| 1. Identificação | ||||
| Tipo de Referência | Artigo em Revista Científica (Journal Article) | |||
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Site | mtc-m21d.sid.inpe.br (namespace prefix: upn:44QHRCS) | |||
| Identificador | 8JMKD2USNNW34T/4CTK68L | |||
| Repositório | sid.inpe.br/mtc-m21d/2025/02.10.12.03 (acesso restrito) | |||
| Última Atualização | 2025:02.10.12.03.11 (UTC) simone | |||
| Repositório de Metadados | sid.inpe.br/mtc-m21d/2025/02.10.12.03.11 | |||
| Última Atualização dos Metadados | 2025:02.17.06.08.20 (UTC) administrator | |||
| DOI | 10.1016/j.acags.2025.100223 | |||
| ISSN | 2590-1974 | |||
| Chave de Citação | GarciaIslPruSanChe:2025:IrRiMa | |||
| Título | Irrigated rice-field mapping in Brazil using phenological stage information and optical and microwave remote sensing  | |||
| Ano | 2025 | |||
| Mês | Feb. | |||
| Data de Acesso | 27 out. 2026 | |||
| Tipo de Trabalho | journal article | |||
| Tipo Secundário | PRE PI | |||
| Número de Arquivos | 1 | |||
| Tamanho | 4018 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Garcia, Andre Dalla Bernardina 2 Islam, MD Samiul 3 Prudente, Victor Hugo Rohden 4 Sanches, Ieda Del'Arco 5 Cheng, Irene | |||
| ORCID | 1 0000-0001-9063-4397 | |||
| Grupo | 1 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 2 3 4 DIOTG-CGCT-INPE-MCTI-GOV-BR | |||
| Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 University of Alberta 3 University of Michigan 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 University of Alberta | |||
| Endereço de e-Mail do Autor | 1 andre.garcia@inpe.br 2 3 4 ieda.sanches@inpe.br | |||
| Revista | Applied Computing and Geosciences | |||
| Volume | 25 | |||
| Páginas | e100223 | |||
| Acervo Hospedeiro | urlib.net/www/2021/06.04.03.40 upn:44QHRCS | |||
| Histórico (UTC) | 2025-02-10 12:04:01 :: simone -> administrator :: 2025 2025-02-17 06:08:20 :: administrator -> simone :: 2025 | |||
| 3. Conteúdo e estrutura | ||||
| É a matriz ou uma cópia? | é a matriz | |||
| Estágio do Conteúdo | concluido | |||
| Transferível | 1 | |||
| Tipo do Conteúdo | External Contribution | |||
| Tipo de Versão | publisher | |||
| Palavras-Chave | Deep learning False-color image composites Irrigated rice-field segmentation Multi-spectral band Rice phenological stages Satellite images | |||
| Resumo | Irrigated rice-field mapping methodologies have been rapidly evolving as a result of advanced remote sensing (RS) technology. However, current methods rely on extensive time-series data and a wide range of multi-spectral bands. These methods often struggle with classification accuracy with contaminated satellite data due to environmental factors or acquisition device constraints, e.g., cloud cover, shadows, noise, and the temporal and spectral resolution trade-off. Our goal is map irrigated rice-field by using a suitable satellite image band composition instead of time-series data. We divide the growth cycle into different rice phenological stages: beginning, middle and end of season, as well as the season transition periods. Near-infrared (NIR), short-wave infrared (SWIR) and red bands of MultiSpectral Instrument - MSI/Sentinel-2 (optical RS), along with polarizations of VV (verticalvertical) and VH (verticalhorizontal) of Sentinel-1 C-band Synthetic Aperture Radar (SAR) (microwave RS), were used to create ten different false-color image composites. Ground truth maps from two consecutive growth seasons (2017/2018 and 2018/2019) served as references. We applied a modified version of the Fusion Adaptive Patch Network (FAPNET), named as Patch Layer Adaptive Network (PLANET) convolutional neural network (CNN) to obtain binary rice mapping, which was evaluated using the traditional Mean Intersection over Union (MIoU) and Dice coefficient. Analytic results show that the end of season is the most suitable for obtaining a reliable classification based on optical and SAR sensors. Although complex rice-field pose challenges, our predictions consistently scored a MIoU above 0.9. We conclude that choosing the right phenological stage for rice mapping combined with deep learning model can greatly improve the classification results. These results indicate that the choice of composition significantly impacts classification accuracy, especially in more complex environments. | |||
| Área | SRE | |||
| Conteúdo da Pasta doc | acessar | |||
| Conteúdo da Pasta source | não têm arquivos | |||
| Conteúdo da Pasta agreement |
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| 4. Condições de acesso e uso | ||||
| Idioma | en | |||
| Arquivo Alvo | 1-s2.0-S2590197425000059-main.pdf | |||
| Grupo de Usuários | simone | |||
| Grupo de Leitores | administrator simone | |||
| Visibilidade | shown | |||
| Permissão de Leitura | deny from all and allow from 150.163 | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Repositório Espelho | urlib.net/www/2021/06.04.03.40.25 | |||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/46KUATE | |||
| Lista de Itens Citando | sid.inpe.br/bibdigital/2022/04.03.22.23 - 122 sid.inpe.br/bibdigital/2013/10.18.22.34 - 94 | |||
| Divulgação | PORTALCAPES; SCOPUS. | |||
| 6. Notas | ||||
| Campos Vazios | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark nextedition notes number parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||