Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management

Abstract

Abstract Soil resource management is fundamentally integral to environmental sustainability and agricultural productivity. The digital elevation model (DEM) is the fundamental data for analyzing landform surfaces, which introduces an opportunity to obtain a broad spectrum of terrain factors to simplify interpreting the patterns and processes in the geoscience field. The accuracy and resolution of DEM are crucial for their effective use, and many algorithms have been developed to interpolate digital elevation data from a set of known points. Although primary topographic variables derived from grid datasets are important, secondary variables, such as the relief index (RFI), play a more critical role in understanding the complicated relationship between soil properties and landform attributes. The RFI is attained from a DEM by calculating the elevation range within a given neighborhood surrounding a central cell. It is an essential predictor of soil natural resource management that measures the degree of differentiation surface relief. In addition, it is beneficial for perceiving the landscape and its management. This study presents a comprehensive zonal analysis comparing the RFI values derived from multiple interpolation-based DEMs. It investigates deterministic and geostatistical interpolators, such as inverse distance weighted and natural neighbor across distinct zones with diverse topographical characteristics. The findings indicated a high correlation between the RFI and the reliability of the DEM, and the natural neighbor technique provided superior performance against others. The results revealed that the choice of spatial interpolation technique significantly affects the accuracy and reliability of RFI models.