Multidimensional soil salinity data mining and evaluation from different satellites
文献类型：期刊论文
| 作者 | Cao, Xiaoyi2,3,4,7; Chen, Wenqian1; Ge, Xiangyu2,3,4; Chen, Xiangyue7; Wang, Jingzhe5,6; Ding, Jianli2,3,4 |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
 |
| 出版日期 | 2022-11-10 |
| 卷号 | 846页码:16 |
| 关键词 | Soil salinity Remote sensing Multidimensionality Data mining Integrated algorithm |
| ISSN号 | 0048-9697 |
| DOI | 10.1016/j.scitotenv.2022.157416 |
| 通讯作者 | Ding, Jianli(watarid@xju.edu.cn) |
| 英文摘要 | Soil salinization, a common land degradation mode, restricts the ecological environment and is a global issue due to climate change. Accurately, quickly and effectively monitoring soil salinity is critical for governmental institutions that develop hazard prevention and mitigation strategies. Remote sensing (RS) technology provides a viable alternative to traditional field work due to its large area coverage, abundant spectral information and nearly constant observations. Key issues in RS-based soil salinity monitoring include the lack of both data-mining techniques for obtaining spectral band information and comprehensive considerations of synergies among different spectra. The main objective of this study was to provide in-depth explorations of data mining and integration algorithms from different satellites to multidimensionally evaluate soil salinity models. The Ebinur Lake Wetland Reserve (Xinjiang Province, China) was selected as a case study. First, ground-measured visible and near infrared (VIS-NIR) spectral data were combined with the RS band to simulate Landsat 8 (L8) and Sentinel 2 (S2) and 3 (S3) data. Second, one-dimensional RS bands and 15 soil salinity and vegetation indices were selected, and 15 spectral data transformations (reciprocal, differential, absorbance, etc.) were obtained. Two- and three-dimensional spectral indices were constructed, and the response relationships between different spectral indices and soil electrical conductivity (FE) were comprehensively explored. Finally, an integrated multidimensional algorithm was used to estimate soil salinity in high-performance models for the three satellites. The results showed that all data-mining-based model combinations performed well fora!! satellites (R-2 > 0.80). However, with multidimensional model combinations, S3 presented the highest predictive capability (R-2 = 0.89, RMSE = 2.57 mS.cm(-1), RPD = 2.05), followed by S2 (R-2 = 0.86, RMSF. = 2.71 mS.cm(-1), RPD = 1.90) and 1.8 (R-2 = 0.85, RMSF. = 2.84 mS.cm(-1), RPD = 1.87). Therefore, data mining with integration algorithms in model combinations performs significantly better than previous models and could be considered a promising method for obtaining improved results from soil salinity susceptibility models in similar cases. |
| WOS关键词 | NATURE-RESERVE ELWNNR ; REMOTE-SENSING DATA ; YELLOW-RIVER DELTA ; EBINUR LAKE ; SALT CONTENT ; ORGANIC-CARBON ; WET SEASONS ; XINJIANG ; PREDICTION ; LANDSAT |
| 资助项目 | Key Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region[2021D01D06] ; National Natural Science Foundation of China[41961059] ; Joint Fundation-Youth Fundation in Guangdong of China[2019A1515110692] ; Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University[2022LSDMIS05] ; State Key Laboratory of Resources and Environmental Information System |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000852634700011 |
| 出版者 | ELSEVIER |
| 资助机构 | Key Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region ; National Natural Science Foundation of China ; Joint Fundation-Youth Fundation in Guangdong of China ; Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University ; State Key Laboratory of Resources and Environmental Information System |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/184752]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ding, Jianli |
| 作者单位 | 1.Qingdao Univ Technol, Sch Informat & Control Engn, Qingdao 266520, Peoples R China 2.Xinjiang Univ, Key Lab Smart City & Envirorm Modelling, Higher Educ Inst, Urumqi 830017, Peoples R China 3.Xinjiang Univ, Xinjiang Key Lab Oasis Ecol, Urumqi 830017, Peoples R China 4.Xinjiang Univ, Coll Geog & Remote Sensing Sci, Urumqi 830017, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 6.Shenthen Polytech, Sch Artificial Intelligence, Shenzhen 518055, Peoples R China 7.Lanzhou Univ, Key Lab Semiarid Climate Change, Minist Educ, Coll Atmospher Sci, Lanzhou 730000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Xiaoyi,Chen, Wenqian,Ge, Xiangyu,et al. Multidimensional soil salinity data mining and evaluation from different satellites[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2022,846:16. |
| APA | Cao, Xiaoyi,Chen, Wenqian,Ge, Xiangyu,Chen, Xiangyue,Wang, Jingzhe,&Ding, Jianli.(2022).Multidimensional soil salinity data mining and evaluation from different satellites.SCIENCE OF THE TOTAL ENVIRONMENT,846,16. |
| MLA | Cao, Xiaoyi,et al."Multidimensional soil salinity data mining and evaluation from different satellites".SCIENCE OF THE TOTAL ENVIRONMENT 846(2022):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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