The mark of vegetation change on Earth's surface energy balance

Response of alpine vegetation function to climate change in the Tibetan Plateau: A perspective from solar-induced chlorophyll fluorescence

Vegetation change in the Tibetan Plateau (TP) is a crucial indicator of climate change in alpine regions. Previous studies have reported an overall greening trend in the vegetation structure across the TP, especially in its northeastern part, in response to a warming climate. However, variations in the vegetation function and the possible drivers remain poorly understood. Considering the optimal temperature for plants in TP is usually higher than the current temperature, our hypothesis is the function and structure of alpine vegetation have changed synchronously over past few decades. To test this hypothesis, we analyzed satellite-observed solar-induced chlorophyll fluorescence (SIF) and leaf area index (LAI) in the Yellow River source (YRS) region in the northeastern TP to quantify the long-term trends in vegetation functional and structural states, respectively. The results suggest that from 1982 to 2018, SIF increased significantly in 77.71 % of the YRS area, resulting in a significant upward trend of 0.52 × 10-3 mW m-2 nm-1 sr-1 yr-1 (p < 0.001) for the regional-mean SIF. This represents a 16.1 % increase in SIF, which is close in magnitude to the increase in LAI over the same period. The synchronous changes between vegetation function and structure suggest that improved greenness corresponds to a similar level of change in carbon uptake across YRS. Additionally, we used a multiple regression approach to quantify the contribution of climatic factors to SIF changes in YRS. Our analyses show that the increases in SIF were primarily driven by rising temperatures. Spatially, temperature dominated SIF changes in most parts of YRS, except for certain dry parts in the northern and western YRS, where precipitation had a greater impact. Our results are crucial for a comprehensive understanding of climate regulations on vegetation structure and function in high-elevation regions.

Spatiotemporal evolution and driving factors analysis of fractional vegetation coverage in the arid region of northwest China

The arid region of northwest China (ARNC) is the most ecologically fragile region in China, and is characterized by harsh natural conditions, severe soil erosion, and poor soil fertility. Understanding long-term vegetation changes in this region is critical for effective environmental monitoring and climate change adaptation. Fractional vegetation coverage (FVC) is a key parameter for characterizing the ecological conditions of the ARNC. However, the reliance on low-resolution FVC and NDVI data due to the lack of medium-resolution data has limited our understanding of the environmental dynamics in this region. Therefore, this study addressed this gap by utilizing Landsat data to generate FVC data, enabling a detailed investigation of the spatial-temporal variations and driving factors of vegetation in the ARNC from 2000 to 2020. The results indicated the following: (1) The FVC was generally low, with an average of 0.191. The FVC was greater in the northwest and lower in the southeast in terms of spatial distribution features. The trend of FVC change in ARNC showed significant spatial variability, with degradation outweighing improvement. (2) The coefficient of variation of FVC was 0.377, indicating significant temporal fluctuations, with more stable conditions in the northwest than in the southeast. (3) The spatial differentiation of the FVC in ARNC was primarily driven by land cover types, evapotranspiration, and precipitation, with explanatory powers exceeding 30 % each. This study is significant because it provides a comprehensive understanding of vegetation dynamics in one of China's most vulnerable regions, offering critical insights for ecological restoration, desertification control, and sustainable development. The findings underscore the importance of targeted ecological governance to address the challenges posed by environmental degradation in the ARNC.

LUCC-induced dust aerosol change increase surface and reduce atmospheric direct radiative forcing in Northern China

Land use and land cover change (LUCC) can alter surface properties, such as albedo, roughness, and vegetation coverage, directly affecting dust emissions and aerosol concentrations, leading to variations in direct radiative forcing (DRF) of dust aerosols and consequently impacting the climate. This study utilized the Weather Research and Forecasting model with Chemistry (WRF-Chem) to quantify the impact of LUCC in northern China from 2000 to 2020 on dust aerosol DRF. Results indicated that LUCC's influence on shortwave radiative forcing of dust was significantly greater than its influence on longwave radiative forcing and exhibited obvious seasonal variations. Overall, LUCC can cause net direct radiative forcing to increase by 5.3 W m-2 at the surface and decrease by 7.8 W m-2 in the atmosphere. Different types of LUCC transformation showed distinct impacts on dust aerosol DRF, with the conversion from sparse vegetation to barren land had the most significant effect on net radiative intensity, resulting in a decrease of 8.1 W m-2 at the surface, an increase of 12.2 W m-2 in the atmosphere, and an increase of 4.1 W m-2 at the top of the atmosphere. Conversely, the conversion from barren land to sparse vegetation led to surface cooling and atmospheric warming. These findings are of great significance for enhancing our knowledge of the effects of LUCC on the radiative balance of dust aerosols.

Forest fire size amplifies postfire land surface warming

Climate warming has caused a widespread increase in extreme fire weather, making forest fires longer-lived and larger1-3. The average forest fire size in Canada, the USA and Australia has doubled or even tripled in recent decades4,5. In return, forest fires feed back to climate by modulating land-atmospheric carbon, nitrogen, aerosol, energy and water fluxes6-8. However, the surface climate impacts of increasingly large fires and their implications for land management remain to be established. Here we use satellite observations to show that in temperate and boreal forests in the Northern Hemisphere, fire size persistently amplified decade-long postfire land surface warming in summer per unit burnt area. Both warming and its amplification with fire size were found to diminish with an increasing abundance of broadleaf trees, consistent with their lower fire vulnerability compared with coniferous species9,10. Fire-size-enhanced warming may affect the success and composition of postfire stand regeneration11,12 as well as permafrost degradation13, presenting previously overlooked, additional feedback effects to future climate and fire dynamics. Given the projected increase in fire size in northern forests14,15, climate-smart forestry should aim to mitigate the climate risks of large fires, possibly by increasing the share of broadleaf trees, where appropriate, and avoiding active pyrophytes.

Decreased cloud cover partially offsets the cooling effects of surface albedo change due to deforestation

Biophysical processes of forests affect climate through the regulation of surface water and heat fluxes, which leads to further effects through the adjustment of clouds and water cycles. These indirect biophysical effects of forests on clouds and their radiative forcing are poorly understood but highly relevant in the context of large-scale deforestation or afforestation, respectively. Here, we provide evidence for local decreases in global low-level clouds and tropical high-level clouds from deforestation through both idealized deforestation simulations with climate models and from observations-driven reanalysis using space-for-time substitution. The decreased cloud cover can be explained by alterations in surface turbulent heat flux, which diminishes uplift and moisture to varying extents. Deforestation-induced reduction in cloud cover warms the climate, partially counteracting the cooling effects of increased surface albedo. The findings from idealized deforestation experiments and space-for-time substitution exhibit disparities, with global average offsets of, respectively, approximately 44% and 26%, suggesting the necessity for further constraints.

Deforestation amplifies climate change effects on warming and cloud level rise in African montane forests

Tropical montane forest ecosystems are pivotal for sustaining biodiversity and essential terrestrial ecosystem services, including the provision of high-quality fresh water. Nonetheless, the impact of montane deforestation and climate change on the capacity of forests to deliver ecosystem services is yet to be fully understood. In this study, we offer observational evidence demonstrating the response of air temperature and cloud base height to deforestation in African montane forests over the last two decades. Our findings reveal that approximately 18% (7.4 ± 0.5 million hectares) of Africa's montane forests were lost between 2003 and 2022. This deforestation has led to a notable increase in maximum air temperature (1.37 ± 0.58 °C) and cloud base height (236 ± 87 metres), surpassing shifts attributed solely to climate change. Our results call for urgent attention to montane deforestation, as it poses serious threats to biodiversity, water supply, and ecosystem services in the tropics.

