Search Results (854)

Soil salinity severely threatens global ecosystems and agriculture, making accurate monitoring an ongoing priority. Currently, efficiently utilizing multi-source datasets to enhance monitoring accuracy while minimizing computational resources remains a critical challenge. This study evaluated several modeling strategies, including full-dataset modeling, variance inflation factor (VIF), Boruta, particle swarm optimization, ant colony optimization and recursive feature elimination (RFE), and validated results across diverse regions (Almaty, Kazakhstan; Shandong, China). We further validated the results using multiple algorithms, including linear regression, partial least squares regression, extreme gradient boosting, k-nearest neighbor and random forest (RF), with topsoil (0–20 cm) electrical conductivity inverted via the optimal method. Results indicate that input feature numbers substantially impact model performance: regional-scale feature selection is indispensable, with RFE outperforming full-dataset modeling (R² improves by up to 0.28, while RMSE decreases by 2.21 dS m⁻¹) and VIF performing the worst. Transferability is also demonstrated in Almaty and Shandong. Additionally, the RF algorithm shows superior performance in soil salinity mapping (overall accuracy = 0.73; kappa coefficient = 0.65). And, the RFE and SHAP results highlight CRSI, BI, and MSAVI2 as particularly important predictors for estimating soil salinity in our study area. Collectively, this study highlights the critical importance of feature optimization and interpretability in soil attribute mapping through the integration of multi-source remote sensing data. Full article

Soil salinization severely threatens agricultural sustainability in saline–alkali regions, and high-throughput, efficient screening of salt-tolerant rice varieties is critical to mitigating this threat. Traditional evaluation methods are constrained by low throughput, limited spatiotemporal resolution, and the lack of standardized indicators. To address these gaps, this study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice. Field experiments were conducted with 12 rice lines at five key growth stages in Ningxia, China, with synchronous ground spectral measurements and UAV image acquisition on the same day for each stage. Five feature selection methods were employed to screen salt stress-sensitive hyperspectral bands, with classification accuracy validated via a Support Vector Machine (SVM) model. The results showed that: (1) rice spectral characteristics varied dynamically across growth stages, and first-order differential transformation effectively amplified subtle spectral variations in stress-sensitive regions; (2) the Minimum Redundancy–Maximum Relevance (mRMR) method outperformed other methods, achieving 100% classification accuracy at key growth stages, with sensitive bands dominated by red edge bands (58.33%); (3) the constructed Salt Stress Index (SIR) showed strong correlations with classical vegetation indices and rice yield, and could clearly distinguish salt-tolerant and salt-sensitive rice varieties, with stable performance against field environmental noise; and (4) band matching between UAV and Sentinel-2 data enabled multi-scale data fusion and regional-scale salt stress monitoring. This framework realizes the transformation from qualitative spectral description to quantitative salt tolerance evaluation, providing standardized technical support for salt-tolerant rice breeding and precision management of saline–alkali lands. Full article

This Special Issue, entitled “Digital Soil Mapping for Agri-Environmental Management and Sustainability”, gathers nine studies from around the globe that illustrate how digital soil mapping (DSM) is being applied to support agri-environmental management and sustainability. Field- and farm-scale studies are emphasized, where informed decisions are essential for efficient day-to-day management and profitability. The articles highlight the integration of remote/proximal sensing, along with modern machine learning techniques, to produce high-resolution soil maps, soil fertility and nutrient management zoning, and to monitor salinity and soil moisture to inform irrigation and land management. Another key focus is improving sampling strategies and assessing prediction uncertainty and model interpretability. This collection sets future DSM priorities, including cost-effective sampling, robust uncertainty assessments, and reliable cost–benefit and risk assessment approaches that link map accuracy/uncertainty to management outcomes and economic performance. Full article

