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

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 3; p. 512
• Main Authors: Wei, Qinyu, Nurmemet, Ilyas, Gao, Minhua, Xie, Boqiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2022
• Subjects: Accuracy; Adaptability; Agricultural development; Algorithms; Arid regions; Arid zones; Artificial neural networks; Back propagation networks; back-propagation neural network; Dielectric constant; Environmental monitoring; Evapotranspiration; Groundwater; Groundwater data; Inversion; Landsat; Learning algorithms; Machine learning; Model accuracy; Monitoring; Neural networks; Oases; PALSAR; Particle swarm optimization; Regression analysis; Remote sensing; Salinity; Salinity effects; Salinization; Semi arid areas; Semiarid lands; Soil ecology; Soil management; Soil salinity; soil salinization; Soils; Support vector machines; support vector regression; Surface roughness; Sustainable development; Synthetic aperture radar; Topography; Vegetation; Taklamakan Desert; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14030512

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.