Sage Journals: Discover world-class research

Abstract

Land cover changes (LCCs) influence land surface temperature through biogeophysical (BGP) and biogeochemical (BGC) processes. Yet, their combined, spatially varying effects remain inadequately quantified for major LCCs in China over the past two decades. This study integrates biogeophysical and biogeochemical processes to assess how LCCs affect local land surface temperature (LST) across China from 2000 to 2020, leveraging satellite-derived LST data and transient climate response estimates from CMIP5 models. We find that LCCs occurred in 5.87% of China’s land area, driven by ecological restoration, cropland expansion, and urbanization. These LCCs caused a BGP warming of 0.000181°C nationally, which was counteracted by a BGC cooling of -0.000199°C (a magnitude equivalent to 110% of the BGP warming) associated with a net carbon sink of 1.33 GtCO₂. The resulting combined net cooling effects indicate that LCCs overall played a mitigating role against background warming. Regionally, the combined effects showed strong warming in eastern China, linked to urbanization and cropland degradation, and cooling in western China, driven by ecological restoration. BGC effects dominated the local LST response over BGP effects in most (80%) LCC areas except urbanization. Local synergy between BGP and BGC was observed in 61% of LCC areas, where they intensify each other’s local effects. These findings reveal distinct synergistic and tradeoff relationships between BGP and BGC effects associated with LCCs and their spatial patterns. It also highlights the potential of effective land use/cover management as a natural climate solution for climate mitigation.

Keywords

land cover change land surface temperature biogeophysical effects biogeochemical effects synergy tradeoff China

Introduction

Human-induced land cover changes (LCCs) are one of the primary driving forces of climate change at global and regional scales through biogeochemical cycles (BGC) and biogeophysical processes (BGP) (Bathiany et al., 2010; Feddema et al., 2005; Kalnay and Cai, 2003; Luo et al., 2020; Pan et al., 2011). LCCs contributed approximately 30% of historical anthropogenic emissions through biogeochemical processes and are responsible for 0.2°C of global warming (Friedlingstein et al., 2022; Intergovernmental Panel on Climate Change (IPCC), 2023). Meanwhile, LCCs alter biogeophysical processes and influence regional and local climate by changing evapotranspiration (ET), albedo, turbulent fluxes, and surface roughness, generating biogeophysical effects (Bonan, 2008). Different from BGC effects, which operate at global scale, the BGP effects of historical LCC vary regionally and even with opposite signs (either warming or cooling) for the same land conversion, creating synergy or tradeoff between these two effects (Arrouays et al., 2006; Bellamy et al., 2005; Don et al., 2011; Guo and Gifford, 2002; Sanderman et al., 2017). Failing to account for these effects may undermine the climate-mitigation benefits of land management, such as afforestation (Hasler et al., 2024; Kristensen et al., 2024). Therefore, incorporating both BGC and BGP effects is a necessity for a more comprehensive assessment of LCC impacts on land surface temperature (LST).

Since 2000, China has undergone accelerating and diverse changes in land cover, driven by rapid economic growth, urbanization, and land-use policies including ecological restoration and conservation of cultivated lands (Fan et al., 2025; Liu et al., 2010; Wu et al., 2024a). For example, ecological restoration programs (e.g., the Grain to Green program) and afforestation efforts in China increased forest area by 494,317 km² from 2006 to 2016 (Guo et al., 2022), positioning China as a leader in vegetation greening (Chen et al., 2019). Rapid urbanization has expanded construction land by 13.6 times from 1978 to 2017 (Gong et al., 2019). Meanwhile, the cultivated land balance policy preserved cropland by limiting its conversion to other land uses (Song, 2017).

The climate impacts of these land cover changes have been extensively studied, focusing on either the BGC or the BGP perspective. Recently, there has been growing attention to the synergy and trade-off between the BGP and BGC effects, which can either enhance or counteract each other at the regional and local levels (Li et al., 2024b; Shen et al., 2025). It is known that the BGC cooling of boreal afforestation through carbon sequestration would be largely offset by BGP warming because of increased albedo, while the BGP effects would enhance the BGC cooling of tropical afforestation through evaporation cooling (Li et al., 2015; Montenegro et al., 2009; Windisch et al., 2021). Similar synergy and trade-off patterns are reported for various land conversions in China. Forest expansion produced a carbon sink of 99.2 TgC yr^-1 from 2001 to 2020 (Liao et al., 2024), accounting for nearly 49% of the national terrestrial carbon sink (Yue et al., 2024). Such BGC cooling of forestation is strengthened by the BGP cooling of forests south of 48°N but counteracted by BGP warming in forests in northeast China (Li et al., 2024b). In contrast, urbanization, which converts natural land to impermeable surfaces, led to a direct loss of terrestrial carbon storage of 13.89 TgC in China from 2000 to 2020 (Wu et al., 2024b) and substantial CO₂ emissions (Liu et al., 2019b), resulting in significant BGC warming. The urbanization-induced BGC warming is also enhanced by BGP warming, driven by reduced evapotranspiration, lowered albedo, anthropogenic heat emissions, and urban morphology (Manoli et al., 2019; Ouyang et al., 2022). Furthermore, BGP cooling resulting from the expansion of irrigated croplands (Liu et al., 2022b) could be offset by the BGC warming due to carbon losses when croplands replace forests, grassland, and wetlands (Chaplin-Kramer et al., 2015; Molotoks et al., 2018).

To combine the BGP and BGC effects of LCC, earth system models and satellite data analyses have attempted to evaluate them within a consistent methodological framework. On the one hand, Earth system models with carbon cycles can dynamically simulate and compare the climatic effects of BGP and BGC directly through numerical experiments (Bathiany et al., 2010). On the other hand, the evaluation of BGC and BGP effects based on satellite data relies on specific metrics that translate these two processes into comparable units, such as radiative forcing (Arneth et al., 2010), CO₂-equivalent (Montenegro et al., 2009; Windisch et al., 2021), and local LST changes (Duveiller et al., 2018; Li et al., 2024b). However, most metrics only consider radiative processes, neglecting non-radiative processes and atmospheric feedback (Bright et al., 2017). Transient climate response to emissions (TCRE) derived from fully coupled Earth system models includes various Earth system feedbacks. It serves as an integrated measure that best characterizes the Earth system’s temperature response to CO₂ emissions and has been used to convert the carbon metric of BGC effects into the temperature metric of BGP effects, making them comparable (Amali et al., 2025; Li et al., 2024b; Windisch et al., 2021).

The majority of previous studies in China have focused on specific land conversions, such as forestry (Huang et al., 2018; Li et al., 2024b; Mykleby et al., 2017), leaving other prevalent LCCs unquantified with respect to their BGP and BGC effects. This study addresses the gap by providing a comprehensive assessment of the BGC and BGP effects of various LCCs in China from 2000 to 2020 on local LST, a more intuitive metric for informing climate consequences. The BGC effects were quantified by LCC-induced changes in carbon density, which were translated into local LST changes through transient climate responses from CMIP5 models, while BGP effects were assessed using LST changes from MODIS satellite LST data. Our findings provide a qualitative assessment of the combined effects of the BGC and BGP for different LCCs and their spatial patterns. This knowledge helps design and improve the effectiveness of land use/cover management as a natural climate solution through its biogeophysical and biogeochemical effects.

