Remote Sensing-Based Mapping of Soil Health Descriptors Across Cyprus
Journal
Environments
Date Issued
August 17, 2025
Author(s)
DOI
10.3390/ environments12080283
Abstract
Accurate and spatially detailed soil information is essential for supporting sustainable land
use planning, particularly in data-scarce regions such as Cyprus, where soil degradation
risks are intensified by land fragmentation, water scarcity, and climate change pressure.
This study aimed to generate national-scale predictive maps of key soil health descriptors
by integrating satellite-based indicators with a recently released geo-referenced soil dataset.
A machine learning model was applied to estimate a suite of soil properties, including
organic carbon, pH, texture fractions, macronutrients, and electrical conductivity. The
resulting maps reflect spatial patterns consistent with previous studies focused on Cyprus
and provide high resolution insights into degradation processes, such as organic carbon
loss, and salinization risk. These outputs provide added value for identifying priority
zones for soil conservation and evidence-based land management planning. While predictive
uncertainty is greater in areas lacking ground reference data, particularly in the
northeastern part of the island, the modeling framework demonstrates strong potential
for a national-scale soil health assessment. The outcomes are directly relevant to ongoing
soil policy developments, including the forthcoming Soil Monitoring Law, and provide
spatial prediction models and indicator maps that support the assessment and mitigation
of soil degradation.
use planning, particularly in data-scarce regions such as Cyprus, where soil degradation
risks are intensified by land fragmentation, water scarcity, and climate change pressure.
This study aimed to generate national-scale predictive maps of key soil health descriptors
by integrating satellite-based indicators with a recently released geo-referenced soil dataset.
A machine learning model was applied to estimate a suite of soil properties, including
organic carbon, pH, texture fractions, macronutrients, and electrical conductivity. The
resulting maps reflect spatial patterns consistent with previous studies focused on Cyprus
and provide high resolution insights into degradation processes, such as organic carbon
loss, and salinization risk. These outputs provide added value for identifying priority
zones for soil conservation and evidence-based land management planning. While predictive
uncertainty is greater in areas lacking ground reference data, particularly in the
northeastern part of the island, the modeling framework demonstrates strong potential
for a national-scale soil health assessment. The outcomes are directly relevant to ongoing
soil policy developments, including the forthcoming Soil Monitoring Law, and provide
spatial prediction models and indicator maps that support the assessment and mitigation
of soil degradation.
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