Abstract
Soil moisture was retrieved from radar data using an inverse model based on Integral Equation Model (IEM). ESTAR images of brightness temperature obtained during the same period were inverted independently for soil moisture. The results at individual sampling sites were first compared against gravimetric soil moisture observations for Washita '94, and the RMS errors for both applications were between 3% and 4%. Subsequently, we investigated the use of high resolution SAR-derived soil moisture fields to estimate sub-pixel variability in ESTAR derived fields. The objective was to determine whether scaling arguments can be used to disaggregate ESTAR data to finer spatial resolution based on the geomorphic layout of the landscape. The differences in the ESTAR and SAR retrieved soil moisture were related to the amount of vegetation present at that pixel. Furthermore, we also investigated the problem of consistency between the two systems. For this purpose, SAR-derived soil moisture was aggregated to ESTAR resolution, and these estimates were used along with land surface attributes to derive the corresponding brightness temperature fields (i.e., backward retrieval). Estimated and observed brightness temperature fields were compared and analyzed to establish the aggregation kernel inherent to ESTAR, that is, how the instrument actually processes/integrates sub-pixel variability.
| Original language | English (US) |
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| Pages | 1917-1920 |
| Number of pages | 4 |
| State | Published - 1999 |
| Externally published | Yes |
| Event | Proceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'99) 'Remote Sensing of the Systems Earth - A Challenge for the 21st Century' - Hamburg, Ger Duration: Jun 28 1999 → Jul 2 1999 |
Conference
| Conference | Proceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'99) 'Remote Sensing of the Systems Earth - A Challenge for the 21st Century' |
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| City | Hamburg, Ger |
| Period | 6/28/99 → 7/2/99 |
ASJC Scopus subject areas
- Computer Science Applications
- General Earth and Planetary Sciences