干旱区生态环境知识资源中心(Arid): Prediction of soil organic matter using Landsat 8 data and machine learning algorithms in typical karst cropland in China

Arid

DOI	10.1016/j.eja.2024.127323
	Prediction of soil organic matter using Landsat 8 data and machine learning algorithms in typical karst cropland in China
	Chang, Naijie; Chen, Di
通讯作者	Chen, D
来源期刊	EUROPEAN JOURNAL OF AGRONOMY
ISSN	1161-0301
EISSN	1873-7331
出版年	2024
卷号	160
英文摘要	Soil organic matter (SOM) is crucial for karst ecosystems, affecting cropland health, climate change mitigation, and rocky desertification control. However, there are limited research on cropland SOM prediction in karst areas with complex topography and diverse microclimates. Here, we compared the performance of four machine learning algorithms-random forest (RF), support vector regression (SVR), multilayer perceptron regression (MLP), and gradient boosting regression trees (GBRT)-for predicting cropland SOM in a typical karst landform area in 2019. Our results indicated that the GBRT model achieved the highest prediction accuracy with an R2 2 of 0.69, MAE of 2.19 g/kg, RMSE of 3.37 g/kg, and LCCC of 0.82. Using the GBRT model and spatial data on climate, topography, and remote sensing, we predicted SOM for each 30 m x 30 m grid cell. The analysis revealed higher SOM content in the northeastern and southwestern regions and lower content in the central area, ranging from 13.95 to 47.81 g/kg, with an average of 27.16 g/kg. Lime soil had the highest SOM content, while purple soil had the lowest. Paddy fields showed significantly higher SOM than dry land. Over the past 40 years, SOM content has slightly increased, while its spatial distribution has remained stable.
英文关键词	Digital soil mapping Soil organic matter Machine learning Landsat 8 Karst cropland
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001303728500001
WOS关键词	CARBON STOCKS ; DYNAMICS ; PATTERNS
WOS类目	Agronomy
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403699
推荐引用方式 GB/T 7714	Chang, Naijie,Chen, Di. Prediction of soil organic matter using Landsat 8 data and machine learning algorithms in typical karst cropland in China[J],2024,160.
APA	Chang, Naijie,&Chen, Di.(2024).Prediction of soil organic matter using Landsat 8 data and machine learning algorithms in typical karst cropland in China.EUROPEAN JOURNAL OF AGRONOMY,160.
MLA	Chang, Naijie,et al."Prediction of soil organic matter using Landsat 8 data and machine learning algorithms in typical karst cropland in China".EUROPEAN JOURNAL OF AGRONOMY 160(2024).