Search Results (112)

Vegetation indices (VIs) have been extensively employed as proxies for gross primary productivity (GPP). However, it is unclear how the spatial resolution effects the performance of VIs in GPP estimation in different biomes when matching the flux tower footprint. Here, we examined the relationship with tower GPP between Landsat-footprint VIs and MODIS-footprint VIs. We first calculated Landsat-footprint VIs (e.g., Normalized Difference Vegetation Index (NDVI), enhanced vegetation index (EVI), two-band EVI (EVI2), near-infrared reflectance of vegetation (NIRv) and kernel Normalized Difference Vegetation Index (kNDVI)) averaged over all the pixels within the footprint and MODIS-footprint VIs with 3 × 3 km or 1 × 1 km around the tower, respectively. We then examined the relationship between Landsat- and MODIS-footprint VIs and tower GPP at monthly scale over 76 FLUXNET sites across ten vegetation types worldwide. The results showed that Landsat-footprint VIs had stronger performance than MODIS-footprint VIs for GPP estimation in all ecosystems, with high improvement on croplands, wetlands, and grasslands and moderate improvements on mixed forest, evergreen needleleaf forest, and deciduous broadleaf forest. Moreover, NIRv showed a stronger correlation with tower-based GPP than NDVI, EVI, EVI2, and kNDVI in ten ecosystems both at 30 m and 500 spatial resolutions. Our findings highlighted the critical role of VIs with high spatial resolution and footprint-aware matching in GPP estimation. Full article

Chinese forest ecosystems are key carbon sinks that significantly contribute to lowering carbon emissions. Accurate Net Ecosystem Productivity (NEP) estimations are essential for evaluating their carbon sequestration capabilities and overall health. This study employed the Physiological Principles Predicting Growth-Satellites (3-PGS) and soil heterotrophic respiration models to simulate China’s forest carbon sinks and sources distribution from 2013 to 2023. Then, climatic factors influencing NEP changes were examined through the application of a geographical detector model. The net carbon sequestered was 1.71 ± 0.09 PgC with an annual average of 0.156 ± 0.0071 PgC, signifying a substantial carbon sink in China’s forest. The annual NEP was highest in evergreen broadleaf forests (352.12 gC m⁻²) and lowest in deciduous needleleaf forests (148.31 gC m⁻²). NEP in China’s forests increased by a rate of 1.67 gC m⁻² annually, with most regions exhibiting a 275.32 gC m⁻² annual carbon sink. The geographical detector model analysis showed that solar radiation, precipitation, and vapor pressure deficit were the main drivers of NEP change, while temperature and frost days had a secondary influence. Furthermore, the interaction between solar radiation and temperature variables showed the greatest impact. This study can enhance the understanding of carbon sink and source distribution in China, serve as a reference for regional carbon cycle research, and provide key insights for policymakers in developing effective climate strategies. Full article

Drought, a complex and frequent natural hazard in the context of global change, poses a major threat to key forest ecosystems in the carbon cycle. However, current research lacks a systematic and quantitative analysis of the multi-factor drivers of drought sensitivity based on lagged and accumulative effects. To address this gap, a drought sensitivity model was established by integrating both lagged and accumulative effects derived from long-term remote sensing datasets. To leverage both predictive power and interpretability, the XGBoost–SHAP framework was employed to model nonlinear associations and identify the threshold effects of driving factors. In addition, the Geodetector model was applied to examine spatially explicit interactions among multiple drivers, thereby uncovering the coupling effects that jointly shape forest drought sensitivity across China. The results reveal the following: (1) Drought had lagged and accumulative effects on 99.52% and 95.55% of forest GPP, with evergreen broadleaf forest showing the strongest effects and deciduous needleleaf forest the weakest. (2) Evergreen needleleaf forests have the highest proportion of extremely high drought sensitivity (16.94%), while deciduous needleleaf forests have the least (1.02%), and the drought sensitivity index declined in 67.12% of forests over decades. (3) Temperature and precipitation are the primary drivers of drought sensitivity, with clear threshold effects. Evergreen forests are mainly driven by climatic factors, while forest age is a key driver in deciduous needleleaf forests. (4) Interactive effects among multiple factors significantly amplify spatial variations in drought sensitivity, with water–heat coupling dominating in evergreen forests and structure–climate interactions prevailing in deciduous forests. Full article

