Search Results (1,525)

The use of biostimulants in agriculture is increasing; however, their effects on raspberry remain insufficiently understood. The aim of this study was to evaluate the impact of foliar-applied biostimulants on yield and growth in three primocane raspberry cultivars grown under field conditions using multispectral imaging based on unmanned aerial vehicles. An experiment included a control and four foliar biostimulant treatments based on animal-derived amino acids, plant-derived amino acids, seaweed extract, and seaweed extract combined with animal-derived amino acids. Biostimulant effects on primocane raspberry were found to vary substantially depending on cultivar, environmental conditions, and formulation type, with measurable impacts on both yield formation and vegetative growth. These responses were further supported and characterized using multispectral UAV-based mutlispectral imaging, which enabled effective detection of treatment-related physiological changes. This approach was based on the analysis of relative percentage changes between consecutive measurements of selected vegetation indices, allowing the identification of dynamic physiological responses over time. These findings highlight the need for a more targeted approach to biostimulant use, taking into account cultivar-specific responses and environmental variability. Future research should extend this framework to a broader range of genotypes, cultivation systems, and biostimulant formulations, while integrating remote sensing with other analytical methods to better understand plant physiological responses. Such developments may support the transition toward data-driven and precision-guided biostimulant application strategies in sustainable crop production. Full article

Accurate landslide segmentation from remote sensing imagery is important for geohazard assessment and emergency response, yet it remains challenging because landslide regions are often spectrally confused with bare soil, riverbeds, shadows, and disturbed surfaces while also suffering from severe foreground–background imbalance. To address these issues, we propose an Efficient Spectrally Guided Hierarchical Fusion Network (SGH-Net) for multi-modal landslide segmentation. Instead of directly concatenating heterogeneous inputs at the image level, SGH-Net adopts an asymmetric encoder–decoder design in which a pretrained EfficientNet-B4 extracts RGB features, while two lightweight guidance encoders capture complementary multispectral band and DEM-derived terrain cues. These guidance features are progressively injected into the RGB backbone through multi-stage Guided Attention Blocks, enabling selective feature recalibration and reducing cross-modal interference. In addition, a hybrid Dice–Focal loss is used to alleviate class imbalance. Experiments on the Landslide4Sense dataset show that SGH-Net achieves the best overall performance among the compared methods under the adopted evaluation protocol, reaching 81.15% IoU and a 77.86% F1-score. Compared with representative multi-modal baselines, the proposed method delivers more accurate boundary delineation and fewer false alarms while maintaining favorable model complexity. These results indicate that modality-guided hierarchical fusion is an effective and efficient strategy for multi-modal landslide segmentation. Full article

China’s rapidly aging population poses a significant challenge to the equitable allocation of primary healthcare resources. Conventional accessibility assessments often rely solely on economic indicators, overlooking the ecological constraints that shape human settlement and service provision. To address this problem, this study proposes a socio-ecological framework integrating remote sensing data with spatial accessibility modeling. This study employs the Vegetation Nighttime Condition Index (VNCI)—a fusion of VIIRS nighttime lights and MODIS NDVI—as a proxy for human activity intensity under ecological constraints. The spatial accessibility of primary healthcare for the elderly (aged 65+) is evaluated across 31 provinces in mainland China using the Enhanced Two-Step Floating Catchment Area (2SFCA) method. Furthermore, a coupling coordination model and the Relative Development Index (RDI) are applied to examine the relative alignment between healthcare accessibility and the socio-ecological development context represented by VNCI. Empirical results reveal a distinct East–West gradient. Eastern coastal regions exhibit high accessibility; however, the coupling analysis identifies that healthcare accessibility lags behind high socio-ecological development intensity (low RDI). Conversely, western and rural regions generally suffer from a “low-level trap,” characterized by both low accessibility and weak socio-ecological coordination. The findings demonstrate that satellite-derived indices like VNCI effectively capture fine-scale human-environment interactions, offering a basis for spatially differentiated healthcare planning. Full article

