Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard–Stone (K–S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21–0.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07–79.6 dS m⁻¹), the spectral reflectance of salinized soil in the MSI data ranged from 0.09–0.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R² = 0.88, root mean square error (RMSE) = 4.89 dS m⁻¹, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.

Chlorophylls are the most important pigments for photosynthesis and are related to the phenology and health status of plants. Monitoring the chlorophyll content and density of Populus euphratica leaves via remote sensing technology has great significance for restoring and reconstructing damaged desert ecosystems. In this study, Populus euphratica leaves were collected in the public welfare forest reserve of Awati county, Xinjiang, China. Spectral variables and the chlorophyll content and density of leaves were obtained, simple linear regression and multiple linear regression models were built to predict leaf chlorophyll content and density. The results showed that partial least-squares regression models are the most appropriate for chlorophyll content estimation and its determination coefficient and root mean square error were 0.630 ∼ 0.745 and 0.031 ∼ 0.240, respectively. The standard deviation to root mean square error ratio was greater than 1.8 and the ratio of the standard error of the laboratory to the standard error of prediction was close to 1 for the partial least-squares regression models. These results confirmed that chlorophyll content can be accurately predicted and the proposed partial least-squares regression models were adequate for estimating Populus euphratica chlorophyll content rapidly and nondestructively, representing an alternative to conventional measurement methods. The rapid monitoring of chlorophyll content can reflect the health condition of Populus euphratica in a timely manner and provide technical support for ecological reconstruction in arid areas.

Soil salinization is a major obstacle to land productivity, crop yield and crop quality in arid areas and directly affects food security. Soil profile salt data are key for accurately determining irrigation volumes. To explore the potential for using Landsat 8 time-series data to monitor soil salinization, 172 Landsat 8 images from 2013 to 2019 were obtained from the Alar Reclamation Area of Xinjiang, northwest China. The multiyear extreme dataset was synthesized from the annual maximum or minimum values of 16 vegetation indices, which were combined with the soil conductivity of 540 samples from soil profiles at 0~0.375 m, 0~0.75 m and 0~1.00 m depths in 30 cotton fields with varying degrees of salinization as investigated by EM38-MK2. Three remote sensing monitoring models for soil conductivity at different depths were constructed using the Cubist method, and digital mapping was carried out. The results showed that the Cubist model of soil profile electrical conductivity from 0 to 0.375 m, 0 to 0.75 m and 0 to 1.00 m showed high prediction accuracy, and the determination coefficients of the prediction set were 0.80, 0.74 and 0.72, respectively. Therefore, it is feasible to use a multiyear extreme value for the vegetation index combined with a Cubist modeling method to monitor soil profile salinization at a regional scale.

The study of chlorophyll fluorescence parameters is very important for understanding plant photosynthesis. Monitoring cotton chlorophyll fluorescence parameters via spectral technology can aid in understanding the photosynthesis, growth, and stress of cotton fields in real time and provide support for cotton growth regulation and planting management. In this study, cotton plot experiments with different water treatments were set up to obtain the spectral reflectance of the cotton canopy, the maximum photochemical quantum yield (Fv/Fm), and the photochemical quenching coefficient (qP) of leaves at different growth stages. Support vector machine regression (SVR), random forest regression (RFR), and artificial neural network regression (ANNR) were used to establish a fluorescence parameter inversion model of the cotton canopy leaves. The results show that the original spectrum was transformed by multivariate scattering correction (MSC), the standard normal variable (SNV), and continuous wavelet transform (CWT), and the model constructed with Fv/Fm passed accuracy verification. The SNV-SVR model at the budding stage, the MSC-SVR model at the early flowering stage, the SNV-SVR model at the full flowering stage, the MSC-SVR model at the flowering stage, and the CWT-SVR model at the full boll stage had the highest estimation accuracy. The accuracies of the three spectral preprocessing and qP models were verified, and the MSC-SVR model at the budding stage, SNV-SVR model at the early flowering stage, MSC-SVR model at the full flowering stage, SNV-SVR model at the flowering stage, and CWT-SVR model at the full boll stage presented the highest estimation accuracies.

Carotenoids play a crucial role in the photosynthesis process in plants. Estimating and modeling the carotenoid content in Populus pruinosa leaves via high-spectrum technology is highly important for health status monitoring. This study involved acquiring the spectral reflectance of Populus pruinosa leaves at different times, followed by smoothing the data with a Savitzky—Golay filter, and then using methods such as first derivative (FD), continuous wavelet transform (CWT), and first-order derivative combined with continuous wavelet transform (CWT+FD), creating three spectral transformation methods. Two- and three-dimensional vegetation indices were then constructed in a unified manner. Two modeling methods, backpropagation neural network (BPNN) and support vector regression (SVR), were employed to estimate the leaf carotenoid density by combining the vegetation indices. The results show that after the spectral reflectance of the canopy of Populus pruinosa is processed by FD, CWT, and CWT+FD on the basis of SG smoothing, it can effectively highlight the spectral characteristics of Populus pruinosa leaves, and the local spectral absorption features are more significant. Compared with the three spectral preprocessing methods, the results showed that the correlation between the values processed by the FD + CWT method and the leaf carotenoid density is the highest. The constructed three-band vegetation index exhibited a 4.26% stronger correlation with carotenoid density than did the two-band vegetation index. Among the three-band index-based models, the SVR model outperforms the BPNN model. For chlorophyll density, the SVR model based on the three-band index processed using CWT+FD achieves the best performance. The coefficient of determination (R²) for the SVR model set was 0.782, the root-mean-square error (RMSE) was 0.022, and the relative percentage deviation (RPD) was 0.206. For the validation set, the (R²) value was 0.648, the RMSE was 0.023, and the RPD was 1.526, indicating the best model accuracy.