ALBERT

Description: Soil moisture estimates are crucial for hydrologic modeling and agricultural decision-support efforts. These measurements are also pivotal for long-term inquiries regarding the impacts of climate change and the resulting droughts over large spatial and temporal scales. However, it has only been the past decade during which ground-based soil moisture sensory resources have become sufficient to tackle these important challenges. Despite this progress, random and systematic errors remain in ground-based soil moisture observations. Such errors must be quantified (and/or adequately minimized) before such observations can be used with full confidence. In response, this paper calibrates and analyzes US Climate Reference Network (USCRN) profile estimates at each of three sensors collocated at each USCRN location. With each USCRN location consisting of three independent, Hydraprobe measurements, triple collocation analysis of these sensory triads reveals the random error associated with this particular sensing technology in each individual location. This allows quantification of the accuracy of these individual profiles, the random errors associated with these measurements in different geographic locations, and offers the potential for more adept quality control procedures in near real time. Averaged over USCRN gauge locations nationally, this random error is determined to be approximately 0.012 m 3 /m 3 .

Description: A field experiment was performed in a grassland at the NOAA-CREST–Soil Moisture Advanced Radiometric Testbed (CREST-SMART) facility, which includes a mobile L-band dual-polarized radiometer with an in situ soil temperature and soil moisture observation network, located near Millbrook, NY. During the day-long field campaign, intensive spatiotemporal measurements of L-band brightness temperatures, surface temperature, soil moisture, and soil temperature at 3-, 7-, and 12-cm depths were collected during three passes at 0830, 1130, and 1430 h. During the second and third passes, half of the field was irrigated. Soil roughness and water content of the short grass that remained after mowing the study area were measured. The Tau-Omega radiative transfer model was used to assess the performance of the soil moisture retrieval using measured soil temperatures at different depths. In addition, the collected microwave observations at the three different times of the day were used to assess the impact of the diurnal variation of soil temperature on the performance of soil moisture retrieval. Obtained results showed that the root mean square error (RMSE) decreased throughout the day to reach 0.03 m 3 /m 3 for the afternoon pass when 12-cm soil temperature values were used in the radiative transfer model. In addition, during the three different passes, the lowest RMSE was consistently obtained when the 12-cm soil temperature was used, which suggests that, for this investigated site, soil temperature at the 12-cm depth can be a surrogate for soil effective temperature when L-band microwave temperatures are used. In terms of diurnal variability, observations from the afternoon pass led to the highest agreement between observed and retrieved soil moisture values.

Description: The COsmic-ray Soil Moisture Observing System (COSMOS) rover may be useful for validating satellite-based estimates of near-surface soil moisture, but the accuracy with which the rover can measure 0- to 5-cm soil moisture has not been previously determined. Our objectives were to calibrate and validate a COSMOS rover for mapping 0- to 5-cm soil moisture at spatial scales suitable for evaluating satellite-based soil moisture estimates. Region-specific calibrations for the COSMOS rover were developed using field-average soil moisture measured using impedance probes and the oven-drying method on volumetric samples. The resulting calibrations were applied to map soil moisture on two dates in a 16- by 10-km region around the Marena, Oklahoma, In Situ Sensor Testbed (MOISST) in north central Oklahoma and one date in a 34- by 14-km region in the Little Washita River watershed in southwestern Oklahoma. The mapped soil moisture patterns were consistent with the regional spatial variability in surface soil texture and with soil wetting by an intervening rainfall. The rover measured field-average soil moisture with a root mean squared difference (RMSD) of 0.03 cm 3 cm –3 relative to impedance probes. Likewise, the regional-average 0- to 5-cm soil moisture determined by the rover was within ±0.03 cm 3 cm –3 of the best available independent estimates for each region. These results demonstrate that a COSMOS rover can be used effectively for 0- to 5-cm soil moisture mapping and for determining the average soil moisture at spatial scales suitable for satellite calibration and validation.

