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Estimation of Winter Wheat Residue Coverage Based on GF-1 Imagery and Machine Learning Algorithm

文献类型： 外文期刊

作者： Zhu, Qilei 1 ; Xu, Xingang 1 ; Sun, Zhendong 1 ; Liang, Dong 3 ; An, Xiaofei 4 ; Chen, Liping 4 ; Yang, Guijun 1 ; Huang, Linsheng 3 ; Xu, Sizhe 1 ; Yang, Min 1 ;

作者机构： 1.Minist Agr & Rural Affairs, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

3.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applic, Hefei 230601, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

关键词： wheat residue coverage; GF-1 remote sensing satellite; Sentinel-2 remote sensing satellite; machine learning regression model

期刊名称：AGRONOMY-BASEL （ 影响因子：3.949； 五年影响因子：4.117 ）

ISSN：

年卷期： 2022 年 12 卷 5 期

页码：

收录情况： SCI

摘要： Crop residue is an important component of farmland ecosystems, which is of great significance for increasing soil organic carbon, mitigating wind erosion and water erosion and conserving soil and water. Crop residue coverage (CRC) is an important parameter to characterize the number and distribution of crop residues, and also a key indicator of conservation tillage. In this study, the CRC of wheat was taken as the research object. Based on the high-resolution GF-1 satellite remote sensing imagery from China, decision tree (DT), gradient boosting decision tree (GBDT), random forest (RF), least absolute shrinkage and selection operator (LASSO), extreme gradient boosting regression (XGBR) and other machine learning algorithms were used to carry out the estimation of wheat CRC by remote sensing. In addition, the comparisons with sentinel-2 imagery data were also utilized to assess the potential of GF satellite data for CRC estimates. The results show the following: (1) Among the spectral indexes using shortwave infrared characteristic bands from sentinel-2 imagery, the dead fuel index (DFI) was the best for estimating wheat CRC, with an R-2 of 0.54 and an RMSE of 10.26%. The ratio vegetation index (RVI) extracted from visible and near-infrared characteristic bands from GF-1 data performed the best, with an R-2 of 0.46 and an RMSE of 11.39%. The spectral index extracted from GF-1 and sentinel-2 images had a significant response relationship with wheat residue coverage. (2) When only the characteristic bands from the visible and near-infrared spectral ranges were applied, the effects of the spatial resolution differences of different images on wheat CRC had to be taken into account. The estimations of wheat CRC with the high-resolution GF-1 data were significantly better than those with the Sentinel-2 data, and among multiple machine learning algorithms adopted to estimate wheat CRC, LASSO had the most stable capability, with an R-2 of 0.46 and an RMSE of 11.4%. This indicates that GF-1 high-resolution satellite imagery without shortwave infrared bands has a good potential in applications of monitoring crop residue coverage for wheat, and the relevant technology and method can also provide a useful reference for CRC estimates of other crops.

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