Search Results (184)

Reliable spatial evidence can support quality assurance and quality control for land use, land-use change and forestry (LULUCF), but land-cover and species-distribution layers do not by themselves identify IPCC Forest Land or species-pure stands. We estimated 2010 and 2020 above- and below-ground living biomass carbon within tree-covered European beech (Fagus sylvatica L.) distribution and occurrence footprints in Greece. Our operational hypothesis was that increasingly restrictive species masks would materially alter the mapped extent and carbon estimates. ESA Climate Change Initiative Biomass v6, ESA WorldCover 2021, European Forest Genetic Resources Programme (EUFORGEN) polygons, and Forest Information System for Europe (FISE) relative probability of presence layers were processed in Google Earth Engine. Biomass was converted with IPCC default carbon fractions and root:shoot ratios, and the results were summarized nationally and for GAUL Level-2 units. The broad EUFORGEN footprint covered 22,133 km², whereas the Combined overlap of EUFORGEN, FISE relative probability of presence ≥ 0.50, and tree cover covered 2742 km². Within the Combined footprint, the pixel mean living biomass carbon density was 60.33 Mg C ha⁻¹ in 2010 and 62.50 Mg C ha⁻¹ in 2020, and the area-integrated change was +0.58 Tg C; the area-normalized regional change was positive in 13 of 17 units and negative in 4. Across masks, the mean decadal change ranged from −0.50 to +3.37 Mg C ha⁻¹ and the approximate area-integrated totals from −1.10 to +0.58 Tg C. These scenario-conditioned estimates are neither official national greenhouse gas inventory estimates nor tests of statistical significance; instead, they provide reproducible spatial screening while making mask sensitivity and unpropagated uncertainty explicit. Full article

Terrestrial vegetation cover is a critical component of the global carbon cycle, annually assimilating a substantial fraction of anthropogenic CO₂ emissions. However, regional estimates of gross primary production (GPP) and net primary production (NPP) remain insufficiently studied, especially for ecologically sensitive areas such as the sub-Mediterranean landscapes of southeastern Crimea. The aim of this study is to calculate and map the spatio-temporal distribution of GPP and NPP across southeastern Crimea over the period 2001–2025 using Earth remote sensing data and geoinformation modeling. This study employed MODIS products (MOD17A2H collection 061) processed in the Google Earth Engine cloud platform, together with temperature and precipitation data (ClimateEU, CHIRPS). Statistical analysis included calculation of the carbon use efficiency (CUE) coefficient and correlation analysis. The results show that the mean GPP for southeastern Crimea is 1.13 kg C/m² and the mean NPP is 0.61 kg C/m², which exceed global average values. Maximum productivity is characteristic of natural forest communities (sessile oak, beech and juniper forests), whereas anthropogenically transformed landscapes (agricultural land, urban coenoses) exhibit the lowest values. The mean CUE is 0.54, with the highest values (0.63–0.66) recorded for agrocoenoses and steppes, and the lowest (0.49–0.57) for forests. A positive correlation between productivity and precipitation and a negative correlation with air temperature were identified, especially for forest ecosystems. This study fills a gap in regional primary productivity assessments and can serve as a basis for ecosystem monitoring under climate change and anthropogenic pressure. Full article

