Approaching holistic crop type mapping in Europe through winter vegetation classification and the Hierarchical Crop and Agriculture Taxonomy
The process of crop type mapping generates land use maps, which serve as critical tools for efficient evaluation of production factors and impacts of agricultural practice. Yet, despite the necessity for comprehensive solutions in space and time, the state of research still exhibits significant limi...
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Elsevier
2024-11-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224005156 |
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| author | David Gackstetter Marco Körner Kang Yu |
| author_facet | David Gackstetter Marco Körner Kang Yu |
| author_sort | David Gackstetter |
| collection | DOAJ |
| description | The process of crop type mapping generates land use maps, which serve as critical tools for efficient evaluation of production factors and impacts of agricultural practice. Yet, despite the necessity for comprehensive solutions in space and time, the state of research still exhibits significant limitations in these two dimensions: (1) From a temporal perspective, the primary focus of past research in crop type mapping has been on the economically most meaningful, main-season crops, thereby largely neglecting the explicit study of off-season vegetation despite its pivotal roles in year-round management cycles. (2) Viewed spatially, study areas in crop type mapping show distinct limitations from a multi- and transnational standpoint, despite intense cross-regional and international interrelations of agricultural production and an increasing number of countries publishing crop reference data. With a focus on Europe, this research aims to tackle the two described shortcomings (a) by investigating to what extent a selection of major off-season, winter vegetation types in continental Europe can be classified and (b) by analyzing the transnational applicability of the Hierarchical Crop and Agriculture Taxonomy (HCAT) for remote sensing-based crop type mapping across the European Union (EU). This study uses ESA’s Sentinel-2 satellite data, EU’s administrative farming declarations, and HCAT labels to analyze off-season farming measures, based on a study period from late summer to spring, in Austria, France, Germany, and Slovenia. We demonstrate that deep learning models effectively identify major productive and agroecogically significant winter vegetation in continental Europe. HCAT proves thereby valuable for transnational crop classification, excelling in mixed-country experiments and showing potential for transfer learning. This study’s findings provide a solid foundation for advancing transnational as well as winter and all-year crop type mapping, thereby serving as contribution towards temporally and spatially holistic research on agricultural practices’ sociocultural, economic, and environmental impacts. |
| format | Article |
| id | doaj-art-e04b103be30c4732bd5621ac5028f942 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-e04b103be30c4732bd5621ac5028f9422024-11-16T05:10:02ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-11-01134104159Approaching holistic crop type mapping in Europe through winter vegetation classification and the Hierarchical Crop and Agriculture TaxonomyDavid Gackstetter0Marco Körner1Kang Yu2Hans Eisenmann-Forum for Agricultural Sciences, Technical University of Munich, Liesel-Beckmann-Str. 2, Freising, 85354, Germany; TUM School of Life Sciences, Department of Life Science Engineering, Precision Agriculture Lab, Technical University of Munich, Dürnast 9, Freising, 85354, Germany; TUM School of Engineering and Design, Department of Aerospace and Geodesy, Chair of Remote Sensing Technology, Technical University of Munich, Arcisstr. 21, Munich, 80333, Germany; Corresponding author at: Hans Eisenmann-Forum for Agricultural Sciences, Technical University of Munich, Liesel-Beckmann-Str. 2, Freising, 85354, Germany.TUM School of Engineering and Design, Department of Aerospace and Geodesy, Chair of Remote Sensing Technology, Technical University of Munich, Arcisstr. 21, Munich, 80333, GermanyHans Eisenmann-Forum for Agricultural Sciences, Technical University of Munich, Liesel-Beckmann-Str. 2, Freising, 85354, Germany; TUM School of Life Sciences, Department of Life Science Engineering, Precision Agriculture Lab, Technical University of Munich, Dürnast 9, Freising, 85354, GermanyThe process of crop type mapping generates land use maps, which serve as critical tools for efficient evaluation of production factors and impacts of agricultural practice. Yet, despite the necessity for comprehensive solutions in space and time, the state of research still exhibits significant limitations in these two dimensions: (1) From a temporal perspective, the primary focus of past research in crop type mapping has been on the economically most meaningful, main-season crops, thereby largely neglecting the explicit study of off-season vegetation despite its pivotal roles in year-round management cycles. (2) Viewed spatially, study areas in crop type mapping show distinct limitations from a multi- and transnational standpoint, despite intense cross-regional and international interrelations of agricultural production and an increasing number of countries publishing crop reference data. With a focus on Europe, this research aims to tackle the two described shortcomings (a) by investigating to what extent a selection of major off-season, winter vegetation types in continental Europe can be classified and (b) by analyzing the transnational applicability of the Hierarchical Crop and Agriculture Taxonomy (HCAT) for remote sensing-based crop type mapping across the European Union (EU). This study uses ESA’s Sentinel-2 satellite data, EU’s administrative farming declarations, and HCAT labels to analyze off-season farming measures, based on a study period from late summer to spring, in Austria, France, Germany, and Slovenia. We demonstrate that deep learning models effectively identify major productive and agroecogically significant winter vegetation in continental Europe. HCAT proves thereby valuable for transnational crop classification, excelling in mixed-country experiments and showing potential for transfer learning. This study’s findings provide a solid foundation for advancing transnational as well as winter and all-year crop type mapping, thereby serving as contribution towards temporally and spatially holistic research on agricultural practices’ sociocultural, economic, and environmental impacts.http://www.sciencedirect.com/science/article/pii/S1569843224005156Winter vegetation monitoringTransnationalityCrop taxonomyDeep learningMulti-temporal earth observationAll-season crop type mapping |
| spellingShingle | David Gackstetter Marco Körner Kang Yu Approaching holistic crop type mapping in Europe through winter vegetation classification and the Hierarchical Crop and Agriculture Taxonomy International Journal of Applied Earth Observations and Geoinformation Winter vegetation monitoring Transnationality Crop taxonomy Deep learning Multi-temporal earth observation All-season crop type mapping |
| title | Approaching holistic crop type mapping in Europe through winter vegetation classification and the Hierarchical Crop and Agriculture Taxonomy |
| title_full | Approaching holistic crop type mapping in Europe through winter vegetation classification and the Hierarchical Crop and Agriculture Taxonomy |
| title_fullStr | Approaching holistic crop type mapping in Europe through winter vegetation classification and the Hierarchical Crop and Agriculture Taxonomy |
| title_full_unstemmed | Approaching holistic crop type mapping in Europe through winter vegetation classification and the Hierarchical Crop and Agriculture Taxonomy |
| title_short | Approaching holistic crop type mapping in Europe through winter vegetation classification and the Hierarchical Crop and Agriculture Taxonomy |
| title_sort | approaching holistic crop type mapping in europe through winter vegetation classification and the hierarchical crop and agriculture taxonomy |
| topic | Winter vegetation monitoring Transnationality Crop taxonomy Deep learning Multi-temporal earth observation All-season crop type mapping |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224005156 |
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