Local surface warming assessment in response to vegetation shifts over arid lands of Central Asia (2001-2020)

The Northern Eurasia Earth Science Partnership Initiative (NEESPI) was established to address the large-scale environmental change across this region. Regardless of the increasingly insightful literature addressing vegetation change across Central Asia, the biogeophysical warming effects of vegetation shifts still need to be clarified. To contribute, the utility of robust satellite observation is explored to evaluate the surface warming effects of vegetation shifts across Central Asia, which is among NEEPSI's hotspots. We estimated an average increase of +1.9 °C in daytime local surface temperature and + 1.5 °C in the nighttime due to vegetation shift (2001-2020). Meanwhile, the mean local latent heat increased by 4.65Wm-2, following the mild reduction of emitted longwave radiation (-0.8Wm-2). We found that vegetation shifts led to local surface warming with a bright surface, noting that the average air surface temperature was revealed to have increased significantly (2001-2020). This signal was driven mainly by agricultural expansion in western Kazakhstan stretching to Tajikistan and Xinjiang, then deforestation confined in Tajikistan, southeast Kazakhstan, and the northwestern edge of Xinjiang, and finally, grassland encroachment occurred massively in the west to central Kazakhstan. These findings address the latest information on Central Asia's vegetation shifts that may be substantial in landscape change mitigation plans.

Accounting for albedo change to identify climate-positive tree cover restoration

Restoring tree cover changes albedo, which is the fraction of sunlight reflected from the Earth's surface. In most locations, these changes in albedo offset or even negate the carbon removal benefits with the latter leading to global warming. Previous efforts to quantify the global climate mitigation benefit of restoring tree cover have not accounted robustly for albedo given a lack of spatially explicit data. Here we produce maps that show that carbon-only estimates may be up to 81% too high. While dryland and boreal settings have especially severe albedo offsets, it is possible to find places that provide net-positive climate mitigation benefits in all biomes. We further find that on-the-ground projects are concentrated in these more climate-positive locations, but that the majority still face at least a 20% albedo offset. Thus, strategically deploying restoration of tree cover for maximum climate benefit requires accounting for albedo change and we provide the tools to do so.

Intensifying urban imprint on land surface warming: Insights from local to global scale

Increasing urbanization exacerbates surface energy balance perturbations and the health risks of climate warming; however, it has not been determined whether urban-induced warming and attributions vary from local, regional, to global scale. Here, the local surface urban heat island (SUHI) is evidenced to manifest with an annual daily mean intensity of 0.99°C-1.10°C during 2003-2018 using satellite observations over 536 cities worldwide. Spatiotemporal patterns and mechanisms of SUHI tightly link with climate-vegetation conditions, with regional warming effect reaching up to 0.015°C-0.138°C (annual average) due to surface energy alterations. Globally, the SUHI footprint of 1,860 cities approximates to 1% of the terrestrial lands, about 1.8-2.9 times far beyond the urban impervious areas, suggesting the enlargements of the imprint of urban warming from local to global scales. With continuous development of urbanization, the implications for SUHI-added warming and scaling effects are considerably important on accelerating global warming.

Hydrological impacts of vegetation cover change in China through terrestrial moisture recycling

Terrestrial moisture recycling (TMR), characterized by a continuous process comprising green water flow (i.e., terrestrial evaporation), atmospheric transport, and terrestrial precipitation, functions as a nexus connecting hydrosphere, atmosphere, biosphere, and anthroposphere. During this process, land cover changes that impact green water flow can modify regional and remote precipitation patterns, potentially yielding far-reaching effects on water resources and human livelihoods. However, the comprehensive patterns of moisture recycling and transfer across eco-geographical regions in China, and their connection with various land cover types and vegetation transitions, remain insufficiently evaluated. This study employed an atmospheric moisture tracking model to quantify China's TMR pattern and evaluate the hydrological impacts of vegetation cover changes in China's ecosystems through TMR. The results demonstrate a significant moisture recycling ratio (52.4 %) and a considerable recycled volume (1.9 trillion m3/a) over China, characterized by pronounced moisture transfer from south to north and southwest to northeast. Among various land cover types, grasslands, croplands, and forests play pivotal supportive roles in China's TMR, contributing 738.8, 470.0, and 450.0 billion m3/a of precipitation in China, respectively. Moreover, the potential transition of vegetation between forest and cropland exerts the most significant and extensive impact on China's hydrological cycle. The conversion from forest to cropland leads to a total decrease of 44.7 billion m3/a in precipitation, whereas reforestation from cropland corresponds to a precipitation increase of 74.9 billion m3/a. This study provides a quantitative approach to comprehending the TMR pattern and its relationship with ecosystems, substantiating the significance of a comprehensive water management framework that considers the contribution of atmospheric moisture recycling and the impact of vegetation cover change.

Remote sensing and spectroscopy of lichens

Lichens are combinations of two symbiotic organisms, a green alga or cyanobacterium and a fungus. They grow in nearly all terrestrial ecosystems and survive in habitats, which are very dry or cold, or too poor in nutrients to maintain vegetation growth. Because lichens grow on visible surfaces and exhibit spectral properties, which are clearly different from, for example, vegetation, it is possible to distinguish them in remote sensing data. In this first systematic review article on remote sensing of lichens, we analyze and summarize which lichen species or genera, and in which habitats and geographical regions, have been remotely sensed, and which remote sensing or spectroscopic technologies have been used. We found that laboratory or in situ measured spectra of over 70 lichen species have been reported to date. We show that studies on remote sensing of lichens fall under seven broad themes: (1) collection of lichen spectra for quantification of lichen species or characteristics, (2) pollution monitoring with lichens as ecological indicators, (3) geological and lithological mapping, (4) desert and dryland monitoring, (5) animal habitat monitoring, (6) land cover or vegetation mapping, and (7) surface energy budget modeling.

One-third of cropland within protected areas could be retired in China for inferior sustainability and effects

Cropland encroachment on protected areas causes natural habitat loss, which may damage ecosystem functions and biodiversity conservation, leading to trade-offs between crop production and habitat conservation. Here we investigate the sustainable utilization and effects of cropland within protected areas, on which we make trade-offs with an established framework. Analyses show that 3.08 million hectares of cropland are identified within protected areas, about two-thirds less sustainable or less beneficial. 41 % and 26 % are expected to be retired for inferior sustainability and effects under the strictest and laxest habitat protection scenario, respectively. Although these retirements would cause a loss of crop benefits of 0.3-0.7 billion US dollars, they could bring ecological benefits of 2.9-3.6 billion US dollars annually. Approximately 11 % of cropland within protected areas is identified in poor agricultural conditions, and 19 % occupies areas of high protection importance, both of which are recommended for priority retirement. This study reveals the characteristics and impacts of cropland in protected areas and provides a quantified trade-off approach for conflicts between cropland and natural habitats, with important implications for production, conservation, and their trade-offs.

