@Article{SanchezIpiaPCACLSMSFQ:2020:CoClCo,
author = "Sanchez Ipia, Alber Hamersson and Picoli, Michelle Cristina
Ara{\'u}jo and C{\^a}mara, Gilberto and Andrade Neto, Pedro
Ribeiro de and Chaves, Michel Eust{\'a}quio Dantas and Lechler,
Sarah and Soares, Anderson Reis and Marujo, Rennan de Freitas
Bezerra and Sim{\~o}es, Rolf Ezequiel de Oliveira and Ferreira,
Karine Reis and Queiroz, Gilberto Ribeiro",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {University of M{\"u}nster} and {Instituto Nacional
de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Comparison of cloud cover detection algorithms on sentinel-2
images of the Amazon tropical forest",
journal = "Remote Sensing",
year = "2020",
volume = "12",
number = "8",
pages = "e1284",
month = "Apr.",
keywords = "remote sensing, amazon forest, clouds, Sentinel–2, Fmask, Sen2Cor,
MAJA, s2cloudless.",
abstract = "Tropical forests regulate the global water and carbon cycles and
also host most of the worlds biodiversity. Despite their
importance, they are hard to survey due to their location, extent,
and particularly, their cloud coverage. Clouds hinder the spatial
and radiometric correction of satellite imagery and also
diminishing the useful area on each image, making it difficult to
monitor land change. For this reason, our purpose is to identify
the cloud detection algorithm best suited for the Amazon
rainforest on Sentinel2 images. To achieve this, we tested four
cloud detection algorithms on Sentinel2 images spread in five
areas of the Amazonia. Using more than eight thousand validation
points, we compared four cloud detection methods: Fmask 4, MAJA,
Sen2Cor, and s2cloudless. Our results point out that FMask 4 has
the best overall accuracy on images of the Amazon region (90%),
followed by Sen2Cors (79%), MAJA (69%), and S2cloudless (52%). We
note the choice of method depends on the intended use. Since MAJA
reduces the number of false positives by design, users that aim to
improve the producers accuracy should consider its use.",
doi = "10.3390/RS12081284",
url = "http://dx.doi.org/10.3390/RS12081284",
issn = "2072-4292",
language = "en",
targetfile = "sanchez_comparison.pdf",
urlaccessdate = "04 maio 2024"
}