Remote Sensing-Based Mapping of Soil Health Descriptors Across Cyprus
Journal
Environments
Date Issued
August 17, 2025
Author(s)
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.
Funding
The authors acknowledge the ‘EXCELSIOR’: ERATOSTHENES: Excellence
Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment
H2020Widespread Teaming project (www.excelsior2020.eu). The ‘EXCELSIOR’ project has received
funding from the European Union’s Horizon 2020 research and innovation program under Grant
Environments 2025, 12, 283 20 of 22
Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate
General for the European Programmes, Coordination and Development and the Cyprus University
of Technology. 2. The authors acknowledge the ‘GreenCarbonCY’: Transitioning to Green agriculture
by assessing and mitigating Carbon emissions from agricultural soils in Cyprus. The GreenCarbonCy
project has received funding from the European Union—Next Generation, the Recovery and Resilience
Plan “Cyprus_tomorrow”, and the Research & Innovation Foundation of Cyprus under the
Restart 2016-2020 Program with contract number CODEVELOP-GT/0322/0023.
Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment
H2020Widespread Teaming project (www.excelsior2020.eu). The ‘EXCELSIOR’ project has received
funding from the European Union’s Horizon 2020 research and innovation program under Grant
Environments 2025, 12, 283 20 of 22
Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate
General for the European Programmes, Coordination and Development and the Cyprus University
of Technology. 2. The authors acknowledge the ‘GreenCarbonCY’: Transitioning to Green agriculture
by assessing and mitigating Carbon emissions from agricultural soils in Cyprus. The GreenCarbonCy
project has received funding from the European Union—Next Generation, the Recovery and Resilience
Plan “Cyprus_tomorrow”, and the Research & Innovation Foundation of Cyprus under the
Restart 2016-2020 Program with contract number CODEVELOP-GT/0322/0023.
File(s)![Thumbnail Image]()
Name
environments-12-00283.pdf
Size
4.6 MB
Format
Adobe PDF
Checksum (MD5)
e4a2c86598e28d9ce77987374324a12e