Soil moisture (SM) retrieval in agricultural regions during the growing seasons is particularly challenging due to high spatial variability and dynamic vegetation conditions. The retrievals have been problematic even when the passive signatures at different spatial scales match well since they depend upon the accuracy of vegetation information such as the vegetation water content (VWC). The VWC used in the Soil Moisture Active Passive (SMAP) single channel retrieval algorithm (SCA) is derived from remotely sensed, climatologically-based Normalized Difference Vegetation Index (NDVI), which does not respond to real-time vegetation dynamics and is prone to saturation. This study explored the differences and seasonal trend in passive signatures and SM at satellite- and field-scales and investigated uncertainties in retrievals arising from different approaches used to estimate VWC from optical and radar indices. It used high temporal resolution, ground-based data collected during the SMAP Validation-Microwave Water, Energy Balance Experiment in 2016 (SMAPVEX16-MicroWEX) during a growing season of corn and soybean. Overall, the brightness temperatures (T_B) from SMAP matched well with the upscaled, ground-based T_B, with a root mean square differences (RMSDs) of about 5 K. In contrast, the SMAP SM retrievals were worse during rapid vegetation growth in the mid-season, with higher RMSDs compared to the upscaled in situ SM, than those in the late-season. In addition, the ground-based T_B from corn and soybean were similar in the early and the late seasons, while their emission differences were > 40 K in the mid season. This indicates the importance of accurate VWC information, particularly during the early and late growing seasons, to account for sub-pixel heterogeneities in agricultural regions. VWC obtained from five optical and radar indices were used in the SMAP SCA for soil moisture retrieval for the entire growing season of corn. The NDVI-based VWC provided SM retrievals that were consistently lower compared to those using in situ VWC, with a higher RMSD of 0.030 m³/m³ and a negative bias of 0.020 m³/m³ for VWC > 4 kg/m². The Normalized Difference Water Index (NDWI)-derived VWC resulted in lower SM retrieval RMSD of 0.022 m³/m³ when compared with in situ SM. Among the three radar indices, vertically polarized cross-pol ratio (CR_vv)-derived VWC provided similar RMSDs in retrieved SM as the NDWI-derived VWC during the growing season. The radar vegetation index (RVI)-derived VWC improved in the late season compared to the in situ VWC and resulted in SM retrievals with RMSDs similar to the CR_vv-derived retrievals. Results presented here suggest that SMAP SCA SM retrievals could be improved through the use of near-real time NDWI and CR_vv-derived vegetation information. Microwave data are available regardless of cloud cover, so the guaranteed availability of CR_vv to capture seasonal and interannual variability is advantageous.

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The latest developments in radar mission concepts suggest that subdaily synthetic aperture radar will become available in the next decades. The goal of this study was to demonstrate the potential value of subdaily spaceborne radar for monitoring vegetation water dynamics, which is essential to understand the role of vegetation in the climate system. In particular, we aimed to quantify fluctuations of internal and surface canopy water (SCW) and understand their effect on subdaily patterns of L-band backscatter. An intensive field campaign was conducted in north-central Florida, USA, in 2018. A truck-mounted polarimetric L-band scatterometer was used to scan a sweet corn field multiple times per day, from sowing to harvest. SCW (dew, interception), soil moisture, and plant and soil hydraulics were monitored every 15 min. In addition, regular destructive sampling was conducted to measure seasonal and diurnal variations of internal vegetation water content. The results showed that backscatter was sensitive to both transient rainfall interception events, and slower daily cycles of internal canopy water and dew. On late-season days without rainfall, maximum diurnal backscatter variations of >2 dB due to internal and SCW were observed in all polarizations. These results demonstrate a potentially valuable application for the next generation of spaceborne radar missions.@en

