Terrestrial Hydrology Research Group

Princeton University

Princeton TMI Soil Moisture Retrievals 1998-2004

Data Access

The dataset is freely available but we ask that you leave a few details about yourself and how you intend to use the dataset. Also, please cite the references below (Gao et al., 2004, 2006) if you use the data. Data for 1998-2004 are available at different levels of quality control and temporal resolution:

  • Level 1: retrievals for each orbit, with level 1A being soil moisture retrieved for each TMI overpass, using the LSMEM (Gao et al. 2004), without consideration of the retrieval quality considerations discussed in Gao et al. (2006). Level 1B is the same as level 1A but with the precipitation masks applied.
  • Level 2: daily averaged fields using level 1B data with averaged soil moisture values for locations with multiple TMI overpasses on that day that passed the active precipitation quality flag (see section 4a, Gao et al. (2006)).
  • Level 3: daily-averaged quality-screened fields. Level 2 data fields with areas screened out because of retrieval concerns from heavy vegetation, snow cover, and frozen soil (see sections 4b and 4c, Gao et al. (2006)). The quality control data for each condition (heavy vegetation, snow cover, frozen soil, and water contamination) is provided with one heavy vegetation mask for each month.

The data format is binary Little-Endian, 4-bytes for each grid box, with 464 columns by 112 rows. Areas masked out have a value of 0; areas where there are no TMI retrievals have a value of 9.999 x e20. All retrieved soil moisture values are greater than 0 so no conflict with the mask files will occur.

Gao H., E. F. Wood, M. Drusch, W. T. Crow, and T. J. Jackson. 2004: Using a microwave emission model to estimate soil moisture from ESTAR observations during SGP99. J. Hydrometeor, 5, 49-63.

Gao, H., Wood, E. F., Jackson, T. J., Drusch, M., and Bindlish, R., 2006: Using TRMM/TMI to retrieve surface soil moisture over the southern United States from 1998 to 2002. J. Hydrometeorology, 7(1), 23-38, DOI: 10.1175/JHM473.1

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Abstract

Gao, H., Wood, E. F., Jackson, T. J., Drusch, M., and Bindlish, R., 2006: Using TRMM/TMI to retrieve surface soil moisture over the southern United States from 1998 to 2002. J. Hydrometeorology, 7(1), 23-38, DOI: 10.1175/JHM473.1

Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8¬°√£patial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields.


Iceberg over Labrador sea.

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