Background & Summary

Land use/land cover (LULC) plays a crucial role in the interactions between the human system and the Earth system1, which relates directly to a wide range of issues that involve big stakes, e.g., biodiversity2, energy balance3, carbon cycle2, hydrologic cycle4, and climate extremes5. In this regard, LULC in China has been undergoing dramatic changes in the past few decades, with nationwide and worldwide social-environmental consequences1,6,7. For instance, since the “Reform and Opening-up” in 1978, China’s rapid urban growth has prevailed by invading a large proportion of croplands8,9,10. However, after implementing afforestation and reforestation programs, China has shown a significant vegetation greening trend, contributed mainly by forests (42%) and croplands (32%)1,11. These complicated changes are subject to the influences of a variety of social and economic factors. Further investigating and predicting future LULC in China is of vital importance for future land use policy decisions and sustainable management of ecosystems. This could provide the crucial information to balance the anthropogenic climate change and social-economic development.

Combining socio-economic scenarios, emission pathways, and diverse sectoral information in a unified framework can be used to assess future LULC under different policies and global mitigation targets. Integrated Assessment Models (IAMs) are commonly used to quantify outcomes of LULC under different Shared Socioeconomic Pathways (SSPs, representing alternative socio-economic developments)12 and Representative Concentration Pathways (RCPs, representing greenhouse gas concentration trajectories)13, such as the Asian-Pacific Integrated Model/computable general equilibrium (AIM/CGE)14, Integrated Model to Assess the Global Environment (IMAGE)15, and Global Change Analysis Model (GCAM)16. Among them, GCAM, an open-source global integrated and multi-sector model adopted by the Intergovernmental Panel on Climate Change (IPCC)17, has been widely used to project future LULC under diverse socioeconomic and emission scenarios at both regional and global scales18,19,20. GCAM V5.2 explicitly incorporates modules of water supply and demands which is vital in the agriculture and land use sectors21. Besides, GCAM V5.2 also considers the assumptions of water technological advancements under different SSPs, which has significant impacts on water demands in a water-constrained world22.

Mapping spatially explicit LULC patterns at a high spatial resolution is important for analyzing the local spatial details of LULC and understanding the local interactions among human activities and ecological processes in the alternative future23. Some research pointed out that LULC data with coarse spatial resolution could ignore a large proportion of small urban patches, with a severe underestimation of urban land area and urban growth24. By contrast, LULC data with a fine spatial resolution (like 1-km) could provide more necessary spatial details and accurately reflect the heterogeneous spatial characteristics of LULC24. However, the LULC data produced by IAMs are typically in the subregion levels (e.g., the regional, agroecological or water-basin levels)16,25,26 or with coarse spatial resolution14,27,28. Combining IAMs under global scenarios with spatially-explicit LULC models at a coherent framework provides a feasible scheme to project future LULC with a finer spatial resolution18,19,20. For example, the Future Land Use Simulation (FLUS) model has been used to generate spatially-explicit LULC data by combining IAMs19,24, which can simulate high-spatial-resolution LULC change with generally higher accuracy than the single neural network-based cellular automata (CA) model29,30, the Conversion of Land Use and its Effects at Small regional extent (CLUE-S)31, and other models24,32,33.

Recently, a set of SSP-RCP frameworks have been proposed34,35 to describe potential pathways under diverse socio-economic and emission conditions. However, comprehensive assessments of China’s LULCs under full combinations of SSP and RCP scenarios with high resolutions remain to be conducted. It can facilitate the thorough analysis of our uncertain future under challenges of mitigation and adaptation35 and is also crucial for the net-zero emission research36,37. Some studies produced the LULC projections under the combinations of SSP and RCP scenarios at a coarse spatial resolution, such as the 0.5-degree LULC data projected by AIM/GEC14 and five arc-minute gridded LULC data produced by IMAGE27. In contrast, some other studies generated the future LULC data with a high spatial resolution but only covered very limited scenarios. For example, Dong et al.19 developed 1-km resolution LULC data in China using the integrated GCAM and FLUS model. Cao et al.20 spatialized global LULC data at a 1-km resolution based on the integrated GCAM and CA model. However, missing some important scenarios could hinder the applications in ecological and hydrological modelling38,39. For example, SSP5-RCP1.9, which is a combination of a strict climate target and a fossil-fueled development scenario, may be required to represent extreme conditions of human activities in the modelling. Projecting high-spatial-resolution LULC with all possible combinations of SSP and RCP, enables a comprehensive analysis of LULC under different socio-economic assumptions and mitigation policies and can support a deep understanding of local LULC dynamics with more spatial details.