Observations from spaceborne radar contain considerable information about vegetation dynamics.The ability to extract this information could lead to improved soil moisture retrievals and the increased capacity to monitor vegetation phenology and water stress using radar data.The purpose of this review paper is to provide an overview of the current state of knowledge with respect to backscatter from vegetated (agricultural) landscapes and to identify opportunities and challenges in this domain.Much of our understanding of vegetation backscatter from agricultural canopies stems from SAR studies to perform field-scale classification and monitoring.Hence, SAR applications, theory, and applications are considered here too.An overview will be provided of the knowledge generated from ground-based and airborne experimental campaigns that contributed to the development of crop classification, crop monitoring, and soil moisture monitoring applications.A description of the current vegetation modeling approaches will be given.A review of current applications of spaceborne radar will be used to illustrate the current state of the art in terms of data utilization.Finally, emerging applications, opportunities and challenges will be identified and discussed.Improved representation of vegetation phenology and water dynamics will be identified as essential to improve soil moisture retrievals, crop monitoring, and for the development of emerging drought/water stress applications.

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The NASA Soil Moisture Active/Passive (SMAP) and the ESA Soil Moisture and Ocean Salinity (SMOS) missions include microwave radiometers at L-band that provides global observations of SM at 36 and 25km, respectively, with a repeat coverage of every 2-3 days. Agricultural regions, with their highly dynamic vegetation and spatial heterogeneity are particularly challenging for soil moisture retrieval algorithms. The Microwave Water and Energy Balance Experiment was conducted as part of the SMAP Validation Experiment (SMAPVEX16-MicroWEX) during the summer of 2016 in a predominantly agricultural region in Iowa, USA. During SMAPVEX16-MicroWEX, ground-based observations of active and passive signatures were obtained every 15-30 minutes during a growing season of corn and soybean from May 23 through September 2, 2016. The field site was within the South Fork Watershed at the Sweeney Farms, near the city of Buckeye. The University of Florida L-band Automated Radar System (UF-LARS) observed the backscatter from corn. The brightness temperatures (TB) at the corn site were observed by the University of Michigan L-Band Radiometer (UMLMR), while those at the soybean site were observed by the University of Florida L-band Microwave Radiometer (UFLMR). Concurrent and colocated observations of soil, vegetation, and micro-meteorological conditions were also conducted at both the sites. The passive signatures from both the corn and the soybean sites were found to be similar during the early season, as both the fields were nearly bare terrains. As expected, the TB diverge during the mid-season, when the vegetation water content (VWC) of the corn is about 2 kg/m2. Interestingly, the TB of the two crops are similar again toward the end of the season, when VWC of the soybean crop reaches about 2 kg/m2. Preliminary modeling results show that physically-based emission models significantly underestimate vegetation opacity for a mature soybean canopy. These findings provide insights into retrieval algorithms for soil moisture in agricultural terrains.@en

An ensemble Kalman Filter-based data assimilation framework that links a crop growth model with active and passive (AP) microwave models was developed to improve estimates of soil moisture (SM) and vegetation biomass over a growing season of soybean. Complementarities in AP observations were incorporated to the framework, where the active observations were used to optimize surface roughness and update vegetation biomass, while passive observations were used to update SM. The framework was implemented in a rain-fed agricultural region of the southern La-Plata Basin during the 2011-2012 growing season, through a synthetic experiment and AP observations from the Aquarius mission. The synthetic experiment was conducted at a temporal resolution of 3 and 7 days to match the current AP missions. The assimilated estimates of SM in the root zone and dry biomass were improved compared to those from the cases without assimilation, during both 3-and 7-day assimilation scenarios. Particularly, the 3-day assimilation provided the best estimates of SM in the near surface and dry biomass with reductions in RMSEs of 41% and 42%, respectively. The absolute differences of assimilated LAI from Aquarius were <0.29 compared to the MODIS LAI indicating that the performance of assimilation was similar to the MODIS product at a regional scale. This study demonstrates the potential of assimilation using AP observations at high temporal resolution such as those from soil moisture active passive (SMAP) for improved estimates of SM and vegetation parameters.

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