How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?

Remote Sensing

Andreas Tewes¹  Gunther Krauss¹  Thomas Gaiser¹  Manuel Nolte²  Christian Kerkhoff²  Fabian Schäfer²  Holger Hoffmann² 

[1] Crop Science Research Group, Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany;xarvio^TM BASF Digital Farming GmbH, Im Zollhafen 24, 50678 Köln, Germany;

关键词: data assimilation;    leaf area index;    crop modeling;    soilgrids;    weighted mean;    ensemble kalman filter;    sentinel-2;   

DOI  :  10.3390/rs12060925

来源: DOAJ

【 摘 要 】

The combination of Sentinel-2 derived information about sub-field heterogeneity of crop canopy leaf area index (LAI) and SoilGrids-derived information about local soil properties might help to improve the prediction accuracy of crop simulation models at sub-field level without prior knowledge of detailed site characteristics. In this study, we ran a crop model using either soil texture derived from samples that were taken spatially distributed across a field and analyzed in the lab (AS) or SoilGrids-derived soil texture (SG) as model input in combination with different levels of LAI assimilation. We relied on the LINTUL5 model implemented in the SIMPLACE modeling framework to simulate winter wheat biomass development in 40 to 60 points in each field with detailed measured soil information available, for 14 fields across France, Germany, and the Netherlands during two growing seasons. Water stress was the only growth-limiting factor considered in the model. The model performance was evaluated against total aboveground biomass measurements at harvest with regard to the average per-field prediction and the simulated spatial variability within the field. Our findings showed that a) per-field average biomass predictions of SG-based modeling approaches were not inferior to those using AS-texture as input, but came with a greater prediction uncertainty, b) relying on the generation of an ensemble without LAI assimilation might produce results as accurate as simulations where LAI is assimilated, and c) sub-field heterogeneity was not reproduced well in any of the fields, predominantly because of an inaccurate simulation of water stress in the model. We conclude that research should be devoted to the testing of different approaches to simulate soil moisture dynamics and to the testing in other sites, potentially using LAI products derived from other remotely sensed imagery.

【 授权许可】

Unknown