Data and methods

Data

Land cover data

We used the European Space Agency Climate Change Initiative (ESA CCI) land cover dataset at 300m (Harper et al., 2023) to extract land cover changes in China from 2000 to 2020 (https://doi.org/10.5194/essd-15-1465-2023, last access: 31 March 2023). We combined 33 land cover types into seven broad categories: cropland, forest, grassland, water, urban areas, sparse vegetation, and bare land (Online Supplementary Table S1). Note that shrubland was included as forests following the definition of forests in China (State Administration for Market Regulation and Standardization Administration of the People’s Republic of China, 2017, 2020; Zeng et al., 2015), and wetlands were grouped into water bodies due to their similar physical characteristics.

Land surface temperature (LST)

The MODIS Aqua LST (MYD11A2, https://doi.org/10.5067/MODIS/ MYD11A2.061, last access: 4 February 2024) data (Wan et al., 2021) at 1km were used to quantify the potential biogeophysical effects of LCC. The original eight-day data from 2003 to 2019 were averaged to a multi-year daily mean LST to reduce the influence of climate variability.

Carbon density

The biomass carbon density map at 300m for the year 2010 was obtained from the Oak Ridge National Laboratory DAAC (https://doi.org/10.3334/ORNLDAAC/1763, last access: 22 April 2020). The aboveground biomass is produced by land-cover-specific remotely sensed data with input parameters from the literature; the belowground biomass map is made by integrating matching data and empirical models (Spawn et al., 2020). Above and belowground biomasses were summed to obtain total biomass carbon for this analysis.

The soil carbon density map at multiple depths in 2010 (Liu et al., 2020, 2022a) is available from the National Earth System Science Data Center of China at a 1km resolution (http://soil.geodata.cn, last access: 8 March 2022; Soil SubCenter, National Earth System Science Data Center, National Science & Technology Infrastructure of China, 2022).

Transient Climate Response to Emissions

Transient Climate Response to Emissions (TCRE) data from CMIP5 describe the LST responses to cumulated CO₂ emissions in doubling CO₂ experiments, which translates the carbon fluxes from BGC effects into LST changes consistent with the BGP effects. Here we used TCRE calculated by Windisch et al. (2021) from 21 CMIP5 models at 1° resolution (ACCESS1-0, bcc-csm1-1, BNU-ESM, CCSM4, CNRM-CM5, CSIRO-Mk3-6-0, EC-EARTH, FGOALS-s2, GFDL-ESM2G, GISS-E2-H, HadGEM2-ES, ACCESS1-3, BCC-CSM1-1-M, CanESM2, CNRM-CM5-2, CSIRO-Mk3L-1-2, FGOALS-g2, GFDL-CM3, GFDL-ESM2M, GISS-E2-R, and INM-CM4).

All the above datasets were resampled from their original spatial resolutions to 0.01° for categorical variables, such as land cover data, using the majority method, and for continuous high-resolution variables, including land surface temperature, soil density, and biomass carbon density, using the average method. For TCRE data with coarse spatial resolution, the nearest neighbor method was used.

Methods

Detection of land cover changes

Land cover changes are identified using land cover data between 2000 and 2020 at a resolution of 0.01°. The large number of specific land conversions were grouped into five broad categories (Table 1), including urbanization (from other to urban land), ecological degradation (from higher to lower vegetation coverage), cropland degradation (from croplands to lower vegetation coverage), cropland expansion (from other to croplands), and ecological restoration (from lower to higher vegetation coverage). Land conversions with water bodies were excluded from our analyses because they have distinct biogeophysical and biogeochemical effects compared to terrestrial land cover.

Table 1.

Broad land conversion categories and their specific land cover changes. Vegetation coverage for each land cover, from high to low, is roughly defined as: forests > grassland, sparse vegetation > urban > bare land.

Land cover change categories	Converted from	Converted to
1. Urbanization	Others	Urban land
2. Ecological degradation	Forests	Grasslands, sparse vegetation, bare land
	Grasslands	Sparse vegetation, bare land
	Sparse vegetation	Bare land
3. Cropland degradation	Croplands	Sparse vegetation, bare land
4. Cropland expansion	Others	Croplands
5. Ecological restoration	Grasslands	Forests
	Sparse vegetation	Forests, grasslands
	Bare land	Forests, grasslands, sparse vegetation
	Urban	Bare land

Estimation of BGP effects

The biogeophysical effects of LCCs refer to the LST changes induced by conversion from land type M to type N due to their different biogeophysical properties. The potential biogeophysical effects of LCC can be estimated using a space-for-time approach to control for the influence of long-term climate change, decadal variability, and other factors on the observed LST changes (Li et al., 2016b). The potential effect is calculated as the mean LST differences between adjacent stable land cover (without LCC) type M and N within a 10×10 pixel window (i.e., window size of 0.1°×0.1°) to exclude the influence of non-LCC factors, as shown in Equation (1):

\begin{matrix} Δ L S T_{p o t e n t i a l M \to N}^{B G P} (x, y) = \bar{L S T_{N}} (x, y) - \bar{L S T_{M}} (x, y) \end{matrix}

(1)

where $Δ L S T_{p o t e n t i a l M \to N}^{B G P} (x, y)$ represents the potential BGP effects of LCC on LST from land cover M to N, $\bar{L S T_{N}} (x, y)$ and $\bar{L S T_{M}} (x, y)$ represent the multi-year mean LST from 2003 to 2019 averaged for unchanged land cover M and N in window (x, y). To control for the topographic influence, pixels with elevation differences greater than 100 m from the window’s mean elevation were excluded. Since the potential effect represents the LST change of 100% land conversion, the actual BGP effects on LST $(Δ L S T_{M \to N}^{B G P} (x, y))$ were estimated as the potential effect multiplying the areal fraction of land conversion from M to N at 0.1° (Equation (2)):

\begin{matrix} Δ L S T_{a c t u a l M \to N}^{B G P} (x, y) = Δ L S T_{p o t e n t i a l M \to N}^{B G P} (x, y) \times f_{M \to N} (x, y) \end{matrix}

(2)

where $Δ L S T_{a c t u a l M \to N}^{B G P} (x, y)$ represents actual BGP effects from land cover changes from type M to N, $f_{M \to N} (x, y)$ represents the areal fraction of LCC from M to N in the window (x, y). For LCC areas without valid potential effects, we used the interpolated value from nearby available grids (Online Supplementary Figure S1). The estimated potential effect ranged from -5 to 5K, which is much larger than the reported MODIS LST measurement errors of ±1 K (Wan, 2014).

