Remote Sensing and GIS Applications in Environmental Sciences

The land use impact evaluation on soil properties through remote sensing in an arid area in southern Iran

Document Type : Original Article

Authors

1 Assistant Professor of Department of water engineering, Faculty of Agriculture, Minab higher Education center, University of Hormozgan, Bandar Abbas, Iran

2 Assistant Professor, Hormoz Studies and Research Center, University of Hormozgan, Bandar Abbas, Iran

10.22034/rsgi.2025.62479.1087

Abstract

Soil is a vital component of terrestrial ecosystems, facilitating energy and material cycling between the atmosphere and the biosphere. Advances in Earth observation data, such as Sentinel imagery and tools like Google Earth Engine (GEE), have improved soil parameter mapping. This study evaluated GEE-derived soil parameters and their relationship with topsoil properties across different land-use types in Iran’s Roudan Basin. Laboratory analysis of 205 topsoil samples (0–10 cm) measured sand, clay, pH, soil organic carbon (SOC), and soil moisture (SM). Copernicus satellite imagery (100 m resolution) was used to validate land-use maps. Statistical analyses, including Pearson correlation coefficient (r), mean absolute error (MAE), and root-mean-square error (RMSE), compared lab data with GEE outputs. Results indicated varying estimation accuracy: SM and clay were reliably predicted (r = 0.92 and r = 0.89 in farmland), while pH and sand showed lower accuracy (r = 0.33 and r = 0.55). Spatial analysis revealed that farmland and gardens retained the most moisture due to high clay and low sand content. SOC correlated positively with clay (r = 0.82) and negatively with sand and moisture (r = −0.89), highlighting the texture’s role in carbon dynamics. Land-use classification achieved high accuracy (95%, Kappa = 0.93), with poor rangeland having the least error, while water bodies exhibited misclassifications due to seasonal variations.

Keywords

Main Subjects

Soil is an essential component of terrestrial ecosystems and plays a crucial role in the cycling of energy and materials between the atmosphere and the biosphere. The mapping of soil parameters such as soil organic carbon (SOC) and soil moisture has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). This study aimed to assess soil parameters derived from GEE imagery and examine their relationship with topsoil properties across various land-use types in the Roudan Basin, located in Hormozgan Province, southern Iran. To this goal, to measure characteristics such as sand and clay content, pH, SOC, and soil moisture (SM) in the laboratory for 205 topsoil samples (0-10 cm) collected from various land uses. to assess the accuracy of the land use maps in the region using Copernicus satellite imagery, which has a spatial resolution of 100 meters. Statistical methods Correlation coefficient (r), mean absolute error (MAE), and root-mean-square error (RMSE), were employed to analyze soil samples to GEE image outputs. Results showed estimation accuracy varied by property and land use, so that soil moisture and clay content were reliably estimated with values r = 0.92 and r = 0.89 in farmland, whereas pH and sand estimations were less accurate with values r = 0.33 and r = 0.55 in farmland. Spatial analysis using GEE demonstrated that farmland and gardens retained the highest soil moisture, attributed to high clay content and low sand presence, which enhanced water retention and reduced drainage risk. Correlation analysis revealed that SOC is positively associated with clay content (r = 0.82) and negatively with sand content and moisture (r = -0.89), emphasizing textural influences on soil carbon dynamics. The result of land-use classification showed high accuracy (95%, Kappa = 0.93), with poor rangeland demonstrating the least classification error. However, water areas exhibited higher misclassification rates, attributed to seasonal fluctuations.

Keywords: Land Use, soil Property, soil organic carbon, Google Earth Engine, Roudan Watershed

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Remote Sensing and GIS Applications in Environmental Sciences
Volume 5, Issue 15
August 2025
Pages 97-72
  • Receive Date: 12 July 2024
  • Revise Date: 07 May 2025
  • Accept Date: 04 August 2025