Normalization Method and Application for MODIS TEB Assessments Using Earth Scene Measurements

Selected Earth targets are commonly used for satellite sensor calibration assessments (e.g., sensor stability and inter-comparisons). Moreover, typical scenes used for the calibration assessment of the thermal emissive bands (TEBs) include Dome Concordia (Dome-C), ocean, desert, and deep convective clouds (DCC). Reference data used for these calibration assessments can come from another band, another instrument, or ground measurements. The Dome-C site, covered with uniformly distributed permanent snow, is normally used for the assessment of the TEBs at cold temperatures. Furthermore, ocean and desert measurements prove useful for scenes with higher temperatures. The DCC, one of the most consistent and coldest targets, can be used for the TEBs calibration and product stability assessments. Moderate Resolution Imaging Spectroradiometer (MODIS) band 31 (similar to 11 mu m) can be used as a reference for these scenes. However, measurements over these scenes have seasonal variations, and the DCC brightness temperatures (BTs) have asymmetrical distributions. These features can introduce additional uncertainty to the stability assessments. A normalization method is applied by using an empirical model to derive reference-dependent BTs. Using the developed empirical model, measurements can be normalized to a reference BT in order to enhance the calibration assessment's accuracy. This method is evaluated using all four scene types (i.e., ocean, desert, snow (Dome-C), and DCC) and applied to all the Terra and Aqua MODIS TEBs. Stability assessments over the instruments' entire data records are presented and discussed. The technique can be applied in future efforts to support MODIS TEBs calibration assessments.

Automated summary

This study discusses the calibration assessment of satellite sensors using selected Earth targets. Different types of scenes, such as Dome-C, ocean, desert, and deep convective clouds, are used for calibration assessments based on temperature variations. A normalization method is applied to enhance the accuracy of the calibration assessment.