# Presentation: 557547

## Historical Climatology of Satellite-Derived

**Global Land Surface Moisture **

**Figure 1**

**Figure 2**

- The Land Parameter Retrieval Model (LPRM) is being used to produce a multi-sensor historical climatology of satellite-derived global land surface moisture.
- This technology has a strong physical basis and may be applied at any appropriate microwave frequency. Validation experiments have shown that the data retrievals compare well with global observations of soil moisture fields. This 28-year data set will be useful for a variety of energy balance-based modeling applications, e.g. climate modeling, weather forecasting, and drought prediction.

**a. Average monthly global soil moisture for July 1980 from SMMR**

**b. Average monthly global soil moisture for July 2003 from AMSR-E**

**Daily land surface soil moisture in Oklahoma from AMSR-E**

## Relevance for future science and relationship to Decadal Survey: Soil moisture is a critical control on water and energy cycles, as well as weather, climate, hydrological and agricultural prediction. Continuity of these data is assured by current AMSR-E and TRMM TMI observations, as well as future missions such as SMAP (2010-2013 launch) and GPM (2013 launch).

**E-mail: **Manfred.Owe@nasa.gov
**Phone: **301-614-5783
**References:**
- Owe, M., R.A.M. de Jeu, and T.R.H. Holmes, 2007. Multi-sensor historical climatology of satellite-derived global land surface moisture, Journal of Geophysical Research (In Press)
- Meesters, A.G.C.A., R.A.M. de Jeu, and M. Owe, 2005, “Analytical derivation of the vegetation optical depth from the microwave polarization difference index”, *IEEE Trans. Geoscience and Remote Sensing Letters 2(2):121-123.*
- De Jeu, R.A.M. and M. Owe, 2003, “Further validation of a new methodology for surface moisture and vegetation optical depth retrieval”, *Int. J. Remote Sensing*, Vol. 24(22):4559-4578.
- Owe, M., R.A.M. de Jeu, J. Walker, 2001. A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave Polarization Difference Index”, *IEEE Trans. Geosci. and Remote Sensing, *Vol. 39(8):1643-1654.
**Data Sources:  **This project is a collaborative effort between researchers from NASA-GSFC (remote sensing data, algorithm development and refinement, testing, validation, and data hosting) and the Vrije Universiteit Amsterdam in the Netherlands (algorithm development and refinement, testing, validation, operational data retrieval, and data hosting).
- Near-surface (~0–1cm) soil moisture observations are derived from historical to currently active satellite microwave sensors. The 28-year, multi-sensor data set begins in November 1978 with Nimbus-7 SMMR, and includes observations from SSM/I, TRMM TMI, and AMSR-E. Microwave sensors range from C–band (SMMR, AMSR–E) to X–band (SMMR, TRMM, AMSR–E) to Ka band (SSM/I). The data will be hosted on servers located at both the Vrije Universiteit Amsterdam and the GES DISC at NASA Goddard. Sub-setting routines will be developed so that users may select specific spatial and temporal data windows.
**Technical Description of Images:**
***Figure 1: *** Global composites of mean monthly land surface soil moisture for July 1980, derived from Nimbus-7 SMMR (C-band) measurements (top), and for July 2003, derived from AMSR-E (C-band) (bottom). The retrieval algorithm is physically based, and performs at any appropriate microwave frequency. Microwave emissions are attenuated by dense vegetation, and therefore, the model will be unable to provide moisture observations in regions such as tropical rain forests and northern boreal forests. However, moisture retrievals are readily obtained in regions of light or no vegetation, which include arid and semi-arid regions of the world. These regions are also most adversely affected by water shortages, and would benefit most from this technology.
***Figure 2:***  Annual time series of daily land surface soil moisture (red line) over a test site in Oklahoma, as observed by AMSR-E at a frequency of 6.9 GHz. Also indicated in the figure, is the daily average soil moisture (at 5 cm depth) from five ground stations (solid dark line), with both minimum and maximum measurements indicated as well (dashed lines).
**Scientific significance:** Land surface soil moisture is useful for a variety of environmental modeling applications, e.g. climate modeling, weather forecasting, agriculture production, and drought prediction. Long term historical observations will be especially useful for model initialization and validation purposes.
**Relevance for future science and relationship to Decadal Survey: **Soil moisture is a critical control on water and energy cycles, as well as weather, climate, hydrological and agricultural prediction. Continuity of these data is assured by current AMSR-E and TRMM TMI observations, as well as future missions such as SMAP (2010-2013 launch) and GPM (2013 launch).