Search

Abstract Global warming has led to the enhancement of diurnal (daytime-nighttime) compound heat waves, which can severely affect the population’s physical health and productive life; this is particularly the case for vulnerable populations that are more susceptible to psychological and physiological harm with the future normalization of heatwave scenarios. In this study, we attributed the changes in population exposure under diurnal compound high temperatures (HTs) and extremely high temperatures (EHTs) to the influence of climatic (exposure duration) and population factors and their combined influence to determine the relationship between exposure duration and the changes in exposure of vulnerable populations. Diurnal compound HT and EHT covered a land area of 4,016,800 and 1,984,200 km2, respectively. The heat-exposed area spread step-like in all directions, centering on the 24-h high-exposure area with a strong gradient effect. The area of prolonged exposure shifted from the Yellow-Huai-Huai Plain across the geographical boundary between the south and the north to the middle and lower reaches of the Yangtze River Plain. Meanwhile, the average maximum value of vulnerable population exposure reached 186,800 and 93,100 person-h, with high values in Zhengzhou, Chongqing, Wuhan, Chengdu, Beijing, and other cities. Changes in vulnerable population exposure were dominated first by demographic factors and then by climatic factors, with the dominance of climatic factors evident in areas of prolonged exposure. Under prolonged exposure to daytime-nighttime compound EHT, changes in the exposure of child and older populations were dominated by climatic factors with a contribution rate of 87.16% and 87.43%, respectively. Therefore, suitable cooling measures should be proposed for vulnerable populations under prolonged exposure, focusing more on the physical and mental health of children and older adults exposed to compound EHT.

Abstract The apparent optimum air temperature for vegetation photosynthesis (Topt) is a key temperature parameter in terrestrial ecosystem models estimating daily photosynthesis or gross primary production (GPP, g C/m2/day). To date, most models use biome-specific Topt (Topt-biome) parameter values. Given vegetation acclimation and adaptation to local climate, site-specific Topt (Topt-site) is needed to reduce uncertainties in estimating daily GPP across the scales from site to region and the globe. Previous studies have demonstrated using the Enhanced Vegetation Index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images and daytime air temperature data to estimate the Topt-site at the eddy covariance tower sites. This study used MODIS-derived EVI and ERA5 climate data to estimate and generate global Topt-site data products from 2000 to 2019. The Topt-site of individual pixels within a biome has large variation, which clearly cannot be represented accurately by the widely used Topt-biome. Therefore, using this global dataset of Topt-site estimates might significantly affect GPP simulation in current ecosystem models.

Rapeseed cultivation on winter-fallow fields enables a seasonal switch between agricultural and bioenergy output. In view of the spectral features during the rapeseed flowering period (RFP), numerous remote sensing studies identified and produced the maps of rapeseed fields. The RFP is frequently identified by field surveys, visual imagery interpretation, or empirical crop knowledge in a specific region, none of which is appropriate for large-scale rapeseed identification and mapping. In this research, we combine social media data on the RFP fields, Sentinel-2 imagery, and deep learning algorithm to identify and produce maps of yellow-flowering rapeseed fields. In the context of citizen science and crowdsourcing, the social media data on the fields is regarded as reference data and utilized to generate the spatial distribution of the RFP and select temporal Setninel-2 imagery. We develop and evaluate the novel method in Anhui Province, China. The resultant rapeseed map in 2018 has a user’s accuracy and a producer’s accuracy of 0.93 and 0.99, respectively. To test the generalization of the knowledge-based method, we apply the deep neural network (DNN) model trained in Anhui Province to produce the maps of yellow-flowering rapeseed fields in Hubei Province and Shaanxi Province, China. The overall accuracy of the resultant rapeseed maps for Hubei Province and Shaanxi Province is 0.97 and 0.95, respectively. The proposed method provides a new way to produce rapeseed maps on a large scale, which could be used to support agricultural planning and ecological system management.

The widely distributed surface water bodies (e.g., lakes and reservoirs) are important components of water resources on Earth, their spatial–temporal dynamics can pose significant effects on regional water resources. Previous studies generally attributed variability in terrestrial water storage (TWS) to groundwater dynamics, hardly considering surface water changes, making the influence of the changing surface water storage on TWS unclear. Here, by taking Hulun Lake as an example, which occupies 94.0 % of the total water areas in the target basin, we quantitatively revealed the impacts of changes in lake water volume on TWS. Using cloud computing-based water body monitoring and altimetry satellite-based water level measurements, we found continuous lake shrinkage (25 km2/yr) before 2012 while rapid expansion after that. By coupling water areas with levels, annual lake volumes were estimated. We showed that lake volumes also significantly declined (−0.52 km3/yr) before 2012, and then rapidly restored (0.65 km3/yr) due to the increasing water recharge from rivers. The increasing rate of lake water volume was higher than that of TWS (0.15 km3/yr), indicating the great contribution of lake volume increases to TWS gains. Our study highlights the considerable impacts of the changing surface water bodies on regional water resources and it is essential to consider storage changes in surface water when attributing the drivers of TWS variability in regions experiencing dramatic lake changes. Our method proposed is also applicable to evaluating the impacts of surface water changes on TWS in other similar areas around the world.

Abstract Mapping grazing intensity (GI) using satellites is crucial for developing adaptive utilization strategies according to grassland conditions. Here we developed a monitoring framework based on a paired sampling strategy and the classification probability of random forest algorithm to produce annual grazing probability (GP) and GI maps at 10-m spatial resolution from 2015 to 2021 for the largest temperate meadow in China (Hulun Buir grasslands), by harmonized Landsat 7/8 and Sentinel-2 images. The GP maps used values of 0–1 to present detailed grazing gradient information. To match widely used grazing gradients, annual GI maps with ungrazed, moderately grazed, and heavily grazed levels were generated from the GP dataset with a decision tree. The GI maps for 2015–2021 had an overall accuracy of more than 0.97 having significant correlations with the statistical data at city (r = 0.51) and county (r = 0.75) scales. They also effectively captured the GI gradients at site scale (r = 0.94). Our study proposed a monitoring approach and presented annual 10-m grazing information maps for sustainable grassland management.

