ALBERT

Description: This article describes one of the first successful examples of multisensor, multivariate land data assimilation, encompassing a large suite of soil moisture, snow depth, snow cover and irrigation intensity environmental data records (EDRs) from Scanning Multi-channel Mi-crowave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), the Advanced Scatterometer (ASCAT), the Moderate-Resolution Imaging Spectroradiometer (MODIS), the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission and the Soil Moisture Active Passive (SMAP) mission. The analysis is performed using the NASA Land Information System (LIS) as an enabling tool for the U.S. National Climate Assessment (NCA). The performance of NCA Land Data Assimilation System (NCA-LDAS) is evaluated by comparing to a number of hydrological reference data products. Results indicate that multivariate assimilation provides systematic improvements in simulated soil moisture and snow depth, with marginal effects on the accuracy of simulated streamow and ET. An important conclusion is that across all evaluated variables, assimilation of data from increasingly more modern sensors (e.g. SMOS, SMAP, AMSR2, ASCAT) produces more skillful results than assimilation of data from older sensors (e.g. SMMR, SSM/I, AMSR-E). The evaluation also indicates high skill of NCA-LDAS when compared with other LSM products. Further, drought indicators based on NCA-LDAS output suggest a trend of longer and more severe droughts over parts of Western U.S. during 1979-2015, particularly in the Southwestern U.S., consistent with the trends from the US drought monitor, albeit for a shorter 2000-2015 time period.

Description: The vast extent and inaccessibility of boreal forest ecosystems are barriers to routine monitoring of forest structure and composition. In this research, we bridge the scale gap between intensive but sparse plot measurements and extensive remote sensing studies by collecting forest inventory variables at the plot scale using an unmanned aerial vehicle (UAV) and a structure from motion (SfM) approach. At 20 Forest Inventory and Analysis (FIA) subplots in interior Alaska, we acquired overlapping imagery and generated dense, 3D, RGB (red, green, blue) point clouds. We used these data to model forest type at the individual crown scale as well as subplot-scale tree density (TD), basal area (BA), and aboveground biomass (AGB). We achieved 85% cross-validation accuracy for five species at the crown level. Classification accuracy was maximized using three variables representing crown height, form, and color. Consistent with previous UAV-based studies, SfM point cloud data generated robust models of TD (r(sup 2) = 0.91), BA (r(sup 2) = 0.79), and AGB (r(sup 2) = 0.92), using a mix of plot- and crown-scale information. Precise estimation of TD required either segment counts or species information to differentiate black spruce from mixed white spruce plots. The accuracy of species-specific estimates of TD, BA, and AGB at the plot scale was somewhat variable, ranging from accurate estimates of black spruce TD (+/1%) and aspen BA (2%) to misallocation of aspen AGB (+118%) and white spruce AGB (50%). These results convey the potential utility of SfM data for forest type discrimination in FIA plots and the remaining challenges to develop classification approaches for species-specific estimates at the plot scale that are more robust to segmentation error.

Description: This publication documents the scientific advances associated with new instrument systems and accessories built to improve above- and in-water observations of the apparent optical properties (AOPs) for a diversity of water masses, including optically complex waters. The principal objective is to be prepared for the launch of next-generation ocean color satellites with the most capable commercial off-the-shelf (COTS) instrumentation in the shortest time possible. The technologies described herein are entirely new hybrid sampling capabilities, so as to satisfy the requirements established for next-generation missions. Both above- and in-water instruments are documented with software options for autonomous control of data collection activities as applicable. The instruments were developed for the Hybridspectral Alternative for Remote Profiling of Optical Observations for NASA Satellites (HARPOONS) vicarious calibration project. The state-of-the-art accuracy required for vicarious calibration also led to the development of laboratory instruments to ensure the field observations were within uncertainty requirements. Separate detailed presentations of the individual instruments provide the hardware designs, accompanying software for data acquisition and processing, and examples of the results achieved.

Description: We use the recently released Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Version 4.1 (V4) lidar data to study the smoke plumes transported from Southern African biomass burning areas. Significant improvements in the CALIPSO V4 Level 1 calibration and V4 Level 2 algorithms lead to a better representation of their optical properties, with the aerosol subtype improvements being particularly relevant to smoke over this area. For the first time, we show evidence of smoke particles increasing in size, as demonstrated by their particulate color ratios, as they are transported over the South Atlantic Ocean from the source regions over Southern Africa. We hypothesize that this is due to hygroscopic swelling of the smoke particles and is reflected in the higher relative humidity in the middle troposphere for profiles with smoke. This finding may have implications for radiative forcing estimates over this area and is also relevant to the ORACLES field mission.

Description: This publication documents the scientific advances associated with new instrument systems and accessories built to improve above- and in-water observations of the apparent optical properties (AOPs) of optically complex waters. The principal objective is to be prepared for the launch of next-generation ocean color satellites with the most capable commercial off-the-shelf (COTS) instrumentation in the shortest time possible. The Hybridspectral Alternative for Remote Profiling of Optical Observations for NASA Satellites (HARPOONS) is presented as a case example of technologies conceived, developed, and deployed operationally in support of next-generation mission requirements. The field trials, field commissioning, and operational demonstration resulted in a technology readiness level (TRL) value of 9 for a diversity of laboratory and field instrument systems. Separate detailed presentations of the individual instruments provide the hardware designs, accompanying software for data acquisition and processing, and examples of the results achieved. For the laboratory components, calibration and characterization procedures are described along with an estimation of the sources of uncertainty, which culminates in a full uncertainty budget for the radiometers deployed to the field.

Description: This publication documents the scientific advances associated with new instrument systems and accessories built to improve above- and in-water observations of the apparent optical properties (AOPs) of aquatic ecosystems. The perspective is to obtain high quality data in offshore, nearshore, and inland waters with equal efficacy. The principal objective is to be prepared for the launch of the next-generation ocean color satellites with the most capable commercial off-the-shelf (COTS) instrumentation in the shortest time possible. The technologies described herein are designed to either improve legacy radiometric systems or to provide entirely new hybrid sampling capabilities, so as to satisfy the requirements established for diverse remote sensing requirements. Both above- and in-water instrument suites are documented with software options for autonomous control of data collection activities. The latter includes an airborne instrument system plus unmanned surface vessel (USV) and buoy concepts.

Description: BRDF defines anisotropy of the surface reflection. It is required to specify the boundary condition for radiative transfer (RT) modeling used in aerosol retrievals, cloud retrievals, atmospheric modeling and other applications. Ground based measurements of reflected radiance draw increasing attention as a source of information about anisotropy of surface reflection. Derivation of BRDF from surface radiance requires atmospheric correction. This study develops a new method of retrieving BRDF on its whole domain making it immediately suitable for further atmospheric RT modeling applications. The method is based on the integral equation relating surface reflected radiance, BRDF and solutions of two auxiliary atmosphere-only RT problems. The method requires kernel-based BRDF. The weights of the kernels are obtained with a quickly converging iterative procedure. RT modeling has to be done only one time before the start of iterative process.

Description: Analysis of sun photometer measured and satellite retrieved aerosol optical depth (AOD) data has shown that major aerosol pollution events with very high fine mode AOD (〉1.0 in mid-visible) in the China/Korea/Japan region are often observed to be associated with significant cloud cover. This makes remote sensing of these events difficult even for high temporal resolution sun photometer measurements. Possible physical mechanisms for these events that have high AOD include a combination of aerosol humidification, cloud processing, and meteorological co-variation with atmospheric stability and convergence. The new development of Aerosol Robotic network (AERONET) Version 3 Level 2 AOD with improved cloud screening algorithms now allow for unprecedented ability to monitor these extreme fine mode pollution events. Further, the Spectral Deconvolution Algorithm (SDA) applied to Level 1 data (L1; no cloud screening) provides an even more comprehensive assessment of fine mode AOD than L2 in current and previous data versions. Studying the 2012 winter-summer period, comparisons of AERONET L1 SDA daily average fine mode AOD data showed that Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing of AOD often did not retrieve and/or identify some of the highest fine mode AOD events in this region. Also, compared to models that include data assimilation of satellite retrieved AOD, the L1 SDA fine mode AOD was significantly higher in magnitude, particularly for the highest AOD events that were often associated with significant cloudiness.

Description: Advanced Land Surface Models (LSM) offer a powerful tool for studying hydrological variability. Highly managed systems, however, present a challenge for these models, which typically have simplified or incomplete representations of human water use. Here we examine recent groundwater declines in the US High Plains Aquifer (HPA), a region that is heavily utilized for irrigation and that is also affected by episodic drought. To understand observed decline in groundwater and terrestrial water storage during a recent multi-year drought, we modify the Noah-MP LSM to include a groundwater irrigation scheme. To account for seasonal and interannual variability in active irrigated area, we apply a monthly time-varying greenness vegetation fraction (GVF) dataset within the model. A set of five experiments were performed to study the impact of groundwater irrigation on the simulated hydrological cycle of the HPA and to assess the importance of time-varying GVF when simulating drought conditions. The results show that including the groundwater irrigation scheme improves model agreement with ALEXI ET data, mascon-based GRACE TWS data and depth-to-groundwater measurements in the southern HPA, including Texas and Kansas, and that accounting for time-varying GVF is important for model realism under drought. Results for the HPA in Nebraska are mixed, likely due to the model's weaknesses in representing subsurface hydrology in this region. This study highlights the value of GRACE datasets for model evaluation and development and the potential to advance the dynamic representations of the interactions between human water use and the hydrological cycle.

Description: With satellite soil moisture (SM) retrievals becoming widely and continuously available, we aim to develop a method to objectively integrate the drought indices into one that is more accurate and consistently reliable. The datasets used in this paper include the Noah land surface modelbased SM estimations, AtmosphereLandExchangeInverse modelbased Evaporative Stress Index, and the satellite SM products from the Advanced Scatterometer, WindSat, Soil Moisture and Ocean Salinity, and Soil Moisture Operational Product System. Using the Triple Collocation Error Model (TCEM) to quantify the uncertainties of these data, we developed an optically blended drought index (BDI_b) that objectively integrates drought estimations with the lowest TCEMderived rootmeansquareerrors in this paper. With respect to the reported drought records and the drought monitoring benchmarks including the U.S. Drought Monitor, the Palmer Drought Severity Index and the standardized precipitation evapotranspiration index products, the BDI_b was compared with the sample average blending drought index (BDI_s) and the RMSEweighted average blending drought indices (BDI_w). Relative to the BDI_s and the BDI_w, the BDI_b performs more consistently with the drought monitoring benchmarks. With respect to the official drought records, the developed BDI_b shows the best performance on tracking drought development in terms of time evolution and spatial patterns of 2010Russia, 2011USA, 2013New Zealand droughts and other reported agricultural drought occurrences over the 20092014 period. These results suggest that model simulations and remotely sensed observations of SM can be objectively translated into useful information for drought monitoring and early warning, in turn can reduce drought risk and impacts.

Description: A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fw(sub LBand)) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency (Tb) observations from AMSR2. The resulting (fw(sub LBand)) global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The (fw(sub LBand)) annual averages corresponded favourably (R=0.84, p〈0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly (fw(sub LBand)) averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable (fw(sub LBand)) performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) (fw(sub LBand)) results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m (fw(sub LBand)) retrievals showed favourable spatial accuracy for water (70.71%) and land (98.99%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new (fw(sub LBand)) algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk.

Description: The assimilation of remotely sensed soil moisture information into a land surface model has been shown in past studies to contribute accuracy to the simulated hydrological variables. Remotely sensed data, however, can also be used to improve the model itself through the calibration of the model's parameters, and this can also increase the accuracy of model products. Here, data provided by the Soil Moisture Active/Passive (SMAP) satellite mission are applied to the land surface component of the NASA GEOS Earth system model using both data assimilation and model calibration in order to quantify the relative degrees to which each strategy improves the estimation of near-surface soil moisture and streamflow. The two approaches show significant complementarity in their ability to extract useful information from the SMAP data record. Data assimilation reduces the ubRMSE (the RMSE after removing the long-term bias) of soil moisture estimates and improves the timing of streamflow variations, whereas model calibration reduces the model biases in both soil moisture and streamflow. While both approaches lead to an improved timing of simulated soil moisture, these contributions are largely independent; joint use of both approaches provides the highest soil moisture simulation accuracy.

Description: Productivity of northern latitude forests is an important driver of the terrestrial carbon cycle and is already responding to climate change. Studies ofthe satellite-derived Normalized Difference VegetationIndex (NDVI) for northern latitudes indicate recent changes in plant productivity. These detected greening and browning trends are often attributedto a lengthening of the growing season from warming temperatures. Yet, disturbance-recovery dynamics are strong drivers of productivity and can mask direct effects of climate change. Here, we analyze 1-km resolution NDVI data from 1989to 2014 for the northern latitude forests of the Greater Yellowstone Ecosystem for changes in plant productivity to address the following questions:(1) To what degree has greening taken place in the GYE over the past three decades? and (2) What is the relative importance of disturbance and climate in explaining NDVI trends? We found that the spatial extents of statistically significant productivity trends were limited to local greening and browning areas. Disturbance history, predominately fire disturbance, was a major driver of these detected NDVI trends. After accounting for fire-,insect-, and human-caused disturbances, increasing productivity trends remained. Productivity of northern latitude forests is generally considered temperature-limited; yet, we found that precipitation was a key driver of greening in the GYE.

Description: Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19-91.19 Gg compared to 42.0-61.71 Gg, 42.59-58.75 Gg and 93.98-111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003-2016. The results suggested a range of 2.56-63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area.

