- Chair: James Smith, ESE
- Advisor: Venkataraman Lakshmi, ESE
- Jon Goodall, ESE
- Julianne Quinn, ESE
- Xi Yang, EVSC
- Steven Chase, NASA JPL
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Title: The Improvement of Science Quality and Utility of Soil Moisture Estimations from Satellite-Based Passive Microwave Remote Sensing
Soil moisture is an important measure of the exchange of water and energy between the land and atmosphere. Passive microwave remote sensors onboard Earth observation satellites have served as the most promising tool for quantifying the water content stored in the surface soil layer at a quasi-global scale. In recent decades, the utilization of satellite-based soil moisture retrievals has benefited a variety of applications, including detection of extreme climate events, water resource and irrigation management, and numerical weather predictions. However, the available passive microwave soil moisture datasets are still unable to entirely satisfy the needs of climatological studies and applications due to the insufficient retrieval quality over particular land surface conditions, the coarse spatiotemporal resolution, the absence of information associated with nonuniform vertical moisture gradients, and the unavailability of a consistent long-term satellite-based soil moisture data record. Given these limitations, this dissertation aims to enhance the quality and utility of current passive microwave-based surface soil moisture data by attempting to resolve the above-mentioned issues.
This dissertation first outlines the analyses for retrievals that are impacted by water bodies (i.e., lakes) and organic matter in soils to provide clues for refining the operational algorithm of deriving soil moisture from observed brightness temperatures at L-band (1.41 GHz). Three state-of-the-art global surface water temperatures datasets have been evaluated by comparing against in-situ measurements located in the North America and by a worldwide-scale inter-comparison. While the lake surface water temperature products of GloboLakes and C-GLOPS exhibited negative biases of -0.27 K and -0.31 K and the lake mix-layer temperature product of ERA5 Land displayed a median bias of 1.56 K, all the three datasets have shown overall comparable performance with Pearson Correlation (R) of 0.87, 0.92 and 0.88. An integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led me to conclude that lake mix-layer temperature from the ERA5 Land Reanalysis product is the optimal selection for water correction of passive soil moisture retrievals. Regarding the substantial degradation of the Soil Moisture Active Passive (SMAP) soil moisture retrievals over organic-rich soils, nine advanced dielectric mixing models have been alternatively placed into the SMAP single channel algorithm at the vertical polarization (SCA-V). Then, their respective retrievals were benchmarked against in-situ observations in Alaska covered by abundant soil organic matter (SOM). Although all models presented a similar level of accuracy with the unbiased root-mean square error (ubRMSE) and R around 0.05 m3/m3 and 0.47, Mironov 2019 consistently outperformed the other models by a slight margin. Based on a holistic analysis of the model inputs, physical basis, and validated accuracy, the separate use of Mironov 2009 and Mironov 2019 in the SMAP SCA-V for mineral soils (SOM < 15%) and organic soils (SOM ≥ 15%) was recommended. Such investigations identified the lake mix-layer temperature from ERA5 Land and the Mironov 2019 as the preferred options to mitigate the water contamination and the effects of SOM in the passive microwave remote sensing retrieval of soil moisture, which will greatly improve the accuracy of the next-generation L-band soil moisture dataset.
Subsequently, the thesis describes the study that fills temporal gaps in SMAP data by incorporating a satellite-based precipitation product. A simplified water balance equation has been applied to estimate the amount of water in the top layer of soil over a 12-hour period based on observed precipitation and information about how water loses through the soil. Over the conterminous United States (CONUS), the derived SMAP-based 12-hourly soil moisture product displayed a great accuracy (ubRMSE: 0.06 m3/m3 and R: 0.63) when compared to in-situ measurements and captured most soil moisture peaks caused by heavy rainfall. Having continuous soil moisture data and information about how water loses through the soil can help researchers better understand land-surface hydrology. Meanwhile, the proposed scheme can be potentially extended into the spatial disaggregation of a coarse-scale precipitation product, and the construction of a long-term soil moisture record.
After that, this thesis presents the use of a layered radiative transfer model to forward simulate brightness temperature emanated from the topsoil layers with the ultimate objective to invert that model and infer vertically heterogeneous moisture profiles from passive microwave observations. The incoherent microwave emission over a four-layer stratified soil medium defined by the ERA5 land profiled soil moisture and soil temperature was estimated by applying a band matrix approach. The L-band (1.41 GHz) brightness temperatures from uniform and non-uniform soil medium were modeled and compared to identify the dominant factors that causes the discrepancies of their radiative transfer solutions. This numerical approach provides a versatile modeling tool for stratified soil media of arbitrary number of lossy layers for different frequencies, polarization, and observations angles.
In summary, this dissertation is dedicated to improving the scientific quality and utility of the state-of-the-art SMAP soil moisture retrievals by addressing several identified drawbacks. The outcomes of this dissertation hold great promise for changing how radiometer observations are interpreted in the future while the newly yielded soil moisture data with temporal continuity and higher accuracy will help researchers quantitatively understand the linkages between water balance components and deepen the understanding of the terrestrial-atmosphere interactions in the context of climate change.