Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks
文献类型：期刊论文
| 作者 | Ge, Xiangyu1,2,6; Ding, Jianli1,2,6,8; Teng, Dexiong3; Xie, Boqiang1,2,6; Zhang, Xianlong4; Wang, Jinjie1,2,6; Han, Lijing1,2,6; Bao, Qingling1,2,6; Wang, Jingzhe5,7 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2022-08-01 |
| 卷号 | 112页码:14 |
| 关键词 | Gaofen-5 Soil salinization Fractional order derivative Machine learning Digital soil mapping |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2022.102969 |
| 通讯作者 | Ding, Jianli(watarid@xju.edu.cn) |
| 英文摘要 | Soil salinization has hampered the achievement of sustainable development goals (SDGs) in many countries worldwide. Several countries have recently launched hyperspectral remote sensing satellites, opening new av-enues for accurate soil-salinity monitoring. Among them, Gaofen-5 (GF-5) from China has a high comprehensive performance, including a spectral resolution of 5 nm, 330 bands, and signal-to-noise ratio of 700. However, the potential of GF-5 for estimating soil salinity is not well understood. In this study, we proposed a strategy that includes bootstrap methods, fractional order derivative (FOD) techniques and decision-level fusion models to exploit the soil salinity diagnostic information and reduce estimation uncertainty in the Ebinur Lake oasis in northwestern China. The results showed that the GF-5 data were suitable for assessing soil salinity. The FOD technique enhanced the correlation between soil salinity and spectra, identified more diagnostic bands, improved the accuracy of soil salinity estimation, and reduced model uncertainty. The low-order FOD outperformed the high-order FOD. The spectra processed by the 0.9 order derivative were the most correlated with soil salinity (r =-0.76). The model driven by the 0.8 order derivative produced the optimal estimated model (R2 = 0.95, root mean square error (RMSE) = 3.20 dS m-1 and a ratio of performance to interquartile distance (RPIQ) = 5.96). The model driven by the 0.8 order derivative had less uncertainty than the models based on the original and integer-order derivative (first-and second-derivatives) spectra. This study provides a reference for estimating soil salinity from GF-5 data using the proposed framework with low uncertainty and high accuracy. GF-5 data have great potential for assessing environmental problems and facilitating further SDGs. |
| WOS关键词 | ORGANIC-MATTER CONTENT ; YELLOW-RIVER DELTA ; UNCERTAINTY ; PREDICTION ; REGION ; WATER ; OLI ; MSI |
| 资助项目 | Shanghai Institute of Technical Physics of the Chinese Acad-emy of Sciences ; Key Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region[2021D01D06] ; National Natural Science Foundation of China[41961059] ; Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University[2022LSDMIS05] ; State Key Laboratory of Resources and Environmental Information System, China |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000844161000003 |
| 出版者 | ELSEVIER |
| 资助机构 | Shanghai Institute of Technical Physics of the Chinese Acad-emy of Sciences ; Key Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region ; National Natural Science Foundation 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, China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/166615]  |
| 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.Xinjiang Univ, Xinjiang Key Lab Oasis Ecol, Urumqi 830017, Peoples R China 2.Xinjiang Univ, Key Lab Smart City & Environm Modelling, Higher Educ Inst, Urumqi 830017, Peoples R China 3.Chinese Acad Sci, Inst Appl Ecol, Shenyang 110164, Peoples R China 4.Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China 5.Shenzhen Polytech, Sch Artificial Intelligence, Shenzhen 518055, Peoples R China 6.Xinjiang Univ, Coll Geog & Remote Sensing Sci, Urumqi 800017, Peoples R China 7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 8.Xinjiang Univ, Coll Geog & Remote Sensing Sci, 777 Huarui St, Urumqi 830017, Xinjiang, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ge, Xiangyu,Ding, Jianli,Teng, Dexiong,et al. Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2022,112:14. |
| APA | Ge, Xiangyu.,Ding, Jianli.,Teng, Dexiong.,Xie, Boqiang.,Zhang, Xianlong.,...&Wang, Jingzhe.(2022).Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,112,14. |
| MLA | Ge, Xiangyu,et al."Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 112(2022):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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