Description: Assessment of actual evapotranspiration (ET) is essential as it controls the exchange of water and heat energy between the atmosphere and land surface. ET also influences the available water resources and assists in the crop water assessment in agricultural areas. This study involves the assessment of spatial distribution of seasonal and annual ET using Surface Energy Balance Algorithm for Land (SEBAL) and provides an estimation of future changes in ET due to land use and climate change for a portion of the Narmada river basin in Central India. Climate change effects on future ET are assessed using the ACCESS1-0 model of CMIP5. A Markov Chain model estimated future land use based on the probability of changes in the past. The ET analysis is carried out for the years 2009-2011. The results indicate variation in the seasonal ET with the changed land use. High ET is observed over forest areas and crop lands, but ET decreases over crop lands after harvest. The overall annual ET is high over water bodies and forest areas. ET is high in the premonsoon season over the water bodies and decreases in the winter. Future ET in the 2020s, 2030s, 2040s, and 2050s is shown with respect to land use and climate changes that project a gradual decrease due to the constant removal of the forest areas. The lowest ET is projected in 2050. Individual impact of land use change projects decreases in ET from 1990 to 2050, while climate change effect projects increases in ET in the future due to rises in temperature. However, the combined impacts of land use and climate changes indicate a decrease in ET in the future.

Description: Due to instrument sensitivities and algorithm detection limits, level 2 (L2) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 532 nm aerosol extinction profile retrievals are often populated with retrieval fill values (RFVs), which indicate the absence of detectable levels of aerosol within the profile. In this study, using 4 years (20072008 and 20102011) of CALIOP version 3 L2 aerosol data, the occurrence frequency of daytime CALIOP profiles containing all RFVs (all-RFV profiles) is studied. In the CALIOP data products, the aerosol optical thickness (AOT) of any all-RFV profile is reported as being zero, which may introduce a bias in CALIOP-based AOT climatologies. For this study, we derive revised estimates of AOT for all-RFV profiles using collocated Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and, where available, AErosol RObotic NEtwork (AERONET) data. Globally, all-RFV profiles comprise roughly 71 % of all daytime CALIOP L2 aerosol profiles (i.e., including completely attenuated profiles), accounting for nearly half (45 %) of all daytime cloud-free L2 aerosol profiles. The mean collocated MODIS DT (AERONET) 550 nm AOT is found to be near 0.06 (0.08) for CALIOP all-RFV profiles. We further estimate a global mean aerosol extinction profile, a so-called noise floor, for CALIOP all-RFV profiles. The global mean CALIOP AOT is then recomputed by replacing RFV values with the derived noise-floor values for both all-RFV and non-all-RFV profiles. This process yields an improvement in the agreement of CALIOP and MODIS over-ocean AOT.

Description: Data products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were recently updated following the implementation of new (version 4) calibration algorithms for all of the level 1 attenuated backscatter measurements. In this work we present the motivation for and the implementation of the version 4 nighttime 532 nm parallel channel calibration. The nighttime 532 nm calibration is the most fundamental calibration of CALIOP data, since all of CALIOPs other radiometric calibration procedures i.e., the 532 nm daytime calibration and the 1064 nm calibrations during both nighttime and daytime depend either directly or indirectly on the 532 nm nighttime calibration. The accuracy of the 532 nm nighttime calibration has been significantly improved by raising the molecular normalization altitude from 30-34 km to 36-39 km to substantially reduce stratospheric aerosol contamination. Due to the greatly reduced molecular number density and consequently reduced signal-to-noise ratio (SNR) at these higher altitudes, the signal is now averaged over a larger number of samples using data from multiple adjacent granules. As well, an enhanced strategy for filtering the radiation-induced noise from high energy particles was adopted. Further, the meteorological model used in the earlier versions has been replaced by the improved MERRA-2 model. An aerosol scattering ratio of 1.01 0.01 is now explicitly used for the calibration altitude. These modifications lead to globally revised calibration coefficients which are, on average, 2-3% lower than in previous data releases. Further, the new calibration procedure is shown to eliminate biases at high altitudes that were present in earlier versions and consequently leads to an improved representation of stratospheric aerosols. Validation results using airborne lidar measurements are also presented. Biases relative to collocated measurements acquired by the Langley Research Center (LaRC) airborne high spectral resolution lidar (HSRL) are reduced from 3.6% 2.2% in the version 3 data set to 1.6% 2.4 % in the version 4 release.

Description: The societal benefits of understanding climate change through the identification of global carbon dioxide sources and sinks led to the recommendation for NASA's Active Sensing of Carbon Dioxide Emissions over Nights, Days, and Seasons space-based mission for global carbon dioxide measurements. For more than 15 years, the NASA Langley Research Center has developed several carbon dioxide active remote sensors using the differential absorption lidar technique operating at 2-m wavelength. Recently, an airborne double-pulsed integrated path differential absorption lidar was developed, tested, and validated for atmospheric carbon dioxide measurement. Results indicated 1.02% column carbon dioxide measurement uncertainty and 0.28% bias over the ocean. Currently, this technology is progressing toward triple-pulse operation targeting both atmospheric carbon dioxide and water vapor-the dominant interfering molecule on carbon dioxide remote sensing. Measurements from the double-pulse lidar and the advancement of the triple-pulse lidar development are presented. In addition, measurement simulations with a space-based IPDA lidar, incorporating new technologies, are also presented to assess feasibility of carbon dioxide measurements from space.

Description: The Global Energy and Water cycle Exchanges (GEWEX) Data and Assessments Panel (GDAP) initiated the GEWEX Water Vapor Assessment (G-VAP), which has the main objectives to quantify the current state of the art in water vapour products being constructed for climate applications and to support the selection process of suitable water vapour products by GDAP for its production of globally consistent water and energy cycle products. During the construction of the G-VAP data archive, freely available and mature satellite and reanalysis data records with a minimum temporal coverage of 10 years were considered. The archive contains total column water vapour (TCWV) as well as specific humidity and temperature at four pressure levels (1000, 700, 500, 300 hPa) from 22 different data records. All data records were remapped to a regular longitude-latitude grid of 2deg 2deg. The archive consists of four different folders: 22 TCWV data records covering the period 2003-2008, 11 TCWV data records covering the period 1988-2008, as well as 7 specific humidity and 7 temperature data records covering the period 1988-2009. The G-VAP data archive is referenced under the following digital object identifier (doi): https://doi.org/10.5676/EUM_SAF_CM/GVAP/V001. Within G-VAP, the characterization of water vapour products is, among other ways, achieved through intercomparisons of the considered data records, as a whole and grouped into three classes of predominant retrieval condition: clear-sky, cloudy-sky and all-sky. Associated results are shown using the 22 TCWV data records. The standard deviations among the 22 TCWV data records have been analysed and exhibit distinct maxima over central Africa and the tropical warm pool (in absolute terms) as well as over the poles and mountain regions (in relative terms). The variability in TCWV within each class can be large and prohibits conclusions about systematic differences in TCWV between the classes.

Description: We use the recently released Version 4 (V4) lidar data products from CALIPSO to study the smoke plumes transported from Southern African biomass burning areas. The significant improvements in CALIPSO V4 Level 1 calibration and the V4 Level 2 aerosol subtyping algorithms, the latter being particularly relevant to biomass burning smoke over this area, lead to a better representation of their optical properties. For the first time, we show evidence of smoke particles increasing in size, evidenced in their particulate color ratios, as they are transported over the South Atlantic Ocean from the source regions over Southern Africa. This is likely due to hygroscopic swelling of the smoke particles and is reflected in the higher relative humidity in the middle troposphere for profiles with smoke. This finding may have implications for radiative forcing estimates over this area and is relevant to the ORACLES field mission that is currently underway.

Description: Previous research has revealed inconsistencies between the Collection 5 (C5) calibrations of certain channels common to the Terra and Aqua MODerate-resolution Imaging Spectroradiometers (MODIS). To achieve consistency between the Terra and Aqua MODIS radiances used in the Clouds and the Earths Radiant Energy System (CERES) Edition 4 (Ed4) cloud property retrieval system, adjustments were developed and applied to the Terra C5 calibrations for channels 1-5, 7, 20, and 26. These calibration corrections were developed independently of those used for MODIS Collection 6 (C6) data, which became available after the CERES Ed4 processing had commenced. The comparisons demonstrate that the corrections applied to the Terra C5 data for CERES Edition 4 generally resulted in Terra- Aqua radiance consistency that is as good as or better than that of the C6 datasets. The C5 adjustments resulted in more consistent Aqua and Terra cloud property retrievals than seen in the previous CERES edition. Other calibration artifacts were found in one of the corrected channels and in some of the uncorrected thermal channels after Ed4 began. Where corrections were neither developed nor applied, some artifacts are likely to have been introduced into the Ed4 cloud property record. For example, the degradation in the Aqua MODIS 0.65- m channel in both the C5 and C6 datasets affects trends in cloud optical depth retrievals. Thus, despite the much-improved consistency achieved for the Terra and Aqua datasets in Ed4, the CERES Ed4 cloud property datasets should be used cautiously for cloud trend studies because of those remaining calibration artifacts.

Description: Atmospheric reanalysis have become an important source of data for weather and climate research, owing to the continuity of the data, but especially because of the multitude of observational data included (radiosondes, commercial aircraft, retrieved data products and radiances). However, the presence of assimilated observations can vary based on numerous factors, and so it is difficult or impossible for a researcher to say with any degree of certainty how many and what type of observations contributed to the reanalysis data they are using at any give point in time or space, or their contribution to the eventual analyzed fields. For example, quality control, transmission interruptions, and station outages can occasionally affect data availability. While orbital paths are known, drift in certain instruments, cloud clearing, and the large number of available instruments makes it challenging to know which satellite is observing any region at any point in the diurnal cycle. Furthermore, there is information from the statistics generated by the data assimilation that can help understand the model and the quality of the reanalysis. Typically, the assimilated observations and their innovations are saved in observation-space data formats and are not easily made available to reanalysis users.Here, we present an early version of a data set has been developed to make the MERRA-2 assimilated observations available for rapid and general use, by simplifying the data format. The observations are binned to a grid similar as MERRA-2 and saved as netCDF. This data collection includes the mean and number of observations in the bin as well as its variance. The data will also include the innovations from the data assimilation, the forecast departure and the analysis increment, as well as bias correction (for satellite radiances). In this paper, we present the data format (called MERRA-2 Gridded Innovations and Observations or GIO) and its strengths and limitations with some initial testing and validation of the methodology.

Description: Simulations of hydrological fields as well as water, energy and carbon fluxes from the land surface to the atmosphere are crucial for a wide range of applications, including agricultural advisories, forecasts of (short-term) atmospheric behavior and seasonal weather predictions including forecasts of extreme events, such as heatwaves or droughts. The NASA Soil Moisture Active Passive (SMAP) mission Level-4 (L4) Eco-Hydrology (ECO) project aims to improve modeled estimates of the terrestrial water, energy and carbon fluxes and states by developing a fully-coupled hydrology-vegetation data assimilation (DA) algorithm. The DA system is developed for the NASA Goddard Earth Observing System version 5 (GEOS-5) Catchment-CN land surface model, which combines land hydrology components of the GEOS-5 Catchment model with dynamic vegetation components of the Community Land Model version 4. Catchment-CN fully couples the terrestrial water, energy and carbon cycles, allowing feedbacks from the land hydrology to the biosphere and vice versa. For SMAP L4 ECO a calibration of the Catchment-CN vegetation parameterization against observations of the fraction of absorbed photosynthetically active radiation (FPAR) from the Moderate Resolution Imaging Spectroradiometer (MODIS) is implemented to improve the model's standalone skill. Next, the DA algorithm used to produce the SMAP L4 soil moisture product is adapted to Catchment-CN to assimilate SMAP brightness temperatures and inform the model's land hydrology component. The DA system is further extended to assimilate MODIS FPAR observations in order to constrain the model's dynamic vegetation component. In this presentation, we demonstrate that the Catchment-CN parameter calibration leads to more realistic vegetation simulations and reduces the root mean squared error between modeled and observed vegetation states across the model's various plant functional types. We also show that the assimilation of SMAP observations is able to improve the average correlation, bias and unbiased RMSE between the modeled surface and root zone soil moisture estimates, and ground observations from the SMAP core validation sites.

Description: There is high uncertainty in the direct radiative forcing of black carbon (BC), an aerosol that strongly absorbs solar radiation. The observation-constrained estimate, which is several times larger than the bottom-up estimate, is influenced by the spatial representativeness error due to the mesoscale inhomogeneity of the aerosol fields and the relatively low resolution of global chemistry-transport models. Here we evaluated the spatial representativeness error for two widely used observational networks (AErosol RObotic NETwork and Global Atmosphere Watch) by downscaling the geospatial grid in a global model of BC aerosol absorption optical depth to 0.1 0.1. Comparing the models at a spatial resolution of 2 2 with BC aerosol absorption at AErosol RObotic NETwork sites (which are commonly located near emission hot spots) tends to cause a global spatial representativeness error of 30%, as a positive bias for the current top-down estimate of global BC direct radiative forcing. By contrast, the global spatial representativeness error will be 7% for the Global Atmosphere Watch network, because the sites are located in such a way that there are almost an equal number of sites with positive or negative representativeness error.

