Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index

Fast and nondestructive discrimination of coal types based on spectral feature parameters

Coal type identification is the basic work of coal quality inspection, which is of great significance to the normal operation of power generation, metallurgy, and other industries. The traditional coal-type identification method is complicated and requires comprehensive determination of various chemical parameters to obtain more accurate analysis results. Hyperspectral detection and analysis technology has the advantages of being simple, fast, nondestructive, and safe, and is widely used in a variety of fields. In this study, typical spectral feature parameters of coal samples were extracted based on hyperspectral data, and the parameters' sensitivity to coal types was explored using one-way ANOVA. The results showed that the coal spectral feature parameters of DI1-2μm and AD2.2μm significantly differed with coal species, indicating that the two parameters were class-sensitive features. When DI1-2μm and AD2.2μm were used to construct the Fisher discriminant model, the coal types could be discriminated with high accuracy. At the same time, the correlation between the extracted spectral feature parameters and the physicochemical parameters of bituminous coal and anthracite was analyzed. The results showed that there was a certain basis for using the extracted spectral feature parameters as the sensitive spectral characteristics of the model, and the application potential of the spectral characteristics of coal in the nondestructive prediction analysis of coal parameters was further discussed.

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms

The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2 = 0.99, RMSE=0.61) and measured dataset (R2 = 0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.

Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China

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.

6-Benzyladenine alleviates NaCl stress in watermelon (Citrullus lanatus) seedlings by improving photosynthesis and upregulating antioxidant defences

Soil salinity is a growing problem in agriculture, plant growth regulators (PGRs) can regulate plant response to stress. The objective of this study was to evaluate the effects of exogenous 6-benzyladenine (6-BA) on photosynthetic capacity and antioxidant defences in watermelon (Citrullus lanatus L.) seedlings under NaCl stress. Two watermelon genotypes were subjected to four different treatments: (1) normal water (control); (2) 20mgL-1 6-BA; (3) 120mmolL-1 NaCl; and (4) 120mmolL-1 NaCl+20mgL-1 6-BA. Our results showed that NaCl stress inhibited the growth of watermelon seedlings, decreased their photosynthetic capacity, promoted membrane lipid peroxidation, and lowered the activity of protective enzymes. Additionally the salt-tolerant Charleston Gray variety fared better than the salt-sensitive Zhengzi NO.017 variety under NaCl stress. Foliar spraying of 6-BA under NaCl stress significantly increased biomass accumulation, as well as photosynthetic pigment, soluble sugar, and protein content, while decreasing malondialdehyde levels, H2 O2 content, and electrolyte leakage. Moreover, 6-BA enhanced photosynthetic parameters, including net photosynthetic rate, stomatal conductance, intercellular CO2 concentration, and transpiration rate; activated antioxidant enzymes, such as superoxide dismutase, catalase, and peroxidase; and improved the efficiency of the ascorbate-glutathione cycle by stimulating glutathione reductase, dehydroascorbate reductase, and monodehydroascorbate reductase, as well as ascorbic acid and glutathione content. Principal component analysis confirmed that 6-BA improved salt tolerance of the two watermelon varieties, particularly Zhengzi NO.017, albeit through two different regulatory mechanisms. In conclusion, 6-BA treatment could alleviate NaCl stress-induced damage and improve salt tolerance of watermelons by regulating photosynthesis and osmoregulation, activating the ascorbate-glutathione cycle, and promoting antioxidant defences.

An Efficient Approach for Inverting the Soil Salinity in Keriya Oasis, Northwestern China, Based on the Optical-Radar Feature-Space Model

Soil salinity has been a major factor affecting agricultural production in the Keriya Oasis. It has a destructive effect on soil fertility and could destroy the soil structure of local land. Therefore, the timely monitoring of salt-affected areas is crucial to prevent land degradation and sustainable soil management. In this study, a typical salinized area in the Keriya Oasis was selected as a study area. Using Landsat 8 OLI optical data and ALOS PALSAR-2 SAR data, the optical remote sensing indexes NDVI, SAVI, NDSI, SI, were combined with the optimal radar polarized target decomposition feature component (VanZyl_vol_g) on the basis of feature space theory in order to construct an optical-radar two-dimensional feature space. The optical-radar salinity detection index (ORSDI) model was constructed to inverse the distribution of soil salinity in Keriya Oasis. The prediction ability of the ORSDI model was validated by a test on 40 measured salinity values. The test results show that the ORSDI model is highly correlated with soil surface salinity. The index ORSDI₃ (R² = 0.656) shows the highest correlation, and it is followed by indexes ORSDI₁ (R² = 0.642), ORSDI₄ (R² = 0.628), and ORSDI₂ (R² = 0.631). The results demonstrated the potential of the ORSDI model in the inversion of soil salinization in arid and semi-arid areas.

Quantitative Estimation of Saline-Soil Amelioration Using Remote-Sensing Indices in Arid Land for Better Management

Soil salinity and sodicity are significant issues worldwide. In particular, they represent the most dominant types of degraded lands, especially in arid and semi-arid regions with minimal rainfall. Furthermore, in these areas, human activities mainly contribute to increasing the degree of soil salinity, especially in dry areas. This study developed a model for mapping soil salinity and sodicity using remote sensing and geographic information systems (GIS). It also provided salinity management techniques (leaching and gypsum requirements) to ameliorate soil and improve crop productivity. The model results showed a high correlation between the soil electrical conductivity (ECe) and remote-sensing spectral indices SIA, SI3, VSSI, and SI9 (R2 = 0.90, 0.89, 0.87, and 0.83), respectively. In contrast, it showed a low correlation between ECe and SI5 (R2 = 0.21). The salt-affected soils in the study area cover about 56% of cultivated land, of which the spatial distribution of different soil salinity levels ranged from low soil salinity of 44% of the salinized cultivated land, moderate soil salinity of 27% of salinized cultivated land, high soil salinity of 29% of the salinized cultivated land, and extreme soil salinity of 1% of the salinized cultivated land. The leaching water requirement (LR) depths ranged from 0.1 to 0.30 m ha−1, while the gypsum requirement (GR) ranged from 0.1 to 9 ton ha−1.

