1. Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm
- Author
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Zhou Xinxing, Fei Yun, Zhang Pengpeng, Changwei Tan, Wang Zhixiang, Dunliang Wang, and Wenshan Guo
- Subjects
Satellite Imagery ,Multivariate statistics ,010504 meteorology & atmospheric sciences ,0211 other engineering and technologies ,02 engineering and technology ,01 natural sciences ,Root mean square ,Remote Sensing ,Mathematical and Statistical Techniques ,Geoinformatics ,Triticum ,Mathematics ,Principal Component Analysis ,Multidisciplinary ,Remote Sensing Imagery ,Geography ,Applied Mathematics ,Simulation and Modeling ,Statistics ,Eukaryota ,Agriculture ,Plants ,Principal component analysis ,Wheat ,Physical Sciences ,Engineering and Technology ,Medicine ,Algorithms ,Research Article ,Computer and Information Sciences ,Mean squared error ,Imaging Techniques ,Science ,Crops ,Research and Analysis Methods ,Normalized Difference Vegetation Index ,Linear regression ,Grain Proteins ,Grasses ,Least-Squares Analysis ,Statistical Methods ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing ,Organisms ,Biology and Life Sciences ,Enhanced vegetation index ,Remote Sensing Technology ,Multivariate Analysis ,Earth Sciences ,Predictive modelling ,Crop Science ,Cereal Crops ,Forecasting - Abstract
Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), Rblue (reflectance at blue band), Rgreen (reflectance at green band), Rred (reflectance at red band) and Rnir (reflectance at near infrared band). The partial least square (PLS) algorithm was used to construct and validate the multivariate remote sensing model of predicting wheat GPC. The research showed a close relationship between wheat GPC and 12 remote sensing variables other than Rblue and Rgreen of the spectral reflectance bands. Among them, except PSRI and Rblue, Rgreen and Rred, other remote sensing vegetation indexes had significant multiple correlations. The optimal principal components of PLS model used to predict wheat GPC were: NDVI, SIPI, PSRI and EVI. All these were sensitive variables to predict wheat GPC. Through modeling set and verification set evaluation, GPC prediction models' coefficients of determination (R2) were 0.84 and 0.8, respectively. The root mean square errors (RMSE) were 0.43% and 0.54%, respectively. It indicated that the PLS algorithm model predicted wheat GPC better than models for linear regression (LR) and principal components analysis (PCA) algorithms. The PLS algorithm model’s prediction accuracies were above 90%. The improvement was by more than 20% than the model for LR algorithm and more than 15% higher than the model for PCA algorithm. The results could provide an effective way to improve the accuracy of remotely predicting winter wheat GPC through satellite images, and was conducive to large-area application and promotion.
- Published
- 2020