This study utilized the North American PhenoCam network to evaluate phenological characteristics and their relationships with geographic and climatic factors across deciduous broadleaf (n = 39) and evergreen needleleaf (n = 13) forests over the past decade. Using high temporal resolution near-surface imagery, key phenological indicators including the start, end, and length of growing season were derived and analyzed using linear regression and structural equation modeling. The results revealed substantial spatial variation; the evergreen needleleaf sites exhibited earlier starts to the growing season (112 vs. 130 Julian date), later ends to the growing season (286 vs. 264 Julian date), and longer lengths for the growing season (172 vs. 131 days) compared with the deciduous broadleaf sites. Latitude was significantly related to the start of the growing season and the length of the growing season at the deciduous broadleaf sites (R² = 0.28–0.41, p < 0.01), while these relationships were weaker at the evergreen needleleaf sites, and elevation had mixed effects. The mean annual temperature strongly influenced the phenology for both forest types (R² = 0.18–0.76, p < 0.01), whereas longitude, distance to the coast, and precipitation had negligible effects. Temporal trends in the phenological indicators were sporadic across both the deciduous broadleaf and evergreen needleleaf sites. Structural equation modeling revealed distinct causal pathways for each forest type, highlighting complex interactions among the geographical and climatic variables. At the deciduous broadleaf sites, geographical factors (latitude, elevation, and distance to the nearest coast) predominated the mean annual temperature, which in turn significantly affected phenological development (χ² = 2.171, p = 0.975). At the evergreen needleleaf sites, geographical variables had more complex effects on the climatic factors, start of the growing season, and end of the growing season, with the end of the growing season emerging as the primary determinant of growing season length (χ² = 0.486, p = 0.784). The PhenoCam network provides valuable fine-scale phenological dynamics, offering great insights for forest management, biodiversity conservation, and understanding carbon cycling under climate change. Full article

Drought disturbances are becoming more frequent with global warming. Accurately assessing the regulatory effect of drought on vegetation phenology is key to understanding terrestrial ecosystem response mechanisms in the context of climate change. Previous studies on cumulative and lagged effects of drought on vegetation growth have mostly focused on a single vegetation type or the overall vegetation NDVI, overlooking the possible influence of different adaptation strategies of different vegetation types and differences in drought effects on different phenological nodes. This study investigates the cumulative and lagged effects of drought on vegetation phenology across a region of East Asia from 2001 to 2020 using NDVI data and the Standardized Precipitation Evapotranspiration Index (SPEI). We analyzed the start of growing season (SOS) and end of growing season (EOS) responses to drought across four vegetation types: deciduous needleleaf forests (DNFs), deciduous broadleaf forests (DBFs), shrublands, and grasslands. Results reveal contrasting phenological responses: drought delayed SOS in grasslands through a “drought escape” strategy but advanced SOS in forests and shrublands. All vegetation types showed earlier EOS under drought stress. Cumulative drought effects were strongest on DNFs, SOS, and shrubland SOS, while lagged effects dominated DBFs and grassland SOS. Drought impacts varied with moisture conditions: they were stronger in dry regions for SOS but more pronounced in humid areas for EOS. By confirming that drought effects vary by vegetation type and phenology node, these findings enhance our understanding of vegetation adaptation strategies and ecosystem responses to climate stress. Full article

