Crop classification based on temporal signatures of Sentinel-1 observations over Navarre province, Spain

Arias Cuenca, María; Campo-Bescós, Miguel; Álvarez Mozos, Jesús

Publication:
Crop classification based on temporal signatures of Sentinel-1 observations over Navarre province, Spain

dc.contributor.author	Arias Cuenca, María
dc.contributor.author	Campo-Bescós, Miguel
dc.contributor.author	Álvarez Mozos, Jesús
dc.contributor.department	Ingeniería	es_ES
dc.contributor.department	Ingeniaritza	eu
dc.date.accessioned	2020-07-06T08:41:21Z
dc.date.available	2020-07-06T08:41:21Z
dc.date.issued	2020
dc.description.abstract	Crop classification provides relevant information for crop management, food security assurance and agricultural policy design. The availability of Sentinel-1 image time series, with a very short revisit time and high spatial resolution, has great potential for crop classification in regions with pervasive cloud cover. Dense image time series enable the implementation of supervised crop classification schemes based on the comparison of the time series of the element to classify with the temporal signatures of the considered crops. The main objective of this study is to investigate the performance of a supervised crop classification approach based on crop temporal signatures obtained from Sentinel-1 time series in a challenging case study with a large number of crops and a high heterogeneity in terms of agro-climatic conditions and field sizes. The case study considered a large dataset on the Spanish province of Navarre in the framework of the verification of Common Agricultural Policy (CAP) subsidies. Navarre presents a large agro-climatic diversity with persistent cloud cover areas, and therefore, the technique was implemented both at the provincial and regional scale. In total, 14 crop classes were considered, including different winter crops, summer crops, permanent crops and fallow. Classification results varied depending on the set of input features considered, obtaining Overall Accuracies higher than 70% when the three (VH, VV and VH/VV) channels were used as the input. Crops exhibiting singularities in their temporal signatures were more easily identified, with barley, rice, corn and wheat achieving F1-scores above 75%. The size of fields severely affected classification performance, with ~14% better classification performance for larger fields (>1 ha) in comparison to smaller fields (<0.5 ha). Results improved when agro-climatic diversity was taken into account through regional stratification. It was observed that regions with a higher diversity of crop types, management techniques and a larger proportion of fallow fields obtained lower accuracies. The approach is simple and can be easily implemented operationally to aid CAP inspection procedures or for other purposes. © 2020 by the authors.	en
dc.description.sponsorship	This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (MINECO/FEDER-UE) through a project (CGL2016-75217-R) and a grant (BES-2017-080560). It was also partly founded by project PyrenEOS EFA 048/15, that has been 65% cofinanced by the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra programme (POCTEFA 2014-2020).	en
dc.format.extent	29 p.
dc.format.mimetype	application/pdf	en
dc.identifier.doi	10.3390/rs12020278
dc.identifier.issn	2072-4292
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/37335
dc.language.iso	eng	en
dc.publisher	MDPI	en
dc.relation.ispartof	Remote Sensing, 2020, 12(2), 278	en
dc.relation.projectID	info:eu-repo/grantAgreement/ES/1PE/CGL2016-75217-R	en
dc.relation.publisherversion	https://doi.org/10.3390/rs12020278
dc.rights	© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.	en
dc.rights.accessRights	info:eu-repo/semantics/openAccess	en
dc.rights.accessRights	Acceso abierto / Sarbide irekia	es
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Crop classification	en
dc.subject	Sentinel-1	en
dc.subject	SAR	en
dc.subject	Time series	en
dc.subject	Common Agricultural Policy	en
dc.title	Crop classification based on temporal signatures of Sentinel-1 observations over Navarre province, Spain	en
dc.type	info:eu-repo/semantics/article	en
dc.type	Artículo / Artikulua	es
dc.type.version	info:eu-repo/semantics/publishedVersion	en
dc.type.version	Versión publicada / Argitaratu den bertsioa	es
dspace.entity.type	Publication
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