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Utilizing Satellite Observations for Improved Crop Model Implementations at Regional Scales

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Special Issue Editors

Dr. Vikalp Mishra

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Guest Editor

NASA Marshall Space Flight Center, SERVIR/SPoRT, Huntsville, AL 35805, USA
Interests: remote sensing in hydrology; agriculture and food security; crop modeling

Dr. Christopher R. Hain

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Guest Editor

NASA, 320 Sparkman Drive, Huntsville, AL 35805, USA
Interests: surface energy balance modeling; soil moisture retrieval; hydrologic data assimilation and drought monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Water Lee Ellenburg

E-Mail Website
Co-Guest Editor

Earth System Science Center, University of Alabama in Huntsville, Huntsville, AL 35805, USA
Interests: agrometeorology; drought; hydrology; water resources; modeling

Special Issue Information

Dear Colleagues,

Agricultural simulation models can be a key component in addressing issues of global food security that includes monitoring and prediction of agricultural drought and its impacts; yields (production); precision agriculture; and agriculture water resources. Crop models typically depend on accurate estimates of numerous inputs, which for many areas of the world are typically not available. Sparse meteorological inputs (e.g., temperature precipitation), in combination with inconsistent management options, tend to increase uncertainties within crop model results. However, some of these uncertainties may be mitigated by utilizing remotely sensed data, such as soil moisture; optical vegetation indices; leaf area index; reference and actual evapotranspiration; land surface temperature; etc. directly or indirectly. In this Special Issue, we seek research that puts forward the use of earth observation data into crop modeling directly (forcing/assimilation) or indirectly (coupled with other land surface models) for improved crop model performance, particularly in data-limited regions of the world at regional scales.

Dr. Vikalp Mishra
Dr. Christopher Hain
Guest Editors

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Keywords

Crop modeling
Data assimilation
Soil moisture
Leaf area index

Published Papers (2 papers)

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23 pages, 2109 KiB

Open AccessArticle

Forecasting Sunflower Grain Yield by Assimilating Leaf Area Index into a Crop Model

by Ronan Trépos, Luc Champolivier, Jean-François Dejoux, Ahmad Al Bitar, Pierre Casadebaig and Philippe Debaeke

Remote Sens. 2020, 12(22), 3816; https://doi.org/10.3390/rs12223816 - 20 Nov 2020

Cited by 9 | Viewed by 2663

Abstract

Forecasting sunflower grain yield a few weeks before crop harvesting is of strategic interest for cooperatives that collect and store grains. With such information, they can optimize their logistics and thus reduce the financial and environmental costs of grain storage. To provide these predictions, data assimilation approaches involving the crop model SUNFLO are used. The methods are based on the re-estimation of soil conditions and on the sequential update of crop model states using an ensemble Kalman filter. They combine the simulation of the crop model and time series of leaf area index (LAI) derived from remote sensors and extracted over 281 fields near Toulouse, France. A sensitivity analysis is used to identify the most relevant model inputs to consider into the data assimilation process. Results show that data assimilation leads to statistically significant better predictions than the simulation alone (from an RMSE of 9.88 q·ha

^{- 1}

to an RMSE 7.49 q·ha

^{- 1}

). Significant improvement is achieved by relying on smoothed LAI rather than raw LAI. Nevertheless, there is still an over estimation of the grain yield that can be partially explained by the limiting factors observed on the fields and the forecast yield still need improvements to meet the required applications’ accuracy. Full article

(This article belongs to the Special Issue Utilizing Satellite Observations for Improved Crop Model Implementations at Regional Scales)

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21 pages, 2305 KiB

Open AccessArticle

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?

by Andreas Tewes, Holger Hoffmann, Manuel Nolte, Gunther Krauss, Fabian Schäfer, Christian Kerkhoff and Thomas Gaiser

Remote Sens. 2020, 12(6), 925; https://doi.org/10.3390/rs12060925 - 13 Mar 2020

Cited by 17 | Viewed by 4039

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. Full article

(This article belongs to the Special Issue Utilizing Satellite Observations for Improved Crop Model Implementations at Regional Scales)

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Utilizing Satellite Observations for Improved Crop Model Implementations at Regional Scales

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