Search Results (484)

Farmland shelterbelts are important linear vegetation infrastructures in oasis agricultural landscapes. Their accurate extraction is essential for shelterbelt inventory and farmland management, but remains challenging because shelterbelts are narrow, elongated, locally discontinuous, and spectrally similar to croplands, orchards, roadside vegetation, bare soil, and irrigation-related features. This study developed a GF-2-based deep learning workflow for farmland shelterbelt extraction in the 11th Regiment of Alar City, Xinjiang, China. Four representative semantic segmentation models, namely U-Net, U-Net with scSE attention, U-Net++, and DeepLabV3+, were trained and evaluated using four-band GF-2 optical imagery under a unified experimental setting. Model performance was assessed using Precision, Recall, F1-score, Intersection over Union (IoU), overall accuracy, and Kappa coefficient. Patch-level statistical comparison and visual interpretation were further conducted to examine performance differences, shelterbelt continuity, boundary integrity, omission errors, and background confusion. The results showed that U-Net achieved the best overall performance, with a Precision of 94.58%, Recall of 94.77%, F1-score of 94.67%, IoU of 89.88%, overall accuracy of 99.71%, and Kappa coefficient of 0.9452. Compared with U-Net with scSE attention, U-Net++, and DeepLabV3+, U-Net better preserved the continuity and boundary integrity of narrow shelterbelts in regular field-boundary networks. The other models showed varying degrees of omission, boundary fragmentation, or confusion with spectrally similar agricultural objects. The best-performing U-Net model was then applied to the complete study area, and the extracted shelterbelt area was approximately 6.5 km², accounting for about 4.37% of the cultivated land area. These results indicate that GF-2 optical imagery combined with semantic segmentation can support fine-scale farmland shelterbelt mapping in oasis agricultural landscapes. They also show that model evaluation for narrow linear vegetation features should consider not only pixel-level accuracy but also spatial continuity, boundary integrity, and typical error patterns. The proposed workflow provides a practical reference for GF-2-based farmland shelterbelt inventory, high-resolution linear vegetation mapping, and shelterbelt monitoring in arid oasis agricultural landscapes. Full article

Groundwater depth is a key control on soil salinization in arid irrigation districts, but seasonal groundwater depth thresholds for different salinization risks remain poorly constrained. Here, we examined the Yichang irrigation area of the Hetao Irrigation District using groundwater depth observations and 0–60 cm soil salinity samples collected before spring irrigation and during the crop-growing season. Indicator Kriging was used to map threshold-based probability zones for groundwater depth and soil salinity in the 0–60 cm root zone; soil salinity thresholds of 2 and 3 g kg⁻¹ represented light-or-higher and moderate-or-higher salinization, respectively, and high-probability matching rates were used to identify the corresponding critical groundwater depths. Groundwater depth showed moderate spatial variability in both seasons, whereas soil salt content showed strong spatial variability. The critical groundwater depths for light and moderate salinization were 2.6 and 2.2 m before spring irrigation and 2.2 and 1.8 m during the growing season, respectively. Soil salt content in the 0–20, 20–40, 40–60 and 0–60 cm layers decreased with increasing groundwater depth, with the 0–60 cm layer well described by an exponential response function. These findings provide a spatially explicit basis for seasonal groundwater regulation, salinization risk zoning and field-scale water–salt management in arid irrigation districts. Full article

