Search Results (2,463)

Increasing water scarcity and pollution have intensified the need for low-cost wastewater treatment in developing regions. Constructed wetlands (CWs) offer a nature-based solution for pollutant removal but often fail, on their own, to meet discharge and reuse standards. This study evaluated four CW systems planted with Cyperus latifolius, Juncus effusus, Phragmites mauritianus, and Pennisetum purpureum, integrated with banana fibre filtration as a polishing step. The CWs alone achieved ammonium removal of 74–83%, nitrate 78–85%, phosphorus 86–91%, and COD 78–83%. Banana fibre filtration enhanced overall removal efficiencies to 93–96%, 94–96%, 81–88%, and 82–87% for ammonium, phosphorus, nitrate, and COD, respectively. Pennisetum purpureum had the highest aboveground nitrogen accumulation (74.4 g N/m²), with its coupled system achieving the highest nitrate removal, whereas Juncus effusus had the highest phosphorus accumulation (93.1 g P/m²), with its coupled system showing the best overall removal of ammonium, phosphorus, and COD. The integrated system reduced fecal coliforms and Escherichia coli by 6–8 log and eliminated detectable Salmonella and Shigella. Treated effluent met FAO irrigation, Rwanda discharge, and European Union (EU) standards for all evaluated parameters except phosphorus, which remained above the stricter EU limit. Despite bench-scale operation over four months, these findings demonstrate a low-cost, nature-based treatment approach suitable for decentralized wastewater treatment and reuse, with harvested wetland biomass offering additional potential for animal feed, energy, or fibre valorization. Full article

Calibration of crop model parameters using optimization algorithms can substantially improve model performance, particularly under different environmental scenarios. Here, we take greenhouse drip-irrigated tomato as an example. Two temperature treatments (high temperature, TH: 35 °C ≤ T_max ≤ 40 °C; non-high temperature, TD: T_max ≤ 35 °C) and two water treatments (well watered, WH: 100%E_pan; water deficit, WD: 60%E_pan, where E_pan is the pan evaporation coefficient) were combined and implemented in 2024 and 2025. Canopy cover, yield, aboveground biomass, and water consumption of tomatoes were measured, and then a global sensitivity analysis was conducted using the extended Fourier amplitude sensitivity test (EFAST). Thereafter, in order to evaluate the performance of AquaCrop-OSPy under different combinations of temperature and water conditions, the whale optimization algorithm (WOA) was coupled with the AquaCrop-OSPy model, and an automatic parameter optimization framework was developed using the aforementioned measured indicators as objective functions. The results showed that CC_x, Senescence_CD, CGC_CD, WP, HI₀, and K_cb were highly sensitive parameters of the AquaCrop-OSPy model across different temperature and water treatments. Compared with the traditional trial and error (TAE) method, WOA demonstrated superior global optimization capability and significantly improved model performance. Validation indicated that the root mean square error (RMSE) was ≤4.52% for canopy cover, ≤0.67 t/hm² for yield, and ≤0.98 t/hm² for aboveground biomass. Notably, even after WOA optimization, water consumption simulation under combined high temperature and water deficit conditions still showed some deviation, which was attributed to limitations in soil water simulation and the oversimplification of model mechanisms under combined stress. Nevertheless, the model remained reliable for estimating canopy cover, yield, and biomass, while ET simulation under combined stress should be interpreted with caution. Therefore, future improvements of AquaCrop-OSPy should focus on addressing the effects of soil water and refining the inhibitory feedback of physiological stress to better adapt the model to the combined conditions of high temperature and water deficit. Full article