In addition, the future urban land change has not been well considered in the existing future LULC data. Urban land is a key driver for many environmental and societal changes across scales40 and is also crucial for studying LULC projections32,41. Some models currently assume that there is no urban land change in the future, such as GCAM42. However, there has been a dramatic change of global urban land in the past decades43, implying that assuming a constant coverage of the urban land in the future is unrealistic24,40. Some other studies projected urban shrinkage simply based on the empirical relationship between urban land, Gross Domestic Product (GDP), population, and other factors19,27. Nevertheless, the decrease of the urban population does not necessarily lead to numerous land conversions from urban to non-urban areas44,45, especially in China46. Recently, Chen et al. (2020a) developed a 1-km gridded dataset of globally future urban land expansion under SSPs, which provides a more reasonable projection of future urban land change. This dataset offers us a great opportunity to calibrate urban land change in LULC predictions.

The overarching goal of this study is to develop a high-resolution gridded LULC dataset in China under a full SSP/RCP matrix from 2020 to 2100, where the urban land change is well incorporated. First, we generated the LULC projections for China under all possible combinations of SSP and RCP using GCAM at the water-basin level. Then, we used the urban land dataset developed by Chen et al. (2020a) to calibrate the urban land demand (the projected total area of urban) projected by GCAM. Finally, we downscaled the water-basin-level LULC projections to 1-km grids using the FLUS model. This newly gridded dataset fills the gap between high spatial resolution and limited scenarios in the current LULC predictions, which can enhance climate change research under diverse socioeconomic and emission assumptions, provide support for making policies to limit global warming to below 2 °C or 1.5 °C by 2100, the target of the Paris agreement36 and help reduce the uncertainties of the Earth system modelling. It will be helpful to those researches focusing on individual socio-economic or emission conditions. Besides, this dataset will be valuable for wide unified and comparable multi-scenario-based research, such as ecosystem services47, biodiversity48,soil erosion by water49, and carbon neutrality in China.

Methods

Overall framework

Figure 1 shows the methodological framework of the integrated GCAM-FLUS model for producing high-resolution LULC dataset in China. First, we used the GCAM model to project the LULC demands of China from 2020 to 2100 under all possible SSP-RCP scenarios (24 scenarios in total, see Table 1) with a 10-year interval at a regional scale. Second, we harmonized the LULC types with a reclassification scheme (see Table 2) and calibrated the land demands of GCAM-generated LULC data based on the historical LULC data collected from the European Space Agency Climate Change Initiative (CCI-LC)50. Further, we calibrated the urban land demands using a well-validated future urban expansion dataset under SSPs produced by Chen et al. (2020a), freely downloaded from http://www.geosimulation.cn/GlobalSSPsUrbanProduct.html. Finally, we integrate comprehensive SSP-RCP with a land use model (FLUS) to downscale the GCAM-based LULC data into 1-km spatial resolution.

The 24 SSP-RCP scenarios (listed in Table 1) are composed of five baseline scenarios that do not include any climate mitigation policy and 19 combined scenarios. In this study, the climate conditions are represented by three RCP levels (2.6, 4.5, 6.0 Wm−2) and two additional forcing levels (1.9 and 3.4 Wm−2). The level of 8.5 Wm−2 is not included because the forcing levels of all five baseline scenarios in our simulations are lower than 8.5 Wm−2. The two additional forcing levels are related to current policy targets36 and belong to 1.5 °C and 2 °C scenarios14,38, which play a key role in reducing the impact and risks of climate change51,52.

Projection of future regional LULC demands

GCAM, a market-equilibrium dynamic-recursive model, well represents the interactions of five sub-systems: energy, water, agriculture and land use, economy, and climate25. GCAM is one of the marker models used to quantify SSP and RCP scenarios and has been widely used to analyze future LULC changes under different scenarios. In the newly released GCAM v5.2, the land-use module subdivides 32 global geo-political regions into the water-basin levels, and there are totally 24 subregions in China (Figure S1). GCAM uses a logit model to represent the sharing of the economic decisions for land use in one region. Thus, there is a distribution of profit behind each competing land use. GCAM also uses a nesting strategy to reflect the differences in alternatives through different LULC types with logit exponents. Besides, GCAM implements both SSP and RCP scenarios. This study used GCAM v5.2 to project the water-basin level demands of different LULC types from 2020 to 2100 with a 10-year interval under the 24 scenarios.

