Classification of agricultural land use and derivation of biophysical parameter using SAR and optical data

Abstract

Agricultural monitoring is essential for global issues, which can be reflected by the terms of food security and ensuring ecosystem services. Due to the high spatial and temporal resolution of the remote sensing sensors enormous potential for precision farming has been worked out in cooperation between science and the private sector. However, the quality of field specific yield estimations, for instance, is highly influenced by the accuracy of the underlying information like land use, plant development, and stress indicators. Thus, particular knowledge about the accuracy of all the relevant indicators is crucial for agricultural monitoring. The chair of remote sensing at the University of Wuerzburg has gained a lot of expertise in this context by working on their three latest ongoing projects with agricultural focus. Within the framework of these projects, a classification and assessment tool with graphical user interface (MELanGe) was developed, which can be used for land use mapping and biophysical parameter derivation.