Against the backdrop of global climate change, climate change has become a major factor affecting the suitability of living environments. Therefore, in this study, based on multisource remote sensing data and geospatial analysis methods, we selected the annual land surface temperature (LST) and normalized difference vegetation index (NDVI) in 2022 of the Chengdu–Chongqing urban agglomeration in China as driving indicators. By extracting three meteorological indicators of meteorological stations throughout the year (temperature [T], humidity [U], and wind speed [K]) in study area to construct the comfortable index of human body (IBC) to quantify regional climatic comfort. The results were as follows: First, the IBC averages were 75.620 (summer) > 60.448 (autumn) > 59.922 (spring) > 42.743 (winter), showing a spatial pattern of high values in the middle and low values on the four sides. T was the primary factor influencing annual IBC. Second, we discussed the relationship between IBC and NDVI, air quality index (AQI), LST, and relief degree of land surface (RDLS) under ordinary least square method, geographically weighted regression (GWR), and multiscale GWR (MGWR) models. LST is the most important factor affecting the spatial distribution of annual climate comfort, and the other four factors have different effects on IBC in different seasons. Third, the climate comfort of natural-type local climate zone (LCZ) was high in spring and summer, while the IBC of LCZ corresponding to building type was high in autumn and winter. This study is crucial for understanding the suitability of urban living environments and provides a reference for urban development planning.

Achieving the sustainable development goals puts new demands on land use/land cover (LULC) forecasting and thermal environment improvement. Local climate zone (LCZ) characterization reduces LULC uncertainty in climate research. This study designed a future multiscenario LCZ thermal environment assessment framework that integrates remote sensing images, a patch-level land use simulation model, and a contribution algorithm. An urban heat island (UHI) classification was used to represent the degree of heat hazard. We sought to quantitatively evaluate the spatiotemporal distribution of LCZ and its heat hazard and explore the coordination mode of LULC patterns and the thermal environment. We considered four future scenarios: a natural development scenario (NDS), urban development scenario (UDS), food security scenario, and ecological protection scenario. The results show that the Shenyang metropolitan area has experienced large-scale land urbanization, forming the main UHI areas with Shenyang–Fushun, Liaoyang–Anshan, and Fuxin as the core. This is expected to become the first area for heat hazard management in the future. The areas that produce heat hazards are mainly distributed as LCZ D and building-LCZs. By 2060, the average heat hazard in the UDS is expected to increase by 1.035 °C and that in the NDS by 0.286 °C compared to that in 2020. From the perspective of the “historical future,” the study results provide a theoretical reference for the continuous improvement of environmental quality in the new era, promoting the coordinated development of metropolitan areas and actively responding to climate change.

The escalation of greenhouse gas emissions has led to a continuous rise in land surface temperature (LST). Studies have highlighted the substantial influence of urban morphology on LST; however, the impact of different dimensional indicators and their gradient effects remain unexplored. Selecting the urban area of Shenyang as a case, we chose various indicators representing different dimensions. By employing XGBoost for regression analysis, we aimed to explore the effects of urban 2-D and 3-D morphology on seasonal LST and its gradient effect. The following results were obtained: 1) the spatial pattern of LST in spring and winter in Shenyang was higher in the suburbs than in the center; 2) the correlation patterns of the indicators in spring and winter were similar, except for the proportion of woodland and grass, digital elevation model, and sky view factor, which exhibited opposing trends in summer and autumn; 3) vegetation and construction had the highest influence on LST in the 2-D index, followed by building forms and natural landscapes in the 3-D urban morphology; and 4) the influence of each indicator varied significantly across different gradients. Among all the indicators, the landscape index, social development, building forms, and skyscape had the highest impacts on urban areas. Vegetation and built-up areas had a greater influence on suburban areas. The findings of this study can assist in adjusting urban morphology and provide valuable recommendations for targeted improvements in thermal environments, thereby contributing to urban sustainable development.

The southern Great Plains (SGP) grassland of the United States has been largely encroached by evergreen forest in recent decades. The response of the grassland water and carbon cycles to the encroachment is not clear yet. Given so, this study quantified the changes in gross primary production (GPP), evapotranspiration (ET), and ecosystem water use efficiency (EWUE) between grassland pixels with evergreen forest encroachment and neighboring pure grassland pixels (500 m). We also assessed the spatial variation of the changes in relation to precipitation (mm), air temperature (°C), and evergreen forest coverage (%). These analyses were repeated, respectively for the years of 2004, 2008, 2016, and 2019 as robustness check. Results suggest that across the 4 years, 62–72% of encroached grassland pixels exhibit higher annual GPP, 67–74% exhibit higher annual ET, whereas 65–71% exhibit lower annual EWUE. The change ratio of ET is positively correlated with that of GPP but negatively correlated with that of EWUE. Additionally, the spatial variation in the change ratios of annual GPP and ET can be explained to a certain degree by the encroachment amount. These results further clarify the response of water and carbon cycles to evergreen forest encroachment in the SGP grassland.

Water resources are the premise of sustainable agricultural development in Northeast China (NEC), the country’s largest granary. Revealing the long-term evolution of water resources from multiple perspectives, such as surface water area (SWA) and groundwater storage (GWS), is crucial for understanding their current status and predicting future trends to formulate adaptive strategies to promote sustainable water management. Here, firstly, we annually mapped surface water in 1987–2023 using the Google Earth Engine (GEE) cloud service, all these historical 30-m Landsat records, and a mature surface water detection algorithm; then, we calculated annual GWS using Gravity Recovery and Climate Experiment (GRACE) satellite data and compared the trends of SWA and GWS to analyze the current status and evolution direction of water resources. We found continuous expansion of SWA (169.3 km2/yr) since 2002 after a 15-year consecutive decline, while GWS significantly declined (2.37 mm/yr). Quantitative attribution analyses identified the increasing artificial lakes and aquaculture ponds as the major drivers of surface water expansion, with a contribution of 85.0 %, suggesting that the expanding surface water bodies seen from space were just an illusion of increasingly abundant water resources. In addition, we revealed the increasing water evaporation (0.2 km3/yr) and the ratio (0.005–0.2 %/yr) of water evaporation to evapotranspiration (ET) with surface water expansion, indicating that the contribution of water evaporation to ET is more prominent and evaporative water loss has become a new threat to local water security and deserves much more attention. Our research can not only bring new insights into the current status of water security in China’s largest granary but also provide a typical case for reference in analyzing water resource changes in other similar regions.

