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

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.

Simulating Urban Growth Using the Cellular Automata Markov Chain Model in the Context of Spatiotemporal Influences for Salem and Its Peripherals, India

“…Machine learning has significantly developed for LU/LC classification in recent years. Most of the research focuses on support vector machine (SVM) classification [37][38][39][40][41]. Although there is no optimal classifier, a comparative study based on performance indicators will aid in preparing a refined dataset [42][43][44].…”

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

“…Recently, in the field of remote sensing tasks, PolSAR feature learning methods in combination with DL algorithms have been fully utilised in many researches [37]. For example, they have been used in agriculture [70], mine classification [71], and agriculture monitoring [72]. To simultaneously obtain spatial-polarimetric information suitable for a deep network, DL algorithms have been proposed for PolSAR data classification [73].…”

Monitoring Soil Salinity Using Machine Learning and the Polarimetric Scattering Features of PALSAR-2 Data

“…Among them, one-tenth of the saline land in the world is distributed in China, most in the northern and northwestern regions of the country [5]. Saline land in Xinjiang, situated northwest of China, includes 36.8% of the saline soils in the country [6]. The Keriya Oasis in Xinjiang is a typical area of salinity.…”