Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China

Qinyu Wei,Ilyas Nurmemet,Minhua Gao,Boqiang Xie

doi:10.3390/rs14030512

Abstract

Soil salinization is a global problem that damages soil ecology and affects agricultural development. Timely management and monitoring of soil salinity are essential to achieve the most sustainable development goals in arid and semi-arid regions. It has been demonstrated that Polarimetric Synthetic Aperture Radar (PolSAR) data have a high sensitivity to the soil dielectric constant and soil surface roughness, thus having great potential for the detection of soil salinity. However, studies combining PALSAR-2 data and Landsat 8 data to invert soil salinity information are less common. The particle swarm optimization (PSO) algorithm is characterized by simple operation, fast computation, and good adaptability, but there are relatively few studies applying it to soil salinity as well. This paper takes the Keriya Oasis as an example, proposing the PSO-SVR and PSO-BPNN models by combining PSO with support vector machine regression (SVR) and back-propagation neural network (BPNN) models. Then, PALSAR-2 data, Landsat 8 data, evapotranspiration data, groundwater burial depth data, and DEM data were combined to conduct the inversion study of soil salinity in the study area. The results showed that the introduction of PSO generated a satisfactory estimating performance. The SVR model accuracy (R2) improved by 0.07 (PALSAR-2 data), 0.20 (Landsat 8 data), and 0.19 (PALSAR + Landsat data); the BP model accuracy (R2) improved by 0.03 (PALSAR-2 data), 0.24 (Landsat 8 data), and 0.12 (PALSAR + Landsat data), and then combined with the model inversion plots, we found that PALSAR + Landsat data combined with the PSO-SVR model could achieve better inversion results. The fine texture information of PALSAR-2 data can be used to better invert the soil salinity in the study area by combining it with the rich spectral information of Landsat 8 data. This study complements the research ideas and methods for soil salinization using multi-source remote sensing data to provide scientific support for salinity monitoring in the study area.

Highlights

Soil salinization is one of the main types of land degradation that affects the ecological environment and crop production security worldwide [1,2]
The study on soil salinization in the Keriya Oasis consists of the following: (1) Taking the study area as an example, we introduce particle swarm optimization into the study of salinization; the PSO-support vector regression (SVR), PSO-back-propagation neural network (BPNN), SVR, and BPNN machine learning methods are used to model the inversion of soil salinity in this area, and the models are compared and analyzed to identify the most suitable inversion model for the study area
We combine particle swarm algorithm and machine learning to propose a method to combine multi-source remote sensing data for soil salinity inversion, and the main findings are as follows: (1) By comparing the simulation results of different models, we found that the prediction accuracy of both the BPNN and SVR models was significantly improved by adding PSO, and the prediction values of the PSO-SVR model are improved by 0.07 compared to the R2 of the SVR model (PALSAR-2 data), 0.20

Summary

Introduction

Soil salinization is one of the main types of land degradation that affects the ecological environment and crop production security worldwide [1,2]. Excessive salt accumulation can affect the growth and development process of crops through infiltration and ionic stress, and can affect the soil structure, eventually reducing the fertility of arable land into a wasteland and affecting food production [6,7,8,9,10]. Xinjiang is the largest province in China in terms of land area, and its saline soil area accounts for 60.6% of the total saline soil surface in the country. As a region with mainly agriculture and animal husbandry, salinization has seriously threatened the sustainable development of agriculture and ecological security in Xinjiang [2,8,15]

Methods

Results

Discussion

Conclusion