Earth observation data-driven cropland soil monitoring: A review

Nikolaos Tziolas*, Nikolaos Tsakiridis, Sabine Chabrillat, José A.M. Demattê, Eyal Ben-Dor, Asa Gholizadeh, George Zalidis, Bas van Wesemael

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


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.

Original languageEnglish
Article number4439
JournalRemote Sensing
Issue number21
StatePublished - 1 Nov 2021


  • Carbon farming
  • Common agricultural policy
  • Deep learning
  • Earth observation
  • Food security
  • Hyperspectral
  • Soil organic carbon
  • Spectral signatures


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