Search Results (217)

Urban cities have been intensely prone to floods during extreme rainfall events and water scarcity issues during dry periods in recent years. In this context, identifying rainwater harvesting potential (RWHP) regions in urban environments provides a sustainable approach to mitigate both urban flooding and water security, thereby improving urban stormwater management. Geospatial mapping of RWHP has tried to consider various hydrometeorological, topographical and other geospatial datasets, but integrating socio-economic factors over urban environments has not been explored much. The present study integrated remote sensing and hydrological-based information, such as slope, soil type, drainage density, geomorphology, topographic wetness index (TWI), land use land cover (LULC), rainfall, runoff coefficient, proximity to roads, and proximity to settlements for geospatial mapping of RWH potential zones for Hyderabad city using multi-criteria decision analysis (MCDA) and weighted overlay analysis (WOA). The resulting RWH potential map indicates that 80.20% of the area falls within the “low” potential category, 17.53% as “moderate”, 2.0% as “very low”, and only 0.25% as “high” potential, mainly in the southeastern portion near the Hussain Sagar outlet. These categories are spatially verified using Sentinel-2 LULC and Google Earth imagery to assess the qualitative plausibility of the mapped RWH potential zones. Northwestern areas, with loamy soils and mild slopes, demonstrate suitability for rooftop collection and percolation structures, highlighting the effectiveness of the proposed modelling framework for sustainable stormwater management for urban environments. Full article

Maize cultivation in tropical Oxisols during the second growing season faces significant climatic risks, where spatial heterogeneity in soil water retention often dictates economic viability. This study integrated a trimodal sensing approach, combining multispectral, thermal, and LiDAR data, with proximal physiological measurements to map isohydric responses and yield variability. Conducted in the Brazilian Cerrado, the research monitored a one-hectare maize field using UAV-based sensors alongside ground truth evaluations of gas exchange, leaf water potential, and soil moisture. Results revealed high yield variability (6.6 to 13.4 Mg ha⁻¹) primarily governed by clay content-mediated water availability. Maize exhibited strict isohydric behavior, maintaining homeostatic leaf water potential through preventive stomatal closure, which limited CO₂ assimilation in zones with lower water retention. A significant statistical decoupling was observed between plant height and final grain yield, as water stress impacted reproductive stages more severely than vegetative growth. Furthermore, the Temperature Vegetation Dryness Index (TVDI) served as a robust proxy for biomass vigor rather than mere water deficit. These results confirm that yield variability in tropical Oxisols was not a product of hydraulic failure, but rather a consequence of carbon limitation necessitated by the crop’s conservative hydraulic management to maintain leaf water potential within safe thresholds. 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

Timely and accurate soil property information is critical for sustainable agriculture and precision nutrient management. Conventional laboratory methods are accurate but costly and labor-intensive, restricting their feasibility for high-density soil mapping. Low-cost, portable near-infrared (NIR) spectroscopy presents a promising alternative for rapid, on-site, and non-destructive soil analysis. This study aimed to evaluate the potential of a low-cost, portable NIR sensor (NeoSpectra) for the quantitative prediction of key soil properties in paddy fields from Southeastern China. The target properties were soil organic matter (SOM), total nitrogen (TN), pH, and particle size fractions (clay, silt, and sand). A total of 995 soil samples were collected from representative paddy fields in the region and spectra measurements were conducted in the laboratory on air-dried samples. We developed and compared the performance of multiple machine learning algorithms, including partial least squares regression (PLSR), Cubist, random forest (RF) and memory-based learning (MBL), to build robust calibration models. The predictive models showed substantial performance for SOM and TN, indicating high accuracy (R² > 0.75, LCCC > 0.85, RPD > 2) for quantitative prediction. Predictions for pH, silt, sand, and clay were less accurate (R² of 0.48–0.53, LCCC of 0.67–0.71, RPD of 1.39–1.49), suggesting the sensor’s utility is limited to indicating general trends for these properties. Among the tested algorithms, MBL consistently provided the most accurate and robust predictions across the majority of soil properties. Our findings demonstrate that the low-cost portable NIR sensor, when coupled with appropriate machine learning algorithms, is a powerful and viable tool for the rapid and reliable estimation of critical paddy soil fertility properties (SOM and TN). This technology has significant potential to support field-level soil health monitoring, precision fertilization strategies, and sustainable land management in the agricultural systems of Southeastern China. Full article

