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

Rolf H. Reichle, Gabrielle J.M. de Lannoy, Qing Liu, Randal D. Koster, John S. Kimball, Wade T. Crow, Joseph V. Ardizzone, Purnendu Chakraborty, Douglas W. Collins, Austin L. Conaty, Manuela Girotto, Lucas A. Jones, Jana Kolassa, Hans Lievens, Robert A. Lucchesi, Edmond B. Smith

Research output: Contribution to journalArticlepeer-review


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 autocorrelations 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.

Original languageEnglish
Pages (from-to)3217-3237
Number of pages21
JournalJournal of Hydrometeorology
Issue number12
StatePublished - Dec 1 2017


  • Data assimilation
  • Hydrology
  • Kalman filters
  • Land surface
  • Remote sensing
  • Soil moisture


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