Abstract
A growing world population, increasing prosperity in emerging countries, and shifts in energy and food demands necessitate a continuous increase in global agricultural production. Simultaneously, risks of extreme weather events and a slowing productivity growth in recent years has caused concerns about meeting the demands in the future. Crop monitoring and timely yield predictions are an important tool to mitigate risk and ensure food security. A common approach is to combine the temporal simulation of dynamic crop models with a geospatial component by assimilating remote sensing data. To ensure reliable assimilation, handling of uncertainties in both models and the assimilated input data is crucial. Here, we present a new approach for data assimilation using particle swarm optimization (PSO) in combination with statistical distance metrics that allow for flexible handling of model and input uncertainties. We explored the potential of the newly proposed method in a case study by assimilating canopy cover (CC) information, obtained from Sentinel-2 data, into the AquaCrop-OS model to improve winter wheat yield estimation on the pixel- and field-level and compared the performance with two other methods (simple updating and extended Kalman filter). Our results indicate that the performance of the new method is superior to simple updating and similar or better than the extended Kalman filter updating. Furthermore, it was particularly successful in reducing bias in yield estimation.
Highlights
After decades of continuously rising yields, recent years have seen a slowing down in agricultural productivity growth in Europe
We describe the performance of yield predictions of AquaCrop-OS on the field-level, pixel-level, and pixel-to-field aggregated level
The simple updating scheme had no effect in terms of accuracy, but inverted the bias to an underestimation
Summary
After decades of continuously rising yields, recent years have seen a slowing down in agricultural productivity growth in Europe. Decreasing global production may be expected under certain climate scenarios [1,2]. A growing world population, rising income per capita, and increasing demand for energy are expected to drive demand for agricultural products [3,4]. A common approach is the use of dynamic biophysical crop models that simulate the soil–plant–atmosphere interface [5]. These models can simulate environmental interactions and field management, but have a limited capacity to represent geospatial information on larger scales
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