D. W. Nelson

Bio: D. W. Nelson is an academic researcher. The author has contributed to research in topics: Organic matter & Carbon. The author has an hindex of 1, co-authored 1 publications receiving 5444 citations.

SoilGrids250m: Global gridded soil information based on machine learning

Abstract: This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

A rapid and precise method for routine determination of organic carbon in soil

Abstract: A simple method for routine determination of organic carbon in soil by a modified Mebius procedure is described It involves (a) digestion of the soil sample with an acidified dichromate (K2Cr2O7‐H2SO4) solution for 30 minutes in a Pyrex digestion tube in a 40‐tube block digester preheated to 170°C and (b) estimation of the unreacted dichromate by titration of the cooled digest with an acidified solution of ferrous ammonium sulfate with use ofN‐phenylanthranilic acid as an indicator The method is more rapid and precise than the Mebius procedure commonly used for routine analysis of soils for organic carbon, and the only equipment required for its use is equipment now commonly used for routine Kjeldahl analysis of soils for total nitrogen