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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #386408

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Investigating the efficacy of the SMAP downscaled soil moisture product for drought monitoring based on information theory

item WU, Z. - Hohai University
item QIU, J. - Sun Yat-Sen University
item Crow, Wade

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/1/2021
Publication Date: 12/20/2021
Citation: Wu, Z., Qiu, J., Crow, W.T. 2021. Investigating the efficacy of the SMAP downscaled soil moisture product for drought monitoring based on information theory. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15:1604-1616.

Interpretive Summary: Soil moisture estimates obtained from satellite-based sensors are potentially of value for a broad range of agricultural applications. However, their relatively coarse spatial resolution (>30 km) prevents them from resolving individual farms and fields. While downscaling techniques have been developed to sharpen the resolution of satellite-derived soil moisture products, they have not been rigorously evaluated. This paper uses information theory to robustly evaluate the degree to which soil moisture downscaling approaches can describe fine-scale variations in crop health and productivity. In this way we identify specific limitations in existing soil moisture downscaling approaches that reduce their effectiveness for agricultural drought monitoring at the farm and field-scale. The results of this study will eventually be used to track the availability of root-zone soil moisture at fine spatial scales and better mitigate the impact of soil moisture extremes on local agricultural productivity.

Technical Abstract: Agricultural drought can cause significant reductions in crop productivity and affect global food security. As a direct determinant of agricultural drought, soil moisture (SM) information can be routinely obtained from high-quality microwave retrievals available at a global scale — such as datasets generated by the Soil Moisture Active Passive (SMAP) mission. However, the relative coarse spatial resolution (~25 km) of microwave SM retrievals potentially reduces their applicability to drought monitoring over complex cropland landscapes. In this study, using mutual information (MI) theory, we investigate the efficacy of the downscaled SMAP/Sentinel-1 L2 3-km EASE-Grid SM product (SPL2) for the detection of agricultural drought over northwestern China in Heihe River Basin (HRB). The SPL2 is generated by merging SMAP enhanced radiometer data with Sentinel-1 radar observations. To evaluate the efficiency of SPL2 the downscaled algorithm, the SMAP Enhanced L3 radiometer 9-km EASE-Grid SM product (SPL3) is also utilized as a non-downscaled baseline. Over croplands, comparing normalized MI (NMI) values sampled between the NDVI time series and 3-km SPL2 with NMI values between NDVI and SPL3 (resampled to 3-km resolution), we find that the Sentinel-1 C-band backscatter coefficient s explains more 3-km NDVI information than the SPL3 enhanced radiometer brightness temperature (Tb), as the NMI between svh (svv) and NDVI is 15% (8%), larger than that between SPL3 Tb and NDVI (5%). However, compared to the SPL3 Tb baseline, the information from downscaled SPL2 Tb on NDVI is reduced by approximately 3%, and the SPL2 algorithm extracts only 7% (10%) of the total information available from both enhanced SPL3 Tb and Sentinel-1 svh (svv). Overall, the C-band backscatter signal provides valuable information for vegetation monitoring via revealing more spatial details and heterogeneity. However, additional efforts should be focus on SPL2 merging algorithms that increase information extraction efficiency regarding vegetation status and therefore maximize the value of downscaled SMAP SM products for agricultural drought applications.