The soil water content (SWC) is a critical factor in agricultural production. To achieve real-time and nondestructive monitoring of the SWC, an experiment was conducted to measure the hyperspectral reflectance of soil samples with varying levels of water content. The soil samples were divided into two parts, SWC higher than field capacity (super-θ_f) and SWC lower than field capacity (sub-θ_f), and the outliers were detected by Monte Carlo cross-validation (MCCV). The raw spectra were processed using Savitzky–Golay (SG) smoothing and then the spectral feature variable of SWC was extracted by using a combination of competitive adaptive reweighted sampling (CARS) and random frog (Rfrog). Based on the extracted feature variables, an extreme learning machine (ELM), a back-propagation artificial neural network (BPANN), and a support vector machine (SVM) were used to establish the prediction model. The results showed that the accuracy of retrieving the SWC using the same model was poor, under two conditions, i.e., SWC above and below θ_f, mainly due to the influence of the lower accuracy of the super-θ_f part. The number of feature variables extracted by the sub-θ_f and super-θ_f datasets were 25 and 18, respectively, accounting for 1.85% and 1.33% of the raw spectra, and the variables were widely distributed in the NIR range. Among the models, the best results were achieved by the BPANN model for both the sub-θ_f and the super-θ_f datasets; the R²p, RMSEp, and RRMSE of the sub-θ_f samples were 0.941, 1.570%, and 6.685%, respectively. The R²p, RMSEp, and RRMSE of the super-θ_f samples were 0.764, 1.479%, and 4.205%, respectively. This study demonstrates that the CARS–Rfrog–BPANN method was reliable for the prediction of SWC.

Unconventional water resources (e.g., saline water, etc.) for irrigation as a promising supplementary water source can alleviate the freshwater shortage in the agriculture of red soil areas in Southern China. It should be noted that the presence of soluble salt in this water source may have detrimental influences on soil water infiltration and crop growth. Understanding the effect of unconventional water irrigation (UWI) on water infiltration in red soil is important. Previous studies have shown that the salinity of UWI can alter soil hydraulic properties to change soil water movement in saline soils. However, the underlying mechanism and factors of water infiltration in red soil under UWI with different salinity levels remain limited. Therefore, a laboratory experiment (one-dimensional vertical infiltration experiment and centrifuge method) was conducted to evaluate the effect of UWI with different salinity levels [0 (the distilled water, CK), 1 (S1), 2 (S2), 3 (S3), 5 (S5), and 10 (S10) g L⁻¹] on the soil water infiltration process, soil water characteristic curve (SWCC), soil water constants estimated using the SWCC, saturated and unsaturated hydraulic conductivity (K_S and K) as well as the soil chemistry of soil profile [pH, electrical conductivity (EC), and Na⁺ and Cl⁻ contents]. The primary factors of soil water infiltration were identified using stepwise regression and path analysis methods. The results showed that UWI salinity decreased water infiltration by 1.53–7.99% at the end of infiltration in red soil, following the order of CK > S1 > S5 > S2 > S3 > S10. Moreover, UWI could enhance soil water availability with an increase of 8.55–12.68% in available water capacity. In contrast, lower K_S and K were observed in S1–S10, and there was a negative linear relationship between irrigation salinity and K_S. UWI also produced the EC, Na⁺, and Cl⁻ accumulations in the soil profile. As the salinity level of UWI increased, the accumulations aggravated. Soil acidification was found in S1–S5, while soil alkalization was observed in S10. Additionally, α, PWP, and K_S were the primary factors influencing the water infiltration of red soil. This study can help optimize the soil infiltration model under UWI and establish a foundation for unconventional water management in the red soil regions of Southern China and other similar regions. In addition, the undisturbed red soil under agricultural unconventional water irrigation and the long-term effect of unconventional water application should be considered.

Jatropha curcas L. is a promising bioenergy source, and its seedling stage is sensitive to salinity. Nitrogen application presents an effective strategy for alleviating the adverse consequences of salinity stress. However, the responses of plant growth and physiology of Jatropha curcas L. seedlings to nitrogen application under salinity stress remain unclear. As a result, a one-year greenhouse plot experiment was conducted to investigate the effects of nitrogen application on the plant growth, antioxidant enzyme activity, and photosynthesis of Jatropha curcas L. seedlings under saline conditions. Experiment treatments consisted of three salinity stresses (mild salinity stress, S1: 2 g/kg; moderate salinity stress, S2: 4 g/kg; and severe salinity stress, S3: 6 g/kg), four nitrogen application rates (N0: 0 gN/plant; N1: 20 gN/plant; N2: 60 gN/plant; and N3: 100 gN/plant), and a control treatment (CK) which was without salinity stress and nitrogen application. The results showed that salinity stress substantially reduced plant growth of Jatropha curcas L. seedlings. As the salinity stress increased, the reduction in plant growth also increased. The S3 treatment had the lowest leaf area, leaf biomass, and total biomass, which decreased by an average of 70.4%, 66.3%, and 69.9%, respectively, compared to CK. Nitrogen application could compensate for these impairments of plant growth from salinity stress by promoting antioxidant enzyme activity and photosynthesis. As for mild and moderate salinity stresses, the maximum plant growth was found in the N3 treatment, with the maximum antioxidant enzyme activity, photosynthetic pigment, photosynthetic characteristic, and chlorophyll fluorescence. As for severe salinity stress, higher plant growth was found in N2 and N3 treatments, and there were no significant differences between N2 and N3 treatments. It also should be noted that the maximum photosynthetic characteristic and chlorophyll fluorescence were found in N2 treatment under severe salinity stress. In conclusion, nitrogen application could be an alternative strategy to improve the salinity tolerance of Jatropha curcas L. growth. The nitrogen application rate of 100 gN/plant could be recommended for low and moderate salinity stresses, while 60 gN/plant could be recommended for severe salinity stress. However, higher nitrogen application rate (>100 gN/plant) under mild and moderate salinity stress and the effects of reactive oxygen species under salinity stress should be further evaluated.

