Earth Observation Data-Driven Cropland Soil Monitoring: A Review
| Autor: | Sabine Chabrillat, Eyal Ben-Dor, Nikolaos L. Tsakiridis, Nikolaos Tziolas, George C. Zalidis, Bas van Wesemael, José Alexandre Melo Demattê, Asa Gholizadeh |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Soil map
Earth observation Computer science business.industry Best practice Science Environmental resource management carbon farming deep learning earth observation Sensor fusion Representativeness heuristic Data-driven soil organic carbon hyperspectral spectral signatures Sustainability General Earth and Planetary Sciences Leverage (statistics) business SOLO AGRÍCOLA |
| Zdroj: | Remote Sensing, Vol 13, Iss 4439, p 4439 (2021) Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP Remote Sensing |
| ISSN: | 2072-4292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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