Monitoring forest carbon (C) stocks is essential to better assess their role in the global carbon balance, and to better model and predict long-term trends and inter-annual variability in atmospheric CO2 concentrations. On a national scale, national forest inventories (NFIs) can provide estimates of forest carbon stocks, but these estimates are only available in certain countries, are limited by time lags due to periodic revisits, and cannot provide spatially continuous mapping of forests. In this context, remote sensing offers many advantages for monitoring above-ground biomass (AGB) on a global scale with good spatial (50–100 m) and temporal (annual) resolutions. Remote sensing has been used for several decades to monitor vegetation. However, traditional methods of monitoring AGB using optical or microwave sensors are affected by saturation effects for moderately or densely vegetated canopies, limiting their performance. Low-frequency passive microwave remote sensing is less affected by these saturation effects: saturation only occurs at AGB levels of around 400 t/ha at L-band (frequency of around 1.4 GHz). Despite its coarse spatial resolution of the order of 25 km × 25 km, this method based on the L-VOD (vegetation optical depth at L-band) index has recently established itself as an essential approach for monitoring annual variations in forest AGB on a continental scale. Thus, L-VOD has been applied to forest monitoring in many continents and biomes: in the tropics (especially in the Amazon and Congo basins), in boreal regions (Siberia, Canada), in Europe, China, Australia, etc. However, no reference study has yet been published to analyze L-VOD in detail in terms of capabilities, validation and results. This paper fills this gap by presenting the physical principles of L-VOD calculation, analyzing the performance of L-VOD for monitoring AGB and reviewing the main applications of L-VOD for tracking the carbon balance of global vegetation over the last decade (2010–2019).

Summary: Global warming has led to a surge in heat health risks (HHRs), the impacts of which are particularly pronounced in metropolitan areas of developing countries. In the current study, six metropolitan areas - Beijing, China; Cairo, Egypt; Jakarta, Indonesia; Mumbai, India; Rio de Janeiro, Brazil; and Tehran, Iran - were selected as the study area to further differentiate the built-up landscapes by utilizing the concept of local climate zones. Moreover, we assessed the similarities and differences in HHR associated with the landscape. Results revealed a 30.67% higher HHR in compact built-up landscapes than in the open built-up type. Urban green spaces played an effective but differentiated role in mitigating HHR. That is, low vegetation in urbanized areas and trees in suburban areas significantly mitigated HHR. Collectively, our findings emphasize the role of effective planning and management in addressing HHR and provide empirical support for implementing HHR mitigation and adaptation strategies.

The carbon sinks of North American boreal forests have been threatened by global warming and forest disturbances in recent decades, but knowledge about the carbon balance of these forests in recent years remains unknown. We tracked annual aboveground carbon (AGC) changes from 2016 to 2021 across the forest regions of NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) core study domain, using Vegetation Optical Depth derived from low-frequency passive microwave observations. The results showed that these forests showed a net AGC increase of + 28.49 Tg C/yr during the study period, with total AGC gains of + 219.34 Tg C/yr counteracting total AGC losses of −190.86 Tg C/yr. Forest degradation (-162.21 Tg C/yr), defined as a reduction in the capacity of forest to provide goods and services, contributes 5 times more to the total AGC loss than forest cover loss (-28.65 Tg C/yr), defined as the complete removal of tree cover. This indicates that degradation has dominated AGC loss in the region.

Abstract It is well established that nighttime light brightness value, which is measured from satellites, correlates with economic prosperity across the globe. Researchers have diverged over whether economic factors cluster in coastal areas or move to interior areas. By using nighttime light data and applying the random forest algorithm to measure the proportion of global “near regions” GDP, it was seen that global GDP decreased from 67.25% in 2000 to 63.02% in 2018. This research reveals that under the continuous promotion of economic globalization, there is still a spatial imbalance of economic development between global “near regions” and “far regions”; however, economic factors are gradually shifting to interior areas and forming a “coastal remoteness” evolution pattern. Within the intercontinental range, there are obvious differences in the evolution patterns and spatial structure of economic development between the sub-regions. The reduction of overseas transportation costs and diseconomies of scale are the primary reasons for the evolution of “coastal remoteness” in global economic development. Our findings can facilitate future policymaking and the management of global coastal and interior areas, as well as establish new horizons for relevant research topics within the context of land and marine-coordinated development.

Abstract Urban parks can effectively reduce surface temperatures, which is an important strategic approach to reducing the urban heat island effect. Quantifying the cooling effect of urban parks and identifying their main internal influencing factors is important for improving the urban thermal environment, achieving maximum cooling benefits, and improving urban sustainability. In this study, we extracted data frobut this is often unrealisticm 28 urban parks in Zhengzhou, China. We combined multivariate data, such as Landsat 8 data, to retrieve the land surface temperature (LST), extract the park interior landscape, and quantify the cooling effect using three cooling indices: park cooling distance (L ∆max), temperature difference magnitude (∆T max), and temperature gradient (G temp). Furthermore, the relationship between the internal landscape characteristics of the park and the average LST and cooling indices of the park was analyzed. The results showed that different buffer ranges affect the LST-distance fitting results of urban parks, and a 300-m buffer zone is the optimal fitting interval. However, specific parks should be analyzed to select the optimal buffer range and reduce the cooling index calculation errors. Additionally, the mean values of LST, ∆T max, L ∆max, and G temp for the 28 parks in Zhengzhou were 34.11, 3.22 °C, 194.02 m, and 1.78 °C/hm, respectively. Park perimeter (PP), park area, internal green area (GA), and landscape shape index (LSI) were both significantly correlated with ∆T max and the main factors associated with maintaining a low LST in parks. L∆max was mainly affected by the GA, LSI, and perimeter-area ratio, whereas G temp was positively correlated with PP. Finally, the threshold value of efficiency for parks in Zhengzhou was 0.83 ha, and comprehensive parks showed optimal cooling in every aspect.

Forest aboveground biomass (AGB) is crucial as it serves as a fundamental indicator of the productivity, biodiversity, and carbon storage of forest ecosystems. This paper presents a targeted literature review of advancements in AGB estimation methods. We conducted an extensive review of published literature using Web of Science, ResearchGate, Semantic Scholar, and Google Scholar. Our findings highlight the importance of accurate AGB estimation in the studies of terrestrial carbon cycle, forest ecosystem management, and climate change. Moreover, AGB estimation contributes valuable ecological knowledge and supports effective natural resource management. Unfortunately, during the data collection process for AGB estimation, we have identified two critical yet often overlooked issues: (1) the reliability of manual survey accuracy, and (2) the impact of overlap between ground plots and remote sensing pixels on AGB estimation. Drawing on existing technologies and literature analysis, we propose a potentially effective solution to address these challenges. In conclusion, accurate estimation and mapping of forest parameters, such as AGB, will remain a priority in forestry research for the foreseeable future. To ensure the practical applicability of AGB research findings, our future research efforts will focus on understanding manual survey accuracy and determining the optimal overlap between plots and pixels.