Estimation of forest biomass and surface fuels is essential for wildfire risk assessment, carbon accounting, and ecosystem management in semi-arid forests. This study evaluated Sentinel-2 spectral indices and Gamma generalized linear models (GLMs) for estimating aboveground live biomass, forest floor biomass, and downed woody debris in oak–pine forests of Northern Mexico. Spectral predictors were grouped according to chlorophyll activity, vegetation vigor, physiological condition, and soil/background correction effects. Measurements of live biomass, forest floor biomass, and downed woody debris were linked to Sentinel-2 spectral information. Aboveground live biomass was most strongly associated with the chlorophyll-related index CIre8A (p < 0.0001), whereas both forest floor biomass and downed woody debris showed stronger relationships with indices associated with red–NIR reflectance gradients, vegetation senescence, and soil/background correction effects, including the Normalized Difference Vegetation Index (NDVI), the Alpha-weighted Red–NIR Vegetation Index (PVIα), the Normalized Pigment Chlorophyll Index (NPCI), and the Atmospherically Resistant Vegetation Index (ARVI) (p < 0.05). Single-index Gamma GLMs showed significant predictive relationships (p < 0.05), supporting the use of individual Sentinel-2 indices as practical predictors of biomass and surface fuels. In contrast, multivariate models combining several spectral indices were affected by collinearity and parameter instability. Principal Component Analysis (PCA) integrated four representative indices into a single orthogonal predictor. The first principal component (Prin1) explained 90.30%–95.25% of total spectral variance, eliminated collinearity effects (VIF = 1), and produced highly significant Gamma GLMs across all biomass components (p ≤ 0.0009). PCA-based models also yielded lower AIC and BIC values, providing the most parsimonious framework when multiple spectral predictors were combined. These results indicate that individual spectral indices offer practical alternatives for biomass estimation, whereas PCA provides a robust approach for integrating complementary spectral information while maintaining model stability. Full article

This study aimed to investigate the accumulation of aboveground biomass, organ allocation patterns, and their driving mechanisms in Moso bamboo (Phyllostachys edulis) forests along different elevational gradients and to compare regional differences in growth processes. A total of 54 sample plots were established along an elevational gradient from 50 to 550 m across three different regions, with 100 m elevational intervals. Two-way ANOVA, regression analysis, Tukey’s HSD multiple comparisons, and generalized additive models (GAMs) were used to examine distribution patterns. (1) Individual bamboo biomass followed a unimodal pattern with increasing elevation, peaking at 150 m (16.20 ± 3.88 kg culm⁻¹), which was significantly higher than at other elevations (p = 0.048). Allometric covariance analysis showed that the b value did not differ significantly among elevations (p = 0.882), indicating a stable diameter at breast height (DBH)-biomass relationship. (2) Stand biomass was highest at 50 m (34.75 ± 10.97 t·ha⁻¹) and declined with elevation to 18.54 ± 7.13 t·ha⁻¹ at 550 m, revealing a decoupling from the elevational trend of individual biomass. (3) Branch and leaf dry mass allocation exhibited a “higher at both ends, lower in the middle” pattern. Culm allocation was highest at 150 m (80.3%), though differences among elevations were not statistically significant (p = 0.591). (4) Stand density decreased with elevation, while mean DBH first increased and then decreased, reaching a maximum at 450 m (9.77 cm). Differences in stand density and DBH among elevations were highly significant (p < 0.001). (5) ANCOVA showed that after controlling for mean DBH, the effect of elevation on individual biomass was substantially weakened (p = 0.051), with partial η² declining from 0.48 to 0.21 (a 56% reduction), indicating that DBH accounted for a substantial portion of the elevation effect on individual biomass.The individual biomass of Moso bamboo in central-southern Jiangxi peaked at approximately 150 m elevation. Elevation was associated with biomass mainly through its association with DBH (a size effect), rather than through changes in allocation ratios or allometric relationships. The pathway “elevation→DBH→individual biomass” appears to be the primary mediating pathway explaining the decoupling, although a causal interpretation requires further experimental validation. These findings provide a theoretical basis for elevation-differentiated management of Moso bamboo forests. Full article

Continuous monitoring of vineyard dynamics is essential for optimizing viticultural practices and assessing plant health. While the seasonal behaviors of satellite-derived vegetation indices are widely studied, robust parametric modeling of these temporal trends remains underexplored. Building upon initial clues derived from Italian vineyards, this study proposes a novel analytical framework based on the consistent parabolic temporal signature of optical and Synthetic Aperture Radar (SAR) indices. Focusing on the elevated Trinity Canyon Vineyards in Armenia, we model the yearly evolution and temporal aggregations of these indices using a parabolic fitting approach. Our results suggest that the parabola vertex, which we hypothesize corresponds to the absolute maximum of vegetative activity, remains remarkably stable across diverse vine types, satellite orbits, and years. While this stable behavior suggests an underlying phenological or structural consistency, distinct exceptions to this trend have also been identified and considered. Furthermore, to bridge the gap between remote sensing observables and agronomic traits, we investigated the relationship between the fitted parabolic parameters and the Winkler index, which is used here as an estimator of above-ground biomass (AGB). By correlating the vegetation indices’ temporal dynamics with biomass growth and by isolating specific anomalies driven by environmental or anthropogenic factors, this work offers a basis for a predictive methodology that enables tracking vineyard structural development. Full article