Hyperspectral remote sensing provides essential biochemical and structural information for crop disease monitoring, yet its application to cotton Verticillium wilt has largely focused on single-period evaluations or multi-temporal classifications. Such approaches overlook the progressive nature of this vascular disease, whose pigment, water, and mesophyll responses evolve over time, making temporal hyperspectral information critical for reliable severity estimation but still insufficiently utilized. To overcome this limitation, we conducted daily time-series observations on cotton leaves and collected 2895 hyperspectral reflectance measurements and 770 high-resolution RGB images together with disease severity records, generating a temporally dense spectral-severity dataset spanning symptom-free to severe stages. Five categories of disease-related vegetation indices were derived and organized into 5-day spectral–temporal slices. Based on these features, we introduce a dual-branch Transformer-TCN model that integrates global temporal dependencies captured by self-attention with local temporal variations resolved by dilated causal convolutions for severity inversion. The model delivers the strongest performance with an R² of 0.8813, exceeding multiple single and hybrid time-series alternatives by 0.0446–0.1407 in R², equivalent to a relative improvement of 5.33–19.00%. Temporal spectral features also outperform their non-temporal counterparts, highlighting that disease progression dynamics captured by time-series spectra are critical for reliable severity retrieval. Feature contribution analysis indicates that the blue red index BRI provides the highest contribution, consistent with the single-index time-series modelling results. Photosynthesis- and water-related indices provide secondary but complementary support. Collectively, our results demonstrate that the dual-branch Transformer-TCN model can capture complex spectral–temporal relationships between cotton Verticillium wilt and disease severity, providing methodological support for crop disease monitoring and evaluation. Full article

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems. Full article

Satellite remote sensing has become essential for water quality assessment across inland and coastal environments, with rapid improvements in recent years. Significant advances have been made in detecting optically active parameters (such as chlorophyll-a, suspended matter, and turbidity), showing consistently strong performance across multiple studies. Specifically, the median validation performance (R²) derived from the quantitative synthesis indicates R² = 0.82 for chlorophyll-a (interquartile range—IQR: 0.75–0.90), R² = 0.80 for total suspended matter (IQR: 0.78–0.85), and R² = 0.88 for turbidity (IQR: 0.85–0.90). Conversely, the retrieval of optically inactive parameters (such as nutrients like total phosphorus and total nitrogen) remains more context dependent. It exhibits moderate, more variable results, with median R² = 0.68 (IQR: 0.64–0.74) for total phosphorus and R² = 0.75 (IQR: 0.70–0.80) for total nitrogen. These findings clearly illustrate the varying success of retrievals of optically active and inactive parameters and underscore the inherent difficulties of indirect estimation methods. However, high reported accuracy has yet to translate into transferable, uncertainty-informed, and operational monitoring systems. This gap stems from structural issues in validation design, physics integration, uncertainty management, and multi-sensor compatibility rather than data limitations alone. We present a PRISMA-guided, distribution-aware quantitative synthesis of 152 peer-reviewed studies (1980–2025), based on a systematic search protocol, to evaluate satellite-based retrievals of both optically active and inactive parameters. Instead of simply averaging performance, we analyse the empirical distributions of validation metrics, considering the validation protocol, sensor type, parameter category, degree of physics integration, and uncertainty quantification. The synthesis demonstrates that validation strategy often influences reported results more than the algorithm class itself, with accuracy inflated under non-independent cross-validation methods and notable variability between studies concealed by mean-based reports. Across four decades, four persistent structural challenges remain: limited transferability across sites and sensors beyond calibration areas; weak or implicit physical integration in many data-driven models; lack of or inconsistency in uncertainty quantification; and fragmented multi-sensor harmonisation that restricts operational scalability. To address these issues, we introduce two evidence-based coding frameworks: a physics-integration taxonomy (P0–P4) and an uncertainty-quantification hierarchy (U0–U4). Applying these frameworks shows that most studies remain focused on low-to-moderate levels of physics integration and primarily consider uncertainty at the prediction stage, with limited attention to upstream sources throughout the observation and inference process. Building on this structured synthesis, we propose a transferable, physics-informed, and uncertainty-aware conceptual framework that links model architecture, validation robustness, and probabilistic uncertainty to well-founded design principles. By shifting satellite water quality modelling from isolated algorithm demonstrations towards integrated, evidence-based system design, this study promotes scalable, decision-grade environmental monitoring amid the accelerating impacts of climate change. Full article

