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Improving estimation of soil organic matter content by combining Landsat 8 OLI images and environmental data: A case study in the river valley of the southern Qinghai-Tibet Plateau

文献类型：期刊论文

作者Yu, Qing1,2; Yao, Tianci1,2; Lu, Hongwei1; Feng, Wei1,2; Xue, Yuxuan1,2; Liu, Binxiao1,2
刊名COMPUTERS AND ELECTRONICS IN AGRICULTURE
出版日期2021-06-01
卷号185页码:12
关键词Soil organic matter Environmental factors Landsat 8 OLI images Qinghai-Tibet Plateau Precision agriculture
ISSN号0168-1699
DOI10.1016/j.compag.2021.106144
通讯作者Lu, Hongwei(luhw@igsnrr.ac.cn)
英文摘要The Qinghai-Tibet Plateau (QTP) is a typical ecologically fragile area. Once the surface vegetation degenerates, it may not be restored. This requires the development of soil organic matter (SOM) monitoring method without destroying the surface, so as to ensure the sustainable development of plateau agriculture. This work investigated the environmental factors that are significantly related to SOM content in the river valley of the southern QTP. These environmental factors include soil hydrothermal factors (soil moisture content and soil temperature), topographic factors (elevation and slope) and vegetation factor (NDVI). The original band reflectivity (OR) of Landsat 8 OLI images and the band reflectivity after the first-order derivative (FDR) and the second-order derivative (SDR) processing were combined with the above environmental factors to estimate SOM content. The results showed that the accuracy of the model was improved obviously by adding environmental factors. The estimation effect of back propagation neural network (BPNN) model was better than that of geographically weighted regression (GWR) model, partial least squares regression (PLSR) model and multivariable linear regression (MLR) model. GWR model can also meet the estimation requirements, while PLSR and MLR models cannot achieve effectively the estimation of SOM content. FDR-BPNN model considering environmental factors was the best model for estimating SOM content, with R2 being 0.947, RMSEC being 4.701 g.kg- 1 and MAEV being 5.485 g.kg 1. Moreover, the model had the lowest uncertainty and the highest stability. This study will provide a good insight for the monitoring of SOM content in the future, and provide basic data support for the implementation of precision agriculture in the QTP.
WOS关键词NEAR-INFRARED SPECTROSCOPY ; ARTIFICIAL NEURAL-NETWORK ; SPATIAL-DISTRIBUTION ; NIR SPECTROSCOPY ; IN-SITU ; CARBON ; PREDICTION ; VARIABILITY ; REGRESSION ; FRACTIONS
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20040301] ; Second Tibetan Plateau Scientific Expedition and Research Program (STEP)[2019QZKK1003] ; National Key Research and Development Program of China[2019YFC0507801]
WOS研究方向Agriculture ; Computer Science
语种英语
WOS记录号WOS:000648959700003
出版者ELSEVIER SCI LTD
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; Second Tibetan Plateau Scientific Expedition and Research Program (STEP) ; National Key Research and Development Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/162828]  
专题中国科学院地理科学与资源研究所
通讯作者Lu, Hongwei
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Qing,Yao, Tianci,Lu, Hongwei,et al. Improving estimation of soil organic matter content by combining Landsat 8 OLI images and environmental data: A case study in the river valley of the southern Qinghai-Tibet Plateau[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2021,185:12.
APA Yu, Qing,Yao, Tianci,Lu, Hongwei,Feng, Wei,Xue, Yuxuan,&Liu, Binxiao.(2021).Improving estimation of soil organic matter content by combining Landsat 8 OLI images and environmental data: A case study in the river valley of the southern Qinghai-Tibet Plateau.COMPUTERS AND ELECTRONICS IN AGRICULTURE,185,12.
MLA Yu, Qing,et al."Improving estimation of soil organic matter content by combining Landsat 8 OLI images and environmental data: A case study in the river valley of the southern Qinghai-Tibet Plateau".COMPUTERS AND ELECTRONICS IN AGRICULTURE 185(2021):12.

入库方式： OAI收割

来源：地理科学与资源研究所

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