Estimation of BGC effects

The BGC effects of LCC arise from its associated carbon fluxes (emission or absorption), which influence the global climate through climate sensitivity (the LST response to a unit change in atmospheric CO₂, as reflected in TCRE). We first defined the susceptible carbon density (SCD) to land cover changes as the sum of aboveground, belowground biomass, and the top 30 cm soil carbon following Windisch et al. (2021). The potential SCD changes induced by land conversion from type M to N $(Δ S C D_{M \to N})$ were estimated as the differences in SCD between M and N, using a similar moving window approach as BGP effects (Equation (3)):

\begin{matrix} Δ S C D_{M \to N} = \bar{S C D_{N}} - \bar{S C D_{M}} \end{matrix}

(3)

where $\bar{S C D_{N}}$ and $\bar{S C D_{M}}$ are the mean SCD of unchanged pixels of land cover M and N in the window. The actual CO₂ emission can be estimated by SCD changes multiplied by the area of LCC at 0.1° resolution, assuming the lost carbon goes to the atmosphere (Equation (4)):

\begin{matrix} {E_{C O_{2}}}_{M \to N}^{B G C} (i, j) = 3.67 \times Δ S C D_{M \to N} \times A r e a_{M \to N} \end{matrix}

(4)

where ${E_{C O_{2}}}_{M \to N}^{B G C} (i, j)$ represents carbon emissions generated by LCC from land cover M to N in grid cell (i, j), $A r e a_{M \to N} (i, j)$ represents the area of LCC from M to N in grid cell (i, j), and 3.67 is the mass conversion ratio from C to CO₂.

Translating BGC effects to local LST changes

We used TCRE from CMIP5 to translate the BGC effects from carbon emissions to local LST changes. Here, TCRE is estimated from 1pt CO₂ experiments (Windisch et al., 2021) as in Equation (5):

\begin{matrix} T C R E (i, j) = \frac{Δ L S T {(i, j)}_{d o u b l i n g}}{Δ C O_{2 d o u b l i n g}} \end{matrix}

(5)

where TCRE(i,j) stands for transient climate response to cumulative emission (unit: °C/ppm) in grid cell (i,j), ∆ $LST {(i, j)}_{doubling}$ represents LST change (unit: °C) of grid cell (i,j) and ∆ $C O_{2 d o u b l i n g}$ represents changes in atmospheric CO₂ concentration (unit: ppm) in 1pt CO₂ experiment when atmospheric CO₂ concentration is doubled compared to pre-industrial level.

Next, we calculated changes in global CO₂ concentration (∆ ${CO}_{2 global},$ unit: ppm) caused by CO₂ emissions (unit: Gt) from LCC in a grid cell (i,j) with Equation (6):

\begin{matrix} Δ C O_{2 g l o b a l} (i, j) = {E_{C O_{2}}}_{M \to N}^{B G C} (i, j) \times \frac{0.5}{7.81} \end{matrix}

(6)

where ${E_{C O_{2}}}_{M \to N}^{B G C} (i, j)$ represents carbon emissions (unit: Gt) by LCC from land cover M to N in grid cell (i, j), 0.5 approximates the airborne fraction of CO₂ because nearly half of anthropogenic CO₂ emission remains in the atmosphere due to land and ocean absorption, and 7.81 represents a factor to convert ppm to GtCO₂.

The global-level LST change caused by a specific amount of CO₂ emission $Δ L S T_{g l o b a l}^{B G C} (i, j)$ is estimated by Equation (7):

\begin{matrix} Δ L S T_{g l o b a l}^{B G C} (i, j) = T C R E (i, j) \times Δ C O_{2 g l o b a l} (i, j) \end{matrix}

(7)

Since the BGC effects of LCC on LST through carbon emission in terms of CO₂, which is well-mixed in the atmosphere and affects climate at the global scale, we used grid cell area Area(i,j) and Earth’s surface area to scale global LST changes to the equivalent local LST changes at the grid level following Equation (8):

\begin{matrix} Δ L S T_{l o c a l}^{B G C} (i, j) = Δ L S T_{g l o b a l} (i, j) \times \frac{A_{S F C}}{A r e a (i, j)} \end{matrix}

(8)

where $Δ L S T_{l o c a l}^{B G C} (i, j)$ represents the local temperature response due to BGC effects, ∆ $L S T_{g l o b a l} (i, j)$ represents the global temperature response caused by LCC at grid cell (i, j), $A r e a (i, j)$ is the grid cell area (i, j), and $A_{S F C}$ stands for the Earth’s surface area.

Results

Land cover changes from 2000 to 2020

China experienced significant land cover changes from 2000 to 2020, with a total change area of 557,411 km², equivalent to 5.87% of China’s land area. The top three LCC categories, collectively accounting for 85% of changes, were ecological restoration (43% of the LCC area), cropland expansion (24% of the LCC area), and urbanization (18% of the LCC area) (Figure 1).

Figure 1.

Land cover changes in China between 2000 and 2020. Maps show the areal percentage of all land cover changes (a), and different land conversion categories, including urbanization (b), ecological degradation (c), cropland degradation (d), cropland expansion (e), and ecological restoration (f). Land cover changes are aggregated to be displayed at 0.1°. Land conversions in each subplot are denoted as cropland (C), forests (F), grassland (G), urban land (U), sparse vegetation (S), and bare land (B).

The ecological restoration LCCs (i.e., land conversions from lower to higher vegetation coverage) have been widely occurring in China (245,356 km²; Figure 1f), with primary conversions from croplands and bare land to forests and grasslands due to the “Grain to Green” and afforestation policies (Figure 2). Cropland expansion LCCs were prominent in the Northeast China Plain and northeastern regions (129,066 km²; Figure 1e), mainly from forests and grasslands (Figure 2). Urbanization LCCs were mostly converted from croplands and grasslands (99,919 km²; Figure 2), and were primarily located in East China, including the North China Plain, Yangtze River Delta, and Pearl River Delta (Figure 1b). Ecological degradation LCCs (i.e., land conversions from higher to lower vegetation coverage) occurred across China (84,178 km²; Figure 1c), which involved conversions from grasslands to sparse vegetation and bare land (Figure 2). Cropland degradation LCCs were mainly found in parts of northwestern and northeastern regions (5,287 km²; Figure 1d), where croplands were converted to sparse vegetation and bare land (Figure 2).

Figure 2.

Area statistics of land conversion in China from 2000 to 2020. Each bar represents the area of specific land conversion. Capital letters stand for land cover converted to U (urban area), G (grassland), S (sparse vegetation), B (bare land), C (cropland), and F (forest). The color of each bar indicates the original land cover that has been converted.

For individual land cover, there was an overall increased area of forests (24,524 km²), urban lands (103,336 km²), and sparse vegetation (22,510 km²), but decreased area of cropland (78,401 km²) and grassland (-19,210 km²) from 2000 to 2020 (Online Supplementary Table S2).

