Search Results (595)

Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide reliable global SM and VOD products, they are limited by coarse spatial resolution and infrequent revisit times. Global Navigation Satellite System Reflectometry (GNSS-R) observations, particularly from the Cyclone GNSS (CYGNSS) mission, offer an improved spatiotemporal sampling rate. This study presents a deep learning framework based on an artificial neural network (ANN) for the simultaneous retrieval of SM and VOD from CYGNSS observations across the contiguous United States (CONUS). Ancillary input features, including specular point latitude and longitude (for spatial context), CYGNSS reflectivity and incidence angle (for surface signal characterization), total precipitation and soil temperature (for hydrological context), and soil clay content and surface roughness (for soil properties), are used to improve the estimates. Results demonstrate strong agreement between the predicted and reference values (SMAP SM and SMOS VOD), achieving correlation coefficients of R = 0.83 and 0.89 and RMSE values of 0.063 m³/m³ and 0.088 for SM and VOD, respectively. Temporal analyses show that the ANN accurately reproduces both seasonal and daily variations in SMAP SM and SMOS VOD (R ≈ 0.89). Moreover, the predicted SM and VOD maps show strong agreement with the reference SM and VOD maps (R ≈ 0.93). Additionally, ANN-derived VOD demonstrates strong consistency with above-ground biomass (R ≈ 0.77), canopy height (R ≈ 0.95), leaf area index (R = 96), and vegetation water content (R ≈ 0.90). These results demonstrate the generalizability of the approach and its applicability to broader environmental sensing tasks. Full article

Drought is a pervasive and intensifying climate hazard with profound implications for food security, water availability, and socioeconomic stability, particularly in sub-Saharan Africa. In Kenya, where over 80% of the landmass comprises arid and semi-arid lands (ASALs), recurrent droughts have become a critical threat to agricultural productivity and climate resilience. This study presents a comprehensive spatiotemporal analysis of seasonal drought dynamics in Kenya for June–July–August–September (JJAS) from 2000 to 2024, leveraging remote sensing-based drought indices and geospatial analysis for climate risk planning. Using the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Soil Moisture Anomaly (SMA), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomaly, a Combined Drought Indicator (CDI) was developed to assess drought severity, persistence, and impact across Kenya’s four climatological seasons. Data were processed using Google Earth Engine and visualized through GIS platforms to produce high-resolution drought maps disaggregated by county and land-use class. The results revealed a marked intensification of drought conditions, with Alert and Warning classifications expanding significantly in ASALs, particularly in Garissa, Kitui, Marsabit, and Tana River. The drought persistence analysis revealed chronic exposure in drought conditions in northeastern and southeastern counties, while cropland exposure increased by over 100% while rangeland vulnerability rose nearly 56-fold. Population exposure to drought also rose sharply, underscoring the socioeconomic risks associated with climate-induced water stress. The study provides an operational framework for integrating remote sensing into early warning systems and policy planning, aligning with global climate adaptation goals and national resilience strategies. The findings advocate for proactive, data-driven drought management and localized adaptation interventions in Kenya’s most vulnerable regions. 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

Accurate prediction of soil texture is essential for effective soil management, precision agriculture, and hydrological modeling. This study proposes a novel, data-driven approach for estimating soil texture without the need for laboratory-based analysis. High-frequency in situ soil moisture measurements from EnviroSCAN (Sentek Technologies, Stepney, Australia) sensors and satellite-derived vegetation indices (NDVI) from Sentinel-2 were collected across 25 sites in Hungary. Temporal soil moisture dynamics were encoded using a Long Short-Term Memory (LSTM) neural network, designed to capture soil-specific hydrological response behavior from time-series data. The resulting latent embeddings were subsequently used within an ordinal regression framework to predict ordered soil texture classes, explicitly enforcing physical consistency between classes. Model performance was evaluated using leave-one-soil-out cross-validation, achieving an overall classification accuracy of 0.54 and a mean absolute error (MAE) of 0.50, indicating predominantly adjacent-class errors. The proposed approach demonstrates that soil texture can be inferred from dynamic environmental responses alone, offering a transferable alternative to fraction-based regression models and supporting scalable sensor calibration and digital soil mapping in data-scarce regions. Full article

