Name
Title | ||
|---|---|---|
Soil Moisture Data Product Generated From Nasa Smap Observations With Noaa Ancillary Data |
Abstract | ||
|---|---|---|
NASA Soil Moisture Active/Passive (SMAP) satellite was launched on January 31 st, 2015 and has been providing level-1B radiometer brightness temperature (L1B-TB) data with an official latency of 12 hours since April 2015. The primary application users of the SMAP radiometer observations include numerical weather prediction operations that require shorter data latency (e. g. less than 6 hours). With slightly simplified algorithms of the L1B-TB data, NASA SMAP project is also providing near real time (NRT) L1B-TB data to NOAA operational users with a latency ranging from 2-6 hours. With this latency NOAA NWP models could use most of those SMAP half-orbit data that arrives within the 6 hour cut-off time limit. Before a radiance data assimilation capability for NOAA NWP models is developed, NOAA NESDIS is retrieving soil moisture from the NRT L1B-TB data through its Soil Moisture Product System (SMOPS) and makes surface soil moisture directly available for assimilation into the NWP models with the shortest possible turn-around time. Unlike using NASA GEOS model surface and soil temperature and multi-year average vegetation index data, SMOPS soil moisture retrieval algorithm uses the surface temperature data from operational Global Forecast System (GFS) and the vegetation index (NDVI) data from the near real time Suomi-NPP VIIRS observations. This paper introduce the structure of SMOPS, the soil moisture retrieval algorithm for SMAP data, and validation of the soil moisture data products against in the situ soil moisture measurements collected from the Soil Climate Analysis Networks of US Department of Agriculture and the Tibetan Soil Moisture Networks of Chinese Academy of Sciences. The soil moisture data retrieved from SMAP NRT TB data through SMOPS are also evaluated against the NASA official SMAP Level 2/3 soil moisture products. Preliminary validation results indicate that the SMAP soil moisture product from the SMAP NRT TB data and NESDIS SMOPS has similar quality to the NASA official products, but has shorter latency which may be critical to NOAA NWP operations. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7730364 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
satellite soil moisture, SMAP, SMOPS | Meteorology,Ancillary data,Global Forecast System,Computer science,Remote sensing,Normalized Difference Vegetation Index,Water content,Data assimilation,Radiance,Radiometer,Numerical weather prediction | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiwu Zhan | 1 | 83 | 15.33 |
| Jicheng Liu | 2 | 1 | 1.37 |
| Jun Wen | 3 | 0 | 0.34 |
| Limin Zhao | 4 | 0 | 0.34 |
| Marco Vargas | 5 | 0 | 0.34 |
| Fuzhong Weng | 6 | 226 | 56.79 |