Improving Estimates of Soil Salt Content by Using Two-Date Image Spectral Changes in Yinbei, China

Xibo Xu,Kangning Li,Sijia Wang,Yunhao Chen,Yongguang Li,Mingguo Wang

doi:10.3390/rs13204165

Abstract

Soil salt content (SSC) is normally featured with obvious spatiotemporal variations in arid and semi-arid regions. Space factors such as elevation, temperature, and spatial locations are usually used as input variables for a model to estimate the SSC. However, whether temporal patterns of salt-affected soils (identified as temporal spectral patterns) can indicate the SSC level and be applied as a covariate in a model to estimate the SSC remains unclear. Hence, temporal changes in soil spectral patterns need to be characterized and explored as to their use as an input variable to improve SSC estimates. In this study, a total of 54 field samples and a time-series of Sentinel-2 multispectral images taken at monthly intervals (from October 2017 to April 2018) were collected in the Yinbei area of western China. Then, two-date satellite images were used to quantify significant spectral changes over time using spectral change vector analysis, and four two-date-based index methods were used to characterize soil spectral changes. Lastly, the optimal two-date-based spectral indices and multispectral bands were used as input variables to build the estimation models using a random forest algorithm. Results showed that the two-date-based spectral index could be applied as an input variable to improve the accuracy of SSC estimation at a regional scale. Temporal changes in salt-induced spectral patterns can be indicated by the band difference in the wavelength range from 400 nm to 900 nm. Three two-date-based indices designated as D28a (i.e., the band difference between band 2 from an image acquired in April 2018 and band 8a from an image acquired in December 2017), D22, and D28 were the optimal parameters for characterizing salt-induced spectral changes, which were dominated by the total brightness, chloride, and sulfate accumulation of the soils. The model did not yield satisfactory estimation results (RPD = 1.49) when multispectral bands were used as the input variables. Multispectral bands coupled with two two-date-based indices (D22 and D28a) used as the input variables produced the best estimation result (R2 = 0.92, RPD = 3.27). Incorporating multispectral bands and two-date-based indices into the random forest model provides a remotely-sensed strategy that effectively supports the monitoring of soil salt content.

Highlights

IntroductionSoil salinization is an important land degradation and desertification process that usually occurs in arid and semi-arid environments and poses a threat to the ecological balance
The results presented in this study provide a solution for analyzing the temporal changes in soil spectral patterns and increasing the explanatory ability of a multispectral remote sensing model for Soil salt content (SSC), thereby promoting the ability to estimate SSC in an arid land
Results showed that band difference in the visible near-infrared range (400–900 nm) among two-date satellite bands was an effective method for characterizing dynamic spectral reflectance of the salt-affected soils

Summary

Introduction

Soil salinization is an important land degradation and desertification process that usually occurs in arid and semi-arid environments and poses a threat to the ecological balance. Soils containing excessive salt adversely impact the material exchange in green plant cells, and reduce grain yield and agricultural productivity [1,2]. Soil salinization is a worldwide issue resulting from anthropogenic activities (e.g., excessive agricultural practices and flood irrigation) and global climate warming [3,4]. Soil productivity for 40 to 45% of the global land surface area has decreased due to different levels of salinity on salt-affected soil [5]

Methods

Results

Discussion

Conclusion