Fungal endophytes are microorganisms that inhabit plant tissues without causing disease and emerge as critical mediators of plant stress tolerance, nutrient acquisition, and ecosystem resilience under diverse climate change scenarios. Their unique position within the host allows them to modulate physiological responses more closely than external microbiota. This review explores how endophytic fungi contribute to plant adaptation under climate-induced stresses such as heat, salinity, drought, pollution, and nutrient limitation, with a focus on molecular crosstalk, functional trait modules, and metabolic trade-offs. Key findings emphasize multilayered signaling systems, including MAMP/DAMP recognition, phytohormone regulation, immune tuning, ROS dynamics, and effector deployment, while emerging mechanisms such as cross-kingdom RNA and extracellular vesicle (EV)-mediated exchange are discussed as promising but currently limited in empirical validation within many endophytic systems. Endophytes also enhance nutrient exchange through conditional carbon-for-benefit trade and may shape rhizosphere microbiota and soil activities through plant-mediated inputs. Integrative multi-omics approaches provide predominantly correlational insights into the mechanistic basis of these effects, linking molecular function to ecosystem and community outcomes. These insights have potential applications in climate-resilient agriculture, phytoremediation, and ecosystem restoration; however, their large-scale implementation requires further field-based validation and context-specific assessment. Future priorities should focus on trait-based selection, ecological modeling, and biosafety evaluation to translate microbial functions into reliable field-level strategies that support sustainable crop performance under accelerating environmental stress. Full article

Neltuma juliflora (Sw.) Raf. (syn. = Prosopis juliflora (Sw.) DC.) is among the world’s most aggressive woody invaders, yet its ecological impacts remain poorly quantified in hyper-arid environments, where soils are calcareous and ecosystems recover slowly from disturbance. In this study, we tested two hypotheses: (1) the presence of N. juliflora changes native plant diversity, as well as soil and key physicochemical properties in hyper-arid Qatar, and (2) agricultural farms act as primary sources of N. juliflora invasion. Using a comparative observational design across 62 sites (45 invaded and 17 non-invaded), we applied a generalised additive model (GAM) and a generalised linear mixed model (GLMM) to quantify invasion drivers and the impact of invasion on perennial species diversity, respectively. Additionally, we used the Wilcoxon rank-sum test to compare the soil properties in the invaded and non-invaded sites. Our results indicate that N. juliflora is positively associated with farms, with the probability of occurrence declining by ca. 20% for each kilometre farther away from agricultural farms. This pattern suggests substantial propagule pressure from agricultural farms. Perennial species richness declined from 7.5 species at 0% N. juliflora cover to 4.8 species at full cover (36% reduction). Invaded sites were characterised by higher amounts of coarse sand (16%); reduced silt–clay fractions (5%); and elevated salinity indicators, including electrical conductivity (0.744 dS m⁻¹) and total dissolved solids (476 mg L⁻¹), while major N–P–K pools remained unchanged. These findings demonstrate measurable invasion-related changes in soil conditions and native perennial diversity in hyper-arid ecosystems and highlight the role of agricultural land use as a key driver of biological invasion. From a sustainability perspective, early detection, targeted control near agricultural and grazing zones, and integration of invasive species monitoring into land-use planning frameworks are essential to prevent further ecosystem degradation, protect biodiversity, and enhance the resilience of desert landscapes under increasing climate and land-use pressures. Full article

This study aims to investigate the relationship between basin hydrology and estuarine processes such as dynamics that influence salinity and water quality in the Shatt Al-Arab River, southern Iraq. Extensive samplings were conducted at 25 sites along the river course over one hydrological year. Runoff estimates were obtained using the soil conservation service–curve number (SCS-CN) model. During winter, peak rainfall (76.8 mm month⁻¹) and runoff (12.38 mm month⁻¹) promote the shortest salt wedge extension (8 km) and the highest water quality (median water quality index (WQI) = 22). In contrast, during fall, minimal rainfall (6.51 mm month⁻¹) and runoff (0.14 mm month⁻¹) result in a salt wedge extension of 109 km and the lowest water quality (median WQI = 250). Strong correlations between rainfall–runoff estimates, salt wedge extension, and water quality parameters demonstrate that water quality status can be predicted using hydrological inputs alone. Thus, this study introduces a novel quantification of the flushing influence required to maintain the Shatt Al-Arab River’s ecological health. A strong (r² = 0.87) significant (p < 0.05) negative correlation was detected between the runoff coefficient (a proxy indicator of catchment wetness) and the standard deviation of WQI. Such a negative correlation implies that hydrological flushing fosters water quality stability. Principal component analysis (PCA) further revealed how natural and anthropogenic sources contribute to water quality. The findings illustrate how seasonal hydrological variability control mixing processes, salt wedge propagation, and water quality in estuarine-influenced river systems, presenting a framework adaptable to similar systems worldwide. Full article

