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Table 1 Spatial soil information currently available for modeling fire in the United States. 1SS = soil survey; DSM = digital soil map; PRS = proxy remote sensing for soil properties. 2Both represents both raster and vector formats available. 31:250 000 in continental US and 1:1 000 000 in Alaska

From: Digital soil mapping for fire prediction and management in rangelands

Dataset Group1 Data type Extent Resolution Advantage for pre or post fire Disadvantage for pre or post fire Method of development Reference
HWSD SS Raster Global 1:1 000 000 to 1:5 000 000 Extensive spatial coverage Coarse resolution; limited interpretations Merged European Soil Database, soil map of China, regional SOTER databases, and Soil Map of the World Wieder et al. 2014
STATSGO2 SS Vector Continental 1:250 000 to 1:1 000 0003 Extensive spatial coverage Coarse resolution; limited interpretations Soil-landscape paradigm; tacit knowledge; field and laboratory sampling and analysis Soil Survey Staff 2018c
SSURGO and gSSURGO SS Both2 Continental 1:12 000 to 1:63 360 Extensive spatial coverage; numerous interpretations and properties Variability within map units; some areas without data Soil Survey Staff 2018a and 2018b
SoilGrids DSM Raster Global 250 m and 1 km raster cells Extensive spatial coverage; quantified model uncertainty; data gaps filled Varying sample density in each soil type Probabilistic based machine learning Hengl et al. 2017
US48 SoilGrids100m+ DSM Raster Continental 100 m raster cells Ramcharan et al. 2018
POLARIS DSM Raster Continental 30 m raster cells Chaney et al. 2016
Local DSM maps DSM Both Regional and local Variable (as detailed as 5 m raster cells) Fine spatial resolution; some data gaps filled; quantified model uncertainty Varying sample density in each soil type; difficult to locate; often specific goals Many methods ranging from regression to machine learning Grunwald 2009; many others
Direct soil moisture (e.g., SMAP, SMOS, GRACE) PRS Raster Global 3 to 36 km raster cells Near current data availability Limited data record; Coarse spatial scale Satellite remote sensing with ground validation Entekhabi et al. 2010; Jensen et al. 2018
Indirect soil moisture PRS Both Regional and local Variable Quantitative and process-based information Relies on empirical relationships and subject to model uncertainty E.g., vegetation indices, inverse process-based models, land surface models Abatzoglou 2013; Waring 2016