Search Results (569)

Changes in vegetation phenology affect ecosystem carbon uptake and water use, thereby regulating water use efficiency (WUE). However, in alpine ecosystems of the Qinghai-Tibetan Plateau (QTP), uncertainty remains regarding the phenological information characterized by different remote-sensing data sources and its associations with WUE. Using solar-induced chlorophyll fluorescence (SIF) and MODIS normalized difference vegetation index (NDVI) data from 2001 to 2018, we derived the start of growth (SOG) and end of growth (EOG) using multiple phenology extraction methods. WUE was calculated using gross primary productivity (GPP) and evapotranspiration (ET) data. We then employed trend analysis, statistical modeling, and a machine learning interpretive framework to systematically evaluate spatiotemporal differences in phenology derived from SIF and NDVI and their associations with WUE. The results showed that: (1) WUE generally increased across the QTP at approximately 0.15 g C m⁻² mm⁻¹ decade⁻¹, with significant increases mainly in the central-eastern and southeastern regions. Both NDVI- and SIF-derived SOG advanced at rates of −1.08 and −1.14 doy decade⁻¹, respectively. In contrast, EOG showed clear data source divergence: EOG_NDVI was delayed by 0.62 doy decade⁻¹, whereas EOG_SIF advanced by −0.48 doy decade⁻¹. SOG_SIF occurred on average 6.6 days later than SOG_NDVI, EOG differences were larger, with EOG_SIF occurring 17.2 days earlier than EOG_NDVI on average. Trend consistency was also higher for SOG than for EOG, whereas opposite EOG trends accounted for 25.3%. (2) After accounting for climatic covariates, SIF- and NDVI-derived phenological indicators showed distinct model-based associations with WUE, but their explanatory contributions were generally weaker than those of key climatic variables. (3) GAM results further showed that SOG was generally negatively associated with standardized WUE in both phenological datasets, whereas the EOG–WUE partial association differed between SIF and NDVI, with positive associations for EOG_SIF and negative associations for EOG_NDVI. This study highlights the differences between SIF- and NDVI-derived phenological indicators and their model-based associations with WUE, providing complementary remote-sensing information for interpreting vegetation phenology and WUE dynamics on the QTP. Full article

Aridification and hotter droughts are triggering forest die-off events characterized by high mortality rates and declines in forest productivity. The western Mediterranean Basin is a climate change hotspot where many of these die-off events have affected several tree and shrub species in recent decades. Yet, the responses of canopy greenness and cover, radial growth, and gross primary productivity (GPP) to climate in these die-off sites remain poorly understood across species and biomes. Here, we examined 44 sites across Spain, covering humid, dry sub-humid, and semi-arid biomes, and including nine tree and one shrub species. We obtained and correlated monthly climate data, satellite-derived vegetation indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index), tree-ring metrics (basal area increment, ring-width indices), and GPP. We assessed climate trends and relationships between climate, vegetation indices, growth, GPP, and resilience after five extreme drought years in the period 1984–2023. Climate warming impacted all sites, increasing vapor pressure deficit and reducing soil moisture availability, with semi-arid sites warming the most. Vegetation indices and growth showed the largest declines during extreme droughts in dry sub-humid and semi-arid sites. Correlations with climate variables highlighted strong sensitivity to drought stress, particularly regarding growth metrics. During die-off events, GPP significantly declined in the growing season, but no legacy effects were observed afterwards. Vegetation indices and growth partially recovered one year after drought, with resilience peaking for GPP in semi-arid sites. Hotter droughts constrain GPP and growth, especially in dry sub-humid and semi-arid forests. Forests and shrublands experiencing die-off are diagnostic monitors of drought-induced thresholds in ecosystem productivity. Full article