Drought is one of the major disasters constraining crop production. The accurate identification of the dominant environmental factors that drive drought stress at different growth stages of maize is essential for developing stage-specific and precise water management strategies, enhancing drought resistance, and ensuring food security. However, a key challenge is quantifying the nonlinear interactions among multiple environmental factors. This study focuses on the rain-fed agricultural region of Northwest China. To address the limited availability of drought event samples in this region and the inadequacy of traditional statistical methods in capturing complex inter-factor relationships, we integrate a small-sample modeling framework based on an improved Conditional Generative Adversarial Network (CGAN) with an attribution framework that employs SHapley Additive exPlanations (SHAP) for interpretability analysis. We incorporate ten environmental factors derived from multi-source remote sensing: temperature (T_max, T_min, T_mean), precipitation (P), evapotranspiration (ET), soil moisture at 0–10 cm (SM_0–10) and at 10–40 cm (SM_10–40), and solar-induced chlorophyll fluorescence (SIF_max, SIF_min, SIF_mean). Sample sets were established for different maize phenological stages. The CGAN model was employed to achieve high-precision estimation of maize drought severity levels, while the SHAP method was used to quantitatively analyze the dominant factors and their contributions at each phenological stage. The results show that the CGAN model achieved coefficients of determination (R²) of 0.963, 0.972, and 0.979 for the seedling, jointing–tasseling, and maturity stages, respectively, demonstrating excellent nonlinear modeling capability under small samples. SHAP analysis reveals a clear dynamic evolution of dominant factors across phenological stages. Evapotranspiration (ET) dominated in the seedling stage, reflecting the primary role of surface water–heat balance, while the jointing–tasseling stage transitioned to a co-dominance of ET, topsoil moisture (SM_0–10), and minimum SIF, indicating intensified crop transpiration and physiological stress under the meteorological drought framework, and the maturity stage shifted to an absolute dominance centered on mean temperature (T_mean), highlighting the critical impact of heat stress. This study provides a data-driven quantitative perspective for understanding maize drought mechanisms and offers a scientific basis for formulating differentiated drought management strategies for different growth stages. Furthermore, it demonstrates the potential of integrating CGAN with SHAP for agricultural remote sensing and drought attribution research in data-scarce regions. Full article

Effective management of wastewater discharges requires understanding the spatial distribution of pollutants both within engineered infrastructure and in receiving environments. However, spatial data sparsity constrains comprehensive assessment. This critical review examines the role of Geographic Information Systems (GIS) and statistical interpolation techniques in bridging these data gaps for wastewater-derived pollutants. Moving beyond a simple compilation of methods, this paper provides a synthesizing framework that categorizes and evaluates interpolation techniques-from deterministic and geostatistical approaches to emerging machine learning (ML) and hybrid models- based on their ability to address specific challenges in wastewater systems. A key contribution is a systematic review and meta-analysis following PRISMA guidelines, synthesizing evidence from 22 studies that directly compare interpolation methods for wastewater-relevant parameters (BOD₅, COD, nutrients, heavy metals) in both engineered systems and impacted water bodies. Results indicate that machine learning methods significantly outperform traditional approaches, with a pooled 21% reduction in RMSE compared to Ordinary Kriging (95% CI: 15–27%). However, subgroup analyses reveal context dependency: ML advantages are most pronounced for organic pollutants (29% reduction) and data-rich environments (27% reduction with n > 100), while geostatistical methods remain competitive for physical parameters (8% reduction, non-significant) and data-sparse scenarios (12% reduction with n < 50). Co-Kriging achieves 15% RMSE reduction over Ordinary Kriging when auxiliary variables are available. The review explores applications in pollutant tracking, infrastructure planning, and environmental impact assessment, highlighting how integration of real-time sensor data (IoT) and remote sensing is transforming static maps into dynamic monitoring tools. Finally, a forward-looking research roadmap is presented, emphasizing hybrid modeling frameworks, digital twin integration, and improved uncertainty communication for decision support. By quantitatively synthesizing the current state-of-the-art and identifying critical knowledge gaps, this review aims to guide future research towards more intelligent, adaptive, and reliable spatial assessments of wastewater-derived pollutants. Full article

Water scarcity in semi-arid closed-basin systems is increasingly driven by hydrological and land transformation processes. This study integrates multi-temporal remote sensing and physicochemical data to examine spatio-environmental drivers of surface water decline in Lake Manyara. Normalized Difference Water Index (NDWI) maps derived from dry-season Landsat imagery (July 2015 and July 2025) were used to quantify surface water dynamics, while supervised Maximum Likelihood land use/land cover (LULC) classification provided a characterized existing spatial context of the study area. Physicochemical parameters derived from recent field observations were evaluated using Carlson’s Trophic State Index (TSI). Results indicate a 31.7% reduction in dry-season surface water extent, from 232.4 km² in 2015 to 158.7 km² in 2025, accompanied by a marked spectral shift toward more negative NDWI values, reflecting extensive lakebed exposure. Agricultural expansion and bare land surfaces were spatially associated with stronger negative NDWI patterns (r ≈ −0.64, p < 0.05). Water quality assessment revealed extreme hypereutrophic conditions (TSI = 98.07), characterized by elevated phosphorus, nitrate, and chlorophyll-a, and high ionic concentrations. The findings demonstrate that hydrological contraction, eutrophication, and catchment land transformation are interconnected processes intensifying water scarcity in semi-arid lake systems. Full article

Peri-urban areas (PUAs), as transitional zones between urban and rural regions, play a critical role in supporting food systems and agricultural livelihoods, yet they are increasingly pressured by rapid urban expansion. Reliable spatial delineation of PUAs remains challenging, as administrative boundaries often fail to capture their functional and spatial heterogeneity. This study proposes a multi-dimensional, spatially explicit framework to delineate peri-urban areas using Indonesia as a case study. Eighteen indicators representing six analytical dimensions—land use/land cover, economic, demographic, infrastructural, spatial accessibility, and landscape structure—were derived from remote sensing and GIS-based data sources and integrated into a composite scoring system using equal-weighted and AHP-weighted approaches. The framework was applied to four major cities on Java Island (Jakarta, Surabaya, Bandung, and Yogyakarta) to generate continuous peri-urban probability surfaces, which were validated using expert surveys across 25 districts in the Jakarta and Bandung metropolitan areas. The results show that the framework effectively captures the spatial heterogeneity and gradients of peri-urban areas, with the equal-weighted approach exhibiting statistically significant agreement with expert assessments (Pearson’s r = 0.517, p = 0.008; Spearman’s ρ = 0.522, p = 0.008; Kendall’s τ = 0.387, p = 0.008), consistently outperforming the AHP-weighted model across all validation metrics. The proposed approach provides a transferable spatial mapping framework for monitoring peri-urban dynamics in rapidly urbanizing regions using remote sensing and GIS. Full article

This study develops and validates an integrated framework that combines UAV multispectral imagery and canopy structural metrics with gridded climatic variables to predict soybean (Glycine max (L.) Merrill) foliar potassium (K) status and grain yield. Field experiments were conducted over three consecutive growing seasons (2022–2023, 2023–2024, and 2024–2025) under different potassium fertilisation strategies and environmental conditions. Machine learning models, particularly the random forest algorithm, were applied to multisource datasets, including UAV-derived canopy structural traits (height and canopy area), spectral indices (NDVI), meteorological variables, and fertilisation information. The foliar K prediction models achieved high accuracy (R² up to 0.85), while the yield prediction models achieved R² values between 0.71 and 0.81. The inclusion of the potassium rate and fertilisation strategy further improved model performance, highlighting the strong influence of potassium supply and fertilisation management on plant physiological responses. Interestingly, compared with those required to stabilise grain yield, foliar potassium saturation occurred at substantially higher K₂O rates, indicating the occurrence of luxury potassium uptake. The association of UAV-derived canopy metrics with this pattern suggests that remote sensing may help detect subtle nutritional dynamics that are not directly reflected in yield responses. Model interpretability using SHAP analysis identified relationships within the analysed dataset that were consistent with physiological expectations, with positive contributions associated with canopy vigour and negative contributions associated with thermal stress. In addition, probabilistic SHAP analysis provided a decision-oriented perspective by quantifying yield probabilities under contrasting potassium management regimes and climate scenarios. Overall, within the experimental conditions studied, the proposed framework enabled a rapid assessment of crop nutritional status, yield prediction, and the evaluation of fertilisation strategies. The integration of UAV data, climatic variables, and machine learning provides an interpretable basis for potassium management and soybean yield forecasting within the experimental conditions studied, while broader transferability requires external validation. Full article

Coral reefs around Hainan Island in the northern South China Sea represent a marginal reef system exposed to interacting climatic and anthropogenic stresses. This study used an optimized MaxEnt model, remote-sensing-derived coral reef occurrence data, key environmental variables, and CMIP6 climate projections to assess habitat suitability, identify key environmental thresholds associated with suitability change, and examine areas with potential refugial significance. The optimized model showed high predictive performance (mean AUC = 0.947). Bathymetry was the dominant predictor of habitat suitability, while sea surface temperature (SST) and dissolved oxygen (DO) concentration were also important predictors. Predicted suitability declined markedly when water depth exceeded 8.9 m or when multiannual mean SST exceeded 26.8 °C. Under current climate conditions, suitable habitat was limited in extent and showed strong spatial heterogeneity. Future projections indicated severe habitat contraction under SSP2-4.5 and SSP5-8.5, whereas under SSP1-1.9 suitable habitat contracted sharply by the 2050s but partially re-emerged by the 2090s. Under SSP1-1.9, parts of eastern Hainan, especially the coastal waters of southern Wenchang, Qionghai, and Wanning, may retain refugial potential. These results help clarify future spatial patterns of habitat persistence and decline, providing a scientific reference for regional conservation prioritization and adaptive management. Full article

This study has explored the effectiveness of machine learning methods for high-precision spatial interpolation of meteorological variables, aiming to provide accurate atmospheric delay corrections for high-precision edge and corner nets observation in complex-terrain environments such as the Xiluodu Hydropower Station, thereby enhancing the accuracy of deformation monitoring. Considering the significant limitations of traditional interpolation methods such as Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) in capturing spatial variability under complex topographic conditions, we systematically introduced machine learning algorithms including Random Forest (RF)and eXtreme Gradient Boosting (XGBoost, XGB) to compare their performance with traditional methods for high-density interpolation of sparsely distributed temperature, relative humidity, and surface pressure, respectively. Concurrently, we proposed an enhanced XGB model incorporating center-point features (XGB-C) which frames spatial interpolation as a supervised learning problem that learns physical mapping from synoptic backgrounds to local microclimates instead of relying on geometric distances alone. The interpolation performance indices (RMSE, MAE, and R²) were evaluated with daily meteorological observations from 47 stations (38 for training, 9 for testing) during 2023–2024. Results demonstrate that machine learning methods significantly outperform traditional approaches, with XGB-C achieving the highest accuracy (R² ≈ 1.00 for pressure, 0.97 for humidity, 0.83 for temperature). Moreover, the interpolation performance also exhibits a dependence on seasons and the station location. Greater challenges are shown in the summer season and in the “Urban and Built-Up” and “Croplands” areas. These findings highlight the substantial advantages of machine learning, particularly the proposed XGB-C, for meteorological interpolation in mountainous hydropower station environments where accurate atmospheric correction is crucial for deformation monitoring. This also lays a solid foundation for developing operational ML-based interpolation models trained with high-quality labels derived from unmanned aerial vehicle (UAV) remote sensing data. Full article

