Identification of wet areas in agricultural lands using remote sensing data

Abstract

Wet areas in agricultural lands are usually not fully or properly managed due to problematic accessibility by heavy machinery and are associated with lower crop yields. There are neither studies regarding spatial distribution of wet agricultural areas in Latvia nor large scale soil maps. Being aware of these wet areas, it would be possible to plan actions for effective management of these areas, starting with a scale of landscape. A geographic information system model could serve as an assistant for decision-making, such as, a direct support for the management of amelioration systems, change of land use and management patterns or granting support payments. Remote sensing data like Sentinel-2 satellite images and LiDAR (Light detecting and ranging) technology can be used to identify local wet areas. The focus of this article is to evaluate different remote sensing indices and methods that can be used to identify wet areas in agricultural lands using open access data and software. From 52 indices, which were analysed with soil moisture field measurements in 33 sample plots, only two of them showed statistical significance in linear regression model (p<0.05): normalized height model in resolution of 25 meters (r2=0.45) and visible blue spectral band in April (r2=0.39). Results from this study help to focus on different aspects of remote sensing data usage and methodology for future improvements in order to fully implement LiDAR and Sentinel-2 data for identification of wet areas in agricultural lands.