First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

摘要: The study presents the preliminary results of two classification exercises assessing capabilities pre-operational (August 2015) Sentinel-2 (S2) data for mapping crop types and tree species. In first case study, an S2 image was used to map six summer species in Lower Austria as well winter crops/bare soil. Crop type maps are needed account crop-specific water use agricultural statistics. information is also useful parametrize growth models yield estimation, retrieval vegetation biophysical variables using radiative transfer models. second aimed seven different deciduous coniferous Germany. Detailed about distribution important forest management assess potential impacts climate change. our assessment, were produced at 10 m spatial resolution by combining ten spectral channels with 20 pixel size. A supervised Random Forest classifier (RF) deployed trained appropriate ground truth. both studies, confirmed its expected produce reliable land cover maps. Cross-validated overall accuracies ranged between 65% (tree species) 76% (crop types). high value red-edge shortwave infrared (SWIR) bands mapping. Also, blue band sites. S2-bands near amongst least channels. object based analysis (OBIA) classical pixel-based achieved comparable results, mainly cropland. As only single date acquisitions available this full could not be assessed. future, twin satellites will offer global coverage every five days therefore permit concurrently exploit unprecedented temporal resolution.