Analog electronics, i.e., circuits that process continuously varying signals, have quietly powered the backbone of agricultural automation long before the advent of modern digital technologies. Yet, the accelerating focus on digitalization, IoT, and AI in precision agriculture has largely overshadowed the enduring, indispensable role of analog components in sensing, signal conditioning, power conversion, and actuation. This paper provides a comprehensive state-of-the-art review of analog electronics applied to agricultural systems. It revisits historical milestones, from early electroculture and soil-moisture instrumentation to modern analog front-ends for biosensing and analog electronics for alternatives source of energy and weed control. Emphasis is placed on how analog electronics enable real-time, low-latency, and energy-efficient interfacing with the physical world, a necessity in farming contexts where ruggedness, simplicity, and autonomy prevail. By mapping the trajectory from electroculture experiments of the 18th-century to 21st-century transimpedance amplifiers, analog sensor nodes, and low-noise instrumentation amplifiers in agri-robots, this work argues that the true technological revolution in agriculture is not purely digital but lies in the symbiosis of analog physics and biological processes. Full article

As one of the world’s most extensive and fragile ecosystems, East Asian karst regions are pivotal for carbon sustainability, yet they are exhibiting starkly divergent responses to environmental pressures. While Southwest China has undergone extensive, policy-driven ecological restoration, many parts of the Association of Southeast Asian Nations (ASEAN) region face severe degradation from unregulated agricultural expansion. To understand the underlying drivers of this divergence, this study conducts a comprehensive comparative analysis of the carbon–water trade-offs in these contiguous karst areas from 2000 to 2023. We identify two dominant eco-functional profiles: a “stable carbon sink–moderate water consumption” pattern in Southwest China (15.38% of the area) and a “potentially unstable carbon sink–high water consumption” pattern widespread in ASEAN (24.00%). By integrating the carbon–water risk zoning with MSPA and MCR models, we identified key ecological sources and corridors to map the regional ecological security pattern. The results show high-risk zones (e.g., eastern Myanmar) not only align with fragmented ecological corridors but also exacerbate structural connectivity loss. This approach innovatively links metabolic risks to landscape resilience. Importantly, we found threat drivers differ in the two areas: atmospheric drought (VPD) has become the dominant constraint in ASEAN and soil moisture deficit in the Southwest China. These findings offer a spatially explicit framework for targeted governance and caution against transferring restoration strategies between divergent ecohydrological contexts. Full article

Surface soil moisture (SSM) is essential for crop growth, irrigation management, and drought monitoring. However, conventional field-based measurements offer limited spatial and temporal coverage, making it difficult to capture environmental variability at scale. This study introduces a multimodal soil moisture estimation framework that combines synthetic aperture radar (SAR), optical imagery, vegetation indices, digital elevation models (DEM), meteorological data, and spatio-temporal metadata. To strengthen model performance and adaptability, an intermediate fine-tuning strategy is applied to two datasets comprising 10,571 images and 3772 samples. This approach improves generalization and transferability across regions. The framework is evaluated across diverse agro-ecological zones, including farmlands, alpine grasslands, and environmentally fragile areas, and benchmarked against single-modality methods. Results with RMSE 4.5834% and $R^2$ 0.8956 show consistently high accuracy and stability, enabling the production of reliable field-scale soil moisture maps. By addressing the spatial and temporal challenges of soil monitoring, this framework provides essential information for precision irrigation. It supports site-specific water management, promotes efficient water use, and enhances drought resilience at both farm and regional scales. Full article

