Tropical rainforests have complex responses to seasonal climatic variations. Compared to the Amazon and Asian rainforests, the Congo rainforest exhibits a more widespread bimodal seasonal pattern, yet it remains understudied. Here, we use three independent satellite-based vegetation indices to investigate the seasonal variations of the Congo rainforest, including solar-induced chlorophyll fluorescence (SIF), the two-band enhanced vegetation index (EVI2), and vegetation optical depth (VOD), which represent vegetation canopy photosynthesis, greenness, and water content, respectively. We find widespread asynchronous bimodal seasonality among the three vegetation indices, suggesting alternating physiological and structural variations of the Congo rainforest. These bimodal seasonality shifts are driven by temporally diverse environmental factors. The variations in photosynthesis are predominantly driven by temperature from January to June of the first growing season, and by precipitation and temperature from July to December of the second growing season. The bimodal seasonal pattern of vegetation greenness is primarily constrained by precipitation across most of the region. In contrast, variations in vegetation water content are closely related to terrestrial water storage, with a temporal lag of 1 to 2 months. These findings highlight the intertwined seasonality of the vegetation canopy traits and their environmental controls in the Congo rainforest, providing an improved understanding of the vegetation-climate feedback for predicting rainforest responses to climate change.

Precise mapping of forest vegetation, including both dominant tree species and other vegetation types, is essential for forest management and biodiversity conservation, yet traditional high-resolution imagery is limited in spatial coverage. The high revisit frequency of Sentinel-2 provides dense time-series observations that capture phenological differences among species across broad areas. In this study, we compiled all cloud-free Sentinel-2 images from 2023 for the Eastern Qilian Mountains, China. In total, 12 thematic classes were defined in this study, including six dominant tree taxa (five tree species and one Picea genus group), five vegetation-type classes (mixed forests, shrubland, and grassland), and one nonvegetation class. Vegetation phenology metrics were derived using two methods: a curve-fitting approach (TIMESAT) and a harmonic analysis of time-series (HANTS). We integrated these phenological features with the original spectral bands, Sentinel-1 SAR backscatter, texture metrics, and topographic variables as inputs to multiple classifiers (random forest, support vector machine, convolutional neural network, vision transformer, and extreme gradient boosting). We also applied Shapley Additive exPlanations (SHAP) to interpret model feature importance. Without phenological inputs, the best model achieved 92.5% overall accuracy. Incorporating phenological variables from TIMESAT increased accuracy to 96.1%—slightly higher than using HANTS-derived features (95.5%). SHAP analysis revealed that time-series phenological and spectral variables contribute most to species discrimination, while SAR backscatter and topographic metrics provide crucial complementary information. These findings demonstrate that exploiting Sentinel-2’s temporal dynamics alongside multisource data markedly improves tree species classification in complex mountainous forests, and highlight the added value of advanced phenology extraction methods and explainable machine learning techniques.

The Advanced Geostationary Radiation Imager (AGRI) onboard the FengYun-4B (FY4B) satellite-a newgeneration geostationary (GEO) platform-offers spatial and radiometric resolutions comparable to those of polar-orbiting satellites such as EOS-MODIS, but with substantially higher temporal resolution. This enhanced temporal capability expands the potential of GEO observations beyond meteorology into terrestrial sciences. Precise geometric accuracy is essential for quantitative remote sensing, as the reliability of any downstream retrieval algorithm depends on accurate geolocation. Operational correction of geometric errors is challenging due to the scarcity of ground control points and large data volumes. Here, we evaluated the geolocation accuracy of FY4B/AGRI imagery using a full year of data and developed an integrated geometric correction workflow combining the Phase-Only Correlation method based on Fast Fourier Transform (FFT-POC) with a ray-tracing orthorectification process. In the original imagery, significant geometric instabilities were observed: east-west offsets (COFF) frequently fluctuated between +/- 5 and +/- 10 pixels (reaching +/- 15 pixels) due to diurnal thermal deformation and operational maneuvers, whereas north-south offsets (LOFF) remained comparatively stable within +/- 5 pixels. These systematic errors were fully corrected by the FFT-POC step, while the subsequent orthorectification effectively eliminated terrain-induced parallax distortions exceeding 3 pixels in high-altitude regions. The corrected FY4B/AGRI data offers accurate geolocation to support operational hyper-temporal applications such as disaster monitoring and carbon cycle sciences.