Calibration of LULC types and land demands

There are some inconsistencies of LULC types between the output of GCAM and the initial LULC map (i.e., CCI-LC) used for spatial downscaling. GCAM mainly includes nine LULC types composed of Cropland, Forest, Pasture, Grassland, Shrub, Urban land, Tundra, Rock, Ice and Desert, and Biomass. By contrast, CCI-LC mainly includes Cropland, Grassland, Shrub, Forest, Bareland, Ice, Water and Urban areas. Besides, there are considerable differences in land areas between the GCAM-derived future land demands and CCI-LC dataset. These inconsistencies may cause significant errors and uncertainties in the future LULC simulations. Therefore, we first built a reclassification scheme composed of 8 LULC types (Table 2) and used it to harmonize the LULC types of these two datasets. Then, we further adjusted the GCAM-derived future land demands by:

$$A{(t+1)}^{k}=\left\{\begin{array}{c}\left(1+\frac{A{\left(t+1\right)}_{GCAM}^{k}-A{\left(t\right)}_{GCAM}^{k}}{A{\left(t\right)}_{GCAM}^{k}}\right)\times A{(t)}_{CCI-LC}^{k}\quad t=2010\\ \left(1+\frac{A{\left(t+1\right)}_{GCAM}^{k}-A{\left(t\right)}_{GCAM}^{k}}{A{\left(t\right)}_{GCAM}^{k}}\right)\times A{(t)}^{k}\quad 2020\le t\le 2100\end{array}\right.$$
(1)

where $$A{(t+1)}^{k}$$ is the calibrated area of type k at year t + 1, $$A{\left(t+1\right)}_{GCAM}^{k}$$ is the area of type k at year t-1 calculated by GCAM, and $$A{(t)}_{CCI-LC}^{k}$$ is the area of type k at year t from the CCI-LC data.

Calibration of urban demand

GCAM assumes that the future urban demands remain constant, which is unreasonable40 and needs to be re-adjusted. Chen et al. (2020a) generated a 1-km future urban land expansion dataset based on the established relationships between urban land demand, GDP and urbanization rate. This dataset has a much higher spatial resolution which could capture more spatial details of urban land patterns than other urban expansion data and shows excellent performance in terms of the Figure of Merit (FoM)32. In addition, this urban land dataset considers more reasonable situations in the urban shrinkage stage45,46. In this study, we used this urban land dataset to calibrate future GCAM-derived urban demands. We first calculated the urban areas of 24 subregions under different SSP scenarios based on this dataset, and used it to update the future urban demands of GCAM-derived LULC data. Then, we used the methods developed by Li et al. 24 to adjust the areas of the remaining LUCCs proportionally. Equation 2 describes how we deduct the demand of urban encroachment in other LULC types.

$${A}_{adjust}{(t)}^{k}=\left(A{(t)}^{U}-A{(0)}^{U}\right)\times {H}_{n}^{k\to U}$$
(2)

where $${A}_{adjust}{(t)}^{k}$$ is the area of type k encroached by urban at year t, $$A{(t)}^{U}A$$ is the projected area of urban land (U) for year t, $$A{(0)}^{U}$$ is the original area of urban in 2010, n is the number of LULC types (except urban, water and ice), and $${H}_{n}^{k\to U}$$ is the empirical proportion of land loss encroached by urban of type k and the sum of $${H}_{n}^{k\to U}$$ of all n types is 1.

In this study, we assumed that urban could encroach other LULC types except for water and ice, and the urban demand under the same SSP scenario is identical. We used the CCI-LC data in 2000 and 2010 to calculate the proportion of land loss due to the urban encroachment on each LULC type and derive $${{\rm{H}}}_{{\rm{n}}}^{{\rm{k}}\to {\rm{U}}}$$ for each subregion (Table 3). Then we combined $${{\rm{H}}}_{{\rm{n}}}^{{\rm{k}}\to {\rm{U}}}$$ and aforementioned urban land dataset to adjust the demands of other LULC types in the GCAM-derived LULC data.

