Integrating Optical and Microwave Satellite Observations for High Resolution Soil Moisture Estimate and Applications in CONUS Drought Analyses

  • Donglian Sun George Mason University
  • Yu Li George Mason University
  • Xiwu Zhan College Park
  • Chaowei Yang George Mason University
  • Ruixin Yang George Mason University
Ariticle ID: 3468
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Keywords: Soil moisture, High spatial resolution, Regional drought, Microwave and optical satellite remote sensing

Abstract

In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated pre_x005fcipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash†drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible.

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Published
2024-02-27
How to Cite
Sun, D., Li, Y., Zhan, X., Yang, C., & Yang, R. (2024). Integrating Optical and Microwave Satellite Observations for High Resolution Soil Moisture Estimate and Applications in CONUS Drought Analyses. Remote Sensing, 13(1). https://doi.org/10.18282/rs.v13i1.3468
Section
Original Research Articles