Soil salinity in wheat represents a severe threat to global productivity, requiring a deep understanding of physiological adaptation mechanisms to ensure food security in the context of continuous agricultural land degradation. The study aim was to assess the impact of a salinity gradient (0–75 mM NaCl) on the dynamics of stomatal opening and chlorophyll content of the varieties Glosa, Taisa, Boema and Granny. The methodology integrated four joint classes, of which two were from detailed physiological parameters, stomatal features and chlorophyll content, and two morphological characteristics, growth visual indices and biomass allocation. All data was corroborated into an original hierarchical synthesis model presented in a multi-layered sunburst plot. The most relevant results indicate that the concentration of 45 mM NaCl represents the osmotic adjustment threshold, where the active accumulation of ions decreases the internal osmotic potential, facilitating an influx of water that maximizes guard cell turgor and, implicitly, stomatal width. Maximal physiological parameters and biomass ranked the variety Granny first, followed by Taisa. Despite stomatal increases, Boema ranked third and Glosa showed overall decreased development and the lowest plant biomass. These findings validate the use of interconnected effects analysis as a screening tool for identifying the salinity responses of wheat varieties. Full article

Arid salt marsh ecosystems endure chronic water scarcity and high salinity stress, with the stability of their functions inextricably linked to the pivotal role of the rhizosphere microenvironment of halophytes. This study focused on three typical halophytes (Kalidium cuspidatum, Nitraria tangutorum, Reaumuria soongarica) in the Jiantan wetland, and deeply explore how these halophytes differently regulate the soil microenvironment through the rhizosphere effect. The results showed that the rhizosphere soil of Kalidium cuspidatum had higher pH, Na⁺, and K⁺ contents, while the rhizosphere soil of R. soongarica had higher total carbon, soil organic carbon, alkali-hydrolyzable nitrogen, and microbial biomass. Microbial community analysis revealed that rhizosphere soil of fungal diversity was significantly higher in K. cuspidatum than in R. soongarica, with distinct differences in bacterial and fungal community structures. These differences were closely associated with factors such as Na⁺, Olsen phosphorus, microbial biomass carbon and alkali-hydrolyzable nitrogen. Among the dominant phyla, Proteobacteria and Ascomycota predominate, with Desulfobacterota and Mortierellomycota exhibiting the highest explanatory power (>48%) for physicochemical property variations. The microbial network of rhizosphere soil of R. soongarica has the highest complexity (with 633 nodes and 3300 edges), but the proportion of positive correlation edges was the lowest (21.58%). Structural equation modeling indicates that soil physical properties indirectly influence network complexity by negatively regulating chemical properties and microbial biomass, while microbial diversity had a direct positive effect on dominant phylum composition and network complexity. This study elucidated the differentiated adaptive strategies of rhizosphere microenvironment-microbe interactions in halophytes, providing a theoretical basis for wetland ecological restoration. Full article

Biochar and plant growth-promoting rhizobacteria (PGPR) are promising for coastal saline–alkali soil remediation, but their combined effect is often limited by nutrient scarcity. This study investigated whether nutrient-laden biochar (saturated with livestock wastewater) synergizes with a PGPR inoculant (Paenibacillus mucilaginosus PM12) to enhance maize productivity by reshaping the rhizosphere microbiome. A field experiment included five treatments: control (CK), sheep manure biochar alone (BC), nutrient-laden biochar (NBC), BC + PGPR (MBC), and NBC + PGPR (MNBC). The MNBC treatment showed the most pronounced improvements, increasing maize yield by 52.5% compared to CK, while reducing soil pH by 0.30 units and enhancing soil organic matter, total nitrogen, and available phosphorus. Metagenomic analysis revealed that MNBC uniquely enriched beneficial genera (e.g., Nocardioides) and saprotrophic Basidiomycota, while suppressing pathogenic Fusarium. This restructuring elevated the genetic potential for nitrogen transformation, phosphorus solubilization, and carbon metabolism. Structural equation modeling identified increased soil available phosphorus and total nitrogen as the primary direct drivers of yield enhancement. The integration of nutrient-laden biochar and PGPR creates a synergistic system that reclaims saline–alkali soil by alleviating stress, supplying nutrients, and directing the assembly of a functional microbiome. Full article