Downscaling of regional LULC projections

Based on the GCAM-derived LULC demands calibrated through the steps abovementioned, we used the FLUS model to simulate the future LULC of China at 1-km resolution. FLUS integrates the top-down system dynamics and bottom-up CA and can explicitly simulate the spatial trajectories of multiple LULC types33. The first part of FLUS aims to train and estimate the occurrence probabilities of LULC on a specific grid cell based on artificial neural networks (ANN). Specifically, we first collected the CCI-LC data in 2010 and 15 driving factors (shown in Table 4) as the training data. The driving factors were mainly selected based on relevant studies and can reflect different heterogeneous characteristics (i.e., climate, soil, topography, population, economics, transportation, etc.) related to LULC24,32,33. All these driving factors are reprojected into 1-km × 1-km grids with a spatial reference of the Albers equal-area conic projection. Then we trained the ANN model based on a 1% uniform sample rate for each subregion and used the trained model to estimate the occurrence probabilities (OP, determined by the characteristics of each pixcel) of each pixel. The adopted ANN model has one input layer, one hidden layer with 10 neurons and one output layer in this study. Each neuron of the input layer is associated with a driving factor, and that of the output layer corresponds to OP for a specific LULC type. The sigmoid activation function was used for the hidden layer. The second part of the model, CA, considers OP, conversion cost, neighborhood condition and competition among the different LULC types to estimate the combined probability for LULC conversion. In this step, the LULC type with a higher OP estimated by the previous step is more likely to be predicted as the target LULC type. In contrast, those with a relatively lower OP can still be converted based on the roulette selection mechanism. During the allocation stage, we adopted several assumptions: first, urban expansion is irreversible; second, water and ice are not involved in LULC conversion; and third, bareland can only be infringed by urban or stay unchanged in the future, considering that GCAM cannot project the bareland change in the future and the Bareland can only change due to the urban expansion (see the previous section). Under these assumptions, combined with CCI-LC data in 2010 as an initial LULC map, we used the FLUS model to produce 1-km LULC dataset in China from 2020 to 2100.

Data Records

The generated LULC dataset with 1-km spatial resolution and 10-year time step from 2020–2100 covers 24 SSP-RCP scenarios in total (Table 2). The dataset is publicly available in https://doi.org/10.6084/m9.figshare.14776128.v153 and http://www.geosimulation.cn/. All the data is stored in a commonly-used geotiff format with a spatial reference of the Albers equal-area conic projection, which can be easily accessed by ARCGIS, ENVI, MATLAB, etc. For the file naming and structure, all the files with the same SSP-RCP scenarios were grouped into the same folders with the name of “SSP-RCP” and each geotiff file is named as “SSP_RCP_Year.tif”, where “SSP” and “RCP” denote the SSP and RCP scenarios, and “Year” denotes the year of the LULC data. For example, the file storing the LULC data under SSP1-RCP1.9 in 2100 is named as “SSP1-RCP19_2100.tif”. Taking “SSP1-RCP19_2100.tif” as an example, Figure S2 shows the 2100 LULC spatial distributions under SSP1-RCP1.9. In each geotiff file, different integer values represent different LULC types: 1 Urban; 2 Cropland; 3 Grassland; 4 Shrub; 5 Forest; 6 Water; 7 Bareland; and 8 Ice.

Technical Validation

We used the CCI-LC data in 2000 to train the FLUS model and then used the CCI-LC data in 2010 to evaluate the reliability of our downscaled dataset. We also compared our gridded dataset with the widely-used Land-use Harmonisation (LUH2) data at a 0.25 degree resolution54, used in the Coupled Model Inter-comparison Project Phase 6 (CMIP6)55. Specifically, we chose all the overlapping scenarios between our dataset and LUH2, including SSP1-RCP1.9, SSP1-RCP1.6, SSP2-RCP4.5, SSP4-RCP3.4, and SSP5-RCP3.4 for comparison. In addition, considering the inconsistency in LULC types between our dataset and LUH2, we re-grouped the LUH2 data into five broad LULC types: Cropland, Forest, Bareland, Grassland, and Urban.

