During crop growth, leaf photosynthetic capacity changes continuously, and the vertical distribution of leaf nitrogen (N-a, in g m(-2)) and chlorophyll (Chl(a), in mu g cm(-2)) affects photosynthesis in different canopy layers. Understanding stratified photosynthesis is vital for the accurate prediction of crop photosynthetic capacity. We conducted a two-year field study on winter wheat and paddy rice in Eastern China, measuring the leaf maximum carboxylation rate (V-cmax25), maximum electron transport rate (J(max25)), N-a, and Chl(a) every 7-10 days from greening to maturity. We analyzed vertical variations in these parameters in the upper (T-1), middle (T-2), and lower (T-3) canopy layers and explored relationships between N-a/Chl(a) and V-cmax25. The results showed significant vertical variations: V-cmax25 and J(max25) in T-1 were higher than in T-2, and T-2 was higher than T-3. The vertical distribution of N-a and V-cmax25 was more pronounced than that of Chl(a). The correlation between N-a and V-cmax25 increased from T-1 to the lower layers, while the V-cmax25-Chl(a) correlation decreased. A single V-cmax25 estimation model based on N-a performed well across layers (R-2 = 0.619, RMSE = 15.751 & micro;mol m(-2) s(-1)). Differentiating T-1 from T-2/T-3 improved the Chl(a)-based models. N-a was better than Chl(a) for characterizing the V-cmax25 vertical variation, with the Chl(a)-based models requiring separation of T-1 from T-2/T-3. This study provides key insights for remote sensing of photosynthetic parameters and improves the understanding of crop canopy photosynthesis.

The Hulun Lake Basin in northern China harbors extensive temperate grasslands, whose carbon uptake and ecosystem functioning are effectively represented by gross primary productivity (GPP). However, the long-term response of grassland GPP to regional climate change remains insufficiently understood, due to the limited ability of global models to capture local ecosystem variability. This study improved GPP estimation in the Hulun Lake Basin by integrating additional grassland flux observations from China into a Random Forest model (MCF). We subsequently examined the spatiotemporal dynamics of GPP and identified the dominant climatic drivers and their long-term trends under changing climate conditions. Results show that the MCF model significantly outperformed the model trained solely on FLUXNET 2015 Tier 2 data in both accuracy and trend patterns of grassland GPP. A 23-year MCF simulation in the Hulun Lake Basin showed a clear west–east GPP gradient, averaging 676.36 g C m− 2 yr− 1 and totaling 70.13 Tg C yr− 1. Most of the region exhibited increasing GPP trends, with an average growth rate of 6.85 g C m− 2 yr− 1, resulting in an annual total increase of approximately 0.45 Tg C yr− 1. SHAP analysis demonstrated that shifts in precipitation and temperature are reshaping the dominant factors contributing to GPP increases. Structural equation modeling further revealed that precipitation enhanced GPP through both direct and indirect pathways, with the direct effect accounting for 63.6

Objectives Urban heat island effects are intensifying under climate change and rapid urbanization. However, how large-scale climate anomalies such as the El Niño–Southern Oscillation (ENSO) interact with urban morphology to shape surface urban heat island intensity (SUHII) remains unclear. This study compares Shanghai and Suzhou to examine how SUHII responds to ENSO intensity across Local Climate Zones (LCZs). Methods Summer (June–August) land surface temperature data (2018–2022) were downscaled from MODIS using a random forest model and integrated with 100 m LCZ maps. SUHII was calculated relative to LCZ D and decomposed into inter- and intra-LCZ components. Linear and quadratic regressions were applied to quantify SUHII sensitivity to ENSO intensity, represented by the Oceanic Niño Index (ONI). Results ENSO intensity appears to modulate SUHII. La Niña phases strengthen inter-SUHII in both cities, whereas El Niño generally weakens it. The ONI–SUHII linkage is LCZ-dependent, with compact built types exhibiting the strongest sensitivity; in Shanghai, compact LCZs show a response slope of −0.98 °C per ONI, exceeding open, industrial, and vegetated types. Intra-SUHII follows a nonlinear pattern, reaching minima under near-neutral ENSO conditions and increasing during stronger El Niño or La Niña phases. City-scale morphology further appears to condition this sensitivity: Shanghai’s monocentric and high-density structure may contribute to the concentration of heat cores and amplifies ENSO-related variability, resulting in higher mean inter-SUHII (by 0.54 °C) and stronger ONI sensitivity than in polycentric Suzhou, where more dispersed urban form and cooling elements may help limit heat buildup. Conclusions These findings underscore the critical role of urban morphology in modulating climate-induced surface heat burden, offering valuable insights for climate-resilient urban planning in rapidly developing regions.