This study adapted and applied a spatially distributed analytical model to estimate the annual representative leached fraction and the annual potential leached mass of atrazine in the Cauquenes catchment in Chile under contrasting Mediterranean hydroclimatic conditions. The model was based on van der Zee and Boesten and Rakonjac et al. and was modified to account for the strong seasonality of precipitation and evapotranspiration by using representative daily hydrological conditions derived from monthly averages. Spatially distributed soil, climate, land-cover, and atrazine application data were integrated at the pixel scale, including locally corrected soil organic carbon, hydraulic properties, precipitation, evapotranspiration, leaf area index, and annual atrazine dose. The model was applied to two contrasting years, 2018 and 2023, and outputs were aggregated at the pixel, land-cover, hotspot, and catchment scales. The results showed a marked hydroclimatic control on potential atrazine leaching. In the drier year, 2018, both the annual representative leached fraction and the annual potential leached mass were generally very low across the catchment, whereas in the wetter year, 2023, moderate-to-high leaching values became much more spatially extensive, and hotspot areas expanded substantially. At the catchment scale, potential leached mass increased from 0.088 kg in 2018 to 179.784 kg in 2023, while the percentage of applied mass potentially leached increased from 5.50 × 10⁻⁵% to 0.112%. Land-cover classes influenced the results both through the spatial allocation of atrazine application and through LAI-dependent partitioning of evapotranspiration. Global sensitivity analysis using the Morris method identified K_OC and DT50 as the dominant controls on annual potential leached mass, and spatial uncertainty propagation was performed. Overall, the proposed framework provides a potential annual screening estimate and may serve as a preliminary screening tool to prioritize areas for targeted monitoring and future model benchmarking in Chile. Full article

In arid and semi-arid regions, groundwater plays an important ecohydrological role in sustaining ecosystem stability under climate-warming-induced surface-water uncertainty. Disentangling precipitation and groundwater recharge effects on vegetation growth remains challenging, limiting robust identification of groundwater-dependent vegetation ecosystems (GDVEs) and quantitative ecological groundwater level estimation. Taking the Daihai Basin, a typical inland closed-lake basin, as a case study, we integrated multi-source remote-sensing data (2005–2025) with in situ groundwater monitoring to develop a comprehensive framework for ecohydrological response analysis and management quantification. Using an improved Mann–Kendall test together with spatiotemporal correlation analyses, we analyzed the spatial relationships between vegetation dynamics and groundwater depth. Results show: (1) basin-wide vegetation exhibits a greening trend (Sen’s slope = 0.00014) with spatial heterogeneity; (2) vegetation dependence on groundwater displays a clear threshold behavior, with low-cover areas (fractional vegetation cover, FVC < 0.3) showing relatively strong groundwater dependency (r = 0.698) whereas high-cover areas exhibit a weaker relationship; and (3) approximate ecological groundwater reference thresholds are estimated as 1.0 m (90% assurance) for forest land and 0.6 m for grass land (80% assurance). The proposed GDVE identification scheme provides a scientific reference for adaptive groundwater management and ecological assessment. Full article

This study examines long-term mangrove vegetation dynamics in the Cananéia–Iguape Coastal System (CICS), southeastern Brazil, with emphasis on their relevance for coastal ecosystem monitoring and sustainability. Land-use and land-cover (LULC) data from MapBiomas were combined with MODIS-derived vegetation indices, namely the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), and Fractional Vegetation Cover (FVC) to assess spatial variability and temporal trends from 2003 to 2024. Spatial anomalies were calculated as deviations from long-term mean conditions, whereas temporal trajectories were evaluated using the non-parametric Mann–Kendall test and Sen’s slope estimator. The results indicate limited spatial variability, with 98.35% of the study area for the NDVI and 99.51% for the EVI showing no detectable deviations from long-term averages. Within mangrove areas, however, statistically significant positive trends were identified for the NDVI (Z_MK = 2.43; p = 0.02), EVI (Z_MK = 2.03; p = 0.04), and FVC (Z_MK = 2.43; p = 0.02), suggesting a gradual increase in spectral greenness and FVC-derived vegetation density. The moderate correlation between mangrove extent and the NDVI (r = 0.61; p < 0.05) indicates that the mapped mangrove area is partially associated with variations in spectral greenness, although this relationship should not be interpreted as direct evidence of ecological recovery or improved ecosystem conditions. Overall, the findings point to low-magnitude but consistent vegetation index changes in a predominantly stable mangrove system. The integration of LULC information, spectral indices, and FVC provides a consistent regional-scale basis for interpreting mangrove dynamics in heterogeneous coastal environments and for guiding long-term monitoring efforts. Full article

