Error Propagation in Microwave Soil Moisture and Vegetation Optical Depth Retrievals

• Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Main Authors: Feldman, Andrew F., Chaparro, David, Entekhabi, Dara
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Institute of Electrical and Electronics Engineers 2021
• Subjects: Soil moisture; Vegetation mapping; Satellites; Microwave measurement; Soil measurements; Optical sensors; Moisture; National Snow and Ice Data Center
• Online Access: http://hdl.handle.net/10261/263099; https://doi.org/10.1109/JSTARS.2021.3124857

Satellite soil moisture and vegetation optical depth [(VOD); related to the total vegetation water mass per unit area] are increasingly being used to study water relations in the soil-plant continuum across the globe. However, soil moisture and VOD are typically jointly estimated, where errors in the optimization approach can cause compensation between both variables and confound such studies. It is thus critical to quantify how satellite microwave measurement errors propagate into soil moisture and VOD. Such a study is especially important for VOD given limited investigations of whether VOD reflects in situ plant physiology. Furthermore, despite new approaches that constrain (or regularize) VOD dynamics to reduce soil moisture errors, there is limited study of whether regularization reduces VOD errors without obscuring true vegetation temporal dynamics. Here, we find that, across the globe, VOD is less robust to measurement error (more difficult for optimization methods to find the true solution) than soil moisture in their joint estimation. However, a moderate degree of regularization (via time-constrained VOD) reduces errors in VOD to a greater degree than soil moisture and reduces spurious soil moisture-VOD coupling. Furthermore, despite constraining VOD time dynamics, regularized VOD variations on subweekly scales are both closer to simulated true VOD time series and have global VOD post-rainfall responses with reduced error signatures compared to VOD retrievals without regularization. Ultimately, we recommend moderately regularized VOD for use in large scale studies of soil-plant water relations because it suppresses noise and spurious soil moisture-VOD coupling without removing the physical signal. SMAP L1C brightness temperatures used to retrieve soil moisture and vegetation optical depth are available from the National Snow and Ice Data Center (NSIDC) (https://nsidc.org/data/SPL1CTB_E). The MT-DCA soil moisture and VOD datasets are freely available at https://doi.org/10.5281/zenodo.5579549. The DCA ...