Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model

Authors

Jianxi Huang, China Agricultural UniversityFollow
Liyan Tian, China Agricultural University
Shunlin Liang, Beijing Normal University
Hongyuan Ma, China Agricultural University
Inbal Becker-Reshef, University of Maryland
Yanbo Huang, USDA-ARS
Wei Su, China Agricultural University
Xiaodong Zhang, China Agricultural University
Dehai Zhu, China Agricultural University
Wenbin Wu, Chinese Academy of Agricultural Sciences

Document Type

Article

Date of this Version

2015

Citation

Agricultural and Forest Meteorology 204 (2015) 106–121

Comments

© 2015 Elsevier B.V.

This document is a U.S. government work and is not subject to copyright in the United States.

http://dx.doi.org/10.1016/j.agrformet.2015.02.001

Abstract

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions (R² = 0.48; RMSE= 151.92 kg ha⁻¹) compared with the unassimilated results (R² = 0.23;RMSE= 373.6 kg ha⁻¹) and the TM LAI results (R² = 0.27; RMSE= 191.6 kg ha⁻¹). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates.