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Land Consumption Classification Using Sentinel 1 Data: A Systematic Review

Author

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  • 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
    Sapienza School for Advanced Studies, Sapienza University of Rome, viale Regina Elena, 00161 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)

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.

Suggested Citation

  • Sara Mastrorosa & Mattia Crespi & Luca Congedo & Michele Munafò, 2023. "Land Consumption Classification Using Sentinel 1 Data: A Systematic Review," Land, MDPI, vol. 12(4), pages 1-25, April.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:932-:d:1129505
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    References listed on IDEAS

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    1. Hameeda Sultan & Wajid Rashid & Jianbin Shi & Inam ur Rahim & Mohammad Nafees & Eve Bohnett & Sajid Rashid & Muhammad Tariq Khan & Izaz Ali Shah & Heesup Han & Antonio Ariza-Montes, 2022. "Horizon Scan of Transboundary Concerns Impacting Snow Leopard Landscapes in Asia," Land, MDPI, vol. 11(2), pages 1-22, February.
    2. Zhiwen Yang & Hebing Zhang & Xiaoxuan Lyu & Weibing Du, 2022. "Improving Typical Urban Land-Use Classification with Active-Passive Remote Sensing and Multi-Attention Modules Hybrid Network: A Case Study of Qibin District, Henan, China," Sustainability, MDPI, vol. 14(22), pages 1-27, November.
    3. Andrea Strollo & Daniela Smiraglia & Roberta Bruno & Francesca Assennato & Luca Congedo & Paolo De Fioravante & Chiara Giuliani & Ines Marinosci & Nicola Riitano & Michele Munafò, 2020. "Land consumption in Italy," Journal of Maps, Taylor & Francis Journals, vol. 16(1), pages 113-123, January.
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