Vegetation diversity in urban areas is crucial for maintaining urban ecological security. Rapid urbanization significantly affects vegetation diversity. However, the absence of effective regional-scale monitoring methods makes quantitative assessments challenging. This study used the remote sensing-based Rao's Q index to assess both the direct and indirect effects of urbanization on vegetation functional diversity in Suzhou, China, in 2021. Our results showed that the modified Rao's Q method effectively captured the spatial distribution patterns of urban vegetation functional diversity. Urban cores showed a 76.9 % reduction in functional diversity (Rao's Q = 0.003) compared to suburban areas (Rao's Q = 0.013), underscoring the substantial impact of urbanization on vegetation diversity. Direct effects increased with urbanization intensity (β); for every 1 % increase in β, Rao's Q declined by 1 %. Indirect effects displayed threshold behavior: they promoted diversity when β < 0.2, but became detrimental when β > 0.2 due to heightened environmental stress and human activities. In 2021, indirect effects mitigated the loss of functional diversity by an average of about 10 % in suburban areas. Partial correlation analysis revealed land surface temperature (LST) and urban greening (UG) as key factors influencing positive indirect impacts. LST enhanced functional diversity in low-urbanization zones, while intensive human activities accelerated its decline. UG was identified as a vital strategy for enhancing vegetation diversity in highly urbanized areas. These findings deepen our understanding of the impact of urbanization on vegetation functional diversity and offer valuable insights for developing adaptive policies to safeguard ecosystem services and improve urban living quality.

Urban vegetation is pivotal in enhancing regional ecological balance and sequestering significant amounts of carbon dioxide (CO2) through photosynthesis, thereby contributing substantially to regional carbon budgets. However, the gross primary productivity (GPP) of urban vegetation remains underexplored due to the absence of robust estimation methodologies, often leading to its exclusion from global and regional carbon budgets. Advances in vegetation indices (VIs) offer promising solutions for improving the accuracy and spatial resolution of urban GPP estimation. In this study, we compared the performance of the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and kernel normalized difference vegetation index (kNDVI) calculated from Landsat 5/7 images in estimating flux-site-level GPP and incorporated meteorological factors to construct a high-performance VI-GPP model for urban GPP estimation. Our findings demonstrated that the EVI, NIRv, and kNDVI exhibited stronger correlations with GPP dynamics and higher R2 values than did the NDVI in linear VI-GPP relationships across most plant functional types (PFTs). Exceptions were observed in evergreen broadleaf forest (EBF), evergreen needle-leaf forest (ENF), and savanna (SAV), where GPP variations were strongly influenced by temperature, shortwave radiation, and vapor pressure. Incorporating these meteorological factors significantly enhanced GPP estimation accuracy for these PFTs. Among the indices, the NIRv achieved the highest overall model performance, with an R2 of 0.60 and a root-mean-square error (RMSE) of 2.05 g C m- 2 d- 1 across PFTs. The kNDVI demonstrated unique advantages for specific PFTs, such as deciduous broadleaf forest (DBF) and ENF. Compared with existing VI-GPP relationships created with coarse-spatial-resolution remote sensing data, our model was more suitable for high-spatial- resolution GPP estimation in urban areas. Our results highlight the performance of the NIRv and kNDVI in urban vegetation GPP estimation and provide a solution for estimating fine-resolution GPP to reveal the importance of urban vegetation to regional carbon budgets.

