CaC
Title of the Research Project:
Optimization of Crop Modeling Using Synthetic High-Resolution Time Series
Main Goals of the Research Project:
- Assimilate multi-source remote sensing combined with gridded meteorological information into a crop growth model
- Validate physical crop parameters (leaf area index, biomass) for yield estimation, simulated by a crop model vs. in situ field database for winter wheat
- Include a meteorological reanalysis of data from the European Centre for Medium-Range Weather Forecasts (ECMWF)
- Implement image fusion techniques (Sentinel-2, Landsat 8, MODIS) in order to generate high-resolution time series (30 m) and improve yield estimation accuracy.
Short Description of the Research Project
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.
German Postdoc-Tandem-Partner:
Dr. Muhammad Usman, Martin Luther University Halle-Wittenberg, Germany