Location: Hydrology and Remote Sensing LaboratoryTitle: Analyzing effects of crops on SMAP satellite-based soil moisture using a rainfall–runoff model in the U.S. Corn Belt
|JADIDOLESLAM, N. - University Of Iowa|
|HORNBUCKLE, B. - Iowa State University|
|MANTILLA, R. - University Of Iowa|
|KRAJEWSKI, W. - University Of Iowa|
Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/23/2021
Publication Date: 11/26/2021
Citation: Jadidoleslam, N., Hornbuckle, B., Mantilla, R., Krajewski, W., Cosh, M.H. 2021. Analyzing effects of crops on SMAP satellite-based soil moisture using a rainfall–runoff model in the U.S. Corn Belt. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15:247-260. https://doi.org/10.1109/JSTARS.2021.3131133.
Interpretive Summary: In this study, soil moisture estimation from multiple data sources are evaluated to assess potential satellite-based soil moisture errors in the state of Iowa. Different products are evaluated with reference field sensor observations and model predictions to identify areas of low and high agreement between different products. The results indicate that satellite-based soil moisture estimates are drier than model output and field sensor observations. However, the dry Bias varies in different regions depending on vegetation properties. Findings from this study highlight the importance of accounting for satellite-based soil moisture errors due to vegetation properties and use in hydrologic modeling applications in agricultural-dominated regions.
Technical Abstract: L-band microwave satellite missions provide soil moisture information potentially useful for food and drought predictions. However, these observations are also sensitive to vegetation that makes satellite-based soil moisture estimations prone to errors in heavily vegetated regions. In this study, authors evaluate the soil moisture estimations from multiple data sources to assess the potential satellite-based soil moisture errors in the state of Iowa, located in a heavily agricultural region. For this purpose, they use soil moisture observations from field sensors, SMAP (Soil Moisture Active Passive), SMOS (Soil Moisture Ocean Salinity), and soil moisture predictions from two distributed hydrologic models. First, they evaluate different soil moisture products with reference field sensor observations. Then, they compare satellite based soil moisture products with model predictions to identify regions of low and high agreement between different products. Finally, they analyze error spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices, and SMAP and SMOS vegetation optical depth to characterize the potential errors in satellite-based soil moisture over their study domain. Their results indicate that satellite-based soil moisture estimations are drier than models and field sensor observations. However, the dry Bias varies in in different regions depending on vegetation optical depth. Findings of this study highlights the importance of accounting for space-dependent errors of satellite-based soil moisture for their hydrologic applications in heavily agricultural regions