Forest aboveground live biomass carbon storage (hereafter referred to as “forest carbon storage”) is an important indicator of forest vegetation carbon sequestration, and its spatial patterns and associated factors are highly heterogeneous. Identifying these variations can improve the understanding of carbon accumulation in complex mountain forests and support fine-scale carbon assessment in similar ecosystems. The results showed the following. (1) The total forest carbon storage in the study area was 3.75 × 10⁶ t C, with a carbon density of 44.83 t C·hm⁻². Pinus massoniana and hard broad-leaved tree species were the main contributors, and carbon storage peaked at the mature forest stage. (2) Carbon storage exhibited significant spatial clustering (Moran’s I = 0.312), with high-value areas concentrated within the national nature reserve and low-value areas distributed in regions with frequent human activities. (3) The GWR model outperformed the ordinary least squares model, with R² increasing to 0.88 and residual spatial autocorrelation reduced by 42.22%. (4) The positive effect of stand volume increased from northeast to southwest, the effect of stand age differed between eastern and western areas, and shrub layer height, soil depth, and slope exhibited region-specific positive and negative effects, with significant interactions among factors. Full article

To address water scarcity, environmental pollution from excessive fertilization, and the need for stable grain yields in the North China Plain (NCP), a 3-year field experiment (2021–2024) was conducted to explore the effects of water and nitrogen (N) management under subsurface drip irrigation on winter wheat–summer maize rotation system. This study included three irrigation treatments (irrigated to 80% (D1), 75% (D2), and 70% (D3) of field water-holding capacity (WHC) when soil water content dropped below 65%, 60%, and 55% of WHC, respectively) under N210 and four N application rates (0 (N0), 150 (N150), 210 (N210), and 270 (N270) kg N ha⁻¹ for each season) under D1. The results showed that D1 and N210 all significantly improved leaf area index (LAI) and aboveground biomass by reducing chlorophyllase and pheophytinase activities (delaying leaf senescence) and enhancing nitrate reductase (NR), glutamine synthetase (GS), and glutamate synthase (GOGAT) activities (promoting N assimilation). Furthermore, compared with D3, D1 increased winter wheat and summer maize yields by 12.7% and 24.3%, respectively, and improved partial factor productivity (PFP) by 14.0–46.0%. Redundancy analysis (RDA) and structural equation modeling confirmed that LAI, biomass, and N-transforming enzyme activities were the key drivers of yield. This study demonstrated that the combination of D1 and N210 treatments were the optimal water and N fertilizer management strategies for achieving water conservation, N reduction, and stable high yields in drip-irrigated rotation systems of the NCP. Full article

Aboveground biomass (AGB) in grasslands is a key biophysical indicator for evaluating grassland productivity, ecosystem functioning, and carbon storage. However, accurate regional-scale AGB estimation in complex mountainous terrain remains challenging because of fragmented topography, strong environmental gradients, and heterogeneous grassland patches. This study evaluated the applicability of satellite embedding features for grassland AGB estimation in the Three Parallel Rivers region of Yunnan Province, China. Based on 135 field plots surveyed during the 2022 growing season, 64-dimensional annual satellite embedding features were extracted, and a conventional feature system derived from Sentinel-1, Sentinel-2, and DEM data was constructed for comparison. Four feature systems, namely Traditional-9, Traditional-40, Emb-9-PCA, and Emb-64, were evaluated using six regression models, including RF, SVR, GPR, XGBoost, LightGBM, and Elastic Net. Random five-fold cross-validation was used to compare feature systems and model combinations, while spatial cross-validation was further applied to assess model robustness under spatially independent conditions. The results showed that satellite embedding features outperformed conventional remote sensing features. Under random five-fold cross-validation, the Emb-64-based XGBoost model achieved the best performance, with an R² of 0.7949 ± 0.0405, an RMSE of 0.1388 ± 0.0191 t/ha, and an MAE of 0.1141 ± 0.0203 t/ha. Under spatial cross-validation, XGBoost retained the highest mean performance, with an R² of 0.7660 ± 0.1003, an RMSE of 0.1417 ± 0.0111 t/ha, and an MAE of 0.1165 ± 0.0124 t/ha. SHAP and Spearman correlation analyses further indicated that important embedding dimensions were associated with AGB and selected conventional environmental variables. Regional mapping showed that predicted grassland AGB ranged from 0.17 to 1.26 t/ha, with a mean value of 0.79 t/ha, and exhibited significant positive spatial autocorrelation. Bootstrap-based uncertainty analysis indicated that higher uncertainty mainly occurred in fragmented mountainous areas, grassland edges, and transition zones. These findings suggest that satellite embedding features provide a promising high-dimensional representation for grassland AGB estimation in complex mountainous landscapes, while their ecological interpretability and transferability still require further investigation. Full article