Understanding ecosystem carbon processes relies heavily on the reliable assessment of gross primary productivity (GPP) yet remains challenging in the Inner Mongolia grasslands due to data scarcity and high uncertainty among existing products. We developed a ConvTransformer-based framework that exploits complementary information from satellite observations and meteorological datasets to enhance the representation of complex spatiotemporal dependencies in grassland ecosystems. Grounded in leave-one-site-out cross-validation across six eddy covariance sites, the model achieved average performance metrics of R² = 0.59, RMSE = 1.40 g C m⁻² d⁻¹, Bias = −0.31 g C m⁻² d⁻¹, and KGE = 0.46, outperforming traditional machine learning models (RF, GBRT, and SVR) as well as the light use efficiency model (EC-LUE) in both accuracy and robustness. Using this framework, we generated a daily GPP dataset at spatial granularity of 1 km for the Inner Mongolia grasslands from 2003 to 2018. The results reveal a clear spatial gradient, with GPP decreasing from southeast to northwest. Comparisons with established products, including FLUXCOM, BESS V2, and PML V2, show strong spatial consistency and reduced discrepancies, supporting the reliability of the estimates. Overall, the proposed framework provides an effective approach for characterizing regional carbon dynamics and supports long-term ecological monitoring in semi-arid regions. Full article

Shrub encroachment represents a widespread shift in vegetation structure, yet its influence on the relationship between vegetation greening and ecosystem productivity across global aridity gradients remains poorly understood. Focusing on global grasslands as the study domain, we systematically examined shrub-encroached areas across diverse climatic zones spanning aridity gradients from arid to humid regions. Here, we integrated multi-source remote sensing datasets, including MODIS land cover, leaf area index (LAI), and gross primary productivity (GPP), with a global aridity index to systematically detect shrub encroachment. By combining trend analysis, Pettitt change-point detection, and a moving-window pairwise comparison approach, we characterized the spatiotemporal dynamics of encroachment and quantified its differential effects on vegetation dynamics. Our results showed that the global extent of shrub encroachment expanded continuously from 2002 to 2022, with an abrupt change detected between 2010 and 2014. Relative to comparable non-encroached coverage pixels (NSEC), comparable shrub-encroached coverage pixels (CSEC) exhibited generally increasing trends in LAI, fractional vegetation cover (FVC), and GPP, with enhancement magnitudes showing strong aridity gradient dependence, peaking in semi-arid and dry sub-humid regions, indicating that shrub encroachment is substantially regulated by water limitation. Furthermore, joint trend analysis revealed that the concurrent increase in both LAI and GPP (LAI+GPP+) was the dominant pattern, observed in approximately 70% of encroached areas, although decoupling persisted at high latitudes and in certain humid regions (LAI+GPP−). These findings demonstrate that shrub encroachment is fundamentally regulated by moisture gradients and exhibits pronounced spatial heterogeneity, providing new evidence for understanding carbon cycling dynamics and informing grassland ecosystem management under increasing aridity. Full article

Rubber plantations are a key component of managed forest ecosystems. Quantifying the carbon budget is essential for assessing their carbon sequestration potential and informing sustainable management practices. However, previous studies have focused primarily on individual carbon pools or specific regions, lacking a comprehensive assessment of the carbon budget in rubber plantation ecosystems (RPEs). This study systematically synthesizes the carbon budget of RPEs based on 678 data points extracted from 58 publications. The results indicate that (1) The carbon stock of RPEs (including plant, soil (0–100 cm), and litter carbon stocks) shows an accumulation trend with stand age, increasing from an average of 113.41 ± 21.63 tC ha⁻¹ in young plantations to 252.64 ± 24.61 tC ha⁻¹ in over-mature plantations. (2) RPEs exhibit high photosynthetic capacity and significant carbon sequestration potential during rotation phase, with mean gross primary productivity (GPP) of 22.99 ± 2.14 tC ha⁻¹ yr⁻¹, mean ecosystem respiration (Reco) of 13.92 ± 2.87 tC ha⁻¹ yr⁻¹, and net ecosystem carbon exchange (NEE) of −9.07 ± 1.91 tC ha⁻¹ yr⁻¹. (3) The carbon sequestration capacity of RPEs is influenced by stand age, and carbon sink capacity varies across different planting regions. (4) RPEs act as carbon sinks during rotation phase (−9.07 ± 1.91 tC ha⁻¹ yr⁻¹), with mean carbon storage of 196.13 ± 23.58 tC ha⁻¹ (comprising plant biomass, litterfall, and soil carbon stocks of 70.25 ± 17.47, 2.50 ± 1.30, and 123.38 ± 14.47 tC ha⁻¹, respectively). This synthesis provides representative baseline values for RPEs carbon dynamics, offering a scientific foundation for assessments of carbon sequestration potential and management practices. Full article