Energy Partitioning and Latent Heat Flux Driving Factors of the CAM Plant Pineapple (Ananas comosus (L.) Merril) Grown in the South Subtropical China

Elucidation of different vegetation energy partitioning and environmental control factors at the agro-ecosystem levels is critical for better understanding and scientific management of farmland. Pineapple (Ananas comosus (L.) Merril) is a tropical plant widely cultivated in the southern subtropical region of China; however, the energy partitioning of crassulacean acid metabolism (CAM) plants like pineapple and their interactions with the environment remain not well understood. In this study, we investigated the energy partitioning patterns of pineapple fields and latent heat flux (LET) response to environmental factors using the Bowen ratio energy balance system and meteorological observation field data. The results showed that the CAM plant pineapple energy partitioning was significantly different from the common C3 and C4 crops during the study period, which was mainly attributed to the complex interactions between CAM plant transpiration and the environment. Specifically, sensible heat flux was the main component of net radiation (Rn), followed by the LET, accounting for 65.0% and 30.8% of the Rn, respectively. Soil heat flux accounts for a very small fraction (4.2%). The mean values of the Bowen ratio were 2.09 and 1.41 for sunny and cloudy days during the daytime and 0.74 and 0.46 at night, respectively. LET is a key factor in responding to crop growth status and agricultural water management, and the path analysis indicates that its variation is mainly influenced directly by Rn with a direct path coefficient of 0.94 on sunny days, followed by vapor pressure deficit, air temperature and relative humidity, which indirectly affect LET through the Rn pathway, whereas soil moisture and wind speed have a low effect on LET. On cloudy days, the effect of Rn on LET was overwhelmingly dominant, with a direct path coefficient of 0.91. The direct path coefficients of the remaining factors on LET were very small and negative. Overall, this study is an important reference for enhancing the impact of pineapple as well as CAM plants on the surface energy balance and regional climate.

Local temperature responses to actual land cover changes present significant latitudinal variability and asymmetry

Land cover changes (LCCs) affect surface temperatures at local scale through biophysical processes. However, previous observation-based studies mainly focused on the potential effects of virtual afforestation/deforestation using the space-for-time assumption, while the actual effects of all types of realistic LCCs are underexplored. Here, we adopted the space-and-time scheme and utilized extensive high-resolution (1-km) satellite observations to perform the first such assessment. We showed that, from 2006 to 2015, the average temperature in the areas with LCCs increased by 0.08 K globally, but varied significantly across latitudes, ranging from -0.05 to 0.18 K. Cropland expansions dominated summertime cooling effects in the northern mid-latitudes, whereas forest-related LCCs caused warming effects elsewhere. These effects accounted for up to 44.6% of overall concurrent warming, suggesting that LCC influences cannot be ignored. In addition, we revealed obvious asymmetries in the actual effects, i.e., LCCs with warming effects occurred more frequently, with stronger intensities, than LCCs with cooling effects. Even for the mutual changes between two covers in the same region, warming LCCs generally had larger magnitudes than their cooling counterparts due to asymmetric changes in transition fractions and driving variables. These novel findings, derived from the assessment of actual LCCs, provide more realistic implications for land management and climate adaptation policies.

Amazon deforestation causes strong regional warming

Tropical deforestation impacts the climate through complex land-atmosphere interactions causing local and regional warming. However, whilst the impacts of deforestation on local temperature are well understood, the regional (nonlocal) response is poorly quantified. Here, we used remote-sensed observations of forest loss and dry season land-surface temperature during the period 2001 to 2020 to demonstrate that deforestation of the Amazon caused strong warming at distances up to 100 km away from the forest loss. We apply a machine learning approach to show nonlocal warming due to forest loss at 2-100 km length scales increases the warming due to deforestation by more than a factor 4, from 0.16 K to 0.71 K for each 10-percentage points of forest loss. We estimate that rapid future deforestation under a strong inequality scenario could cause dry season warming of 0.96 K across Mato Grosso state in southern Brazil over the period 2020 to 2050. Reducing deforestation could reduce future warming caused by forest loss to 0.4 K. Our results demonstrate the contribution of tropical deforestation to regional climate warming and the potential for reduced deforestation to deliver regional climate adaptation and resilience with important implications for sustainable management of the Amazon.

Water-energy-vegetation nexus explain global geographical variation in surface urban heat island intensity

Surface urban heat island (SUHI) is a key climate risk associated with urbanization. Previous case studies have suggested that precipitation (water), radiation (energy), and vegetation have important effects on urban warming, but there is a lack of research that combines these factors to explain the global geographic variation in SUHI intensity (SUHII). Here, we utilize remotely sensed and gridded datasets to propose a new water-energy-vegetation nexus concept that explains the global geographic variation of SUHII across four climate zones and seven major regions. We found that SUHII and its frequency increase from arid zones (0.36 ± 0.15 °C) to humid zones (2.28 ± 0.10 °C), but become weaker in the extreme humid zones (2.18 ± 0.15 °C). We revealed that from semi-arid/humid to humid zones, high precipitation is often coupled with high incoming solar radiation. The increased solar radiation can directly enhance the energy in the area, leading to higher SUHII and its frequency. Although solar radiation is high in arid zones (mainly in West, Central, and South Asia), water limitation leads to sparse natural vegetation, suppressing the cooling effect in rural areas and resulting in lower SUHII. In extreme humid regions (mainly in tropical areas), incoming solar radiation tends to flatten out, which, coupled with increased vegetation as hydrothermal conditions become more favorable, leads to more latent heat and reduces the intensity of SUHI. Overall, this study offers empirical evidence that the water-energy-vegetation nexus highly explains the global geographic variation of SUHII. The results can be used by urban planners seeking optimal SUHI mitigation strategies and for climate change modeling work.

A review of applied research on low-carbon urban design: based on scientific knowledge mapping

The construction of low-carbon cities is an essential component of sustainable urban development. However, there is a lack of a comprehensive low-carbon city design and evaluation system that incorporates "carbon sink accounting-remote sensing monitoring-numerical modelling-design and application" in an all-around linkage, multi-scale coupling, and localized effects. This paper utilizes the Citespace tool to evaluate low-carbon city design applications by analyzing literature in the Web of Science (WOS) core collection database. The results reveal that low-carbon cities undergo four stages: "measurement-implementation-regulation - management." The research themes are divided into three core clustering evolutionary pathways: "extension of carbon sink functions," "spatialisation of carbon sink systems," and "full-cycle, full-dimensional decarbonisation." Applications include "Utility studies of multi-scale carbon sink assessments," "Correlation analysis of carbon sink influencing factors," "Predictive characterisation of multiple planning scenarios," and "Spatial planning applications of urban sink enhancement." Future low-carbon city construction should incorporate intelligent algorithm technology in real-time to provide a strong design basis for multi-scale urban spatial design with the features of "high-precision accounting, full-cycle assessment and low-energy concept."

Observational evidence of legacy effects of the 2018 drought on a mixed deciduous forest in Germany

Forests play a major role in the global carbon cycle, and droughts have been shown to explain much of the interannual variability in the terrestrial carbon sink capacity. The quantification of drought legacy effects on ecosystem carbon fluxes is a challenging task, and research on the ecosystem scale remains sparse. In this study we investigate the delayed response of an extreme drought event on the carbon cycle in the mixed deciduous forest site 'Hohes Holz' (DE-HoH) located in Central Germany, using the measurements taken between 2015 and 2020. Our analysis demonstrates that the extreme drought and heat event in 2018 had strong legacy effects on the carbon cycle in 2019, but not in 2020. On an annual basis, net ecosystem productivity was [Formula: see text] higher in 2018 ([Formula: see text]) and [Formula: see text] lower in 2019 ([Formula: see text]) compared to pre-drought years ([Formula: see text]). Using spline regression, we show that while current hydrometeorological conditions can explain forest productivity in 2020, they do not fully explain the decrease in productivity in 2019. Including long-term drought information in the statistical model reduces overestimation error of productivity in 2019 by nearly [Formula: see text]. We also found that short-term drought events have positive impacts on the carbon cycle at the beginning of the vegetation season, but negative impacts in later summer, while long-term drought events have generally negative impacts throughout the growing season. Overall, our findings highlight the importance of considering the diverse and complex impacts of extreme events on ecosystem fluxes, including the timing, temporal scale, and magnitude of the events, and the need to use consistent definitions of drought to clearly convey immediate and delayed responses.