The temperate evergreen needleleaf forest (ENF), primarily composed of Mongolian Scots pine (Pinus sylvestris var. mongolica), plays a pivotal role in the “The Great Green Wall” Shelterbelt Project in northern China as a major species for windbreak and sand fixation. Solar-induced chlorophyll fluorescence (SIF) has emerged as a revolutionary remote sensing signal for quantifying photosynthetic activity and gross primary production (GPP) at the ecosystem scale. Meanwhile, eddy covariance (EC) technology has been widely employed to obtain in situ GPP estimates. Although a linear relationship between SIF and GPP has been reported in various ecosystems, it is mainly derived from satellite SIF products and flux-tower GPP observations, which are often difficult to align due to mismatches in spatial and temporal resolution. In this study, we analyzed synchronous high-frequency SIF and EC-derived GPP measurements from a Mongolian Scots pine plantation during the seasonal transition (August–December). The results revealed the following. (1) The ENF acted as a net carbon sink during the observation period, with a total carbon uptake of 100.875 gC·m⁻². The diurnal dynamics of net ecosystem exchange (NEE) exhibited a “U”-shaped pattern, with peak carbon uptake occurring around midday. As the growing season progressed toward dormancy, the timing of CO₂ uptake and release gradually shifted. (2) Both GPP and SIF peaked in September and declined thereafter. A strong linear relationship between SIF and GPP (R² = 0.678) was observed, consistent across both diurnal and sub-daily scales. SIF demonstrated higher sensitivity to light and environmental changes, particularly during the autumn–winter transition. Cloudy and rainy conditions significantly affect the relationship between SIF and GPP. These findings highlight the potential of canopy SIF observations to capture seasonal photosynthesis dynamics accurately and provide a methodological foundation for regional GPP estimation using remote sensing. This work also contributes scientific insights toward achieving China’s carbon neutrality goals. Full article

Soil nutrient and fertility assessments provide a direct measure for evaluating forest management effects. In this study, we examined soil nutrient content in Chinese fir (Cunninghamia lanceolata) plantations under four reconstruction patterns: pure plantation, introduced broadleaf, introduced needleleaf, and introduced mixed broadleaf-needleleaf. The soil fertility index (SFI) evaluation model was constructed based on partial least squares path modeling (PLS-PM), revealing the influence of stand characteristics on SFI in early stand reconstruction. The results showed that, compared to pure plantations, total nutrient content increased in the introduced needleleaf pattern by 13.94% to 21.15% and available nutrient content by 18.21% to 26.91%. In contrast, both introduced broadleaf and mixed broadleaf-needleleaf exhibited a declining trend. Significant differences were observed among the reconstruction patterns (p < 0.05). In the SFI evaluation model, soil chemistry total nutrient (SCT) and soil chemistry available nutrient (SCA) made significant contributions. The weights of SCT and SCA in SFI were 0.52 and 0.48, respectively. The SFI of four patterns ranged from 0.43 to 0.58, indicating relatively low soil fertility. Compared to pure plantations, introduced trees did not enhance soil fertility in early stand reconstruction. The SFI of the introduced needleleaf was significantly higher than that of the other two reconstruction patterns (p < 0.05). Stand construction (including diameter at breast height, tree density, and tree biomass) explained 14.69% of SFI variation, with a contribution of 31.72% in the surface soil layer (0~20 cm). Tree biomass significantly influenced SFI variation, accounting for over 40% of the total stand factors. Retained tree biomass had a substantially greater effect than introduced tree biomass, contributing twice as much to SFI variation. PLS-PM could effectively reflect the soil nutrient status and accurately estimate the weight of soil fertility. In early stand reconstruction, retained tree biomass might be the major influence on soil fertility variation. We suggest determining reasonable thinning intensity to retain enough Chinese fir and promote the growth of introduced trees. This study introduces a novel approach to soil fertility assessment and provides theoretical support for formulating effective forest management strategies in the early reconstruction of Chinese fir plantations. Full article

Accurately understanding the changes in global light-response parameters (i.e., maximum light use efficiency, LUEmax) is essential for improving the simulation of terrestrial ecosystem’s photosynthetic carbon cycling under climate change, but a comprehensive understanding and assessments are still lacking. In this study, LUEmax was quantified using data from 23 global flux stations, and the change patterns in LUEmax across various vegetation types and climate zones were analyzed. The extent of significant increases or decreases in LUEmax during different phenological stages of vegetation growth was evaluated using trend analysis methods. The contribution rates of environmental factors were determined using the Geodetector method. The results show that the LUEmax values of the same vegetation type varied across different climate types. More variable climates (e.g., polar and alpine climates) are associated with more significant fluctuations in LUEmax. Conversely, more stable climates (e.g., temperate climates) tend to show more consistent LUEmax values. Within the same climate type, evergreen needleleaf forests (ENF) and deciduous broadleaf forests (DBF) generally exhibited higher LUEmax values in temperate and continental climates, whereas the LUEmax values of wetlands (WET) were relatively high in polar and alpine climates. The mechanisms driving variations in LUEmax across different vegetation types exhibited significant disparities under diverse environmental conditions. For ENF and DBF, LUEmax is predominantly influenced by temperature and radiation. In contrast, the LUEmax of GRA, WET, and croplands is more closely associated with vegetation indices and temperature factors. The findings of this study play an important role in advancing the theoretical development of gross primary productivity (GPP) models and enhancing the accuracy of carbon sequestration simulations in terrestrial ecosystems. Full article

The interaction between trees’ water needs during drought and the signals that appear in their canopies is not fully understood. The first visually detectable signs, which we describe as early warning signals in tree canopies, are often not noticeable at first glance. When these signs become widely apparent, tree decline is already underway. In this study, we focus on identifying early visible signs of drought stress in the tree crowns, such as very small leaves, premature needle/leaf discolouration and abscission, and defoliation. We provide guidance on recognising initial signs, offer specific examples, and comprehensively analyse each signal. Our focus is on signs in the tree crowns that appear during intense and prolonged droughts, which we confirmed by calculating the Standardised Precipitation Evapotranspiration Index (SPEI). Our findings are based on 20 years (2004–2024) of continuous fieldwork and data collection from permanent sample plots in Serbia, which was conducted as part of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). We also conducted a comprehensive review of the literature and key findings related to the early signs we address. This research was further motivated by the signs observed in the tree crowns during the summer of 2024 due to extreme climatic events, which classify this year as one of the hottest recorded in Serbia. However, we still cannot conclusively determine which specific trees will die back based solely on these early warning signals, as some trees manage to withstand severe drought conditions. Nonetheless, the widespread appearance of these indicators is a clear warning of significant ecosystem instability, potentially leading to the decline of individual trees or larger groups. Full article

The spatiotemporal variability of precipitation profoundly influences terrestrial carbon fluxes, driving shifts between carbon source and sink dynamics through gross primary productivity (GPP) and ecosystem respiration (ER). As a result, the sensitivities of GPP and ER to precipitation (S_GPP and S_ER), along with their differential responses, are pivotal for understanding ecosystem reactions to precipitation changes and predicting future ecosystem functions. However, comprehensive evaluations of the spatiotemporal variability and differences in S_GPP and S_ER remain notably scarce. In this study, we utilized eddy covariance flux data to investigate the spatial patterns, temporal dynamics, and differences in S_GPP and S_ER. Spatially, S_GPP and S_ER were generally strongly correlated. Among different ecosystems, the correlation between S_GPP and S_ER was lowest in mixed forest and highest in broadleaf and needleleaf forest. Within the same ecosystem, S_GPP and S_ER exhibited considerable variation but showed no significant differences. In contrast, they differed significantly across ecosystems, with pronounced variability in their magnitudes. For example, shrubland exhibited the highest values for S_GPP, whereas needleleaf forest showed the highest values for S_ER. Temporally, S_ER demonstrated more pronounced changes than S_GPP. Different ecosystems displayed distinct trends: shrubland exhibited an upward trend for both metrics, while grassland showed a downward trend in both S_GPP and S_ER. Forest, on the other hand, maintained stable S_GPP but displayed a downward trend in S_ER. Additionally, S_GPP and S_ER exhibited a notable non-linear response to changes in the aridity index (AI), with both showing a rapid decline followed by stabilization. However, S_ER demonstrated a wider adaptive range to precipitation changes. Generally, this research enhances our understanding of the spatiotemporal variations in ecosystem carbon fluxes under changing precipitation patterns. Full article