Abstract Wind‐induced crop lodging can reduce agricultural production and impact food security. However, a systematic evaluation of large‐scale crop lodging and its drivers is lacking, mostly due to the limited observations available. Such knowledge gaps hinder the application of effective management practices to mitigate yield losses. Here, we quantify maize‐lodging induced by three consecutive typhoons in Northeast China in 2020 using direct crop canopy height observations from the Global Ecosystem Dynamics Investigations (GEDI) instrument and optical satellite imagery. We show that the canopy water‐related vegetation index provides a robust wall‐to‐wall mapping of crop lodging, supported by changes in GEDI canopy height. Our spatially contiguous lodging maps show that lodging intensity is mainly due to typhoon intensity (64.34%), with maize plantation fraction (11.94%) and phenology (7.94%) as secondary factors. The shelterbelt forest may partly alleviate the lodging risk, but its effect requires further investigation. Considering the recent expansion of maize cultivation and the poleward shift of landfalling typhoons alongside climate change, the lodging risk may increase for maize and further threaten China's food security.

Over the past 4 decades, Southwest China has the fast vegetation growth and aboveground biomass carbon (AGC) accumulation, largely attributed to the active implementation of ecological projects. However, Southwest China has been threatened by frequent extreme drought events recently, potentially countering the expected large AGC increase caused by the ecological projects. Here, we used the L-band vegetation optical depth to quantify the AGC dynamics over Southwest China during the period 2013-2021. Our results showed a net AGC sink of 0.064 [0.057, 0.077] Pg C year−1 (the range represents the maximum and minimum AGC values), suggesting that Southwest China acted as an AGC sink over the study period. Note that the AGC loss of 0.113 [0.101, 0.136] Pg C year−1 was found during 2013-2014, which could mainly be attributed to the negative influence of extreme droughts on AGC changes in Southwest China, particularly in the Yunnan province. For each land use type (i.e., dense forests, persistent forests, nonforests, afforestation, and forestry), the largest AGC stock increase of 0.032 [0.028, 0.036] Pg C year−1 was found in nonforests, owing to their widespread land cover rate over Southwest China. For AGC density (i.e., AGC per unit area), the afforestation areas showed the largest AGC density increase of 0.808 [0.724, 0.985] Mg C ha−1 year−1, reflecting the positive effect of afforestation on AGC increase. Moreover, the karst areas exhibited a higher increasing rate of AGC density than nonkarst areas, suggesting that the karst ecosystems have a high carbon sink capacity over Southwest China.

National territory spatial planning guides national spatial development and the spatial blueprint of sustainable development [...]

Abstract Flash drought, characterized by unusually rapid drying, can have substantial impact on many socioeconomic sectors, particularly agriculture. However, potential changes to flash drought risk in a warming climate remain unknown. In this study, projected changes in flash drought frequency and cropland risk from flash drought are quantified using global climate model simulations. We find that flash drought occurrence is expected to increase globally among all scenarios, with the sharpest increases seen in scenarios with higher radiative forcing and greater fossil fuel usage. Flash drought risk over cropland is expected to increase globally, with the largest increases projected across North America (change in annual risk from 32% in 2015 to 49% in 2100) and Europe (32% to 53%) in the most extreme emissions scenario. Following low-end and medium scenarios compared to high-end scenarios indicates a notable reduction in annual flash drought risk over cropland.

Cropping intensity maps at high spatial resolution play a crucial role in guiding agricultural policies and ensuring food security. So far, most of nationwide cropping intensity maps have been developed from satellite images at moderate or coarse resolutions. In this study, we first assembled and integrated time-series dataset with high spatial resolution, specifically Landsat-7, Landsat-8 and Sentinel-2 imagery in 2017. We then used an object- and phenology-based algorithm and integrated images to create a 10-m resolution cropping intensity map over China. The map evaluation results revealed an overall accuracy of 96.68 ± 0.01 % and a Kappa coefficient of 0.90. In 2017, single cropping dominated the agricultural practices in China, with an approximate area of 1.189 × 106 km2 ± 7.90 × 103 km2, constituted 79.26 % of the entire cropland area. Simultaneously, double and triple cropping covered approximately 0.306 × 106 km2 ± 8.03 × 103 km2 and 5.00 × 103 ± 1.75 × 103 km2, corresponding to 20.41 % and 0.33 % of the entire cropland area, respectively. On average, the national multiple cropping index (MCI) was 1.21. The results in the study prove the reliability of the generated mapping products and high potential of the developed mapping framework (the algorithm and integrated datasets), which can be readily applied to quantify the interannual changes of cropping pattern on a nationwide level with a high spatial resolution.

Grassland degradation (within-state change) and desertification (state conversion from grasslands to deserts) are different states in the complex dynamic process of grassland deterioration. However, the dynamics and associated drivers of the two states have been rarely examined separately, which is critical for early warning and conservation policy making. Here, the grassland degradation and desertification in Central Asia (CA) were studied due to their evident vulnerability to climate extremes and human activities. We performed a two-step approach to identify the critical state zones first (i.e., grasslands, deserts, and the desertification zones) and then assess the vegetation dynamics within each zone. Finally, we quantified the roles of CO2 fertilization effect, climate change, climate variability, land use (LU), and anthropogenic climate change (ACC) on the vegetation dynamics in different state zones. The results showed that the areas of grasslands and sparse vegetation regions (including deserts and desertification zones) were stable from 2000 to 2020, but the trends of vegetation greenness were divergent with a reduction in grasslands and an increase in sparse vegetation regions. Furthermore, climate change and climate variability were the main driving force affecting grassland degradation in CA. Yet the vegetation greening in the desertification and desert zones was mainly driven by the rising CO2 and LU. Moreover, ACC degraded 20.63% of grasslands and sparsely vegetated lands in CA on the baseline of 2000. The results highlighted the risks of grassland degradation in CA. This study proposed a methodology to examine grassland degradation and desertification in detail and quantify the associated driving factors (especially ACC) at the pixel scale, which provided some insights on developing region-precise strategies for grassland conservation in CA and other drylands.

Abstract Pseudo-human settlements (PHS) reflect the subjective wishes and needs of residents. An in-depth characterization of PHS aids in clarifying the actual development needs and future development directions of the city from the residents’ perspective. China is currently undergoing high-speed urbanization and advocates improving the quality of cities. It is particularly important to test the achievements of urbanization through PHS. This study quantitatively analyses spatiotemporal characteristics and the realistic driving mechanism of PHS in 286 Chinese cities through spatial autocorrelation analysis and geographic detectors. The results indicate that (1) the PHS level exhibited notable hierarchical characteristics and spatial differences (high in the eastern coastal regions and low in the western inland regions), with weak positive autocorrelation characteristics; (2) nighttime light index, per capital GRP, and population size are major drivers of heterogeneity of development level and functional structure of PHS; and (3) the functional structure followed an emergence-development-maturity-decline-extinction life cycle. Overall, the development of PHS must play a leading role in high-level areas, and urban decision-makers should clarify and adapt to local preferences of PHS and predict future changes. The findings of this study can help China and other countries promote realistic development by unveiling the characteristics of PHS.