Northern expansion is not compensating for southern declines in North American boreal forests

Climate change is expected to shift the boreal biome northward through expansion at the northern and contraction at the southern boundary respectively. However, biome-scale evidence of such a shift is rare. Here, we used remotely-sensed tree cover data to quantify temporal changes across the North American boreal biome from 2000 to 2019. We reveal a strong north-south asymmetry in tree cover change, coupled with a range shrinkage of tree cover distributions. We found no evidence for tree cover expansion in the northern biome, while tree cover increased markedly in the core of the biome range. By contrast, tree cover declined along the southern biome boundary, where losses were related largely to wildfires and timber logging. We show that these contrasting trends are structural indicators for a possible onset of a biome contraction which may lead to long-term carbon declines.

Neglected implications of land-use and land-cover changes on the climate-health nexus

Climate change can substantially affect temperature-related mortality and morbidity, especially under high green-house gas emission pathways. Achieving the Paris Agreement goals require not only drastic reductions in fossil fuel-based emissions but also land-use and land-cover changes (LULCC), such as reforestation and afforestation. LULCC has been mainly analysed in the context of land-based mitigation and food security. However, growing scientific evidence shows that LULCC can also substantially alter climate through biogeophysical effects. Little is known about the consequential impacts on human health. LULCC-related impact research should broaden its scope by including the human health impacts. LULCC are relevant to several global agendas (i.e. Sustainable Development Goals). Thus, collaboration across research communities and stronger stakeholder engagement are required to address this knowledge gap.

Impacts of land cover and management change on top-of-canopy and below-canopy temperatures in Southeastern Kenya

Impacts of land cover conversion have been studied well from the top-of-canopy level using satellite observations. Yet, the warming or cooling impacts of land cover and management change (LCMC) from below-canopy level remain less explored. Here, we studied the below-canopy temperature change from field to landscape level across multiple LCMC in southeastern Kenya. To study this, in situ microclimate sensors, satellite observations, and high-resolution below-canopy temperature modelling approaches were used. Our results show that from field to landscape scale, forest to cropland conversion, followed by thicket to cropland change, generate higher surface temperature warming than other conversion types. At field scale, tree loss increases the mean soil temperature (measured at 6 cm below ground) more than the mean below-canopy surface temperature but its impact on the diurnal temperature range was higher on surface temperature than soil temperature in both forest to cropland and thicket to cropland/grassland conversions. At landscape scale, compared with top-of-canopy land surface temperature warming, which was estimated at Landsat overpass time (∼10:30 a.m.), forest to cropland conversion generates ∼3 °C higher below-canopy surface temperature warming. Land management change, through fencing of wildlife conservation areas and limiting mobility of mega browsers, can have an impact on woody cover and induce more below-canopy surface temperature warming than top-of-canopy in comparison with non-conservancy areas. These results indicate that human induced land changes can generate more below-canopy warming than inferred from top-of-canopy satellite observations. Together, the results highlight the importance of considering the climatic impacts of LCMC from both top-of-canopy and below-canopy level for effective mitigation of anthropogenic warming from land surface changes.

Does plant ecosystem thermoregulation occur? An extratropical assessment at different spatial and temporal scales

To what degree plant ecosystems thermoregulate their canopy temperature (T_c ) is critical to assess ecosystems' metabolisms and resilience with climate change, but remains controversial, with opinions from no to moderate thermoregulation capability. With global datasets of T_c , air temperature (T_a ), and other environmental and biotic variables from FLUXNET and satellites, we tested the 'limited homeothermy' hypothesis (indicated by T_c & T_a regression slope < 1 or T_c  < T_a around midday) across global extratropics, including temporal and spatial dimensions. Across daily to weekly and monthly timescales, over 80% of sites/ecosystems have slopes ≥1 or T_c  > T_a around midday, rejecting the above hypothesis. For those sites unsupporting the hypothesis, their T_c -T_a difference (ΔT) exhibits considerable seasonality that shows negative, partial correlations with leaf area index, implying a certain degree of thermoregulation capability. Spatially, site-mean ΔT exhibits larger variations than the slope indicator, suggesting ΔT is a more sensitive indicator for detecting thermoregulatory differences across biomes. Furthermore, this large spatial-wide ΔT variation (0-6°C) is primarily explained by environmental variables (38%) and secondarily by biotic factors (15%). These results demonstrate diverse thermoregulation patterns across global extratropics, with most ecosystems negating the 'limited homeothermy' hypothesis, but their thermoregulation still occurs, implying that slope < 1 or T_c  < T_a are not necessary conditions for plant thermoregulation.

Compound Heat Vulnerability in the Record-Breaking Hot Summer of 2022 over the Yangtze River Delta Region

Hourly meteorological data and multisource socioeconomic data collected in the Yangtze River Delta (YRD) region were used to analyze its heat vulnerability during the record-breaking hot summer of 2022 in both daytime and nighttime. Over forty consecutive days, daytime temperatures exceeded 40 °C, and 58.4% of the YRD region experienced 400 h with temperatures hotter than 26 °C during the nighttime. Only 7.5% of the YRD region was under low heat risk during both daytime and nighttime. Strong heat risk combined with strong heat sensitivity and weak heat adaptability led to strong heat vulnerability during both daytime and nighttime in most areas (72.6%). Inhomogeneity in heat sensitivity and heat adaptability further aggravated the heterogeneity of heat vulnerability, leading to compound heat vulnerability in most regions. The ratios of heat-vulnerable areas generated by multiple causes were 67.7% and 79.3% during daytime and nighttime, respectively. For Zhejiang and Shanghai, projects designed to decrease the urban heat island effect and lower the local heat sensitivity are most important. For Jiangsu and Anhui, measures aiming to decrease the urban heat island effect and improve heat adaptability are most important. It is urgent to take efficient measures to address heat vulnerability during both daytime and nighttime.

Spatiotemporal Characterization of Land Surface Temperature in Relation Landuse/Cover: A Spatial Autocorrelation Approach

The land use and land cover (LULC) characteristics of Ghaziabad have experienced dynamic changes because of the city's ongoing industrialization and urbanisation processes. These shifts can be directly attributed to human actions. These shifts can be directly attributed to human actions. Thermal variation in the study area necessitates LULC analysis. Landsat and Sentinel satellite data for 2011 and 2021 were used to map LULC, estimate land surface temperature (LST) and analysis spatial autocorrelation among the variables using ArcGIS software and the Google Earth Engine (GEE) cloud platform. A sharp descent is observed in the cropland while built-up area has increased during the study period. With the increase in the built-up surface in the area, the ambient temperatures have also increased from 18.70 °C in 2011 to 21.81 °C in 2021 leading to urban heat island effect. At all spatial scales, spatial autocorrelation is a characteristic property of most ecological parameters. The spatial clustering of LST in an ecosystem can play a crucial role in determining the dynamics of LULC.The Moran’s, I show that there is a considerable level of spatial autocorrelation in the values of LST and highly clustered pattern for both the years. Monitoring and understanding the surface thermal environment is crucial to discerning the causes of climate change.