Climate change and rising human water demand are intensifying water scarcity across arid Asia, but their relative contributions to terrestrial water storage (TWS) decline in endorheic basins remain poorly quantified. Using satellite gravity observations from GRACE and GRACE‑FO, which measure changes in TWS, we show that TWS anomalies across Asian endorheic basins decreased by 64.26 ± 2.43 Gt yr −1 during 2002–2023. Multi-dataset attribution for 2002-2021 reveals that while climate-driven changes account for roughly half of the total TWS decline (−29.41 Gt yr⁻¹), increasing human water demand severely exacerbates both the rate and magnitude of this depletion. Human water use accounts for 38.0 ± 4.55% (−21.30 Gt yr⁻¹) of the total TWS loss, primarily through irrigation and groundwater abstraction. Depletion is spatially heterogeneous, with faster losses near human activity centres and in drier areas with greater irrigation demand. Current global hydrological models substantially underestimate the observed magnitude of TWS depletion and fail to reproduce these spatial hotspots. Our results demonstrate that localized human water demand is driving severe basin-scale water depletion, underscoring the urgent need for a policy shift from supply-side infrastructure to transboundary demand management to avert irreversible ecological and societal collapse across Earth's fragile drylands.

Recent satellite observations have revealed the patterns and drivers of large-scale afternoon photosynthetic depression (APD). However, existing APD monitoring is limited because APD does not directly reflect physiological stress since it can occur under normal conditions, and monthly metrics used in previous studies obscure rapid, sub-daily fluctuations. To address this gap, we developed an Afternoon Photosynthetic Depression Index (APDI) using the morning–afternoon contrast in photosynthetic efficiency from eddy-covariance flux tower data. Then using two satellite-based hourly gross primary production (GPP) products (BEPS, FLUXCOM), we assessed the ability of APDI to detect extreme APD events (EAPD), and quantify their coupling with extreme vapor pressure deficit (EVPD) globally. Results showed that APDI effectively captured afternoon depression in photosynthesis, which intensified under increasing environmental stress. Site and global analyses showed the strongest temporal correlations of APDI–VPD, and significantly higher APDI in drylands compared to non-drylands (p < 0.001). Further validation showed that BEPS GPP agreed better with site observations (R2 = 0.45–0.46) than FLUXCOM (R2 = 0.34–0.36). Independent geostationary GPP and solar-induced chlorophyll fluorescence (SIF) datasets further confirmed the strong association between APDI and environmental drivers. Using BEPS-derived APDI, EAPD and EVPD occurred globally and showed spatial co-occurrence with mean frequency of 1.55 and 1.76 events yr−1, lasting 6.34 and 8.55 days with severity of 2.56 and 17.05 kPa, respectively. The EAPD duration and severity exhibited significant increasing trends from 2001 to 2019 (0.031 days yr−1 and 0.020 yr−1), and exhibited temporally coherent variations with EVPD (0.057 days yr−1 and 0.144 kPa yr−1), underscoring their link with atmospheric dryness. Quantifying GPP losses reveals that while EAPD accounts for only 0.6% of annual production, it drives 9.6% of interannual productivity fluctuations on average, particularly during extreme climatic episodes. In conclusion, this study provides a diagnostic tool to quantify the impacts of atmospheric dryness on ecosystem functioning, offering a new perspective for assessing terrestrial ecosystem vulnerability to extreme atmospheric dryness.

