ALBERT

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.

Beschreibung: 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.