Coastal urban soils (CUS) exhibit distinct physical–chemical properties due to the combined effects of intense interaction among anthropogenic and natural pressures, resulting in a unique, complex and highly vulnerable system. While CUS are vital for the urban coastal environment and its inhabitants, numerous factors significantly threaten their sustainability. The rapid urban expansion and climate change exacerbate these factors, making CUS even more susceptible to degradation. Despite this, most remote sensing syntheses have either addressed urban soils in general or coastal environments without explicitly considering urban soils as a distinct, coastal-specific component. This review aims to fill this gap by providing an in-depth analysis of the application of remote sensing technologies for CUS monitoring and management. One hundred fifty-six studies published between 1982 and 2025 were considered and evaluated based on their spatial distribution, methodological evolution and the main challenges and opportunities in this field. Remote sensing has strong potential for large-scale, non-invasive and cost-effective soil monitoring, enabling the assessment of key soil properties, identification of degradation hotspots and temporal tracking of changes. However, gaps remain, including data processing complexity, the need for ground validation and limitations in spatial/spectral resolution. Despite significant technological advancements, the application of remote sensing to CUS remains relatively underdeveloped compared to other coastal sectors. Harmonized methodologies, improved data integration, and more targeted studies specifically focusing on CUS are further required in the future; research should prioritize standardized protocols, the integration of remote sensing data from multiple sources, and the advancement of machine learning techniques.
Remote Sensing for Monitoring and Managing Urban Soils in Coastal Areas: A Review / Ganga, A., Auzzas, A., Silva, R.B., Capra, G.F.. - In: SOIL USE AND MANAGEMENT. - ISSN 0266-0032. - 42:3(2026). [10.1111/sum.70263]
Remote Sensing for Monitoring and Managing Urban Soils in Coastal Areas: A Review
Ganga, Antonio;Auzzas, Alessandro;Silva, Rafael Barroca;Capra, Gian Franco
2026-01-01
Abstract
Coastal urban soils (CUS) exhibit distinct physical–chemical properties due to the combined effects of intense interaction among anthropogenic and natural pressures, resulting in a unique, complex and highly vulnerable system. While CUS are vital for the urban coastal environment and its inhabitants, numerous factors significantly threaten their sustainability. The rapid urban expansion and climate change exacerbate these factors, making CUS even more susceptible to degradation. Despite this, most remote sensing syntheses have either addressed urban soils in general or coastal environments without explicitly considering urban soils as a distinct, coastal-specific component. This review aims to fill this gap by providing an in-depth analysis of the application of remote sensing technologies for CUS monitoring and management. One hundred fifty-six studies published between 1982 and 2025 were considered and evaluated based on their spatial distribution, methodological evolution and the main challenges and opportunities in this field. Remote sensing has strong potential for large-scale, non-invasive and cost-effective soil monitoring, enabling the assessment of key soil properties, identification of degradation hotspots and temporal tracking of changes. However, gaps remain, including data processing complexity, the need for ground validation and limitations in spatial/spectral resolution. Despite significant technological advancements, the application of remote sensing to CUS remains relatively underdeveloped compared to other coastal sectors. Harmonized methodologies, improved data integration, and more targeted studies specifically focusing on CUS are further required in the future; research should prioritize standardized protocols, the integration of remote sensing data from multiple sources, and the advancement of machine learning techniques.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