Accurate monitoring of global biodiversity relies on robust habitat characterization. While traditional optical Dynamic Habitat Indices (DHIs) effectively capture ecosystem physiological energy, their efficacy is fundamentally limited by persistent cloud obscuration and canopy saturation, which obscure the physical architecture of habitats. To overcome these limitations, we propose a novel macro-scale habitat assessment framework that integrates multi-year baseline optical Gross Primary Productivity (GPP) and microwave Vegetation Optical Depth (VOD) to capture stable climatological habitat regimes. We evaluated the predictive performance of single-source and fused DHIs on global terrestrial vertebrate species richness using Random Forest models, aiming to decipher complex non-linear ecological constraints and interacting thresholds. Results demonstrate that the fusion model yields a significant enhancement in predictive accuracy for species richness. Contrary to the prevailing assumption that microwave data primarily serve to "gap-fill" optical observations in cloudy tropical regions, our findings reveal that predictive gains (ΔR 2 ) peak in mid-to-high latitude realms (e.g., Nearctic and Palearctic). This underscores that the core contribution of VOD lies in characterizing habitat structural heterogeneity across complex environments, rather than mere cloud-filling. Furthermore, taxa-specific responses elucidate the ecological mechanisms underlying this optical-microwave synergy. In densely vegetated, closed-canopy forests where optical signals saturate, microwave observations effectively capture the critical sub-canopy hydro-thermal microclimates and vertical structures essential for amphibians and birds. Ultimately, this study facilitates a vital transition in Earth Observation from unidimensional "surface greenness" toward a multidimensional "physical structural architecture," providing a robust, mechanistically driven framework for global biodiversity monitoring amidst environmental change.

The ecologically fragile Asian Endorheic Basins (AEB) recently exhibited a pronounced productivity increase despite regional drying—a paradox known as the ‘greening despite drying’ dilemma. To quantify its drivers, we integrated satellite observations with Dynamic Global Vegetation Models (DGVMs) from 2001 to 2023. We show that 25.2% of the AEB experienced significant productivity gains, heavily driven by rapid irrigation expansion (43.2%), while climate change and CO2 fertilization played minor roles. Crucially, Terrestrial Water Storage Anomaly (TWSA) analysis reveals these gains coincide with accelerated water depletion, confirming an unsustainable water-carbon trade-off where productivity gains are achieved at the expense of regional water storage. Furthermore, state-of-the-art DGVMs significantly underestimated this irrigation-driven productivity trend, incorrectly attributing it primarily to climate change. Our findings underscore the growing dominance of anthropogenic influences in shaping dryland landscapes. This model-observation mismatch highlights an urgent need to improve DGVM representations of irrigation activities and the severe sustainability risks posed by groundwater depletion. Irrigation-driven greening in Asian endorheic basins has boosted vegetation productivity despite accelerated water depletion, revealing an unsustainable water-carbon trade-off, based on satellite observations and dynamic vegetation model comparisons from 2001 to 2023.

Highlights What are the main findings? What are the implications of the main findings?Highlights What are the main findings? What are the implications of the main findings?Abstract Monitoring species richness patterns across large spatial scales is essential for addressing the global biodiversity crisis. Dynamic Habitat Indices (DHIs), derived from satellite-based productivity data, have proven valuable for predicting species distributions. The original DHI framework comprises three complementary sub-indices, each corresponding to a key ecological hypothesis linking productivity and biodiversity: annual cumulative productivity (DHI Cum; available energy hypothesis), annual minimum productivity (DHI Min; environmental stress hypothesis), and the coefficient of variation in productivity (DHI CV; environmental stability hypothesis). However, current DHI formulations primarily focus on intra-annual vegetation productivity dynamics, thereby overlooking the ecological significance of inter-annual productivity variability. To address this limitation, we propose an extended DHI suite that integrates both seasonal (intra-annual) and long-term (inter-annual) productivity metrics. Using a random forest regression approach, we demonstrate that incorporating this extended DHI suite significantly improves predictions of global vertebrate species richness (cross-validated R 2 = 0.89, RMSE = 68.20) compared to using seasonal metrics alone (R 2 = 0.86). Notably, inter-annual productivity variation emerged as the most influential predictor, strongly supporting the environmental stability hypothesis. This was followed by importance in seasonal minimum productivity (environmental stress) and cumulative productivity (available energy). Our findings reveal the critical, complementary roles of seasonal and inter-annual productivity dynamics in shaping global faunal species richness patterns. This enhanced framework provides a robust scalable tool for assessing species richness distributions and informing conservation strategies amid accelerating climate shifts and anthropogenic pressures.

