A New ground open water detection scheme using Sentinel-1 SAR images

Abstract

ABSTRACT The detection of groundwater is essential not only for scientific research but also for agricultural purposes. This research aims to improve the accuracy and reliability of detecting ground standing water in cropland during the spring/early summer season in eastern South Dakota, USA, by reducing misclassification between water and vegetation. The study utilizes Sentinel-1 synthetic aperture radar (SAR) data. We selected and surveyed 159 ground sites, comprising 78 water sites and 81 nonwater sites, located between Brookings and Sioux Falls, SD, USA. The proposed scheme consists of three steps: 1) developing a modified speckle filter to reduce speckle noise while preserving image details, 2) characterizing the data for water and nonwater sites and providing parameter estimation using the Method of log-cumulants (MoLC) with generalized Gamma distribution (GΓD), and 3) applying Markov random field (MRF) for SAR data classification. The developed scheme demonstrates good performance with a site-based overall detection accuracy of 93.7%. In addition, it provides a computationally efficient solution for water detection, which can be applied in various applications such as crop insurance, precision agriculture and drought monitoring.