IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product

Rolf H. Reichle, Qing Liu, Joseph V. Ardizzone, Wade T. Crow, Gabrielle J.M. DE LANNOY, John S. Kimball, Randal D. Koster

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The NASA Soil Moisture Active Passive (SMAP) mission Level<4 Soil Moisture (L4_SM) product provides global, 9<km resolution, 3<hourly surface and root<zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L<band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation<based precipitation. This paper describes and evaluates the use of satellite< and gauge<based precipitation from the NASA Integrated Multi<satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm begin-ning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG<Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge<only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ~0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge<only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation<minus<forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6.

Original languageEnglish
Pages (from-to)1699-1723
Number of pages25
JournalJournal of Hydrometeorology
Issue number10
StatePublished - Oct 2023


  • Kalman filters
  • Land surface
  • Land surface model
  • Precipitation
  • Remote sensing
  • Soil moisture


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