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 (TB) from SMAP matched well with the upscaled, ground-based TB, 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 TB 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 (CRvv)-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 CRvv-derived retrievals. Results presented here suggest that SMAP SCA SM retrievals could be improved through the use of near-real time NDWI and CRvv-derived vegetation information. Microwave data are available regardless of cloud cover, so the guaranteed availability of CRvv to capture seasonal and interannual variability is advantageous.
Bibliographical noteFunding Information:
The authors thank anonymous reviewers for their helpful comments and suggestions. The SMAPVEX16-MicroWEX was funded by grant from the NASA-Terrestrial Hydrology Program (award number: NNX16AQ24G). The work of S. C. Steele-Dunne was supported by a Vidi Grant 14126 from the Dutch Technology Foundation TTW, which is part of the Netherlands Organization for Scientific Research (NWO), and partly funded by the Ministry of Economic Affairs. The work of A. Monsivais-Huertero was supported by the National Council of Science and Technology of Mexico (project AEM-2014-1-247741). In addition to the financial support from various agencies above, the SMAPVEX16-MicroWEX team is thankful to David and Annette Sweeney; John Prueger, Forrest Goodman, and Jerry Hatfield from USDA-ARS; and Prof. Brian Hornbuckle and his research team at Iowa State University for providing field support during the field campaign.
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- Active and passive microwave
- Soil moisture
- Vegetation water content