Work

Analysis of spatial and temporal variability of soil salinity in the Yellow River delta

Year: 2025

Type: conference-paper

Abstract: Constructing regression models from remote sensing data has become a hot research topic for monitoring soil salinization information, but previous studies have analyzed fewer changes in long time series. Therefore, this study constructed a remote sensing inversion model of soil salinity content based on Landsat 9 OLI multispectral images by machine learning, and validated, compared and optimized it, and then inverted soil salinity content of arable land in 2015, 2018, 2021, and 2024 in the study area based on the optimal model, which provided data support for regional salinization management. Therefore, this study constructed a remote sensing inversion model based on Landsat 9 OLI multispectral images by machine learning method, validated, compared and optimized the model, inverted the soil salinity of cultivated land in the study area in 2015, 2018, 2021, and 2024 based on the optimal model, and analyzed the change of the optimal model in a long time series, which provided data support for the management of regional salinization.

DOI: 10.1117/12.3065941

Authors: Xinyi Luan, Jicai Ning, Zhiqiang Gao, Maosi Chen

Institutions: Yantai Institute of Coastal Zone Research, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Colorado State University

Language: English

Topic: Soil Geostatistics and Mapping

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Funder: Chinese Academy of Sciences

OpenAlex ID: W4414315359