Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data

Xiaobin Xu,Xiaoyu Song,Liping Chen,Zhenhai Li,Ying Du,Raffaele Casa,Xiaohe Gu,Yu Zhao,Guijun Yang,Chunqi Zhao,Xiuliang Jin,Cong Teng

doi:10.3390/rs12081349

Abstract

Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual expansion and regional transfer still exist. The present experiment was carried out in wheat producing areas of Beijing (BJ), Renqiu (RQ), Quzhou, and Jinzhou in Hebei Province. First, the spectral consistency of Landsat 8 Operational Land Imager (LS8) and RapidEye (RE) was compared with Sentinel-2 (S2) satellites at the same ground point in the same period. The GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Medium-range Weather Forecasts using hierarchical linear model (HLM) method. The prediction and spatial expansion of regional GPC were validated. Results were as follows: (1) Spectral information calculated from S2 imagery were highly consistent with LS8 (R2 = 1.00) and RE (R2 = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R2 = 0.524) demonstrated higher accuracy than the empirical linear model (R2 = 0.286) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in RQ, BJ, Xiaotangshan (XTS) in 2018, and XTS in 2019 were ideal with root mean square errors of 0.61%, 1.13%, 0.91%, and 0.38%, and relative root mean square error of 4.11%, 6.83%, 6.41%, and 2.58%, respectively. This study has great application potential for regional and inter-annual quality prediction.

Highlights

The market demand for wheat quality is receiving increasing attention because of the continuous improvement of the living standards of people
This study focused on the following issues based on the ground and multisource satellite data of different years and regions: (1) The consistency of data from different satellite sensors was discussed; (2) wheat Grain protein content (GPC) prediction based on hierarchical linear model (HLM) was constructed by considering the interaction among environmental, genotype, and remote sensing (RS) monitoring; and (3) the wheat quality classification with GPC was mapped at regional scale
The spectral reflectance of different satellites in the same area and on the same day is extracted to verify the feasibility of multi-source remote sensing satellite complementation

Summary

Introduction

The market demand for wheat quality is receiving increasing attention because of the continuous improvement of the living standards of people. Grain protein content (GPC) is an important standard of wheat quality and directly restricts the utilization and commercial value of wheat [1]. Monitoring and forecasting GPC are of great significance for guiding farmers and enterprises in wheat harvesting and cultivation [2]. The laboratory detection and analytical method are accurate; their destructive multipoint sampling is time consuming and labor intensive, their detection cost is high, and their representativeness is poor [3]. The remote sensing (RS) technology with certain advantages, such as real-time, fast, and non-destructive, had been widely used in the acquisition of crop production information. Researchers had realized the instantaneous and large-scale monitoring of wheat growth status and environment through RS data

Methods

Results

Discussion

Conclusion