Mediterranean agriculture is increasingly constrained by severe and recurrent water scarcity. Rising temperatures, declining precipitation, and more frequent droughts are driving higher crop water demand and reducing rainfall reliability. Combined with growing competition for limited freshwater resources, these climatic pressures pose a major challenge to the long-term sustainability and productivity of farming systems in the region. In this context, urban treated wastewater (UTWW) is increasingly recognized as a strategic alternative resource. Regulation (EU) 2020/741 establishes minimum quality requirements for agricultural water reuse based on the fit-for-purpose principle. The Italian regulatory framework requires regional authorities to plan water reuse by mapping reclamation facilities, water demand, and distribution networks. From this perspective, to support the planning process, this study describes the application of a Geographic Information System (GIS)-based methodology to assess the potential for UTWW reuse in Sicily by integrating national datasets (regularly updated) and comparing reclaimed water supply with irrigation demand from both qualitative and quantitative perspectives. UTWW supply was estimated by integrating data from the Italian National Institute of Statistics (ISTAT) Census of Water for Civil Use and the European Environment Agency (EEA) dataset, while irrigation demand was derived by combining Copernicus crop data, ISTAT irrigated-to-total UAA ratios and crop-specific irrigation demand (Seasonal Specific Volumes) derived from the SIGRIAN database. Two scenarios were analyzed for grasslands, vineyards, olive groves, and orchards. Scenario 1 included only wastewater treatment plants (WWTPs) located inside irrigation areas, whereas Scenario 2 also included selected WWTPs located outside irrigation areas where orographic and distance constraints allowed potentially feasible connections. Under Regulation (EU) 2020/741, potential compatibility with the required water quality classes was identified at the screening level for all crop categories considered, which require classes B, C or D. From a quantitative perspective, Scenario 1 identified 19 WWTPs serving 11 out of 38 irrigation areas with an aggregated uncapped demand coverage of 53%; only two areas reached full local demand satisfaction. Scenario 2 added 23 external WWTPs, increased potentially served areas to 20, and raised the aggregate uncapped coverage to 73%. When surplus volumes were capped within each irrigation area, effective demand satisfaction was 12% and 22% in Scenarios 1 and 2, respectively, highlighting the importance of storage, conveyance and inter-area transfer. The results indicate that treated wastewater reuse could contribute to agricultural water security and climate-resilient water management in Mediterranean regions. The framework supports the first-order identification of candidate reuse areas, but site-specific assessments of effluent quality, risk management, costs, and seasonal storage remain necessary before implementation. Full article

Agricultural drought threatens rainfed agriculture in northeast Thailand, where variable monsoon rainfall, limited irrigation access, and extensive cropland increase vulnerability. This study developed a Google Earth Engine-based Agricultural Drought Risk Index (ADRI) for the Chi River Basin using six benchmark years (2000, 2005, 2010, 2015, 2020, and 2025). CHIRPS precipitation, MODIS-derived vegetation health, ERA5-Land soil moisture, irrigation accessibility, and agricultural land exposure were normalized and integrated by weighted linear combination. The analysis quantified risk-class areas, irrigated–rainfed contrasts, persistent hotspots, weight sensitivity, and spatial agreement with the official Land Development Department recurring-drought map. Moderate risk dominated most years, but high-risk area expanded to 60.7% in 2015, coincident with severe rainfall deficits during the 2015–2016 El Niño event. Conditions improved in 2020 and 2025 as rainfall, vegetation health, and soil moisture recovered. Rainfed areas consistently had higher ADRI values than irrigated areas, and persistent hotspots were concentrated in southeastern and downstream agricultural zones. The principal spatial and temporal patterns remained stable under ±10% weight perturbations. External validation identified ADRI > 2.90 as the optimal threshold, with raster-level precision, recall, and F1 of 0.779, 0.884, and 0.828, respectively; the 998-point sample produced an F1 of 0.832. ADRI therefore provides a practical basin-scale screening framework for drought monitoring, adaptation prioritization, and agricultural water-management planning. Full article

Climate change, increasing aridification, and water scarcity are intensifying the vulnerability of irrigated agroecosystems in Central Asia to drought and secondary soil salinization. In this study, we assessed the spatio-temporal vulnerability of irrigated land in the Turkestan Region (Kazakhstan) using SPEI-3, NDVI, NDSI, Landsat imagery (2000–2025), spatial analysis, and copula modeling. A strong positive relationship was found between SPEI-3 and NDVI (Pearson’s r = 0.861; Spearman’s r = 0.851), indicating vegetation is highly sensitive to moisture availability. Moderate and severe droughts reduced NDVI values by 15–25%, exceeding 30% in the most vulnerable areas, while persistent salinity hotspots were identified in the southern and southeastern irrigated zones. Copula modeling quantified the relationship between SPEI-3 and NDVI using 23 paired annual observations (2000–2022). The best-fitting model, selected using the Akaike Information Criterion (AIC = −31.074) and Bayesian Information Criterion (BIC = −29.939), revealed a nonlinear and asymmetric dependence between drought conditions and vegetation response. The probability of concurrent drought and vegetation degradation reached 0.55–0.70 in the most vulnerable areas. Integrated vulnerability mapping identified the Maktaaral, Zhetysay, Shardara, and Otyrar districts as the most vulnerable territories. The proposed framework supports drought vulnerability assessment and sustainable management of irrigated agroecosystems under climate change. Full article

