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Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Authors :
Nikolaos Tziolas
Nikolaos Tsakiridis
Sabine Chabrillat
José A. M. Demattê
Eyal Ben-Dor
Asa Gholizadeh
George Zalidis
Bas van Wesemael
Source :
Remote Sensing, Vol 13, Iss 21, p 4439 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

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.

Details

Language :
English
ISSN :
20724292
Volume :
13
Issue :
21
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.716094acd77047b99c4e7c1efb851f39
Document Type :
article
Full Text :
https://doi.org/10.3390/rs13214439