Data for: How Much More Carbon Can Be Realistically Captured from Grassland Vegetation? Quantitative Assessment Using Focal Analysis on Soil-Topography-Vegetation Unit in the Inner Mongolia

The data provided in this work were processed from five original datasets, including 1) soil map from the Harmonized World Soil Database (ver. 1.2) (http://www.fao.org/soils-portal), 2) topography data (DEM from http://earthexplorer.usgs.gov), 3) Climate dataset (http://cdc.cma.gov.cn), 4) vegetation type map (from http://www.nsii.org.cn/chinavegetaion), and 5) MODIS net primary productivity (NPP) (MODIS 17A3, http://e4ftl01.cr.usgs.gov/MOLT), which were downloaded for the Inner Mongolia Autonomous Region (IMAR) of China. The time window for most of the datasets was during 2000-2014. Topography, after classifying the DEM into three groups (<500m, 500m~1500m, and >1500m), and monthly precipitation and temperature data collected from about 680 weather stations from the climate dataset were applied to make climate grid maps at 1 km×1 km using an ANUSPLIN approach (Price et al., 2000). The climate grid maps were used to model potential NPP (PNPP) using the Miami NPP model (Adams et al., 2004; Gang et al., 2014; Lieth, 1973). Tiles from MODIS 17A3 were mosaicked and taken to represent actual NPP (ANPP) of the grassland vegetation. A differential analysis between PNPP and ANPP at pixel level, was presented to represent a theoretical potential space in carbon capture. The maps of soil (S), topography (T), and vegetation (V) were overlaid to segment the area into spatially homogeneous S-T-V patches. Three types of focal statistics, including mean (Mean), maximum (Max), and 95% percentile threshold (95%PCT), of ANPP for each S-T-V unit were computed as the target level for ANPP. The gap from ANPP to each target level for each S-T-V patch was computed as being the practically realistic potential space. The gaps for the entire IMAR area were aggregated. The temporally averaged maps of the gaps derived from the pixel-based and focal analysis approaches along with ANPP and PNPP were provided. The temporal trajectories of spatially averaged gaps as well as ANPP and PNPP were illustrated.