Description: The US Geostationary Operational Environmental Satellite R Series (GOES-R) was launched on November 19, 2016and was designated GOES-16 upon reaching geostationary orbit ten days later. After checkout and calibration, GOES-16 was relocated to its operational location of 75.2 degrees west and officially became GOES East on December 18, 2017. The Advanced Baseline Imager (ABI) is the primary instrument on the GOES-R series for imaging Earth's surface and atmosphere to significantly improve the detection and observation of severe environmental phenomena. A team supporting the GOES-R Flight Project at NASA's Goddard Space Flight Center developed algorithms and software for independent verification of ABI Image Navigation and Registration (INR), which became known as the INR Performance Assessment Tool Set (IPATS). In this paper, we will briefly describe IPATS on top concept level, and then introduce the Landsat chips, chip registration algorithms, and how IPATS measurements are filtered. We present GOES-16 navigation (NAV) errors from flight data from January 2017 to May 2018. The results show a) IPATS characterized INR variations throughout the post-launch test phase; and b) ABI INR has improved over time as post-launch tests were performed and corrections applied. Finally, we will describe how estimated NAV errors have been used to assess and understand satellite attitude anomalies and scale errors etc. This paper shows that IPATS is an effective tool for assessing and improving GOES-16 ABI INR and is also useful for INR long-term monitoring.

Description: In this work we evaluate the near-surface air temperature datasets from the ERA-Interim, JRA55, MERRA2, NCEP1, and NCEP2 reanalysis projects. Reanalysis data were first compared to observations from weather stations located on wheat areas of the United States and Ukraine, and then evaluated in the context of a winter wheat yield forecast model. Results from the comparison with weather station data showed that all datasets performed well (r2〉0.95) and that more modern reanalysis such as ERAI had lower errors (RMSD ~ 0.9) than the older, lower resolution datasets like NCEP1 (RMSD ~ 2.4). We also analyze the impact of using surface air temperature data from different reanalysis products on the estimations made by a winter wheat yield forecast model. The forecast model uses information of the accumulated Growing Degree Day (GDD) during the growing season to estimate the peak NDVI signal. When the temperature data from the different reanalysis projects were used in the yield model to compute the accumulated GDD and forecast the winter wheat yield, the results showed smaller variations between obtained values, with differences in yield forecast error of around 2% in the most extreme case. These results suggest that the impact of temperature discrepancies between datasets in the yield forecast model get diminished as the values are accumulated through the growing season.

Description: Remotely sensing the water status of plant canopies remains a long term goal of remote sensing research. Existing approaches to remotely sense canopy water status, such as the Crop Water Stress Index (CWSI) and the Equivalent Water Thickness (EWT), have limitations. The CWSI, based upon remotely sensing canopy radiant temperature in the thermal infrared spectral region, does not work well in humid regions, requires estimates of the vapor pressure deficit near the canopy during the remote sensing over-flight and, once stomata close, provides little information regarding the canopy water status. The EWT is based upon the physics of water-light interaction in the 900-2000nm spectral region, not plant physiology. Our goal is development of a remote sensing technique for estimating plant water status based upon measurements in the VIS/NIR spectral region, taking advantage of polarization physics. This technique would potentially provide remote sensing access to plant dehydration physiology to the changes in cellular photochemistry and structure associated with water deficits in leaves. In this research, we used crossed optical polarization filters to measure the VIS/NIR light reflected from the leaf interior, R, as well as the leaf transmittance, T, for 78 corn (Zea mays) and soybean (Glycine max) leaves having relative water content (RWC) values between 0.60 and 0.98. Our results show that as RWC decreases R increases while T decreases. Our results tie R and T changes in the VIS/NIR to leaf physiological changes linking the light scattered out of the drying leaf interior to its relative water content and to changes in leaf cellular structure and pigments.

Description: The San Francisco Bay (SFB) is the largest estuary on the west coast of the United States. It is an important transition zone between marine, freshwater, and inland terrestrial watersheds. The SFB is an important region for the cycling of nutrients and pollutants and it supports nurseries of ecologically and commercially important fisheries, including some threatened species. Phytoplankton community structure influences food web dynamics, and the taxonomy of the phytoplankton may be more important in determining primary food quality than environmental factors. As such, estimating food quality from phytoplankton community composition can be a robust tool to understand trophic transfer of energy. Recent work explores phytoplankton food quality in SFB through the use of microscopy and phytoplankton chemotaxonomy to evaluate how changes in phytoplankton composition may have influenced the recent trophic collapse of pelagic fishes in the northern part of the SFB. The objective of this study is to determine if the approach can also be applied to imaging spectroscopy data in order to quantify phytoplankton food quality from space. Imaging spectroscopy data of SFB from the Airborne VisibleInfrared Imaging Spectrometer (AVIRIS) was collected during the Hyperspectral Infrared (HyspIRI) Airborne Campaign in California (2013 2015) and used in this study. Estimates of ocean chlorophyll and phytoplankton community structure were determined using standard ocean chlorophyll algorithms and the PHYtoplankton Detection with Optics (PHYDOTax) algorithms. These were validated using in situ observations of phytoplankton composition using microscopic cell counts and phytoplankton chemotaxonomy from the US Geological Surveys ship surveys of the SFB. The findings from this study may inform the use of future high spectral resolution satellite sensors with the spatial resolution appropriate for coastal systems (e.g., HyspIRI) to assess food quality from space.

Description: Localized, correlated biases exist in OCO-2 (Orbiting Carbon Observatory-2) data. These biases do not improve with averaging. These have been noted by O'Dell (various OCO-2 telecons), Worden (2017), Kulawik (2018). We propose to utilize information from adjacent observations to reduce this bias. This works on the assumption that grouping the observations allows the system to find a better, global minimum. Pooling adjacent observations could allow more accurate characterization of interferents (e.g. retrieving additional albedo parameters), reducing entanglement with CO2.

Description: ICESat-2 carries NASA's next-generation laser altimeter, ATLAS, (Advanced Topographic Laser Altimeter System), designed to measure changes in ice sheet height, sea ice freeboard, and vegetation canopy height. ATLAS contains a photon-counting lidar which transmits green (532-nm) pulses at 10kHz. Each pulse is split into 3 pairs of beams (one strong and one weak). Approximately 1014 photons per pulse travel from ATLAS through the atmosphere to reflect off the Earth's surface. Some return back into the ATLAS telescope where they are recorded. Photons from sunlight and instrument noise at the same wavelength are also recorded. The flight software time tags all photons within a 500m to 6 km range window and generates histograms. Using the histograms, it selects a telemetry window which varies from 20m over flat surfaces to hundreds of meters over rougher terrain. ATL03 contains the time, height (relative to the WGS-84 ellipsoid), latitude and longitude of every photon within the telemetry window. The basic challenge is to determine which of these photons were reflected off the surface. We have developed an algorithm that identifies these signal photons and assigns a confidence level (low, medium, or high) to each signal photon based on the signal to noise ratio. We present an overview of the signal identification algorithm and show the results on actual ICESat-2 data over ice sheet, sea ice, vegetated, and water surfaces. Higher level ATLAS products work with aggregations of the photons in order to determine the ellipsoidal height of the Earth, canopy height and structure, and other quantities of geophysical interest.

Description: We present the development of the reflective coating by magnetron sputtering deposition onto precisely-fabricated thin X-ray mirrors. Our goal is to remove distortion induced by the coating and then keep their surface pro les. We first addressed the uniform coating to minimize the distortion by introducing a mask to control the spatial distribution of the coating thickness. The uniformity was finally achieved within 1%. We next tried a platinum single-layer coating on a glass substrate with a dimension of 200 mm 125 mm. The distortion caused by the frontside coating with a thickness of 320 A was found to be at most 1 m, smaller than the previous results obtained from the non-uniform coating. We then carried out the platinum coating with the same amount of the thickness on the backside surface of the glass substrate. The surface pro le of the glass substrate was fully recovered, indicating that the residual stress was successfully balanced by the backside coating. Furthermore, we tried to an iridium single-layer coating with a thickness of 150 Aon the silicon mirrors. The frontside coating caused the degradation of the imaging quality by 7:5 arcsec in half-power width. However, the backside coating with the same amount of the thickness reduced this degradation to be 3:4 arcsec. Finally, an additional backside coating with a thickness of 100 A and the annealing to relax the residual stress were found to eliminate the distortion completely; the final degradation of the imaging quality was only 0:4 arcsec.

Description: As NASA prepares to launch ICESAT-II there are significant efforts underway to plan for aircraft under-flights to calibrate the onboard lasers and to validate data products. This presentation will begin with an overview of recent airborne science missions including Operation Ice Bridge that have focused observations on the arctic. This will be followed by results from recent NASA sponsored unmanned aircraft missions and a discussion how these new observing technologies can contribute to future research in the Arctic. Information on recent icing wind tunnel testing and comparisons with icing prediction models will also be shared leading to discussions on specific challenges in adapting systems to harsh arctic conditions.

Description: Access to space and the preservation of the near-Earth space environment is of critical significance. Increased interest in issues surrounding space traffic management and the continued assessment and discussion of orbital debris at the United Nations Committee on the Peaceful Uses of Outer Space (COPUOS) illustrates the significance of the topic of orbital debris. There are currently over 20,000 tracked objects in the publicly available satellite catalog on Space-Track.org. The catalog is maintained by the US Air Force Space Command using a network of optical and radar ground-based sensors and is believed to be complete for a characteristic size of 10 cm or larger in low Earth orbit (LEO). Based on the work of the NASA Orbital Debris Program Office (ODPO) over approximately the past 40 years it is understood that the small debris population (〈 10 cm) increases by orders of magnitude as characteristic size decreases. With population estimates ranging from 500,000 to 1,000,000 small debris objects (〉 5 mm) in orbit, it is currently not practical to track and maintain precision orbits on every object. Instead the NASA ODPO uses powerful ground-based radars to sample the low Earth Orbit (LEO) environment and assign approximate orbits to each detection. This poses an interesting signal processing challenge as we are trying to detect the smallest objects possible on the edge of the radar's sensitivity. For approximately the last 30 years, NASA ODPO has partnered with the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL) to utilize the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR - formerly the Long-Range Imaging Radar or simply Haystack) and the Haystack Auxiliary (HAX) radar to collect orbital debris radar data. Additionally, the ODPO collaborates with the NASA Jet Propulsion Laboratory (JPL) to use the Goldstone Solar System Radar. The orbital debris detections from these radars serve as inputs for statistical risk models used by the human spaceflight and satellite communities to assess risk to spacecraft posed by orbital debris. In this paper, we will describe the history of orbital debris radar measurements conducted by NASA, provide an overview of current radar measurements techniques and facilities, discuss the signal processing software used for orbital debris measurements and the inference of debris size and orbital parameters from these measurements, and discuss how orbital debris radar measurements are validated for use in models that are used throughout the aerospace industry.

Description: Management of aquatic weeds in complex watersheds and river systems present many challenges to assessment, planning and implementation of management practices for floating and submerged aquatic invasive plants. The Delta Region Areawide Aquatic Weed Project (DRAAWP), a USDA sponsored area-wide project, is working to enhance planning, decision-making and operational efficiency in the California Sacramento-San Joaquin Delta. Satellite and airborne remote sensing are used map (area coverage and biomass density), direct operations, and assess management impacts on plant communities. Archived satellite records enable review of results following previous climate and management events and aide in developing long-term strategies. Examples of remote sensing aiding effectiveness of aquatic weed management will be discussed as well as areas for potential technological improvement. Modeling at local and watershed scales using the SWAT modeling tool provides insight into land-use effects on water quality (described by Zhang in same Symposium). Controlled environment growth studies have been conducted to quantify the growth response of invasive aquatic plants to water quality and other environmental factors. Environmental variability occurs across a range of time scales from long-term climate and seasonal trends to short-term water flow mediated variations. Response time for invasive species response are examined at time scales of weeks, day, and hours using a combination of study duration and growth assessment techniques to assess water quality, temperature (air and water), nitrogen, phosphorus, and light effects. These provide response parameters for plant growth models in response to the variation and interact with management and economic models associated with aquatic weed management. Plant growth models are to be informed by remote sensing and applied spatially across the Delta to balance location and type of aquatic plant, growth response to altered environments and phenology. Initial utilization of remote sensing tools developed for mapping of aquatic invasive plants improved operational efficiency in management practices. These assessment methods provide a comprehensive and quantitative view of aquatic invasive plants communities in the California Delta.

Description: The purpose of the workshop was to invite statisticians, applied mathematicians, computer scientists, data system architects, experts in remote sensing technology, and Climate and Earth System scientists to review, discuss, and plan research on issues related to large-scale, efficient analysis of distributed data using spatial statistical methods. Our motivation in organizing this event was to catalyze interchange among experts on the fast-emerging problem of analysis of distributed data. As part of SAMSI's 2017-2018 Program on Mathematical and Statistical Methods for Climate and the Earth System, a Working Group on Remote Sensing was established to address statistical and mathematical research problems in the analysis of remote sensing data. The Working Group has five subgroups: 1) Spatial Retrieval Methodology (the so-called \Spatial-X" subgroup); 2) Spatial Analysis for Hyperspectral Data (the so-called \Spatial-Y" subgroup); 3) Emulators for Complex Forward Models; 4) Optimization for Remote Sensing Retrievals; and 5) Theory of Data Systems (ToDS). The ToDS subgroup spent the first half of this academic year formulating a framework in which to consider the joint problem of a) optimizing statistical methods for environments where data are distributed and too large to move to a central location, and b) the design of data system infrastructures within which to implement those statistical methods. To x ideas, the Workshop focused on spatial statistical methods. To date there are many new spatial statistical methods designed with massive data sets in mind, in the literature. However, very few have been implemented for remote sensing data, and none have been implemented in operational settings like those used by NASA and NOAA. A major impediment to their use in these cases is that the data are not only massive, but are stored in different physical locations. These data must be brought together in some way in order to estimate spatial covariance functions, but moving data to a central location for analysis is tedious at best and impossible at worst. Some remote data reduction is almost certainly necessary, but how much? What are the consequences for inference? The fundamental issue underlying these questions is how to navigate the trade-space between costs and uncertainty in the estimates or inferences that are ultimately produced.

