1. Identificação | |
Tipo de Referência | Artigo em Revista Científica (Journal Article) |
Site | mtc-m21d.sid.inpe.br |
Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
Identificador | 8JMKD3MGP3W34T/46NBK42 |
Repositório | sid.inpe.br/mtc-m21d/2022/04.18.13.57 (acesso restrito) |
Última Atualização | 2022:04.18.13.57.12 (UTC) simone |
Repositório de Metadados | sid.inpe.br/mtc-m21d/2022/04.18.13.57.12 |
Última Atualização dos Metadados | 2023:01.03.16.46.04 (UTC) administrator |
DOI | 10.1016/j.jhydrol.2022.127784 |
ISSN | 0022-1694 |
Chave de Citação | XuYaYaXuHuGoLi:2022:DoSMSo |
Título | Downscaling SMAP soil moisture using a wide & deep learning method over the Continental United States |
Ano | 2022 |
Mês | June |
Data de Acesso | 18 maio 2024 |
Tipo Secundário | PRE PI |
Número de Arquivos | 1 |
Tamanho | 30957 KiB |
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2. Contextualização | |
Autor | 1 Xu, Mengyuan 2 Yao, Ning 3 Yang, Haoxuan 4 Xu, Jia 5 Hu, Annan 6 Gonçalves, Luís Gustavo Gonçalves de 7 Liu, Gang |
Grupo | 1 2 3 4 5 6 DIMNT-CGCT-INPE-MCTI-GOV-BR |
Afiliação | 1 China Agricultural University 2 Northwest Agriculture and Forestry University 3 Tongji University 4 China Agricultural University 5 University College London 6 Instituto Nacional de Pesquisas Espaciais (INPE) 7 China Agricultural University |
Endereço de e-Mail do Autor | 1 2 3 4 5 6 gustavo.degoncalves@gmail.com 7 liug@cau.edu.cn |
Revista | Journal of Hydrology |
Volume | 609 |
Páginas | e127784 |
Nota Secundária | A1_INTERDISCIPLINAR A1_GEOGRAFIA A1_GEOCIÊNCIAS A1_ENGENHARIAS_III A1_ENGENHARIAS_I A1_CIÊNCIAS_AMBIENTAIS A1_CIÊNCIAS_AGRÁRIAS_I A2_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA A2_BIODIVERSIDADE B1_MEDICINA_I B1_CIÊNCIAS_BIOLÓGICAS_I B2_ASTRONOMIA_/_FÍSICA C_ENGENHARIAS_II |
Histórico (UTC) | 2022-04-18 13:57:12 :: simone -> administrator :: 2022-04-18 13:57:14 :: administrator -> simone :: 2022 2022-04-18 13:58:06 :: simone -> administrator :: 2022 2023-01-03 16:46:04 :: administrator -> simone :: 2022 |
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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 | SMAP Soil moisture downscaling The Continental United States Wide & Deep learning method |
Resumo | Soil moisture (SM) plays a critical role in drought monitoring, agricultural management, flood forecasting, and other practical applications. However, the relatively coarse spatial resolutions of SM products derived from passive microwave satellite retrievals (approximately 2555 km) greatly hamper their local-scale applications. In this research, we proposed an SM downscaling framework based on the Wide & Deep Learning (WDL) method to improve the spatial resolution of the level-3 daily composite of Soil Moisture Active Passive (SMAP) radiometer SM product (L3_SM_P). In this method, horizontally and vertically polarized Brightness Temperature (TBh, and TBv, respectively), surface reflectance and Land Surface Temperature (LST), topographic attributes, soil properties, climate types, and landcover types collected in the Continental United States (CONUS) during the annual unfrozen season (April 1st to November 1st) from 2015 to 2017 were used as auxiliary datasets to downscale the spatial resolution of the SMAP SM (L3_SM_P) product from its original 36 km to 1 km. Precipitation and in-situ SM measurements obtained from 211 sites distributed across the International Soil Moisture Network (ISMN) over the CONUS were utilized to validate the downscaled SM. The results demonstrated that the correlation (R) between the downscaled and the in-situ SM ranged from 0.325 to 0.997; the average R value was 0.715. The unbiased Root Mean Square Error (ubRMSE) values ranged from 0.010 to 0.141 m3/m3, with an average ubRMSE of 0.041 m3/m3, which meets the accuracy of SMAP SM requirement of ubRMSE approximately 0.04 m 3/m3. The downscaled SM also showed good temporal consistency with the in-situ SM and exhibited a high response to the precipitation data. The downscaled SM not only maintained high spatial consistency with the original SMAP SM but also provides more detailed spatial SM variations. |
Área | MET |
Arranjo | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Downscaling SMAP soil... |
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 | xu_2022.pdf |
Grupo de Usuários | simone |
Grupo de Leitores | administrator simone |
Visibilidade | shown |
Política de Arquivamento | denypublisher denyfinaldraft24 |
Permissão de Leitura | deny from all and allow from 150.163 |
Permissão de Atualização | não transferida |
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5. Fontes relacionadas | |
Unidades Imediatamente Superiores | 8JMKD3MGPCW/46KUATE |
Divulgação | WEBSCI; PORTALCAPES; MGA; COMPENDEX. |
Acervo Hospedeiro | urlib.net/www/2021/06.04.03.40 |
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6. Notas | |
Campos Vazios | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url |
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7. Controle da descrição | |
e-Mail (login) | simone |
atualizar | |
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