To quantitatively assess the simulated LULC during the downscaling process, we calculated the overall accuracy and the Cohen’s Kappa coefficient of each subregion by validating our dataset against the CCI-LC data. Compared with the Kappa coefficient, FoM can avoid overestimating the accuracy and has been demonstrated to effectively evaluate the accuracy and has been demonstrated to be effective to evaluate the accuracy of simulating LULC changes56,57. Therefore, we further used the FoM metrics to assess the consistencies between the simulated LULC and remote sensing data (CCI-LC). Specifically, FoM represents the ratio of the correct predicted change to the sum of the observed and predicted change:

$${\rm{FoM}}=\frac{{\rm{B}}}{{\rm{A}}+{\rm{B}}+{\rm{C}}+{\rm{D}}}$$
(3)

where A represents the false area where the observed change is predicted as persistence, B represents the correct area where the observed change is predicted as change correctly, C represents the false area where the observed change is predicted as a change in the wrong LULC type, and D represents the false area where the observed persistence is predicted as change. Its value ranges from 0 to 1, and larger FoM represents a better performance on LULC simulations.

In addition, we used the Pearson correlation coefficient and root mean square difference (RMSD) to assess the spatio-temporal consistencies between our gridded LULC dataset and LUH2 data during 2020–2100.

Validation of the downscaling process

The statistical accuracy metrics of the LULC simulations of 2010 in each subregion and the whole of China are shown in Table 5. The Kappa coefficient ranges from 0.43 to 0.75 and the overall accuracy ranges from 0.66 to 0.88 across different subregions. These two metrics have values of 0.64 and 0.79 for China. FoM varies from 0.10 to 0.17 in different subregions, and has a value of 0.13 for China. This result is identical to existing studies which showed that the FoM values were usually in the range of 0.1 to 0.3, due to the path-dependent effects32,33,58. The confusion matrix of the simulated LULC compared to CCI-LC data in 2010 shows that the number of mis-classified pixels is small (Table 6). These results demonstrate that the simulated LULC have a good agreement with CCI-LC in 2010.

We also compared the LULC spatial distributions of the actual LULC map retrieved by remote sensing data (CCI-LC, used as the base map), our downscaled data and LUH2 data in 2010 (Figure S3). It is worth noting that LUH2 data used a different historical data source55. Overall, they show similar spatial patterns with minor differences in most regions. The Pearson’s correlation coefficients between our data and base map are 0.98, 0.99, 0.95, 0.96, and 0.90 for Cropland, Forest, Bareland, Grassland and Urban. The differences between LUH2 and base map are mainly distributed in the eastern and south eastern China, where the base map shows a lower proportion of Bareland and Grassland, but a higher proportion of Urban. We further compared the difference of the land amount proportion of each LULC type within a 10-km x 10-km gird between our simulations and base map (Figure S4), which shows that the overall spatial pattern of our simulated LULC is consistent with the base map. Some differences between them are mainly distributed in the northwest of China, where our simulation overestimates the area of Bareland and underestimates the areas of Grassland and Cropland. This may be because from 2000 to 2010, some areas of Bareland were converted into Grassland and Cropland in the northwest China. However, our downscaling strategy assumes the Bareland can only be infringed by urban. Thus, the conversion from Bareland to other LULC types is omitted, which can lead to the overestimation of the areas of Bareland and underestimation of the area of Grassland and Cropland in this region. The fraction differences of all the eight LULC types within each 10-km × 10-km gird range from −15% to 15% and most of them are smaller than ± 5% (Fig. 2). All these results demonstrate that the downscaling process can accurately simulate the LULC spatial distributions.

Spatio-temporal consistency with LUH2

We first analyzed the temporal change of the eight LULC types’ land amount in our dataset from 2010 to 2100 (Figure S5). Notably, Ice and Water are assumed to be intact in our LULC projections, and thus they will keep unchanged over time. Forest will increase in SSP1-, SSP2-, SSP4-based scenarios, but whether Forest increases or decreases in SSP3- and SSP5-based scenarios depends on specific RCP settings. Shrub shows an increasing trend in all the SSP scenarios. Grassland shows a decreasing trend in most of the SSP2-, SSP3-, SSP4-, and SSP5-based scenarios, a slight increase in the baseline scenarios of SSP4 and SSP5, and trivial changes in SSP1-based scenarios. Crop has a decreasing trend in all SSP1-, SSP4- and SSP5-based scenarios, but some SSP2- and SSP3-based scenarios show an increasing trend. In terms of RCP, more Croplands are required in RCP1.9, RCP2.6, and RCP3.4 than RCP4.5, RCP6, and baseline scenarios. Forest, Shrub, and Grassland will increase faster in RCP1.9, RCP2.6, and RCP3.4 than RCP4.5, RCP6, and baseline scenarios.

