Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Characterizing the topographic index as a tool to represent spatial soil moisture: The effect of classification approach, digital elevation model type, and resolution. |
| Συγγραφείς: |
Asfaw, Binyam Workeye, Fuka, Daniel R., Collick, Amy S., White, Robin R., Easton, Zachary M. |
| Πηγή: |
Vadose Zone Journal; Jul2025, Vol. 24 Issue 4, p1-17, 17p |
| Θεματικοί όροι: |
Soil moisture, Digital elevation models, K-means clustering, Classification, Topography, Hydrologic models, Spatial variation |
| Περίληψη: |
Grouping topographic indices (TIs) values, a topographic metric incorporating upslope contributing area and slope gradient, into topographic index classes (TIC) to estimate soil moisture variability has been shown to improve hydrologic model characterization of complex landscapes. However, the performance of TIC may be enhanced by understanding the inputs in its derivation. In this study, in situ soil moisture measurements across 25 locations in a 4.2 ha field and the k‐means clustering machine learning algorithm were used to identify the optimum number of classes for characterizing field soil moisture. The effect of using equal‐interval, equal‐area, k‐means, and Fisher classification approaches; U.S. Geological Survey (USGS) (1 and 9 m); and drone‐captured light detecting and ranging (LiDAR) digital elevation models (DEMs) re‐gridded to 0.25, 0.5, 1, 2, 3, 4, 5, and 10 m resolution was evaluated. The k‐means clustering, along with elbow analysis and Gap* statistics, identified the spatial pattern of the soil moisture in the field into three groups. Spearman's correlation between a three‐class TIC and the identified soil moisture patterns showed wide variability due to DEM resolution for TIC from drone‐captured LiDAR DEMs—from r = −0.41 to −0.17 for high resolution (0.25–4 m) to r = 0.52 to 0.58 for low resolution (10 m), compared to TIC derived from 1 and ∼9 m resolution USGS DEMs (r = 0.53–0.64). TIC generated using the equal‐interval approach best correlated with identified soil moisture patterns. Selecting the right classification approach, DEM type, and resolution are important considerations when using TI to estimate soil moisture patterns. Core Ideas: k‐Means clustering analysis showed field scale spatial soil moisture may be clustered into three dissimilar units.Digital elevation model (DEM) type and resolution influence the representation of spatial soil moisture using the topographic index.Topographic index classes (TIC) derived from low‐resolution publicly available DEM outperforms drone light detecting and ranging (LiDAR) DEM for soil moisture estimation.The choice of classification approach affects the performance of TIC to represent spatial soil moisture patterns. Plain Language Summary: Soil moisture varies across space and over time making estimating soil moisture conditions challenging. Representing this variability in hydrological models, in a simplified manner, aids soil moisture estimation. To do so requires information on the number of homogeneous soil moisture units for a field. Research has shown topographic attributes that can be used to derive relations that describe soil moisture variability across space. Calculating these topographic attributes is not straight forward as the performance of the derived information depends on the information content of the topographic data used. Topographic attributes need to be classified into an equivalent number of classes equal to the number of homogeneous soil moisture units. Using soil moisture measurements, this study investigates the number of homogenous soil moisture units in a field; determines the effect of using different sources and scales of topographic data; as well as the effect of classification techniques. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Biomedical Index |