Phenological shifts in boreal forests alter land-atmosphere energy exchange, but annual averages mask pronounced seasonal contrasts in the magnitude and pathways of this feedback. Here we show that climate-driven phenological instability generates a systematic warming asymmetry: surface warming from leaf area loss exceeds the cooling from an equivalent gain. During the growing season, additional foliage yields diminishing cooling returns as transpiration approaches its physiological limits, constraining latent heat flux. During the dormant season, the canopy-snow albedo contrast makes shortwave radiation dominant; because this radiative perturbation is converted into surface temperature highly non-linearly, foliage loss still produces a net warming exceeding the cooling from an equivalent gain. Per unit leaf area change, the median asymmetry index indicates warming roughly 2.0-2.3 times stronger than cooling in spring and autumn, and about an order of magnitude stronger in winter, where a very small greening-side sensitivity inflates the ratio; in summer both responses approach the detection limit and the asymmetry is not directionally robust. Phenological instability therefore acts as a seasonally structured positive biophysical feedback in northern ecosystems. This study uses satellite data and shows that in boreal forests the warming from seasonal leaf area loss outweighs the cooling from an equivalent gain. This asymmetry is driven by transpiration limits in the growing season, and by canopy-snow albedo contrasts in winter.

Leaf chlorophyll content (LCC) is closely linked to vegetation photosynthetic capacity and provides essential information for assessing plant physiological status and ecosystem productivity. Remotely sensed vegetation indices (VIs) are widely used to characterize LCC, but many existing chlorophyll-related VIs are affected by canopy structural effects and may saturate under medium-to-high LCC conditions, especially in dense forest canopies. To address this challenge, this study developed a Sentinel-2-compatible red-edge structure-resistant chlorophyll index (SCI) to improve the trade-off between LCC sensitivity and resistance to LAI-induced structural interference. SCI was developed using global sensitivity analysis and three-dimensional radiative transfer simulations based on LESS, PROSPECT-D, and the General Spectral Vectors soil model. It combines a chlorophyll-sensitive red-edge contrast with a structure-compensation term using Sentinel-2 red, red edge 1, red edge 2, and near-infrared (NIR) bands. SCI was evaluated using simulated datasets, ground-based measurements, and NEON–Sentinel-2 validation data. In simulations, SCI showed a high chlorophyll contribution (84.0%), a negligible LAI contribution (2.5%), and the strongest VI–LCC relationship (R2 = 0.84, RMSE = 8.60 μg cm−2) with negligible LAI dependence (R2 = 0.00). SCI also achieved the strongest LCC relationship in ground-based validation (R2 = 0.66, RMSE = 6.85 μg cm−2) and the highest overall association in NEON–Sentinel-2 validation. Vegetation-type stratification further showed that SCI performed favorably in vegetation types with more complex canopy structures and broader LCC dynamics, such as forests and cultivated crops. This study demonstrates that SCI can reduce LAI disturbance and maintain a near-linear response across a broad LCC range, making it a promising indicator for large-scale LCC monitoring and ecosystem assessment under global environmental change.