Biogeophysical effects of land cover changes

Land cover changes in China resulted in a BGP warming at the national level (0.000181°C, averaged across China’s land) (Figure 3a), with significant variations across land conversion categories and regions. The spatial pattern of BGP effects reflects the effects of individual land conversion categories and their spatial distribution. Specifically, the dominant BGP warming in eastern China was driven by urbanization, which accounted for 54% of LCC areas. In contrast, the rest of the LCC areas, mainly located in western China, exhibited cooling due to ecological restoration and cropland expansion.

Figure 3.

Biogeophysical effects of land cover changes on LST. Spatial pattern of LST changes for all land cover changes (a), urbanization (b), ecological degradation (c), cropland degradation (d), cropland expansion (e), and ecological restoration (f) at 0.5° resolution. Land conversions in each subplot are denoted as cropland (C), forests (F), grassland (G), urban land (U), sparse vegetation (S), and bare land (B). Histograms in the lower-left corner of the subplots show distributions of LST changes for each LCC category, with the statistical significance of the mean value by one-sample t-test annotated next to each histogram.

Among different LCC categories, BGP warming effects were widely observed in urbanization (0.000314°C, Figure 3b), ecological degradation (0.000049°C, Figure 3c), and cropland degradation LCCs (0.000006°C, Figure 3d). Urbanization LCCs led to a widespread increase in LST across 81% of their LCC areas (Figure 3b), known as the urban heat island effect (Manoli et al., 2019). Ecological degradation (Figure 3c) and cropland degradation (Figure 3d) both experienced conversions from higher to lower vegetation coverage, resulting in warming in 56% and 62% of their LCC areas, respectively. Transitions from grasslands to bare land (69.93%) and to sparse vegetation (40.68%) contributed most to the warming of ecological degradation.

BGP cooling effects appeared in cropland expansion (-0.000075°C, Figure 3e) and ecological restoration LCCs (-0.000108°C, Figure 3f). The mild cooling effects of cropland expansion (Figure 3e) come from the strong cooling (-0.001161°C) in northern drylands of China receiving intensive irrigation (Liu et al., 2019a), combined with weak but prevalent warming (0.000570°C) in southern China where forests were converted to croplands. Ecological restoration, which involves converting land from lower to higher vegetation coverage, had an intense cooling effect in most regions (Figure 3f), except in parts of northeastern China and the alpine Qinghai-Tibet Plateau. In southern China, conversions from bare land to grasslands and from croplands to forests led to cooling, while in northern China, conversions from grasslands to forests and from croplands to grasslands led to warming, likely due to the dominance of albedo warming effects over evaporative cooling in dry regions (Li et al., 2016a).

Biogeochemical effects of land cover changes

LCCs induced a net carbon sink at the national level of 1.33 GtCO₂ (Online Supplementary Figure S2), which translates to a BGC cooling (-0.000199 °C) (Figure 4a). Regionally, BGC cooling in southwest and northwest China (51.69% of LCC area) was linked to ecological restoration and crop expansion, whereas warming in the southeast and northeast was due to crop expansion and urbanization.

Figure 4.

Biogeochemical effects of China’s Land cover changes. Spatial pattern of LST changes for all land cover changes (a), urbanization (b), ecological degradation (c), cropland degradation (d), cropland expansion (e), and ecological restoration (f) at 0.5°. Land conversions in each subplot are denoted as cropland (C), forests (F), grassland (G), urban land (U), sparse vegetation (S), and bare land (B). Histograms in the lower-left corner of the subplots show distributions of LST changes for each LCC category, with the statistical significance of the mean value by one-sample t-test annotated next to each histogram.

Most LCCs caused BGC warming, including urbanization (0.000212°C, Figure 4b), ecological degradation (0.000210°C, Figure 4c), cropland degradation (0.000011°C, Figure 4d), and cropland expansion (0.000490°C, Figure 4e). The greatest warming of urbanization was driven by the considerable carbon losses from soil and biomass when converting croplands, grasslands, and forests to impervious surfaces (Pouyat et al., 2002). Degraded vegetation associated with ecological degradation and cropland degradation caused warming due to a reduction in carbon density (Guo and Gifford, 2002), especially the conversion from forests to grasslands. Cropland expansion had contrasting BGC effects, shifting from a warming in the east to cooling in western China. In the humid eastern regions, cropland expansion emits carbon because the replaced natural vegetation, such as forests, has a higher carbon density than crops (Burke et al., 1989). In contrast, in the dry western regions, croplands receiving irrigation have a higher carbon density than natural grasslands or bare ground, in particular above-ground biomass carbon and soil inorganic carbon (Dun et al., 2021; Zi et al., 2025), leading to increased carbon stock (Emde et al., 2021).

Ecological restoration was the only LCC category that generated the most substantial BGC cooling (Figure 4f), across 83.26% of its LCC area, primarily due to conversions from croplands and grasslands to forests. The greatest cooling was observed during transitions from croplands or grasslands to forests in the southwest Hengduan Mountains, northeast Changbai Mountains, and northeast Greater Khingan Mountains and Tianshan Mountains.

Combined biogeophysical and biogeochemical effects of land cover changes

The combined BGC and BGP effects were determined by their synergy (with the same sign) and tradeoff (with the opposite sign), and the one with a greater magnitude determines the sign of the net effects. The BGC and BGP effects produced a net cooling of -0.000018°C (Table 2) at the national level due to their tradeoff, with BGC cooling (-0.000199 °C) partially offset by BGP warming (0.000181°C). The net cooling suggests that LCCs in China still serve as a mitigation against the greenhouse-gas-induced warming (Figure 5a).

Table 2.

Combined biogeophysical and biogeochemical effects for different land conversion categories. Positive and negative carbon fluxes for the BGC effect mean emission and uptake, respectively.

LCC category	Combined (°C)	BGP (°C)	BGC (°C/GtCO₂)	Synergy (%)
Urbanization	0.000527	0.000314	0.000212/1.3418	71.79%
Ecological degradation	0.000259	0.000049	0.000210/1.1296	55.55%
Cropland degradation	0.000017	0.000006	0.000011/-0.0084	56.42%
Cropland expansion	0.000415	-0.000075	0.000490/5.4630	63.36%
Ecological restoration	-0.001233	-0.000108	-0.001125/-9.2569	61.23%
All changes	-0.000018	0.000181	-0.000199/-1.3307	61.20%

Figure 5.

Combined biogeophysical and biogeochemical effects of land cover changes. Spatial pattern of LST changes for all land cover changes (a), urbanization (b), ecological degradation (c), cropland degradation (d), cropland expansion (e), and ecological restoration (f) at 0.5°. Inserted maps in the lower left parts show synergy (red regions) and trade-off (blue regions) of biogeophysical and biogeochemical effects.