Agricultural systems are under growing pressure, as soil degradation threatens food security and sustainable land use. Early detection through soil monitoring and precision agriculture is vital to prevent irreversible damage and enable timely conservation. This study evaluates a combined procedure based on electrical resistivity tomography and frequency-domain electromagnetic induction measurements, together with discrete soil sampling, to electrically characterize the soil, identify layers, and map the A horizon depth in a non-disturbing way. This work includes the design and implementation of a mounting electrode system, which reduces the installation time of electrical resistivity tomography surveys by 60% while maintaining data quality. The data were acquired in the oldest long-term agronomic experiment in South America, comprising seven rotation systems with three replicates each, totaling 21 rainfed plots, and representing contrasting management scenarios. Soil A horizon thickness maps of the entire experiment were obtained through two procedures. A comparison between mapping inputs, including all plots and only bare-soil plots, revealed minimal differences in unvegetated areas but notable discrepancies under plant cover, where vegetation increased fluctuations and noise. The present study provides a methodology for accurately assessing the spatial variability of the A horizon thickness by means of proximal sensing techniques. This contributes to the challenge of gathering fundamental soil information in a fast and cost-effective manner, critical for precision agricultura. Full article

Floods represent one of the most frequent and damaging natural hazards in Mediterranean mountain regions, where intense rainfall and complex topography amplify runoff and inundation risk. This study aims to delineate flood-susceptible zones in the Monti Lucretili area of central Italy, an environmentally sensitive and culturally significant landscape that hosts archeological remains and UNESCO listed dry-stone heritage using an integrated Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) approach. Fifteen (15) conditioning factors, including elevation, slope, rainfall, soil, lithology, land use/land cover, drainage density, and proximity to rivers and roads, were derived from open-access satellite remote sensing and spatial datasets. The AHP model produced a flood susceptibility index ranging from 1.806 to 4.465, reclassified into five categories from very low to very high zones. The resulting map indicates that low- and moderate-susceptibility zones dominate the study area, while high and very high classes are primarily concentrated along valleys and drainage corridors. Model validation indicates strong regional-scale predictive performance, with 85.36% of modeled flood-prone areas located within high- to very-high-susceptibility zones and an AUC value of 0.82. Overall, the study highlights the potential of open-access AHP–GIS modeling as a practical screening tool for flood susceptibility assessment and heritage-aware spatial planning in Mediterranean environments. Full article

Sustainable soil–water management under climate and socio-economic pressures requires predictive capability that is both mechanistic and continuously corrected by observations. Data assimilation (DA) provides the formal machinery to merge models with heterogeneous measurements—from satellite evapotranspiration and soil moisture to cosmic-ray neutron sensing, proximal geophysics, lysimeters, and groundwater hydrographs—while propagating uncertainty. This review (based on 90 references) synthesizes frontiers in DA and modeling for soil–water systems across scales, emphasizing (i) multi-source observation operators and scaling; (ii) coupled crop–vadose–groundwater modeling frameworks and their structural hypotheses; (iii) modern DA methods (ensemble, variational, particle-based, and hybrid physics–ML) for joint estimation of states, parameters, and biases; and (iv) emerging digital twins that enable predict-then-verify management loops for irrigation, recharge enhancement, and drought risk reduction. We highlight how tracer-aided and isotope-informed components can improve evapotranspiration partitioning and recharge threshold detection, and how agent-based or socio-hydrological coupling can represent human decision feedback. Finally, we outline research gaps in uncertainty quantification, benchmarking, reproducibility, and governance needed to operationalize trustworthy soil–water digital twins for resilient food and water systems. Full article