To clarify the effects of precipitation changes on greenhouse gas (CO₂, CH₄, N₂O) emissions from the lakeshore wetland ecosystem of Qinghai Lake, this study establishes five precipitation treatments (D25%, D75%, CK, I25%, I75%) using a rainfall manipulation device. During the growing season, greenhouse gas fluxes were measured via the static chamber–gas chromatography method, while soil temperature, moisture, total carbon (TC), total nitrogen (TN), pH, electrical conductivity (EC), and aboveground and belowground biomass were simultaneously monitored. The results showed that at 11:00, both increased and decreased precipitation inhibited CO₂ and CH₄ emissions (with D25 showing the strongest inhibition for CO₂ and I75 for CH₄); however, increased precipitation promoted N₂O emissions (I75 strongest), while decreased precipitation suppressed them (D25 strongest). At 15:00, only D25 inhibited CO₂ emissions, whereas all other treatments promoted them (I75 strongest). CH₄ fluxes under all treatments were higher than those in CK, indicating a promoting effect (I25 strongest). N₂O emissions responded to precipitation changes as follows: wetting promoted emissions (I25 optimal), while drying suppressed them (D75 stronger). Correlation analyses revealed treatment- and time-specific patterns. At 11:00, significant negative correlations between CO₂ and soil temperature and significant positive correlations between CH₄ and soil temperature were observed only under I75; soil moisture was significantly positively correlated with CH₄ under D25; TN significantly inhibited all three gases under wetting treatments; and TC showed a highly significant negative correlation with N₂O under I75 (p < 0.01). At 15:00, soil temperature generally promoted CO₂ and CH₄ emissions (except for CO₂ under D75); soil moisture promoted CH₄; TC was negatively correlated with both CO₂ and N₂O; pH was positively correlated with CO₂ only under CK; the correlation between EC and CO₂ shifted from positive (CK) to negative (I75); aboveground biomass suppressed CO₂ under I25 and I75 but promoted N₂O under D25. In summary, precipitation changes significantly modulate greenhouse gas emissions and their relationships with soil factors, with strong temporal and treatment dependencies. Future studies should integrate long-term observations and process-based models to further quantify the comprehensive effects of precipitation changes on the greenhouse gas budget of this wetland ecosystem. Full article

Phenolic acids are considered important allelochemicals that can restrict nutrient uptake and inhibit plant growth. Most existing studies have focused on growth phenotypes and single nutrient changes under phenolic acid stress, while the coupled C:N:P balance responses of plant–soil systems remain poorly characterized. Ecological stoichiometry offers a holistic perspective to reveal the nutrient supply–demand mismatch underlying allelopathic inhibition by analyzing elemental ratio dynamics, thus deepening the mechanistic understanding of allelopathy beyond traditional single-indicator observations. Using lettuce (Lactuca sativa L.) as the test plant, this study investigated the effects of cinnamic acid (CA) and p-hydroxybenzoic acid (PHA) stress, combined with nitrogen (N), phosphorus (P), and their combined application (NP) on plant growth and plant C, N, and P stoichiometric characteristics. Soil C, N, and P contents and stoichiometric characteristics were further analyzed as supplementary indicators. The results showed that phenolic acid stress significantly reduced lettuce biomass: PHA decreased aboveground biomass by 40.11% and belowground biomass by 44.14%, with a stronger inhibitory effect than CA. Both phenolic acids induced marked stoichiometric imbalance, characterized primarily by a sharp decline in leaf nitrogen content; N content dropped by 49.80% under CA and 70.75% under PHA, corresponding to a 64.43% and 162.79% increase in leaf C:N ratio, and a 37.63% and 63.19% decrease in leaf N:P ratio, respectively. Nutrient addition alleviated these responses to varying degrees: N addition mainly promoted biomass recovery and elevated plant N status, P addition optimized elemental ratios, and combined NP application yielded the strongest overall recovery. Correlation and redundancy analyses indicated that soil C, N, and P stoichiometric characteristics were closely associated with plant growth, with soil organic carbon and total phosphorus identified as major explanatory variables associated with plant growth. Overall, phenolic acid stress altered plant stoichiometric characteristics and growth, as well as soil C, N, and P stoichiometric characteristics, while nutrient addition partially alleviated these responses. These findings provide a stoichiometric perspective for understanding allelopathic stress and may offer a theoretical basis for nutrient management under continuous cropping conditions. Full article