This study examines climate change impacts on evapotranspiration in Inner Mongolia, analyzing potential (PET) and actual (AET) evapotranspiration shifts across diverse land-use classes using the SEBAL model and SSP2-4.5 and SSP5-8.5 projections (2030–2050) relative to a 1985–2015 baseline. Our findings reveal substantial PET increases across all LULC types, with Non-Vegetated Lands consistently showing the highest absolute PET values across scenarios (931.19 mm under baseline, increasing to 975.65 mm under SSP5-8.5) due to limited vegetation cover and shading effects, while forests, croplands, and savannas exhibit the most pronounced relative increases under SSP5-8.5, driven by heightened atmospheric demand and vegetation-induced transpiration. Monthly analyses show pronounced PET increases, particularly in the warmer months (June–August), with projected SSP5-8.5 PET levels reaching peaks of over 500 mm, indicating significant future water demand. AET increases are largest in densely vegetated classes, such as forests (+242.41 mm for Evergreen Needleleaf Forests under SSP5-8.5), while croplands and grasslands exhibit more moderate gains (+249.59 mm and +167.75 mm, respectively). The widening PET-AET gap highlights a growing vulnerability to moisture deficits, particularly in croplands and grasslands. Forested areas, while resilient, face rising water demands, necessitating conservation measures, whereas croplands and grasslands in low-precipitation areas risk soil moisture deficits and productivity declines due to limited adaptive capacity. Non-Vegetated Lands and built-up areas exhibit minimal AET responses (+16.37 mm for Non-Vegetated Lands under SSP5-8.5), emphasizing their limited water cycling contributions despite high PET. This research enhances the understanding of climate-induced changes in water demands across semi-arid regions, providing critical insights into effective and region-specific water resource management strategies. Full article

The leaf area index (LAI) is a key indicator of vegetation canopy structure and growth status, crucial for global ecological environment research. The Moderate Resolution Spectral Imager-II (MERSI-II) aboard Fengyun-3D (FY-3D) covers the globe twice daily, providing a reliable data source for large-scale and high-frequency LAI estimation. VI-based LAI estimation is effective, but species and growth status impacts on the sensitivity of the VI–LAI relationship are rarely considered, especially for MERSI-II. This study analyzed the VI–LAI relationship for eight biomes in China with contrasting leaf structures and canopy architectures. The LAI was estimated by adaptively combining multiple VIs and validated using MODIS, GLASS, and ground measurements. Results show that (1) species and growth stages significantly affect VI–LAI sensitivity. For example, the EVI is optimal for broadleaf crops in winter, while the RDVI is best for evergreen needleleaf forests in summer. (2) Combining vegetation indices can significantly optimize sensitivity. The accuracy of multi-VI-based LAI retrieval is notably higher than using a single VI for the entire year. (3) MERSI-II shows good spatial–temporal consistency with MODIS and GLASS and is more sensitive to vegetation growth fluctuation. Direct validation with ground-truth data also demonstrates that the uncertainty of retrievals is acceptable (R² = 0.808, RMSE = 0.642). Full article

In the context of global warming, the accelerated degradation of circum-Arctic permafrost is releasing a significant amount of carbon. InSAR can indirectly reflect the degradation of permafrost by monitoring its deformation. This study selected three typical permafrost regions in North America: Alaskan North Slope, Northern Great Bear Lake, and Southern Angikuni Lake. These regions encompass a range of permafrost landscapes, from tundra to needleleaf forests and lichen-moss, and we used Sentinel-1 SAR data from 2018 to 2021 to determine their deformation. In the InSAR process, due to the prolonged snow cover in the circum-Arctic permafrost, we used only SAR data collected during the summer and applied a two-stage interferogram selection strategy to mitigate the resulting temporal decorrelation. The Alaskan North Slope showed pronounced subsidence along the coastal alluvial plains and uplift in areas with drained thermokarst lake basins. Northern Great Bear Lake, which was impacted by wildfires, exhibited accelerated subsidence rates, revealing the profound and lasting impact of wildfires on permafrost degradation. Southern Angikuni Lake’s lichen and moss terrains displayed mild subsidence. Our InSAR results indicate that more than one-third of the permafrost in the North American study area is degrading and that permafrost in diverse landscapes has different deformation patterns. When monitoring the degradation of large-scale permafrost, it is crucial to consider the unique characteristics of each landscape. Full article

