Mostrar el registro sencillo del ítem

dc.contributor.authorLlodrà Llabrés, Joana María 
dc.contributor.authorLópez Martínez, Francisco Javier 
dc.contributor.authorPostma, Thedmer
dc.contributor.authorPérez Martínez, María del Carmen 
dc.contributor.authorAlcaraz Segura, Domingo 
dc.date.accessioned2024-04-10T09:41:04Z
dc.date.available2024-04-10T09:41:04Z
dc.date.issued2023
dc.identifier.citationInternational Journal of Applied Earth Observation and Geoinformation 125 (2023) 103605 [10.1016/j.jag.2023.103605]es_ES
dc.identifier.urihttps://hdl.handle.net/10481/90589
dc.description.abstractThe fundamental role of water for life and the threats to water bodies around the world have highlighted the need for their conservation. Remote sensing is a tool that allows us to monitor water bodies in a rapid, systematic, accurate and economical way, being complementary to traditional field sampling methods. The main aim of this review is to synthesise the use of the Sentinel-2 satellite for chlorophyll-a monitoring, an indicator of the trophic state of aquatic ecosystems, and assess the role of each parameter on chlorophyll-a retrieval. To this end, indices, models, atmospheric corrections and field sampling details used so far in chlorophyll-a monitoring of aquatic ecosystems using Sentinel-2 imagery were analysed. Sentinel-2 was chosen because it has suitable features for monitoring water bodies (spatial, temporal and spectral resolution), despite not having been specifically designed for that purpose. The indices aphy(B4)/a*phy(B4), B7(1/B4-1/B5), B5-(B6 + B4)/2 and B3/B4 performed best in lakes and B2 + B3 + B4 + B5, B3/B6 and (B5-B4)/(B5 + B4) in reservoirs. The atmospheric correction ELM performed worse than Sen2Cor and ATCOR in lakes. In reservoirs, ATCOR performed best and C2XC and Dark Object Subtraction performed worse. Finally, classical machine learning and deep learning models outperformed traditional linear and non-linear models. An integrated vision of remote sensing with Ecology could improve some weaknesses found in the reviewed articles, such as the lack of methodological details in field sampling or knowledge of the dynamics and functioning of the ecosystem to achieve the most optimal sampling of the system. By doing so the field of remote sensing would have a higher aplicability. Some further investigations are needed on small water bodies (area < 0.1 km2), which have been scarcely studied by remote sensing, although accounting for >90% of the water bodies worldwide.es_ES
dc.description.sponsorshipEuropean Union’s Horizon 2020 research and innovation programme under grant agreement No 641762es_ES
dc.description.sponsorship"Convenio de Colaboración entre la Consejería de Medio Ambiente y Ordenación del Territorio y la Universidad de Granada para el desarrollo de actividades vinculadas al Observatorio de Cambio Global de Sierra Nevada, en el marco de la Red de Observatorios de Cambio Global de Andalucía"es_ES
dc.description.sponsorshipeLTER H2020 project “European Long-Term Ecosystem and Socio-Ecological Research Infrastructure” funded by the European Union’s Horizon 2020 programme under grant agreement No 654359es_ES
dc.description.sponsorshipProject LACEN (OAPN 2403-S/2017) which has been co-funded by the Ministry of Ecological transition in their National Park Autonomous Agency action line.es_ES
dc.description.sponsorshipProject “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada” (LifeWatch-2019-10-UGR-01), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014-2020 (POPE), LifeWatch-ERIC action linees_ES
dc.description.sponsorshipAid For University Teacher Training FPU 2019 (FPU19/04878) by the Spanish Ministry of Universitieses_ES
dc.description.sponsorshipMaría Zambrano postdoctoral grant by the Spanish Ministry of Universities and Next Generation European Union fundses_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectRemote sensing es_ES
dc.subjectInland aquatic ecosystemses_ES
dc.subjectWater quality es_ES
dc.titleRetrieving water chlorophyll-a concentration in inland waters from Sentinel-2 imagery: Review of operability, performance and ways forwardes_ES
dc.typejournal articlees_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/641762es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/654359es_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.1016/j.jag.2023.103605
dc.type.hasVersionVoRes_ES


Ficheros en el ítem

[PDF]

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 Internacional