This follow-up study investigates the long-term environmental sustainability and remediation outcomes of the UPPER (‘Urban Productive Parks for Sustainable Urban Regeneration’-UIA04-252) project in Latina, Italy, focusing on Nature-Based Solutions (NbS) applied to urban green infrastructure. By integrating proximal and satellite-based remote sensing methodologies, the research evaluates persistent improvements in vegetation health, soil moisture dynamics, and overall environmental quality over multiple years. Building upon the initial monitoring framework, this case study incorporates updated data and refined techniques to quantify temporal changes and assess the ecological performance of NbS interventions. In more detail, ground-based data from meteo-climatic, air quality stations and remote satellite data from the Sentinel-2 mission are adopted. Ground-based measurements such as temperature, humidity, radiation, rainfall intensity, PM10 and PM2.5 are carried out to monitor the overall environmental quality. Updated satellite imagery from Sentinel-2 is analyzed using advanced band ratio indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and the Normalized Difference Moisture Index (NDMI). Comparative temporal analysis revealed consistent enhancements in vegetation health, with NDVI values significantly exceeding baseline levels (NDVI 2022–2024: +0.096, p = 0.024), demonstrating successful vegetation establishment with larger gains in green areas (+27.0%) than parking retrofits (+11.4%, p = 0.041). However, concurrent NDWI decline (−0.066, p = 0.063) indicates increased vegetation water stress despite irrigation infrastructure. NDMI improvements (+0.098, p = 0.016) suggest physiological adaptation through stomatal regulation. Principal Component Analysis (PCA) of meteo-climatic variables reveals temperature as the dominant environmental driver (PC2 loadings > 0.8), with municipality-wide NDVI-temperature correlations of r = −0.87. These multi-scale findings validate sustained NbS effectiveness in enhancing vegetation density and ecosystem services, yet simultaneously expose critical water-limitation trade-offs in Mediterranean semi-arid contexts, necessitating adaptive irrigation management and continued monitoring for long-term urban climate resilience. The integrated monitoring approach underscores the critical role of continuous, multi-scale assessment in ensuring long-term success and adaptive management of NbS-based interventions. Full article

This study presents a geospatial assessment and modeling of the water–energy–food (WEF) nexus to enrich the sustainable paddy cultivation of the North Central Province (NCP) of Sri Lanka in the Dry Zone. Increasing climatic variability and limited resources have raised concerns about the need for efficient resource management to restore food security globally. The study analyzed the three components of the WEF nexus for their synergies and trade-offs using GIS and remote sensing applications. The food productivity potential was derived using the Normalized Difference Vegetation Index (NDVI), Soil Organic Carbon (SOC), soil type, and land use, whereas water availability was assessed using the Normalized Difference Water Index (NDWI), Soil Moisture Index (SMI), and rainfall data. Energy potential was mapped using WorldClim 2.1 datasets on solar radiation and wind speed and the proximity to the national grid. Scenario modeling was conducted through raster overlay analysis to identify zones of WEF constraints and synergies such as low food–low water areas and high energy–low productivity areas. To ensure the accuracy of the created model, Pearson correlation analysis was used to internally validate between hotspot layers (representing extracted data) and scenario layers (representing modeled outputs). The results revealed a strong positive correlation (r = 0.737), a moderate positive correlation for energy (r = 0.582), and a positive correlation for food (r = 0.273). Those values were statistically significant at p > 0.001. These results confirm the internal validity and accuracy of the model. This study further calculated the total greenhouse gas (GHG) emissions from paddy cultivation in NCP as 1,070,800 tCO_2eq yr⁻¹, which results in an emission intensity of 5.35 tCO_2eq ha⁻¹ yr⁻¹, with CH₄ contributing around 89% and N₂O 11%. This highlights the importance of sustainable cultivation in mitigating agricultural emissions that contribute to climate change. Overall, this study demonstrates a robust framework for identifying areas of resource stress or potential synergy under the WEF nexus for policy implementation, to promote climate resilience and sustainable paddy cultivation, to enhance the food security of the country. This model can be adapted to implement similar research work in the future as well. Full article