Ongoing global climate change and intensified human activities have increased the frequency and intensity of droughts, posing a serious threat to global ecosystems and agricultural sustainability. However, the seasonally differentiated effects of droughts on cropland phenology and productivity, especially in Northeast China, remain insufficiently understood, limiting the assessment of agro-ecosystem vulnerability and the development of effective adaptation strategies. In this study, the standardized precipitation evapotranspiration index (SPEI) was used to assess the frequency and severity of extreme drought in Northeast China based on run theory. Cropland phenology parameters and productivity were derived from time-series MODIS normalized difference vegetation index (NDVI), and gross primary productivity (GPP) products, which were smoothed using a Savitzky–Golay (S–G) filter. Correlation analyses were conducted to examine regional associations between SPEI-defined drought conditions and cropland phenology and productivity. Results show that: (1) Drought events occurred frequently in the central and southern parts of Northeast China, particularly in the Songnen Plain (5.22 events per decade) and the Liaohe Plain (4.89 events per decade); (2) the Songnen Plain showed significant increases (Sen’s slope > 0, p < 0.05) across all drought metrics over 2001–2020, which coincided with LOS shortening (−0.18 d a⁻¹) and GPP decline (−9.12 g C m⁻² a⁻¹); in contrast, the Sanjiang Plain exhibited slight declines (Sen’s slope, p > 0.05) in drought metrics, resulting in LOS lengthening (0.06 d a⁻¹) and GPP increases (7.84 g C m⁻² a⁻¹); and (3) drought impacts were strongly season-dependent, with autumn droughts showing a stronger association with reductions in crop productivity in local areas of Northeast China. These findings highlight the need to account for crop responses to drought events, which is essential for developing measures to cope with drought and protecting regional food security. Full article

The Huang–Huai–Hai Plain supports one of China’s most important grain production systems, but crop production there is persistently constrained by limited water availability and recurrent drought. Common regional drought indicators are useful for monitoring dry conditions, yet they do not explicitly represent crop water demand and irrigation input, which reduces their suitability for agricultural risk assessment. In this study, a crop-oriented framework was developed for winter wheat and summer maize by linking crop water requirement, effective rainfall, irrigation supply, drought-event detection, and productivity-risk estimation. A standardized crop water deficit index (SCWDI) was developed from crop water balance and integrated with run theory, monthly correlation analysis, and a Copula–Bayesian framework to detect drought events, identify crop-sensitive periods, and quantify the probability and triggering threshold of gross primary productivity (GPP) loss. During 2001–2022, the Huang–Huai–Hai Plain experienced an average of 1.15 drought events per year, with pronounced spatial differences. The main sensitive period was June for summer maize and March–April for winter wheat. Summer maize showed a stronger drought response, with a mean triggering threshold of −1.54, whereas winter wheat required more severe stress to trigger concentrated productivity loss (−2.54). Under extreme drought, the probability of summer-maize GPP loss exceeded 80% in both the Beijing–Tianjin–Hebei region and Henan. These results provide a basis for growth-stage-oriented irrigation prioritization and spatially differentiated drought management under agricultural water scarcity. Full article

The eastern coast of China, characterized by a pronounced climatic gradient and diverse ecosystems, is an ideal region for exploring the spatiotemporal dynamics of carbon fluxes and their drivers. Based on observations from eight flux tower sites, together with meteorological, remote sensing, and ecohydrological variables from 2001 to 2022, this study developed Back Propagation (BP), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) models to estimate regional gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP). Among them, RF performed best, achieving validation R² values of 0.92, 0.84, and 0.83 for GPP, ER, and NEP, respectively, and was therefore selected for regional upscaling. The regional mean GPP, ER, and NEP were 1578.38, 1286.05, and 334.56 g C m⁻² yr⁻¹, respectively, indicating that the region functioned as a net carbon sink during the study period. GPP, ER, and NEP exhibited a clear spatial gradient, with higher values in the south and lower values in the north. Total regional NEP increased from 344.12 Tg C in 2001 to 517.73 Tg C in 2022, reflecting a continuous strengthening of terrestrial carbon sink strength. Forests contributed most to the regional carbon sink, while the ecosystem-level NEP contribution of croplands increased over time; by contrast, the total carbon sink of wetlands declined because of area loss. These results suggest that ecological restoration, vegetation greening, and land cover optimization jointly enhanced the carbon sink along the eastern coast of China. These findings have important implications for ecological management and green low-carbon development along the eastern coast of China. Full article