Description: Past strategies for retrieving cloud optical properties from remote sensing assumed significant limits for desired parameters such as semi-infinite optical thickness, single scattering albedo equaling unity (non-absorbing scattering), absence of spectral dependence of the optical thickness, etc., and only one optical parameter could be retrieved (either optical thickness or single scattering albedo). Here, we demonstrate a new method based on asymptotic theory for thick atmospheres, and the presence of a diffusion domain within the clouds that does not put restrictions and makes it possible to get two or even three optical parameters (optical thickness, single scattering albedo and phase function asymmetry parameter) for every wavelength independently. We applied this method to measurements of angular distribution of solar radiation above, inside and below clouds, obtained with NASA's Cloud Absorption Radiometer (CAR) over two cases of marine stratocumulus clouds; first case, offshore of Namibia and the second case, offshore of California. The observational and retrieval errors are accounted for by regularization, which allows stable and smooth solutions. Results show good potential for parameterization of the shortwave radiative properties (reflection, transmission, radiative divergence and heating rate) of water clouds.

Description: The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34 C in lake pixels (is) greater than180 m from land, 4.89 C at the land-water boundary, and 1.11 C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.

Description: The smart city approach requires collection of interdisciplinary data and information from multiple sources and integration with modern technologies to provide a new and cost-effective way for researchers and decision makers to study and manage cities. In this book chapter, we introduce NASA satellite-based global and regional observations with emphasis on the hydrologic cycle (e.g., precipitation, wind, temperature, soil moisture) for smart cities. These products, consisting of both near-real-time and historical datasets, are publicly available free of charge and can be used for global and regional research and applications. Examples of using these datasets in smart cities are included. The chapter is organized as follows, first, a brief overview of NASA global satellite-based data products, followed by data services and tools, two examples of using satellite-based datasets in megacities, and finally summary and future plans.

Description: EPIC (Earth Polychromatic Imaging Camera) on board the DSCOVR (Deep Space Climate Observatory) spacecraft is the first earth science instrument located near the earth-sun gravitational plus centrifugal force balance point, Lagrange 1. EPIC measures earth-reflected radiances in 10 wavelength channels ranging from 317.5 to 779.5 nm. Of these channels, four are in the UV range 317.5, 325, 340, and 388 nm, which are used to retrieve O3, 388 nm scene reflectivity (LER: Lambert equivalent reflectivity), SO2, and aerosol properties. These new synoptic quantities are retrieved for the entire sunlit globe from sunrise to sunset multiple times per day as the earth rotates in EPIC's field of view. Retrieved ozone amounts agree with ground-based measurements and satellite data to within 3 %. The ozone amounts and LER are combined to derive the erythemal irradiance for the earth's entire sunlit surface at a nadir resolution of 1818 km2 using a computationally efficient approximation to a radiative transfer calculation of irradiance. The results show very high summertime values of the UV index (UVI) in the Andes and Himalayas (greater than 18), and high values of UVI near the Equator at equinox.

Description: The Orbiting Carbon Observatory-2 (OCO-2), launched in July 2014, is capable of measuring Solar-Induced chlorophyll Fluorescence (SIF), a functional proxy for terrestrial gross primary productivity (GPP). Although its primary mission is to measure the column-averaged mixing ratio of CO2 (Xco(sub 2)) to constrain global carbon source/sink distribution, one of the OCO-2 spectrometers allows for a robust SIF retrieval solely based on solar Fraunhofer lines. Here we present a technical overview of the OCO-2 SIF product, aiming to provide the scientific community guidance on best practices for data analysis, interpretation, and application. This overview consists of the retrieval algorithms, OCO-2 specific bias correction, retrieval uncertainty evaluation, cross-mission comparison with other existing SIF products, and a global-scale examination of the SIF-GPP relationship. With the initial three years of data (September 2014 onward), we compared OCO-2 SIF with retrievals from Greenhouse Gases Observing Satellite (GOSAT) and Global Ozone Monitoring Experiment-2 (GOME-2), and examined its relationship with FLUXCOM and MODIS GPP datasets. Our results show that OCO-2 SIF, along with GOSAT products, closely resemble the mean spatial and temporal patterns of FLUXCOM GPP from regions to the globe. Compared with GOME-2, however, OCO-2 depicts a more realistic spatial contrast between the tropics and extra-tropics. The linear relationship between OCO-2 SIF and existing modeled GPP products diverges somewhat across biomes at the global scale, consistent with previous GOSAT or GOME-2 based findings when modeled GPP products were used, but in contrast to a consistent cross-biome SIF-GPP relationship obtained at flux tower sites with OCO-2 products. This contrast suggests a critical need to reconcile differences in diverse SIF and GPP products and the relationships among them. Overall, the OCO-2 SIF products are robust and valuable for monitoring the global terrestrial carbon cycle and for constraining the carbon source/sink strengths of the Earth system. Finally, insights are offered for future satellite missions optimized for SIF retrievals.

Description: The NASA GEOS composition forecast model (GEOS-CF) provides global, high-resolution (25 km) air quality forecasts in near-real time. This system combines the operational GEOS-5 weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to provide detailed chemical analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). The resolution of the forecasts is the highest compared to current, publicly-available global composition forecasts.Air quality observations are an indispensable tool to evaluate the model's ability to capture the strong temporal and spatial gradients of air pollutants across the globe. We show how comparisons against near-real time observations available through OpenAQ (www.openaq.org) demonstrate the model's overall success in reproducing surface concentrations of ozone, nitrogen dioxide, and PM2.5. This analysis also helps identifying current limitations of the model, for example over South America. The model-observation mismatches are most likely caused by uncertainties in the emissions data. Using the example of Rio de Janeiro, we show how the model skill can be improved by using local, high-resolution emission inventories in combination with air quality data.

Description: Global navigation satellite system (GNSS) phase measurements of the total electron content (TEC) and ionospheric delay are sensitive to sudden increases in electron density in the layers of the Earths ionosphere. These sudden ionospheric disruptions, or SIDs, are due to enhanced X-ray and extreme ultraviolet radiation from a solar flare that drastically increases the electron density in localized regions. SIDs are solar flare signatures in the Earths ionosphere and can be observed with very low frequency (VLF ~ 3-30 kHz) monitors and dual-frequency GNSS (L1 = 1575.42 MHz, L2 = 1227.60 MHz) receivers that probe lower (D-region) to upper (F-region) ionospheric layers, respectively.

Description: Change detection from satellite sensor vegetation indices (VIs) presents an opportunity to monitor trends and disturbances at the regional scale for southern California's Mojave and Lower Colorado Deserts. Renewable energy sites are being constructed in this region on public lands under the Bureau of Land Management (BLM). We have developed a framework for VI change detection over the past two decades, with initial focus on three sites, Joshua Tree National Park, Mojave National Preserve, and a proximal group of Development Focus Areas (DFAs), for comparison between protected and development-targeted lands. Three Terra MODIS VIs (normalized difference [NDVI], enhanced [EVI], soil-adjusted [SAVI]) were evaluated in the Breaks for Additive Season and Trend (BFAST) setting for the regional MODIS 250-m resolution grid to estimate significant time series shifts (breakpoints) from February 2000 to May 2018. All three VIs tended to detect the maximum number of breakpoints at a grid location, but cross-correlations with precipitation and comparison with timing of wildfire burns near the study sites for breakpoint density (proportion of area with a breakpoint) showed that NDVI had the strongest response to these major disturbances, supporting its use for subsequent analysis. Time series of NDVI breakpoint change densities for individual solar energy sites did not have a consistent vegetation response following construction. Bootstrapping showed that the DFAs had significantly larger kurtosis and variance in the positive NDVI breakpoint distribution than did the protected sites, but there was no significant difference in the negative distribution for all three sites. The inconsistent post-construction NDVI signal and the large number of breakpoints overall suggested that the largest changes in vegetation cover density were tied to seasonal precipitation amounts. The distributional results indicated that existing site-specific conditions were the main control on VI responses, given the history of human disturbances in the DFAs. Although the results do not support persistent VI disturbances resulting from recent solar energy development, continued monitoring and examination of other ecological variables and surface temperatures will be vital to the long-term protection of this desert environment.

Description: Near-nadir observations of the Libya 4 site from the S-NPP VIIRS Day-Night Band (DNB) and Moderate resolution Bands (M bands) are used to assess the detector calibration stability and half-angle mirror (HAM) side differences. Almost seven years of Sensor Data Records products are extracted from the Libya 4 site center over an area of 3232 pixels. The mean values of the radiance from individual detectors per HAM side are computed separately. The comparison of the normalized radiance between detectors indicates that the detector calibration differences are wavelength dependent and the differences have been slowly increasing with time for short wavelength bands, especially for M1-M4. The maximum annual average differences between DNB detectors are 0.77% in 2017 at HAM-A. For the M bands, the maximum detector differences in 2017 are 1.7% for M1, 1.8% for M2, 1.3% for M3, 1.2% for M4, 0.67% for M5, 0.75% for M7, 0.57% for M8, 13% for M9, 0.63% for M10, and 0.66% for M11. The average HAM side A to B difference in 2017 are 0.00% for DNB, 0.22% for M1, 0.17% for M2, 0.15% for M3, 0.09% for M4, -0.07% for M5, 0.02% for M7, 0.01% for M8, 1.4% for M9, 0.01% for M10, and 0.03% for M11. Results for M6 are not available due to the signal saturation and M9 results are not accurate because of the low reflectance from the desert site and the strong atmospheric absorption in this channel. The results in this study help scientists better understand each detectors performance and HAM side characteristics. Additionally, they provide evidence and motivation for future VIIRS calibration improvements.

Description: The AQUA, SNPP, and NOAA 18-20 PM sun-synchronous satellites were designed with similar local time, local solarzenith angles, and overlapping temporal coverage. Although the satellites are expected to have fixed local equator-crossing time, during the satellite lifetime, the equator-crossing times of these satellites drift. For NOAA 18-19, the driftin equator-crossing time is significant (few hours) and no correction has been done over the lifetime. For SNPP andAQUA, correction in the orbital inclination angle was periodically performed to maintain the equator-crossing timearound the designed value. The impact of systematic drift of the local observation time during the satellite life cycle canbe significant and should be accounted for when using multi-year time series of satellite products in long-termenvironmental studies. In this paper, the equator-crossing time drift of AQUA, SNPP, and NOAA 18-20, the correctionof SNPP and AQUA equator-crossing time via orbital inclination angle change, and the consequent local solar zenithangle variation are evaluated. The impact of such drift on low-latitude mean brightness temperature trend derived fromthe similar ~11 m thermal emissive channel of AQUA MODIS CH31, SNPP Visible Infrared Imaging RadiometerSuite (VIIRS) CH15 and NOAA 18-19 HIRS CH08 are analyzed. The drift in the mean brightness temperature measuredby these sensors is combined as a function of local time and analyzed using diurnal cycle analysis. The mean brightnesstemperature drift for SNPP VIIRS is reconciled within the context of much larger temperature drift of NOAA 18-19.

Description: Bellingham, Washington is located near the American-Canadian border in the northwestern part of Washington state, and is bordered on its west side by the Lummi reservation. Between Lummi and Bellingham lies Bellingham bay, which has a history of harmful algae related closures dating back to 1978. The subject of this work is a genus of dinoflagellates: Alexandrium, within which many species have the capacity to produce a suite of toxins known as saxitoxin. These toxins bioaccumulate in bivalves, which in turn cause paralytic shellfish poisoning in marine consumers (mammals, birds, and fish), including humans. Symptoms in humans can range from tingling and numbness to difficulty or inability to breathe, resulting in death. Because of the longstanding history of shellfish gathering among Salish tribes and the fact that Washington is the leading U.S. producer of farmed bivalves, harmful algae blooms impact both native and non-native peoples living in the Salish Sea area negatively. The objective of this work is to identify factors which influence Alexandrium blooms in Bellingham Bay, as well as predict Alexandrium blooms in the future. The method of doing so involves two processes: an initial statistical modeling phase to find in situ and remote sensing observations correlated to bloom density (including, but not limited to: water temperature, chlorophyll-a, salinity, color dissolved organic matter, and discharge rate of local rivers), followed by use of that data as a training set for a recursive neural network. This predictive capacity may inform future closures, help ensure the safety of shellfish consumers, and act as a baseline for future modeling efforts in the region.

Description: Observations made in Gale Crater by instruments on the MSL Curiosity Rover show that the diurnal amplitude of the surface pressure is increased and the depth of the Convective Boundary Layer (CBL) is decreased relative to other lander locations on flatter regions of Mars (Haberle et al., 2014; Moores et al., 2015). Mesoscale modeling studies of Gale Crater suggest that crater circulations produce these effects. Tyler & Barnes (2013) show that local upslope/downslope flows along the crater rim and Mt. Sharp amplify the diurnal pressure cycle. These same flows are thought to be at least partly responsible for the suppression of the CBL because upward air flow at the rim and in the center (due to Mt. Sharp) forces subsidence over the lowest regions of the crater during the day. Regional flows, largely due to the location of Gale near the dichotomy boundary, may also play a role in shaping the circulation internal to the crater. Whether the behavior of the CBL and the amplified diurnal pressure cycle are phenomena observed in craters morphologically different from Gale (i.e. bowl-shaped, irregular, degraded) is not yet understood. We will explore these questions by characterizing the behavior of these processes as they are shaped by the morphology of craters greater than 100 km in diameter. We use the NASA Ames Mars Global Circulation Model (GCM) that now utilizes the NOAA/GFDL cubed-sphere finite-volume dynamical core to examine ~100 craters of varying size and shape from a database of known Martian craters (Robbins & Hynek, 2014). Run at 7.5 km resolution, the GCM is capable of resolving surface winds, temperature, and pressure inside craters of this size allowing for the analysis of dozens of craters simulated at various seasons and within the context of synoptic and global-scale phenomena.