Accurate vineyard mapping is important for agricultural resource monitoring, land-use management, and inventory updating in arid regions. However, vineyard identification in Xinjiang, China, is challenged by fragmented parcels, exposed soil backgrounds, irrigation-driven heterogeneity, and strong accumulated-temperature gradients. This study developed a multi-source remote-sensing framework for vineyard mapping and inventory updating by integrating Sentinel-1/2 data, terrain variables, growing-degree-day-derived agrothermal zones (ATZs), and seasonal-difference features. RF, LightGBM, XGBoost, 1D-CNN, and a Stacking ensemble were evaluated using polygon-level in-distribution testing and Leave-One-ATZ-Out cross-zone validation. The in-distribution test was used to assess vineyard separability under similar sample distributions, whereas cross-ATZ validation was used to evaluate model transferability across heterogeneous thermal domains. Tree-based models and Stacking achieved near-ceiling performance under the in-distribution setting, but cross-ATZ validation revealed substantial performance degradation, indicating that conventional local validation can overestimate operational transferability. RF achieved the highest mean cross-zone F1-score, while Stacking achieved the highest cross-zone AP and Recall and provided a flexible probability surface for thresholding, mosaicking, vector post-processing, and patch-level mapping. In Gaochang District, the Stacking-derived result identified 22,998.84 ha of potential vineyard area, achieved an Area Recall of 0.7908 against the historical inventory, and covered 88.03% of existing parcels at ≥30% spatial overlap. The workflow also identified 1356 candidate vineyard patches for inventory updating covering 2502.32 ha for subsequent inventory verification. These results demonstrate that multi-source feature integration and probability-based ensemble mapping can support vineyard mapping and inventory updating in arid regions, while cross-zone validation is essential for assessing operational generalization. Full article

Accurately acquiring soil moisture content (SMC) and its spatial distribution is of great significance for water-saving irrigation and sustainable agricultural development in arid regions. However, the complex soil background noise and weak moisture absorption features in the Xinjiang region severely restrict the accuracy and reliability of remote sensing inversion for SMC. To address the challenges of difficult feature capture and low estimation accuracy in soil moisture monitoring, this study utilized GF-5 satellite hyperspectral data and ground-measured SMC data. First, the effects of Fractional-Order Differentiation (FOD) at orders 0–2 (with a 0.2 step size) on spectral moisture response were systematically evaluated. Next, 60 dual-band spectral indices (DBIs) were constructed from full-band combinations under the optimal differentiation orders, and highly correlated indices were selected as candidate features. Finally, three variable screening methods were coupled with three machine learning models to construct nine SMC inversion schemes, and the optimal model combination was employed to map the spatial distribution of SMC in the study area. Results showed that FOD at orders 0.8–1.2 effectively enhanced spectral responses at soil moisture absorption bands, the introduction of DBI concentrated high-correlation band combinations in moisture-sensitive regions, and the optimal scheme (BSS-PLSR) demonstrated good predictive performance and stability. These findings provide data support for precision irrigation decision-making and soil moisture management in arid farmlands. Full article

Groundwater is a principal irrigation water source worldwide; however, its quality is increasingly diminished by rapid urbanization, improper agricultural practices, and accelerating industrial activities. Groundwater management is especially important in areas where soil salinization and erosion are more probable, such as arid and semi-arid zones. In view of this, the Armavir region of the Republic of Armenia was selected as the study area, being an intensively cultivated agricultural zone. The objective of this study was to assess and map the quality of groundwater for irrigation using advanced methods, taking into account both climatic conditions and anthropogenic influences. A total of 72 groundwater samples were collected during the irrigation season from 41 unconfined and 31 confined aquifer wells. Key hydrochemical parameters (pH, EC, TDS, Cl⁻, HCO₃⁻, CO₃²⁻, Na⁺, K⁺, Ca²⁺ and Mg²⁺), irrigation indices (SAR, Na%, MH, RSC and PI), and graphical methods (Gibbs, USSL and Wilcox diagrams) were applied to assess groundwater quality. An integrated assessment was performed using the Irrigation Water Quality Index (IWQI), and spatial distribution was evaluated through geostatistical analysis and GIS mapping. Although certain individual hydrochemical parameters indicated limitations for irrigation in localized areas, particularly within the unconfined aquifer, the integrated IWQI assessment revealed that groundwater predominantly falls within the good to excellent categories across the study area, with more favorable conditions observed in the confined aquifer. These findings constitute an essential prerequisite for counteracting soil salinization and promoting sustainable agricultural development. Full article