Over the past two decades, extreme climate and weather events have become increasingly frequent in the United States, and the carbon–water cycle of corn ecosystems has shown high sensitivity to climate change. However, traditional simulation methods that rely on coarse-scale reanalysis data are unable to reflect changes in local water and heat conditions accurately. This study combines in situ meteorological observations with remote sensing, using a long short-term memory model to simulate the daily gross primary productivity (GPP) and evapotranspiration (ET) of 684 corn-growing meteorological stations in the United States. In summer, GPP and ET showed a spatial pattern of gradual decrease from the humid eastern region to the arid western region, and the multi-year daily averages at meteorological stations showed a single-peak pattern. The sensitivity of GPP and ET changes is mainly influenced by leaf area index (LAI) and shortwave radiation downward changes, which together explain more than 90% of the main variation in GPP and ET at the meteorological stations. The 2012 drought caused a general decline in GPP and ET, with the peak occurring approximately 15 days earlier than usual. Water use efficiency (GPP/ET) decreased at 85% of the sites (p < 0.05), but photosynthesis per unit leaf area (GPP/LAI) increased at 63% of the sites (p < 0.05). This study demonstrates the importance of meteorological station-scale data for understanding carbon–water flux dynamics in cornfields. Integrating the models developed in this study with medium-to-long-term climate projections will further guide climate-informed agricultural water management and provide reliable accounting and pricing tools for agricultural land carbon markets and carbon trading. Full article

Soil respiration (Rs) is influenced by various factors, including soil temperature (ST), soil moisture (SM), and vegetation growth. Accurately and quantitatively estimating Rs from remote sensing data is essential for understanding the carbon cycle in desertification ecosystems. However, selecting appropriate vegetation representation factors poses a significant challenge during the remote sensing inversion. Sun-Induced Chlorophyll Fluorescence (SIF) is used extensively to monitor vegetation diseases and pests, assess drought conditions, and estimate Gross Primary Production (GPP). Nevertheless, the applicability of SIF for estimating Rs from remote sensing data and whether Rs modeling surpasses traditional vegetation indices requires further investigation. This study focuses on the Hongshaquan mining area, utilizing UAV hyperspectral, thermal infrared, and in situ monitoring data, combined with four machine learning methods: Random Forest (RF), Partial Least Squares (PLS), Support Vector Machine (SVM), and Back Propagation Neural Network Algorithm (BP) to establish a model for estimating Rs from remote sensing data. The determination coefficient (R²) and root mean square error (RMSE) were used to assess the performance of Rs inversion models characterized by SIF, Normalized Difference Vegetation Index (NDVI), and Near-Infrared Reflectance of Vegetation (NIRv) improved by radiance. The feasibility and modeling potential of estimating Rs from remote sensing data using SIF were explored. The results indicate that vegetation significantly impacts Rs in desertification mining area ecosystems, and the inversion accuracy of Rs improved by 26.8% after incorporating vegetation factors. The RF model displayed the best overall performance among the four machine learning methods. When the Salinity Index (SI) and Temperature Vegetation Dryness Index (TVDI) were treated as fixed components of the modeling independent variable, the modeling accuracy of the various vegetation representation factors ranked from highest to lowest as follows: SIF > NIRv > NDVI, with corresponding R² values of 0.63, 0.58, and 0.57, and RMSEs of 0.08 μmol·m⁻²·s⁻¹, 0.12 μmol·m⁻²·s⁻¹, and 0.13 μmol·m⁻²·s⁻¹, respectively. The research findings suggest that SIF holds significant promise for remote sensing estimation of Rs. The use of SIF can enhance the accuracy of Rs estimation. Full article

