PMID- 32160185 OWN - NLM STAT- MEDLINE DCOM- 20200619 LR - 20200619 IS - 1932-6203 (Electronic) IS - 1932-6203 (Linking) VI - 15 IP - 3 DP - 2020 TI - Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm. PG - e0228500 LID - 10.1371/journal.pone.0228500 [doi] LID - e0228500 AB - 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. FAU - Tan, Changwei AU - Tan C AUID- ORCID: 0000-0001-6591-8136 AD - 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. FAU - Zhou, Xinxing AU - Zhou X AD - 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. FAU - Zhang, Pengpeng AU - Zhang P AD - 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. FAU - Wang, Zhixiang AU - Wang Z AD - 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. FAU - Wang, Dunliang AU - Wang D AD - 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. FAU - Guo, Wenshan AU - Guo W AD - 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. FAU - Yun, Fei AU - Yun F AD - National Tobacco Cultivation and Physiology and Biochemistry Research Centre/Key Laboratory for Tobacco Cultivation of Tobacco Industry, Henan Agricultural University, Zhengzhou, China. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20200311 PL - United States TA - PLoS One JT - PloS one JID - 101285081 RN - 0 (Grain Proteins) SB - IM MH - *Algorithms MH - Grain Proteins/*analysis MH - Least-Squares Analysis MH - Remote Sensing Technology/*methods MH - Satellite Imagery/*methods MH - Triticum/*chemistry/*metabolism PMC - PMC7065814 COIS- The authors have declared that no competing interests exist. EDAT- 2020/03/12 06:00 MHDA- 2020/06/20 06:00 PMCR- 2020/03/11 CRDT- 2020/03/12 06:00 PHST- 2019/07/18 00:00 [received] PHST- 2020/01/16 00:00 [accepted] PHST- 2020/03/12 06:00 [entrez] PHST- 2020/03/12 06:00 [pubmed] PHST- 2020/06/20 06:00 [medline] PHST- 2020/03/11 00:00 [pmc-release] AID - PONE-D-19-20309 [pii] AID - 10.1371/journal.pone.0228500 [doi] PST - epublish SO - PLoS One. 2020 Mar 11;15(3):e0228500. doi: 10.1371/journal.pone.0228500. eCollection 2020.