DOI: 10.1016/j.ecolind.2020.106288
论文题名: Mapping soil organic carbon content using multi-source remote sensing variables in the Heihe River Basin in China
作者: Zhou T. ; Geng Y. ; Chen J. ; Liu M. ; Haase D. ; Lausch A.
刊名: Ecological Indicators
ISSN: 1470160X
出版年: 2020
卷: 114 语种: 英语
英文关键词: Boosted regression tree
; Digital soil mapping
; Random forests
; Remote sensing
; Soil organic carbon
Scopus关键词: Agronomy
; Climate change
; Decision trees
; Forecasting
; Forestry
; Image enhancement
; Land use
; Mapping
; Mean square error
; Organic carbon
; Random forests
; Remote sensing
; Soil surveys
; Synthetic aperture radar
; Topography
; Watersheds
; Boosted regression trees
; Coefficient of determination
; Digital soil mappings
; Root mean squared errors
; Soil organic carbon
; Soil organic carbon content
; Spatial distribution patterns
; Sustainable soil management
; Soil quality
英文摘要: Soil organic carbon (SOC) has a large impact on soil quality and global climate change. It is therefore important to be able to predict SOC accurately to promote sustainable soil management. Although the synthetic aperture radar (SAR) has many advantages and has been widely used in soil science research, it has rarely been used in previous SOC mapping studies based on remote sensing images. The purpose of this study was to investigate the ability of multi-temporal Sentinel-1A data in SOC prediction, by comparing the predictive performance of random forest (RF) and boosted regression tree (BRT) models in the Heihe River Basin in northwestern China. A set of 162 topsoil (0–20 cm) samples were taken and 15 environmental variables were obtained including land use, topography, climate, and remote sensing images (optical and SAR data). Using a cross-validation procedure to evaluate the performance of the models, three statistical indices were calculated. Overall, both RF and BRT models effectively predicted SOC content, exhibiting similar performance and producing similar spatial distribution patterns of SOC. The results showed that the addition of multi-temporal Sentinel-1A images improved prediction accuracy, with the root mean squared error (RMSE), the mean absolute error (MAE) and the coefficient of determination (R2) improving by 9.0%, 8.3% and 13.5%, respectively. Furthermore, the combination of all environmental variables had the best prediction performance explaining 75% of SOC variation. The most important environmental variables explaining SOC variation were precipitation, elevation, and temperature. The multi-temporal Sentinel-1A data in RF and BRT models explained 9% and 7%, respectively. The results from our case study highlight the usefulness of multi-temporal Sentinel-1 data in SOC mapping. © 2020 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158068
Appears in Collections: 气候变化与战略
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作者单位: Humboldt-Universität zu Berlin, Department of Geography, Unter den Linden 6, Berlin, 10099, Germany; Helmholtz Centre for Environmental Research – UFZ, Department of Computational Landscape Ecology, Permoserstraße 15, Leipzig, 04318, Germany; Nanjing Agricultural University, College of Resources and Environmental Sciences, Weigang 1, Nanjing, 210095, China; Liaoning Technical University, School of Surveying and Geoscience, Zhonghua Road 47, Fuxin, 123000, China
Recommended Citation:
Zhou T.,Geng Y.,Chen J.,et al. Mapping soil organic carbon content using multi-source remote sensing variables in the Heihe River Basin in China[J]. Ecological Indicators,2020-01-01,114