Despite a national-level tradeoff, there was substantial heterogeneity in the combined BGC and BGP effects across regions and land conversion categories, characterized by strong warming in the eastern regions and cooling in the western regions. Locally, synergy was observed in 61.20% of LCC areas, indicating that the BGC and BGP effects more often enhance each other and produce stronger local impacts. Owing to a larger magnitude, BGC effects dominated over BGP effects in most LCC areas of China (79.96%, Online Supplementary Figure S3). This dominance also manifested in ecological degradation, cropland degradation, cropland expansion, and ecological restoration, except in the case of urbanization.

The combined net warming effects of ecological degradation (Figure 5c, 0.000259°C) and cropland degradation (Figure 5d, 0.000017°C) resulted from a synergy between BGP and BGC’s warming effects in more than 54% of their LCC areas. Such synergy also produced combined cooling (-0.001233°C) for ecological restoration, with the BGP effect enhancing BGC cooling by 9.6% (Figure 5f, synergy in 61.23% of LCC areas). However, a tradeoff cooling was present in the northeast high-latitude regions, as the ecological restoration-induced BGP warming partially offset the BGC cooling. Similarly, for cropland expansion, a tradeoff warming was found (Figure 5e, 0.000415°C), because BGC warming was mitigated by BGP cooling by 15.3%. Due to heterogeneity, 63.36% of the cropland expansion areas still showed synergy, suggesting that the cooling in the north and the warming in the south would be intensified. Unlike other LCC categories, the BGP effects of urbanization dominated the BGC effects with synergy in 71.79% of LCC areas, resulting in the strongest combined warming (0.000527°C) among all LCC categories (Figure 5b).

Discussion

Mechanisms of biogeophysical and biogeochemical effects

The biogeophysical effects of LCCs assessed in our study stem from the influence of altered biogeophysical properties, such as ET and albedo, and their associated radiative and non-radiative processes (Bright et al., 2017). For conversion between vegetation, since natural vegetation generally has a higher ET (cooling effect) and lower albedo (warming effect), the loss of natural vegetation associated with ecological degradation, cropland degradation, and cropland expansion leads to warming through reduced ET cooling. Conversely, vegetation gain through ecological restoration generates cooling through strengthened ET (Chen et al., 2022; Li et al., 2024a). However, in northeast China at high latitudes, because albedo effects outweigh ET effects under a cold climate (Hasler et al., 2024), vegetation gain and loss result in warming and cooling, respectively, which is consistent with studies for boreal and arid regions (Alkama and Cescatti, 2016; Li et al., 2015; Perugini et al., 2017; Rohatyn et al., 2022). For urbanization, BGP warming effects arise from reduced ET due to vegetation removal, increased impervious surfaces, and anthropogenic heat release (Bo et al., 2018; Manoli et al., 2019; Yang et al., 2019).

LCCs affect the carbon cycle by disturbing biomass and soil carbon storage, and their influence on climate depends on whether carbon is released or absorbed. When LCCs such as vegetation restoration enhance carbon storage, a BGC cooling effect is generated, as observed in most of our study area. In contrast, LCCs with vegetation loss, such as ecological degradation, cropland degradation, and urbanization, show BGC warming. Certain LCCs have opposite BGC effects depending on the location. Cropland expansion in arid areas has a BGC cooling effect, rather than a warming effect, as observed in southern China. This is because human activities, such as irrigation, result in higher biomass and carbon density in croplands of drylands than in natural vegetation (Zi et al., 2025). For urbanization, we considered only LCC-induced carbon density changes in this study, which may underestimate the BGC effects of urbanization, as substantial carbon emissions from fossil fuel combustion occur in urban areas (Huo et al., 2022). If considered, these would significantly increase the BGC effects above current estimates.

Synergy and tradeoff between biogeophysical and biogeochemical effects

Our results indicate a broad synergy between BGP and BGC effects, driven by coordinated changes in carbon and biogeophysical factors across most LCC areas of China. Vegetation gain increases biomass and soil carbon as well as ET, thereby promoting cooling from both BGC and BGP perspectives, whereas vegetation loss yields a synergistic warming. Tradeoffs mainly stem from spatial variations in biogeophysical effects. They are more likely to occur during land cover conversions in arid and snow-covered cold regions, where limited ET and strong albedo effects prevail (Rohatyn et al., 2022) (e.g., the northeast and drylands of China). Under these circumstances, BGP effects can either mitigate the BGC warming or offset the cooling associated with different land conversions. For instance, BGP warming of ecological restoration in high-latitude areas would weaken BGC cooling effects (Kristensen et al., 2024). In contrast, BGP-driven warming of urbanization would intensify BGC warming across most areas.

In terms of magnitude, the BGC effects are greater than the BGP effects and thus dominate local LST change in most of China, consistent with previous studies that employed similar methods (Li et al., 2024b; Windisch et al., 2021). Since locally emitted carbon is well-mixed in the atmosphere, BGC effects on climate are not confined to local LCC areas but are manifested globally. Therefore, the comparison between BGP and BGC effects relies on translating BGC effects from CO₂ emissions to LST changes via TCRE and from global to local changes via a scaling factor. The reference global area selected for local scaling (Equation (8)) affects the magnitude of estimated local equivalent LST changes. A smaller reference global area, such as the global land area (Shen et al., 2025; Zhu et al., 2023), leads to a mathematically smaller value than our selection of Earth’s land surface area and affects the regional dominance between BGP and BGC effects (reducing the BGC dominance from 79.76% of LCC area in Online Supplementary Figure S3 to 71%). Compared with other metrics, such as radiative forcing (Betts, 2000; Zhao and Jackson, 2014) and Greenhouse gas values (Anderson-Teixeira et al., 2012), the benefit of TCRE is that it incorporates complex Earth system feedback (e.g., non-radiative and nonlocal processes), serving as a more holistic measure to bridge BGC and BGP processes and representing LCC impacts in more intuitive LST changes than the CO₂-equivalent metric (MacDougall and Friedlingstein, 2015).

Although the national-level net cooling of LCCs combining BGP and BGC effects from all land conversions appears small (-0.000018°C) relative to observed temperature changes and variability, the small national signal is expected because it reflects the complex and spatially varying synergistic and tradeoff relationships between BGP and BGC effects that may cancel each other across different land conversion categories (Table 1). On the one hand, the net combined effects are a consequence of overall BGP warming and BGC cooling of similar magnitude, which counteract each other. On the other hand, the spatially heterogeneous warming and cooling associated with each LCC category of either BGP or BGC processes also cancel out each other. Technically, the LCC-induced LST changes, which only occurred in 5.87% of China’s land area and only a fraction of land at each grid, are muted when averaging over the entire national land. Last but not least, we emphasized that LCCs have a greater impact at the local scale than at the regional scale, with ecological and social implications for agriculture, human health, and so on.

Uncertainty and limitation

We employed a space-for-time method to quantify the impact of LCCs on LST using a potential effect based on multi-year average differences in LST between stable land covers (Li et al., 2015), which differs from estimates based on temporal changes in LST from actual land conversion processes (Alkama and Cescatti, 2016; Li et al., 2016b). The real-world LCCs are typically dynamic and occur at a finer scale, often involving land management and degradation processes without a change in land classification (De Hertog et al., 2022). The increasing human management complicates the impacts of LCC (Luyssaert et al., 2014). These more complex land cover and land use change dynamics need further exploration to understand their climate consequences.

