The Applicability of Remote Sensing Models of Soil Salinization Based on Feature Space

Jing Liu,Li Zhang,Qiangjun Yang,Zhibin Zhang,Hongqi Wu,Tong Dong,Juanle Wang,Qinglong Geng,Yanmin Fan

doi:10.3390/su132413711

Abstract

Soil salinization is a major challenge for the sustainable use of land resources. An optimal remote sensing inversion model could monitor regional soil salinity across diverse geographical areas. In this study, the feature space method was used to study the applicability of the inversion model for typical salt-affected soils in China (Yanqi Basin (arid area) and Kenli County (coastal area)), and to obtain soil salinity grade distribution maps. The salinity index (SI) surface albedo (Albedo)model was the most accurate in both arid and coastal regions with overall accuracy reaching 93.3% and 88.8%, respectively. The sensitivity factors for the inversion of salinity in both regions were the same, indicating that the SI-Albedo model is applicable for monitoring salinity in arid and coastal areas of China. We combined Landsat 8 Operational Land Imager image data and field data to obtain the distribution pattern of soil salinity using the SI-Albedo model and proposed corresponding countermeasures for soil salinity in the Yanqi Basin and Kenli County according to the degree of salinity. This study on soil salinity in arid and coastal areas of China will provide a useful reference for future research on soil salinity both in China and globally.

Highlights

Land degradation is a major global environmental problem [1,2,3]
Soil salinization is disturbed by the natural environment and human activities when capillary action or evapotranspiration leads to the rise of groundwater and the accumulation of soluble salts at the surface, resulting in different degrees of salinization [4]
Compared to other classification methods, such as goodness-of-fit, the Jenks natural break classification method has the advantage of having the least variance within classes and the greatest variance between classes and was able to minimize the sum of variance within each salinity class [30]

Summary

Introduction

Land degradation is a major global environmental problem [1,2,3]. An important element of land degradation, significantly impacts the global ecological environment. Soil salinization is disturbed by the natural environment and human activities when capillary action or evapotranspiration leads to the rise of groundwater and the accumulation of soluble salts at the surface, resulting in different degrees of salinization [4]. Soil salinization leads to a decrease in soil fertility, acid-base imbalance, and an increasingly poor land environment and intensifies the conflict between socioeconomic development and environmental protection [5]. It is important to extract soil salinization information dynamically, quickly, and accurately on a regional scale, monitor the spatial distribution characteristics of saline soils, and quantify the degree of soil salinization to manage soil salinization and prevent further deterioration of the ecological environment. Managing soil salinization provides security for the development of efficient agriculture, working towards achieving the goal of sustainable development of agricultural production and the social economy

Objectives

Methods

Results

Discussion

Conclusion