Work

Improving the accuracy of digital soil mapping using remote sensing and topography covariates in the Central Dry Zone of Myanmar

Year: 2025

Type: article

Abstract: Remote sensing has been extensively applied in Digital Soil Mapping to support land resource management and environmental monitoring. However, in humid tropical regions, the effectiveness of optical sensors is often constrained by persistent cloud cover. As a result, many studies in these areas rely primarily on topographic variables as covariates. This study aims to achieve high-accuracy mapping by combining remote sensing images and topographic variables in the Pyawbwe township of the Central Dry Zone of Myanmar, covering 1650 km 2 . This study explored (1) the correlation between soil properties and remote sensing predictors retrieved from monthly composite images of Landsat 5 bands on the accuracy of soil property predictions and (2) the improved prediction accuracy of Digital Soil Mapping when topographic variables were combined with Landsat 5 images. Correlation and regression analyses show significant relationships between spectral bands and soil properties. Integrating Landsat imagery with topographic data consistently improved the prediction of soil properties using Random Forest (RF) and Cubist models, with R 2 values more than double in some cases compared to models using topography alone. Particularly for clay content (R 2 improved from 0.27 to 0.54 for RF and 0.14 to 0.57 for Cubist), effective cation exchange capacity (from 0.17 to 0.48 for RF and 0.07 to 0.42 for Cubist), and sand content (from 0.15 to 0.46 for RF and 0.10 to 0.50 for Cubist). Results from linear correlation analysis show that clay, silt, sand, effective cation exchange capacity, and exchangeable magnesium display the highest correlations with near-infrared and shortwave infrared bands during the fallow period in March, May, and December. The results suggest the potential for using remote sensing to interpret soil fertility, texture, and nutrient-supplying capacity during these periods.

Source: Geoderma Regional

DOI: 10.1016/j.geodrs.2025.e01001

Authors: Akari Win, Budiman Minasny, Anthony Ringrose-Voase, Ho-Jun Jang

Institutions: The University of Sydney, Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food

Language: English

Topic: Soil Geostatistics and Mapping

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Zero hunger

Open Access status: hybrid

Estimated APC paid: $2,310