Global climate change is causing various negative impacts on urban ecosystems and energy systems. To effectively mitigate and adapt to these changes, it is important to understand the contributions of background climate and local effects to urban thermal environment variation. This study utilized the empirical orthogonal function (EOF) approach to deconstruct long-term MODIS land surface temperature (LST) datasets to obtain the main features of change in daytime and nighttime thermal environments. Local bivariate spatial autocorrelation analysis was used to explore the underlying causes of these changes. The main EOF modes explained 73.14% and 81.33% of daytime and nighttime thermal environment variation, respectively. The correlation coefficient between the time coefficient of the main modes and the average LST was > 0.99, reflecting the role of global effect caused by background climate change. The secondary EOF modes explained 12.51% and 4.12% of daytime and nighttime thermal environment variation, respectively, and were spatially correlated with changes in landscape thermal intensity, reflecting local effect caused by landscape change and anthropogenic heat emissions. In expansion and renewal areas, industrial zones and compact high-rise buildings had the most obvious warming effect on the daytime thermal environment, while mid-to-high-rise buildings had the most obvious warming effect on the nighttime thermal environment. The results of this study provide valuable insights into the mechanisms of background climate and local effects on the urban thermal environment, and provide a reference for formulating effective strategies for mitigating and adapting to change in urban areas, and for promoting sustainable development.

North American boreal forests are known to be an important carbon pool in boreal ecosystems, but have experienced extensive tree mortality and carbon loss due to multiple agents of stand-replacing disturbances in recent decades. However, the impacts of these stand-replacing disturbances on forest dynamics are still unknown. We used a recently developed remote-sensing based stand-replacing disturbance product, coupled with aboveground biomass (AGB), gross primary productivity (GPP) and leaf area index (LAI) datasets to estimate the impacts of stand-replacing disturbances (e.g., fires, logging and insect outbreaks) on the carbon balance of western North American boreal forests during 2000–2012. Our results showed that fire, logging and insect outbreaks resulted in AGB losses of 23.4, 16.6, and 4.7 Tg/yr, respectively. In the post-disturbance periods, AGB did not recover to its pre-disturbed levels in the 10th year, which is longer than the recovery time of GPP and LAI. Furthermore, the losses of AGB, GPP and LAI in fire events were the dominant factors for forest recovery after stand-replacing fire. Vapor Pressure Deficit (VPD), soil clay content, temperature and precipitation were the important factors for forest recovery after stand-replacing insect outbreaks and stand-replacing logging. When removing the impact of environmental factors, our results showed a smaller magnitude of AGB, GPP and LAI loss relative to the results including these factors, although similar recovery trajectories were observed among the two results. The results have important implications for understanding the effects of stand-replacing disturbances on the carbon dynamics of boreal forests, which is required to adopt effective forest management strategies after disturbance.

The urban development boundary (UDB) is a core issue in territorial spatial planning and remains a research focus in urban development. However, in the past, land use simulations of coastal cities have had some limitations. For example, new urban land outside the research boundary could not be effectively simulated, resulting in a large gap between the simulation results and reality. In this study, an artificial neural network (ANN) cellular automata (CA) model based on a new perspective, the Offshore Island Connection Line (collectively, OICL-ANN-CA), was developed to address this problem. We first proposed the delineation principles and methods of OICL and applied it to the Jinpu New Area of Dalian City, Liaoning Province, China. Based on the conversion probability and land use simulation results obtained by ANN and CA, this study validated the UDB simulations for 2000–2020 in the Jinpu New Area and predicted the UDB for 2020–2035 under three scenarios: historical inertia development, ecological security protection, and ecological and economic balance. The results indicate that, compared with the traditional perspective model (the ANN-CA model based on the sea–land boundary), the OICL-ANN-CA model exhibited better simulation accuracy (the figure of merit was approximately 35% higher) and effectively simulated new urban land outside the traditional boundary. This simulation of urban land expansion is more consistent with recent development in the study area. In addition, the predicted results of UDB in 2035 also demonstrate the benefits of this model for detecting reclaimed land. This study illustrates that the OICL-ANN-CA model is a more capable method for capturing changes in new coastal urban land and can produce realistic simulations. It provides a reference for delimiting the UDB and defining the research boundary for the Jinpu New Area and other fast-growing coastal cities in China.

Eucalyptus plantations promote the economic development of forestry in southern China, but many studies have reported their negative environmental impacts, such as high water resource usage of certain species of Eucalyptus plants and losses in biodiversity. To date, annual maps of Eucalyptus plantations at large scales with high spatial resolutions are not yet available. Here, we investigated the spectral properties of Eucalyptus plantations and developed a knowledge-based Eucalyptus plantation mapping algorithm. We produced annual maps of Eucalyptus plantation at 10-m spatial resolution in the Guangxi Zhuang Autonomous Region (Guangxi), China, using our proposed algorithm and images of ALOS PALSAR-2, Sentinel-2, and Landsat (ETM+/OLI) in a single year. First, we generated annual evergreen forest maps using PALSAR-2 and Landsat/Sentinel-2-based vegetation index time series data. Second, we distinguished Eucalyptus plantations from the evergreen forest layer using the unique biophysical features of Eucalyptus plantations, the Sentinel-2 red edge bands, and Landsat/Sentinel-2-based enhanced vegetation index (EVI). Our resultant 2020 Eucalyptus plantation map had high producer's, user's, and overall accuracies of 0.85, 0.89, and 0.96, respectively. There were 3.10 × 106 ha of Eucalyptus plantation in Guangxi in 2020. Among the 14 administrative units, Wuzhou City had the largest Eucalyptus plantation area in Guangxi, followed by Nanning, Baise, and Chongzuo cities. We demonstrated the potential of knowledge-based mapping approaches for identifying evergreen forest and Eucalyptus plantations in complex and fragmented landscapes where cloud cover is frequent. Our 10-m Eucalyptus plantation map is the most current dataset available and can be used to assist the sustainable production of Eucalyptus, ecological assessments, and conservation.

