Abstract
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.
Highlights
A successful use of dynamic crop models at sub-field level could provide valuable information to precision farming applications, such as detailed yield forecasts, timing of pesticide application and estimation of potential for variable rate application of fertilizers in the field
Spatial heterogeneity in the biomass predictions of the SG-based assimilation approaches could only be induced by a) the differences in soil texture information if the field stretched over more than one grid cell, and/or b) the assimilation of S2-derived LAI information that was extracted from 10 m resolution pixels, because all other input variables were identical in the model for each point in the field
The analysis of the results showed that: 1. Mean values of the calculated metrics (RMSE, mean absolute percentage error (MAPE), Bias) across all fields did not substantially differ between ensemble-based SG and AS approaches (i.e., EM, Ensemble Kalman filter (EnKF), weighted mean (WM)), but standard deviations were greater for SG-based approaches
Summary
A successful use of dynamic crop models at sub-field level could provide valuable information to precision farming applications, such as detailed yield forecasts, timing of pesticide application and estimation of potential for variable rate application of fertilizers in the field. Modern earth observation satellite systems with high spatial and temporal resolution in the optical domain allow for the retrieval and monitoring of biophysical crop canopy variables [1]. The assimilation of this information into dynamic crop simulation models could help to account for the sub-field heterogeneity of crop growth [2] and advance the potential usage of those for precision agriculture applications. The idea of data assimilation for dynamic crop models is to incorporate one or several observations of model state variables during the period of crop growth Based on these measurements, the model can be modified and used to make predictions about the future states of the crop [3]. Defined as the total one-sided area of leaf tissue per unit ground surface area, it is one of the key parameters in crop growth analysis because of its influence on light interception, biomass production, plant growth, and on crop yield, and it is critical to understanding the function of many crop management practices [16]
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