Uniform application of fertilizers and pesticides continues to dominate global agriculture despite significant spatial variability in soil and crop conditions. This mismatch results in avoidable yield gaps, excessive chemical waste, and environmental pressures, including nutrient leaching and greenhouse gas emissions. The integration of Artificial Intelligence (AI) and Remote Sensing (RS) has emerged as a transformative framework for diagnosing this variability and enabling site-specific, climate-responsive management. This systematic synthesis reviews evidence from 2000–2025 to assess how AI–RS technologies optimize agrochemical efficiency. A comprehensive search across Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar were used. Following rigorous screening and quality assessment, 142 studies were selected for detailed analysis. Data extraction focused on sensor platforms (Landsat-8/9, Sentinel-1/2, UAVs), AI approaches (Random Forests, CNNs, Physics-Informed Neural Networks), and operational outcomes. The synthesized data demonstrate that AI–RS systems can predict critical soil attributes, specifically salinity, moisture, and nutrient levels, with 80–97% accuracy in some cases, depending on spectral resolution and algorithm choice. Operational implementations of Variable-Rate Application (VRA) guided by these predictive maps resulted in fertilizer reductions of 15–30%, pesticide use reductions of 20–40%, and improvements in water-use efficiency of 25–40%. In fields with high soil heterogeneity, these precision strategies delivered yield gains of 8–15%. AI–RS technologies have matured from experimental methods into robust tools capable of shifting agrochemical science from reactive, uniform practices to predictive, precise strategies. However, widespread adoption is currently limited by challenges in data standardization, model transferability, and regulatory alignment. Future progress requires the development of interoperable data infrastructures, digital soil twins, and multi-sensor fusion pipelines to position these technologies as central pillars of sustainable agricultural intensification. Full article

The detection of subsurface stromatolites remains challenging due to their complex morphology and heterogeneous composition. This study assesses the combined application of Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) for identifying microbialites in two contrasting geological and climatic settings: the Neoproterozoic Salitre Formation in Brazil and the Mesozoic microbialite-bearing limestones in northern Spain. High-resolution ERT profiles processed with raster-based blob detection algorithms revealed subcircular high-resistivity anomalies consistent with the studied microbialite morphologies, with strong resistivity contrasts observed between microbialites and host matrices despite variations in absolute values linked to lithology and soil moisture. In parallel, GPR surveys analyzed with a peak detection algorithm delineated domal reflectors and clusters of high-amplitude reflections that directly captured the internal architecture of stromatolitic buildups. With decimetric vertical resolution, GPR offered unrivaled insights into internal morphology, complementing the broader-scale imaging capacity of ERT. The complementary strengths of both methods are clear: ERT excels at mapping distribution and stratigraphic context, while GPR provides unparalleled resolution of internal structures. Crucially, this work advances previous efforts by explicitly demonstrating that integrated ERT-GPR approaches, when combined with algorithm-based interpretation, can resolve microbialite morphology, distribution and internal architecture with a level of objectivity not previously achieved. Beyond methodological refinement, these findings open new avenues for reconstructing microbialite development and preservation in ancient carbonate systems and hold strong potential for application in other geological contexts where complex carbonate structures challenge traditional geophysical imaging. Full article

This study explores the potential of Precision Agriculture (PA) to optimize top-dressing nitrogen (N) fertilization in rainfed barley under drought conditions in Central Catalonia (Spain). Efficient N management is critical in Mediterranean dryland winter cereal systems, where water scarcity and environmental regulations limit fertilization strategies. Two plots (2.93 ha and 1.80 ha) were zoned using soil apparent electrical conductivity (ECa) and elevation data obtained with the VERIS 3100 ECa soil surveyor. An on-farm experimental design tested four N dose rates (0 kg N/ha, 32 kg N/ha, 64 kg N/ha, and 96 kg N/ha) across two management zones per plot. Yield data were collected using a combine harvester equipped with a yield monitor and were mapped using geostatistical methods. A linear model (ANOVA) was used to analyze barley yield (kg/ha at 13% moisture), with nitrogen rate and soil zone (management class) as explanatory factors. Results showed low average yields (~1200 kg/ha–1300 kg/ha) due to severe water stress during the 2022–2023 season. Non-fertilized plots (N0) and those receiving moderate (N64) or high fertilization (N96) achieved the best performance, with the latter likely enhancing crop N uptake during the post-stress recovery period. In contrast, low fertilization (N32) proved less effective. Marginal return analysis supported variable-rate N application only in one plot, whereas under drought conditions, a no-fertilization strategy proved more suitable in the other. Ultimately, additional trials conducted under more favourable climatic scenarios are necessary to assess and validate the effectiveness of Precision Agriculture-based fertilization strategies in rainfed barley. Full article