As a rapidly developing extreme drought event, flash drought poses an increasingly serious threat to agricultural production, ecosystem carbon sequestration, and regional ecological security. However, systematic understanding remains limited regarding the occurrence characteristics of flash drought across different cropland types and the mechanisms by which it affects gross primary productivity (GPP). Using root-zone soil moisture, meteorological variables, and GPP data for China from 2000 to 2020, this study characterized flash drought events across different cropland ecosystems, quantified the response frequency and intensity of GPP, and further explored the dominant driving factors using eXtreme Gradient Boosting and SHapley Additive exPlanations. The results showed that flash drought occurred more frequently in cropland than in non-cropland areas, and that rainfed cropland experienced flash drought more frequently and developed more rapidly than irrigated cropland. The mean GPP response frequency in cropland was 0.43, indicating that nearly half of flash drought events suppressed GPP. Regions with high sensitivity were mainly concentrated in northwestern and northeastern China, with northwestern China showing the lowest resistance to flash drought. Climatic background and hydro-meteorological anomalies were the dominant factors controlling GPP responses in cropland, and the dominant driving factors differed significantly among cropland types, exhibiting pronounced nonlinear and threshold effects. This study reveals the spatial heterogeneity and driving mechanisms of flash drought impacts across different cropland ecosystems in China and provides a scientific basis for agricultural drought-risk assessment and differentiated adaptive management. Full article

Changing hydrothermal regime leads to pronounced changes in growth and ranges of Siberian tree species that are mostly negative at the southern part of the trees’ habitat. Here we analyzed the response of Larix sibirica and Populus laurifolia to moisture changes in unique refugia that border the Mongolian desert in Southern Siberia. The great age of old-growth larch trees (>500 years) suggests that the refugia have existed throughout the Holocene. We aimed to (1) analyze larch and poplar growth and range response to the changing temperature and moisture regime, (2) explore the potential migration of trees into the desert, and (3) analyze Gross Primary Productivity (GPP) dynamics within the refugia and adjacent desert. We used on-ground surveys, remote sensing data, and dendroecological analysis. We found that since the warming onset (c. 1980), larch and poplar trees have increased their growth and population within and beyond the refugia (+300% for poplar and +45% for larch). Both species’ growth has been controlled by atmospheric and soil droughts (measured by the Standardized Precipitation Evapotranspiration Index (SPEI) and Self-Calibrating Palmer Drought Severity Index (scPDSI)) and by microtopography-dependent moistening. Summer winds impair trees’ growth via increased evapotranspiration. Both species were migrating to the southern sandy dunes. Although poplar is less drought-resistant than larch, it was shifting ahead of larch (5.6 m/year vs. 0.8 m/year). The mean and maximum treeline shifts were 260 and 450 m for poplar and 35 m and 70 m for larch. P. laurifolia occupied new climate-caused niches ahead of drought-resistant L. sibirica due to its higher prolificacy. We found a “desert greening” phenomenon, i.e., a significantly increasing GPP trend (R² = 0.31) in both refugia and sandy dunes. The GPP increase correlated with tree growth increase (r² = 0.36–0.39). The larch and poplar migration to the desert contradicts the predicted shrinkage of the tree ranges within their southern boundary. However, the projected increase in the moisture deficit by 2080–2100 may impair this phenomenon. Nevertheless, current changes in the hydrology regime are favorable for larch and poplar growth and expansion into the adjacent Mongolian desert. Full article

