I would like to use SoilGrids to derive a global map of potential natural soil organic carbon stocks at a 10km resolution, in order to estimate potential future impacts of land conversion. Two possible approaches came to mind that I tried, and I was wondering what your thoughts on these were.
1. I divided the world into 2x2 km blocks and assigned to each block the maximum SOC value of all points in the full 250m dataset that are contained in a block. The idea was that while many points in the full dataset may correspond to managed land (cropland, pasture, managed forests), there would still be points in a 2x2 km area that reflect near-natural conditions. The derived 2x2 km map was then be aggregated (linearly) to a 10km map.
In addition to 2x2 km blocks, I repeated the analysis with 1x1 km blocks and 3x3 km blocks, without the result changing substantially.
2. I used maps of the extent of cropland and pasture area for the year 2000. From that, I estimated potential natural SOC prior to land conversion by assuming that the SoilGrids SOC values on cropland/pasture are equal to the potential SOC minus a certain fraction (that I get from literature) that is lost when land is converted.
I was hoping that the two methods would lead to moderately similar results, but found that, except for a very small number of areas, that was not the case. The map that I obtained with the first method had higher SOC values mostly at very high latitudes, which is not where cropland/pasture areas tend to be located and SOC increased using the second method.
I'd be very happy to hear your thoughts on which of those methods you think seems more suitable, and of course if you've got any other ideas.
Many thanks in advance,
Felix