Effect of different atmospheric correction algorithms on Sentinel-2 imagery classification accuracy in a semiarid mediterranean area

Valdivieso Ros, Carmen; Alonso Sarria, Francisco; Gomariz Castillo, Francisco

Publication:
Effect of different atmospheric correction algorithms on Sentinel-2 imagery classification accuracy in a semiarid mediterranean area

dc.contributor.author	Valdivieso Ros, Carmen
dc.contributor.author	Alonso Sarria, Francisco
dc.contributor.author	Gomariz Castillo, Francisco
dc.contributor.department	Geografía
dc.date.accessioned	2024-12-27T09:53:51Z
dc.date.available	2024-12-27T09:53:51Z
dc.date.issued	2021-05-01
dc.description	© 2021 by the authors. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/. This document is the Published version of a Published Work that appeared in final form in Water. To access the final edited and published work see https://doi.org/10.3390/rs13091770
dc.description.abstract	Multi-temporal imagery classification using spectral information and indices with random forest allows improving accuracy in land use and cover classification in semiarid Mediterranean areas where the high fragmentation of the landscape caused by multiple factors complicates the task. Hence, since data come from different dates, atmospheric correction is needed to retrieve surface reflectivity values. The Sen2Cor, MAJA and ACOLITE algorithms have proven their good performances in these areas in different comparative studies, and DOS is a basic method that is widely used. The aim in this study was to test the feasibility of its application to the data set to improve the values of accuracy in classification and the performance in properly labelling different classes. Additionally, we tried to correct accuracy and separability mixing predictors with different algorithms. The results showed that, using a single algorithm, the general accuracy and kappa index from ACOLITE were the highest, 0.80 and 0.76, but the separability between problematic classes was slightly improved by using MAJA. Any combination of the different algorithms tested increased the values of classification, although they may help with separability between some pairs of classes.	es
dc.format	application/pdf	es
dc.format.extent	23	es
dc.identifier.citation	Remote Sensing, 13, 1770, 2021
dc.identifier.doi	https://doi.org/10.3390/rs13091770
dc.identifier.issn	Electronic: 2073-4441
dc.identifier.uri	http://hdl.handle.net/10201/147821
dc.language	eng	es
dc.publisher	MDPI	es
dc.relation	This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness/Agencia Estatal de Investigación/FEDER (Fondo Europeo de Desarrollo Regional) Grant Number CGL2017-84625-C2-2-R. C.V.R. is grateful for the financing of the pre-doctoral research by the Ministerio de Ciencia, Innovación y Universidades from the Government of Spain (FPU18/01447).	es
dc.relation.publisherversion	https://www.mdpi.com/2072-4292/13/9/1770	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Atribución 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	Supervised classification	es
dc.subject	Atmospheric correction	es
dc.subject	ACOLITE	es
dc.subject	Sen2Cor	es
dc.subject	MAJA	es
dc.subject	DOS	es
dc.subject	Random forest	es
dc.subject.other	CDU::9 - Geografía e historia	es
dc.title	Effect of different atmospheric correction algorithms on Sentinel-2 imagery classification accuracy in a semiarid mediterranean area	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es