A comprehensive analysis of the crop effect on the urban-rural differences in land surface phenology

The response of land surface phenology (LSP) to the urban heat island effect (UHI) is a useful biological indicator for understanding how vegetated ecosystems will be affected by future climate warming. However, vegetation cover in rural areas is often dominated by cultivated land, whose phenological timing is considerably influenced by agricultural managements (e.g., timing of sowing and harvesting), leading to biased conclusions derived from the urban-rural LSP differences. To demonstrate this problem, we investigated the crop influence on the phenological response to a warmer environment resulting from the UHI effect. We partitioned cities in the United States into cultivated and non-cultivated categories according to the proportion of crops in rural areas. We then built continuous buffer zones starting from the urban boundary to explore the urban-rural LSP differences considering the UHI effect on them. The results suggest crop inclusion is likely to lead to >14 days of urban-rural differences at both the start of the season (SOS) and the end of the season (EOS) between cultivated and non-cultivated cities. The temperature sensitivity (S_T) of SOS is overestimated by approximately 2.7 days/°C, whereas the EOS is underestimated by 3.6 days/°C. Removing crop-dominated pixels (i.e., above 50 %) can minimize the influence of crop planting/harvesting on LSP and derive reliable results. We, therefore, suggest explicit consideration of crop impacts in future studies of phenological differences between urban and rural areas and the UHI effect on LSP in urban domains, as presented by this comprehensive study.

Biophysical impacts of earth greening can substantially mitigate regional land surface temperature warming

Vegetation change can alter surface energy balance and subsequently affect the local climate. This biophysical impact has been well studied for forestation cases, but the sign and magnitude for persistent earth greening remain controversial. Based on long-term remote sensing observations, we quantify the unidirectional impact of vegetation greening on radiometric surface temperature over 2001-2018. Here, we show a global negative temperature response with large spatial and seasonal variability. Snow cover, vegetation greenness, and shortwave radiation are the major driving factors of the temperature sensitivity by regulating the relative dominance of radiative and non-radiative processes. Combined with the observed greening trend, we find a global cooling of -0.018 K/decade, which slows down 4.6 ± 3.2% of the global warming. Regionally, this cooling effect can offset 39.4 ± 13.9% and 19.0 ± 8.2% of the corresponding warming in India and China. These results highlight the necessity of considering this vegetation-related biophysical climate effect when informing local climate adaptation strategies.

Tropical surface temperature response to vegetation cover changes and the role of drylands

Vegetation cover creates competing effects on land surface temperature: it typically cools through enhancing energy dissipation and warms via decreasing surface albedo. Global vegetation has been previously found to overall net cool land surfaces with cooling contributions from temperate and tropical vegetation and warming contributions from boreal vegetation. Recent studies suggest that dryland vegetation across the tropics strongly contributes to this global net cooling feedback. However, observation-based vegetation-temperature interaction studies have been limited in the tropics, especially in their widespread drylands. Theoretical considerations also call into question the ability of dryland vegetation to strongly cool the surface under low water availability. Here, we use satellite observations to investigate how tropical vegetation cover influences the surface energy balance. We find that while increased vegetation cover would impart net cooling feedbacks across the tropics, net vegetal cooling effects are subdued in drylands. Using observations, we determine that dryland plants have less ability to cool the surface due to their cooling pathways being reduced by aridity, overall less efficient dissipation of turbulent energy, and their tendency to strongly increase solar radiation absorption. As a result, while proportional greening across the tropics would create an overall biophysical cooling feedback, dryland tropical vegetation reduces the overall tropical surface cooling magnitude by at least 14%, instead of enhancing cooling as suggested by previous global studies.

Analysis of the Green Development Effects of High-Speed Railways Based on Eco-Efficiency: Evidence from Multisource Remote Sensing and Statistical Data of Urban Agglomerations in the Middle Reaches of the Yangtze River, China

As part of the modern transport infrastructure, high-speed railways (HSRs) have been considered an important factor affecting eco-efficiency (EE). This study used multisource remote sensing and statistical data from 185 counties representing urban agglomerations in the middle reaches of the Yangtze River (UAMRYR) in China from 2009 to 2018. The study integrated ArcGIS analysis, the Super-SBM (super slack-based measure) model, and the DSPDM (dynamic spatial panel Durbin model) to explore the spatial effects of HSRs on EE. The results showed that the coordinates of the interannual centers of gravity for EE and HSRs both fell in the same county, possessing similar parameter values for the standard deviation elliptical, a negative spatial mismatch index, and obvious spatial mismatch characteristics. In different spatially dislocated areas, the spatial effects of HSRs on EE are variable. Overall, the short-term effects are more intense than the long-term effects, and both the long-term and short-term effects are dominated by the effects of spatial spillover. A new perspective is proposed to explore the green development effects of HSRs, with a view to providing policy implications for the enhancement of EE and the planning of HSRs.

Protected areas provide thermal buffer against climate change

Climate change is pushing temperatures beyond the thermal tolerance of many species. Whether protected areas (PAs) can serve as climate change refugia for biodiversity has not yet been explored. We find that PAs of natural (seminatural) vegetation effectively cool the land surface temperature, particularly the daily maximum temperature in the tropics, and reduce diurnal and seasonal temperature ranges in boreal and temperate regions, as compared to nonprotected areas that are often disturbed or converted to various land uses. Moreover, protected forests slow the rate of warming more at higher latitudes. The warming rate in protected boreal forests is up to 20% lower than in their surroundings, which is particularly important for species in the boreal where warming is more pronounced. The fact that nonprotected areas with the same type of vegetation as PAs show reduced warming buffer capacity highlights the importance of conservation to stabilize the local climate and safeguard biodiversity.

Deforestation intensifies daily temperature variability in the northern extratropics

While the biogeophysical effects of deforestation on average and extreme temperatures are broadly documented, how deforestation influences temperature variability remains largely unknown. To fill this knowledge gap, we investigate the biogeophysical effects of idealized deforestation on daily temperature variability at the global scale based on multiple earth system models and in situ observations. Here, we show that deforestation can intensify daily temperature variability (by up to 20%) in the northern extratropics, particularly in winter, leading to more frequent rapid extreme warming and cooling events. The higher temperature variability can be attributed to the enhanced near-surface horizontal temperature advection and simultaneously is partly offset by the lower variability in surface sensible heat flux. We also show responses of daily temperature variability to historical deforestation and future potential afforestation. This study reveals the overlooked effects of deforestation or afforestation on temperature variability and has implications for large-scale afforestation in northern extratropic countries.

Response of enhanced vegetation index changes to latent/sensible heat flux and precipitation over Pakistan using remote sensing

For a sustainable development and ecological integrity, it is of worth importance to monitor land use/ land cover (LULC) changes and related land-atmosphere fluxes. To serve this purpose, we have used moderate resolution imaging spectroradiometer (MODIS) retrieved-enhanced vegetation index (EVI), MERRA-2 re-analysis surface heat fluxes (latent heat flux, sensible heat flux and specific humidity), TRMM rainfall data, and OMI retrieved aerosol index (AI) over Pakistan during 2000 to 2021. High EVI (0.66) is observed in May 2021 as compared to May 2000 over Muzaffarabad, Srinagar, north and northwest of Khyber Pakhtunkhwa, east of Punjab and along the Indus River in Sindh. The highest increase in vegetative area is observed in Baluchistan (~ 366%), followed by Manavadar (~ 60%), Khyber Pakhtunkhwa (~ 41%), Sindh (~ 37%), and Punjab (~ 20%) whereas Gilgit-Baltistan and Jammu and Kashmir show reduction in vegetative area by 21% and 11% respectively. The coefficient of determination (R²) is found to be highest between rainfall and latent heat flux (R² = 0.59) followed by rainfall and specific humidity (R² = 0.35), and rainfall and sensible heat flux (R² = 0.06). The latent heat flux shows increasing trend at the rate of 0.003 Wm^-2 winter^-1, 0.0065 Wm^-2 pre-monsoon^-1 and 0.0272 Wm^-2 post-monsoon^-1 during 1980-2021 whereas sensible heat flux shows decreasing trend at the rate of 0.00056 Wm^-2 winter^-1, 0.00249 Wm^-2 pre-monsoon^-1 and 0.0037 Wm^-2 post-monsoon^-1 during 1980-2021. Specific humidity depicts increasing trend at the rate of 0.0002 Wm^-2 winter^-1, 0.0038 Wm^-2 pre-monsoon^-1 and decreasing trend at the rate of 0.0080 Wm^-2 post-monsoon^-1 during 1980-2021. The interannual variations in AI show highest AI of 2.28 in 2021 with maximum positive percentage anomaly of 28.06% during 2007.