Highlights What are the main findings? The performance of GPP models with varying levels of complexity differs substantially across different LAI levels and environmental stress conditions. The process-based FvCB model is more responsive to canopy structure and light distribution, and its resilience to environmental stress increases with LAI. What are the implications of the main findings? Differences in model structures and in the representation of photosynthetic processes lead to substantial variability in GPP estimates. The 3D simulations provide a more realistic testing ground, and compared with the simplified LUE models, the FvCB model better captures differences in canopy sensitivity to environmental stress across LAI levels.Highlights What are the main findings? The performance of GPP models with varying levels of complexity differs substantially across different LAI levels and environmental stress conditions. The process-based FvCB model is more responsive to canopy structure and light distribution, and its resilience to environmental stress increases with LAI. What are the implications of the main findings? Differences in model structures and in the representation of photosynthetic processes lead to substantial variability in GPP estimates. The 3D simulations provide a more realistic testing ground, and compared with the simplified LUE models, the FvCB model better captures differences in canopy sensitivity to environmental stress across LAI levels.Abstract Gross primary production (GPP) models are widely used to estimate carbon fluxes at local and global scales, and play a crucial role in understanding the dynamics of terrestrial carbon cycling. While numerous studies have compared the performance of various GPP models, most evaluations rely on in situ GPP derived from eddy covariance flux towers, which may be constrained by estimation uncertainties and limited spatial representativeness. In this study, we employed a three-dimensional (3D) simulation framework characterized by high accuracy and strong environmental controllability to evaluate the performance of GPP models of varying complexity (FvCB, MOD17, VPM, and MVPM) under different leaf area index (LAI) levels and environmental stress conditions. The results revealed significant differences among the models at both instantaneous and daily scales. Under high-temperature stress, the performance of VPM was most comparable to that of MOD17. FvCB and MOD17 exhibited strong consistency in their sensitivity to environmental variations, whereas MVPM generally produced lower GPP estimates but showed the highest responsiveness to environmental changes. The process-based FvCB model was the most sensitive to canopy structure and light distribution, and its resilience to environmental stress increased with LAI. These findings provide a novel methodological perspective for evaluating GPP models and offer important insights into the structural and mechanistic factors driving performance differences among the models.

A direct empirical relationship between gross primary productivity (GPP) estimated by the eddy covariance method and satellite vegetation indices (VIs) has been widely observed across diverse ecosystems globally. Building on this observed covariation, VIs are frequently utilized as critical parameters - such as the fraction of absorbed photosynthetically active radiation (fPAR) - within light use efficiency (LUE) and greenness-based models for carbon cycle monitoring. However, actual canopy carbon assimilation is jointly governed by slowly evolving structural parameters and highly dynamic functional traits, such as physiological efficiency. The extent to which the macro-scale VI-GPP covariance is driven by structural scaffolding, and how this structural signal decouples from physiological function under environmental stress, remains to be systematically quantified. Here, we synthesized half-hourly eddy covariance measurements from 328 globally distributed sites and paired them with a rigorously angle-normalized Enhanced Vegetation Index (nadir view and fixed solar zenith angle at 30 degrees, EVI_SZA30). By applying a nonlinear light-response curve model across 54,720 high-frequency temporal windows, we mechanistically disentangled observed actual GPP (GPP_EC) into baseline photosynthetic capacity (P_c) and intrinsic quantum yield (alpha). Our results demonstrate that the macroscopic covariance between EVI_SZA30 and GPP_EC (R^2=0.554) is primarily driven by the index's robust ability to track structural capacity (P_c, R^2=0.538). In contrast, EVI_SZA30 exhibits limited sensitivity to high-frequency variations in functional traits like physiological efficiency (alpha, R^2=0.038). Particularly in water-limited biomes (e.g., open shrublands and woody savannas), intense environmental stress triggers rapid stomatal regulation while the physical canopy structure remains relatively stable. Consequently, the correlation between EVI and P_c becomes notably stronger than its correlation with actual GPP_EC, highlighting a pronounced structural-physiological decoupling. Because discrete overpasses by sun-synchronous polar-orbiting satellites face intrinsic temporal constraints in capturing sub-daily physiological down-regulation (e.g., midday photosynthetic depression), future monitoring paradigms could greatly benefit from the continuous, high-frequency observations provided by next-generation geostationary (GEO) satellites to bridge the gap between structural parameters and transient ecosystem function.

Groundwater-dependent ecosystems (GDEs) provide valuable ecosystem services but are threatened by climate change and increased human groundwater use. Sustainable groundwater management requires knowledge of the spatial distribution of GDEs, but this remains a challenge. Here we developed an effective workflow for mapping GDEs and their dependence on groundwater at 30 m resolution over the Hexi Corridor, northwestern China, by integrating multi-source Earth observations. The method integrates seasonal Landsat NDVI and NDWI indices with an unsupervised ISODATA clustering algorithm and groundwater depth datasets to classify GDEs. The accuracy of the mapping results was validated by multiple measurements. Results show that 34.23% of the study area is GDE, mainly in shallow water table and arid regions, making them vulnerable to exploitation. We also found that the rate of decline of the GRACE terrestrial water storage anomaly (TWSA), a satellite proxy for groundwater dynamics, was faster in areas with high GDE occurrence, suggesting that urgent groundwater management actions are needed to avoid the potential loss of valuable services provided by these ecosystems to the human society. While demonstrated in northwestern China, the workflow is adaptable to other global regions, enabling consistent and dynamic monitoring of GDEs. The GDE map has been uploaded to the data repository for free public access:(http://datadryad.org/stash/share/mODbi5SlXI_oeV1E_rWs5SD7eZEz-HfDKgB8JxET-ZA).

