Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions

- Characteristics of a maize cultivation region were effectively extracted.
- Exponential NDVI-LAI model showed a high determination coefficient and accuracy.
- LAI extracted from ETM+ data was integrated into the coupled hydrology-crop growth model using EnKF.
- This study is encouraging for forecasting crop status and estimating crop yield at a regional scale.

Regional crop yield prediction is a significant component of national food policy making and security assessments. A data assimilation method that combines crop growth models with remotely sensed data has been proven to be the most effective method for regional yield estimates. This paper describes an assimilation method that integrates a time series of leaf area index (LAI) retrieved from ETM+ data and a coupled hydrology-crop growth model which links a crop growth model World Food Study (WOFOST) and a hydrology model HYDRUS-1D for regional maize yield estimates using the ensemble Kalman filter (EnKF). The coupled hydrology-crop growth model was calibrated and validated using field data to ensure that the model accurately simulated associated state variables and maize growing processes. To identify the parameters that most affected model output, an extended Fourier amplitude sensitivity test (EFAST) was applied to the model before calibration. The calibration results indicated that the coupled hydrology-crop growth model accurately simulated maize growth processes for the local cultivation variety tested. The coefficient of variations (CVs) for LAI, total above-ground production (TAGP), dry weight of storage organs (WSO), and evapotranspiration (ET) were 13%, 6.9%, 11% and 20%, respectively. The calibrated growth model was then combined with the regional ETM+ LAI data using a sequential data assimilation algorithm (EnKF) to incorporate spatial heterogeneity in maize growth into the coupled hydrology-crop growth model. The theoretical LAI profile for the near future and the final yield were obtained through the EnKF algorithm for 50 sample plots. The CV of the regional yield estimates for these sample plots was 8.7%. Finally, the maize yield distribution for the Zhangye Oasis was obtained as a case study. In general, this research and associated model could be used to evaluate the impacts of irrigation, fertilizer and field management on crop yield at a regional scale.