A reduced latency regional gap-filling method for SMAP using random forest regression.

Earth sciences Remote sensing Soil science

Journal

iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038

Informations de publication

Date de publication:
20 Jan 2023
Historique:
received: 19 08 2022
revised: 09 11 2022
accepted: 19 12 2022
entrez: 9 1 2023
pubmed: 10 1 2023
medline: 10 1 2023
Statut: epublish

Résumé

The soil moisture active/passive (SMAP) mission represents a significant advance in measuring soil moisture from satellites. However, its large spatial-temporal data gaps limit the use of its values in near-real-time (NRT) applications. Considering this, the study uses NRT operational metadata (precipitation and skin temperature), together with some surface parameterization information, to feed into a random forest model to retrieve the missing values of the SMAP L3 soil moisture product. This practice was tested in filling the missing points for both SMAP descending (6:00 AM) and ascending orbits (6:00 PM) in a crop-dominated area from 2015 to 2019. The trained models with optimized hyper-parameters show the goodness of fit (R

Identifiants

DOI: 10.1016/j.isci.2022.105853 PMID: 36619984 PMC: PMC9817173
pubmed: 36619984
doi: 10.1016/j.isci.2022.105853
pii: S2589-0042(22)02126-5
pmc: PMC9817173
doi:

Types de publication

Journal Article

Langues

eng

Pagination

105853

Informations de copyright

© 2022 The Author(s).

Déclaration de conflit d'intérêts

The authors declare no competing interests.

Références

Sci Total Environ. 2018 Jun 1;625:496-509
pubmed: 29291564
J Hydrometeorol. 2017 Dec;18(12):3217-3237
pubmed: 30364509
Front Big Data. 2020 Apr 09;3:10
pubmed: 33693385

Auteurs

Xiaoyi Wang (X)

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, National Cooperative Innovation Center for Water Safety and Hydro-science, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing 210098, China.

Haishen Lü (H)

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, National Cooperative Innovation Center for Water Safety and Hydro-science, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing 210098, China.

Wade T Crow (WT)

USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705-2350, USA.

Gerald Corzo (G)

Hydroinformatics Chair Group, IHE Delft Institute for Water Education, 2611AX Delft, the Netherlands.

Yonghua Zhu (Y)

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, National Cooperative Innovation Center for Water Safety and Hydro-science, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing 210098, China.

Jianbin Su (J)

National Tibetan Plateau Data Center, Key Laboratory of Tibetan Environmental Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China.

Jingyao Zheng (J)

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, National Cooperative Innovation Center for Water Safety and Hydro-science, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing 210098, China.

Qiqi Gou (Q)

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, National Cooperative Innovation Center for Water Safety and Hydro-science, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing 210098, China.

Classifications MeSH