Description: GEONEX is a processing pipeline that produces a suite of satellite land surface products using data streams from the latest geostationary (GEO) sensors including the GOES016/ABI and the Himawari-8/AHI. The suite, created collaboratively by scientists from NASA and NOAA, includes top-of-atmosphere (TOA) reflectances, land surface reflectances (LSRs), vegetation indices, LAI/fPAR, and other downstream products. As a key component of the GEONEX product processing, we have adapted the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce LSRs from the TOA data. Because the algorithm depends on building "stacks" of images, we first run internal geo-registration checks to ensure geo-spatial accuracy and consistency of the input (L1B) data before transferring them from the geostationary projection into a tile system in geographic grids. Scan-time is inferred from metadata and applied to calculate the sun-sensor angles for each grid cell. The MAIAC algorithm is run to detect clouds/shadows, estimate aerosol optical thickness (AOT), perform atmospheric corrections, and generate LSRs. We have processed 18-months (from 2016/04 onward) of AHI data over East Asia and Oceania at a 10-minute time step and 10-months (from 2018/01 onward) of ABI data over North and South Americas at a 15-minute time step. As a verification measure, we compare the GEONEX (AHI/ABI) surface reflectances with the standard MODIS products (MOD09GA) and the MODIS MAIAC products over pixels that have similar sun-view geometries. The results indicate general linear relationships between GEONEX and corresponding MODIS LSRs. In particular, the RMSEs between GEONEX and MOD09 data are comparable to those between MOD09 and MODIS MAIAC products, suggesting that the uncertainties of GEONEX LSRs fall into an acceptable range. However, direct comparisons of LSRs over pixels with different sun-view angles are not as straightforward and require more modeling efforts to correct the directional effects. Evaluation of such angular influences on the downstream products (e.g., vegetation indices) is also under investigation.

Description: The glaciers of the Three Sisters volcanoes in Cascadia have retreated dramatically over the past century. In order to understand ongoing chemical weathering and solute transport in the proglacial valleys, waters were sampled from glacier outwash streams, local snowmelt, and proglacial springs and lakes at Collier and Diller Glaciers. To understand weathering and transport processes in the proglacial plains, infrared orbital remote sensing data was used to map compositional variability and highlight weathering products, which were then ground-truthed with laboratory mineralogical and chemical analyses of sediments. The hydrochemistry is significantly affected by a sub- and proglacial mafic weathering system lacking carbonate minerals. Here we report major ion concentrations in meltwaters for the summer 2016 and 2017 melt seasons. Total cation concentrations range from 3 to 250 eq/l and dissolved bicarbonate concentrations range from 2 to 200 eq/l. Other dissolved anions are negligible compared to bicarbonate. Dissolved silica concentrations range from 2 to 260 mol/l, comparable to total dissolved cation concentrations. The highest cation and silica concentrations were measured in moraine-sourced springs. Compositional remote sensing analysis identified alteration zones in the proglacial plains at both Collier and Diller indicating potential hydrated silica. This analysis is consistent with laboratory analysis of sediment samples, which indicate the presence of poorly crystalline phases weathering products, including hydrated silica. Weathered materials are preferentially deposited on moraines due to aeolian and glacial transport, as well as intra-moraine alteration, and at abandoned stream terraces due to fluvial transport. Geochemical measurements indicate that the predominant form of chemical weathering in these periglacial mafic systems is the carbonation of feldspar as well as reactive volcanic glass. The presence of poorly crystalline silicates, as indicated by remote sensing datasets and laboratory analysis, is consistent with rapid weathering of feldspars and glass and formation of Fe-Al-Si-bearing mineraloids in these proglacial valleys. This weathering regime has wide-ranging implications for atmospheric CO2 drawdown due to cold-climate volcanic rock weathering.

Description: A variety of both existing and developing sensors would benefit from near real time communication of high bandwidth data. To cite just one example, sensors that could more accurately report real-time positions of marine mammals would be useful in reducing whale-ship collisions. Similar considerations are relevant for maritime port and harbor security, including detection and alerts for divers or autonomous underwater vehicles (AUVs) that could pose a risk to ships. Especially in ports and harbors, field experiments have confirmed that acoustic communication in these cluttered and noisy shallow water environments, compounded with vertical reflecting surfaces formed by piers and pilings, can limit the reliability and utility of underwater acoustic communications. Moreover, many sensors have greater bandwidth requirements than acoustic communications are able to provide. We here discuss the development of high repetition rate multispectral LED optical systems initially developed for imaging, but also capable of simultaneous data transmission at rates of approximately100 kilobits per second. Results are discussed for the multispectral images from coral reefs in Guam, and data transmission experiments from underwater to surface vessels. Subsequent field efforts will extend data transmission from AUVs to unmanned aircraft systems (UAS).

Description: This paper presents a generic approach developed to derive surface reflectance over land from a variety of sensors. This technique builds on the extensive dataset acquired by the Terra platform by combining MODIS and MISR to derivean explicit and dynamic map of band ratio's between blue and red channels and is a refinement of the operational approach used for MODIS and LANDSAT over the past 15 years. We will present the generic approach and the application to MODIS VIIRS, LANDSAT and Sentinel 2 data's and its validation using the AERONET data.

Description: Defining design alternatives constitutes one of the critical initial steps of the systems engineering process. Once these design alternatives have been identified, assessing which alternatives best satisfy the projects objectives can prove to be challenging when dealing with complex decision frameworks. Complex systems often involve the participation of different interest groups, who have different value systems and are focused on distinct aspects of the project. For example, design alternatives might be assessed predominantly for their technical merit by one group of stakeholders, while a different group might be more inclined to assess the design alternatives primarily based on programmatic values. MESCAL (Monitoring the Evolving State of Clouds and Aerosol Layers) is an ongoing NASA / CNES (Centre National dEtudes Spatiales) joint study for an active remote sensing Earth observing satellite. Several design alternatives have been identified and the assessment of these alternatives requires consideration of a variety of factors. This paper presents the approach that was used to support a global assessment of the MESCAL design alternatives. A mapping of the interactions between mission, instrument, and science requirements was modeled to support the assessment of the trade space. In addition, a set of metrics was developed to structure the assessment that was conducted. Finally, this paper also discusses the general applicability of these metrics to other science mission concepts in the formulation phase.

Description: We present an optimal estimation based retrieval framework, the Microphysical Aerosol Properties from Polarimetry (MAPP) algorithm, designed for simultaneous retrieval of aerosol microphysical properties and ocean color bio-optical parameters using multi-angular polarized radiances. Polarimetric measurements from the airborne NASA Research Scanning Polarimeter (RSP) were inverted by MAPP to produce atmosphere and ocean products. The RSP MAPP results are compared with co-incident lidar measurements made by the NASA High Spectral Resolution Lidar HSRL-1 and HSRL-2 instruments. Comparisons are made of the aerosol optical depth (AOD) at 355 and 532 nm, lidar column-averaged measurements of the aerosol lidar ratio and ngstrm exponent, and lidar ocean measurements of the particulate hemispherical backscatter coefficient and the diffuse attenuation coefficient. The measurements were collected during the 2012 Two-Column Aerosol Project (TCAP) campaign and the 2014 Ship-Aircraft Bio-Optical Research (SABOR) campaign. For the SABOR campaign, 73% RSP MAPP retrievals fall within 0.04 AOD at 532 nm as measured by HSRL-1, with an R value of 0.933 and root-mean-square deviation of 0.0372. For the TCAP campaign, 53% of RSP MAPP retrievals are within 0.04 AOD as measured by HSRL-2, with an R value of 0.927 and root-mean-square deviation of 0.0673. Comparisons with HSRL-2 AOD at 355 nm during TCAP result in an R value of 0.959 and a root-mean-square deviation of also 0.0694. The RSP retrievals using the MAPP optimal estimation framework represent a key milestone on the path to a combined lidar + polarimeter retrieval using both HSRL and RSP measurements.

Description: Mercury Cadmium Telluride electron initiated avalanche photodiodes demonstrated a breakthrough in lidar active remote sensing technology. A lidar detection system, based on an array of these devices, was integrated and characterized for 2-m applications. Characterization experiments were focused on evaluating the dark current, gain and responsivity variations with bias voltage. Quantum efficiency and input dynamic range including noise-equivalent-power and maximum detectable power, were calculated from these results. Operating the detection system using four pixels at 77.6 K, 12 V bias resulted in a current responsivity of 615.8 A/W and a voltage responsivity of 1.45 GV/W. Minimum detectable power of 14 pW was obtained, which is equivalent to 5.7 fW/Hz(exp 1/2) noise-equivalent-power, indicating an average noise-equivalent-power of 1.4 fW/Hz(exp 1/2) per pixel. Work is in progress to integrate and validate this detection system using a newly developed triple-pulse integrated path differential absorption lidar for simultaneous and independent atmospheric measurements of water vapor and carbon dioxide.

Description: One of the scientific instruments aboard the NOAA-20 satellite is the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS regularly performs on-orbit radiometric calibration of its reflective solar bands, primarily through observations of an onboard sunlit solar diffuser (SD). The incident sunlight passes through an attenuation screen (the SD screen) and scatters off the SD to provide a radiance source for the calibration. The on-orbit change of the SD bidirectional reflectance distribution function (BRDF), denoted as the H-factor, is determined by an onboard solar diffuser stability monitor (SDSM). The eight SDSM detectors observe the sun through another attenuation screen (the SDSM screen) and the sunlit SD almost at the same time to measures the SD BRDF change. The products of the SD screen transmittance and the BRDF at t=0 and the SDSM screen transmittance were measured prelaunch. Large undulations in the H-factor were seen when using the prelaunch screen transmittances. Fifteen on-orbit yaw maneuvers were performed to validate and to further characterize the screens. Although significantly improved, the H-factor from the yaw maneuver data determined screen transmittance still has undulations as large as about 0.7-0.8%, revealing that the angular step size of the yaw maneuvers is too large. In this paper, we add regular on-orbit data to the yaw maneuver data to further improve the relative products and the relative SDSM screen transmittance. The H-factor time series derived from the newly determined screen transmittance is much smoother than that derived from using only the yaw maneuver data and thus improves considerably the radiometric calibration accuracy.

Description: It is well accepted that drought and low moisture conditions are linked with increased wildfire occurrence. However, quantifying the sensitivity of wildfire to surface moisture state has been challenging due to a lack of soil moisture observations at an appropriate spatial scale. Here we apply model simulations of surface soil moisture that numerically assimilate observations from NASAs Gravity Recovery and Climate Experiment (GRACE) mission, combined in a predictive algorithm with the US Forest Services Fire-Occurrence Database. We estimate a relationship between historic surface moisture and wildfire occurrence to produce annual probable wildfire occurrence and burned area at 0.25-degree resolution for the contiguous United States by land-cover classification. Cross-validation indicates increased frequency of smaller fires when the months preceding fire season are wet, while larger fires are more frequent when soils are dry. This demonstrates that assimilated GRACE data holds information that could aid national-scale fire potential assessments for early decision-support.

Description: The NOAA-20 (formerly the Joint Polar Satellite System-1) satellite was launched on November 18, 2017. One of the five scientific instruments aboard the NOAA-20 satellite (N20) is the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS scans the earth surface in 22 spectral bands, of which 14 are denoted as the reflective solar bands (RSBs) with design band central wavelengths from 412 to 2250 nm. The VIIRS regularly performs on-orbit radiometric calibration of its RSBs, primarily through observations of an onboard sunlit solar diffuser (SD). The on-orbit change of the SD bidirectional reflectance distribution function (BRDF) value, denoted as the H-factor, is determined by an onboard solar diffuser stability monitor (SDSM). We have shown that the H-factor for the SD on the VIIRS instrument on the Suomi National Polar-orbiting Partnership (SNPP) satellite is both incident and outgoing sunlight direction dependent. This angular dependence profoundly affects the on-orbit radiometric calibration process and results. Here, we give preliminary results for the angular dependence for the N20 VIIRS SD H-factor, and compare the dependence with that for the SNPP VIIRS.

Description: NASA Earth Science (ES) data is essential to a wide range of GIS research and applications. However, for many GIS users, searching, accessing, using and analyzing NASA ES data can be of a great challenge- ranging from the sheer data volumes, types of science parameters, and to the complexity of data encoding formats. As one of the twelve NASA Science Mission Directorate (SMD) Data Centers, Goddard Earth Sciences (GES) Data and Information Services Center (DISC) archives and distributes petabytes of ES parameters covering atmosphere, land, and ocean fields. Most data are multidimensional and multi-spatiotemporal in nature and are encoded in different science data formats (e.g, HDF, HDF-EOS, netCDF, GRIB, binary), which usually contain multiple variables and different metadata information. By far, GES DISC has been developing a number of services and online tools to help GIS users to easily explore our data products. In this presentation, we will describe our ArcGIS-based data accessing and visualization services and portals, which allow users directly exploring the multi-spatiotemporal ES data in ArcGIS clients without having to pre-download/import the data. The ArcGIS services are also compliant with the Open Geospatial Consortium (OGC) Web Coverage Service (WCS) and Web Map Service (WMS) protocols and can be accessed by any other WCS/WMS clients to get customized GES DISC EO data on-the-fly from such services.