This study quantifies how different land-use types influence surface soil characteristics (0–5 cm) and the dynamics of soil organic carbon (SOC) and nitrogen in the mountainous ecosystems of the Rila Mountains. Across 54 forest and agricultural plots, pH, bulk density, coarse fraction, C:N ratio, SOC, total nitrogen (TN), and their respective stocks were assessed using standard analytical methods and statistical tests (Shapiro–Wilk, ANOVA, Kruskal–Wallis, correlation and regression analysis). Land use significantly affected all soil parameters except pH. Forest soil showed lower bulk density and lower SOC stocks compared with grasslands. Unmown meadows exhibited the highest SOC and TN concentrations and stocks, while potato fields recorded the highest bulk density and elevated TN stocks, reflecting intensive management impacts on surface soil properties. Forest soils displayed species-specific patterns, with Scots pine and Silver fir showing comparatively lower SOC and TN stocks attributable to historical degradation and site limitations. As the study focused on the uppermost soil layer (0–5 cm), the results should be interpreted more as indicators of surface soil dynamics rather than as estimates of total topsoil carbon and nutrient storage. Correlation analysis revealed strong positive relationships among SOC, TN, and the C:N ratio, and strong negative relationships between SOC and both bulk density and coarse fraction in managed agricultural lands. The findings demonstrate that minimizing soil disturbance and maintaining permanent vegetation cover—particularly through conservation of unmanaged grasslands—offer great capacity for enhancing the soil organic matter accumulation in mountainous ecosystems. Full article

Because of high visible and near-infrared (VNIR) reflectance, and deep shortwave infrared (SWIR) absorption, snow and ice are unique among terrestrial land cover. As such, both are well-suited to mapping and monitoring using optical remote sensing. However, to date, almost all studies of snow and ice spectroscopy have been limited to single or small numbers of specific cryospheric environments. These studies serve a diversity of objectives, but together also suggest the importance of the global continuum of snow and ice composition and spectroscopy. The continuum of snow and ice composition gives rise to the characteristics that allow different types of snow and ice to be distinguished optically. Particularly with imaging spectrometers. Characterization of this continuum of reflectance can facilitate development of physical models to quantify snow and ice composition and abundance, particularly in the presence of other types of land cover. In this study, a collection of ~140,000,000 visible through SWIR (VSWIR) reflectance spectra, collected by NASA’s EMIT imaging spectrometer from 56 diverse cryospheric environments, is used to characterize the continuum of snow and ice reflectance. This continuum is characterized using linear dimensionality reduction to quantify the dimensionality and topology of the spectral feature space of snow and ice. The resulting spectral feature space is effectively two-dimensional with a planar spectral feature continuum bounded by dry and wet snow, ice and dark targets (e.g., shadow, water). Because of the near collinearity of snow and ice endmember reflectances, linear spectral mixture models based only on these endmembers are ill-posed and unstable to inversion. However, in landscapes where sufficiently homogeneous seasonal snow is present with other land cover types, the standardized spectroscopic mixture model based on the Substrate, Vegetation and Dark (SVD) continuum can be extended with an instance-specific snow endmember (SVD + snow) to yield plausible areal fraction estimates with small misfits to observed spectra. More generally, the snow–ice-dark continuum can also be represented accurately with an optimal normalized difference index exploiting compositionally distinct differential absorptions at ~650 and ~1230 nm to distinguish dry from wet snow from white and blue ice. This optimized index, referred to as the Continuous Cryosphere Index (CCI), minimizes BRDF effects of topographic slope and aspect relative to illumination, while avoiding the saturation that causes the Normalized Difference Snow Index (NDSI) to conflate wet snow with white and blue ice reflectance. In addition to imaging spectrometers like EMIT, operational sensors like MODIS, VIIRS and WorldView-3 have spectral bands near 650 nm and 1230 nm, so they could also be used for CCI mapping. Full article