Accurate mapping of burned forest areas in mountainous regions is essential for wildfire assessment and post-fire ecological management. This study develops an FS-SNIC-ML workflow that integrates multi-source optical fusion, semi-automatic sample generation, feature selection, and object-based machine-learning classification to support reliable burned-area mapping under complex terrain conditions. A pseudo-invariant feature (PIFS)-based fusion of Sentinel-2 and Landsat 8 imagery was employed to generate cloud-free, gap-free, and spectrally consistent pre- and post-fire reflectance datasets. Burned and unburned samples were constructed using a semi-automatic SAM–GLCM–PCA–Otsu procedure and county-level stratified sampling to ensure spatial representa-tiveness. Feature selection using LR, RF, and Boruta identified dNBR, dNDVI, and dEVI as the most discriminative variables. Within the SNIC-supported GEOBIA framework, four classifiers were evaluated; RF performed best, achieving overall accuracies of 92.02% for burned areas and 94.04% for unburned areas, outperforming SVM, CART, and KNN. K-means clustering of dNBR revealed spatial variation in fire conditions, while geographical detector analysis showed that NDVI, temperature, soil moisture, and their pairwise interactions were the dominant drivers of wildfire hotspot density. The proposed workflow provides an effective and transferable approach for high-precision burned-area extraction and quantification of wildfire-driving factors in mountainous forest regions. Full article

Understanding how carbon is stored and stabilized in temperate agricultural soils is central to addressing one of the defining environmental challenges of our time—climate change. In this review, we bridge quantitative bibliometric insights with a qualitative synthesis of the mechanisms, regional differences, management practices, and models governing soil organic carbon (SOC) dynamics. We systematically analyzed 481 peer-reviewed publications published between 1990 and 2024, retrieved from Scopus and Web of Science, using bibliometric tools such as VOSviewer to map research trends, collaboration networks, and thematic evolution. The bibliometric analysis revealed a marked increase in publications after 2010, coinciding with growing global interest in climate-smart agriculture and carbon sequestration policies. Comparative synthesis across temperate sub-regions—such as the humid temperate plains of Europe, the semi-arid temperate zones, and the temperate black soil region of Northeast China—reveals that the effectiveness of common practices varies with soil mineralogy, texture, moisture regimes, and historical land-use. Reduced tillage (average SOC gain of 0.25 Mg C ha⁻¹ yr⁻¹), cover cropping (0.32 Mg C ha⁻¹ yr⁻¹), and organic amendments such as compost and biochar (up to 1.1 Mg C ha⁻¹ yr⁻¹) consistently enhance SOC accumulation, but with region-specific outcomes driven by these contextual factors. Recognizing such heterogeneity is essential for developing regionally actionable management recommendations. Recent advances in machine learning, remote sensing, and process-based modeling are enabling more accurate and scalable monitoring of SOC stocks, yet challenges remain in integrating micro-scale stabilization processes with regional and global assessments. To address these gaps, this review highlights a multi-method integration pathway—combining field measurements, mechanistic modeling, data-driven approaches, and policy instruments that incentivize adoption of evidence-based practices. By combining quantitative bibliometric analysis with regionally informed mechanistic synthesis, this review provides a holistic understanding of how knowledge about SOC in temperate agroecosystems has evolved and where future opportunities lie. The findings underscore that temperate agricultural soils, when supported by appropriate scientific practices and enabling policy frameworks, represent one of the most accessible natural climate solutions for advancing climate-resilient and sustainable food systems. Full article

