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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 #350045

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Downscaling of surface soil moisture retrieval by combining MODIS/Landsat and in situ measurements

item XU, C. - George Mason University
item QU, J.J. - George Mason University
item HAO - George Mason University
item Cosh, Michael
item Prueger, John
item ZHU, Z. - Us Geological Survey (USGS)
item GUTENBERG, L. - George Mason University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/1/2018
Publication Date: 3/1/2018
Citation: Xu, C., Qu, J., Hao, Cosh, M.H., Prueger, J.H., Zhu, Z., Gutenberg, L. 2018. Downscaling of surface soil moisture retrieval by combining MODIS/Landsat and in situ measurements. Remote Sensing. 10(2):210.

Interpretive Summary: Current microwave soil moisture remote sensing has a problem of resolution which can be challenging for agricultural applications. Optical satellite data also has the problem of cloud interference and poor temporal resolution. But a merging of these products is shown to generate a high resolution soil moisture product with near daily coverage and adequate accuracy. A case study was used to demonstrate this method in Central Iowa, USA in a mainly agricultural region, in coordination with the NASA Soil Moisture Active Passive (SMAP) Mission. This information is useful for agricultural and watershed managers who need field level soil moisture data for understanding water use management.

Technical Abstract: Soil moisture, especially the surface soil moisture (SSM), plays an important role in the development of various natural hazards, such as drought, flooding and landslide that result from extreme weather events. There have been many remote sensing methods for soil moisture retrieval based on the microwave or optical/thermal measurements. Optical/ thermal remote sensing measurements have been popular for surface soil moisture retrieval due to the fine spatial and temporal resolutions. However, because of the limitation in penetration of optical/thermal radiation and cloud cover, optical/thermal methods can only be used for SSM retrieval under clear sky conditions. Soil moisture retrieval with microwave measurements is based on solid physical principles and has advantages in cases of cloud cover. But passive microwave remote sensing has disadvantages because of its low spatial resolution. For applications at local scale, SSM data at high spatial and temporal resolutions are important, especially for agricultural management and decision support systems. Current remote sensing measurements usually have either a high spatial resolution or high temporal resolution, but not both. This study aims to retrieve SSM at both high spatial and temporal resolutions through the fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat data. Based on the universal triangle trapezoid, this study investigated the relationship between land surface temperature (LST) and normalized difference vegetation index (NDVI) under different soil moisture conditions to construct an improved nonlinear model for SSM retrieval based upon LST and NDVI. A case study was conducted in Iowa, USA , from May 1st, 2016 to August 31st, 2016. Daily SSM in an agricultural area during the crop growing season was downscaled to 120 m spatial resolution by fused Landsat 8 with MODIS, with a correlation coefficient of 0.5766, and RMSE from 0.0302 to 0.1124 m3/m3.