Accurate estimation of latent heat flux (LE) and sensible heat flux (H) is essential for determining actual crop evapotranspiration (ET_a) and optimizing irrigation water management. However, uncertainties in characterizing the zero-plane displacement height (d_o) often limit H and LE model accuracy. This study introduces a novel approach to characterize d_o using a dynamic fractional vegetation cover and a new proposed canopy porosity (Φ_dp) term derived from Unmanned Aerial System (UAS) imagery. Field experiments were conducted in 2024 near Greeley, Colorado, USA, at a research farm using fully and deficit-irrigated maize fields. Eddy covariance (EC) systems, handheld multispectral radiometer, and PlanetDove mini-satellite imagery were used in the land surface energy balance (EB). A dynamic heat flux footprint area was implemented based on crop height, atmospheric stability, and wind conditions, to align and integrate those measurements with measured EC heat fluxes. Results indicated that both developed d_o models noticeably outperformed existing d_o methods. The new d_o models reduced the normalized root mean square errors (NRMSE) for H estimation by up to 21.1% in the fully irrigated (FI) field and by 16.9% in the deficit-irrigated (DI) field. Furthermore, a higher index of agreement of up to 0.74 reflected an improved d_o model vs. observation correlation. These findings highlight the potential of incorporating a dynamic canopy porosity and vegetation fractional cover to refine spatially distributed EB-based ET_a modeling and advance agricultural irrigation water management based on remote sensing inputs. Full article

Hyperspectral unmixing aims to estimate endmember spectra and their corresponding abundance fractions at the subpixel scale, which is a critical preprocessing step for quantitative analysis of hyperspectral remote sensing imagery. While deep learning-based methods have achieved remarkable progress, three fundamental challenges remain: (i) reliance on a single shared spatial prior that cannot decouple the heterogeneous spatial patterns of different land covers; (ii) the lack of synergy in jointly optimizing endmember extraction and abundance estimation; (iii) the poor robustness of unsupervised training to complex mixtures, noise, and class imbalance. To address these issues, we propose a novel unsupervised unmixing framework that integrates adaptive orthogonal multi-faceted graph representation with curriculum learning. Specifically, we design an Adaptive Orthogonal Multi-Faceted Graph Generator (AOMFG) to learn a set of independent orthogonal graph structures, achieving spatially informed decoupling of land cover patterns. Then, a dual-branch collaborative optimization network is constructed: a Graph Convolutional Network (GCN) branch that incorporates the learned spatial topological priors for abundance estimation, and a 1D Convolutional Neural Network (1DCNN) branch that employs a query-attention mechanism to adaptively aggregate pure spectral features for endmember extraction. Finally, we introduce a three-stage curriculum learning strategy that progressively fine-tunes the model, which significantly enhances its performance. Extensive experiments on three widely used real-world benchmark datasets demonstrate that our proposed framework consistently outperforms state-of-the-art methods in both endmember extraction and abundance estimation accuracy. Comprehensive ablation studies, parameter sensitivity analysis, and noise robustness tests further validate the effectiveness of each core component. Full article

Arid and semi-arid regions are critical to terrestrial ecosystems and regional carbon cycle regulation, directly contributing to peak carbon and carbon neutrality goals. However, the fragmented landscapes in these regions pose significant challenges to conventional pixel-based classification, which often struggles with mixed pixel issues and lacks biophysical interpretability. To address these limitations, this study develops an Ecologically Constrained Deep Learning Autoencoder (ECO-DEAU) framework for sub-pixel land cover mapping by integrating biophysical constraints. Specifically, ECO-DEAU employs spectral indices to extract standard spectral signatures for five primary land cover types, which serve as initial weights to guide the autoencoder in estimating fractional abundances. The model was trained across ten representative landscape zones in the Inner Mongolia section of the Yellow River Basin and validated against high-resolution Gaofen-2 data. Results demonstrated that ECO-DEAU yielded an average $R^2$ of 0.687, reaching a maximum $R^2$ of 0.749 in spatially heterogeneous transition zones, representing a substantial improvement over the baseline unconstrained Deep Autoencoder (DEAU). By effectively resolving the blind source separation problem and improving decomposition accuracy, ECO-DEAU serves as a robust tool for addressing mixed pixel challenges in heterogeneous environments, thereby facilitating large-scale, high-resolution carbon sink monitoring. Full article

