Literature DB >> 29983456

Precipitation Estimation Using L-Band and C-Band Soil Moisture Retrievals.

Randal D Koster1, Luca Brocca2, Wade T Crow3, Mariko S Burgin4, Gabrielle J M De Lannoy4.   

Abstract

An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge-based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L-band SMAP and SMOS datasets is higher than that obtained with the C-band product, as might be expected given that L-band is sensitive to a thicker layer of soil and thereby provides more information on the response of soil moisture to precipitation. The square of the correlation coefficient between the SMAP-based precipitation estimates and the observations (for aggregations to ~100 km and 5 days) is on average about 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor soil moisture thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.

Entities:  

Keywords:  1854 Precipitation (3354); 1855 Remote sensing (1640, 4337); 1866 Soil moisture

Year:  2016        PMID: 29983456      PMCID: PMC6031929          DOI: 10.1002/2016WR019024

Source DB:  PubMed          Journal:  Water Resour Res        ISSN: 0043-1397            Impact factor:   5.240


  5 in total

1.  River flow prediction in data scarce regions: soil moisture integrated satellite rainfall products outperform rain gauge observations in West Africa.

Authors:  Luca Brocca; Christian Massari; Thierry Pellarin; Paolo Filippucci; Luca Ciabatta; Stefania Camici; Yann H Kerr; Diego Fernández-Prieto
Journal:  Sci Rep       Date:  2020-07-27       Impact factor: 4.379

2.  Estimating Basin-Scale Water Budgets with SMAP Soil Moisture Data.

Authors:  Randal D Koster; Wade T Crow; Rolf H Reichle; Sarith P Mahanama
Journal:  Water Resour Res       Date:  2018-06-01       Impact factor: 5.240

3.  Development and Assessment of the SMAP Enhanced Passive Soil Moisture Product.

Authors:  Steven K Chan; Rajat Bindlish; Peggy O'Neill; Thomas Jackson; Eni Njoku; Scott Dunbar; Julian Chaubell; Jeffrey Piepmeier; Simon Yueh; Dara Entekhabi; Andreas Colliander; Fan Chen; Michael H Cosh; Todd Caldwel; Jeffrey Walker; Aaron Berg; Heather McNairn; Marc Thibeault; José Martínez-Fernández; Frederik Uldall; Mark Seyfried; David Bosch; Patrick Starks; Chandra Holifield Collins; John Prueger; Rogier van der Velde; Jun Asanuma; Michael Palecki; Eric E Small; Marek Zreda; Jean-Christophe Calvet; Wade T Crow; Yann Kerr
Journal:  Remote Sens Environ       Date:  2017-10-13       Impact factor: 10.164

4.  The error structure of the SMAP single and dual channel soil moisture retrievals.

Authors:  Jianzhi Dong; Wade Crow; Rajat Bindlish
Journal:  Geophys Res Lett       Date:  2017-12-20       Impact factor: 4.720

5.  The instantaneous retrieval of precipitation over land by temporal variation at 19 GHz.

Authors:  Yalei You; Christa Peters-Lidard; Nai-Yu Wang; Joseph Turk; Sarah Ringerud; Song Yang; Ralph Ferraro
Journal:  J Geophys Res Atmos       Date:  2018-08-23       Impact factor: 4.261

  5 in total

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