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

Zhenhai Li,Jihua Wang,Haikuan Feng,Xiuliang Jin,Guijun Yang,Xingang Xu,Chunjiang Zhao

doi:10.3390/rs70912400

Abstract

The combination of remote sensing and crop growth models has become an effective tool for yield estimation and a potential method for grain quality estimation. In this study, two assimilation variables (derived from a hyperspectral sensor), called leaf area index (LAI) and canopy nitrogen accumulation (CNA), were jointly used to calibrate the sensitive parameters and initial states of the DSSAT-CERES crop model, to improve simulated output of the grain yield and protein content of winter wheat. The results show that the modified simple ratio (MSR) and normalized difference red edge (NDRE) better estimated LAI and CNA, respectively, compared with the other possible vegetation indices. The integration of both LAI and CNA resulted in a more robust DSSAT-CERES models with than each one alone. The R2 and RMSE values, respectively, of the regression between the simulated (using the two assimilation variables method) and measured LAI were 0.828 and 0.494, and for CNA were 0.808 and 20.26 kg N∙ha−1. These two assimilation variables resulted in grain yield and protein content estimates of winter wheat with a high precision and R2 and RMSE values of 0.698 and 0.726 ton∙ha−1, and 0.758% and 1.16%, respectively. This study provides a more robust method for estimating the grain yield and protein content of winter wheat based on the integration of the DSSAT-CERES crop model and remote sensing data.

Highlights

Wheat (Triticum aestivum L.) is a staple food in North China, where the population accounts for about40% of the country’s total [1,2]
Our results show that the SVCNA method (R2 = 0.714; root mean square error (RMSE) = 0.732 ton∙ha−1) resulted in a better estimation of wheat yield than the SVLAI method (R2 = 0.665; RMSE = 0.868 ton∙ha−1 (Figure 3a,c)
normalized difference red edge (NDRE) had the strongest relationship with canopy N accumulation (CNA) (R2 = 0.794 and RMSE = 37.75 kg N∙ha−1), and this index was measured with wavebands centered at 720 and 790 nm, which were more sensitive to estimating canopy N per unit area than canopy N concentration [48]

Summary

Introduction

Wheat (Triticum aestivum L.) is a staple food in North China, where the population accounts for about40% of the country’s total [1,2]. Wheat (Triticum aestivum L.) is a staple food in North China, where the population accounts for about. 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]. Guerif and Duke [8,11]

Objectives

Methods

Discussion

Conclusion