Albedo impacts of current agricultural land use: Crop-specific albedo from MODIS data and inclusion in LCA of crop production

Agricultural land use and management practices affect the global climate due to greenhouse gas (GHG) fluxes and changes in land surface properties. Increased albedo has the potential to counteract the radiative forcing and warming effect of emitted GHGs. Thus considering albedo could be important to evaluate and improve agricultural systems in light of climate change, but the albedo of individual practices is usually not known. This study quantified the albedo of individual crops under regional conditions, and evaluated the importance of albedo change for the climate impact of current crop production using life cycle assessment (LCA). Seven major crops in southern Sweden were assessed relative to a land reference without cultivation, represented by semi-natural grassland. Crop-specific albedo data were obtained from a MODIS product (MCD43A1 v6), by combining its spatial response pattern with geodata on agricultural land use 2011-2020. Fluxes of GHGs were estimated using regional data and models, including production of inputs, field operations, and soil nitrogen and carbon balances. Ten-year mean albedo was 6-11% higher under the different crops than under the reference. Crop-specific albedo varied between years due to weather fluctuations, but differences between crops were largely consistent. Increased albedo countered the GHG impact from production of inputs and field operations by 17-47% measured in GWP₁₀₀, and the total climate impact was warming. Using a time-dependent metric, all crops had a net cooling impact on global mean surface temperature on shorter timescales due to albedo (3-12 years under different crops), but a net warming impact on longer timescales due to GHG emissions. The methods and data presented in this study could support increasingly comprehensive assessments of agricultural systems. Further research is needed to integrate climatic effects of land use on different spatial and temporal scales, and direct and indirect consequences from a systems perspective.

Surface temperature behavior in view of the conversion of tropical dry forest into anthropic uses, northern Minas Gerais-Brazil

Tropical dry forests (TDFs) are essential for environmental dynamics, especially in terms of climate variations. However, several anthropic factors have threatened the integrity of TDFs, and consequently the surface temperature (ST), which is a proxy variable for several environmental processes in TDFs. So, understanding their behavior is crucial. The objective was to analyze the behavior of surface temperature owing to conversion of TDFs into anthropic uses in northern Minas Gerais between 2007 and 2016. In 9 years, dry forests decreased by 22.9%, with pastures as the central driver (counted 93% of change). Between 2007 and 2016, there was an increase in ST by 1.55 K ± 1.15 K. When TDFs were converted to pastureland, the increase in ST was 2.21 K ± 1.39 K and for crops by 0.57 K ± 1.24 K. The remaining TDFs (2016) had an increase in their thermal average of 1.41 K ± 1.02 K. This analysis is essential for the adoption of conservation actions for the maintenance of ecological corridors in TDFs, considering their importance in the ecosystem context.

Contrasting influences of biogeophysical and biogeochemical impacts of historical land use on global economic inequality

Climate change has significant implications for macro-economic growth. The impacts of greenhouse gases and anthropogenic aerosols on economies via altered annual mean temperature (AMT) have been studied. However, the economic impact of land-use and land-cover change (LULCC) is still unknown because it has both biogeochemical and biogeophysical impacts on temperature and the latter differs in latitudes and disturbed land surface types. In this work, based on multi-model simulations from the Coupled Model Intercomparison Project Phase 6, contrasting influences of biogeochemical and biogeophysical impacts of historical (1850-2014) LULCC on economies are found. Their combined effects on AMT result in warming in most countries, which harms developing economies in warm climates but benefits developed economies in cold climates. Thus, global economic inequality is increased. Besides the increased AMT by the combined effects, day-to-day temperature variability is enhanced in developing economies but reduced in developed economies, which further deteriorates global economic inequality.

Thermal and moisture response to land surface changes across different ecosystems over Heilong-Amur River Basin

The Heilong-Amur River Basin (HARB) in Northeast Asia has experienced distinct land surface changes during the past 40 years due to extensive ecological restoration programs, agricultural management, and grassland grazing in different ecosystems. However, the regional climate impact caused by the long-term spatially heterogeneous land surface changes in this mid-high latitude region is not well documented. Therefore, this study used multi-source satellite measurements records and a high-resolution land-atmosphere coupled regional climate model (WRF) to investigate the land surface changes and their associated thermal and moisture impacts across three main ecosystems over the Heilong-Amur River basin from 1982 to 2018. Firstly, satellite observations indicated an overall greening in HARB, with variations across ecosystems. The significant summer farmland greening is the most representative, with the farmland green vegetation fraction (GVF) remarkably increasing by 7.78% in summer. The forest greening magnitude is stronger in spring (3.42%) than in summer (2.85%), while the grassland vegetation showed some local browning signals in summer. Secondly, our simulated results showed the summer farmland greening accelerated evapotranspiration (ET) by 0.161 mm/d and significantly cools the surface temperature by 0.508 °C averaged at the ecosystem scale, which was highly correlated with the satellite observations but with lower cooling magnitude. The forest greening brought less surface cooling in spring than summer due to the stronger albedo feedback, despite with greater increase in GVF and ET. While with the opposite process, the local grassland browning leads to consistent warming effects, which can be detected from both satellite observations and our simulation results. Finally, our results also found that rainfall increasing averagely at the ecosystem scale can't fully compensate the water emission from enhanced ET due to the surface greening, contributing to soil moisture decline in both farmland and relative dry forests.

Phenology-based seasonal terrestrial vegetation growth response to climate variability with consideration of cumulative effect and biological carryover

Vegetation growth is influenced not only by climate variability but also by its past states. However, the differences in the degree of the climate variability and past states affecting vegetation growth over seasons are still poorly understood, particularly given the cumulative climate effects. Relying on the Normalized Difference Vegetation Index (NDVI) data from 1982 to 2014, the vegetation growing season was decomposed into three periods (sub-seasons) - green-up (GSgp), maturity (GSmp), and senescence (GSsp) - following a phenology-based definition. A distributed lag model was then utilized to analyze the time-lag effect of vegetation growth response to climatic factors including precipitation, temperature, and solar radiation during each sub-season. On this basis, the relative importance of climatic factors and vegetation growth carryover (VGC) effect on vegetation growth was quantified at the phenology-based seasonal scale. Results showed that the longest peak lag of precipitation, temperature, and solar radiation occurred in the GSmp, GSsp, and GSgp, with 1.27 (1.13 SD), 0.89 (1.02 SD), and 0.80 (1.04 SD) months, respectively. The influence of climate variability was strongest in the GSgp, and diminished over the season, while the opposite for the VGC effect. The relative influence of each climatic factor also varied between sub-seasons. Vegetation in more than 58% of areas was more affected by temperature in the GSgp, and the proportion decreased to 34.00% and 31.78% in the GSmp and GSsp, respectively. Precipitation and solar radiation acted as the dominant climatic factors in only 28.80% and 20.88% of vegetation areas in the GSgp, but they increased to 35.21%, 32.61% in the GSmp, and 38.20%, 30.02% in the GSsp, respectively. The increased regions influenced by precipitation were mainly in dry areas especially for the boreal and cool temperate climate zones, while increased regions influenced by solar radiation were primarily located in moist areas of mid-high latitudes of the Northern Hemisphere. By introducing the cumulative climate effect, our findings highlight seasonal patterns of vegetation growth affected by climate variability and the VGC effect. The results provide a more comprehensive perspective on climate-vegetation interactions, which may help us to accurately forecast future vegetation growth under accelerating global warming.

