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Exploitation of SAR and optical Sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index

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Exploitation of SAR and optical Sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index

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dc.contributor.author	Campos Taberner, Manuel
dc.contributor.author	García-Haro, Francisco Javier
dc.contributor.author	Camps-Valls, Gustau
dc.contributor.author	Grau-Muedra, Gonçal
dc.contributor.author	Nutini, Francesco
dc.contributor.author	Busetto, Lorenzo
dc.contributor.author	Katsantonis, Dimitrios
dc.contributor.author	Stavrakoudis, Dimitris
dc.contributor.author	Minakou, Chara
dc.contributor.author	Gatti, Luca
dc.contributor.author	Barbieri, Massimo
dc.contributor.author	Holecz, Francesco
dc.contributor.author	Stroppiana, Daniela
dc.contributor.author	Boschetti, Mirco
dc.date.accessioned	2017-03-07T18:45:41Z
dc.date.available	2017-03-07T18:45:41Z
dc.date.issued	2017
dc.identifier.citation	Campos Taberner, Manuel García-Haro, Francisco Javier Camps Valls, Gustavo Grau-Muedra, Gonçal Nutini, Francesco Busetto, Lorenzo Katsantonis, Dimitrios Stavrakoudis, Dimitris Minakou, Chara Gatti, Luca Barbieri, Massimo Holecz, Francesco Stroppiana, Daniela Boschetti, Mirco 2017 Exploitation of SAR and optical Sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index Remote Sensing 9 3 248
dc.identifier.uri	http://hdl.handle.net/10550/57576
dc.description.abstract	This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R^2>0.93) and good accuracies (RMSE<0.83, rRMSE_m<23.6% and rRMSE_r<16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.
dc.language.iso	eng
dc.relation.ispartof	Remote Sensing, 2017, vol. 9, num. 3, p. 248
dc.subject	Arròs Malalties i plagues
dc.subject	Ciències de la terra
dc.title	Exploitation of SAR and optical Sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index
dc.type	journal article	es_ES
dc.date.updated	2017-03-07T18:45:42Z
dc.identifier.doi	10.3390/rs9030248
dc.identifier.idgrec	116513
dc.rights.accessRights	open access	es_ES