Bioclimatic monitoring at vineyard scale is essential for irrigation management and disease-risk assessment, yet many systems rely on expensive commercial stations or generic IoT nodes with limited validation and little focus on small and medium-sized winegrowers. This application-driven engineering work investigates whether decision-support-grade bioclimatic data for precision viticulture can be obtained from a low-cost station, by proposing a solar-powered proximal node that integrates soil, plant, and atmospheric sensors on a dedicated PCB that communicates via LoRaWAN. The node operates in a 15-min cycle, with sensing parameters selected to provide the minimum information required for key Precision Viticulture applications. It was deployed in a commercial vineyard side by side with a commercial station, quantifying sensor agreement, communication reliability, and energy consumption. The results show low error rates and consistent agronomic interpretation of environmental conditions, disease risk, precipitation events, and soil and water dynamics. The LoRaWAN link reached a 97% packet-delivery ratio with an average consumption of about 2.5 Wh per day. Material cost is approximately 260 €, one order of magnitude lower than a comparable station. These results indicate that, under real vineyard conditions and compared with a commercial reference, the proposed low-cost system provides agronomically useful, reliable bioclimatic monitoring. Full article

Citrus orchards are especially vulnerable owing to low inter-row vegetation cover, and frequent tillage. Here, we combine controlled field experiments with proximal remote sensing–derived geomorphometric variables and machine learning (ML) to identify key factors of erosion in a Mediterranean climate citrus plantation located close to Seville and the National Park of Doñana (Southern Spain) on Gleyic Regosols (clayic, arenic). We conducted rainfall simulations with 30 s sampling, measured infiltration (mini-disc infiltrometer), saturated hydraulic conductivity (Kfs; Guelph permeameter), compaction (penetrologger), and soil respiration (gas analyzer) at multiple points, and derived high resolution morphometric indices from proximal sensing (UAV-LiDAR). Linear models and Random Forests were trained to explain three responses: soil loss, sediment concentration (SC), and runoff. Results show that soil loss is most strongly associated with maximum compaction and Kfs (multiple regression: R² = 0.68; adjusted R² = 0.52; p = 0.063), while SC increases with surface compaction and exhibits weak relationships with topographic metrics. Runoff decreases with average infiltration, which is related to compaction (β = −4.83 ± 2.38; R² = 0.34; p = 0.077). Diagnostic checks indicate centered residuals with mild heteroscedasticity and a few high leverage observations. Random Forests captured part of the variance for soil loss (≈29%) but performed poorly for runoff, consistent with limited sample size and modest nonlinear signal. Morphometric analysis revealed gentle relief but pronounced convergent–divergent patterns that modulate hydrological connectivity. There were strong differences in the experiments conducted close to the trees and in the tractor trails. We conclude that compaction and near surface hydraulic properties are the most influential and measurable controls of erosion at plot scale and the UAV-LiDAR could not give us extra-insights. We highlight that integrating standardized field protocols with proximal morphometrics and ML can be the best method to prioritize a small set of explanatory variables, helping to reduce experimental effort while maintaining explanatory power. Full article

Real-time, in situ soil diagnostics are increasingly relevant for precision agriculture, but their efficacy under varying field and climatic conditions remains underexplored. This study assesses the 2022/23 version of the Stenon FarmLab, a multi-sensor soil analysis tool, over a 10-month period and across 1187 measurements on six fields (five cropped, one grassland) in northeast Germany. Despite the common approach of comparing a field sensor against lab results, in this paper, the FarmLab’s outputs are benchmarked using various approaches, such as time series, correlation, and geostatistical analysis, to fully evaluate the temporal and spatial stability and alignment with known soil heterogeneity. While physical soil parameters such as temperature and soil texture showed robust detection accuracy, key agronomic metrics—including mineral nitrogen (Nmin), soil organic carbon (SOC), and phosphorus—exhibited poor temporal consistency and low correlation with expected spatial patterns. Measurement errors and high sensitivity to weather conditions restrict data quality, particularly under frost and drought. Spatial clustering of more temporally stable parameters (e.g., pH, soil texture) allowed for limited zone delineation. We conclude that while the FarmLab shows partial potential for on-site soil sensing, significant limitations in nutrient measurement reliability currently prevent its use in operational precision agriculture. Enhancements in sensor calibration, environmental compensation, and software are needed for broader applicability. Full article

