Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data
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 deviation...
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MDPI AG
2020-04-01
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Online Access: | https://www.mdpi.com/2072-4292/12/8/1349 |
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author | Xiaobin Xu Cong Teng Yu Zhao Ying Du Chunqi Zhao Guijun Yang Xiuliang Jin Xiaoyu Song Xiaohe Gu Raffaele Casa Liping Chen Zhenhai Li |
author_facet | Xiaobin Xu Cong Teng Yu Zhao Ying Du Chunqi Zhao Guijun Yang Xiuliang Jin Xiaoyu Song Xiaohe Gu Raffaele Casa Liping Chen Zhenhai Li |
author_sort | Xiaobin Xu |
collection | DOAJ |
description | 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 (R<sup>2</sup> = 1.00) and RE (R<sup>2</sup> = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R<sup>2</sup> = 0.524) demonstrated higher accuracy than the empirical linear model (R<sup>2</sup> = 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. |
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language | English |
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spelling | doaj.art-42a747b48bb34c38abe0b9f065bcbcbc2023-11-19T22:40:08ZengMDPI AGRemote Sensing2072-42922020-04-01128134910.3390/rs12081349Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF DataXiaobin Xu0Cong Teng1Yu Zhao2Ying Du3Chunqi Zhao4Guijun Yang5Xiuliang Jin6Xiaoyu Song7Xiaohe Gu8Raffaele Casa9Liping Chen10Zhenhai Li11Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaInstitute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaDAFNE Università della Tuscia, Via San Camillo de Lellis, 01100 Viterbo, ItalyKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, ChinaIndustrialization 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 (R<sup>2</sup> = 1.00) and RE (R<sup>2</sup> = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R<sup>2</sup> = 0.524) demonstrated higher accuracy than the empirical linear model (R<sup>2</sup> = 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.https://www.mdpi.com/2072-4292/12/8/1349Multi-source Remote Sensing ImageryEuropean Center for Medium-range Weather Forecasts (ECMWF) meteorological datagrain protein contenthierarchical linear model |
spellingShingle | Xiaobin Xu Cong Teng Yu Zhao Ying Du Chunqi Zhao Guijun Yang Xiuliang Jin Xiaoyu Song Xiaohe Gu Raffaele Casa Liping Chen Zhenhai Li Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data Remote Sensing Multi-source Remote Sensing Imagery European Center for Medium-range Weather Forecasts (ECMWF) meteorological data grain protein content hierarchical linear model |
title | Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data |
title_full | Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data |
title_fullStr | Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data |
title_full_unstemmed | Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data |
title_short | Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data |
title_sort | prediction of wheat grain protein by coupling multisource remote sensing imagery and ecmwf data |
topic | Multi-source Remote Sensing Imagery European Center for Medium-range Weather Forecasts (ECMWF) meteorological data grain protein content hierarchical linear model |
url | https://www.mdpi.com/2072-4292/12/8/1349 |
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