Vegetation dynamics in the Hulun Lake Basin (HLB), a vulnerable grassland–wetland–forest transition zone in Northeastern Inner Mongolia, North China, are sensitive to climate change, but traditional greenness metrics like the normalized difference vegetation index (NDVI) lack process-level insights. Using the GIMMS NDVI3g dataset (1982–2015) and meteorological data, this study analyzed the spatiotemporal dynamics of the NDVI and vegetation NDVI change rate (VNDVI)—a metric quantifying greening and browning speeds via NDVI temporal variation—employing linear regression and partial correlation analyses. The NDVI exhibited an overall significant upward trend of +0.0028 yr−1 (p < 0.05) across more than 70% of the basin, indicating a persistent greening tendency. The VNDVI revealed an accelerated spring greening rate of +0.8% yr−1 (p < 0.05) and a slowed autumn browning rate of −0.6% yr−1 (p < 0.05), reflecting an extended growing season. Spatial correlation analysis showed that the temperature dominated spring greening (r = 0.52), precipitation governed summer growth (r = 0.64), and solar radiation modulated autumn senescence (r = 0.38). Compared with the NDVI, the VNDVI was more sensitive to both climatic fluctuations and anthropogenic disturbances, highlighting its utility in capturing process-level vegetation dynamics. These findings provide quantitative insights into the mechanisms of vegetation change in the HLB and offer scientific support for ecological conservation in North China’s grassland–forest ecotone.

Over the past century, atmospheric inorganic nitrogen (IN) deposition to terrestrial ecosystems has significantly increased and caused various environmental issues. China has been one of the hotspot regions for IN deposition, yet limited data exist regarding IN deposition fluxes in China at the regional scale. In this study, based on NO 2 and NH 3 columns acquired by satellite sensors, coupled with atmospheric chemical transport model (CTM), mixed-effects model and site observations, we constructed regional-scale IN dry and wet deposition models respectively, and finally proposed a spatially explicit database of IN deposition fluxes in China. The database includes the dry, wet and total deposition fluxes in China during 2011–2020, and the data are presented in raster form with a resolution of 0.25° × 0.25°. Overall, the database is of great importance for monitoring and simulating the trends of IN deposition over a long time series in China.

Urban forests have the potential to sink atmospheric CO2. With the improvement of coverage of vegetation in urban environments, more attention has been paid to the carbon sequestration potential of the urban forest. However, the high fragmentation of urban forests makes it difficult to evaluate their carbon budget on a regional scale. In this study, the GPP-NIRv relationship model was employed to estimate GPP in Suzhou by MODIS, Landsat-8 and Sentinel-2 remote sensing data, and to further explore what kind of remote images can figure out the spatial-temporal pattern of GPP in urban forests. We found that the total GPP of the terrestrial ecosystem in Suzhou reached 8.43, 8.48, and 9.30 Tg C yr-1 for MODIS, Landsat-8, and Sentinel-2, respectively. Monthly changes of GPP were able to be derived by MODIS and Sentinel-2, with two peaks in April and July. According to Sentinel-2, urban forests accounted for the majority of total GPP, with an average of about 44.63%, which was larger than the results from GPP products with coarser resolutions. Additionally, it is clear from the high-resolution images that the decline of GPP in May was due to human activities such as the rotation of wheat and rice crops and the pruning of urban forests. Our results improve the understanding of the contribution of the urban forest to the carbon budget and highlight the importance of high-resolution remote sensing images for estimating urban carbon assimilation.

IntroductionClimate change and land use/cover changes (LUCC) greatly influenced terrestrial carbon sequestration.MethodsThis study evaluated the effects of climate change and LUCC on the carbon sequestration of forest ecosystems from 1991 to 2020 in the coastal area of China using a process-based global vegetation dynamic model (IBIS).Results and discussionThe results showed that temperature had an increasing trend, with a rate of 0.02 °C yr−1; 20.00% of the forest coverage shifted spatially but there was only a 0.31% increase in total area. The carbon sequestration amounts by plants (net primary productivity, NPP) and the net carbon budget (net ecosystem productivity, NEP) in the coastal area of China in 2020 were 666.49 and 58.45 Tg C yr−1, respectively, which increased by 18.31% and 2509.30%, respectively, compared to the values in 1991. The increased forest ecosystem NPP and NEP were mainly due to climate change, while forest cover changes increased NPP by only 0.06% and NEP by 9.75% with a strong regional difference. The NPP showed a significant increasing trend, with a rate of 3.01 Tg C yr−1, which was positively correlated with temperature (0.43, p < 0.01) and precipitation (0.42, p < 0.01). However, the NEP largely varied during 1991-2020 and did not show a significant correlation with precipitation or temperature. These results provide scientific guidance for the government to enact environmental protection and low-carbon land use planning policies.

