Arid
DOI10.3390/rs13040769
Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang
Li, Xiaohang; Ding, Jianli; Liu, Jie; Ge, Xiangyu; Zhang, Junyong
Corresponding AuthorDing, JL (corresponding author), Xinjiang Univ, Coll Resources & Environm Sci, Higher Educ Inst, Key Lab Smart City & Environm Modeling, Urumqi 800046, Peoples R China. ; Ding, JL (corresponding author), Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China.
JournalREMOTE SENSING
EISSN2072-4292
Year Published2021
Volume13Issue:4
Abstract in EnglishAs an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.
Keyword in Englishmachine learning Sentinel-1A Sentinel-2A Sentinel-3A soil organic carbon digital soil mapping
SubtypeArticle
Language英语
OA Typegold
Indexed BySCI-E
WOS IDWOS:000624440800001
WOS KeywordRANDOM FOREST ; TOTAL NITROGEN ; TERRAIN ATTRIBUTES ; SPATIAL-PATTERNS ; REGRESSION TREE ; CLIMATE-CHANGE ; PREDICTION ; STOCKS ; SEQUESTRATION ; TEMPERATURE
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
Document Type期刊论文
Identifierhttp://119.78.100.177/qdio/handle/2XILL650/351479
Affiliation[Li, Xiaohang; Ding, Jianli; Liu, Jie; Ge, Xiangyu; Zhang, Junyong] Xinjiang Univ, Coll Resources & Environm Sci, Higher Educ Inst, Key Lab Smart City & Environm Modeling, Urumqi 800046, Peoples R China; [Li, Xiaohang; Ding, Jianli; Liu, Jie; Ge, Xiangyu; Zhang, Junyong] Xinjiang Univ, Key Lab Oasis Ecol, Urumqi 830046, Peoples R China
Recommended Citation
GB/T 7714
Li, Xiaohang,Ding, Jianli,Liu, Jie,et al. Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang[J],2021,13(4).
APA Li, Xiaohang,Ding, Jianli,Liu, Jie,Ge, Xiangyu,&Zhang, Junyong.(2021).Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang.REMOTE SENSING,13(4).
MLA Li, Xiaohang,et al."Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang".REMOTE SENSING 13.4(2021).
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