Description: In situ soil moisture monitoring networks are critical to the development of soil moisture remote sensing missions as well as agricultural and environmental management, weather forecasting, and many other endeavors. These in situ networks utilize a variety of sensors and installation practices, which confounds the development of a unified reference database for satellite calibration and validation programs. As part of the Soil Moisture Active Passive Mission, the Marena, Oklahoma, In Situ Sensor Testbed (SMAP-MOISST) was initiated to perform inter-comparisons and study sensor limitations. Soil moisture sensors that are deployed in major monitoring networks were included in the study, along with new and emerging technologies, such as the Cosmic Ray Soil Moisture Observing System (COSMOS), passive/active distributed temperature sensing (DTS), and global positioning system reflectometers (GPSR). Four profile stations were installed in May of 2010, and soil moisture was monitored to a depth of 1 m on an hourly basis. The four stations were distributed within a circular domain of approximately 600 m diameter, adequate to encompass the sensing range of COSMOS. The sensors included in the base station configuration included the Stevens Water Hydra Probe, Campbell Scientific 616 and 229, Decagon EC-TM, Delta-T Theta Probe, Acclima, and Sentek EnviroSMART capacitance system. In addition, the Pico TRIME system and additional time-domain reflectometry (TDR) systems were deployed when available. It was necessary to apply site-specific calibration to most sensors to reach an RMSE below 0.04 m 3 m –3 . For most sensor types, a single near surface sensor could be scaled to represent the areal-average of a field domain by simple linear regression, resulting in RMSE values around 0.03 m 3 m –3 .

Description: Calibration and validation of soil moisture satellite products requires data records of large spatial and temporal extent and for diverse land cover types. Obtaining these data, especially for forests, can be challenging. These challenges can include the remoteness of the locations and expense of equipment. The Boreal Ecosystem Research and Monitoring Sites (BERMS) network in Saskatchewan, Canada, is a long-term ecosystem network which includes five soil water content profile stations. These stations provide a critical but incomplete view of the soil water content patterns across a study domain of 10,000 square km; however, the representativeness of these observations for this purpose has not yet been evaluated. In coordination with the Canadian Experiment-Soil Moisture 2010 (CANEX-SM10), a temporary network of surface soil water content sensors was installed during the summer of 2010 to enhance the data resources of the BERMS network. This short term data record was then used as a basis for up-scaling and validating the BERMS network. This large domain is approximately 1200 square km and provides a higher confidence because of the increased number of sampling sites. Using temporal stability analysis, this network verified that the BERMS network could be scaled to a satellite scale footprint with a root mean square error (RMSE) of 0.025 m 3 m –3 , and applied to the entire period of record.

Description: This is an update to the special section "Remote Sensing for Vadose Zone Hydrology—A Synthesis from the Vantage Point" [Vadose Zone Journal 12(3)]. Satellites (e.g., Soil Moisture Active Passive [SMAP] and Soil Moisture and Ocean Salinity [SMOS]) using passive microwave techniques, in particular at L-band frequency, have shown good promise for global mapping of near-surface (0–5-cm) soil moisture at a spatial resolution of 25 to 40 km and temporal resolution of 2 to 3 d. C- and X-band soil moisture records date back to 1978, making available an invaluable data set for long-term climate research. Near-surface soil moisture is further extended to the root zone (top 1 m) using process-based models and data assimilation schemes. Validation of remotely sensed soil moisture products has been ongoing using core monitoring sites, sparse monitoring networks, intensive field campaigns, as well as multi-satellite comparison studies. To transfer empirical observations across space and time scales and to develop improved retrieval algorithms at various resolutions, several efforts are underway to associate soil moisture variability dynamics with land surface attributes in various energy- and water-rich environments. We describe the most recent scientific and technological advances in soil moisture remote sensing. We anticipate that remotely sensed soil moisture will find many applications in vadose zone hydrology in the coming decades.