Climate variability represents a major threat to agricultural systems, particularly in arid and semi-arid regions such as the Beni-Amir irrigated perimeter, located in the Tadla plain in central Morocco. In this perimeter, olive trees are exposed to multiple environmental and management-related factors that are associated with variations in phenology and vegetation vigor, such as successive drought episodes. This study represents a spatio-temporal assessment of drought impact on olive using satellite- derived vegetation indices from Sentinel-2 imagery and precipitation satellite data from CHIRPS over the period 2015–2024. The Standardized Precipitation Index (SPI-12) was used to identify wet and dry phases over this period. The results indicate an alternation of dry and wet periods between 2015 and 2021, followed by a predominance of dry conditions from September 2021. Over the same period, the time series of the Normalized Difference Vegetation Index (NDVI) and the other vegetation indices reveals marked interannual variability and a progressive degradation of olive tree phenological cycles. A land cover map derived from a supervised support vector machine (SVM) under three classification scenarios achieved high overall accuracies exceeding 94%. Post-classification change detection highlights a substantial reduction in mapped olive-growing areas between 2016 and 2024, with an estimated 72% loss of the initial area. The findings reported in this study indicate that the succession of drought episodes may have contributed to olive grove degradation, including disruptions in phenological cycles and a decline in maximum NDVI values. Even the most resilient olive groves appeared affected following the severe drought period after 2021. The study underscores the usefulness of satellite-derived vegetation indices and drought indicators for the effective monitoring of drought-related stress and supporting improved management practices under climate change. Full article

Soil salinization limits agricultural production and the management of soil and water resources in arid irrigated regions. Regional prediction remains difficult, while soil salinity is influenced by hydrology, topography, and land cover. This study developed an explainable automated machine learning (AutoML) framework to map surface soil salinity in the Qingtongxia Irrigation District, Ningxia, China. The analysis combined 108 soil samples (0–10 cm), collected in March-April 2024, with 36 candidate input features from Sentinel-2 spectral indices, topography, soil texture, groundwater depth, and climate. Pearson correlation analysis and recursive feature elimination with cross-validation (RFECV) selected 10 key input features. Among the tested models, AutoML achieved the best validation performance, with R² = 0.78, MAE = 1.94 g/kg, and RMSE = 2.65 g/kg. The resulting 30 m prediction map captured broad regional patterns of soil salinity, with predicted values from 0.34 to 22.91 g/kg and higher salinity in northern and northeastern areas. Shapley additive explanations (SHAP) analysis highlighted brightness index, groundwater depth, clay content, elevation, and salinity index I as influential features. These findings suggest that explainable AutoML can help identify regional salinity risk and guide future sampling and model refinement. Full article

Groundwater is one of the basic requirements for life, economic and social developments in the Bazo River catchment, Rift Valley, Southern Ethiopia. In the study area, availability of water is faced with several problems, such as quality issues due to high levels of fluoride in some samples, spring scarcity in the lowlands, unprotected river water used for drinking, and high demand for good quality water. The aim of this study was to investigate the hydrogeochemical characteristics and to evaluate groundwater quality for domestic and irrigation uses. Thirty-four primary groundwater samples were collected from the field and analyzed in the water quality lab of Arba Minch University. Two water quality indices (WQI and EWQI), a variety of irrigation water quality indices, and GIS-based spatial analysis were utilized in this study. Cations were present in the descending order of Na > Ca > Mg > K > Fe, and anions were HCO₃⁻ > Cl > SO₄ > NO₃⁻ > F. Excepting two samples (BH8 and SP3), the water samples were acceptable for drinking. Sodium, TDS, and fluoride levels were over the limit of drinking water in BH8 and SP3. Rock–water interaction, cation exchange, and silicate mineral weathering were the main hydrogeochemical reactions that controlled groundwater composition in the area, based on Gibb’s diagram, chloro-alkaline indices, and major ions ratios. Groundwater facies were identified as Ca.HCO₃, Na.HCO₃ and mixed Ca-Na/Ca.Mg.Na.HCO₃ types using a Piper plot. The water quality index was computed, and its spatial variations were mapped using GIS. About 82.35% of groundwater samples were excellent for drinking use and 94.12% (SAR) of groundwater were acceptable for irrigation. These study results are useful to help develop inclusive strategies and interventions to address groundwater quality aspects in the study area, underlining the significance of managing and monitoring water resources. The findings underscore the need for effective management and monitoring strategies to ensure sustainable groundwater resources in the Bazo River catchment. Full article