Nutrient stoichiometry provides a powerful framework for linking nutrient limitation to plant community biomass, especially in grasslands undergoing degradation in the agro-pastoral ecotone, where nitrogen (N) fertilization and mowing have become two widespread key management practices. However, their influence on nutrient stoichiometry has received little attention, especially beyond the leaf carbon (C):N:phosphorus (P) ratio. Here, we conducted a field experiment on the Mongolian Plateau wherein we quantified 19 nutrient ratios for soils and three plant components (aboveground plants, litter, and belowground roots) following 12 years of N addition (0, 2, and 10 g N m⁻² year⁻¹) combined with mowing, and grouped these ratios into four sets with C, N, P, and potassium (K) as the numerators. Under N addition, nutrient stoichiometry in plant components and soils changed markedly, whereas mowing management resulted in negligible changes in most C-, N-, and K-based nutrient ratios. Furthermore, mowing and N addition interactively and significantly affect P-based nutrient ratios. The responses of nutrient stoichiometry differed among plant components and soils, and also depended on the level of N input, and these ratios with C and N as numerators generally showed greater variability than those with P and K in the plant–soil system. Plant community biomass was associated with nutrient ratios in both plant components and in soils, although the relationships were not always significant. Long-term N addition resulted in rate-dependent shifts in nutrient stoichiometry, whereas mowing had only weak modifying effects. Extending nutrient stoichiometry framework (including neglected ratios, e.g., N:K) beyond leaf C:N:P to encompass entire plant–soil systems can help local government and ranch owners manage grasslands more cost-effectively because of simple assessment procedures, and could further provide more comprehensive insights into nutrient limitation and main hypotheses of ecological stoichiometry in grassland ecosystems. Full article

ICESat-2/ATLAS photon-counting LiDAR faces several challenges in regional-scale forest aboveground biomass (AGB) estimation. These challenges include sparse sampling, signal saturation, terrain effects, and limited model generalization. To solve these challenges, this study proposes a new synergistic multi-source remote sensing framework for regional-scale AGB estimation by integrating terrain-corrected ICESat-2 canopy height and forest-type heterogeneity. The framework combines structural, spectral, textural, topographic, and climatic information derived from multiple remote sensing datasets to improve biomass estimation accuracy and model robustness across different forest types. In this paper, multi-source datasets were integrated, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission (SRTM), WorldClim, and a terrain-corrected canopy height model (CHM). Subsequently, candidate features were derived such as spectral, textural, topographic, and climatic variables. In terms of the terrain-corrected CHM, canopy structural parameters were extracted from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) ATL08 data after terrain correction based on a high-resolution DEM. Footprint-level AGB samples were first generated using ICESat-2-derived canopy structural parameters through four regression approaches, including Multiple linear regression, Stepwise multiple regression, Ridge regression, and Lasso regression. These generated AGB samples were then used as response variables for subsequent regional-scale modeling. To build accurate AGB estimation model, key features were first identified using correlation analysis. To account for forest structural heterogeneity, three models including random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were developed for regional AGB mapping. To evaluate the performance of the proposed AGB estimation model by integrating terrain-corrected canopy height and forest-type heterogeneity, this study conducted AGB estimation at the Harvard Forest (HARV) site in the United States. The experimental results show that forest-type-specific modeling improves model adaptability and robustness. Among the models (RF, XGBoost and SVM), RF achieved the best performance, with an average coefficient of determination of 0.694. The optimized model was applied to produce a 30 m resolution AGB map. The validation was conducted using airborne LiDAR-derived AGB referenced results. The validation shows that an overall coefficient of determination (R²) of 0.606 and a root mean square error (RMSE) of 16.53 Mg ha⁻¹. These results demonstrate that the proposed new synergistic AGB estimation framework, which integrates terrain-corrected ICESat-2 canopy height with forest-type-specific modeling, provides an accurate and reliable solution for regional-scale forest biomass mapping and carbon stock assessment. Full article