Soil moisture is an essential environmental parameter affecting hydrological cycles, agricultural productivity, and climate systems. Conventional in situ measurements are precise but do not provide the spatiotemporal coverage for large applications. This research provides an extensive framework for estimating and mapping surface soil moisture by integrating Sentinel-1 Synthetic Aperture Radar (SAR) data with machine learning in the Google Earth Engine (GEE) cloud platform. The study area is the agricultural region of Perambalur district in Tamil Nadu State, India. The research took place between September 2018 and January 2019. The dual-polarized (VV and VH) Sentinel-1 C-band images were collected in tandem with ground truth soil moisture data collected through the gravimetric method. A set of SAR indices and engineered features were extracted from the backscattering coefficients (σ°). A random forest (RF) machine learning model was used in this study to estimate soil moisture. The RF model incorporating the complete set of engineered features showed a coefficient of determination (R²) of 0.694 and a root mean square error (RMSE) of 1.823 (Soil moisture %). The complete processing and modeling workflow was encapsulated in the GEE-based software tool (version 1) providing an accessible, user-friendly platform for generating near-real-time maps of soil moisture. This research proves that the combination of Sentinel-1 data with clever machine-learning algorithms in the GEE cloud platform provides a scalable, efficient, and potent tool for operational soil moisture mapping serving applications in precision agriculture and in the management of the water resource. Full article

Soil moisture plays a crucial role in hydrological processes and serves as a key driver of rainfall-induced landslides, especially in regions with steep terrain and intense precipitation. Traditional landslide risk models often oversimplify soil moisture and infiltration dynamics, which limits their predictive accuracy. This study presents a deep learning-based framework for generating high-resolution, spatiotemporal Surface Soil Moisture (SSM) maps for Prince George’s County, Maryland—a region highly susceptible to rainfall-triggered landslides—aimed at improving infiltration modeling and landslide prediction. A Convolutional Long Short-Term Memory (ConvLSTM) network integrates static spatial features (elevation, slope, soil type) with multi-temporal meteorological variables (precipitation, temperature, humidity, wind speed, evapotranspiration) and vegetation indices. The model is trained using dense SSM maps derived from Sentinel-1 SAR data processed through a change detection algorithm, providing a physically meaningful alternative to sparse in-situ observations. To address data imbalance, a two-pass patch extraction strategy was implemented to enhance representation of high-SSM conditions. The framework leverages high-performance computing resources to process large-scale, multi-temporal raster datasets efficiently. Evaluation results show strong predictive performance, with the two-day model achieving ${\mathrm{R}}^2$ = 0.72, correlation = 0.85, RMSE = 0.154, and MAE = 0.103. The results demonstrate the model’s capability to produce fine-resolution, wall-to-wall SSM maps that capture the spatial and temporal dynamics of surface soil moisture, supporting the development of early warning systems and landslide hazard mitigation strategies. Full article

Flash droughts are a significant natural hazard, characterized by rapid onset and potential to cause substantial economic and environmental impacts. This study utilizes ERA5 soil moisture data to identify and define historical flash drought (FD) events in the Northeastern Aegean islands (specifically Chios, Lemnos, Lesvos and Samos). Hourly soil moisture data, spanning from 1990 to the present, covering three soil layers (0–7 cm, 7–28 cm and 28–100 cm), were analyzed and mapped onto a 0.1° × 0.1° grid with a native resolution of approximately 9 km. Additionally, the Standardized Precipitation Evapotranspiration Index (SPEI) was applied to the island of Lesvos, using precipitation and average temperature data from the local meteorological stations. The number and characteristics of these events—including frequency, duration, decline rate, magnitude, intensity, recovery rate and recovery duration—were produced to construct a regional overview of FD risk across the Northeastern Aegean Islands. These results reveal a considerable variability in the spatial, seasonal and temporal distribution of past FD events. Furthermore, this study highlights the value of using satellite-derived soil moisture data for identifying FD events and demonstrates that analyzing this data with field temperature and precipitation measurements enables a more localized and accurate interpretation of past events. This approach facilitates the definition of FD “hotspot” areas, which, when combined with further investigation, can lead to the development of a predictive FD model. Full article