Sugarcane croplands account for ~70% of global sugar production and ~60% of global ethanol production. Monitoring and predicting gross primary production (GPP) and transpiration (T) in these fields is crucial to improve crop yield estimation and management. While moderate-spatial-resolution (MSR, hundreds of meters) satellite images have been employed in several models to estimate GPP and T, the potential of high-spatial-resolution (HSR, tens of meters) imagery has been considered in only a few publications, and it is underexplored in sugarcane fields. Our study evaluated the efficacy of MSR and HSR satellite images in predicting daily GPP and T for sugarcane plantations at two sites equipped with eddy flux towers: Louisiana, USA (subtropical climate) and Sao Paulo, Brazil (tropical climate). We employed the Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM) with C4 photosynthesis pathway, integrating vegetation index data derived from satellite images and on-ground weather data, to calculate daily GPP and T. The seasonal dynamics of vegetation indices from both MSR images (MODIS sensor, 500 m) and HSR images (Landsat, 30 m; Sentinel-2, 10 m) tracked well with the GPP seasonality from the EC flux towers. The enhanced vegetation index (EVI) from the HSR images had a stronger correlation with the tower-based GPP. Our findings underscored the potential of HSR imagery for estimating GPP and T in smaller sugarcane plantations.

Abstract The acceleration of global urbanization has increased the frequency of the urban heat island (UHI) effect and heatwaves, which seriously endanger human health. We used Shenzhen as a case study to examine the daytime and nighttime differences in UHI intensity (UHII), considering different local climate zones (LCZs) and sea–land breezes. The diurnal UHII was >3 °C for 52% of the study period, whereas the nocturnal UHII was >3 °C for only 26% of the study period. The average diurnal and nocturnal building-type UHII values were 2.77 and 1.11 °C higher than those of the natural type, respectively. Sea breezes alleviated the UHI effect with a linear correlation coefficient of −0.68601 between them. Moreover, diurnal and nocturnal UHII showed differences across different gradients, which can help guide urban planning.

Measurement(s) lake area and number Technology Type(s) satellite images and coding Sample Characteristic - Organism lake Sample Characteristic - Environment plateau-climate Sample Characteristic - Location Tibetan Plateau

Biological invasions are considerably altering ecosystem structure and functions, especially in coastal ecosystems that are subject to intensive anthropogenic disturbances. Spartina alterniflora has been recognized as the most serious invasive species in coastal China, which has received considerable attention from the government and the public. There is urgent need to control this invasive species at regional and national scales, but such efforts were impeded by lack of time-series data of Spartina spread. Here, we assessed the pixel- and phenology-based algorithm for mapping Spartina saltmarshes, and applied this algorithm to generate annual Spartina saltmarsh maps (30-m spatial resolution) from 1985 to 2020 by using time series Landsat 5/7/8 images. The resulting maps suggest that Spartina has been expanding since 1990 in coastal China, with three noticeable phases (rapid, moderate, and rapid). Along the latitudinal gradient, Spartina exhibited a longer invasion history and more frequent changes at low latitudes. Although human interventions caused the decline of Spartina in certain areas, rapid natural spread was primarily responsible for its extensive and continual invasion. These results provide insights for efficiently managing this invasive species, enhancing the conservation of coastal wetlands, and promoting the sustainability of coastal wetlands.

Summary: Land surface temperature (LST) is subject to location and environmental influences, which makes quantification difficult in terms of timeliness. Based on 10-d geostationary satellite LST TCI products, we quantitatively evaluated the thermal environment differentiation of various ground objects in North, South, and Northwest China from 2017 to 2021. We found that the thermal condition index (TCI) in Northwest China decreased, whereas it increased in North and South China. In contrast, Moran’s I index increased in Northwest and South China, with strong spatial agglomeration. The TCI for artificial surfaces decreased from North (0.633) to Northwest (0.554) and South China (0.384). The bare land TCI was always the lowest among the land use/land cover (LULC) types in each region. Our results reflect the LULC thermal environment of China against the background of new urbanization and provide theoretical support for scientific planning to improve the ecological environment.

Tourism development has influenced industrial structure changes and has become a major driving force for China's new urbanization. However, the development will negatively impact natural resources and the ecological environment and will become an essential driving factor for land use change. Therefore, understanding the impact of tourism urbanization is crucial for sustainable local development. This study selected the Dachangshan Island in the Changhai County, Dalian, China, as the study area, because it is the only coastal island-type border county in China. During the study period, changes in local environmental factors were analyzed based on land use data, Landsat 5 and Landsat 8 data of 2009, 2014, and 2019. The results showed that: (1) the overall land surface temperature (LST) in the research region shows an increasing trend; the LST in 2014 and 2019 increased by 6.10 and 5.94 °C, respectively, compared with 2009. With respect to specific land types, impervious surfaces maintained a high land surface temperature (25.44, 32.38, and 31.86); however, surface temperatures for cropland, forest, grassland, and water bodies remained stable. (2) The land use land cover (LULC) change analysis from 2009–2019 indicates that impervious surfaces and cropland increased by 0.5653 km2 and 0.9941 km2, while the areas of forest, grassland, and water bodies decreased. The results also showed that forests (−1.3703 km2) are most affected by urbanization. (3) The results of the landscape index calculation showed that the variation at the patch scale is different for different LULC types. The patch density of impervious surfaces decreased, but the aggregation index increased over time, while the patch density of the forest increased continuously. At the landscape scale, overall patch type and distribution remained stable. The purpose of this study is to explore the environmental changes of islands and provide a reference for the sustainable development of islands.

Flash droughts can have devastating impacts but are notoriously difficult to predict. This study identifies global hotspots of flash drought, driven by evaporative demand and precipitation deficits across varying geographic regions and crop-type, providing a framework for flash drought prediction.

Understanding the thermal characteristics and contribution ranking of local climate zones (LCZs) is essential since they can help in maintaining environmental harmony. However, previous studies only considered independent effects and could not analyze the combined effects of LCZ on land surface temperature (LST). In this study, we propose a new method to establish an interaction model between LCZs. Five first-level grids with different scales from 270 to 990 m were established to calculate the area proportion of LCZ. The area proportion of LCZ was then applied in the stepwise regression model to quantitatively analyze its magnitude and direction of impact on the LST. The results suggest that the LCZ types of the study area with the highest and lowest average LST were LCZ2 (compact middle-rise building, 39.82°C) and LCZG (water body, 34.24°C), respectively. However, on most scales, the warming effect of LCZ2 was lower than that of LCZE (bare rock or paver), and the cooling effect of LCZG was lower than that of LCZD (low plants). The optimum results were obtained at a scale of 810 m. At this scale, the warming effect was in the order: LCZE (0.314) > LCZ2 (0.236) > LCZ3 (compact low-rise building, 0.135) > LCZ5 (open middle-rise, 0.084) > LCZ6 (open low-rise, 0.056); the cooling effect was in the order: LCZD (−0.272) > LCZA (dense trees, −0.104) > LCZG (−0.103). These findings can help to elucidate the unique warming and cooling effects of LCZ on the interaction condition and the construction of an urban human settlement.