大气无机氮沉降是全球氮循环的重要过程,对生态系统的可持续发展至关重要.监测大气氮沉降主要包括地面站点监测和大气化学传输模型模拟两种方法;但在区域尺度上两种方法获得的氮沉降通量具有较大差异.因此,急需发展新的方法以补充探讨区域尺度大气氮沉降时空分布格局.卫星遥感技术具有监测全球高时空分辨率大气含氮化合物浓度的能力,已应用于区域尺度氮沉降通量估算.本文系统回顾了监测大气NO2和NH3的主要卫星载荷及其数据特点、基于卫星监测的NO2和NH3柱浓度开展氮沉降估算的基本原理,大气硝态氮和氨态氮的气体、颗粒物和湿沉降遥感估算模型,中国大气无机氮沉降时空分布格局,并指出了当前基于卫星监测信息开展大气无机氮沉降通量估算研究中存在的不足.

The atmospheric IN deposition fluxes database consists of three files. The ‘data file’ provides yearly data on the dry, wet and total IN deposition fluxes. Specifically, dry deposition is the sum of two forms, gaseous and particulate, including NO2，HNO3，NH3，NO3-，NH4+.Wet deposition is the sum of both NO3--N and NH4+-N. The ‘readme file’ describes the ‘data file’ and the units of all variables included. The ‘source file’ includes the full references used in the database, which comprise satellite data and precipitation data.

利用GIS和遥感图像处理软件,依据2001、2005、2010、2015和2020年的遥感影像数据和能源消耗数据,采用碳排放系数法和能源消费算法,对苏州市各历史时期的土地利用碳排放进行了分析.结果表明:(1)建设用地是主要碳源,全市99％以上的碳排放来自建设用地,碳汇主要来自水域((7～8)×104 t·a-1)和林地(约1×104 t·a-1);(2)近20年来,苏州市碳排放始终呈现增长趋势,但增长主要发生在本世纪的前10年,净碳排放量从2001年的922×104t增长到2010年的4 281×104 t,再到2020年的4 656×104t;(3)空间上,呈现中心城区较低,外围区市较高的格局,全市近50％的碳排放来自张家港市.

Global enhanced human activities have deeply influenced grassland ecosystems. Quantifying the impact of human activities on grasslands is crucial to understanding the grassland dynamic change mechanism, such as grassland degradation, and to establishing ecosystem protection measures. In this study, potential net primary productivity (PNPP), actual NPP (ANPP), and the forage harvest NPP (HNPP) were employed to establish the human activities index (HAI) to reveal the spatiotemporal changes of the effects of human activities on grassland ecosystems in eastern Inner Mongolia from 2000 to 2017, and to further explore the relationship between human activities and grassland degradation. The results showed that the total average PNPP, ANPP, and HNPP of grasslands in eastern Inner Mongolia were 187.2 Tg C yr-1, 152.3 Tg C yr-1, and 8.9 Tg C yr-1, respectively, during the period of 2000 to 2017. The HAI exhibited a clear decreasing trend during the study period, with annual mean values ranging from 0.75 to 0.47, which indicates that the NPP loss induced by human activities is weakening, and this trend is dominated by the difference between potential NPP and actual NPP. About 42.4% of the study area was non-degraded grassland, and the declining grassland degradation index (GDI) indicated that the degradation grade in eastern Inner Mongolia improved from moderate to light degradation. A positive relationship was found between HAI and GDI. This relationship was more significant in Xilingol League, which is a typical ecologically fragile area, than that in Xing'an League and Hulunbuir City.