Against the backdrop of global climate change and the “carbon neutrality” target, the ecological quality improvement of the Loess Plateau—a key region for ecological restoration in China—and its impact on vegetation carbon sources hold significant importance for regional carbon balance and ecological security. Based on MODIS and meteorological reanalysis data from 2002 to 2024, this study constructed the Remote Sensing Ecological Index (RSEI). Combined with a carbon source/sink model, it systematically assessed the spatiotemporal coupling evolution characteristics of ecological environment quality and vegetation carbon storage capacity in the Loess Plateau, and explored the synergistic driving mechanisms of major hydrothermal and surface factors. The results indicate the following: (1) From 2002 to 2024, the ecological environment of the Loess Plateau improved significantly, with the RSEI rising from moderate to good. This improvement was accompanied by a marked decrease in surface dryness, an increase in surface wetness, and notable growth in vegetation cover, revealing a positive coupling relationship characterized by “reduced surface dryness—increased surface wetness—enhanced vegetation restoration.” (2) Regional vegetation carbon storage capacity strengthened markedly. Gross Primary Productivity (GPP), Net Primary Productivity (NPP), and Net Ecosystem Productivity (NEP) all showed significant increasing trends, and the proportion of area classified as carbon sink increased substantially. (3) Spatially, carbon sink distribution exhibited a pattern of “higher in the southeast, lower in the northwest.” Sub-regions A and D were identified as core areas with higher ecological quality and carbon sink capacity, whereas sub-regions B and C were more ecologically fragile and served as primary carbon source areas. (4) The implementation of soil and water conservation measures on the Loess Plateau has effectively enhanced regional carbon storage capacity. Vegetation restoration, improved water conditions, and reduced surface dryness have jointly driven the transition of the Loess Plateau ecosystem from a “vulnerable type” to a “recovering type”, while ecological restoration projects have played a certain role in enhancing the carbon sink. This study provides a theoretical basis and scientific–technological support for ecological protection and high-quality development in the Yellow River Basin. Full article

Accurate quantification of ecohydrological processes is essential for effective water and carbon management in terrestrial ecosystems. Traditional simulations mainly rely on mechanistic models, yet their accuracy is often limited by inconsistencies in representing physical processes and uncertainties in parameterization. Integrating remote sensing signals offers a promising way to reduce these uncertainties and enhance model applicability. In this study, in-situ observations from a wheat cropland in the Guanzhong Plain were used to simulate gross primary productivity (GPP) and latent heat flux (LE) by comparing a forward model (STEMMUS-SCOPE) with a remote sensing-driven inverse model (STEMMUS-MLR). We further examined the role of solar-induced chlorophyll fluorescence (SIF), an emerging proxy for photosynthesis, as an input to improve mechanistic modeling of GPP and LE. Results show that STEMMUS-MLR outperformed STEMMUS-SCOPE in estimating water and carbon fluxes, demonstrating that incorporating SIF effectively reduces bias associated with uncertainties in parameters and forcing data. The contribution of SIF was quantified using Random Forest regression and Shapley additive explanations (SHAP), revealing that SIF markedly reduced the dependence of GPP and LE simulations on shortwave radiation (SW), air temperature (Ta), and leaf area index (LAI). These findings highlight the critical role of SIF in ecohydrological modeling of semi-arid cropland ecosystems and provide a scientific basis for advancing process understanding and improving the precision management of water and carbon budgets in terrestrial ecosystems. Full article

Understanding the long-term dynamics of cropland water use efficiency (WUE) and its underlying environmental drivers is essential for ensuring food and water security, particularly for regions facing intensified climate change. Here, we investigated the spatial patterns and long-term trends of gross primary productivity (GPP), evapotranspiration (ET), and WUE in cropland ecosystems across Northeast China during the past two decades as the nation’s primary commodity grain base using the time-series Breathing Earth System Simulator (BESS) products. Subsequently, the ridge regression method was used to quantitatively disentangle the relative contributions of key climatic variables to the observed WUE trends of cropland. Our results revealed a pronounced decreasing gradient in both GPP and ET along the southeast–northwest direction. A significant increase in GPP was observed over the 20-year period (p < 0.01), with 95.94% of the cropland area showing positive trends. ET showed a slight, non-significant increase (p > 0.05), though 82.77% of pixels exhibited positive trends, particularly in the northwest. Consequently, WUE showed a widespread and significant enhancement (p < 0.01), with approximately 98% of cropland pixels exhibiting increasing trends. Attribution analysis identified air temperature as the dominant environmental variable, accounting for 92.4% of the observed WUE increase, while solar radiation and precipitation contributed modestly (3.4% and 3.2%, respectively). Our findings underscore the predominant role of thermal conditions in shaping the carbon–water coupling efficiency of agroecosystems in semi-arid to semi-humid transition zones. This study provides quantitative evidence that warming climate, rather than changes in water availability or radiation, has been the primary climatic factor driving the improved cropland WUE over the past two decades. These insights have important implications for developing adaptive water management strategies to enhance agricultural climate resilience in Northeast China and similar regions worldwide. Full article