Soil salinity severely constrains agricultural productivity and soil health, particularly in arid and semi-arid regions. Conventional salinity assessment methods are labor-intensive, time-consuming, and spatially limited. This study developed a data-scarce workflow integrating proximal sensing (EM38-MK2), very high-resolution multispectral imagery, and machine learning to map soil salinity at field scale in the semi-arid Sehb El Masjoune area, central Morocco. A total of 26 soil samples were analyzed for Electrical Conductivity (EC), and 500 Apparent Electrical Conductivity (ECa) measurements were collected and calibrated using the field samples. Spectral and topographic covariates derived from Unmanned Aerial Vehicle (UAV) and PlanetScope imagery supported model training using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and a Stacked Ensemble Learning Model (ELM). Regression Kriging (RK) was applied to model residuals to improve spatial prediction. ELM achieved the highest accuracy (R² = 0.87, RMSE ≈ 4.15), followed by RF, which effectively captured nonlinear spatial patterns. RK improved PLSR accuracy (by 11.1% for PlanetScope, 13.8% for UAV) but offered limited gains for RF, SVR, and ELM. SHAP analysis identified topographic covariates as the most influential predictors. Both UAV and PlanetScope delineated similar saline–sodic zones. The study demonstrates the following: (1) a scalable, data-efficient workflow for salinity mapping; (2) model and RK performance depend more on algorithmic design than sensor type; (3) interpretable ML and spatial modeling enhance understanding of salinity processes in semi-arid systems. Full article

The advent of hyperspectral remote sensing represented a breakthrough in the accurate, fast, and non-invasive estimation of important soil fertility parameters. The present study utilizes non-imaging hyperspectral data in the spectral range of 350–2500 nm for estimating soil organic carbon (SOC) content. When partial least squares (PLS) scores were taken as independent variables, support vector machine (SVM) outperformed artificial neural network (ANN) and partial least squares regression (PLSR), achieving an R² value of 0.83. After pre-processing, the proximal spectral values were spatially interpolated to construct a synthetic hyperspectral image of the experimental fields. By applying the regression model to this synthetic hyperspectral imagery, a high-resolution SOC map showing the variability of organic carbon content in the soil was generated. Full article

Effective reservoir operation demands a careful balance between flood risk mitigation, water supply reliability, and operational stability, particularly under evolving hydrological conditions. This study applies deep reinforcement learning (DRL) models—Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Deep Deterministic Policy Gradient (DDPG)—to optimize reservoir operations at the Soyang River Dam, South Korea, using 30 years of daily hydrometeorological data (1993–2022). The DRL framework integrates observed and remotely sensed variables such as precipitation, temperature, and soil moisture to guide adaptive storage decisions. Discharge is computed via mass balance, preserving inflow while optimizing system responses. Performance is evaluated using cumulative reward, action stability, and counts of total capacity and flood control violations. PPO achieved the highest cumulative reward and the most stable actions but incurred six flood control violations; DQN recorded one flood control violation, reflecting larger buffers and strong flood control compliance; DDPG provided smooth, intermediate responses with one violation. No model exceeded the total storage capacity. Analyses show a consistent pattern: retain on the rise, moderate the crest, and release on the recession to keep Flood Risk (FR) < 0. During high-inflow days, DRL optimization outperformed observed operation by increasing storage buffers and typically reducing peak discharge, thereby mitigating flood risk. Full article