Dynamic Global Vegetation Models (DGVM) are powerful tools for studying complicated ecosystem processes and global changes. This review article synthesizes the developments and applications of the Integrated Biosphere Simulator (IBIS), a DGVM, over the past two decades. IBIS has been used to evaluate carbon, nitrogen, and water cycling in terrestrial ecosystems, vegetation changes, land-atmosphere interactions, land-aquatic system integration, and climate change impacts. Here we summarize model development work since IBIS v2.5, covering hydrology (evapotranspiration, groundwater, lateral routing), vegetation dynamics (plant functional type, land cover change), plant physiology (phenology, photosynthesis, carbon allocation, growth), biogeochemistry (soil carbon and nitrogen processes, greenhouse gas emissions), impacts of natural disturbances (drought, insect damage, fire) and human induced land use changes, and computational improvements. We also summarize IBIS model applications around the world in evaluating ecosystem productivity, carbon and water budgets, water use efficiency, natural disturbance effects, and impacts of climate change and land use change on the carbon cycle. Based on this review, visions of future cross-scale, cross-landscape and cross-system model development and applications are dis-cussed.

Chlorophyll content is an important indicator of crop health, plant productivity, and environmental stress. Real-time, fast and accurate acquisition of leaf chlorophyll content of crops is of significant for monitoring crop growth. Remote sensing is an effective way to retrieve leaf chlorophyll content of crops at regional and global scales. However, previous studies retrieving leaf chlorophyll content of crops does not fully consider the impact of underlying surface background, limiting retrieval accuracy. To this end, this paper aims at the inversion of rice leaf chlorophyll content from Sentinel-2 remote sensing satellite data using a look-up table based approach. The look-up table was simulated using the PRAOSAIL radiation transfer model. The applicability of chlorophyll indices (CI) calculated from the reflectance of the green band and different red-edge bands and the spectral index (Zarco and Miller, ZM) constructed by two different red edge bands in inverting leaf chlorophyll content was evaluated using field measurements. The greenness index (G) was integrated with CI and ZM to constrain the impact of background on the inversion of leaf chlorophyll content. The main findings of this study are: (1) The accuracy of leaf element content inversion based on the spectral index constructed in different bands is different, and CI740 performed the best (R-2 = 0. 79, RMSE= 9. 02 mu g.cm(-2)), followed by ZM (R-2 =0. 71, RMSE=10. 53 mu g.cm(-2)) , CI705 (R-2 =0. 69, RMSE=9. 17 mu g.cm(-2)), and CI783 (R-2 =0. 67, RMSE= 10. 84 mu g.cm(-2)); (2) The inverted leaf chlorophyll content is significantly affected by the background, especially at the early stage of rice growth. The inverted leaf chlorophyll content was systematically lower than observations (mean relative error (MRE) in the range from 18. 87% to 31. 94%) owing to strong background interference; (3) CI/G and ZM/G can effectively eliminate the influence of background and improve the accuracy of rice leaf chlorophyll inversion. At the early stage of rice growth, inversion based on CI/G and ZM/G significantly improves agreement between inverted and observed leaf chlorophyll content (MRE in the range from 8. 11% to 18.11%). These findings are of great significance for improving the inversion of leaf chlorophyll content under different leaf area index levels of rice from remote sensing data.

科学地预测和分析不同情景下中国碳中和路径有助于碳中和目标的合理推进,但当前研究仍缺少结合碳源—汇变化趋势的综合性分析与应用国际耦合模式情景的系统性分析.本文运用WITCH综合评估模型与IBIS植被动态模型模拟了各共享社会经济路径情景下21世纪中国碳源—汇路径,对中国碳中和时间及路径进行预测分析.研究发现:①中国碳汇逐年值存在3～4 a的周期性波动.RCP6.0气候情景下中国碳汇总量均值稳定在约0.30 Gt C/a;RCP2.6气候情景下中国碳汇总量均值呈下降趋势,到2065-2100年下降至约0.18 Gt C/a.②中国碳排放总量受到世界经济社会发展路径与政策强度的共同影响,在中高强度减排政策下中国碳排放均在2025-2030年达峰后呈下降趋势,其中SSP1/SSP4—高强度碳减排政策情景下碳排放在2060年降至约0.30 Gt C/a并实现碳中和目标.③基于典型碳中和情景的路径研究,实现碳中和目标应积极促进清洁能源技术进步与应用,推进非电力能源向电力能源转变,推广生物质能源及CCS技术,并积极倡导电动汽车的发展.

