## 2 Study Area, Data Sources, and Methodology
### Data Sources  
Monthly climate variables, including temperature, precipitation, sun radiation, and water balance (evapotranspiration and soil moisture) were obtained from two well-known global climate datasets, ERA5-L [43] and TerraClimate [44]. They were combined to provide the necessary meteorological variables for modeling vegetation productivity. All the meteorological data variables were prepared on a monthly scale during the study year.  
This study applied Sentinel-2 (S2) surface reflectance (SR) data (S2_SR) to obtain the normalized difference vegetation index (NDVI) [45]. Clouds in S2_SR were identified from the S2 cloud probability dataset [46] and gap-filled by the smoothed values by performing a linear interpolation and Savitzky-Golay filter from the closest time neighboring sceneswithin three month [47]. The monthly NDVI time series were obtained by the maximum composite from the NDVI in each month.  
Soil properties are largely determined by soil classes. Vegetation carbon uptake and the potential of improvement in the carbon uptake can be constrained by soil cover. Because soil data is hard to obtain, this work selected the Harmonized World Soil Database (HWSD ver. 1.2, [[http://www.fao.org/soils-portal](http://www.fao.org/soils-portal)]([http://www.fao.org/soils-portal](http://www.fao.org/soils-portal)), access on 28 July 2022). HWSD integrates the soil map of China and provides the most up-to-date soil properties that we can obtain for GGA. HWSD incorporates a data table of 48,148 soil profile descriptions related to the various soils associated with each mapping unit at a spatial resolution 1-km [48]. Soil type classes from FAO-90 code (SU_Code90), which includes 194 labels globally, were used for mapping soil type cover in GGA.  
Land (vegetation) cover was obtained from Environmental Systems Research Institute (ESRI) at a fine scale of 10 m [49]. This land use/land cover (LULC) was derived from ESA Sentinel-2 imagery at 10 m resolution. A deep learning model was used to classify land cover by using as a massive training dataset of human-labeled image pixels. The algorithm generated LULC predictions for 10 classes, described in detail below. This outcome was produced by a deep learning model for all major biomes. For the area in GGA, only 5 of the land cover types are present, as shown in Table 1. Waters are excluded from further analysis.  
Landform properties add another constraint/contribution to vegetation carbon uptake. Here, we applied the classified landform cover from the European Soil Data Centre (ESDAC) to indicate landform variations. This product includes 16 landform labels dynamically classified by an unsupervised nested-means algorithm from three ge  
Figure 1: The study area: (**a**) The provincial boundaries of China. (**b**) The administrative boundary of Guangdong province, where the study area (the greater Guangzhou area, GGA) is located. (**c**) The land (vegetation) cover classes in GGA.  
surface texture, and local convexity) from the Shuttle Radar Topography Mission 30 m (SRTM30) digital elevation data [50].  
We selected the most recent year, i.e., 2021, to study the spatial pattern of current carbon uptake and assess the carbon uptake potential from optimal LMPs. The required datasets are listed in Table 2. All the datasets were resampled to a 10 m resolution to match the NDVI to facilitate further analysis.