For the BGP effects of LCC, we only considered the local effects within LCC areas (Luo et al., 2024). Nonlocal biogeophysical effects may occur in areas without LCCs driven by atmospheric and oceanic feedbacks (Davin and de Noblet-Ducoudré, 2010; Li et al., 2020; Luo et al., 2024). These nonlocal BGP effects, such as those from deforestation, may influence the carbon stock of ecosystems in remote non-LCC areas (Li et al., 2022). However, since the magnitude of nonlocal effects depends on the spatial extent of LCCs (Winckler et al., 2019a), quantifying these effects requires climate model simulations with active land-atmosphere interactions, which cannot be accounted for by the time-for-space approach using observational data. Yet, existing modeling evidence indicated a dominance of nonlocal effects in global deforestation experiments (Winckler et al., 2019a), and stronger local (−1°C) than nonlocal effects (0.1–0.4°C) for afforestation in China (Chen et al., 2022). How nonlocal effects would change the combined effects of LCCs needs further investigation.

To combine BGP and BGC effects, we used TCRE from CMIP5 models. However, TCRE also exhibits spatial variations and model dependency, which affect the quantification of BGC effects. Higher TCRE values correspond to greater LST changes for the same amount of carbon emission, as seen in high-latitude regions (Pithan and Mauritsen, 2014) and models with higher climate sensitivity. CMIP6 Earth system models tend to exhibit slightly higher TCRE values than CMIP5, with an effective TCRE of about 2.1 [1.8–2.6] °C per 1000 PgC, mainly due to changes in effective radiative forcing, physical feedbacks, and ocean heat uptake (Cox et al., 2024; Williams et al., 2020). The estimated effects presented in our study are subject to various methodological (e.g., space-for-time approach, global-to-local scaling) and data uncertainties (e.g., land cover, LST, TCRE, and carbon density) and should be interpreted with caution. For example, uncertainty in land cover data and inconsistent definitions of forests (Li, 2017) lead to an underestimation of the increase in forest area in this study compared to the national forest inventory (The Chinese State Forest Administration, 2019), which affects the estimation of BGC and BGP effects.

Although we focused on the impacts of LCCs on local LST, it is important to note that satellite-derived LST is a distinct temperature metric from the near-surface air temperature used in climate assessment studies (Winckler et al., 2019b). Moreover, how transferable the LST effects are to air temperature remains an ongoing research question (Li et al., 2025).

Conclusions

This study quantified the combined BGP and BGC effects of land cover changes on LST in China over the past 20 years. Our results showed that 5.87% of China’s areas experienced LCCs, driven by land conversions from ecological restoration, cropland expansion, urbanization, ecological degradation, and cropland degradation. These LCCs generated a combined cooling effect (-0.000018°C) at the national level, especially in the western regions, where LCC cooling can partially mitigate the warming caused by fossil fuel emissions. The net cooling is contributed by a BGC cooling (-0.000199°C) offset by a BGP warming (0.000181°C). BGC effects dominated local LST changes, while BGP effects act as a synergy in most LCC areas. The combined effects, their synergy, and trade-offs varied spatially and across land conversion categories, offering an opportunity to maximize the climate benefits of LCC through targeted LCCs in specific areas. Our findings underscore the importance of considering both BGP and BGC effects when assessing the impacts of LCCs, providing valuable insights for land management and natural climate solutions.

Supplemental Material

sj-docx-1-tee-10.1177_2754124X261437704 – Supplemental material for Biogeochemical cooling offsets biogeophysical warming from land cover changes in China from 2000 to 2020

Supplemental material, sj-docx-1-tee-10.1177_2754124X261437704 for Biogeochemical cooling offsets biogeophysical warming from land cover changes in China from 2000 to 2020 by Hangzheng Zhong, Yan Li, Yongzhe Chen, Duqi Liu, Hongwen Chen, Shan Sang, Huiqing Lin and Chengcheng Hou in Transactions in Earth, Environment, and Sustainability

Footnotes

Acknowledgements

We acknowledge the data support from Soil SubCenter, National Earth System Science Data Center, National Science & Technology Infrastructure of China (), TCRE data from Windisch, M. G. Hangzhong Zhong thanks Dr. Ru Xu for her help with code.

ORCID iD

Hangzheng Zhong

Author Contributions

Hangzheng Zhong: Writing- review & editing, Writing-original draft, Visualization, Validation, Methodology, Data analysis, Data curation. Yan Li: Writing-review & editing, Visualization, Validation, Supervision, Software, Project administration, Methodology, Data curation, Conceptualization. Yongzhe Chen: Writing-review & editing, Supervision. Duqi Liu: Writing- review & editing. Hongwen Chen: Writing-review, Methodology, Data curation. Shan Sang: Writing- review & editing. Huiqing Lin: Writing- review & editing. Chengcheng Hou: Writing- review & editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study is supported by the National Natural Science Foundation of China (no. 42371102).

Data Availability Statement

Code and data needed to reproduce this study are available at Figshare .

Supplemental material

Supplemental material for this article is available online.

Author biographies

Hangzheng Zhong is a master’s student at Beijing Normal University with a research focus on the biogeophysical and biogeochemical effects of land cover changes.

Yan Li, PhD., is a Professor at Beijing Normal University. His research interests include vegetation-climate interactions and human-natural system coupling.

Yongzhe Chen, PhD., is an assistant professor at Beijing Normal University with a research focus on remote sensing applications in ecology, hydrology, and agriculture.

Duqi Liu is a PhD students at Beijing Normal University.

Hongwen Chen is a PhD students at Beijing Normal University.

Shan Sang is a PhD students at Beijing Normal University.

Huiqing Lin is a PhD students at Beijing Normal University.

Chengcheng Hou is a PhD students at Beijing Normal University.

References

Alkama

Cescatti

(2016) Biophysical climate impacts of recent changes in global forest cover. Science 351(6273): 600–604.

Amali

Schwingshackl

Ito

, et al. (2025) Biogeochemical versus biogeophysical temperature effects of historical land-use change in CMIP6. Earth System Dynamics 16(3): 803–840.

Anderson-Teixeira

Snyder

Twine

, et al. (2012) Climate-regulation services of natural and agricultural ecoregions of the Americas. Nature Climate Change 2(3): 177–181.

Arneth

Harrison

Zaehle

, et al. (2010) Terrestrial biogeochemical feedbacks in the climate system. Nature Geoscience 3(8): 525–532.

Arrouays

Deslais

Badeau

(2006) The carbon content of topsoil and its geographical distribution in France. Soil Use and Management 17(1): 7–11.

Bathiany

Claussen

Brovkin

, et al. (2010) Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model. Biogeosciences 7(5): 1383–1399.

