Author
REICHLE, R. - Goddard Space Flight Center | |
DE LANNOY, G. - University Of Leuven | |
LIU, Q. - Goddard Space Flight Center | |
ARDIZONNE, J. - Goddard Space Flight Center | |
COLLIANDER, A. - Jet Propulsion Laboratory | |
CONATY, A. - Jet Propulsion Laboratory | |
Crow, Wade | |
Jackson, Thomas | |
JONES, L. - University Of Montana | |
KIMBALL, J. - University Of Montana | |
KOSTER, R. - Goddard Space Flight Center | |
MAHANAMA, S.P. - Goddard Space Flight Center | |
SMITH, E. - Goddard Space Flight Center | |
BERG, A. - University Of Guelph | |
BIRCHER, S. - University Of Toulouse | |
Bosch, David | |
CALDWELL, T. - University Of Texas At Austin | |
Cosh, Michael | |
GONZALEZ-ZANORA, A. - University Of Salamanca | |
Holifield Collins, Chandra | |
Livingston, Stanley | |
LOPEZ-BAEZA, E. - University Of Valencia | |
MARTINEZ-FERNANDEZ, J. - National Center For Agriculture And Forestry Technologies (CENTA) | |
MCNAIRN, H. - Agriculture And Agri-Food Canada | |
MOGHADDAM, M. - University Of Michigan | |
PACHECO, A. - Agriculture And Agri-Food Canada | |
PELLARIN, T. - Universite Grenoble Alpes | |
Prueger, John | |
ROWLANDSON, T. - University Of Guelph | |
Seyfried, Mark | |
Starks, Patrick | |
SU, Z. - University Of Twente | |
THIBEAULT, M. - Consejo Nacional De Investigaciones Científicas Y Técnicas(CONICET) | |
ULDALL, F. - Technical University Of Denmark | |
VAN DER VELDE, R. - University Of Twente | |
WALKER, J. - Monash University | |
WU, X. - Monash University | |
ZENG, Y. - University Of Twente |
Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 11/15/2017 Publication Date: 12/15/2017 Publication URL: https://handle.nal.usda.gov/10113/6550426 Citation: Reichle, R., De Lannoy, G., Liu, Q., Ardizonne, J., Colliander, A., Conaty, A., Crow, W.T., Jackson, T.J., Jones, L., Kimball, J., Koster, R., Mahanama, S., Smith, E., Berg, A., Bircher, S., Bosch, D.D., Caldwell, T., Cosh, M.H., Gonzalez-Zanora, A., Holifield Collins, C.D., Livingston, S.J., Lopez-Baeza, E., Martinez-Fernandez, J., McNairn, H., Moghaddam, M., Pacheco, A., Pellarin, T., Prueger, J.H., Rowlandson, T., Seyfried, M.S., Starks, P.J., Su, Z., Thibeault, M., Uldall, F., 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. 18(10):2621-2645. https://doi.org/10.1175/JHM-D-17-0063.1. DOI: https://doi.org/10.1175/JHM-D-17-0063.1 Interpretive Summary: Information about root-zone soil water availability is critical for a range of applications including: agricultural drought monitoring, irrigation scheduling and the optimization of fertilizer application. The current state-of-the-art for estimating root-zone soil moisture is based on combining surface soil moisture information obtained from satellite sensors with a soil water balance model via a process called data assimilation. This paper describes the validation of a new data assimilation system which utilizes observations from the NASA Soil Moisture Active Passive (SMAP) satellite mission to globally estimate root-zone soil moisture availability. It represents the first attempt to continuously generate hourly estimates of root-zone soil moisture in near-real-time. The validation results presented here demonstrate that the data assimilation system is working as expected and producing accurate estimates of root-zone soil moisture. These results represent an important step forward in the application of these new technologies to improve the sustainability of agricultural water use. Technical 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 surface (root-zone) soil moisture measurements for 43 (17) reference pixels at 9-km and 36-km grid-cell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root-zone) soil moisture at 401 (297) 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 (0.028 m3 m-3) at the 9-km scale and 0.034 m3 m-3 (0.024 m3 m-3) at the 36-km scale. The L4_SM estimates improve (significantly for surface soil moisture) over model-only estimates, which have a 9-km surface (root-zone) ubRMSE of 0.043 m3 m-3 (0.031 m3 m-3) and do not benefit from the assimilation of SMAP brightness temperature observations. The same relative performance was found for the time series correlation metric. The sparse networks results corroborate these findings over a greater variety of climate and land cover conditions. |