Grassland Monitoring Based on Sentinel-1

摘要

Grassland occupies a large proportion of utilised agricultural area, especially in mountainous regions. Despite its importance current and reliable data on grassland yields and cutting frequencies with a sufficient spatial coverage are lacking. Both are essential for optimizing the use of grassland, nature conservation and policy consultation. Model approaches for the assessment of grassland yields take cutting dates and frequency into account despite environmental and cultivation factors.The European Earth Observation programme Copernicus provides large quantities of spatial and temporal high resolution data collected by a set of Sentinel satellites. The freely and openly accessible Sentinel-1 radar data form a valid basis for automatedsatellite and ground data processing methods to detect cutting events. These cutting frequencies are a fundamental information source for further analysis — the computation of grassland yields with different model approaches.In this study we like to present our overall approach integrating and analyzing data of different sources. A comparison between two different automated data processing methods to detect cutting frequencies from radar satellite data in three different regions in Bavaria is included. The common statistical detection represents a robust and reliable way by analysing time series of Sentinel-1 radar images of the same acquisition geometry with time intervals of 6 days. In contrast, machine learning techniques offer the opportunity to increase the accuracy and limit cutting dates to more precise time intervals.