Upscaling remote sensing inversion and dynamic monitoring of soil salinization in the Yellow River Delta, China. (April 2023)

• Record Type: Journal Article
• Authors: Li, Yinshuai; Chang, Chunyan; Wang, Zhuoran; Zhao, Gengxing
• Is Part Of: Ecological indicators. Volume 148(2023)
• Publication Date: 2023-04
• Subjects: Soil salinization -- Remote sensing inversion -- Spectral indicator -- Upscaling conversion -- Yellow River Delta, China; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2023.110087
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: Highlights: Soil salinity can be well reflected by vegetation and salinity indicators. The conversion of spectral indicators can effectively reduce the upscaling error. Precise land regionalization will improve the accuracy of scale conversion. The risk of soil salinization gradually decreases from coastal to inland. Soil salinization experienced a change of increasing first and then decreasing. Abstract: As a global problem of soil degradation, salinization has become a major obstacle to the sustainable development of the ecological environment and agriculture in coastal plains. However, the traditional process of salinity survey is too cumbersome, expensive and time-consuming to meet the mapping needs in a large scale. Remote sensing technology has become an important tool for digital soil mapping because of its rich sources, real-time and low cost. In order to meet the objective demand for rapid, accurate, and efficient acquisition and monitoring of soil salinization. This paper collected 61 soil samples from the Kenli District (experimental area) and extracted vegetation and salinity indicators from the Landsat image to construct the salinity inversion model by random forest algorithm. Then, taking the Yellow River Delta as the study area, the conversion coefficient of spectral indicators between Landsat and MODIS images was constructed in the form of the ratio of the mean value. Through optimization, the upscaling conversion method based on land use regionalization was proposed to realize the upscaling inversion and dynamic monitoring of soil salinization. The results showed that: (1) The random forest model based on NDVI, RVI, EVI, SI3, and SI5 can better predict the soil salinity in the experimental area, with R 2 = 0.821 and RMSE = 2.811 (validation accuracy). (2) The upscaling conversion method based on land use regionalization can effectively reduce the statistical error and collinearity of spectral indicators constructed by MODIS images and improve their correlation with OLI data and soil salinity. (3) From coastal to inland, soil salinization gradually decreases in the Yellow River Delta. From 2000 to 2020, soil salinization increased first and then decreased, and the salinized soil accounted for 20.35%∼35.10%. This study used multi-source remote sensing data to realize the collaborative inversion at different scales, which was significant for the quantitative estimation of soil salinity, salinization control, and sustainable agricultural development in coastal plains.