Equality testing for soil grid unit resolutions to polygon unit scales with DNDC modeling of regional SOC pools

Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon (SOC) pool simulation due to their strong influences on the modeling. A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales, namely, 1:50 000 (C5), 1:200 000 (D2), 1:500 000 (P5), 1:1 000 000 (N1), 1: 4 000 000 (N4) and 1:14 000 000 (N14), in the Taihu Region of China. Both soil unit formats were used for regional SOC pool simulation with a DeNitrification-DeComposition (DNDC) process-based model, which spans the time period from 1982 to 2000 at the six map scales. Four indices, namely, soil type number (STN), area (AREA), average SOC density (ASOCD) and total SOC stocks (SOCS) of surface paddy soils that were simulated by the DNDC, were distinguished from all these soil polygon and grid units. Subjecting to the four index values (IV) from the parent polygon units, the variations in an index value (VIV, %) from the grid units were used to assess its dataset accuracy and redundancy, which reflects the uncertainty in the simulation of SOC pools. Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools, matching their respective soil polygon unit map scales. With these optimal raster resolutions, the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy, when VIV < 1% was assumed to be a criterion for all four indices. A quadratic curve regression model, namely, y =–0.80 × 10–6 x 2 + 0.0228x + 0.0211 (R 2 = 0.9994, P < 0.05), and a power function model R = 10.394Ŝ 0.2153 (R 2 = 0.9759, P < 0.05) were revealed, which describe the relationship between the optimal soil grid unit resolution (y, km) and soil polygon unit map scale (1:10 000x), the ratio (R, %) of the optimal soil grid size to average polygon patch size (Ŝ, km2) and the Ŝ, with the highest R 2 among different mathematical regressions, respectively. This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale, and be referenced to other landscape polygon patches’ mesh partition.