SMAP radiometer soil moisture downscaling at global scale
Abstract
The Soil Moisture Active Passive (SMAP) mission provides soil moisture estimates at a grid resolution of 9 km with a 2-3 day global coverage. The SMAP soil moisture product from the L-band radiometer is still too coarse to be applied in hydrological research at fine scale. In recent years, there have been numerous approaches developed to downscale the soil moisture products by integrating higher resolution remote sensing observations from other satellites, land surface model outputs, or applying the advanced mathematical modeling approaches. The Global Land Data Assimilation System (GLDAS) uses land surface modeling and data assimilation techniques to assimilate satellite and ground-based data products and has been providing model outputs at 0.25 degree resolution since 1948-present. In this study, we developed a soil moisture downscaling algorithm based on thermal inertia relationship between soil moisture and land surface temperature variation. This improved algorithm utilizes GLDAS outputs from Noah land surface model and LTDR (Land Long Term Data Record) AVHRR (Advanced Very High Resolution Radiometer) NDVI (Normalized Difference Vegetation Index) data from 1981 - 2018 to disaggregate SMAP soil moisture at global scale. Validation results using in situ observations from various soil moisture networks will be presented. Disaggregated soil moisture estimates will also be compared with NASA LIS (Land Information System) model products.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.H44G..02F
- Keywords:
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- 1816 Estimation and forecasting;
- HYDROLOGY;
- 1836 Hydrological cycles and budgets;
- HYDROLOGY;
- 1847 Modeling;
- HYDROLOGY;
- 1855 Remote sensing;
- HYDROLOGY