Estimation of Winter Wheat Grain Protein Content Based on Multisource Data Assimilation

Pengfei Chen

doi:10.3390/rs12193201

Abstract

Data assimilation is a robust method to predict crop biophysical and biochemical parameters. However, no previous study has attempted to predict grain protein content (GPC) at a regional scale using this method. This study explored the feasibility of designing an assimilation model for wheat GPC estimation using remote sensing, a crop growth model, and a priori knowledge. The data included a field experiment and regional sampling data, and Landsat Operational Land Imager images were employed, with the CERES (Crop Environment REsource Synthesis)-Wheat model used as simulation model. To select an optimal method for data assimilation in GPC prediction, different state variable scenarios and cost function solving algorithm scenarios were compared. Additionally, to determine whether a priori information could improve GPC prediction, the collected leaf area index (LAI) and leaf N content sampling data and the range of GPC in the study region were used to constrain the data assimilation process. Furthermore, the data assimilation method was compared to the use of only the CERES-Wheat model. The results showed that GPC could be predicted by remote sensing observation, a crop growth model, and a priori knowledge at regional scale, where the use of data assimilation improved the GPC prediction compared to using only the CERES-Wheat model.

Highlights

Wheat is a very important grain crop
Studies have shown that the wheat grain protein content (GPC) can be predicted by the data assimilation method using field sampled spectra and the CERES-Wheat model
We showed that GPC could be predicted with moderate accuracy by combing Landsat Operational Land Imager (OLI) data with the CERES-Wheat model

Summary

Introduction

Wheat is a very important grain crop. The grain protein content (GPC, %) is a critical indicator that determines the use and commercial value of the wheat [1]. A combination of factors, such as the plant genetics, field management, and weather conditions determine the GPC [2], which show significant spatial differences [3,4]. Detecting wheat GPC in an accurate, non-destructive, and timely manner at the regional scale is essential for harvesting wheat according to crop quality, thereby ensuring that high-quality crops are sold at appropriate prices. Two main types of non-destructive detection methods are used for wheat GPC prediction, namely the crop growth model simulation method and the remote sensing inversion method. Soil, cultivar, and field management parameters as inputs, wheat growth models, such as APSIM (Agricultural Production Systems sIMulator)-Wheat and CERES-Wheat, can simulate the growth and development of wheat, as well as predict the wheat GPC [5,6,7]. Remote sensing technology uses the vegetation canopy spectrum acquired by sensors to predict the biophysical and biochemical parameters of vegetation based on their spectral characteristics of the reflectance curve

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Conclusion