Description: Ground-based, air-borne, and space-borne remote sensing techniques have evolved over the past several decades and provided many new techniques for estimating various land surface attributes at multiple scales related to mass and energy dynamics. The vadose zone, encompassing land surface, root zone, and the deeper soil profile down to the groundwater table, is a complex domain, particularly related to various hydrologic and biological processes across different scales. In this zone, spatial distributions and temporal dynamics of soil moisture and evapotranspiration (ET), including their interdependence, are critical to climate feedback, hydrology, and plant canopy health. Their temporal and spatial variability across catchments affect surface and subsurface runoff, modulates evaporation and transpiration, determines the extent of groundwater recharge and contaminant transport, and initiates or sustains feedback between the land surface and the atmosphere. With the recent development and deployment of various remote sensing platforms working with different techniques such as optical, microwave, gravitational, infrared, and other sensors, improved temporal and spatial measurement or estimates of soil moisture, evapotranspiration, soil hydraulic parameters, soil salinity, and vegetation attributes are possible. In this special section "Remote Sensing for Vadose Zone Hydrology," 14 contributions on fundamental and applied studies using different remote sensing platforms, including satellite retrieval algorithm development, data assimilation techniques, scaling issues, ground validation, and field applications in the context of vadose zone hydrology are presented. The foci of these papers range across root zone soil moisture retrieval and variability, evapotranspiration dynamics and distribution, agricultural water management, soil hydraulic and mechanical property estimation, ecosystems assessment, land–atmosphere interaction, and land surface hazard assessment. Here we organized the summary of these papers in four overlapping sections including (i) estimation, variability, scaling, and data assimilation of soil moisture by microwave remote sensing; (ii) estimating evapotranspiration by remote sensing and water management applications; (iii) estimating vadose zone properties by remote sensing; and (iv) ground-based soil moisture for calibration and validation of microwave remote sensing.

Description: Soil moisture satellite estimates are available from a variety of passive microwave satellite sensors, but their spatial resolution is frequently too coarse for use by land managers and other decision makers. In this paper, a soil moisture downscaling algorithm based on a regression relationship between daily temperature changes and daily average soil moisture is developed and presented to produce an enhanced spatial resolution soil moisture product. The algorithm was developed based on the thermal inertial relationship between daily temperature changes and averaged soil moisture under different vegetation conditions, using 1/8° spatial resolution North American Land Data Assimilation System (NLDAS) surface temperature and soil moisture data, as well as 5-km Advanced Very High Resolution Radiometer (AVHRR) (1981–2000) and 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and surface temperature (2002–present) to build the look-up table at 1/8° resolution. This algorithm was applied to the 1-km MODIS land surface temperature to obtain the downscaled soil moisture estimates and then used to correct the soil moisture products from Advanced Microwave Scanning Radiometer–EOS (AMSR-E). The 1-km downscaled soil moisture maps display greater details on the spatial pattern of soil moisture distribution. Two sets of ground-based measurements, the Oklahoma Mesonet and the Little Washita Micronet, were used to validate the algorithm. The overall averaged slope for 1-km downscaled results vs. Mesonet data is 0.219, which is better than AMSR-E and NLDAS, while the spatial standard deviation (0.054 m 3 m –3 ) and unbiased RMSE (0.042 m 3 m –3 ) of 1-km downscaled results are similar to the other two datasets. The overall slope and spatial standard deviation for 1-km downscaled results vs. Micronet data (0.242 and 0.021 m 3 m –3 , respectively) are significantly better than AMSR-E and NLDAS, while the unbiased RMSE (0.026 m 3 m –3 ) is better than NLDAS and further than AMSR-E. In addition, Mesonet comparisons of all three soil moisture datasets demonstrate a stronger statistical significance than Micronet comparisons, and the p value of 1-km downscaled is generally better than the other two soil moisture datasets. The results demonstrate that the AMSR-E soil moisture was successfully disaggregated to 1 km. The enhanced spatial heterogeneity and the accuracy of the soil moisture estimates are superior to the AMSR-E and NLDAS estimates, when compared with in situ observations.