Assessment of the SMAP Level-4 surface and root-zone soil moisture product using in situ measurements

Rolf H. Reichle; Gabrielle J.M. De Lannoy; Qing Liu; Joseph V. Ardizzone; Andreas Colliander; Austin Conaty; Wade Crow; Thomas J. Jackson; Lucas A. Jones; John S. Kimball; Randal D. Koster; Sarith P. Mahanama; Edmond B. Smith; Aaron Berg; Simone Bircher; David Bosch; Todd G. Caldwell; Michael Cosh; Ángel González-Zamora; Chandra D.Holifield Collins; Karsten H. Jensen; Stan Livingston; Ernesto Lopez-Baeza; José Martínez-Fernández; Heather McNairn; Mahta Moghaddam; Anna Pacheco; Thierry Pellarin; John Prueger; Tracy Rowlandson; Mark Seyfried; Patrick Starks; Zhongbo Su; Marc Thibeault; Rogier van der Velde; Jeffrey Walker; Xiaoling Wu; Yijian Zeng

doi:10.1175/JHM-D-17-0063.1

Assessment of the SMAP Level-4 surface and root-zone soil moisture product using in situ measurements

Rolf H. Reichle
, Gabrielle J.M. De Lannoy
, Qing Liu
, Joseph V. Ardizzone
, Andreas Colliander
, Austin Conaty
, Wade Crow
, Thomas J. Jackson
, Lucas A. Jones
, John S. Kimball
, Randal D. Koster
, Sarith P. Mahanama
, Edmond B. Smith
, Aaron Berg
, Simone Bircher
, David Bosch
, Todd G. Caldwell
, Michael Cosh
, Ángel González-Zamora
, Chandra D.Holifield Collins

Karsten H. Jensen, Stan Livingston, Ernesto Lopez-Baeza, José Martínez-Fernández, Heather McNairn, Mahta Moghaddam, Anna Pacheco, Thierry Pellarin, John Prueger, Tracy Rowlandson, Mark Seyfried, Patrick Starks, Zhongbo Su, Marc Thibeault, Rogier van der Velde, Jeffrey Walker, Xiaoling Wu, Yijian Zeng

Numerical Terradynamic Simulation Group

Research output: Contribution to journal › Article › peer-review

251 Scopus citations

Abstract

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) "reference pixels" at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m³ m^-3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m³ m^-3 (0.030 m³ m^-3) at the 9-km scale and 0.035 m³ m^-3 (0.026 m³ m^-3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m³ m^-3 (0.032 m³ m^-3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

Original language	English
Pages (from-to)	2621-2645
Number of pages	25
Journal	Journal of Hydrometeorology
Volume	18
Issue number	10
DOIs	https://doi.org/10.1175/JHM-D-17-0063.1
State	Published - Oct 1 2017

Funding

Funding for this work was provided by the NASA SMAP mission. Computational resources were provided by the NASA High-End Computing program through the NASA Center for Climate Simulation. We are grateful for the datasets and data archiving centers that supported this work and appreciate those who make the generation, dissemination, and validation of the L4_SM product possible, including SMAP team members at JPL, GSFC, and NSIDC and staff at NOAA/CPC, NOAA/NCEI, USDA-ARS, USDA-NRCS, the Oklahoma Climatological Survey, and Monash University. Erica Tetlock is acknowledged for her help with the Kenaston network, for which funding was provided by the Canadian Space Agency and by Environment and Climate Change Canada.We thank three anonymous reviewers for their helpful comments.

Funders
USDA-ARS Jornada Experimental Range
NASA Goddard Space Flight Center
Oklahoma State University
Natural Resources Conservation Service
Canadian Space Agency
Monash University

Keywords

Data assimilation
Kalman filters
Land surface model
Satellite observations
Soil moisture
Soil temperature

Access to Document

10.1175/JHM-D-17-0063.1

Cite this

Reichle, R. H., De Lannoy, G. J. M., Liu, Q., Ardizzone, J. V., Colliander, A., Conaty, A., Crow, W., Jackson, T. J., Jones, L. A., Kimball, J. S., Koster, R. D., Mahanama, S. P., Smith, E. B., Berg, A., Bircher, S., Bosch, D., Caldwell, T. G., Cosh, M., González-Zamora, Á., ... Zeng, Y. (2017). Assessment of the SMAP Level-4 surface and root-zone soil moisture product using in situ measurements. Journal of Hydrometeorology, 18(10), 2621-2645. https://doi.org/10.1175/JHM-D-17-0063.1

