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.

Highlights

  • Soil salinization is a global problem and a long-term threat to agricultural production, especially in arid and semi-arid areas

  • The results showed that the average value of the soil pH was 8.38, and the average value of the soil salinity was 58.02 g/kg, which indicated that the soil salinity in the study area was severe

  • The spectral data of the soil were collected from the laboratory and satellite spectral data were obtained for a similar time period; we were able to evaluate the ability of the laboratory and satellite data in establishing the soil content by analyzing the spectral characteristics of the salinized soil

Read more

Summary

Introduction

Soil salinization is a global problem and a long-term threat to agricultural production, especially in arid and semi-arid areas. Agriculture in irrigated areas is of great significance to regional development, which relieves people’s survival pressure in arid and semi-arid areas. People develop more land to meet their development needs, and this is accompanied by soil compaction, fertility declines, acid–base imbalances, soil degradation, and other consequences caused by soil salinization [5]. From the perspective of soil salinization control, the amount of salt in soil is directly proportional to the difficulty of soil recovery. The dynamic monitoring of soil salt content-related indicators and a timely quantitative grasp of the soil salt content are of great significance for the rational development and utilization of land resources and for maintaining the ecological sustainable development of irrigated areas

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call