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dc.contributor.authorArbelo Pérez, Manuel Imeldo 
dc.contributor.authorFernández-Urrutia, Manuel
dc.contributor.authorGil, Artur
dc.contributor.otherFísica
dc.date.accessioned2024-03-05T21:06:18Z
dc.date.available2024-03-05T21:06:18Z
dc.date.issued2023
dc.identifier.urihttp://riull.ull.es/xmlui/handle/915/36921
dc.descriptionhttps://doi.org/10.3390/s23156932
dc.description.abstractRice is a staple food that feeds nearly half of the world’s population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesSensors 2023, 23, 6932
dc.rightsLicencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
dc.titleIdentification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Reviewen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.3390/s23156932
dc.subject.keywordrice cropsen
dc.subject.keywordmultispectralen
dc.subject.keywordmultisourceen
dc.subject.keywordradaren
dc.subject.keywordmachine learningen
dc.subject.keywordvegetation indicesen


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  • DFSCA. Física
    Documentos de investigación (artículos, libros, capítulos de libros, ponencias...) publicados por investigadores del Departamento de Física

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Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
Except where otherwise noted, this item's license is described as Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)