Long time series monitoring method of soil and water loss based on Landsat 8 OLI satellite remote sensing data

Zhikui Yin; Xiang Xie; Wenming Duan; Dongdong Zhang; Zhengkun Zhang; Huan Li

doi:10.1117/12.3035423

19 July 2024 Long time series monitoring method of soil and water loss based on Landsat 8 OLI satellite remote sensing data

Zhikui Yin, Xiang Xie, Wenming Duan, Dongdong Zhang, Zhengkun Zhang, Huan Li

Author Affiliations +

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132M (2024) https://doi.org/10.1117/12.3035423
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

A long-term soil erosion monitoring method based on remote sensing data from the Land Satellite 8 OLI satellite has been proposed. According to the needs of soil erosion calculation, the Chinese soil erosion engineering model is used to calculate the soil erosion modulus. Perform factor calculation analysis to determine factors and calculate annual rainfall erosivity; Obtain soil erosion data and determine the spatial distribution characteristics of soil erosion intensity in the region. By using vegetation optical thickness feature analysis and multispectral remote sensing feature monitoring methods, soil moisture inversion analysis was carried out to extract moisture information parameters, and long-term monitoring of soil erosion based on remote sensing data from the Land Satellite 8 OLI satellite was achieved. The experimental results indicate that this method can improve the accuracy of long-term monitoring of soil erosion.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhikui Yin, Xiang Xie, Wenming Duan, Dongdong Zhang, Zhengkun Zhang, and Huan Li "Long time series monitoring method of soil and water loss based on Landsat 8 OLI satellite remote sensing data", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132M (19 July 2024); https://doi.org/10.1117/12.3035423

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KEYWORDS

Environmental monitoring

Remote sensing

Satellites

Soil science

Landsat

Rain

Vegetation

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