A review of the satellite remote sensing techniques for assessment of runoff and sediment in soil erosion

Author:

Ji Cuicui12345,Cao Yiming1,Li Xiaosong2,Pei Xiangjun34,Sun Bin6,Yang Xuemei7,Zhou Wei8

Affiliation:

1. Schoolof Smart City, Chongqing Jiaotong University , Chongqing , China .

2. Aerospace Information Research Institute, Chinese Academy of Sciences , Beijing , China .

3. State Key Laboratory of Geohazard Prevention and Geo-environment Protection , Chengdu University of Technology , Sichuan , China .

4. College of Ecology and Environment , Chengdu University of Technology , Sichuan , China .

5. Chongqing Institute of Geology and Mineral Resources , Chongqing , China .

6. Institute of Forest Information Techniques, Chinese Academy of Forestry , Beijing , China .

7. Tourism School , Lanzhou University of Arts and Science , Gansu , China .

8. Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences , Southwest University , Chongqing , China .

Abstract

Abstract Soil erosion monitoring is essential for the ecological evaluation and dynamic monitoring of land resources via remote sensing technology. In this paper, we provide new insights into the existing problems and development directions of traditional models, which are supported by new technologies. An important role is played by remote sensing information acquisition technology in the qualitative and quantitative evaluation of soil erosion, and the data and technical support provided are systematically reviewed. We provide a detailed overview of the research progress associated with empirical statistical models and physically driven process models of soil erosion, and the limitations of their application are also summarized. The preliminary integration of remote sensing data sources with high spatial and temporal resolution and new technologies for soil erosion monitoring enables the high-precision quantitative estimation of sediment transport trajectories, the watershed river network density, and the terrain slope, enhancing the accuracy of erosion factor identification, such as spectral feature recognition from erosion information, gully erosion feature extraction, and vegetation coverage estimation. However, the current erosion models, driven by algorithms and models, are not comprehensive enough, particularly in terms of the spatial feature extraction of erosion information, and there are limitations in the applicability and accurate estimation of such models.

Publisher

Walter de Gruyter GmbH

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