Hyperspectral Prediction of Soil Total Salt Content by Different Disturbance Degree under a Fractional-Order Differential Model with Differing Spectral Transformations

Abstract

Soil salinization is an ecological challenge across the world. Particularly in arid and semi-arid regions where evaporation is rapid and rainfall is scarce, both primary soil salinization and secondary salinization due to human activity pose serious concerns. Soil is subject to various human disturbances in Xinjiang in this area. Samples with a depth of 0–10 cm from 90 soils were taken from three areas: a slightly disturbed area (Area A), a moderately disturbed area (Area B), and a severely disturbed area (Area C). In this study, we first calculated the hyperspectral reflectance of five spectra (R, R, 1/R, lgR, 1/lgR, or original, root mean square, reciprocal, logarithm, and reciprocal logarithm, respectively) using different fractional-order differential (FOD) models, then extracted the bands that passed the 0.01 significance level between spectra and total salt content, and finally proposed a partial least squares regression (PLSR) model based on the FOD of the significance level band (SLB). This proposed model (FOD-SLB-PLSR) is compared with the other three PLSR models to predict with precision the total salt content. The other three models are All-PLSR, FOD-All-PLSR, and IOD-SLB-PLSR, which respectively represent PLSR models based on all bands, all fractional-order differential bands, and significance level bands of the integral differential. The simulations show that: (1) The optimal model for predicting total salt content in Area A was the FOD-SLB-PLSR based on a 1.6 order 1/lgR, which provided good predictability of total salt content with a RPD (ratio of the performance to deviation) between 1.8 and 2.0. The optimal model for predicting total salt content in Area B was a FOD-SLB-PLSR based on a 1.7 order 1/R, which showed good predictability for total salt content with RPDs between 2.0 and 2.5. The optimal model for predicting total salt content in Area C was a FOD-SLB-PLSR based on a 1.8 order lgR, which also showed good predictability for total salt content with RPDs between 2.0 and 2.5. (2) Soils subject to various disturbance levels had optimal FOD-SLB-PLSR models located in the higher fractional order between 1.6 and 1.8. This indicates that higher-order FODs have a stronger ability to extract feature data from complex information. (3) The optimal FOD-SLB-PLSR model for each area was superior to the corresponding All-PSLR, FOD-All-PLSR, and IOD-SLB-PLSR models in predicting total salt content. The RPD value for the optimal FOD-SLB-PLSR model in each area compared to the best integral differential model showed an improvement of 9%, 45%, and 22% for Areas A, B, and C, respectively. It further showed that the fractional-order differential model provides superior prediction over the integral differential. (4) The RPD values that provided an optimal FOD-SLB-PLSR model for each area were: Area A (1.9061) < Area B (2.0761) < Area C (2.2892). This indicates that the prediction effect of data processed by fractional-order differential increases with human disturbance increases and results in a higher-precision model.

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF

“…Modeling strategy I used only the single-band feature variables screened by CARS to participate in the modeling, and the results showed that the 1.9 order was the optimal differential order which was the same as the optimal differential order for region I in the study of salinity content estimation in different saline zones by ( Fu C. et al., 2021 ). The characteristic bands were scattered in the visible (408-777 nm), but mainly concentrated in the near-infrared (NIR) shortwave (800-1000 nm), which indicated that the NIR bands performed better in the characterization of soil salinity using vegetation spectra, which was also confirmed in the results of ( Tian et al., 2021 ) and ( Nguyen et al., 2020 ). However, since the single-band variables can only reflect the spectral properties from a one-dimensional perspective, the R 2 of modeling strategy I is only 0.9107, with the lowest coefficient of determination among the three modeling strategies, and the RPD is 1.8736, which is also the weakest in terms of stability.…”

Combining Fractional Derivatives and Supervised Machine Learning: A Review

“…In [32], estimates on top soil organic carbon are given using a Grünwlad Letnikov fractional derivative and Random random forest. Also, (soil) salt content can be estimated using hyperspectral data and a range of ML algorithms, as done in [33][34][35]. Another agricultural application is to assess the nitrogen content in apple tree canopies via support vector machines, random forest algorithms, and fractional-derivative-augmented hyperspectral data, [36].…”

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

“…The sampling zone is divided into three types of soil zones according to the difference of human interference [25], as shown in Figure 1 (The blue rectangle is used to distinguish three different research areas), the Landsat 8 satellite acquisition date was in August 2017: 1 light human interference zone (I Zone), here the soil basically keeps its the original state, and there is high vegetation coverage here, such as Haloxylon ammodendron, also the black biological crusts can be seen on the soil surface. 2 moderate human interference zone (II Zone), here the wasteland soil was opened around the 1950s with 3 m width, but it was abandoned latter.…”