%0 Journal Article %1 dlr117007 %A Stumpf, Felix %A Schmidt, Karsten %A Goebes, Philipp %A Behrens, Thorsten %A Schönbrodt-Stitt, Sarah %A Wadoux, Alexandre %A Xiang, Wei %A Scholten, Thomas %D 2017 %I Elsevier %J Catena %K LSFE Schoenbrodt article myown %P 30--38 %T Uncertainty-guided sampling to improve digital soil maps %U http://elib.dlr.de/117007/ %V 153 %X "Digital soil mapping (DSM) products represent estimates of spatially distributed soil properties. These estimations comprise an element of uncertainty that is not evenly distributed over the area covered by DSM. If we quantify the uncertainty spatially explicit, this information can be used to improve the quality of DSM by optimizing the sampling design. This study follows a DSM approach using a Random Forest regression model, legacy soil samples, and terrain covariates to estimate topsoil silt and clay contents in a small catchment of 4.2 km² in the Three Gorges Reservoir Area, Central China. We aim (i) to introduce a method to derive spatial uncertainty, and (ii) to improve the initial DSM approaches by additional sampling that is guided by the spatial uncertainty. The proposed uncertainty measure is based on multiple realizations of individual and randomized decision tree models. We used the spatial uncertainty of the initial DSM approaches to stratify the study area and thereby to identify potential sampling areas of high uncertainties. Further,we tested howprecisely available legacy samples cover the variability of the covariateswithin each potential sampling area to define the final sampling area and to apply a purposive sampling design. For the final RandomForestmodel calibration,we combined the legacy sample set with the additional samples. This uncertainty-driven DSMrefinement was evaluated by comparing it to a second approach. In this second approach, the additional samples were replaced by a random sample set of the same size, obtained fromthe entire study area. For the comparative analysis, external, bootstrap-, and cross-validation was applied. The DSM approach using the uncertainty-driven refinement performed best. The averaged spatial uncertainty was reduced by 31\% for silt and by 27\% for clay compared to the initial DSM approach. Using external validation, the accuracy increased by the same proportions, while showing an overall accuracy of R² = 0.59 for silt and R² = 0.56 for clay."

@article{dlr117007, abstract = {"Digital soil mapping (DSM) products represent estimates of spatially distributed soil properties. These estimations comprise an element of uncertainty that is not evenly distributed over the area covered by DSM. If we quantify the uncertainty spatially explicit, this information can be used to improve the quality of DSM by optimizing the sampling design. This study follows a DSM approach using a Random Forest regression model, legacy soil samples, and terrain covariates to estimate topsoil silt and clay contents in a small catchment of 4.2 km² in the Three Gorges Reservoir Area, Central China. We aim (i) to introduce a method to derive spatial uncertainty, and (ii) to improve the initial DSM approaches by additional sampling that is guided by the spatial uncertainty. The proposed uncertainty measure is based on multiple realizations of individual and randomized decision tree models. We used the spatial uncertainty of the initial DSM approaches to stratify the study area and thereby to identify potential sampling areas of high uncertainties. Further,we tested howprecisely available legacy samples cover the variability of the covariateswithin each potential sampling area to define the final sampling area and to apply a purposive sampling design. For the final RandomForestmodel calibration,we combined the legacy sample set with the additional samples. This uncertainty-driven DSMrefinement was evaluated by comparing it to a second approach. In this second approach, the additional samples were replaced by a random sample set of the same size, obtained fromthe entire study area. For the comparative analysis, external, bootstrap-, and cross-validation was applied. The DSM approach using the uncertainty-driven refinement performed best. The averaged spatial uncertainty was reduced by 31\% for silt and by 27\% for clay compared to the initial DSM approach. Using external validation, the accuracy increased by the same proportions, while showing an overall accuracy of R² = 0.59 for silt and R² = 0.56 for clay."}, added-at = {2020-09-11T12:04:50.000+0200}, author = {Stumpf, Felix and Schmidt, Karsten and Goebes, Philipp and Behrens, Thorsten and Sch{\"o}nbrodt-Stitt, Sarah and Wadoux, Alexandre and Xiang, Wei and Scholten, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/2bd532f83ce3c2149e58f5316021b2bb4/earthobs_uniwue}, interhash = {e772135f59115fca20dc4dc42a01241e}, intrahash = {bd532f83ce3c2149e58f5316021b2bb4}, journal = {Catena}, keywords = {LSFE Schoenbrodt article myown}, pages = {30--38}, publisher = {Elsevier}, timestamp = {2020-11-26T13:31:06.000+0100}, title = {Uncertainty-guided sampling to improve digital soil maps}, url = {http://elib.dlr.de/117007/}, volume = 153, year = 2017 }