Soil salinity mapping in heterogeneous irrigation districts faces a dual challenge: the high dimensionality of hyperspectral data leads to redundancy, while the scarcity of ground-truth samples restricts the generalization of data-driven models. Traditional regression methods often struggle to capture non-linear spectral responses under such “small-sample” conditions. To address these limitations, this study proposes a semi-supervised retrieval framework coupling Optimal Band Combination Analysis (OBCA) with a Spectral Wasserstein GAN with Gradient Penalty (S-WGAN-GP). We constructed a robust feature set via cross-scenario evaluation and developed a rigorous “Uncertainty-Aware Filtering” protocol to screen synthetic samples generated by a teacher mechanism. The OBCA screening revealed that salinity-sensitive features are robustly clustered in the Green (550–570 nm) and Near-Infrared (NIR, 880–950 nm) regions, with NIR bands demonstrating superior stability across different sites. The proposed S-WGAN-GP successfully densified the feature manifold by generating 1186 high-fidelity synthetic samples. By incorporating these augmented data, the inversion accuracy was substantially improved: the R² of the optimal SVR model increased from 0.36 (baseline) to 0.60 (+66.7%), and the RMSE decreased from 7.06 to 5.57 dSm⁻¹. This study confirms that physically constrained generative augmentation, when combined with rigorous quality control, effectively bridges the distribution gap in limited datasets. The proposed framework offers a transferable and accurate solution for fine-scale soil salinity monitoring in data-scarce arid regions. Full article

Rational fertilization is a key measure for improving grassland productivity; however, the optimal effects of nitrogen (N), phosphorus (P) and potassium (K) rationing vary across grasslands with different salinity–alkalinity conditions. To determine the optimum fertilization ratio for typical saline–alkaline degraded grasslands in the agro-pastoral transition zone of northern China, we carried out an experiment with different ratios of N, P and K to investigate the effects of fertilization on biomass, plant diversity, plant nutrient uptake and soil nutrient contents. The results showed that fertilization increased biomass, plant diversity, nutrient uptake and soil nutrient contents in all levels of saline–alkaline grasslands. Compared with the control, N2P2K2 treatment resulted in the significantly highest biomass, with an increase of 4.52 and 2.39 t ha⁻¹ in slightly and moderately saline–alkaline grasslands; N2P2K1 resulted in the significantly highest biomass, with an increase of 1.14 t ha⁻¹ in severely saline–alkaline grasslands. We integrated plant and soil properties to construct a second-order response surface model (RSM), and our recommended optimum N–P–K fertilization ratios for slightly, moderately and severely saline–alkali grasslands are 103.7–88.1–78.0, 125.5–91.5–74.1 and 85.2–68.1–58.2 kg ha⁻¹, respectively. Reasonable fertilization can improve soil fertility, biomass yield and plant diversity, while excessive fertilization has negative effects on soil and plant traits. Our results provide theoretical support and practical guidance for the scientific fertilization of grasslands with varying salinity and alkalinity. Full article

This study proposes a method for retrieving ocean sea surface salinity (SSS) using C/X-band ocean emissivities in coastal regions, aiming to verify the performance of these unconventional frequencies for SSS retrieval in warm, high-salinity-variation coastal oceans. Since C/X-band brightness temperatures are less sensitive to sea surface salinity than L-band brightness temperatures, it becomes particularly important to develop a sophisticated and effective method for extracting salinity-related signals from C/X-band brightness temperatures. To this end, a wind effect correction process is developed to remove rough sea surface emissivity contributions from total emissivity and derive calm sea emissivity from WindSat’s brightness temperatures. The wind-induced effects are modeled with a third-order polynomial. Then, based on emissivity analysis, a weighted combination of C/X-band calm sea emissivities (with parameter λ) is introduced to reduce SST sensitivity. This λ-based combination is used to retrieve SSS in the Bay of Bengal. Based on the triple-match method and buoy data, the salinity retrieval results are verified and compared with the Soil Moisture Active Passive (SMAP) SSS and Argo in situ SSS. The results show that the use of parameter λ reduces the RMS error of SSS by 0.1–0.2 psu. The RMSE of SSS retrieval is about 0.64 psu, which is comparable to the error of SMAP data. Simultaneously, the SSS retrieval accuracy is significantly influenced by offshore distance. At an offshore distance of 100 km, the salinity retrieval error exceeds 1 psu, while when the offshore distance exceeds 500 km, the salinity retrieval error is better than 0.6 psu. Full article