As a promising alternative water source to alleviate irrigation water scarcity in red soil regions in southern China, low-quality water could enhance regional water resource utilization and promote sustainable agriculture. However, its soluble salt and ions could affect soil solute distribution and transport, potentially hindering crop growth. Undoubtedly, it is necessary to understand the mechanism of solute transport in red soil under low-quality water irrigation with different water salinity levels. Therefore, a one-dimensional vertical water infiltration experiment and a solute breakthrough experiment were conducted to evaluate the solute transport (soluble salt, Na⁺, and Cl⁻) in unsaturated and saturated homogenous red soil at different salinity levels [1 (S1), 2 (S2), 3 (S3), 5 (S5), and 10 (S10) g/L] when irrigated with simulated low-quality water using analytical-grade NaCl. Moreover, the potential factors affecting salt distribution in unsaturated red soil were determined. The findings indicate positive linear relationships between accumulations of three solutes and irrigation water salinity. Generally, the depth of maximum solute concentration increased with the increase in irrigation water salinity. Soluble salt, Na⁺, and Cl⁻ exhibited early breakthrough and trailing in red soil, but higher irrigation water salinity could reduce PV and retardation. A mobile and immobile water model (MIM) showed that convection was dominant in solute transport in red soil under low-quality water irrigation. D decreased as a power function with increasing irrigation water salinity, while v and R decreased linearly. Furthermore, the red soil can adsorb Cl⁻ resulting from its special charge characteristics under low-quality water irrigation, which may be the main source of salt adsorption. Additionally, v > soil pH > βsalt primarily influenced salt distribution in the 0–40 cm soil profile. This study can provide insights into solute transport in red soil under low-quality water irrigation, facilitating soil fertility and structure, as well as low-quality water irrigation strategy optimization.

Background: Soil water content is one of the critical indicators in agricultural systems. Visible/near-infrared hyperspectral remote sensing is an effective method for soil water estimation. However, noise removal from massive spectral datasets and effective feature extraction are challenges for achieving accurate soil water estimation using this technology. Methods: This study proposes a method for hyperspectral remote sensing soil water content estimation based on a combination of continuous wavelet transform (CWT) and competitive adaptive reweighted sampling (CARS). Hyperspectral data were collected from soil samples with different water contents prepared in the laboratory. CWT, with two wavelet basis functions (mexh and gaus2), was used to pre-process the hyperspectral reflectance to eliminate noise interference. The correlation analysis was conducted between soil water content and wavelet coefficients at ten scales. The feature variables were extracted from these wavelet coefficients using the CARS method and used as input variables to build linear and non-linear models, specifically partial least squares (PLSR) and extreme learning machine (ELM), to estimate soil water content. Results: The results showed that the correlation between wavelet coefficients and soil water content decreased as the decomposition scale increased. The corresponding bands of the extracted wavelet coefficients were mainly distributed in the near-infrared region. The non-linear model (ELM) was superior to the linear method (PLSR). ELM demonstrated satisfactory accuracy based on the feature wavelet coefficients of CWT with the mexh wavelet basis function at a decomposition scale of 1 (CWT(mexh_1)), with R², RMSE, and RPD values of 0.946, 1.408%, and 3.759 in the validation dataset, respectively. Overall, the CWT(mexh_1)-CARS-ELM systematic modeling method was feasible and reliable for estimating the water content of sandy clay loam.

The soil water content is a key evaluation factor in the agricultural and ecological fields. The objective of this study was to explore the combination of multi-satellite information to address the issue of low accuracy in soil water content retrieval from single-band, single-source information and to establish a model for the estimation of the soil water content based on a simulated multi-spectral method. An experiment involving the hyperspectral determination of soil samples (Oxisols) with varying values of the water content was conducted. The reflectance of eight multi-spectral satellite sensors was resampled based on the spectral response functions. The vegetation indices (VIs) were established by pairing all available sensor bands. The significant VIs and band reflectance were extracted using the correlation coefficient and out-of-bag (OOB) data importance analysis methods, and then linear models and nonlinear models were established for soil water content estimation. A significant correlation was achieved between the simulated multi-spectral reflectance, VIs, and soil water content. The nonlinear models had a better performance than the linear model. The combined OOB and random forest (OOB-RF) model achieved the highest prediction accuracy, with R² _calibration and R² _prediction values of 0.852 and 0.834, respectively. Overall, it was verified that the OOB-RF modeling method based on multi-spectral remote sensing was feasible for estimating the soil water content.