Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops/Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Yangzhou University, Yangzhou, China.
National Tobacco Cultivation and Physiology and Biochemistry Research Centre/Key Laboratory for Tobacco Cultivation of Tobacco Industry, Henan Agricultural University, Zhengzhou, China.
PLoS One. 2020 Mar 11;15(3):e0228500. doi: 10.1371/journal.pone.0228500. eCollection 2020.
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.
遥感已被用作现代作物生产监测的重要手段，特别是在预测小麦中后期的质量方面。为了进一步提高通过遥感估计籽粒蛋白质含量（GPC）的准确性，本研究分析了从配备宽带 CCD 传感器的环境与灾害监测预报小卫星星座系统（简称 HJ-CCD）获得的 14 个遥感变量与田间冬小麦 GPC 之间的定量关系。这 14 个遥感变量分别是归一化植被指数（NDVI）、土壤调整植被指数（SAVI）、优化土壤调整植被指数（OSAVI）、氮反射指数（NRI）、绿度归一化植被指数（GNDVI）、结构密集色素指数（SIPI）、植物衰老反射率指数（PSRI）、增强植被指数（EVI）、差值植被指数（DVI）、比值植被指数（RVI）、Rblue（蓝光波段反射率）、Rgreen（绿光波段反射率）、Rred（红光波段反射率）和 Rnir（近红外波段反射率）。本研究使用偏最小二乘（PLS）算法构建并验证了预测小麦 GPC 的多元遥感模型。研究结果表明，小麦 GPC 与光谱反射带中除 Rblue 和 Rgreen 之外的 12 个遥感变量密切相关。其中，除 PSRI 和 Rblue、Rgreen 和 Rred 外，其他遥感植被指数具有显著的多重相关性。用于预测小麦 GPC 的 PLS 模型的最优主成分是：NDVI、SIPI、PSRI 和 EVI。这些都是预测小麦 GPC 的敏感变量。通过建模集和验证集评价，GPC 预测模型的决定系数（R2）分别为 0.84 和 0.8，均方根误差（RMSE）分别为 0.43%和 0.54%。这表明 PLS 算法模型对小麦 GPC 的预测效果优于线性回归（LR）和主成分分析（PCA）算法模型。PLS 算法模型的预测精度均在 90%以上，比 LR 算法模型提高了 20%以上，比 PCA 算法模型提高了 15%以上。研究结果可为利用卫星图像提高冬小麦 GPC 远程预测精度提供有效途径，有利于大面积应用和推广。