Description: NASA's Atmospheric Tomography Mission (ATom) deployed in each of the four seasons during 2016-2018, the DC-8 aircraft in order to establish global-scale datasets intended to improve the representation of chemically reactive gases in global atmospheric chemistry models (ACMs). The Global Modeling Initiative (GMI) executed simulations for each ATom flight using the GMI Chemistry Transport Model (GMI-CTM) to provide species concentrations of chemical gases along the DC-8 flight transects. To solve the problem of translating the GMI-CTM simulation data to the unique spatial resolutions of each ATom flight, the GMI ICARTT Processing Software (GMI-IPS) was developed.The GMI-IPS is written in Python and provides data processing, flight extraction, and visualization support for aircraft research projects using ICARTT format, which is a standard format for airborne instrument data. Additionally, the GMI-IPS interpolates global gridded model data from Hierarchical Data Format (HDF) to ICARTT compatible flight transects. Software classes for instruments and collections provided by the ATom DC-8 aircraft such as MER10, MMS, etc. are derived from a common base class. Other functionality provided by the GMI-IPS are: deriving missing flight entries along a transect, reading ICARTT entries from file, and providing Python data structures for storing flight and model information, and more.The GMI-IPS is GIT source controlled, has approximately 30,000 lines of code, and supports parallelization across data collections. It delivered GMI-CTM data for more than forty distinct DC-8 aircraft flights that took place under ATom. The output ICARTT files adhere to format standard V1.1, and pass the scan utility provided by NASA LaRC Airborne Science Data for Atmospheric Composition. This presentation will include a software and methods overview, and results from ATom, including assessments using the GMI-CTM showing how well observations from ATom flight transects represent a broader region.

Description: Seasonal forecasts made by coupled atmosphere-ocean general circulation models (GCMs) are increasingly able to provide skillful forecasts of climate anomalies. At some centers, the capabilities of these models are being expanded to represent carbon-climate feedbacks including ocean biogeochemistry (OB), terrestrial biosphere (TB) interactions, and fires. These advances raise the question of whether such models can support skillful forecasts of carbon fluxes.Here, we examine whether land and ocean carbon flux anomalies associated with the 2015-16 El Nino could have been predicted months in advance. This El Nino was noteworthy for the magnitude of the ocean temperature perturbation, the skill with which this perturbation was predicted, and the extensive satellite observations that can be used to track its impact. We explore this topic using NASA's Goddard Earth Observing System (GEOS) model, which routinely produces an ensemble of seasonal climate forecasts, and a suite of offline dynamical and statistical models that estimate carbon flux processes. Using GEOS forecast fields from 2015-16 to force flux model hindcasts shows that these models are able to reproduce significant features observed by satellites. Specifically, OB hindcasts are able to predict anomalies in chlorophyll distributions with lead times of 3-4 months. The ability of TB hindcasts to reproduce NDVI anomalies is driven by the skill of the climate forecast, which is greatest at short lead times over tropical landmasses. Statistical fire forecasts driven by ocean climate indices are able to predict burned area in the tropics with lead times of 3-12 months. We also integrate the ocean and land hindcast fluxes into the GEOS GCM to examine the magnitude of the atmospheric carbon dioxide anomaly and compare with satellite and ground-based observations.While seasonal forecasting remains an active area of research, these results demonstrate that forecasts of carbon flux processes can support a variety of applications, potentially allowing scientists to understand carbon-climate feedbacks as they happen and to capitalize on more flexible satellite technologies that allow areas of interest to be targeted with lead times of weeks to months. We also provide a first glimpse at the spring 2019 carbon forecast using the GEOS-based forecasting system.

Description: The first Visible Infrared Imaging Radiometer Suite (VIIRS) instrument has been in operation for more than 6 years on-board the S-NPP satellite and the second instrument, with the same design and performance requirements, was launched in November, 2017 on-board the JPSS-1 satellite (named NOAA-20 after reaching its orbit) and is currently in normal operation conditions. This paper provides a brief description of VIIRS on-orbit calibration and characterization activities and presents performance assessments and comparisons of S-NPP and NOAA-20 VIIRS using data collected from their on-board calibrators (OBC) and regularly scheduled lunar observations. Results show that NOAA-20 VIIRS is performing as well or better than S-NPP VIIRS in all of the key performance metrics. The NOAA-20 reflective solar bands, including the day-night band, have experienced less than 1% change in gain in the first 250 days since launch and did not suffer from the contamination related rapid degradation experienced by S-NPP VIIRS. Some of the NOAA20 thermal emissive bands had larger than expected gain degradation after launch due to ice buildup on the dewar window of the long-wave IR focal plane assembly but a mid-mission outgassing operation was able to restore their gains and maintain stable behavior. Though this study is focused on the sensor's key performance parameters, such as detector responses (gains), signal-to-noise ratios, and noise-equivalent temperature differences, challenges identified and lessons learned through different phases of on-orbit calibration and characterization are also discussed.

Description: This work presents a methodology for estimating seasonal snow water equivalent (SWE) from the use of remotely sensed Visible and Near Infrared observations from the Landsat mission. The method is comprised of two main components: (1) a coupled land surface model and snow depletion curve model, which is used to generate an ensemble of predictions of SWE and snow cover area for a given set of (uncertain) inputs, and (2) a reanalysis step, which updates estimation variables to be consistent with the satellite observed depletion of the fractional snow cover time series. This method was applied over the Sierra Nevada (USA) based on the assimilation of remotely sensed fractional snow covered area data over the Landsat 5-8 record (1985-2016). The verified dataset (based on a comparison with over 9000 station years of in situ data) exhibited mean and root-mean-square errors less than 3 and 13 cm, respectively, and correlations with in situ SWE observations of greater than 0.95. The method (fully Bayesian), resolution (daily, 90-meter), temporal extent (32 years), and accuracy provide a unique dataset for investigating snow processes. In particular, this presentation illustrates how the reanalysis dataset was used to provide climatology of the seasonal snowfall accumulation rates, distributions, and variability over the last three decades.

Description: The MODIS instruments on the Terra and Aqua spacecraft are cross-track scanning radiometers that view the Earth scene, a space view port, and the on-board calibrators using a two-sided scan mirror. The reflectivity of the scan mirror varies with the angle of the incident light, and changes in this response versus scan angle (RVS) need to be tracked on orbit in order to maintain accurate calibration. In this paper, we review various methods of RVS calibration of the reflective solar bands using pseudo-invariant desert targets and discuss potential advantages and disadvantages for use in future calibration.

Description: Over the years, data from different satellites has provided invaluable information about Earth's atmosphere, land and oceans. The thermal emissive bands (TEB) on the Moderate Resolution Imaging Spectroradiometer (MODIS) are comprised of 16 spectral bands with wavelengths ranging from 3.7 to 14.4 m. MODIS TEB are calibrated on orbit on a scan-by-scan basis using an on-board blackbody (BB). Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR), launched on 16 February 2016, has 11 spectral bands with wavelengths from 0.55 to 12 m. In this study, we compare the observed brightness temperature from MODIS bands 31 and 32 and SLSTR bands S8 and S9 over Dome C using a 20 20 km region of interest (ROI) centered at (75.102 S,123.395 E). A total of 2989 scenes for Terra, 2963 for Aqua and 1961 for SLSTR from November, 2016 to January 2018 are analyzed. The relative bias between MODIS and SLSTR is evaluated using the near-surface temperature measurements from an Automatic Weather Station (AWS).

Description: The Harmonized Landsat-8 and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a seamless, harmonized surface reflectance record from the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard Landsat-8 and Sentinel-2 remote sensing satellites, respectively. The HLS products are based on a set of algorithms to obtain seamless products from both sensors (OLI and MSI): atmospheric correction, cloud and cloud-shadow masking, geographic co-registration and common gridding, bidirectional reflectance distribution function normalization and bandpass adjustment. As of version 1.3, the HLS v1.3 data set covers 9.12 million km2 and spans from first Landsat-8 data (2013) to present. HLS products provide near-daily surface reflectance information with a common geometric framework, and are suitable for a variety of agricultural and vegetation monitoring tasks, including analysis of crop type, condition, and phenology.

Description: Two MODIS instruments (Terra and Aqua) and two VIIRS instruments (S-NPP and JPSS-1) are currently operated inspace, continuously making global earth observations in the spectral range from visible (VIS) to long-wave infrared(LWIR). These observations have enabled a broad range of environmental data records to be generated and distributed insupport of both operational and scientific community. Despite extensive pre-launch calibration and characterizationperformed for both MODIS and VIIRS instruments and routine on-orbit calibration activities carried out using their onboardcalibrators (OBC), various spacecraft maneuvers have also been designed and implemented to further enhance thesensor on-orbit calibration and data quality. This paper focuses on the use of observations made during spacecraft pitchmaneuvers of MODIS and VIIRS in support of their on-orbit characterization of thermal emissive bands (TEB) responseversus scan-angle (RVS). In the case of Terra MODIS, lunar observations made from instrument nadir view duringspacecraft pitch maneuvers are used to compare with that made regularly through instrument space view (SV) port toevaluate on-orbit changes in RVS and band-to-band registration (BBR) for the reflective solar bands (RSB). In additionto results derived from spacecraft pitch maneuvers performed for MODIS and VIIRS, discussion is provided on theadvantages, challenges, and lessons for future considerations and improvements.

Description: In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Description: NASA's Capacity Building Program (CBP) aims to empower communities to use NASA Earth Observations for environmental decision-making. A new project, the Indigenous Peoples Pilot Project, focuses on building relationships across NASA and indigenous communities through remote sensing training, community engagement, and research opportunities. A recent workshop, held on the lands of the Red Cliff Band of Lake Superior Chippewa in Wisconsin, focused on understanding indigenous knowledge systems and comparisons with western science, and more specifically, NASA Earth Science. In order to meet the workshop goals, North American tribal members and NASA managers participated in storytelling and systems mapping. The storytelling portion focused on personal narratives to understand the barriers, challenges, and pathways to incite change in the context of western scientists working with tribal nations. This was based on the traditional model of oral history and allowed participants to share their unique experiences of failures and successes. The systems mapping portion of the workshop focused on finding leverage points within the "NASA Ecosystem" for creating sustained instrumental change. This included (1) dedicating time and resources to explore how to recognize western science and indigenous knowledge as equal, (2) supporting innovation in communication and knowledge system frameworks, and (3) identifying new partners, allies, and champions for the western science/indigenous relationship. These leverage points were then used to generate recommendations for NASA which included (1) an awareness phase (for NASA and for indigenous communities) for NASA/Indigenous work, (2) creating a cultural immersion for NASA managers, (3) developing a protocol for working with indigenous groups, (4) creating a NASA tribal liaison office, (5) acknowledging and accepting indigenous knowledge systems in funding solicitations and proposals, and (6) incorporation of indigenous knowledge into capacity building activities.

Description: Biomass burning, which includes wildfires, prescribed, and agricultural fires, is an important source of trace gases and particles, and can influence air quality on a local, regional, and global scale. Biomass burning emissions are an important source of several key trace gases including carbon dioxide (CO2) and methane (CH4). With the threat of wildfire events increasing due to changes in land use, increasing population, and climate change, the importance of characterizing wildfire emissions is vital. In this work we characterize trace gas emissions from 9 wildfire events in California between 2013 2016, in some cases with multiple measurements performed during different burn periods of a specific wildfire. During this period airborne measurements of CO2, CH4, water vapor (H2O), ozone (O3), and formaldehyde (HCHO) were made by the Alpha Jet Atmospheric eXperiment (AJAX). Located in the Bay Area of California, AJAX is a joint effort between NASA Ames Research Center and H211, LLC. AJAX makes in-situ airborne measurements of trace gases 2-4 times per month, resulting in 229 flights to date since 2011. Results presented include emission ratios (ER) of trace gases measured by AJAX during fire flights, and comparisons of ERs are made for each fire, which differ in time, location, burning intensity, and fuel type. We also use our airborne measurements to compare with photochemical grid model results to assess model approximations of plume transport and chemical evolution from select wildfires.

Description: The use of unmanned air vehicles (UAVs) to passively collect fog for microbiological analysis is being investigated at NASA Ames, contributing to our understanding of the dynamics and limits of the biosphere. Current improvements to a preliminary payload design include physically modifying the mesh of the passive impactor collection unit (PICU) to increase fog-capture efficiency and a quick- mounting system to improve sample turnaround and contamination control. Increasing the water-capture efficiency of the PICU involved changing the surface roughness of the polypropylene mesh by abrasive sanding. Four scale models of the PICU were created, with three of those models sanded with 80-, 150-, and 240-grit sand paper for a minimum of thirty seconds on each side. Each mesh was individually tested in a fog-simulation chamber to measure collection efficiency. Tests showed that sanding the mesh will increase the water-capture efficiency by at least 3-fold for the 150- and 240-grit. Next steps are to determine the scaling laws and minimum amount of exposure time as a function of the fog density, to acquire enough microorganisms for analysis; a light-weight ground module for the PICU has been developed to allow rapid, easy field testing. Developing a mounting system that will ensure sterile deployment, as well as prompt changes of PICUs, for collection of fog water, utilized computational fluid dynamics (CFD) for the UAV to ensure that the location of the impactors will not be contaminated by particulates in the UAV downwash. The original design allowed the impactors to remain within sterile packaging until mounted on the UAV but did not address the concerns of contamination from aerodynamic ground effects and particulate lofting during takeoff. Design for a raised launch pad would allow for the sterile PICU to be attached to UAV and launched from outside the region of projected particulate lofting. California coastal sites have been scouted for appropriate testing locations. The improvements to the PICU design and operations will allow for multiple sampling points within a single fog event. The returned fog samples will undergo ion chromatography, qPCR, ATP quantification, and other tests to characterize microbial species and relevant biochemistry.