Abstract Global terrestrial vegetation dynamics have been rapidly altered by climate change. A widespread vegetation greenness over a large part of the planet from the 1980s to early this century has been reported, whereas weakening of CO2 fertilization effects and increasing climate extremes and the adverse impact of increasing rate of warming and severity of drought on vegetation growth were also reported. Earth system models project that the land carbon sink will decrease in size in response to an increase in warming during this century. How global vegetation is changing during this century in response to global warming and water availability across spatial and temporal scales remains uncertain. Our understanding of the widespread vegetation greening or browning processes and identifying the biogeochemical mechanisms remain incomplete. Here we use multiple long‐term satellite leaf area index (LAI) records to investigate vegetation growth trends from 1982 to 2018. We find that the widespread increase of growing‐season integrated LAI (greening) since 1980s was reversed (p‐value 45°N). Overall, the reversal of greening trend since 2000 weakened the negative feedback of carbon sequestration on the climatic system and should be considered in the strategies for climate warming mitigation and adaptation. Our findings of the diversity of processes that drive browning across bioclimatic‐zones and ecosystems and of how those driving processes are changing would enhance our ability to project global future vegetation change and its climatic and abiotic consequences.

Urbanization has been accelerating; hence the effect of urban heat island (UHI) has increased. There has been extensive research on spatiotemporal UHI changes and drivers, however, data on the dominant seasonal factors of UHIs and the differences along urban-rural gradients remain limited. Based on Luojia-1A, Landsat 8, and moderate resolution imaging spectroradiometer (MODIS) data, we assessed the seasonal differences in surface UHI (SUHI), normalized differences in vegetation index (NDVI), built-up index (NDBI), and water index (NDWI) and their relationships in the Dalian City, Northeast China. We found that in the urban built-up area, the mean SUHI intensity (SUHII) decreased from that in summer (2.74°C) > autumn (1.65°C) > winter (0.28°C) > spring (−0.79°C). SUHII was more strongly affected by NDWI and NDBI than NDVI, and NDBI and NDWI showed positive and negative correlations with SUHII in different seasons, while NDVI and SUHII were positively correlated in spring and negatively correlated in the other seasons. When analyzing the dominant factors of SUHII, the importance results showed that, in spring, NDBI > NDVI > NDWI, in autumn, NDVI > NDWI > NDBI, in summer and winter, NDWI > NDVI > NDBI. In addition, SUHII changed the most in summer along the urban-rural gradient, decreasing from 2.74°C to −2.74°C. Among these indicators, except for spring NDVI which increased from 0.09 to 0.59 with distance from built-up areas, there was minimal change in NDVI, NDBI, and NDWI along the urban-rural gradient in other seasons (i.e., all were within 0.2). In this study, the difference analysis of SUHI and remote sensing indices along the urban-rural gradient can help to facilitate the rational layout of cities.

High urban temperatures affect city livability and may be harmful for inhabitants. Analyzing spatial and temporal differences in surface temperature and the thermal impact of urban morphological heterogeneity can promote strategies to improve the insulation of the urban thermal environment. Therefore, we analyzed the diurnal variation of land surface temperature (LST) and seasonal differences in the Fifth Ring Road area of Beijing from the perspective of the Local Climate Zone (LCZ) using latest ECOSTRESS data. We used ECOSTRESS LST data with a resolution of 70 m to accurately interpret the effects of urban morphology on the local climate. The study area was dominated by the LCZ9 type (sparse low-rise buildings) and natural LCZ types, such as LCZA/B (woodland), LCZD (grassland), and LCZG (water body), mainly including park landscapes. There were significant differences in LST observed in different seasons as well as day and night. During daytime, LST was ranked as follows: summer > spring > autumn > winter. During night-time, it was ranked as follows: summer > autumn > spring > winter. All data indicated that the highest and lowest LST was observed in summer and winter, respectively. LST was consistent with LCZ in terms of spatial distribution. Overall, the LST of each LCZ during daytime was higher than that of night-time during different seasons (except winter), and the average LST of each LCZ during the diurnal period in summer was higher than that of other seasons. The LST of each LCZ during daytime in winter was lower than that of the corresponding night-time, which indicates that it is colder in the daytime during winter. The results presented herein can facilitate improved analysis of spatial and temporal differences in surface temperature in urban areas, leading to the development of strategies aimed at improving livability and public health in cities.

Urban heat islands (UHIs) and their energy consumption are topics of widespread concern. This study used remote sensing images and building and meteorological data as parameters, with reference to Oke's local climate zone (LCZ), to divide urban areas according to the height and density of buildings and land cover types. While analyzing the heat island intensity, the neural network training method was used to obtain temperature data with good temporal as well as spatial resolution. Combining degree-days with the division of LCZs, a more accurate distribution of energy demand can be obtained by different regions. Here, the spatial distribution of buildings in Shenyang, China, and the law of land surface temperature (LST) and energy consumption of different LCZ types, which are related to building height and density, were obtained. The LST and energy consumption were found to be correlated. The highest heat island intensity, i.e., UHILCZ 4, was 8.17°C. The correlation coefficients of LST with building height and density were −0.16 and 0.24, respectively. The correlation between urban cooling energy demand and building height was −0.17, and the correlation between urban cooling energy demand and building density was 0.17. The results indicate that low- and medium-rise buildings consume more cooling energy.

Wild waterbirds, and especially wild waterfowl, are considered to be a reservoir for avian influenza viruses, with transmission likely occurring at the agricultural-wildlife interface. In the past few decades, avian influenza has repeatedly emerged in China along the East Asian-Australasian Flyway (EAAF), where extensive habitat conversion has occurred. Rapid environmental changes in the EAAF, especially distributional changes in rice paddy agriculture, have the potential to affect both the movements of wild migratory birds and the likelihood of spillover at the agricultural-wildlife interface. To begin to understand the potential implications such changes may have on waterfowl and disease transmission risk, we created dynamic Brownian Bridge Movement Models (dBBMM) based on waterfowl telemetry data. We used these dBBMM models to create hypothetical scenarios that would predict likely changes in waterfowl distribution relative to recent changes in rice distribution quantified through remote sensing. Our models examined a range of responses in which increased availability of rice paddies would drive increased use by waterfowl and decreased availability would result in decreased use, predicted from empirical data. Results from our scenarios suggested that in southeast China, relatively small decreases in rice agriculture could lead to dramatic loss of stopover habitat, and in northeast China, increases in rice paddies should provide new areas that can be used by waterfowl. Finally, we explored the implications of how such scenarios of changing waterfowl distribution may affect the potential for avian influenza transmission. Our results provide advance understanding of changing disease transmission threats by incorporating real-world data that predicts differences in habitat utilization by migratory birds over time.

