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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 #398326
Research Project: From Field to Watershed: Enhancing Water Quality and Management in Agroecosystems through Remote Sensing, Ground Measurements, and Integrative Modeling

Location: Hydrology and Remote Sensing Laboratory

Title: Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland

Author
item FENG, S. - Collaborator
item QUI, J. - Collaborator
item Crow, Wade
item MO, X. - Chinese Academy Of Sciences
item WANG, S. - University Of Illinois
item GAO, L. - University Of Illinois

Submitted to: Journal of Photogrammetry and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/17/2022
Publication Date: 12/22/2022
Citation: Feng, S., Qui, J., Crow, W.T., Mo, X., Wang, S., Gao, L. 2022. Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland. Journal of Photogrammetry and Remote Sensing. 617. Article 129015. https://doi.org/10.1016/j.jhydrol.2022.129015.
DOI: https://doi.org/10.1016/j.jhydrol.2022.129015

Interpretive Summary: Remotely sensed retrievals of surface soil moisture are of great potential value for tracking the extent and severity of agricultural drought. However, to provide accurate estimates of soil moisture availability, these retrievals must first be corrected for the impact of water contained in overlying vegetation. Unfortunately, such estimates of vegetation water content are difficult to obtain within agricultural areas. This paper inter-compares various recent methods for estimating vegetation water content using remote sensing and provides new insight into which of these methods works best within an intensively cultivated region of China. Results from this study will be applied to improve the accuracy of remotely sensed estimates of surface soil moisture and, therefore, enhance our collective ability to globally monitor the extent and severity of agricultural drought from space.

Technical Abstract: Satellite-based soil moisture (SM) retrieval within agricultural regions is challenging due to pronounced vegetation and land cover variations during the growing season. Vegetation water content (VWC) - used to describe vegetation opacity in both the Soil Moisture Active Passive (SMAP) Dual Channel Algorithm (DCA) and Single Channel Algorithm (SCA) - is approximated using the climatological Normalized Difference Vegetation Index (NDVI) time series. To understand the impact of VWC algorithms on SMAP SM retrieval, this study investigated the optimal vegetation indices for VWC estimation, and further evaluated the impact of VWC uncertainty on DCA and SCA-V SM retrievals. Besides NDVI, we use four dynamic vegetation indices (VIs) from Sentinel-2 - including two red-edge VIs and two non-red-edge VIs to estimate growing-season corn VWC and the impact of different empirical relationships on VWC estimates. We find that climatological NDVI from MODIS significantly underestimates VWC, and therefore SM over croplands in Northwest China during the growing season, whereas the dynamic VIs can improve the accuracy of VWC and SM retrievals. With the improved VWC estimation scheme, the empirical method adopted in both DCA and SCA-V to account for the vegetation stem factor is shown to be effective for VWC and SM estimation. The sensitivity analysis shows that SM retrieval based on red-edge VI is more sensitive to none-red-edge VI. In addition, the influence of VWC uncertainty on SM accuracy is shown to be higher in SCA-V than in DCA. The results of this study provide insight into preferred approaches for the accurate estimation of VWC and the improvement on passive SM retrieval algorithms.