Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization
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Earth Observation and Modelling, Dept. of Geography, Kiel University, 24118 Kiel, Germany
2
Algorithmic Optimal Control—CO2 Uptake of the Ocean, Dept. of Computer Science, Kiel University, 24118 Kiel, Germany
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(2), 105; https://doi.org/10.3390/ijgi9020105
Received: 14 December 2019
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Revised: 25 January 2020
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Accepted: 7 February 2020
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Published: 10 February 2020
(This article belongs to the Special Issue Uncertainty Modeling in Spatial Data Analysis)
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.
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Keywords:
particle swarm optimization (PSO); AquaCrop-OS; data assimilation; uncertainty quantification; crop yield estimation; model updating; canopy cover (CC)
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MDPI and ACS Style
Wagner, M.P.; Slawig, T.; Taravat, A.; Oppelt, N. Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization. ISPRS Int. J. Geo-Inf. 2020, 9, 105. https://doi.org/10.3390/ijgi9020105
AMA Style
Wagner MP, Slawig T, Taravat A, Oppelt N. Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization. ISPRS International Journal of Geo-Information. 2020; 9(2):105. https://doi.org/10.3390/ijgi9020105
Chicago/Turabian StyleWagner, Matthias P., Thomas Slawig, Alireza Taravat, and Natascha Oppelt. 2020. "Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization" ISPRS International Journal of Geo-Information 9, no. 2: 105. https://doi.org/10.3390/ijgi9020105
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