In recent decades, forest disturbances have increased in both frequency and intensity, driven by global warming and urbanization. Remote sensing, together with forest disturbance algorithms, offers broad opportunities for forest disturbance monitoring due to its high temporal and spatial resolution. However, operational methods capable of predicting and classifying disturbances while providing official area estimates suitable for national statistics remain scarce. The Three Indices Three Dimensions (3I3D) algorithm has proven effective in identifying forest changes and providing area estimates in Mediterranean ecosystems using Sentinel-2 imagery. Yet, while suitable for change detection, it does not distinguish among disturbance types. Here, we propose a two-step framework for forest disturbance detection and classification, tested in inland Spain for 2018. First, a binary forest change map is produced through an enhanced version of the 3I3D approach. This step incorporates Receiver Operating Characteristic (ROC) analysis to calibrate the algorithm through data-driven threshold selection, allowing adaptation to specific regional conditions. Second, detected changes are classified into four disturbance types: wildfire, clear-cut, thinning, and non-stand replacing disturbance, using Sentinel-2 spectral bands, 3I3D-derived metrics, and geometric descriptors of disturbance patches. Three machine-learning classifiers were compared: Support Vector Machine, Random Forest, and Neural Network. The detection step reached an overall accuracy of 82%, estimating that 1.43% of Spanish forests (264,900 ha) were disturbed in 2018. In the classification step, Random Forest achieved the best performance, with an overall accuracy of 72%. Of the detected disturbed area, 69% corresponded to non-stand replacing disturbances, while the remaining area was classified as thinnings (19%), wildfires (26%), and clear-cuts (55%). By integrating freely available Sentinel-2 imagery, remote sensing algorithms, and photo-interpreted reference datasets, this study provides a scalable and operational approach capable of producing annual disturbance maps that combine both detection and classification of high- and low-intensity disturbances, supporting official national-scale estimates of forest disturbance areas. Full article

Soil salinity and nutrient availability are major constraints affecting crop productivity, soil quality, and agroecosystem sustainability, particularly in coastal regions vulnerable to seawater intrusion. This study provides a comprehensive spatial and temporal assessment of soil properties and quality dynamics on Hainan Island by integrating field observations and multi-temporal remote sensing (RS) datasets. In 2024, a total of 152 sampling sites were surveyed, with three topsoil soil samples collected at each location. Multi-year RS data (2024–2021), including soil salinity reflectance indices (SRSI and SI), the Normalized Difference Vegetation Index (NDVI), and land use and land cover (LULC), were analyzed to evaluate temporal and spatial variability. The soil fertility index was calculated using alkali-hydrolyzed nitrogen (AN), available phosphorus (AP), available potassium (AK), soil pH, and soil organic matter (SOM). The soil quality index was calculated using the same parameters with the addition of chromium (Cr) to account for potential heavy metal contamination. Furthermore, in this study the Inverse Distance Weighting (IDW) method was used for spatial distribution maps of soil properties and other indices. The results indicated that soils were predominantly acidic (pH < 6.0) with generally low electrical conductivity (0.01–0.53 mS cm⁻¹) across inland areas, whereas higher salinity levels (2.28–5.31 mS cm⁻¹) were observed in southern and eastern coastal zones, suggesting potential seawater intrusion. Nutrient concentrations ranged from 60.1 to 150 mg kg⁻¹ (AN), 4 to 332 mg kg⁻¹ (AP), and 50.1 to 100 mg kg⁻¹ (AK). NDVI values (0.70–0.94) indicated high vegetation density over most agricultural landscapes. Significant positive correlations were observed between soil EC and the SRSI (r = 0.781) and SI (r = 0.663; p < 0.01), demonstrating the reliability of RS-derived indices for salinity assessment. The integrated indicator-based framework developed in this study provides a scientific basis for precision agriculture, soil health monitoring, and sustainable land management in coastal agroecosystems. Full article