Long-Term Dynamics and Response to Climate Change of Different Vegetation Types Using GIMMS NDVI3g Data over Amathole District in South Africa

Monitoring vegetation dynamics is essential for improving our understanding of how natural and managed agricultural landscapes respond to climate variability and change in the long term. Amathole District Municipality (ADM) in Eastern Cape Province of South Africa has been majorly threatened by climate variability and change during the last decades. This study explored long-term dynamics of vegetation and its response to climate variations using the satellite-derived normalized difference vegetation index from the third-generation Global Inventory Modeling and Mapping Studies (GIMMS NDVI3g) and the ERA5-Land global reanalysis product. A non-parametric trend and partial correlation analyses were used to evaluate the long-term vegetation changes and the role of climatic variables (temperature, precipitation, solar radiation and wind speed) during the period 1981–2015. The results of the ADM’s seasonal NDVI3g characteristics suggested that negative vegetation changes (browning trends) dominated most of the landscape from winter to summer while positive (greening) trends dominated in autumn during the study period. Much of these changes were reflected in forest landscapes with a higher coefficient of variation (CV ≈ 15) than other vegetation types (CV ≈ 10). In addition, the pixel-wise correlation analyses indicated a positive (negative) relationship between the NDVI3g and the ERA5-Land precipitation in spring–autumn (winter) seasons, while the reverse was the case with other climatic variables across vegetation types. However, the relationships between the NDVI3g and the climatic variables were relatively low (R < 0.5) across vegetation types and seasons, the results somewhat suggest the potential role of atmospheric variations in vegetation changes in ADM. The findings of this study provide invaluable insights into potential consequences of climate change and the need for well-informed decisions that underpin the evaluation and management of regional vegetation and forest resources.

Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System

Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on the spatial and temporal resolution of available thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the Landsat and MODIS ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). In this paper, we compared different STARFM ET fusion implementation strategies over various crop lands in the central California. In particular, the use of single versus two Landsat-MODIS pair images to constrain the fusion is explored in cases of rapidly changing crop conditions, as in frequently harvested alfalfa fields, as well as an improved dual-pair method. The daily 30 m ET retrievals are evaluated with flux tower observations and analyzed based on land cover type. This study demonstrates improvement using the new dual-pair STARFM method compared with the standard one-pair STARFM method in estimating daily field scale ET for all the major crop types in the study area.

1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020-2100

In the past decades, China has undergone dramatic land use/land cover (LULC) changes. Such changes are expected to continue and profoundly affect our environment. To navigate future uncertainties toward sustainability, increasing efforts have been invested in projecting China’s future LULC following the Shared Socioeconomic Pathways (SSPs) and/or Representative Concentration Pathways (RCPs). To supplements existing datasets with a high spatial resolution, comprehensive pathway coverage, and delicate account for urban land change, here we present a 1-km gridded LULC dataset for China under 24 comprehensive SSP-RCP scenarios covering 2020–2100 at 10-year intervals. Our approach is to integrate the Global Change Analysis Model (GCAM) and Future Land Use Simulation (FLUS) model. This dataset shows good performance compared to remotely sensed CCI-LC data and is generally spatio-temporally consistent with the Land Use Harmonization version-2 dataset. This new dataset (available at 10.6084/m9.figshare.14776128.v1) provides a valuable alternative for multi-scenario-based research with high spatial resolution, such as earth system modeling, ecosystem services, and carbon neutrality.

Measurement(s)	Land Use and Land Cover Change
Technology Type(s)	computational modeling technique
Factor Type(s)	Shared Socioeconomic Pathways scenarios • Representative Concentration Pathways scenarios • land use and land cover change
Sample Characteristic - Environment	Land
Sample Characteristic - Location	China

The spatiotemporal response of China's vegetation greenness to human socio-economic activities

Climate change and human socioeconomic activities both strongly impact long-term vegetation greenness. It is more a challenge to evaluate the impacts of socioeconomic activities on vegetative greenness than climate change, partially due to the lack of appropriate quantitative indicators of the former. Here we examined the relationship between the remote sensing nighttime light (NTL) data and the Normalized Difference Vegetation Index (NDVI), which in this study are used as the proxies of socioeconomic activities and vegetation greenness, respectively. We first eliminated the vegetation greenness changes in response to climate change and calculated the human-activities-induced NDVI (HNDVI). After explored the spatiotemporal patterns of the HNDVI and NTL data across China from 1998 to 2018, we studied the relationship between the HNDVI and NTL at the grid and county levels, respectively. Our results show that the mean adjusted DN values of the NTL data (NTLI) continuously increase (+0.2938) across our study area from 1998 to 2018, whereas the HNDVI values fluctuate with a general upward trend (+0.0018). Most grids (91.2%) with increased HNDVI were found in rural areas, particularly in the Northeast forest shelterbelt and the Loess Plateau. By contrast, the HNDVI values in rapidly urbanized areas in Chinese major urban agglomerations mainly show a downward trend, especially in the Yangtze River Delta (YRD) urban agglomeration. The relationships between the NTLI and HNDVI are inconsistent over time and across space, which could be attributed to land use conditions, afforestation projects in rural areas, and greening activities in urban areas over different periods and regions.

Blue-Sky Albedo Reduction and Associated Influencing Factors of Stable Land Cover Types in the Middle-High Latitudes of the Northern Hemisphere during 1982–2015

Land surface albedo (LSA) directly affects the radiation balance and the surface heat budget. LSA is a key variable for local and global climate research. The complexity of LSA variations and the driving factors highlight the importance of continuous spatial and temporal monitoring. Snow, vegetation and soil are the main underlying surface factors affecting LSA dynamics. In this study, we combined Global Land Surface Satellite (GLASS) products and ERA5 reanalysis products to analyze the spatiotemporal variation and drivers of annual mean blue-sky albedo for stable land cover types in the middle-high latitudes of the Northern Hemisphere (30~90°N) from 1982 to 2015. Snow cover (SC) exhibited a decreasing trend in 99.59% of all pixels (23.73% significant), with a rate of −0.0813. Soil moisture (SM) exhibited a decreasing trend in 85.66% of all pixels (22.27% significant), with a rate of −0.0002. The leaf area index (LAI) exhibited a greening trend in 74.38% of all pixels (25.23% significant), with a rate of 0.0014. Blue-sky albedo exhibited a decreasing trend in 98.97% of all pixels (65.12% significant), with a rate of −0.0008 (OLS slope). Approximately 98.16% of all pixels (57.01% significant) exhibited a positive correlation between blue-sky albedo and SC. Approximately 47.78% and 67.38% of all pixels (17.13% and 25.3% significant, respectively) exhibited a negative correlation between blue-sky albedo and SM and LAI, respectively. Approximately 10.31%, 20.81% and 68.88% of the pixel blue-sky albedo reduction was mainly controlled by SC, SM and LAI, respectively. The decrease in blue-sky albedo north of 40°N was mainly caused by the decrease in SC. The decrease in blue-sky albedo south of 40°N was mainly caused by SM reduction and vegetation greening. The decrease in blue-sky albedo in the western Tibetan Plateau was caused by vegetation greening, SM increase and SC reduction. The results have important scientific significance for the study of surface processes and global climate change.

