Statistical normalization of brightness temperature records from the NOAA/AVHRR

Marco Vargas; Felix Kogan; Wei Guo

doi:10.1117/12.892556

15 September 2011 Statistical normalization of brightness temperature records from the NOAA/AVHRR

Marco Vargas, Felix Kogan, Wei Guo

Proceedings Volume 8156, Remote Sensing and Modeling of Ecosystems for Sustainability VIII; 81560Y (2011) https://doi.org/10.1117/12.892556
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

Abstract

The Advanced Very High Resolution Radiometer (AVHRR) sensors onboard The National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites have been measuring electromagnetic radiation emitted by the Earth in the visible (VIS), Near-Infrared (NIR) and Infrared (IR) portions of the electromagnetic spectrum for nearly 30 years. The Global Vegetation Index Vegetation Health product (GVI-x VH) developed from the AVHRR dataset includes the Brightness Temperature (BT) variable calculated from the IR channels, which in turn is used to estimate other environmental variables such as Sea Surface Temperature (SST), Land Surface Temperature (LST), Temperature Condition Index (TCI), and Vegetation Health Index (VTI) among others. However, the satellite measured IR radiances need to be corrected with sufficient accuracy to minimize the uncertainty introduced by a host of sources such as the atmosphere, stratospheric aerosols, and satellite orbital drift before being input into any algorithm to generate remotely sensed products. In this research we have applied a statistical technique based on Empirical Distribution Functions (EDF) to normalize the NOAA GVI-x BT records for the combined effect of the sources of uncertainty mentioned above, avoiding the need for physics based corrections. The normalized results are tested to verify that the normalization improves the data.

Citation Download Citation

Marco Vargas, Felix Kogan, and Wei Guo "Statistical normalization of brightness temperature records from the NOAA/AVHRR", Proc. SPIE 8156, Remote Sensing and Modeling of Ecosystems for Sustainability VIII, 81560Y (15 September 2011); https://doi.org/10.1117/12.892556

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