Evaluating ESA CCI soil moisture in East Africa.

Amy McNally, Shraddhanand Shukla, Kristi R Arsenault, Shugong Wang, Christa D Peters-Lidard, James P Verdin
Author Information
  1. Amy McNally: University of Maryland Earth Systems Science Interdisciplinary Center and Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.
  2. Shraddhanand Shukla: University of California, Santa Barbara, CA.
  3. Kristi R Arsenault: SAIC, Inc., Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.
  4. Shugong Wang: SAIC, Inc., Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.
  5. Christa D Peters-Lidard: Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.
  6. James P Verdin: Center for Earth Resources Observation Science, U.S. Geological Survey, Sioux Falls, SD 57198.

Abstract

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R>0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

Keywords

References

  1. Philos Trans R Soc Lond B Biol Sci. 2005 Nov 29;360(1463):2155-68 [PMID: 16433101]

Grants

  1. /Goddard Space Flight Center NASA
  2. N-999999/Intramural NASA

Word Cloud

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