Next Article in Journal
Improving Remote Species Identification through Efficient Training Data Collection
Previous Article in Journal
Laboratory Salinization of Brazilian Alluvial Soils and the Spectral Effects of Gypsum
Remote Sens. 2014, 6(4), 2664-2681; doi:10.3390/rs6042664
Article

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

1,2,3
, 2,3,* , 1
, 2,3
, 2,3
 and 2,3
Received: 6 January 2014; in revised form: 3 March 2014 / Accepted: 17 March 2014 / Published: 25 March 2014
View Full-Text   |   Download PDF [1987 KB, uploaded 19 June 2014]   |   Browse Figures
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.
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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

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.

AMA Style

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 Sensing. 2014; 6(4):2664-2681.

Chicago/Turabian Style

Jiang, Zhiwei; Chen, Zhongxin; Chen, Jin; Ren, Jianqiang; Li, Zongnan; Sun, Liang. 2014. "The Estimation of Regional Crop Yield Using Ensemble-Based Four-Dimensional Variational Data Assimilation." Remote Sens. 6, no. 4: 2664-2681.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert