Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Tziolas, Nikolaos [School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, 54123 Thessaloniki, Greece] Tsakiridis, Nikolaos [Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 54123 Thessaloniki, Greece] Chabrillat, Sabine [Helmholtz Center Potsdam GFZ German Research Centre for Geosciences, Remote Sensing and Geoinformatics, Telegrafenberg, 14473 Potsdam, Germany] Demattê, José A. M. [Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Av. Pádua Dias 11, CP9, Piracicaba 13418-900, SP, Brazil] Ben-Dor, Eyal [The Remote Sensing Laboratory, Department of Geography, School of Earth Science, Tel-Aviv University, Tel Aviv-Yafo 39040, Israel] Gholizadeh, Asa [Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamycka 129, 16500, Prague, Czech Republic] Zalidis, George [School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, 54123 Thessaloniki, Greece] Van Wesemael, Bas [UCL] et al.[show all]

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