Assessment of Soil Salinization Risk by Remote Sensing-Based Ecological Index (RSEI) in the Bosten Lake Watershed, Xinjiang in Northwest China

Accurate real-time information about the spatial and temporal dynamics of soil salinization is crucial for preventing the aggravation of salinization and achieving sustainable development of the ecological environment. With the Bosten Lake watershed as the study area, in this study, the regional risk factors of soil salinization were identified, the salinization information was extracted, and the remote sensing-based ecological index (RSEI) of soil salinization was assessed through the combined use of remote sensing (RS) and geographic information system (GIS) techniques and measurements of soils samples collected from various field sites. The results revealed that (1) a four period (1990, 2000, 2010, and 2020) RS dataset on soil salinization allowed for the accurate classification of the land use/land cover types, with an overall classification accuracy of greater than 90% and kappa values of >0.90, and the salt index (SI), an RS-derived risk factor of soil salinization, was significantly correlated with the actual measured salt content of the surface soils. (2) The RS-derived elevation and normalized difference vegetation index (NDVI) were significantly correlated with the SI-T. (3) An integrated risk assessment model was constructed for the soil salinization risk in the Bosten Lake watershed, which calculated the integrated risk index values and classified them into four risk levels: low risk, medium risk, high risk, and extremely high risk. (4) Due to the combined effect of the surface water area and terrain, the soil salinization risk gradually decreased from the lake to the surrounding areas, while the corresponding spatial range increased in order of decreasing risk. The areas with different levels of soil salinization risk in the study area during the last 30 years were ranked in decreasing order of medium risk > high risk > extremely high risk > low risk. These findings provide theoretical support for preventing and controlling soil salinization and promoting agricultural production in the study area.

Radar remote sensing-based inversion model of soil salt content at different depths under vegetation

Excessive soil salt content (SSC) seriously affects the crop growth and economic benefits in the agricultural production area. Prior research mainly focused on estimating the salinity in the top bare soil rather than in deep soil that is vital to crop growth. For this end, an experiment was carried out in the Hetao Irrigation District, Inner Mongolia, China. In the experiment, the SSC at different depths under vegetation was measured, and the Sentinel-1 radar images were obtained synchronously. The radar backscattering coefficients (VV and VH) were combined to construct multiple indices, whose sensitivity was then analyzed using the best subset selection (BSS). Meanwhile, four most commonly used algorithms, partial least squares regression (PLSR), quantile regression (QR), support vector machine (SVM), and extreme learning machine (ELM), were utilized to construct estimation models of salinity at the depths of 0-10, 10-20, 0-20, 20-40, 0-40, 40-60 and 0-60 cm before and after BSS, respectively. The results showed: (a) radar remote sensing can be used to estimate the salinity in the root zone of vegetation (0-30 cm); (b) after BSS, the correlation coefficients and estimation accuracy of the four monitoring models were all improved significantly; (c) the estimation accuracy of the four regression models was: SVM > QR > ELM > PLSR; and (d) among the seven sampling depths, 10-20 cm was the optimal inversion depth for all the four models, followed by 20-40 and 0-40 cm. Among the four models, SVM was higher in accuracy than the other three at 10-20 cm (R_P ² = 0.67, R_MSEP = 0.12%). These findings can provide valuable guidance for soil salinity monitoring and agricultural production in the arid or semi-arid areas under vegetation.

Monitoring soil salinization and its spatiotemporal variation at different depths across the Yellow River Delta based on remote sensing data with multi-parameter optimization

Soil salinization is recognized as a key issue negatively affecting agricultural productivity and wetland ecology. It is necessary to develop effective methods for monitoring the spatiotemporal distribution of soil salinity at a regional scale. In this study, we proposed an optimized remote sensing-based model for detecting soil salinity in different depths across the Yellow River Delta (YRD), China. A multi-dimensional model was built for mapping soil salinity, in which five types of predictive factors derived from Landsat satellite images were exacted and tested, 94 in-situ measured soil salinity samples with depths of 30-40 cm and 90-100 cm were collected to establish and validate the predicting model result. By comparing multiple linear regression (MLR) and partial least squares regression (PLSR) models with considering the correlation between predictive factors and soil salinity, we established the optimized prediction model which integrated the multi-parameter (including SWIR1, SI9, MSAVI, Albedo, and SDI) optimization approach to detect soil salinization in the YRD from 2003 to 2018. The results indicated that the estimates of soil salinity by the optimized prediction model were in good agreement with the measured soil salinity. The accuracy of the PLSR model performed better than that of the MLR model, with the R² of 0.642, RMSE of 0.283, and MAE of 0.213 at 30-40 cm depth, and with the R² of 0.450, RMSE of 0.276, and MAE of 0.220 at 90-100 cm depth. From 2003 to 2018, the soil salinity showed a distinct spatial heterogeneity. The soil salinization level of the coastal shoreline was higher; in contrast, lower soil salinization level occurred in the central YRD. In the last 15 years, the soil salinity at depth of 30-40 cm experienced a decreased trend of fluctuating, while the soil salinity at depth of 90-100 cm showed fluctuating increasing trend.