Monitoring agricultural production in developing nations is essential for assessing food security. Nevertheless, persistent cloud cover in tropical regions severely limits optical satellite observations, and ground-truth data for classification validation are typically unavailable. This study developed a remote sensing methodology to classify agricultural land in southern Uasin Gishu County, Kenya, using weather-independent Synthetic Aperture Radar (SAR) imagery without requiring in situ training data. We processed 29 Sentinel-1 C-band VH-polarized scenes through the Alaska Satellite Facility’s Radiometric Terrain Correction pipeline. We computed the Coefficient of Variation (CV) across the 2017 time series to quantify temporal backscatter variance. VH polarization was selected over VV because a preliminary analysis showed that VV sensitivity to water surface dynamics confounded the CV algorithm. Preprocessing masks excluded water bodies, urban areas, and edge pixels to reduce classification errors from non-agricultural sources of temporal variability. Unsupervised ISO Cluster classification partitioned the CV raster into land-cover classes, and a Python-based statistical analysis determined optimal threshold values. Active agriculture pixels (n = 581,807) exhibited a mean CV of 0.469 (SD = 0.087), while non-agricultural pixels (n = 623,484) showed a mean CV of 0.274 (SD = 0.049). The optimal classification threshold of 0.357, determined by the intersection of fitted normal distributions, achieved an overall accuracy of 87.5% (Kappa = 0.73) when validated against Sentinel-2 reference imagery. User’s accuracy for agriculture was 96.6%, indicating that pixels classified as agricultural were highly reliable, while omission errors reducing producer’s accuracy to 84.6% were primarily attributable to edge pixels and land cover types where preprocessing masks or threshold placement excluded pixels exhibiting intermediate temporal dynamics. The classification identified approximately 810 km² of actively cultivated land (54% of the southern study area), corresponding to an estimated 69,500 to 162,200 metric tonnes (assuming 30–70% maize fraction) of potential maize production based on FAO yield data. The methodology provides a replicable, cost-effective tool for food security monitoring in cloud-prone regions where ground-truth data are unavailable. Full article

Current assessments of urban green spaces (UGS) rely largely on two-dimensional (2D) indicators, which fail to capture the three-dimensional (3D) structure necessary for evaluating ecological functions and human exposure. Among these, the Normalized Difference Vegetation Index (NDVI) describes top-down canopy greenness from a nadir perspective, whereas the Green View Index (GVI) quantifies vegetation visibility at street level from a pedestrian perspective. Because the relationship between NDVI and GVI remains unclear, multi-indicator assessments become difficult to interpret, limiting their ability to jointly characterize urban greenery. To address these gaps, we develop a synergy framework that integrates remote sensing with street-view images. First, we aligned the observation scales through street-view depth estimation and converted NDVI into fractional vegetation cover (FVC) through nonlinear mapping to unify measurement units. Correlation experiments revealed that the consistency between GVI and FVC was weak across the city (R² = 0.27) but substantially stronger along arterial roads with continuous vegetation (R² = 0.61). On this basis, we design a Green Synergy Index (GSI) that combines FVC and GVI using fractional power-law adjustments and an interaction term to capture their joint effects. Robustness tests indicate that GSI effectively handles extreme or mismatched cases, differentiates greening patterns, and integrates complementary information from nadir and street views without numerical instability. Furthermore, we assess the consistency between GSI and land surface temperature (LST), showing that the proposed index improves explanatory power compared with FVC and GVI alone (by 5.6% and 8.8%, respectively). Application to the study area yields a mean GSI value of 0.44 on a 0–1 scale, with spatial variations closely associated with road geometry and functional zoning. This enables the identification of mismatched canopy and visibility segments and supports targeted, climate-sensitive green infrastructure planning. Full article