Conservation of global biodiversity requires scalable tools to monitor species richness patterns, and satellite remote sensing offers a promising avenue. However, a great challenge lies in identifying how best to translate satellite data into ecologically meaningful biodiversity metrics. This study examines the effectiveness of dynamic habitat indices (DHIs) derived from satellite vegetation products, including gross primary productivity (GPP), fraction of absorbed photosynthetically active radiation, leaf area index, normalized difference vegetation index, enhanced vegetation index, and solar-induced chlorophyll fluorescence, in capturing global species richness across amphibians, birds, mammals, and reptiles. The DHIs consist of 3 subindices, with each representing an important productivity–species richness hypothesis, namely, annual cumulative productivity (DHI Cum, available energy hypothesis), annual minimum productivity (DHI Min, environmental stress hypothesis), and coefficient of variation of productivity (DHI CV, environmental stability hypothesis). Results showed that DHIs derived from satellite GPP data explain a large proportion of the variance in species richness globally (R2 = 0.70 for amphibians, R2 = 0.78 for birds, R2 = 0.77 for mammals, R2 = 0.77 for reptiles, and R2 = 0.82 when all taxa combined), outperforming other satellite vegetation products. Validation with in situ DHIs calculated from tower-measured GPP at 124 globally FLUXNET sites demonstrated strong agreement with satellite DHIs, supporting the reliability of the satellite GPP-based DHIs. Furthermore, the relatively higher uncertainty of satellite DHIs at low-productivity sites also urges further development of satellite GPP algorithms. Globally, protected areas showed significantly higher DHI Cum and Min and lower DHI CV (P < 0.0001), underscoring their superior habitat quality for biodiversity conservation. These findings highlight the potential of DHIs as a powerful and scalable tool for linking satellite observations to global biodiversity patterns, thus bridging the gap between remote sensing and biodiversity conservation community.

The interplay between terrestrial water storage and vegetation dynamics in arid regions is critical for understanding ecohydrological responses to climate change and human activities. This study examines the coupling between total water storage anomaly (TWSA) and vegetation greenness changes in the Hexi Corridor, an arid region in northwestern China consisting of three inland river basins—Shule, Heihe, and Shiyang—from 2002 to 2022. Utilizing TWSA data from GRACE/GRACE-FO satellites and MODIS Enhanced Vegetation Index (EVI) data, we applied a trend analysis and partial correlation statistical techniques to assess spatiotemporal patterns and their drivers across varying aridity gradients and land cover types. The results reveal a significant decline in TWSA across the Hexi Corridor (−0.10 cm/year, p < 0.01), despite a modest increase in precipitation (1.69 mm/year, p = 0.114). The spatial analysis shows that TWSA deficits are most pronounced in the northern Shiyang Basin (−600 to −300 cm cumulative TWSA), while the southern Qilian Mountain regions exhibit accumulation (0 to 800 cm). Vegetation greening is strongest in irrigated croplands, particularly in arid and hyper-arid regions of the study area. The partial correlation analysis highlights distinct drivers: in the wetter semi-humid and semi-arid regions, precipitation plays a dominant role in driving TWSA trends. Such a rainfall dominance gives way to temperature- and human-dominated vegetation greening in the arid and hyper-arid regions. The decoupling of TWSA and precipitation highlights the importance of human irrigation activities and the warming-induced atmospheric water demand in co-driving the TWSA dynamics in arid regions. These findings suggest that while irrigation expansion cause satellite-observed greening, it exacerbates water stress through increased evapotranspiration and groundwater depletion, particularly in most water-limited arid zones. This study reveals the complex ecohydrological dynamics in drylands, emphasizing the need for a holistic view of dryland greening in the context of global warming, the escalating human demand of freshwater resources, and the efforts in achieving sustainable development.

