Land Consumption Classification Using Sentinel 1 Data: A Systematic Review

Sara Mastrorosa (), Mattia Crespi, Luca Congedo and Michele Munafò
Additional contact information
Sara Mastrorosa: Geodesy and Geomatics Division—DICEA, Sapienza University of Rome, via Eudossiana, 00184 Rome, Italy
Mattia Crespi: Geodesy and Geomatics Division—DICEA, Sapienza University of Rome, via Eudossiana, 00184 Rome, Italy
Luca Congedo: ISPRA—Italian Institute for Environmental Protection and Research, via Vitaliano Brancati, 00144 Rome, Italy
Michele Munafò: ISPRA—Italian Institute for Environmental Protection and Research, via Vitaliano Brancati, 00144 Rome, Italy

Land, 2023, vol. 12, issue 4, 1-25

Abstract: The development of remote sensing technology has redefined the approaches to the Earth’s surface monitoring. The Copernicus Programme promoted by the European Space Agency (ESA) and the European Union (EU), through the launch of the Synthetic Aperture Radar (SAR) Sentinel-1 and the multispectral Sentinel-2 satellites, has provided a valuable contribution to monitoring the Earth’s surface. There are several review articles on the land use/land cover (LULC) matter using Sentinel images, but it lacks a methodical and extensive review in the specific field of land consumption monitoring, concerning the application of SAR images, in particular Sentinel-1 images. In this paper, we explored the potential of Sentinel-1 images to estimate land consumption using mathematical modeling, focusing on innovative approaches. Therefore, this research was structured into three principal steps: (1) searching for appropriate studies, (2) collecting information required from each paper, and (3) discussing and comparing the accuracy of the existing methods to evaluate land consumption and their applied conditions using Sentinel-1 Images. Current research has demonstrated that Sentinel-1 data has the potential for land consumption monitoring around the world, as shown by most of the studies reviewed: the most promising approaches are presented and analyzed.

Keywords: change detection; earth observation; land consumption; machine learning; SAR images; Sentinel-1; soil sealing (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2073-445X/12/4/932/pdf (application/pdf)
https://www.mdpi.com/2073-445X/12/4/932/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:4:p:932-:d:1129505

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().