Wildfires are a recurring dry-season hazard in northern Thailand, contributing to severe air pollution and trans-boundary haze. However, the region lacks the ground-based measurements necessary for monitoring Live Fuel Moisture Content (LFMC), a key variable influencing vegetation flammability. This study presents a preliminary framework for near-real-time (NRT) LFMC estimation using Sentinel-2 multispectral imagery. The system integrates normalized vegetation and moisture-related indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Infrared Index (NDII), and the Moisture Stress Index (MSI) with an NDVI-derived evapotranspiration fraction (ETf) within a heuristic modeling approach. The workflow includes cloud and shadow masking, weekly to biweekly compositing, and pixel-wise normalization to address the persistent cloud cover and heterogeneous land surfaces. Although currently unvalidated, the LFMC estimates capture the relative spatial and temporal variations in vegetation moisture across northern Thailand during the 2024 dry season (January–April). Evergreen forests maintained higher moisture levels, whereas deciduous forests and agricultural landscapes exhibited pronounced drying from January to March. Short-lag responses to rainfall suggest modest moisture recovery following precipitation, although the relationship is influenced by additional climatic and ecological factors not represented in the heuristic model. LFMC-derived moisture classes reflect broad seasonal dryness patterns but should not be interpreted as direct fire danger indicators. This study demonstrates the feasibility of generating regional LFMC indicators in a data-scarce tropical environment and outlines a clear pathway for future calibration and validation, including field sampling, statistical optimization, and benchmarking against global LFMC products. Until validated, the proposed NRT LFMC estimation product should be used to assess relative vegetation dryness and to support the refinement and development of future operational fire management tools, including early warnings, burn-permit regulation, and resource allocation. Full article

Operational ensemble numerical weather prediction models are typically underspread near the land surface, with the Australian Bureau of Meteorology’s (BoM) global system being a typical example. In this study, land surface fraction values, representing the estimated proportions of various land cover types, are perturbed with the aim of increasing the ensemble spread at the surface. The perturbations are achieved by multiplying the existing land surface fraction estimates by spatially correlated random error structures that represent the uncertainties in these estimates. The methodology was trialed over a 75-day period during the Australian summer of 2017–2018 when both perturbed and unperturbed forecasting cycling experiments were run. The results showed that land surface fraction perturbations increased surface temperature, sensible heat flux, and latent heat flux ensemble spread significantly, especially in the tropics and over the Australian region. The screen-level temperature ensemble spread also increased, albeit by a relatively smaller magnitude compared to the surface temperature ensemble spread. Root-mean square error values—as measured relative to reanalysis data—were also found to be smaller in the perturbed runs, leading to significantly improved spread-to-skill ratio values. Full article

Although surface soil moisture (SSM) is particularly important in crop yield prediction, irrigation scheduling optimization, and runoff generation mechanisms, accurate monitoring of time-series SSM is still challenging for agricultural and hydrological research. This study presented an improved approach integrating Sentinel-1 C-band SAR and MODIS optical data (2019–2020) to estimate surface soil moisture. To address vegetation effects, we developed a piecewise function using fractional vegetation coverage (FVC) to correct soil moisture and backscatter extrema and established the normalized difference enhanced vegetation index (NDEVI) to characterize backscatter-vegetation relationships across various land covers. Furthermore, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm identified anomalous surface changes, enabling segmentation of long-term series into invariant periods that satisfy the change detection method assumptions. Validation in the Shandian River Basin demonstrated significant improvement over traditional methods, achieving determination coefficients (R²) of 0.844 and root mean square errors (RMSE) of 0.030 m³/m³. The method effectively captured soil moisture dynamics from precipitation and irrigation events, providing reliable monitoring in heterogeneous landscapes. This integrated approach offers a robust technical framework for multi-source remote sensing of soil moisture in semi-arid areas, enhancing capability for agricultural water resource management. Full article