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Remote Sens. 2014, 6(4), 2664-2681; doi:10.3390/rs6042664

The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation

1
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2
Key Laboratory of Agri-Informatics, Ministry of Agriculture, Beijing 100081, China
3
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Received: 6 January 2014 / Revised: 3 March 2014 / Accepted: 17 March 2014 / Published: 25 March 2014
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Abstract

To improve crop model performance for regional crop yield estimates, a new four-dimensional variational algorithm (POD4DVar) merging the Monte Carlo and proper orthogonal decomposition techniques was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)-Wheat model. Two winter wheat yield estimation procedures were conducted on a field plot and regional scale to test the feasibility and potential of the POD4DVar-based strategy. Winter wheat yield forecasts for the field plots showed a coefficient of determination (R2) of 0.73, a root mean square error (RMSE) of 319 kg/ha, and a relative error (RE) of 3.49%. An acceptable yield at the regional scale was estimated with an R2 of 0.997, RMSE of 7346 tons, and RE of 3.81%. The POD4DVar-based strategy was more accurate and efficient than the EnKF-based strategy. In addition to crop yield, other critical crop variables such as the biomass, harvest index, evapotranspiration, and soil organic carbon may also be estimated. The present study thus introduces a promising approach for operationally monitoring regional crop growth and predicting yield. Successful application of this assimilation model at regional scales must focus on uncertainties derived from the crop model, model inputs, data assimilation algorithm, and assimilated observations. View Full-Text
Keywords: four-dimensional variation; crop model; data assimilation; yield estimation; leaf area index; remote sensing four-dimensional variation; crop model; data assimilation; yield estimation; leaf area index; remote sensing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Jiang, Z.; Chen, Z.; Chen, J.; Ren, J.; Li, Z.; Sun, L. The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation. Remote Sens. 2014, 6, 2664-2681.

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