Bellamy

Loveland

Bradley

, et al. (2005) Carbon losses from all soils across England and Wales 1978–2003. Nature 437(7056): 245–248.

Betts

(2000) Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature 408(6809): 187–190.

Fang

Jian

, et al. (2018) A parallel and optimization approach for land-surface temperature retrieval on a windows-based PC cluster. Sustainability 10(3): 621.

10.

Bonan

(2008) Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320(5882): 1444–1449.

11.

Bright

Davin

O’Halloran

, et al. (2017) Local temperature response to land cover and management change driven by non-radiative processes. Nature Climate Change 7(4): 296–302.

12.

Burke

Yonker

Parton

, et al. (1989) Texture, climate, and cultivation effects on soil organic matter content in U.S. Grassland soils. Soil Science Society of America Journal 53(3): 800–805.

13.

Chaplin-Kramer

Sharp

Mandle

, et al. (2015) Spatial patterns of agricultural expansion determine impacts on biodiversity and carbon storage. Proceedings of the National Academy of Sciences 112(24): 7402–7407.

14.

Chen

Guo

, et al. (2022) The Biophysical Impacts of Idealized Afforestation on Surface Temperature in China: Local and Nonlocal Effects. Journal of Climate 35(23): 4233–4255.

15.

Chen

Park

Wang

, et al. (2019) China and India lead in greening of the world through land-use management. Nature Sustainability 2(2): 122–129.

16.

Chen

Wang

Song

, et al. (2022) Ecological restoration intensifies evapotranspiration in the Kubuqi Desert. Ecological Engineering 175: 106504.

17.

Cox

Williamson

Friedlingstein

, et al. (2024) Emergent constraints on carbon budgets as a function of global warming. Nature Communications 15(1): 1885.

18.

Davin

de Noblet-Ducoudré

(2010) Climatic impact of global-scale deforestation: Radiative versus nonradiative processes. Journal of Climate 23(1): 97–112.

19.

De Hertog

Havermann

Vanderkelen

, et al. (2022) The biogeophysical effects of idealized land cover and land management changes in Earth system models. Earth System Dynamics 13(3): 1305–1350.

20.

Don

Schumacher

Freibauer

(2011) Impact of tropical land-use change on soil organic carbon stocks - a meta-analysis: Soil organic carbon and land-use change. Global Change Biology 17(4): 1658–1670.

21.

Dun

Bai

, et al. (2021) Effect of groundwater irrigation on agricultural soil carbon pool in North China Plain. Research of Environmental Sciences 34(5): 1187–1195.

22.

Duveiller

Hooker

Cescatti

(2018) A dataset mapping the potential biophysical effects of vegetation cover change. Scientific Data 5(1): 180014.

23.

Emde

Hannam

Most

, et al. (2021) Soil organic carbon in irrigated agricultural systems: A meta-analysis. Global Change Biology 27(16): 3898–3910.

24.

Fan

Zhang

, et al. (2025) Agricultural and socioeconomic effects of vegetation restoration on the Loess Plateau, China. Geography and Sustainability 6(5): 100326.

25.

Feddema

Oleson

Bonan

, et al. (2005) The importance of land-cover change in simulating future climates. Science 310(5754): 1674–1678.

26.

Friedlingstein

Jones

O’Sullivan

, et al. (2022) Global carbon budget 2021. Earth System Science Data 14(4): 1917–2005.

27.

Gong

Zhang

(2019) 40-Year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing. Science Bulletin 64(11): 756–763.

28.

Guo

Gong

Dronova

, et al. (2022) Forest cover change in China from 2000 to 2016. International Journal of Remote Sensing 43(2): 593–606.

29.

Guo

Gifford

(2002) Soil carbon stocks and land use change: A meta analysis. Global Change Biology 8(4): 345–360.

30.

Harper

Lamarche

Hartley

, et al. (2023) A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models. Earth System Science Data 15(3): 1465–1499.

31.

Hasler

Williams

Denney

, et al. (2024) Accounting for albedo change to identify climate-positive tree cover restoration. Nature Communications 15(1): 2275.

32.

Huang

Zhai

Liu

, et al. (2018) The moderating or amplifying biophysical effects of afforestation on CO2-induced cooling depend on the local background climate regimes in China. Agricultural and Forest Meteorology 260–261: 193–203.

33.

Huo

Huang

Dou

, et al. (2022) Carbon Monitor Cities, near-real-time daily estimates of CO2 emissions from 1500 cities worldwide. Scientific Data 9(1): 533.

34.

Intergovernmental Panel on Climate Change (IPCC) (2023) Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva: IPCC.

35.

Kalnay

Cai

(2003) Impact of urbanization and land-use change on climate. Nature 423(6939): 528–531.

36.

Kristensen

JÅ

Barbero-Palacios

Barrio

, et al. (2024) Tree planting is no climate solution at northern high latitudes. Nature Geoscience 17(11): 1087–1092.

37.

Sonnenborg

, et al. (2024a) Effect of ecological restoration on evapotranspiration and water yield in the agro-pastoral ecotone in northern China during 2000–2018. Journal of Hydrology 638: 131531.

38.

(2017) Inconsistent estimates of forest cover change in China between 2000 and 2013 from multiple datasets: Differences in parameters, spatial resolution, and definitions. Scientific Reports 7(1): 8748.

39.

Brando

Morton

, et al. (2022) Deforestation-induced climate change reduces carbon storage in remaining tropical forests. Nature Communications 13(1): 1964.

40.

De Noblet-Ducoudré

Davin

, et al. (2016a) The role of spatial scale and background climate in the latitudinal temperature response to deforestation. Earth System Dynamics 7(1): 167–181.

41.

Z-L

, et al. (2025) Observed different impacts of potential tree restoration on local surface and air temperature. Nature Communications 16(1): 2335.

42.

Piao

Chen

, et al. (2020) Local and teleconnected temperature effects of afforestation and vegetation greening in China. National Science Review 7(5): 897–912.

43.

Zhang

Wang

, et al. (2024b) Prioritizing forestation in China through incorporating biogeochemical and local biogeophysical effects. Earth’s Future 12(7): e2024EF004536.

44.

Zhao

Mildrexler

, et al. (2016b) Potential and actual impacts of deforestation and afforestation on land surface temperature. Journal of Geophysical Research Atmospheres 121(24): 14,372–14,386.

45.

Zhao

Motesharrei

, et al. (2015) Local cooling and warming effects of forests based on satellite observations. Nature Communications 6(1): 6603.

46.

Liao

Yue

, et al. (2024) Growing biomass carbon stock in China driven by expansion and conservation of woody areas. Nature Geoscience 17(11): 1127–1134.

47.

Liu

Zhao

, et al. (2022a) Mapping high resolution National Soil Information Grids of China. Science Bulletin 67(3): 328–340.

48.

Liu

Zhang

G-L

Song

, et al. (2020) High-resolution and three-dimensional mapping of soil texture of China. Geoderma 361: 114061.

49.

