Characterizing Drifts in Spaceborne L-Band Radiometers Using Stable Reference Regions: Application to the Aquarius Mission | IEEE Journals & Magazine | IEEE Xplore

Characterizing Drifts in Spaceborne L-Band Radiometers Using Stable Reference Regions: Application to the Aquarius Mission


Abstract:

The global measurement of sea-surface salinity and more precise measurements of soil moisture from space has been enabled by L-band observations from the Soil Moisture an...Show More

Abstract:

The global measurement of sea-surface salinity and more precise measurements of soil moisture from space has been enabled by L-band observations from the Soil Moisture and Ocean Salinity (SMOS) mission, the Aquarius mission, and the Soil Moisture Active Passive (SMAP) mission. These measurements are key components of the global water cycle and the ability to resolve climate scale variations in these variables is extremely valuable for improved understanding of how the hydrologic cycle varies in a changing climate system. It is therefore imperative that the radiometer systems making these measurements be calibrated to remove any spurious instrument temporal drifts. In this paper, models are developed over Antarctica and rainforest regions to track the gain and offset drifts in spaceborne L-band radiometer brightness-temperature (TB) measurements. The Antarctica region is found to be best for tracking small variations (0.1 K over 10 days) for vertically polarized observations near the Brewster angle. The rainforest regions are found to be best for tracking longer term variations (>60 days) in all channels, due to larger uncertainty in the surface temperature knowledge, and excellent for tracking shorter term variations between channels (<;10 days). These reference regions were used to separate a long-term gain drift and a quasi-monthly offset variation in the Aquarius radiometer. These observations eventually led to the characterization of the root cause for the drift and are contributing to improved correction methods based on models for the hardware behavior.
Page(s): 5239 - 5251
Date of Publication: 02 March 2016

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