RETRACTED ARTICLE: Monitoring soil salinization in Manas River Basin, Northwestern China based on multi-spectral index group

Abstract

Large-scale and accurate monitoring soil salinization is essential for controlling soil degradation and sustainable agricultural development. The agricultural irrigation area of the Manas River Basin in the arid area of Northwest China was selected as the test area. The soil salinization monitoring model based on spectral index group was constructed by comparing the accuracy of PCR, PLSR and MLR models using the transformation of multi-spectral index group and index screening. The results showed that there was a certain correlation between the 28 spectral index groups, with a maximum correlation coefficient -0.3689 between the original spectral group and the soil salt content was B10 band. After the transformation of original data for the logarithm Ln(R), exponential e R and square root R 1/2 respectively, the correlation between each index and soil salinity was significantly improved, with the maximum correlation coefficient was up to -0.7564 of R 1/2 . The salt content estimation models were constructed by different data transformation using PLSR, PCR and MLR methods, respectively. This study provides a fast and accurate method for monitoring regional soil salinity content and the results can provide a reference for soil salinity grading management in arid and semi-arid areas.

Scenario-based analysis of the impacts of lake drying on food production in the Lake Urmia Basin of Northern Iran

“…The soil characteristics are impacted by various factors (e.g., vegetation cover, moisture, texture, parent material, etc. ), which must be considered in soil salinity mapping 39 , 40 . Therefore, we applied the Combined Spectral Response Index (CSRI) technique to compute the soil salinity ratio for each study year.…”

Soil Salinity Estimation in Cotton Fields in Arid Regions Based on Multi-Granularity Spectral Segmentation (MGSS)

“…For example, Fu et al [10] performed root mean square ( √ R), reciprocal (1/R), inverse logarithmic (log(1/R)), logarithmic, and logarithmic reciprocal preprocessing after Savitzky-Golay (SG) convolution smoothing on the raw spectra and found that the model accuracy constructed based on SG smoothing and 1/R preprocessing was the highest. Shi et al [11] performed logarithmic, exponential, and square root (R 1/2 ) preprocessing on raw spectral data to construct spectral indices and found that preprocessing obviously improved the soil salinity estimation accuracy compared with the raw spectra data, especially R 1/2 preprocessing. Wang and Li [12] constructed support vector regression (SVR) models after preprocessing raw spectral data using average reflectance (R), the logarithm of the reciprocal of R, and the continuum removal of reflectance (Rcr), and they found that the R-based and Rcr-based SVR model had the highest accuracy in estimating soil Cl − and K + content, respectively.…”