AgriRover was developed to address key operational constraints faced by smallholder vineyards in Peru, including sandy and saline soils, labor shortages, and limited access to advanced agricultural machinery. The platform features an articulated, all-wheel-drive chassis designed to ensure mobility and stability on loose terrain while minimizing soil compaction. This study presents the simulation-driven development of a digital pre-twin, conceived as a virtual prototype prepared for future sensor integration but currently operating without real-time data feedback. The pre-twin was implemented in MATLAB/Simulink (vers. 2024b) using a multibody dynamics model and evaluated through eight scenario-based simulations, varying field geometry, soil type, and slope conditions. The results show stable operation on slopes up to 10°, wheel sinkage values ranging between approximately 20 and 45 mm depending on terrain conditions, and a moderate battery state-of-charge reduction across most scenarios, with higher power demand observed on sandy soils. A scenario-based comparison indicates a potential reduction of approximately 50% in total monitoring time relative to manual field scouting, while advanced sensing, autonomous navigation, and AI-based analytics remain part of future developments. The current pre-twin provides a validated, low-cost foundation for context-specific phenological monitoring and early-stage precision agriculture applications in developing regions. Full article

Soil salinization is a primary bottleneck for the sustainable development of agriculture in the Yellow River Delta (YRD). Conventional remote sensing monitoring predominantly relies on single-phase instantaneous spectral responses, which are highly vulnerable to transient environmental interferences, such as surface moisture fluctuations. This study proposes a novel predictive framework based on legacy vegetation signals. By integrating multi-temporal Sentinel-2 imagery from the 2024 growing season, we quantified the cumulative physiological feedback of crops from the preceding year and developed a spring soil salinity content (SSC) inversion model for 2025 using the LightGBM algorithm. The results demonstrate that the median compositing technique significantly enhances model robustness against outliers. Furthermore, the optimal time window for capturing these legacy signals for spring salinity monitoring was identified as July to September. Compared with traditional immediate monitoring models, the LightGBM model based on previous-season legacy signals achieved superior predictive accuracy (R² = 0.84), effectively mitigating the impact of stochastic noise. This research validates the critical role of long-term vegetation memory in salinity early warning and provides a robust scientific foundation for the precision management of coastal saline-alkali land. Full article

Soil salinization represents a critical constraint to sustainable agriculture in arid and semi-arid regions, where salinity threatens soil productivity, water quality, and ecosystem resilience. Soil salinity pattern prediction is complicated by tightly coupled landscape hydro-climatic processes, wherein the central Sabkha acts as a persistent salt sink, episodic inundation and intense evaporation concentrate dissolved salts, and a shallow saline groundwater table interacts with the semi-arid climate to drive surface salinization. Conventional mapping is laborious and lacks the precision needed to capture the spatio-temporal dynamics of soil salinity across landscapes. This study developed an integrated framework uniting multi-temporal Landsat imagery (2000–2025), hypsometric data, climatic indicators, and in situ soil electrical conductivity (ECe) measurements to model soil salinity dynamics using machine learning (ML), over the Sehb El Masjoune (SEM) semi-arid region, Morocco. A total of 233 soil samples were collected in the investigated area in 2022, 2023, 2024, and 2025 to assess the spatial variability to calibrate and validate modeling findings. To this end, three predictive algorithms, i.e., Gradient-Boosted Trees (GBT), Support Vector Regression (SVR), and Random Forest (RF) were assessed. Our findings showed that SVR achieved the highest predictive capability (R² = 0.76; RMSE = 32.91 dS/m), whereas SVR-based salinity maps revealed a distinct spatial organization of salinization processes, characterized by extremely saline soils (≥64 dS/m) concentrated in the central study area (i.e., SEM center) and a progressive decline toward adjacent agricultural lands (0–8 dS/m). Our results demonstrated that from 2000 to 2025, moderately to highly saline areas (≥16 dS/m) expanded by nearly 10%, driven by recurrent droughts and inefficient drainage. Hydroclimatic analysis confirmed that dry years (SPI: Standardized Precipitation Index ≤ −0.5) promoted net salinity build-up through the expansion and persistence of moderate-to-high salinity classes (≥16 dS/m), whereas wet years (SPI ≥ +0.5) favored temporary leaching and partial recovery, mainly within the low-to-moderate range. This integrative remote sensing–ML approach provides a robust and scalable framework for operational soil salinity monitoring, offering valuable insights for sustainable land-use planning in similar Sabkha’s data-scarce agroecosystems. Full article