ABSTRACT Land conversion for intensive agriculture produces unfavorable changes to soil ecosystems, causing global concern. Soil bacterial communities mediate essential terrestrial ecosystem processes, making it imperative to understand their responses to agricultural perturbations. Here, we used high-throughput sequencing coupled with a functional gene array to study temporal dynamics of soil bacterial communities over 1 year under different disturbance intensities across a U.S. Southern Plains agroecosystem, including tallgrass prairie, Old World bluestem pasture, no-tillage (NT) canola, and conventional tillage (CT) wheat. Land use had the greatest impact on bacterial taxonomic diversity, whereas sampling time and its interaction with land use were central to functional diversity differences. The main drivers of taxonomic diversity were tillage > sampling time > temperature, while all measured factors explained similar amounts of variations in functional diversity. Temporal differences had the strongest correlation with total nitrogen > rainfall > nitrate. Within land uses, community variations for CT wheat were attributed to nitrogen levels, whereas soil organic matter and soil water content explained community variations for NT canola. In comparison, all measured factors contributed almost equally to variations in grassland bacterial communities. Finally, functional diversity had a stronger relationship with taxonomic diversity for CT wheat compared to phylogenetic diversity in the prairie. These findings reinforce that tillage management has the greatest impact on bacterial community diversity, with sampling time also critical. Hence, our study highlights the importance of the interaction between temporal dynamics and land use in influencing soil microbiomes, providing support for reducing agricultural disturbance to conserve soil biodiversity. IMPORTANCE Agricultural sustainability relies on healthy soils and microbial diversity. Agricultural management alters soil conditions and further influences the temporal dynamics of soil microbial communities essential to ecosystem functions, including organic matter dynamics, nutrient cycling, and plant nutrient availability. Yet, the responses to agricultural management are also dependent on soil type and climatic region, emphasizing the importance of assessing sustainability at local scales. To evaluate the impact of agricultural management practices, we examined bacterial communities across a management disturbance gradient over 1 year in a U.S. Southern Plains agroecosystem and determined that intensive management disturbance and sampling time critically impacted bacterial structural diversity, while their interactive effect influenced functional diversity and other soil health indicators. Overall, this study provides insights into how reducing soil disturbance can positively impact microbial community diversity and soil properties in the U.S. Southern Plains.

Resource-based urban agglomerations often encounter greater challenges in the sustainable development of human settlements. The aim of this study is to propose an approach to the coordinated development of competitiveness by analyzing the interaction of human settlements competitiveness (HSC) in resource-based urban agglomerations. Through the compound evaluation model of HSC and urban network analysis, this study finds: 1) the HSC measure increased from 35.12 in 1990 to 52.15 in 2015 and showed a downward trend from 2015 to 2019, with an average value of 47.82 in 2019; 2) The change trend of the relevance network density is the same as that of the HSC, while the difference network density reaches the lowest value of 0.441 when the HSC is the highest, indicating that the HSC of the urban agglomerations has improved to a certain extent but is more unsustainable, and 3) Communities in the relevance network are obviously bounded by the borders of provinces or urban agglomerations, while the communities in the difference network are differentiated into two types: high-competitiveness and low-competitiveness. Using the theory of “co-opetition” to analyze the sustainable development path of resource-based urban agglomerations, the study believes that a coordination mechanism and a guarantee mechanism for benefit distribution should be established between urban agglomerations to curb local protectionism, and promote regional dislocation development. The development gradient level also should be established within the urban agglomeration to narrow the gap between HSC of cities, and innovative development should be the core of promoting industrial transformation and upgrading.

Measurement(s) area of different crop types • area of cropland Technology Type(s) machine learning Factor Type(s) type of crop • year of data collection Sample Characteristic - Environment cultivated environment • cropland ecosystem Sample Characteristic - Location Northeast China • China Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13567526

Urban vegetation experiences multiple natural and human impacts during urbanization, including land conversion, local environmental factors, and human management, which may bring positive or negative impacts on vegetation gross primary productivity (GPP) at multiple scales. In this study, we analyzed the spatial-temporal changes of GPP and three urbanization factors: land urbanization (impervious surface coverage), population urbanization (Population), and economic urbanization Gross domestic product (GDP) at city-district-grid scales in Beijing during 2000–2018. Overall, both GPP and three urbanization factors showed an increased trend. The relationships between GPP and urbanization factors exhibit diverse characteristics at multiple scales: unlike the linear relationship observed at city scale, the relationships at district and grid scales all demonstrated nonlinear relationship, even a U shape between GPP and population/GDP. Furthermore, the positive impact of urbanization on GPP increased and offset the negative impact of land conversion from 9.9% in 2000 to 35% in 2018, indicating that urban management and climate during urbanization effectively promote vegetation photosynthesis and neutralize the negative impact of urban area expansion. Our findings highlight the increased growth offset by urbanization on vegetation and the importance of analysis at a finer scale. Understanding these urbanization types’ impact on vegetation is pivotal in formulating comprehensive strategies that foster sustainable urban development and preserve ecological balance.

Urbanization affects vegetation within city administrative boundary and nearby rural areas. Gross primary production (GPP) of vegetation in global urban areas is one of important metrics for assessing the impacts of urbanization on terrestrial ecosystems. To date, very limited data and information on the spatial-temporal dynamics of GPP in the global urban areas are available. In this study, we reported the spatial distribution and temporal dynamics of annual GPP during 2000–2016 from 8,182 gridcells (0.5° by 0.5° latitude and longitude) that have various proportion of urban areas. Approximately 79.3% of these urban gridcells had increasing trends of annual GPP during 2000-2016. As urban area proportion (%) within individual urban gridcells increased, the means of annual GPP trends also increased. Our results suggested that for those urban gridcells, the negative effect of urban expansion (often measured by impervious surfaces) on GPP was to large degree compensated by increased vegetation within the gridcells, mostly driven by urban management and local climate and environment. Our findings on the continued increases of annual GPP in most of urban gridcells shed new insight on the importance of urban areas on terrestrial carbon cycle and the potential of urban management and local climate and environment on improving vegetation in urban areas.

The authors of this study compile data on spatial and temporal dynamics of surface water bodies across China, covering a time span from 1989 – 2016. The study describes hot-spot areas with strongly decreasing trends in surface water area and terrestrial water storage in North China and discusses implications of water resources and security in China.

The role of paddy rice agriculture in the spatial and temporal dynamics of atmospheric methane concentration remains unclear. Here, Zhang et al. show that regions with dense rice paddies have high satellite-based column averaged CH4 concentrations (XCH4), and that seasonal dynamics of XCH4 mirror those of paddy rice growth.