The hotspot effect denotes a special case of the Bidirectional Reflectance Distribution Function (BRDF) when the solar direction coincides with the sensor viewing direction, which is essential for remote estimation of canopy structure information. In contrast to polar-orbiting satellites, third-generation geostationary (GEO) meteorological satellites provide a new opportunity to investigate the hotpot effect at a modest spatial resolution (similar to 1 km) due to their extremely high observation frequencies. Nevertheless, modeling of the hotspot effect observed by GEO satellites is a significant challenge because their observations of Bidirectional Reflectance Factor (BRF) are usually off the principal plane. Among the existing semi-empirical BRDF models, the Rahman-PintyVerstraete (RPV) model has been widely applied to simulate intricate fields of canopy BRF. However, the RPV model has also been criticized for underestimating the hotspot signatures. Consequently, an enhanced version of the RPV model (i.e., ERPV) was proposed in this study to improve its capability for modeling the hotspot signatures of canopy reflectance. To verify the proposed ERPV model, a reflectance dataset of hotspot effect for different vegetation types was created using the atmospherically corrected Hiwamari-8 reflectance, and the ERPV model was applied to estimate foliage Clumping Index (CI) through constructing hotspot and dark spot within the principal plane. Validation results demonstrated that the land surface reflectance of Himwari-8 could measure the hotspot effect properly for each vegetation type. The EPRV model yielded satisfactory accuracies in capturing the hotspot signatures with Root-Mean-Square-Error (RMSE) of 0.0034 and 0.0056, and Bias of -0.0019 and -0.0028, for the red and near-infrared (NIR) bands, respectively. In contrast, the RMSE and Bias for the original RPV model and three existing kernel-driven BRDF models ranged from 0.0187 to 0.125, and from -0.0149 to -0.114, respectively. Moreover, the estimated CI based on the ERPV model (0.66) was closer to the field measurement (0.65) for a mixed forest site than the RPV-based CI estimate (0.72) and the MODIS CI product (0.705). The findings demonstrate that our ERPV model can not only improve the modeling accuracies of hotspot signatures, but also has the potential to construct reliable BRF within the principal plane for CI retrieval.

Vegetation indices (VIs), with the advantages of being easy to understand, simple form, and robust, have emerged as a pivotal and widespread tool for monitoring and assessing vegetation health and dynamics. Decades of research have produced numerous VIs, broadening their use and impact across various fields, but possibly overwhelming users with too many options. This study conducted a bibliometric review of VI-related literature in the web of science (WOS) database since 1986, examining current trends and issues in data sources, geographic areas, eco-functional areas, applications, and technical methods. It also analyzed the correlation among 86 VIs from global satellite data and assessed the sensitivity of 16 VIs to different parameters using radiative transfer model simulations at leaf and canopy scales. This review revealed that (1) VI research accelerated since 1986, particularly after 2012, largely due to the availability of earth-observing satellite data and new VIs. (2) The central concern of VI is its sensitivity to vegetation parameters, with recent interest in complex terrain effects. (3) VI is difficult to distinguish structural and spectral information. Optimization of soil-adjusted vegetation indices (OSAVI) has the highest sensitivity to leaf area index (LAI), and Sentinel-2 red edge position (S2REP) has the highest sensitivity to chlorophyll among the 16 selected VIs. Overall, VI performance depends on band selection and formula, with an ideal VI balancing sensitivity to vegetation and interference resistance. VI Selection should be tailored to user needs, focusing on relevant vegetation parameters and the study area’s conditions.