Land use change is one of the crucial factors affecting carbon emissions. The continuously increasing CO 2 and global warming have raised concerns about carbon emission reduction in the process of urbanization. In this research, the Markov and multi-objective optimization models were conducted to predict the demands for land use in Nanjing in 2030 and 2060 under the natural growth (NG) and minimum carbon emission (MCE) scenarios to coordinate the needs of economic development, ecological protection and food security as well as the target of carbon emissions reduction in the future. The spatial distribution of land use simulated by the FLUS (Future Land Use Simulation) model was used to evaluate the effects of future land use on carbon emissions. The results showed that 1) The demands for each type of land use in the NG scenario were significantly different from those in the MCE scenario. Considering the goals of food security and ecological protection in the future, the total amount of cultivated land would not decrease in the MCE scenario, and the area of construction land was significantly smaller than that in the NG scenario. 2) The carbon emissions of Nanjing under the MCE scenario would decrease by 3.94 and 11.80 million tons in 2030 and 2060, respectively, accounting for 9.97% and 27.17% of the total carbon emissions. The optimization of land use patterns can effectively reduce carbon emissions in the process of urbanization.

Atmospheric methane (CH4) is an important greenhouse gas that can reflect variations of CH4 emissions and sinks. This study aimed to detect spatial and temporal variations of atmospheric CH4 concentrations in China during 2003–2021 based on CH4 column-averaged dry-air mole fraction (XCH4) products from three satellites, namely, Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), Greenhouse Gases Observing Satellite (GOSAT), and Copernicus Sentinel-5 Precursor (S5P). The results revealed that XCH4 observed from three satellites showed high agreement in spatiotemporal variations and demonstrated good consistency with ground station measurements. The correlation coefficients (r) between the three satellites were 0.72 and 0.73, and the correlation coefficients for the ground stations were 0.79, 0.66, 0.03, 0.21, 0.70, and 0.80. The spatial distribution of XCH4 in China was generally high in the east and low in the west and close to that of CH4 emissions, indicating that CH4 emission sources dominated the spatial variations of atmospheric XCH4. From 2003 to 2006, XCH4 remained stable with an annual growth rate of 0.51 ppb·yr−1 and then abruptly increased with an overall growth rate of 6.96 ppb·yr−1. There were obvious seasonal changes in XCH4, with peaks in autumn and summer and nadir in winter and spring. These seasonal variations of XCH4 were related to CH4 emissions from rice planting. Rice cultivation areas generally had high XCH4 concentrations, and the growth cycle of rice plants significantly contributed to seasonal variations of XCH4 in the main rice planting areas. These results provide scientific data that could encourage decision-makers to enact policies and processes to reduce methane emissions.

Accurate representation of photosynthetic capacity and its seasonal variations is critical for modeling carbon sequestration of cropland ecosystems through photosynthesis. Previous studies indicated that the maximum carboxylation rate at 25 °C (Vcmax25) is the key determinant of photosynthetic capacity and can be mapped according to leaf nitrogen (N) and chlorophyll (Chl) contents. However, whether relationships of Vcmax25 with leaf nitrogen and chlorophyll contents in cropland ecosystems change over the whole growing season remain unclear. To address this question, we conducted intensive field campaigns in winter wheat and paddy rice farmlands of Eastern China, with leaf Vcmax25, the maximum electron transport rate at 25 °C (Jmax25), N, and Chl measured every 7 to 10 days from the tillering stage to maturity. Results showed that Vcmax25 and Jmax25 varied significantly during the growing seasons and maximized at flowering stages. For both winter wheat and paddy rice, Chla (Chl content at the area unit) showed significantly different relationships with Vcmax25 pre- and post- flowering while Na (N content at the area unit) did not. When different empirical models were used for pre- flowering and post- flowering stages, Vcmax25 estimated from Chla was closer to observations (R2 = 0.875, RMSE = 9.479 µmol m−2 s−1) than that estimated from Na (R2 = 0.565, RMSE = 17.718 µmol m−2 s−1). The findings in this study imply that the combination of remotely sensed leaf Chla content and phenology would improve the mapping of Vcmax25 for crops at regional and even global scales.