Liu

Zhang

, et al. (2010) Spatial patterns and driving forces of land use change in China during the early 21st century. Journal of Geographical Sciences 20(4): 483–494.

50.

Liu

Zhang

(2019a) Land surface temperature response to irrigated paddy field expansion: A case study of semi-arid Western Jilin Province, China. Scientific Reports 9(1): 5278.

51.

Liu

Dong

, et al. (2022b) Biophysical effects of paddy rice expansion on land surface temperature in Northeastern Asia. Agricultural and Forest Meteorology 315: 108820.

52.

Liu

Wang

, et al. (2019b) Impacts of urban expansion on terrestrial carbon storage in China. Environmental Science and Technology 53(12): 6834–6844.

53.

Luo

Cao

, et al. (2024) Local and nonlocal biophysical effects of historical land use and land cover changes in CMIP6 models and the intermodel uncertainty. Earth’s Future 12(6): e2023EF004220.

54.

Luo

Jia

Lai

(2020) Quantitative analysis of the contributions of land use change and CO2 fertilization to carbon use efficiency on the Tibetan Plateau. Science of the Total Environment 728: 138607.

55.

Luyssaert

Jammet

Stoy

, et al. (2014) Land management and land-cover change have impacts of similar magnitude on surface temperature. Nature Climate Change 4(5): 389–393.

56.

MacDougall

Friedlingstein

(2015) The origin and limits of the near proportionality between climate warming and cumulative CO2 emissions. Journal of Climate 28(10): 4217–4230.

57.

Manoli

Fatichi

Schlpfer

, et al. (2019) Magnitude of urban heat islands largely explained by climate and population. Nature 573(7772): 55–60.

58.

Molotoks

Stehfest

Doelman

, et al. (2018) Global projections of future cropland expansion to 2050 and direct impacts on biodiversity and carbon storage. Global Change Biology 24(12): 5895–5908.

59.

Montenegro

Eby

, et al. (2009) The net carbon drawdown of small scale afforestation from satellite observations. Global & Planetary Change 69(4): 195–204.

60.

Mykleby

Snyder

Twine

(2017) Quantifying the trade-off between carbon sequestration and albedo in midlatitude and high-latitude North American forests. Geophysical Research Letters 44(5): 2493–2501.

61.

Ouyang

Sciusco

Jiao

, et al. (2022) Albedo changes caused by future urbanization contribute to global warming. Nature Communications 13(1): 3800.

62.

Pan

Birdsey

Fang

, et al. (2011) A large and persistent carbon sink in the world’s forests. Science 333(6045): 988–993.

63.

Perugini

Caporaso

Marconi

, et al. (2017) Biophysical effects on temperature and precipitation due to land cover change. Environmental Research Letters 12(5): 053002.

64.

Pithan

Mauritsen

(2014) Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nature Geoscience 7(3): 181–184.

65.

Pouyat

Groffman

Yesilonis

, et al. (2002) Soil carbon pools and fluxes in urban ecosystems. Environmental Pollution 116(Supplement 1): S107–S118.

66.

Rohatyn

Yakir

Rotenberg

, et al. (2022) Limited climate change mitigation potential through forestation of the vast dryland regions. Science 377(6613): 1436–1439.

67.

Sanderman

Hengl

Fiske

(2017) Soil carbon debt of 12,000 years of human land use. Proceedings of the National Academy of Sciences 114(36): 9575–9580.

68.

Shen

Liu

Zuo

, et al. (2025) Utilizing the synergistic biophysical and biogeochemical processes to estimate the impacts of forest landscape characteristics on land surface temperature and carbon stock in southern China. Journal of Environmental Management 393: 127071.

69.

Soil SubCenter, National Earth System Science Data Center, National Science & Technology Infrastructure of China (2022) Basic attribute dataset of China’s high-resolution national soil information grid (2010–2018). Available at: http://soil.geodata.cn (accessed 15 October 2022).

70.

Song

(2017) Land-use/land-cover change and ecosystem service provision in China. Science of the Total Environment 576: 705–719.

71.

Spawn

Sullivan

Lark

, et al. (2020) Harmonized global maps of above and belowground biomass carbon density in the year 2010. Scientific Data 7(1): 112.

72.

State Administration for Market Regulation and Standardization Administration of the People’s Republic of China (2017) GB/T 21010-2017 Current land use classification. GB/T 21010-2017, National Standard.

73.

State Administration for Market Regulation and Standardization Administration of the People’s Republic of China (2020) GB/T 38590-2020 Technical regulations for continuous forest inventory. GB/T 38590-2020, National Standard.

74.

The Chinese State Forest Administration (2019) China Forest Resources Report (2014–2018). Beijing: China Forestry Publishing House.

75.

Wan

(2014) New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sensing of Environment 140: 36–45.

76.

Wan

Hook

Hulley

(2021) MODIS/Aqua Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V061. Sioux Falls, SD: NASA EOSDIS Land Processes DAAC.

77.

Williams

Ceppi

Katavouta

(2020) Controls of the transient climate response to emissions by physical feedbacks, heat uptake and carbon cycling. Environmental Research Letters 15(9): 0940c1.

78.

Winckler

Lejeune

Reick

, et al. (2019a) Nonlocal effects dominate the global mean surface temperature response to the biogeophysical effects of deforestation. Geophysical Research Letters 46(2): 745–755.

79.

Winckler

Reick

Luyssaert

, et al. (2019b) Different response of surface temperature and air temperature to deforestation in climate models. Earth System Dynamics 10(3): 473–484.

80.

Windisch

Davin

Seneviratne

(2021) Prioritizing forestation based on biogeochemical and local biogeophysical impacts. Nature Climate Change 11(10): 867–871.

81.

, et al. (2024a) Dynamics of land cover changes and driving forces in China’s drylands since the 1970 s. Land Use Policy 140: 107097.

82.

Zhang

Wang

, et al. (2024b) Urbanization promotes carbon storage or not? The evidence during the rapid process of China. Journal of Environmental Management 359: 121061.

83.

Yang

Huang

Tang

(2019) The footprint of urban heat island effect in 302 Chinese cities: Temporal trends and associated factors. The Science of the Total Environment 655: 652–662.

84.

Yue

Ciais

, et al. (2024) Contributions of ecological restoration policies to China’s land carbon balance. Nature Communications 15(1): 9708.

85.

Zeng

Tomppo

Healey

, et al. (2015) The national forest inventory in China: History - results - international context. Forest Ecosystems 2(1): 23.

86.

Zhao

Jackson

(2014) Biophysical forcings of land-use changes from potential forestry activities in North America. Ecological Monographs 84(2): 329–353.

87.

Zhu

Ciais

, et al. (2023) Comparable biophysical and biogeochemical feedbacks on warming from tropical moist forest degradation. Nature Geoscience 16(3): 244–249.

88.

Zhang

, et al. (2025) The biophysical and crop yield effects of irrigation and their changes in China’s drylands. Agricultural Water Management 313: 109471.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

3.79 MB

0.00 MB