Monitoring of Soil Salinization in the Keriya Oasis Based on Deep Learning with PALSAR-2 and Landsat-8 Datasets

Adilai Abulaiti,Jing Zhao,Ilyas Nurmemet,Nuerbiye Muhetaer,Sentian Xiao

doi:10.3390/su14052666

Abstract

Currently, soil salinization is one of the main forms of land degradation and desertification. Soil salinization not only seriously restricts the development of agriculture and the economy, but also poses a threat to the ecological environment. The main purpose of this study is to map soil salinity in Keriya Oasis, northwestern China using the PALSAR-2 fully polarized synthetic aperture radar (PolSAR) L-band data and Landsat8-OLI (OLI) optical data combined with deep learning (DL) methods. A field survey is conducted, and soil samples are collected from 20 April 2015 to 1 May 2015. To mine the hidden information in the PALSAR-2 data, multiple polarimetric decomposition methods are implemented, and a wide range of polarimetric parameters and synthetic aperture radar discriminators are derived. The radar vegetation index (RVI) is calculated using PALSAR-2 data, while the normalized difference vegetation index (NDVI) and salinity index (SI) are calculated using OLI data. The random forest (RF)-integrated learning algorithm is used to select the optimal feature subset composed of eight polarimetric elements. The RF, support vector machine, and DL methods are used to extract different degrees of salinized soil. The results show that the OLI+PALSAR-2 image classification result of the DL classification was relatively good, having the highest overall accuracy of 91.86% and a kappa coefficient of 0.90. This method is helpful to understand and monitor the spatial distribution of soil salinity more effectively to achieve sustainable agricultural development and ecological stability.

Highlights

Received: 5 January 2022As a form of soil degradation, soil salinization destroys resources and reduces agricultural production, and poses a severe threat to the biosphere and ecological environment, becoming an environmental problem worldwide [1,2]
Different polarization decomposition methods were performed on the PALSAR-2 image data, and polarimetric parameter information was extracted
The results demonstrated that the degree, scope, and spatial distribution of soil salinization were different in different regions of the research area

Summary

Introduction

As a form of soil degradation, soil salinization destroys resources and reduces agricultural production, and poses a severe threat to the biosphere and ecological environment, becoming an environmental problem worldwide [1,2]. Soil salinization is a process during which soluble salt accumulates on the soil surface, and it is one of the main forms of land desertification and degradation [3]. More than 3% of soil resources are affected by salt [5]. China has been one of the countries with severe salinization problems [6]. China alone is covered by 10% of the world’s salinized land area [7,8], and Xinjiang is one of the largest saline soil distribution areas [9], accounting for 36.8% of the country’s saline-alkali land area, mainly distributed in the oasis-desert ecosystem in southern Xinjiang (nearly 50%) [10]. Realizing large-scale, high-precision soil salinization monitoring and scientific prediction of salinization risks and hazards are fundamental to alleviating environmental pressure

Objectives

Methods

Results

Conclusion