Woody plants serve as crucial ecological barriers surrounding oases in arid and semi-arid regions, playing a vital role in maintaining the stability and supporting sustainable development of oases. However, their sparse distribution makes significant challenges in accurately mapping their spatial extent using medium-resolution remote sensing imagery. In this study, we utilized high-resolution Gaofen (GF-2) and Landsat 5/7/8 satellite images to quantify the relationship between vegetation growth and groundwater table depths (GTD) in a typical inland river basin from 1988 to 2021. Our findings are as follows: (1) Based on the D-LinkNet model, the distribution of woody plants was accurately extracted with an overall accuracy (OA) of 96.06%. (2) Approximately 95.33% of the desert areas had fractional woody plant coverage (FWC) values of less than 10%. (3) The difference between fractional woody plant coverage and fractional vegetation cover proved to be a fine indicator for delineating the range of desert-oasis ecotone. (4) The optimal GTD for Haloxylon ammodendron and Tamarix ramosissima was determined to be 5.51 m and 3.36 m, respectively. Understanding the relationship between woody plant growth and GTD is essential for effective ecological conservation and water resource management in arid and semi-arid regions.

Gross primary productivity (GPP) through photosynthesis is a crucial ecosystem function that significantly influences food security, the carbon cycle, and climate change. Current remote sensing estimates of GPP rely on look-up tables containing biome-specific parameters to model light-use efficiency (epsilon) using coarse-resolution interpolated meteorology data, resulting in significant uncertainties in global GPP estimates. To address this challenge, we propose a simple ecosystem light-use efficiency (eLUE) model to upscale GPP from FLUXNET tower sites to a global scale. Defined as GPP/photosynthetically active radiation (PAR), eLUE differs from the traditional LUE (GPP/absorbed photosynthetic active radiation (APAR) or epsilon) in that eLUE essentially integrates canopy light absorption (fAPAR) and the physiological efficiency of photosynthesis (epsilon), thus eliminating the need for a separate estimate of e. eLUE was calibrated as a function of MODIS enhanced vegetation index (EVI), and then GPP can be modeled directly as eLUE x PAR. To quantify the carbon cycle error budget, we analytically derived GPP uncertainty based on the law of error propagation. Cross-validation against 120 global FLUXNET sites, encompassing 11 plant functional types (PFTs), demonstrated satisfactory performance of the eLUE model (R-2 = 0.74, RMSE = 2.05 g C m(-2) d(-1), NSE = 0.74), outperforming or performing comparably to more sophisticated models. Our estimate of global total terrestrial GPP, averaged between 2001 and 2024, is 135.12 +/- 11.02 Pg C yr(-1). Meanwhile, we found a significant increasing trend in global total GPP at a rate of 0.26 +/- 0.06 Pg C yr(-1) (p < 0.001) from 2001 to 2024, primarily driven by the enhanced CO(2 )sequestration in terrestrial ecosystems across the Northern Hemisphere. We suggest that our eLUE model, with its simple structure, robust performance and clear error representation, will help constrain the global carbon budget and improve the diagnostic analysis of carbon cycle dynamics and climate change feedback. The eLUE-GPP product, available at both global scale and FLUXNET sites, can be accessed for free at https://doi.org/10.5061/dryad.v9s4mw74h

The UN SDG15 'Life on Land,' aims to promote the sustainable management and use of terrestrial ecosystems, with sub-indicator SDG15.1.2 quantifying the proportion of Key Biodiversity Areas (KBAs) covered by protected areas. However, progress on SDG15.1.2 remains unclear, complicating the prioritization of ecosystems with high conservation potential. Here, we propose an innovative framework that utilizes Big Earth Data (BED) to quantify SDG15.1.2 across five mainland Southeast Asian (MSA) countries. This framework employs the Integrative Multidimensional Biodiversity Index (iMBI) to map KBAs, enabling the derivation of SDG15.1.2 by overlaying KBA maps with protected areas. The results indicate that Cambodia (87.3%) and Thailand (63.9%) have relatively high SDG15.1.2, while Myanmar (13%), Vietnam (23.3%), and Laos (25.1%) exhibit considerably lower values, resulting in a regional average of 29.1% for the MSA. While there was a slight upward trend in SDG15.1.2 from 2000 to 2020, the rate of increase remains insufficient to achieve comprehensive legal protection for the majority of KBAs by 2030. Furthermore, we identified areas with high conservation potential that remain unprotected, providing insights for improving SDG15.1.2. Although the MSA serves as a case study, the proposed framework is adaptable to other regions, facilitating consistent and spatially explicit global tracking of UN SDG15.1.2.