Estimation of Soil Salt and Ion Contents Based on Hyperspectral Remote Sensing Data: A Case Study of Baidunzi Basin, China

Abstract

Soil salinity due to irrigation diversion affects regional agriculture, and the development of soil composition estimation models for the dynamic monitoring of regional salinity is important for salinity control. In this study, we evaluated the performance of hyperspectral data measured using an analytical spectral device (ASD) field spec standard-res hand-held spectrometer and satellite sensor visible shortwave infrared advanced hyperspectral imager (AHSI) in estimating the soil salt content (SSC). First derivative analysis (FDA) and principal component analysis (PCA) were applied to the data using the raw spectra (RS) to select the best model input data. We tested the ability of these three groups of data as input data for partial least squares regression (PLSR), principal component regression (PCR), and multiple linear regression (MLR). Finally, an estimation model of the SSC, Na+, Cl−, and SO42− contents was established using the best input data and modeling method, and a spatial distribution map of the soil composition content was drawn. The results show that the soil spectra obtained from the satellite hyperspectral data (AHSI) and laboratory spectral data (ASD) were consistent when the SSC was low, and as the SSC increased, the spectral curves of the ASD data showed little change in the curve characteristics, while the AHSI data showed more pronounced features, and this change was manifested in the AHSI images as darker pixels with a lower SSC and brighter pixels with a higher SSC. The AHSI data demonstrated a strong response to the change in SSC; therefore, the AHSI data had a greater advantage compared with the ASD data in estimating the soil salt content. In the modeling process, RS performed the best in estimating the SSC and Na+ content, with the R2 reaching 0.79 and 0.58, respectively, and obtaining low root mean squared error (RMSE) values. FDA and PCA performed the best in estimating Cl− and SO42−, while MLR outperformed PLSR and PCR in estimating the content of the soil components in the region. In addition, the hyperspectral camera data used in this study were very cost-effective and can potentially be used for the evaluation of soil salinization with a wide range and high accuracy, thus reducing the errors associated with the collection of individual samples using hand-held hyperspectral instruments.

ECWS: Soil Salinity Measurement Method Based on Electrical Conductivity and Moisture Content

“…The drying residue method has high accuracy, but requires field sampling first, followed by a large amount of indoor analysis work, which is not suitable for rapid large-scale measurements. The sum of salt ion method mainly accumulates the content of all salt ions in the soil leachate, but the results may vary due to different analysis methods [4].…”

Estimation of Soil Salt Content at Different Depths Using UAV Multi-Spectral Remote Sensing Combined with Machine Learning Algorithms

“…Farifteh et al [12] used satellite hyperspectral data and field-measured data to analyze the relationship between soil salinity and spectral reflectance and established a salt estimation model in combination with PLSR and other methods. Wang et al [13] selected the best input data of the model by conducting first derivative analysis (FDA) and principal component analysis (PCA) based on the original hyperspectral data, then established the soil-salt-content-estimation model by using modeling methods, such as PLSR, and drew the spatial distribution map of soil salt. Chen et al [14] took sunflowers as the research object and monitored the soil salt of sunflowers at their flowering and budding stage using a UAV equipped with multispectral and thermal infrared cameras.…”

Soil Salinity Inversion Model Based on BPNN Optimization Algorithm for UAV Multispectral Remote Sensing

“…The traditional method of obtaining SSC is fixed-point sampling and measured by conductivity meter, which uses data measured by a conductivity meter; however, this method is time-consuming and laborious, which is not conducive to large-scale monitoring of farmland in irrigation area [5], [6]. Remote sensing has been widely used in soil salinization monitoring [7], [8], [9]. Satellite remote sensing provides a method to quickly monitor soil salinity in a wide range, Sertel [10] focused on multitemporal monitoring of Tuz Lake Region in order to track changes in areas of salty spots by using Landsat-5 TM and Landsat-8 remote sensing images.…”