Accurate high-resolution mapping of soil organic carbon (SOC) is essential for agricultural management and carbon pool assessment in arid lakeside oases, a fragile aquatic-terrestrial transition ecosystem. However, targeted high-precision SOC mapping for typical lakeside oases remains insufficient: existing models have poor adaptability to the highly fragmented oasis landscapes, and fine-resolution SOC spatial products for the representative Bosten Lake oasis are lacking. To address this inadequacy, we integrated Sentinel-2 imagery with topographic, bioclimatic, and spectral environmental covariates and developed four machine learning models (Random Forest, XGBoost, SVR with RBF kernel, Cubist) for SOC prediction, based on 153 topsoil samples (0–20 cm) collected via stratified random sampling in the study area. Model performance was validated through 5-fold cross-validation, the optimal model was selected for 10 m resolution SOC mapping, and dominant driving factors were identified via SHAP analysis. The results showed that SOC content in the study area ranged from 2.37 to 20.63 g·kg⁻¹ (mean = 10.59 g·kg⁻¹), with moderate spatial variability (CV = 34.86%). The Cubist model achieved the highest mapping accuracy (R² = 0.8166, RMSE = 1.5812 g·kg⁻¹, MAE = 0.9247 g·kg⁻¹). The generated high-resolution SOC map clearly revealed a spatial pattern of high values in the eastern well-irrigated cropland and low values in bare and salinized areas at the oasis edge. The Bare Soil Index (BSI), surface roughness, and Normalized Difference Red Edge Index 1 (NDRE1) were the dominant factors controlling SOC spatial distribution. This study mitigates the inadequacy of high-precision SOC mapping in typical arid lakeside oases, and the proposed framework is readily applicable to other fragmented arid landscapes worldwide and provides reliable spatial data and a scalable technical framework for precision agriculture and sustainable land management in similar fragile ecosystems. Full article

Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R² of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R² increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments. Full article

The middle reaches of the Minjiang River, shaped by the Dujiangyan irrigation system, provide a typical setting for studying long-term human–water interactions. During the Little Ice Age, the water management system as a whole experienced a full cycle of recovery, expansion, and decline from 1644 to 1949 (Qing to Republican period), although subregions exhibited marked spatial heterogeneity. This heterogeneity makes the area an ideal case for comparative analysis; however, previous studies have neither quantitatively reconstructed river–canal changes nor systematically disentangled the composite natural and anthropogenic drivers across different subregions. Using archival documents, historical maps, remote sensing imagery, and water cultural heritage sites, this study reconstructs the evolution and quantifies two change types: anthropogenic construction, including new construction, reconstruction, and modification, and environmentally driven changes such as rerouting, damage, and maintenance. Correlations were analyzed among the four subregions: Inner River, Outer River, Nanhe River, and the Lower Basin to identify driving mechanisms. Results indicate that anthropogenic construction is constrained by natural conditions and driven by population growth, whereas environmentally driven changes are primarily caused by floods and worsened by canal head maintenance failure. The four spatially differentiated driving patterns are: Inner River—human-dominated intervention type; Outer River—flood stress type; Nanhe River—low-disturbance stable type; and Lower Basin—natural–human composite type. This study offers new insights into long-term human–water interactions in large irrigation districts under climate change. Full article

The purpose of this work is to investigate the use of high-resolution ($~100$ m) surface soil moisture (SSM) maps derived from Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify irrigation events occurring in the Riaza irrigation district (Castilla y León region, Spain) from 2017 to 2021. The proposed method is based on the application of the Constant False Alarm Rate (CFAR) algorithm, which is an adaptive and unsupervised thresholding algorithm traditionally used for target detection in SAR images. This algorithm uses a sliding window approach that allows an adaptive threshold estimate for each pixel of the image, depending on the distribution of the surrounding pixels. The analysis was carried out on fields cultivated with maize, sugar beet and sunflower. Results show that the Overall Accuracy (OA) of the detection mainly depends on the time span (TS) between the S-1 passage and the irrigation event, the acquisition timing and the development stage of the vegetation. Indeed, the OA reaches a mean of $78\%$ and $70\%$, respectively, for the $6$ a.m. and $6$ p.m. acquisitions, when the irrigation events occur within $36$ h before the S-1 passage, and it follows a downward trend as the TS increases. On the other hand, when the vegetation reaches the mature stage, the mean OA decreases respectively to $56\%$ and $52\%$. Stemming from the event detection, the study explored the estimation of the total irrigated area in the early growing season, showing promising agreement with in situ data, as evidenced by the low Relative Error ($E_r\approx 5.6\%$). Additionally, the analysis revealed a significant correlation between field-scale mean SSM and irrigation depths ($R=0.89$). Full article