Soil and water conservation measures have good carbon sinking capacity, and the comprehensive management of small watersheds involves plant measures, engineering measures and farming measures, which profoundly affect the capacity of the three major carbon pools of soil, vegetation and water bodies, making them an ideal place to carry out the monitoring and accounting of carbon sinks in soil and water conservation. The purpose of this paper is to monitor and evaluate the carbon sinks of soil and vegetation, to provide techniques and methods for the implementation of dynamic monitoring and evaluation of carbon sinks in soil and water conservation projects, and to provide theoretical and methodological support for the participation of soil and water conservation projects in carbon trading and the study of the formulation of relevant rules. In this study, field sampling and analysis, LiDAR, remote sensing and other related parameters were used to account for the carbon storage of vegetation carbon pools and soil carbon pools in the Luodi River sub-watershed, Changting County, Fujian Province, from 2001 to 2022, and to evaluate the carbon sink capacity of the various soil and water conservation management measures in the sub-watershed. The results show that after 21 years of comprehensive management, various soil and water conservation measures in the Luodi River sub-basin have significantly enhanced the role and capacity of carbon sinks, and the sub-basin’s carbon stock increased by 3.97 × 10⁴ t, with an average annual increase of 1.89 × 10³ t/a. From the perspective of the carbon pools, the carbon stocks of soil and vegetation increased by 73.73% and 346.41%, respectively, from 2001 to 2022. The total carbon sunk in the sub-watershed reached 2.90 × 10⁴ t, of which 1.57 × 10⁴ t was in soil carbon sinks and 1.34 × 10⁴ t was in vegetation carbon sinks. There were differences in the ability of various measures to enhance the increment of the carbon sink, among which the Castanea mollissima and the Fertilized Pinus massoniana Forest had the most obvious increase in carbon sunk, followed by the Mixed Needleleaf and Broadleaf Forest, the Nurture and Management Pinus massoniana Forest, and the Horizontal terraces Pinus massoniana Forest, and lastly, the Closed Management Forest and the Morella rubra. Various soil and water conservation measures have obvious effects of carbon retention, carbon sequestration and sink enhancement, while Castanea mollissima and Fertilized Pinus massoniana Forest and other forests that implement land preparation and afforestation with fertilization and nourishment measures have more significant increases in carbon sink capacity, which is an effective measure to improve the benefits of soil and water conservation and increase the amount of carbon sinks. Full article

The latest versions of the Ross-Li model include kernels that represent isotropic reflection of the surface, describe backward reflection of soil and vegetation systems, characterize strong forward reflection of snow, and adequately consider the hotspot effect (i.e., RossThick-LiSparseReciprocalChen-Snow, RTLSRCS), theoretically able to effectively characterize BRDF/Albedo/NBAR features for various land surface types. However, a systematic evaluation of the RTLSRCS model is still lacking for various land cover types. In this paper, we conducted a thorough assessment of the RTLSRCS and RossThick-LiSparseReciprocalChen (RTLSRC) models in characterizing BRDF/Albedo/NBAR characteristics by using the global POLDER BRDF database. The primary highlights of this paper include the following: (1) Both models demonstrate high accuracy in characterizing the BRDF characteristics across 16 IGBP types. However, the accuracy of the RTLSRC model is notably reduced for land cover types with high reflectance and strong forward reflection characteristics, such as Snow and Ice (SI), Deciduous Needleleaf Forests (DNF), and Barren or Sparsely Vegetated (BSV). In contrast, the RTLSRCS model shows a significant improvement in accuracy for these land cover types. (2) These two models exhibit highly consistent albedo inversion across various land cover types (R² > 0.9), particularly in black-sky and blue-sky albedo, except for SI. However, significant differences in white-sky albedo inversion persist between these two models for Evergreen Needleleaf Forests (ENF), Evergreen Broadleaf Forests (EBF), Urban Areas (UA), and SI (p < 0.05). (3) The NBAR values inverted by these two models are nearly identical across the other 15 land cover types. However, the consistency of NBAR results is relatively poor for SI. The RTLSRC model tends to overestimate compared to the RTLSRCS model, with a noticeable bias of approximately 0.024. This study holds significant importance for understanding different versions of Ross-Li models and improving the accuracy of satellite BRDF/Albedo/NBAR products. Full article