Optimization of Crop Modeling Using Synthetic High-Resolution Time Series
The main objective of the project is to estimate biomasses and crop yields of winter wheat (Triticum aestivum) in the inner Aral Sea Basin of Central Asia. Remote sensing techniques will be integrated into the crop growth modeling to improve irrigation efficiency and other soil and water management aspects. Four major methodological novelties will be implemented, including assimilation of Sentinel-2, data reanalysis, and data fusion techniques using MODIS, Landsat 8 and Sentinel-2. Integration of multi-source remote sensing, ECMWF data and in situ field measurements are evaluated for the accurate estimation of biomasses and crop yields. The project aims to quantify the applicability of crop growth models, including a LUE model with single Sentinel and fused data and assessments of the transferability of the methods into practice.
Dr. Muhammad Usman, Martin Luther University Halle-Wittenberg, Germany