dc.identifier.uri |
http://dx.doi.org/10.15488/15597 |
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dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/15718 |
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dc.contributor.author |
Tziolas, Nikolaos
|
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dc.contributor.author |
Tsakiridis, Nikolaos
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dc.contributor.author |
Chabrillat, Sabine
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dc.contributor.author |
Demattê, José A. M.
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dc.contributor.author |
Ben-Dor, Eyal
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dc.contributor.author |
Gholizadeh, Asa
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dc.contributor.author |
Zalidis, George
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dc.contributor.author |
van Wesemael, Bas
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dc.date.accessioned |
2023-12-04T09:10:35Z |
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dc.date.available |
2023-12-04T09:10:35Z |
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dc.date.issued |
2021 |
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dc.identifier.citation |
Tziolas, N.; Tsakiridis, N.; Chabrillat, S.; Demattê, J.A.M.; Ben-Dor, E. et al.: Earth Observation Data-Driven Cropland Soil Monitoring: A Review. In: Remote Sensing 13 (2021), Nr. 21, 4439. DOI: https://doi.org/10.3390/rs13214439 |
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dc.description.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. |
eng |
dc.language.iso |
eng |
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dc.publisher |
Basel : MDPI |
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dc.relation.ispartofseries |
Remote Sensing 13 (2021), Nr. 21 |
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dc.rights |
CC BY 4.0 Unported |
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dc.rights.uri |
https://creativecommons.org/licenses/by/4.0 |
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dc.subject |
Carbon farming |
eng |
dc.subject |
Common agricultural policy |
eng |
dc.subject |
Deep learning |
eng |
dc.subject |
Earth observation |
eng |
dc.subject |
Food security |
eng |
dc.subject |
Hyperspectral |
eng |
dc.subject |
Soil organic carbon |
eng |
dc.subject |
Spectral signatures |
eng |
dc.subject.ddc |
620 | Ingenieurwissenschaften und Maschinenbau
|
|
dc.title |
Earth Observation Data-Driven Cropland Soil Monitoring: A Review |
eng |
dc.type |
Article |
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dc.type |
Text |
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dc.relation.essn |
2072-4292 |
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dc.relation.doi |
https://doi.org/10.3390/rs13214439 |
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dc.bibliographicCitation.issue |
21 |
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dc.bibliographicCitation.volume |
13 |
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dc.bibliographicCitation.firstPage |
4439 |
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dc.description.version |
publishedVersion |
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tib.accessRights |
frei zug�nglich |
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