Then, we compared the LULC amounts of five overlapped scenarios between our dataset and LUH2 across the five LULC types. Figure 3 shows that the temporal trend of Forest is similar to LUH2 before 2060 and lightly differs from LUH2 after 2060, while the temporal trend of Grassland in our dataset is generally consistent with LUH2. Cropland in our dataset will decrease in all five scenarios, and presents different trends with LUH2. However, the trend of Cropland is similar with the results reported in Chen et.al. (2020b). The Urban shows an increasing trend from 2010 to the mid-21st century, consistent with LUH2. But Urban will remain stable after reaching its maximum values, which is different from the decreasing trends in LUH2. Bareland will slightly decrease from 2010 to mid-21st century and remain unchanged later, which is also different from LUH2. The difference in Urban and Bareland is mainly because these two types are assumed to be intact in GCAM, and we used an urban land dataset under SSPs to calibrate the future LULC change. This urban land dataset assumes that the urban will not convert to other LULC types in the city shrinking stage, which causes that the area of urban will keep unchanged after reaching the peak. Besides, we assume the Bareland can be infringed by urban in the expanding stage, leading to the slight decrease of Bareland from 2010 to the mid-21st century.

We further compared the spatial consistencies of our downscaled gridded LULC dataset with LUH2 from 2010–2100 (Fig. 4). Overall, the two datasets show good consistencies across the five LULC types. The mean values of the Pearson’s correlation coefficients for five sceneries are 0.88 (Crop), 0.76 (Forest), 0.66 (Bareland), 0.78 (Grassland) and 0.82 (Urban) respectively. The relatively low consistency for Bareland may result from two reasons. First, the Bareland change in our dataset is caused by the urban encroachment since the demands generated by GCAM remain stable in the future, but LUH2 adopted different assumptions. Second, the base map we used had a distinct spatial pattern of Bareland compared to LUH2 in 2010 (see Figure S3). The Pearson’s correlation coefficients between our dataset and LUH2 range from 0.56 to 0.92 in SSP1-RCP1.9, 0.56 to 0.92 in SSP1-RCP2.6, 0.68 to 0.91 in SSP2-RCP3.4, 0.69 to 0.92 in SSP4-RCP3.4 and 0.68 to 0.92 in SSP5-RCP4.5. Among the five scenarios, our dataset has the highest Pearson’s correlation coefficients with LUH2 in SSP4-RCP3.4, which can be explained by that LUH2 used the same GCAM model with a different version (v4) for the LULC projection. These results demonstrate that our dataset has a good spatial consistency with LUH2 for different LULC types under different scenarios.

We also compared the temporal consistencies (RMSD) of land area in five LULC types between our dataset and LUH2. As shown in Fig. 5, most of the temporal variation in our dataset is similar to LUH2, with only some minor differences. For all the five scenarios, the RMSD for Cropland ranges from 0.057 to 0.11; for Forest, it ranges from 0.052 to 0.073; for Bareland, it ranges from 0.001 to 0.026; for Grassland, it ranges from 0.062 to 0.11; and for Urban, it ranges from 0.0001 to 0.0006. Among all the LULC types, Urban shows the highest degree of similarity which may be because the urban dataset we used to calibrate the urban demand has a high consistency with LUH232. But there are some differences in the temporal variation of Bareland and Grassland, and most of them occur in the northwest China, possibly because of the differences in the input data, the spatial downscaling strategy, and the new land cover classification scheme we used. The Bareland change is only caused by urban encroachment in our dataset, while this is not the case in LUH2. Some differences also occur in the southeast of China, which may result from the differences in type definition and classification standards between the base map and LUH2 (Figure S3). Overall our dataset shows high spatio-temporal consistency with LUH2. The discrepancy between our dataset and LUH2 can be caused by different IAM models, base map and LULC downscaling methods.

Besides, our dataset can reflect more spatial details because of its high spatial resolution (1-km) compared to LUH2 with coarse spatial resolution of 0.25°  (Fig. 6). This means our dataset can be more helpful to investigate the local impacts of LULC on ecosystem services and many other studies under different socioeconomic and emission conditions.