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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #343480

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Global assessment of the SMAP level-4 surface and root-zone soil moisture product using assimilation diagnostics

item RIECHLE, R. - Goddard Space Flight Center
item DE LANNOY, G. - University Of Leuven
item LIU, Q. - Goddard Space Flight Center
item KOSTER, R. - Goddard Space Flight Center
item KIMBALL, J. - University Of Montana
item Crow, Wade
item ARDIZZONE, J. - Goddard Space Flight Center
item CHAKRABORTY, P. - Goddard Space Flight Center
item COLLINS, D. - Goddard Space Flight Center
item CONASTY, A. - Jet Propulsion Laboratory
item GIROTTI, M. - Jet Propulsion Laboratory
item JONES, L. - University Of Montana
item KOLASSA, J. - Goddard Space Flight Center
item LIEVENS, H. - Ghent University
item LUCCHESI, R. - Goddard Space Flight Center
item SMITH, E. - Goddard Space Flight Center

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/15/2017
Publication Date: 12/28/2017
Citation: Riechle, R., De Lannoy, G., Liu, Q., Koster, R., Kimball, J., Crow, W.T., Ardizzone, J., Chakraborty, P., Collins, D., Conasty, A., Girotti, M., Jones, L., Kolassa, J., Lievens, H., Lucchesi, R., Smith, E. 2017. Global assessment of the SMAP level-4 surface and root-zone soil moisture product using assimilation diagnostics. Journal of Hydrometeorology. 18(12):3217-3237.

Interpretive Summary: Soil moisture is an important climate variable because of its impact on the land surface water, energy, and nutrient cycles. For example, soil moisture partly controls how much of the water from any given rainfall is stored in the soil and becomes available for natural and agricultural plant growth, and how much of that water runs off into creeks, streams, lakes, and reservoirs. Satellite observations that are suitable to derive global estimates of soil moisture are available from the NASA Soil Moisture Active Passive (SMAP) satellite mission, which was launched in early 2015. The satellite, however, does not observe soil moisture directly. Rather, it measures the energy of electromagnetic radiation that is emitted naturally by the Earth’s surface in the low-frequency microwave range, also known as the brightness temperature. The emission of such radiation is strongly impacted by the presence of water, which makes it possible to relate the satellite measurements to the moisture that is present in the soil. The satellite measurements, however, are only sensitive to the moisture in the top few centimeters of the soil, and, for a given location on Earth, are only available once every 2-3 days on average. The SMAP Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product addresses these shortcomings by merging (or assimilating) the satellite observations into a numerical model of the land surface water and energy balance. The paper assesses the L4_SM system based on diagnostics that are by-products of the assimilation algorithm. These diagnostics include the count of the assimilated observations, the differences between the observed and modeled brightness temperatures, and the resulting adjustments to the modeled soil moisture and soil temperature estimates, all of which are available wherever and whenever SMAP observations are assimilated. The results show that the 2-year average of the L4_SM surface and root-zone soil moisture estimates captures the expected global patterns of arid and humid regions. These validation results are a necessary first step before output from the L4_SM system can be widely applied to globally monitor the extent, severity and duration of agricultural drought.

Technical Abstract: The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with ~2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of ~0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under ~3 K), the soil moisture increments (under ~0.01 m3 m-3), and the surface soil temperature increments (under ~1 K). Typical instantaneous values are ~6 K for O-F residuals, ~0.01 (~0.003) m3 m-3 for surface (root-zone) soil moisture increments, and ~0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.