Journal Article

Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China

Peng, Jie, Wang, Tianwei, Zhang, Huaping, Wang, Jiaqiang, Li, Hongyi, Yin, Caiyun, Liu, Weiyang

2021

Overview

Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard–Stone (K–S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21–0.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07–79.6 dS m−1), the spectral reflectance of salinized soil in the MSI data ranged from 0.09–0.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R2 = 0.88, root mean square error (RMSE) = 4.89 dS m−1, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.

• Publisher: MDPI AG
• Subject: Accuracy / Agricultural development / Agricultural land / Agricultural production / Algorithms / altitude / area / arid area / Arid regions / Arid zones / Artificial intelligence / Artificial neural networks / China / classification / correlation / Correlation coefficient / Correlation coefficients / data collection / Datasets / decision support systems / Electrical conductivity / Electrical resistivity / Environmental monitoring / Environmental security / Error analysis / estimation / irrigation / knowledge / Knowledge acquisition / laboratories / Land degradation / Learning algorithms / Learning theory / Machine learning / Modelling / Moisture content / monitoring / Neural networks / Precipitation / prediction / Reflectance / Remote sensing / Root-mean-square errors / Salinity / Salinity effects / Salinization / sampling / Sentinel-2 MSI / Soil conductivity / soil electrical conductivity / Soil salinity / soil salinization / Soils / spectral analysis / Spectral reflectance / Standard error / Statistical analysis / Statistical methods / Support vector machines / Sustainable agriculture / Sustainable development / transportation