Description: Experimental evolution (EE) exposes microbial communities to ecological stressors, simulating dynamics up to near-extinction events. Combined with comparative sequencing and other molecular tools, such data can inform the genetic and other biological mechanisms underlying extremophile adaptation, and other observed effects. Automating this type of experiment using biofluidics can mitigate many traditional obstacles, including delays in assay results and environment adjustment and the need for many replicates. A first-generation device for automating EE procedures, the Automated Adaptive Directed Evolution Chamber (AADEC), was developed at NASA Ames. UV-C radiation was the stressor, an LED-photodiode array measured optical density, magnetic agitation and peristaltic pump systems ensured nutrient availability, and Arduino microcontrollers provided control. Escherichia coli in LB kanamycin media was used for testing and performance verification. A manual laboratory procedure with timed exposure to UV-C was performed to typify tolerance acquisition. Approximately a 106 factor increase in survival ratio was recorded over multiple iterations. Currently, a second-generation device is being developed integrating more real-time sensors: redox potential (ORP), indicating available/consumed metabolic energy; dissolved oxygen (DO), indicating aerobic/anaerobic growth; pH, indicating metabolic products; and electrical conductivity (EC), another indicator of metabolic products. The EC sensor system was constructed and calibrated in-house and matched commercial sensors in the required range. A Raspberry Pi computer automated the electrical system, allowing real-time data acquisition. The fluidics card was made of CNC-milled polycarbonate for biocompatibility. Each sensor parameter can also be used as a selection pressure alone or in combination with others to create extreme microbial environments. As a proof of concept, this work demonstrated sensor operation in one pair of growth-sensor chambers. It can be expanded to a multi-chamber system to enable inter-culture comparisons and multi-population studies. The prior Arduino system will be ported to the RPi system. Future stressors to be added include thermal, reactive oxygen species, and varying nutrient availability.

Description: Management of aquatic weeds in complex watersheds and river systems present many challenges to assessment, planning and implementation of management practices for aquatic invasive plants. The Delta Region Areawide Aquatic Weed Project (DRAAWP), a USDA (U. S. Dept. of Agriculture)-sponsored area-wide project, is working to enhance planning, decision-making and operational efficiency of invasive plant management in the California Sacramento-San Joaquin Delta. Satellite and airborne remote sensing are used to map area of plant coverage and estimate biomass density to aid operations and assess management impacts on plant communities. Modeling at local and watershed scales using the SWAT (Soil Water Assessment Tool) modeling tool provides insight into land-use effects on water quality. Environmental variability in the Delta occurs across a range of time scales from long-term climate and seasonal trends to short-term water flow mediated variations. Controlled environment growth studies have been conducted to quantify the growth response of invasive aquatic plants to water quality and other environmental factors. Response time for invasive species response are examined at time scales of weeks, day, and hours using a combination of study duration and growth assessment techniques to assess water quality, temperature (air and water), and light effects. These provide response parameters for plant growth models in response to environmental variation and interact with management and economic models associated with aquatic weed management. Plant growth models are to be informed by remote sensing and applied spatially across the Delta to balance location and type of aquatic plant, growth response to altered environments and phenology. Initial utilization of remote sensing tools developed for mapping of aquatic invasive plants improved operational efficiency in management practices. These assessment methods provide a comprehensive and quantitative view of aquatic invasive plant communities in the California Delta.

Description: The James Webb Space Telescope (JWST), set to launch in early 2019, is currently undergoing a series of system-level environmental tests to verify its workmanship and end-to-end functionality. As part of this series, the Optical Telescope Element and Integrated Science Instrument Module (OTIS) Cryo-Vacuum (CV) test, the most complex cryogenic test executed to date by NASA, has recently been completed at the Johnson Space Centers Chamber A facility. The OTIS CV test was intended as a comprehensive test of the integrated instrument and telescope systems to fully understand its optical, structural, and thermal performance within its intended flight environment. Due to its complexity, extensive pre-test planning was required to ensure payload safety and compliance with all limits and constraints. A system-level pre-test thermal model was constructed which fully captured the behavior of the payload, ground support equipment, and surrounding test chamber. This thermal model simulated both the transient cooldown to and warmup from a 20K flight-like environment, as well as predicted the payload performance at cryo-stable conditions. The current work is a preliminary assessment of thermal model performance against actual payload response during the OTIS CV test. Overall, the thermal model performed exceedingly well at predicting schedule and payload response. Looking in depth, this work examines both the benefits and shortcomings of assumptions made pre-test to simplify model execution when compared against test data. It explores in detail the role of temperature-dependent emissivities during transition to cryogenic temperatures, as well as the impact that model geometry simplifications have on tracking of critical hardware limits and constraints. This work concludes with a list of recommendations to improve the accuracy of thermal modeling for future large cryogenic tests. It is hoped that the insight gained from the OTIS CV test thermal modeling will benefit planning and execution for upcoming cryogenic missions.

Description: Potential sources of a priori ozone (O3) profiles for use in Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite tropospheric O3 retrievals are evaluated with observations from multiple Tropospheric Ozone Lidar Network (TOLNet) systems in North America. An O3 profile climatology (tropopause-based O3 climatology (TB-Clim), currently proposed for use in the TEMPO O3 retrieval algorithm) derived from ozonesonde observations and O3 profiles from three separate models (operational Goddard Earth Observing System (GEOS-5) Forward Processing (FP) product, reanalysis product from Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), and the GEOS-Chem chemical transport model (CTM)) were: 1) evaluated with TOLNet measurements on various temporal scales (seasonally, daily, hourly) and 2) implemented as a priori information in theoretical TEMPO tropospheric O3 retrievals in order to determine how each a priori impacts the accuracy of retrieved tropospheric (0-10 km) and lowermost tropospheric (LMT, 0-2 km) O3 columns. We found that all sources of a priori O3 profiles evaluated in this study generally reproduced the vertical structure of summer-averaged observations. However, larger differences between the a priori profiles and lidar observations were observed when evaluating inter-daily and diurnal variability of tropospheric O3. The TB-Clim O3 profile climatology was unable to replicate observed inter-daily and diurnal variability of O3 while model products, in particular GEOS-Chem simulations, displayed more skill in reproducing these features. Due to the ability of models, primarily the CTM used in this study, on average to capture the inter-daily and diurnal variability of tropospheric and LMT O3 columns, using a priori profiles from CTM simulations resulted in TEMPO retrievals with the best statistical comparison with lidar observations. Furthermore, important from an air quality perspective, when high LMT O3 values were observed, using CTM a priori profiles resulted in TEMPO LMT O3 retrievals with the least bias. The application of time-specific (non-climatological) hourly/daily model predictions as the a priori profile in TEMPO O3 retrievals will be best suited when applying this data to study air quality or event-based processes as the standard retrieval algorithm will still need to use a climatology product. Follow-on studies to this work are currently being conducted to investigate the application of different CTM-predicted O3 climatology products in the standard TEMPO retrieval algorithm. Finally, similar methods to those used in this study can be easily applied by TEMPO data users to recalculate tropospheric O3 profiles provided from the standard retrieval using a different source of a priori.

Description: Ground reflectance was acquired at the Railroad Valley Playa calibration site in Nevada USA using different methods of collection. The data was collected near the time and date of Landsat 8 OLI and Sentinel-2 satellite overpasses so an inter-comparison could be made with the reflectance products to determine which method was more suitable for vicarious calibration. The field spectrometers and reference panels were characterized before the field campaign. A continuous acquisition method was compared to stop and measure collections. Both acquisition methods were collected along an 80 m east-west transect as well as for a series of north-south transects over an 80 x 320 m area, with the stop and measure method being performed at random sampling locations. The measurements were performed using two field spectrometers by three teams of two people to compare the repeatability. The aim of the field campaign was to determine the variability due to the operator and the method of collection.

Description: NASA's Black Marble nighttime lights product suite (VNP46) is available at 500m resolution since January 2012 with data fro the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-terrain, vegetation, snow, lunar and stray light corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Current and planned validation activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at difference geographic locations and time periods representing the full range of retrieval conditions.

Description: No abstract available

Description: No abstract available

Description: The Joint Polar Satellite System 2 (JPSS-2) is the follow-on for the Suomi-National Polar-orbiting Partnership (S-NPP) and Joint Polar Satellite System 1 (JPSS-1) missions. These spacecrafts provide critical weather and global climate products to the user community. A primary sensor on both JPSS and S-NPP is the Visible-Infrared Imaging Radiometer Suite (VIIRS) with Earth observations covering the Reflective Solar Band (RSB), Thermal Emissive Band (TEB) and Day Night Band (DNB) spectral regions. The VIIRS Sensor Data Records (SDRs) contain the calibrated Earth observations that are used in Environmental Data Record (EDR) products such as Ocean Color/Chlorophyll (OCC) and Sea Surface Temperature (SST). This SDR calibration is performed using unpolarized sources such as the Solar Diffuser (SD) for the RSBs and an On-Board Calibrator BlackBody (OBCBB) for the TEBs. Therefore, polarized Earth scenes will have radiometric bias errors within the SDRs based on how sensitive VIIRS is to polarized illumination and is corrected in some EDR algorithms. This paper will discuss the JPSS-2 VIIRS polarization characterization methodology, polarization sensitivity results and compare its performance to its predecessors S-NPP and JPSS-1 VIIRS. Optical modifications to the JPSS-2 VIIRS sensor to address heritage polarization sensitivity issues will be discussed.

Description: At the interface between the land, oceans, and atmosphere, coastal regions are highly dynamic environments, characterized by strong variability in both water and air quality. Variability in atmospheric composition is associated with highly variable anthropogenic emissions, as well as complex meteorological processes that influence the circulation and accumulation of atmospheric pollutants at the land-ocean interface. Assessing the spatial and temporal dynamics of atmospheric pollutants, aerosols, and absorbing trace gases in coastal areas is critical for improving modeling of coastal tropospheric air quality, developing accurate satellite retrievals of coastal ocean color and biological processes, determining impacts of atmospheric pollution on human health, and assessing the ecological implications of atmospheric pollutant deposition for coastal terrestrial and aquatic ecosystems.Here, we present new measurements of atmospheric trace gas (NO2, and ozone) dynamics across a range of estuarine and coastal waters near urban regions. Measurements were conducted from research vessels using NASA's shipboard Pandora spectrometers, as part of recent multidisciplinary, multiplatform field campaigns, including the 2016 KORUS OC/AQ field campaign in the Yellow Sea and East Sea/Sea of Japan, the 2017/2018 OLWETS field campaign in the Chesapeake Bay estuary, and the 2018 LISTOS field campaign in the Long Island Sound. Shipboard measurements over these coastal waters were integrated with measurements from a ground-based Pandora network to examine differences in air quality over the land and over the ocean. Measurements were combined with air-parcel back-trajectory simulations to determine the origin of air masses over the coastal ocean. Comparisons with satellite retrievals of atmospheric composition reveal the benefits and limitations of polar-orbit satellite observations in capturing variability in atmospheric pollution gradients over land-water boundaries.

Description: Ground reflectance was acquired at the Railroad Valley Playa calibration site in Nevada USA using different methods of collection. The data was collected near the time and date of Landsat 8 OLI and Sentinel-2 satellite overpasses so an inter-comparison could be made with the reflectance products to determine which method was more suitable for vicarious calibration. The field spectrometers and reference panels were characterized before the field campaign. A continuous acquisition method was compared to stop and measure collections. Both acquisition methods were collected along an 80 m east-west transect as well as for a series of north-south transects over an 80 x 320 m area, with the stop and measure method being performed at random sampling locations. The measurements were performed using two field spectrometers by three teams of two people to compare the repeatability. The aim of the field campaign was to determine the variability due to the operator and the method of collection.

Description: GEDI: Global Ecosystem Dynamics Investigation. Selected in late 2014 for $94 M (Class C mission). Multi-beam waveform lidar instrument. Deployed on International Space Station. Launch on SpaceX-17: Nov. 2018. Nominal 2 year mission length.

Description: Calibration of the on-orbit gain changes of the narrow bandwidth reflective solar bands (RSB) of Terra and Aqua MODIS is usually based on the band center wavelength. The relative spectral response (RSR) of each band is assumed to be constant on orbit and the time dependence of an overall gain factor is calculated. Any on-orbit changes to the RSR of the MODIS bands will introduce some error into the calibration and may also have an impact on the Earth scene radiance retrieval. We consider two different ways to track how the RSR of the MODIS RSB may be changing on orbit, and the effect that these changes will have on the calibration. First, we examine in-band RSR measurements from the spectro-radiometric calibration assembly (SRCA) carried on-board both MODIS instruments. Second, we study the broadband degradation of the MODIS scan mirror and how it may be changing the effective out-of-band response of the RSB. We find that RSR changes have a small effect on the radiance calibrated using the on-board solar diffuser, generally less than 0.5% in all cases at any time in the missions, with bands 1, 8, and 9 impacted the most.

Description: The upcoming ESA Fluorescence Explorer (FLEX) mission will incorporate ground-based validations for fluorescence parameters and reflectance indices, drawing on an international network of sensors located at eddy covariance tower sites. A program has been initiated by the OPTIMISE program to develop methods and protocols for this network. A sensor system suite under evaluation by OPTIMISE includes the FLoX hyperspectral spectroradiometers. The NASA team at GSFC is participating in this experiment and we report first results from the 2017 summer measurements made above the canopy at the USDA/ARS Beltsville cornfield using the DFLoX and two other leaf-level measurement systems, the MONI-PAM and the FluoWat.

