Abstract
ABSTRACT Digital elevation models (DEM) used in digital soil mapping (DSM) are commonly selected based on measures and indicators (quality criteria) that are thought to reflect how well a given DEM represents the terrain surface. The hypothesis is that the more accurate a DEM, the more accurate will be the DSM predictions. The objective of this study was to assess different criteria to identify the DEM that delivers the most accurate DSM predictions. A set of 10 criteria were used to evaluate the quality of nine DEMs constructed with different data sources, processing routines and three resolutions (5, 20, and 30 m). Multinomial logistic regression models were calibrated using 157 soil observations and terrain attributes derived from each DEM. Soil class predictions were validated using leave-one-out cross-validation. Results showed that, for each resolution, the quality criteria are useful to identify the DEM that more accurately represents the terrain surface. However, for all three resolutions, the most accurate DEM did not produce the most accurate DSM predictions. With the 20-m resolution DEMs, DSM predictions were five percentage points less accurate when using the more accurate DEM. The 5-m resolution was the most accurate DEM overall and resulted in DSM predictions with 44% accuracy; this value was equal to that obtained with two coarser resolution, lower accuracy DEMs. Thus, identifying the truly best DEM for DSM requires assessment of the accuracy of DSM predictions using some form of external validation, because not necessarily the most accurate DEM will produce the best DSM predictions.
Highlights
Digital soil mapping (DSM) uses statistical models to quantify the correlation of soil attributes with environmental conditions to make predictions at locations not sampled
Predictor variables are often chosen based on their availability, the required level of spatial detail of DSM predictions, and/or knowledge of their ability to explain soil spatial variation (Miller et al, 2015)
The evaluation of digital elevation models (DEM) characteristics and quality for DSM matched those from recent literature
Summary
Digital soil mapping (DSM) uses statistical models to quantify the correlation of soil attributes with environmental conditions to make predictions at locations not sampled In these models, soil attributes in a particular location, whether continuous or categorical, are taken as random dependent variables. A major difficulty is the selection of a single - optimally the best - satellite image and DEM among the many available for a given DSM project. This is because these data carry unknown and varying errors that could reduce the correlation with soil attributes and affect the accuracy of DSM predictions. These errors arise from the complex interplay between the methods of data generation, analytical procedures and characteristics of each site (Fisher; Tate, 2006; Florinsky, 1998; Hirt; Filmer; Featherstone, 2010)
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