Real-time monitoring of cotton above-ground biomass (AGB) is crucial for monitoring crop growth and optimizing management practices. This study evaluated UAV-based RGB and multispectral (MS) imagery for cotton AGB estimation across multiple growth stages under different planting densities and sowing dates in Anyang, China. Spectral features, vegetation indices (VIs), and Gray Level Co-occurrence Matrix (GLCM) texture metrics were extracted and organized into three scenarios: RGB + MS, RGB-only, and MS-only. Recursive feature elimination with cross-validation (RFECV) was applied for feature selection, and six machine learning models were evaluated using both baseline and selected feature sets. Results showed that model performance was strongly influenced by growth stage, sensor configuration, and feature composition. Accuracy was highest at the seedling and squaring stages and decreased at flowering due to canopy complexity and spectral saturation. MS-only and fused features generally performed best at the seedling stage, while RGB-only features were competitive or superior at the squaring stage, highlighting the importance of high-resolution structural information. At flowering, fused RGB–MS features provided the most stable performance, although improvements were limited. RFECV exhibited stage-dependent behavior, improving performance mainly at early growth stages but showing inconsistent benefits later. SHAP analysis revealed a shift from texture-dominated predictors at the seedling stage to balanced feature contributions at squaring and vegetation index (VIs) dominance at flowering. Overall, cotton AGB estimation is a stage-dependent process requiring adaptive sensor and feature selection strategies. Full article

This study addresses the challenge of drought stress in ornamental plants by evaluating the drought response of Cuban oregano (Coleus amboinicus Lour.) and the potential of sea fennel (Crithmum maritimum L.) extracts as biostimulants to mitigate the effects of water deficit. Conducted in a greenhouse under two irrigation regimes, full irrigation (100% water replacement) and deficit irrigation (50%), the research applied foliar treatments of sea fennel extracts prepared via aqueous extraction (WE) and alcoholic extraction (AE) at various concentrations. Drought stress significantly reduced total dry biomass by 48%, leaf number by 33%, and leaf area by 57%, severely impacting both aboveground and root growth. Biostimulant treatments alleviated these negative effects, with alcoholic extracts at 2.5 mL L⁻¹ showing the greatest efficacy by increasing biomass by approximately 60% and restoring leaf area to levels comparable to fully irrigated plants. Aqueous extracts provided moderate improvements. Drought induced an increased root-to-shoot ratio, indicating adaptive biomass allocation, while SPAD values and gas exchange parameters (net photosynthesis and stomatal conductance) declined under stress but improved with biostimulant application, especially with AE treatments. Photosystem II efficiency (Fv/Fm) confirmed stress but showed partial recovery in AE-treated plants. Additionally, WE at 5 mL L⁻¹ enhanced anthocyanin and amino acid accumulation, suggesting metabolic adjustments to drought. Overall, alcoholic sea fennel extracts demonstrated superior potential as sustainable biostimulants to enhance drought tolerance in ornamental plants. Full article

Monitoring the condition of grasslands is essential given their vital role in food security, carbon sequestration and other ecosystem services. Harvested aboveground biomass (HAB) and aboveground net primary production (ANPP) are among the most important grassland state indicators. However, spatially explicit production estimates are largely lacking, and grassland area estimations also remain uncertain. This study addresses these gaps for drought-prone Central European grasslands over 2017–2024. We synthesized grassland extent data, collected extensive field measurements on biomass (BM), and used remote sensing-based biophysical proxies to build an ensemble of six linear models for spatial extrapolation at 10 m resolution. Bayesian framework was used for the linear model fitting that also considers uncertainty of the observations. The ensemble mean ANPP was 310.7 ± 19 gBM m⁻², with modest interannual variability. Upscaled country-wide mean ANPP was 34.3 ± 13.3 Mt year⁻¹. The results indicate that, within the frame of the present study, the remote sensing-based linear model selection has a larger influence on the country totals than the grassland area database selection. The results highlight that both grassland area uncertainty and model construction are major sources of uncertainty in biomass estimation that have to be addressed in future studies. Full article