Description: Hawkeye is an ocean color instrument that is part of the SeaHawk satellite developed for SOCON, the Sustained Ocean Color Observations using Nanosatellites program funded by the Gordon and Betty Moore Foundation and managed by the University of North Carolina Wilmington (UNC-W). HawkEye has spectral characteristics similar to SeaWiFS, but with 8 times finer resolution and a smaller field of view more appropriate for lakes, rivers, and near-shore terrestrial environments. With a volume of only 10 X 10 X 10 cm (a CubeSat 1U), it can produce 8 bands of image data in a single pass, each with 1800 x 6000 pixels, with a resolution of 120 meters per pixel. This paper will present a short summary of instrument design, the spacecraft interface, and lessons learned during this effort. Scientists considering using linear arrays in a pushbroom mode for remote sensing will find this useful. Much of the discussion will center on optical performance, such as flat field calibration, polarization effects, stray light, out-of-band response, and exposure linearity. Images from field tests will be shown.The Hawkeye instrument is an ocean color measuring instrument designed to fly on the SeaHawk satellite developed for SOCON, the Sustained Ocean Color Observations using Nanosatellites program funded by the Gordon and Betty Moore Foundation and managed by the University of North Carolina Wilmington (UNC-W). The Hawkeye instrument measures ocean color in 8 spectral bands, similar to SeaWiFS, except Band 7, which is shifted to a slightly lower wavelength to avoid the oxygen absorption feature that a wider band overlapped on SeaWiFS. The instrument is approximately 1/3rd the volume of the entire satellite, which is a 3U Cubesat manufactured by Clydespace in Glasgow, Scotland. The purpose of this instrument is to ascertain the quality of ocean color data possible with such a small, inexpensive instrument and bus. The nominal orbit is 540 km, and the nominal pixel geometric instantaneous field of view (GIFOV) 120 meters on a side. Each band will produce an image 1800 x 6000 pixels in size, for a total field of view of 216 X 720 km.2) DESIGN CONCEPTThe Hawkeye instrument uses linear arrays in pushbroom mode to collect data over a two dimensional area. The instrument has 4 linear CCD arrays, the Onsemi KLI-4104, to collect the 8 bands of data. Figure 1 illustrates the optical design for two bands, sharing a single array.

Description: No abstract available

Description: No abstract available

Description: The Thermal Infrared Sensor 2 (TIRS-2) will fly aboard the Landsat 9 spacecraft and leverages the Thermal Infrared Sensor (TIRS) design currently flying on Landsat 8. TIRS-2 will provide similar science data as TIRS, but is not a buildto-print rebuild due to changes in requirements and improvements in absolute accuracy. The heritage TIRS design has been modified to reduce the influence of stray light and to add redundancy for higher reliability over a longer mission life. The TIRS-2 development context differs from the TIRS scenario, adding to the changes. The TIRS-2 team has also learned some lessons along the way.

Description: This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone Soil Moisture (L4_SM) data for the Science Data System (SDS) of the Soil Moisture Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project.

Description: The newly launched (November 18, 2017) polar-orbiting satellite of the Joint Polar Satellite System (JPSS-1), now transitioned to NOAA-20, is the follow-on mission to the SNPP (Suomi National Polar-orbiting Partnership) satellite, launched six years ago. NOAA-20 leads SNPP by a half orbit or about 50 minutes. The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key sensor onboard both NOAA-20 and SNPP spacecraft with nearly identical band spectral responses. Similar to the heritage sensor MODIS, VIIRS has on-board calibration components including a solar diffuser (SD) and a solar diffuser stability monitor (SDSM) for the reflective solar bands (RSB), a V-groove blackbody for the thermal emissive bands (TEB), and a space view (SV) as background reference for calibration. This study provides an initial assessment of calibration of the NOAA-20 VIIRS reflective solar bands (RSB) by inter-comparison with measurements from SNPP VIIRS using various vicarious approaches. The first approach is based on a double difference method using observations from simultaneous nadir overpasses (SNO) with Aqua MODIS. The second is from the collected reflectances over the widely used Liby-4 desert site from 16-day repeatable orbits so each data point has the same viewing geometry relative to the site. The third approach is to use the frequent overpasses over the Dome C snow site. Results of this study provide useful information on NOAA-20 VIIRS post-launch calibration assessment and preliminary analysis of its calibration stability and consistency for the first 6 months

Description: The JPSS-1 (now named NOAA-20) VIIRS instrument has been successfully operating on orbit since November 28th, 2017. The Day-Night Band (DNB) is a panchromatic channel covering wavelengths from 0.5 to 0.9 m that is capable of observing the Earth scene in visible/near-Infrared spectral range at spatial resolution of 750 m. The DNB operates at low, mid, or high radiometric gain stages, and it uses an onboard solar diffuser (SD) panel for low gain stage calibration. The SD observations also provide a mean to compute gain ratios between low-to-mid and mid-to-high gain stages. With their large dynamic range and high sensitivity, the DNB detectors can make observations during both daytime and nighttime. This paper provides an early assessment of the DNB on-orbit performance and behavior in the first 90-day post launch test (PLT) period and beyond. The calibration methodology used by the VIIRS Characterization Support Team (VCST) in support of the NASA earth science community will be presented. The trending of OBC dark-offsets, SD gains and gain ratios, and signal-to-noise ratio (SNR) at minimum radiance have been analyzed, especially during key events such as the Nadir and Cryo-cooler doors opening. Furthermore, we performed inter-comparison studies between SNPP and JPSS-1 instruments and evaluated DNB radiometric calibration and characterization, including the SD degradation, detector gains and gain ratios, as well as the calibration comparison between the IDPS LUTs and our VCST delivery results.

Description: Aqua MODIS is the second MODIS instrument of NASA's Earth Observation System and has operated for over sixteen years since its launch in 2002. MODIS has sixteen thermal emissive bands (TEBs) located on two separate cold focal plane assemblies (CFPA). The TEBs are calibrated every scan using observations of an onboard blackbody (BB) and a space view port. Low saturation temperatures (Tsat) of Aqua MODIS bands 33, 35, and 36 cause these bands to saturate when the BB temperature is higher than their Tsat values during a BB warm-up cool-down (WUCD) cycle, therefore impacting the ability to perform nominal calibration. In addition, starting from around 2006, the CFPA temperature showed gradual variation from its nominally-controlled operating temperature due to a loss of its radiative cooler margin and the magnitude of its fluctuation reaching a maximum in 2013. The MODIS Characterization Support Team currently uses a correction that is dependent on the CFPA temperature to provide a gain estimate for the saturated scans during the BB WUCD. This gain estimation has been implemented in the Aqua MODIS Collection 6 (C6) and C6.1 L1B products. This paper evaluates the quality of the calibrated radiance of Aqua MODIS bands 33, 35, and 36 using simultaneous nadir observations from the Atmospheric Infrared Sounder (AIRS), which is also onboard the Aqua satellite. Our analysis results show that the differences between AIRS and Aqua MODIS can be controlled well within the fluctuation range compared to the periods when the BB signals for these bands are not saturated.

Description: The TROPspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (Sentinel-5P) is the first of the Atmospheric Composition Sentinels by the European Space Agency (ESA) that provides measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The early afternoon orbit of Sentinel-5P mission provides a strong synergy with the U.S. Suomi National Polar-orbiting Partnership (S-NPP) satellite, especially in that the S-NPP Ozone Monitoring and Profiling Suite (OMPS) facilitates high vertically resolved stratospheric and lower mesospheric ozone profiles. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) supports over a thousand data collections in the Focus Areas of Atmospheric Composition, Water & Energy Cycles, and Climate Variability and it is the Distributed Active Archive Center (DAAC) that is curating both offline Sentinel-5P TROPOMI and S-NPP OMPS Level-1B (L1B) and Level-2 (L2) products. Through its convenient and enhanced tools/services such as OPeNDAP and L2 Subsetting, GES DISC offers air quality remote sensing user communities facile solutions for complex Earth science data and applications. This presentation will demonstrate TROPOMI and OMPS products including earthview radiance, solar irradiance, and currently available L2 datasets, as well as easy ways to access, visualize and subset data. The implementation of the End User License Agreement (EULA) between NASA GES DISC and all data users accessing data at GES DISC will be emphasized as well.

Description: Historically, at the end of a NASA mission, earth and space science data were stored at NASA's National Space Science Data Center (NSSDC). The original data archive consisted of both magnetic tapes and film media. As data storage technology improved, data from later missions were stored on disks and platters and higher capacity magnetic media for online accessibility. To conserve physical space at NASA archive sites and to meet disaster recovery guidelines, historical data originally stored on magnetic tapes and film were moved to the Federal Archives and Record Center (FRC) as a temporary holding area until its long-term value was determined by NASA. All records at the FRC are controlled by the NASA Records Retention Schedule (NRRS) which determines the disposal date for each record. On that date, responsible NASA parties are notified that all scheduled records should be reviewed and assessed to determine if they continue to hold significant historical, scientific or administrative value. For Earth Science data records being held at FRC, the Earth Science Data and Information System (ESDIS) Project office is the party responsible for making the value assessment that determines which records warrant preservation and which are ready for proper disposal according to NASA guidelines. Once the data's long-term value is determined, ESDIS takes definitive steps to preserve this data for future discovery and access. Deteriorating media containing historic data of value are recalled from FRC and brought back to ESDIS. Through a tedious, laborious process, digital data are recovered and restored to modern formats with improved metadata and documentation to aid discovery. The restored digital products are then incorporated into our modern online archive, and made immediately accessible to the public. In this paper, we will discuss how we identify data-at-risk, ways to minimize data loss, how we plan for recovery, how we delegate recovery activities to our archive facilities, and how we make recovered data more accessible.

Description: Social media data streams are important sources of real-time and historical global information for science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are exploring the Twitter data stream for its potential in augmenting the validation program of NASA Earth science missions, specifically the Global Precipitation Measurement (GPM) mission. We have implemented a tweet processing infrastructure that outputs classified precipitation tweets. Inputs are "passive" tweets, along with a smaller number of tweets from "active" participants, i.e., those knowingly contributing to our effort. The "active" tweets, presumably of higher quality, enrich the Twitter stream. "Active" sources include data scraped from other social media (e.g., public Facebook posts) and data from existing crowdsourcing programs (e.g., mPING reports). In addition, there is likely relevant precipitation information in images and documents that are the end points of links often included in tweets. Information derived from these "active" sources could then be tweeted into the Twitter stream, thus enriching its quality. The objective of our current work is to mine these tweet linked images and documents, using neural networks, to increase the information content and quality related to precipitation. For images, we classified them as either precipitation-related or not. For training and validation, we used images obtained via the Google custom search API. We created two models: (1) by training a simple Convolutional Neural Network and (2) by using transfer learning principles to adapt a pre-trained object recognition model. For documents, both those linked to tweets and the tweet contents, we trained Hierarchical Attention Networks to determine precipitation occurrence, type, and intensity. For training and validation, we used a keyword-filtered tweet data set labelled with ground truth data from Dark Sky (an API to retrieve weather-related labels) and the National Severe Storms Laboratory's Multi Radar/Multi-Sensor (MRMS) system. Our results demonstrated the efficacy of our machine learning approaches for enriching the Twitter stream, to derive information potentially useful for validation of earth science satellite data.

Description: Space-borne earth observation has been important to monitor the earth condition and played a critical role in validating other instruments or modeling's outputs. However, the data from satellite earth observation are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use from many aspects. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), one of the 12 official NASA data centers, archives and distributes rich collections of data from multiple satellite missions and model results. The GES DISC is also the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. Recently, the GES DISC has been evolving and improving its data management and services in order to promote NASA data to be easily discovered and accessed, as well as to facilitate interoperability. We'll show in this presentation how to explore and analyze NASA earth observation data for air quality through a suite of user-friendly tools - from Worldview to Giovanni, demonstrating in using this set of tools prepares us to serve the Sentinel 5P TROPOMI to the community.

Description: Spatial correlation structures can describe the degree to which soil moisture at a specified location co-varies in time with that at other, remote locations. Using four years of warm season SMAP Level 2 near-surface soil moisture data, we compute these spatial correlation structures for points across North America. The character of these structures is seen to differ geographically; the structures found for the west-central US, for example, are significantly more spatially extensive. We then demonstrate how these structures can potentially be used to reconstruct soil moisture fields during the pre-SMAP era. In this exercise, we consider as "truth" the soil moistures produced in a long-term offline land surface model simulation (1980-2014) that utilizes precipitation forcing based on a high density of precipitation gauges. Then, for a given location within the continent, we construct an"estimated" soil moisture time series based solely on historical soil moisture information simulated at least 300 km distant from the location, using the SMAP-based spatial correlation structures to determine how to make best use of the remote information. The reconstructed soil moistures are found to have significant skill relative to the assumed truth, suggesting that the same approach, when applied in areas of low rain gauge density (i.e., in areas for which historically simulated soil moistures are necessarily inaccurate), could provide useful historical soil moisture estimates through the SMAP-guided extraction of relevant information from neighboring gauged regions.

Description: The NASA Rapid Response system started in 2001 by serving static subsets of Near Real Time MODIS (Moderate Resolution Imaging Spectroradiometer) imagery acquired from the Terra satellite. Over this time, Rapid Response has been used to support near real time applications such as wildfire and sea ice mapping for hundreds of thousands of users. In 2011, the toolset expanded to include GIBS, the Global Imagery Browse Services, which provides a web map tiling service of over 700 imagery products, and Worldview (https://worldview.earthdata.nasa.gov/), an interactive web application that showcases the products available in GIBS. This year a new application, Worldview Snapshots, has been added to complete the toolset by providing a flexible, low bandwidth method to download personalized subsets for those who do not need or cannot use a fully featured web mapping application. The original Rapid Response subset tool has been retired and we thank it for its seventeen years of service. Stop by to learn more about the next generation of NASA Rapid Response.