@article{e6f4a6cafe8441d5a69613177ad4c097,
title = "Earth observation data-driven cropland soil monitoring: A review",
abstract = "We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.",
keywords = "Carbon farming, Common agricultural policy, Deep learning, Earth observation, Food security, Hyperspectral, Soil organic carbon, Spectral signatures",
author = "Nikolaos Tziolas and Nikolaos Tsakiridis and Sabine Chabrillat and Dematt{\^e}, {Jos{\'e} A.M.} and Eyal Ben-Dor and Asa Gholizadeh and George Zalidis and {van Wesemael}, Bas",
note = "Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = nov,
day = "1",
doi = "10.3390/rs13214439",
language = "אנגלית",
volume = "13",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "21",
}