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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34T/46NBK42
Repositóriosid.inpe.br/mtc-m21d/2022/04.18.13.57   (acesso restrito)
Última Atualização2022:04.18.13.57.12 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2022/04.18.13.57.12
Última Atualização dos Metadados2023:01.03.16.46.04 (UTC) administrator
DOI10.1016/j.jhydrol.2022.127784
ISSN0022-1694
Chave de CitaçãoXuYaYaXuHuGoLi:2022:DoSMSo
TítuloDownscaling SMAP soil moisture using a wide & deep learning method over the Continental United States
Ano2022
MêsJune
Data de Acesso18 maio 2024
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho30957 KiB
2. Contextualização
Autor1 Xu, Mengyuan
2 Yao, Ning
3 Yang, Haoxuan
4 Xu, Jia
5 Hu, Annan
6 Gonçalves, Luís Gustavo Gonçalves de
7 Liu, Gang
Grupo1
2
3
4
5
6 DIMNT-CGCT-INPE-MCTI-GOV-BR
Afiliação1 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 Autor1
2
3
4
5
6 gustavo.degoncalves@gmail.com
7 liug@cau.edu.cn
RevistaJournal of Hydrology
Volume609
Páginase127784
Nota SecundáriaA1_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
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveSMAP
Soil moisture downscaling
The Continental United States
Wide & Deep learning method
ResumoSoil 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.
ÁreaMET
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4. Condições de acesso e uso
Idiomaen
Arquivo Alvoxu_2022.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentodenypublisher denyfinaldraft24
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/46KUATE
DivulgaçãoWEBSCI; PORTALCAPES; MGA; COMPENDEX.
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
Campos Vaziosalternatejournal 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
7. Controle da descrição
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