Interannual Relationship between Haze Days in December–January and Satellite-Based Leaf Area Index in August–September over Central North China

Haze pollution in central North China has become a hot topic in recent decades due to its serious environmental and health effects. In this work, the interannual relationship between haze days in December–January (DJ_HD) and leaf area index in August–September (AS_LAI) over central North China, along with the possible physical mechanisms involved, are investigated. The relationship varies in different periods, being significant during 1982–2000 (P1) but insignificant during 2001–2014 (P2). During P1, there is an in-phase relationship between AS_LAI and surface evaporation, and an out-of-phase relationship between AS_LAI and surface albedo in August–September. The surface evaporation and albedo anomalies persist to October–November and are associated with lower top-layer volumetric soil water, upward sensible heat flux and downward latent heat flux anomalies in October–November, which act as the bridge in the relationship between AS_LAI and DJ_HD. Both the volumetric soil water and heat fluxes anomalies persist to December–January and correspond to atmospheric circulations similar to the weakened East Asian winter monsoon pattern, which is the dominant system for winter haze events. Thus, the ventilation conditions in December–January are favorable for the accumulation of haze particles. However, during P2, the relationships are not significant between AS_LAI and volumetric soil water or surface soil temperature during October–January. Meanwhile, the East Asian winter monsoon is likely strengthened and tends to be more significantly affected by factors including Arctic sea ice, Arctic Oscillation, etc. Therefore, the effects of AS_LAI on the monsoon may become insignificant and, in turn, the relationship between AS_LAI and DJ_HD becomes insignificant during P2.

Contrasting impacts of forests on cloud cover based on satellite observations

Forests play a pivotal role in regulating climate and sustaining the hydrological cycle. The biophysical impacts of forests on clouds, however, remain unclear. Here, we use satellite data to show that forests in different regions have opposite effects on summer cloud cover. We find enhanced clouds over most temperate and boreal forests but inhibited clouds over Amazon, Central Africa, and Southeast US. The spatial variation in the sign of cloud effects is driven by sensible heating, where cloud enhancement is more likely to occur over forests with larger sensible heat, and cloud inhibition over forests with smaller sensible heat. Ongoing forest cover loss has led to cloud increase over forest loss hotspots in the Amazon (+0.78%), Indonesia (+1.19%), and Southeast US (+ 0.09%), but cloud reduction in East Siberia (-0.20%) from 2002-2018. Our data-driven assessment improves mechanistic understanding of forest-cloud interactions, which remain uncertain in Earth system models.

Vegetation-based climate mitigation in a warmer and greener World

The mitigation potential of vegetation-driven biophysical effects is strongly influenced by the background climate and will therefore be influenced by global warming. Based on an ensemble of remote sensing datasets, here we first estimate the temperature sensitivities to changes in leaf area over the period 2003-2014 as a function of key environmental drivers. These sensitivities are then used to predict temperature changes induced by future leaf area dynamics under four scenarios. Results show that by 2100, under high-emission scenario, greening will likely mitigate land warming by 0.71 ± 0.40 °C, and 83% of such effect (0.59 ± 0.41 °C) is driven by the increase in plant carbon sequestration, while the remaining cooling (0.12 ± 0.05 °C) is due to biophysical land-atmosphere interactions. In addition, our results show a large potential of vegetation to reduce future land warming in the very-stringent scenario (35 ± 20% of the overall warming signal), whereas this effect is limited to 11 ± 6% under the high-emission scenario.

An integrated assessment on the warming effects of urbanization and agriculture in highly developed urban agglomerations of China

Urbanization and agriculture, the two major and concurrent land use activities, can dramatically alter land surface temperature (LST) through multiple biophysical processes. However, previous studies mainly focused on the warming effects of urbanization in large cities and/or urban core areas that may greatly underestimate the land use impacts on regional climate. Using natural forest as a reference, we assessed the LST changes of both urbanization and agriculture in the three most developed urban agglomerations of China (Jing-Jin-Ji, JJJ; Yangtze River Delta, YRD; Pearl River Delta, PRD) according to satellite observations. Results show that the urban-dominated lands warm the daytime LST substantially, especially in the south subtropical PRD (with an annual mean intensity of 5.5 °C), and the highest do not occur in the core cities. The crop-dominated lands also warm the daytime LST dramatically, especially in the temperate semi-humid JJJ (with an annual mean intensity of 3.9 °C). The daytime warming effects increase significantly from 2003 to 2018 mainly due to urban expansion in crop-dominated and mixed lands. The two land uses continue to warm the LST at night though in a lower magnitude in the PRD. However, the urban-dominated lands warm the LST slightly and the crop-dominated lands cool the LST substantially at night in the JJJ and YRD. Overall, the crop-dominated and/or mixed lands dominate the regional LST changes owing to their large areas. We further show that the daytime warming effects of the two land uses are likely caused by the changes of evapotranspiration, whereas the nighttime cooling effects might be mainly due to the changes in surface albedo and roughness. Our results highlight the importance of considering the urbanization in small-medium sized satellite cities and the more widespread agricultural activities in rural areas when assessing the regional climate change and formulating the mitigation strategies.

Impacts of forest loss on local climate across the conterminous United States: Evidence from satellite time-series observations

Forest disturbances alter land biophysics. Their impacts on local climate and land surface temperature (LST) cannot be directly measured by comparing pre- and post-disturbance observations of the same site over time (e.g., due to confounding such as background climate fluctuations); a common remedy is to compare spatially-adjacent undisturbed sites instead. This space-for-time substitution ignores the inherent biases in vegetation between two paired sites, interannual variations, and temporal dynamics of forest recovery. Besides, there is a lack of observation-based analyses at fine spatial resolutions capable of capturing spatial heterogeneity of small-scale forest disturbances. To address these limitations, here we report new satellite analyses on local climate impacts of forest loss at 30 m resolution. Our analyses combined multiple long-term satellite products (e.g., albedo and evapotranspiration [ET]) at 700 sites across major climate zones in the conterminous United States, using time-series trend and changepoint detection methods. Our method helped isolate the biophysical changes attributed to disturbances from those attributed to climate backgrounds and natural growth. On average, forest loss increased surface albedo, decreased ET, and reduced leaf area index (LAI). Net annual warming-an increase in LST-was observed after forest loss in the arid/semiarid, northern, tropical, and temperate regions, dominated by the warming from decreased ET and attenuated by the cooling from increased albedo. The magnitude of post-disturbance warming was related to precipitation; climate zones with greater precipitation showed stronger and longer warming. Reduction in leaf or LAI was larger in evergreen than deciduous forests, but the recovery in LAI did not always synchronize with those of albedo and ET. Overall, this study presents new evidence of biophysical effects of forest loss on LST at finer spatial resolutions; our time-series method can be further leveraged to derive local policy-relevant ecosystem climate regulation metrics or support model-based climate-biosphere studies.

The role of urban trees in reducing land surface temperatures in European cities

Urban trees influence temperatures in cities. However, their effectiveness at mitigating urban heat in different climatic contexts and in comparison to treeless urban green spaces has not yet been sufficiently explored. Here, we use high-resolution satellite land surface temperatures (LSTs) and land-cover data from 293 European cities to infer the potential of urban trees to reduce LSTs. We show that urban trees exhibit lower temperatures than urban fabric across most European cities in summer and during hot extremes. Compared to continuous urban fabric, LSTs observed for urban trees are on average 0-4 K lower in Southern European regions and 8-12 K lower in Central Europe. Treeless urban green spaces are overall less effective in reducing LSTs, and their cooling effect is approximately 2-4 times lower than the cooling induced by urban trees. By revealing continental-scale patterns in the effect of trees and treeless green spaces on urban LST our results highlight the importance of considering and further investigating the climate-dependent effectiveness of heat mitigation measures in cities.