Corn‐yield estimation through assimilation of remotely sensed data into the CSM‐CERES‐Maize model

Abstract

One of the applications of crop simulation models is to estimate crop yield during the current growing season. Several studies have tried to integrate crop simulation models with remotely sensed data through data-assimilation methods. This approach has the advantage of allowing reinitialization of model parameters with remotely sensed observations to improve model performance. In this study, the Cropping System Model-CERES-Maize was integrated with the Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products for estimating corn yield in the state of Indiana, USA. This procedure, inversion of crop simulation model, facilitates several different user input modes and outputs a series of agronomic and biophysical parameters, including crop yield. The estimated corn yield in 2000 compared reasonably well with the US Department of Agriculture National Agricultural Statistics Service statistics for most counties. Using the seasonal LAI in the optimization procedure produced the best results compared with only the green-up LAIs or the highest LAI values. Planting, emergence and maturation dates, and N fertilizer application rates were also estimated at a regional level. Further studies will include investigating model uncertainties and using other MODIS products, such as the enhanced vegetation index.

Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

“…LAI and CNA were individually used as state variables for integrating remote sensing data into the DSSAT-CERES model with the PSO data assimilation method. The results show that each method (SVCNA and SVLAI) estimated LAI or CNA with great accuracy, in agreement with previous research [5], [7], [8], [16] and [17]. However, when only one assimilation variable was used, large errors existed between the simulated and measured values of the other variables; for example, the RMSE of LAI determined with the SVCNA method was 1.207, which was much larger than that determined with the SVLAI method (RMSE = 0.527).…”

“…The productivity of wheat, including grain yield and quality, directly determines its market price and related agriculture policies [3,4], and an advanced knowledge of grain yield and quality is important for this purpose [5,6]. The combination of remote sensing and crop growth simulation models has provided an effective tool for crop grain yield and quality estimation in regional studies [5,7].…”

“…Many studies have been focused on the integration of remote sensing and crop growth simulation models for crop growth monitoring and yield estimation [5,7,8]. Maas [9,10] initially described four techniques (input, updating, re-initialization, and re-parameterization) for leaf area indices (LAI) by incorporating remote sensing data into crop growth model estimations.…”

“…In a regional winter wheat and maize yield simulation, De Wit and Van Diepen [13] used the Ensemble Kalman filter (EnKF) to assimilate soil moisture into the World Food Study (WOFOST) model, where the results indicated assimilation of soil moisture improved the model's relationship with crop yield statistics for 66% and 56% of the regions for winter wheat and maize, respectively. Fang et al [7,14] integrated MODIS LAI, vegetation index, and the Crop System Model (CSM)-Crop Environment Resource Synthesis (CERES)-Maize model for corn yield estimation in Indiana, USA. Morel et al [15] coupled the sugarcane modelling software (MOSICAS) with a remotely sensed time series of the fraction of intercepted photosynthetically active radiation (fIPAR) to optimize the yield estimation by the partial forcing and complete forcing methods.…”

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

“…However, obtaining such data in a timely and accurate manner is challenging if global climate change is considered. Recently, remote sensing data assimilations based on crop growth simulations [4,5] have furnished a potential approach to improving regional crop yield monitoring and forecasting. By making better use of crop-growth models such as the World Food Studies (WOFOST) [6], Erosion Productivity Impact Calculator (EPIC) [7], and Decision Support System for Agro-technology Transfer (DSSAT) [8], crop growth processes can be effectively simulated under different environmental and management conditions while incorporating various limiting factors (e.g., soil, weather, water, and nitrogen) in a dynamic manner [9].…”