Dr. Yi Liu Profile

Dr. Yi Liu

at Univ of Massachusetts Dartmouth

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Proceedings Article | 1 August 2021 Presentation + Paper

Characterizing water and non-water sites from cropland in eastern South Dakota using Sentenel-1 SAR images

Songxin Tan, Yi Liu

Proceedings Volume 11829, 1182906 (2021) https://doi.org/10.1117/12.2593061

KEYWORDS: Synthetic aperture radar, Water, Satellites, Vegetation, Algorithm development, Satellite imaging, Clouds, Agriculture, Polarization, Microwave radiation

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Cropland is extremely important for the global food security. However, soil erosion caused by water affects crop productivity, and the runoff water pollutes both the fresh water and marine ecosystems, leading to severe economic and environmental issues. While there have been many ground water detection techniques using satellite optical imaging sensors, synthetic aperture radar (SAR) proves to be an important alternative. Because microwave can penetrate through the cloud, SAR data often become the only data available when local cloud cover blocks the optical imaging sensors. However, literature reveals that the detection of ground water using SAR data is often obscured by vegetation, which also appears dark in SAR images. This severely reduces the water detection accuracy. In this study, the freely available Sentenel-1 SAR data from the European Space Agency (ESA) Copernicus missions were used to characterize the water and non-water sites in the cropland in eastern South Dakota, USA. A total number of 159 sites were pre-selected from the SAR images. Field surveys were conducted. Of the 159 sites, 78 were identified as water sites and 81 as non-water sites. The water and non-water site data at both VV and VH polarizations were downloaded and analyzed. The density functions of the shifted Rayleigh distributions were estimated using maximum likelihood estimation (MLE). The residue errors are small. The distribution functions between water and non-water sites will facilitate the development of a more accurate classification algorithm for cropland water detection. Such information is important for precision agriculture.

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