@article{ad35d756c4d44a78bc7ac88b6fee71a9,

title = "Assessment of the SMAP Level-4 surface and root-zone soil moisture product using in situ measurements",

abstract = "The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4\_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4\_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and land surface conditions. This study presents an overview of the L4\_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) {"}reference pixels{"} at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4\_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m-3 or better. The ubRMSE for L4\_SM surface (root zone) soil moisture is 0.038 m3 m-3 (0.030 m3 m-3) at the 9-km scale and 0.035 m3 m-3 (0.026 m3 m-3) at the 36-km scale. The L4\_SM estimates improve (significantly at the 5\% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m-3 (0.032 m3 m-3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.",

keywords = "Data assimilation, Kalman filters, Land surface model, Satellite observations, Soil moisture, Soil temperature",

author = "Reichle, \{Rolf H.\} and \{De Lannoy\}, \{Gabrielle J.M.\} and Qing Liu and Ardizzone, \{Joseph V.\} and Andreas Colliander and Austin Conaty and Wade Crow and Jackson, \{Thomas J.\} and Jones, \{Lucas A.\} and Kimball, \{John S.\} and Koster, \{Randal D.\} and Mahanama, \{Sarith P.\} and Smith, \{Edmond B.\} and Aaron Berg and Simone Bircher and David Bosch and Caldwell, \{Todd G.\} and Michael Cosh and {\'A}ngel Gonz{\'a}lez-Zamora and Collins, \{Chandra D.Holifield\} and Jensen, \{Karsten H.\} and Stan Livingston and Ernesto Lopez-Baeza and Jos{\'e} Mart{\'i}nez-Fern{\'a}ndez and Heather McNairn and Mahta Moghaddam and Anna Pacheco and Thierry Pellarin and John Prueger and Tracy Rowlandson and Mark Seyfried and Patrick Starks and Zhongbo Su and Marc Thibeault and \{van der Velde\}, Rogier and Jeffrey Walker and Xiaoling Wu and Yijian Zeng",

note = "Publisher Copyright: {\textcopyright} 2017 American Meteorological Society.",

year = "2017",

month = oct,

day = "1",

doi = "10.1175/JHM-D-17-0063.1",

language = "English",

volume = "18",

pages = "2621--2645",

journal = "Journal of Hydrometeorology",

issn = "1525-755X",

publisher = "American Meteorological Society",

number = "10",

}

Reichle, RH, De Lannoy, GJM, Liu, Q, Ardizzone, JV, Colliander, A, Conaty, A, Crow, W, Jackson, TJ, Jones, LA, Kimball, JS, Koster, RD, Mahanama, SP, Smith, EB, Berg, A, Bircher, S, Bosch, D, Caldwell, TG, Cosh, M, González-Zamora, Á, Collins, CDH, Jensen, KH, Livingston, S, Lopez-Baeza, E, Martínez-Fernández, J, McNairn, H, Moghaddam, M, Pacheco, A, Pellarin, T, Prueger, J, Rowlandson, T, Seyfried, M, Starks, P, Su, Z, Thibeault, M, van der Velde, R, Walker, J, Wu, X & Zeng, Y 2017, 'Assessment of the SMAP Level-4 surface and root-zone soil moisture product using in situ measurements', Journal of Hydrometeorology, vol. 18, no. 10, pp. 2621-2645. https://doi.org/10.1175/JHM-D-17-0063.1

TY - JOUR

T1 - Assessment of the SMAP Level-4 surface and root-zone soil moisture product using in situ measurements

AU - Reichle, Rolf H.

AU - De Lannoy, Gabrielle J.M.

AU - Liu, Qing

AU - Ardizzone, Joseph V.

AU - Colliander, Andreas

AU - Conaty, Austin

AU - Crow, Wade

AU - Jackson, Thomas J.

AU - Jones, Lucas A.

AU - Kimball, John S.

AU - Koster, Randal D.

AU - Mahanama, Sarith P.

AU - Smith, Edmond B.

AU - Berg, Aaron

AU - Bircher, Simone

AU - Bosch, David

AU - Caldwell, Todd G.

AU - Cosh, Michael

AU - González-Zamora, Ángel

AU - Collins, Chandra D.Holifield

AU - Jensen, Karsten H.

AU - Livingston, Stan

AU - Lopez-Baeza, Ernesto

AU - Martínez-Fernández, José

AU - McNairn, Heather

AU - Moghaddam, Mahta

AU - Pacheco, Anna

AU - Pellarin, Thierry

AU - Prueger, John

AU - Rowlandson, Tracy

AU - Seyfried, Mark

AU - Starks, Patrick

AU - Su, Zhongbo

AU - Thibeault, Marc

AU - van der Velde, Rogier

AU - Walker, Jeffrey

AU - Wu, Xiaoling

AU - Zeng, Yijian

PY - 2017/10/1

Y1 - 2017/10/1

N2 - The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) "reference pixels" at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m-3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m-3 (0.030 m3 m-3) at the 9-km scale and 0.035 m3 m-3 (0.026 m3 m-3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m-3 (0.032 m3 m-3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

AB - The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) "reference pixels" at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m-3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m-3 (0.030 m3 m-3) at the 9-km scale and 0.035 m3 m-3 (0.026 m3 m-3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m-3 (0.032 m3 m-3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

KW - Data assimilation

KW - Kalman filters

KW - Land surface model

KW - Satellite observations

KW - Soil moisture

KW - Soil temperature

UR - https://www.scopus.com/pages/publications/85032730324

U2 - 10.1175/JHM-D-17-0063.1

DO - 10.1175/JHM-D-17-0063.1

M3 - Article

AN - SCOPUS:85032730324

SN - 1525-755X

VL - 18

SP - 2621

EP - 2645

JO - Journal of Hydrometeorology

JF - Journal of Hydrometeorology

IS - 10

ER -

Assessment of the SMAP Level-4 surface and root-zone soil moisture product using in situ measurements

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this