Literature DB >> 32661449

SMAP L-Band Microwave Radiometer: Instrument Design and First Year on Orbit.

Jeffrey R Piepmeier1, Paolo Focardi2, Kevin A Horgan1, Joseph Knuble1, Negar Ehsan1, Jared Lucey1, Clifford Brambora1, Paula R Brown2, Pamela J Hoffman2, Richard T French2, Rebecca L Mikhaylov2, Eug-Yun Kwack2, Eric M Slimko2, Douglas E Dawson2, Derek Hudson1, Jinzheng Peng1, Priscilla N Mohammed1, Giovanni De Amici1, Adam P Freedman2, James Medeiros1, Fred Sacks1, Robert Estep1, Michael W Spencer2, Curtis W Chen2, Kevin B Wheeler2, Wendy N Edelstein2, Peggy E O'Neill1, Eni G Njoku2.   

Abstract

The Soil Moisture Active-Passive (SMAP) L-band microwave radiometer is a conical scanning instrument designed to measure soil moisture with 4% volumetric accuracy at 40-km spatial resolution. SMAP is NASA's first Earth Systematic Mission developed in response to its first Earth science decadal survey. Here, the design is reviewed and the results of its first year on orbit are presented. Unique features of the radiometer include a large 6-m rotating reflector, fully polarimetric radiometer receiver with internal calibration, and radio-frequency interference detection and filtering hardware. The radiometer electronics are thermally controlled to achieve good radiometric stability. Analyses of on-orbit results indicate that the electrical and thermal characteristics of the electronics and internal calibration sources are very stable and promote excellent gain stability. Radiometer NEDT < 1 K for 17-ms samples. The gain spectrum exhibits low noise at frequencies >1 MHz and 1/f noise rising at longer time scales fully captured by the internal calibration scheme. Results from sky observations and global swath imagery of all four Stokes antenna temperatures indicate that the instrument is operating as expected.

Keywords:  Calibration; microwave radiometry; polarimetry

Year:  2017        PMID: 32661449      PMCID: PMC7357195          DOI: 10.1109/tgrs.2016.2631978

Source DB:  PubMed          Journal:  IEEE Trans Geosci Remote Sens        ISSN: 0196-2892            Impact factor:   5.600


  3 in total

1.  Error Propagation in Microwave Soil Moisture and Vegetation Optical Depth Retrievals.

Authors:  Andrew F Feldman; David Chaparro; Dara Entekhabi
Journal:  IEEE J Sel Top Appl Earth Obs Remote Sens       Date:  2021-11-13       Impact factor: 3.784

2.  DroughtCast: A Machine Learning Forecast of the United States Drought Monitor.

Authors:  Colin Brust; John S Kimball; Marco P Maneta; Kelsey Jencso; Rolf H Reichle
Journal:  Front Big Data       Date:  2021-12-21

3.  SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US.

Authors:  Noemi Vergopolan; Nathaniel W Chaney; Ming Pan; Justin Sheffield; Hylke E Beck; Craig R Ferguson; Laura Torres-Rojas; Sara Sadri; Eric F Wood
Journal:  Sci Data       Date:  2021-10-11       Impact factor: 6.444

  3 in total

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