Estimating residual uncertainties for lunar irradiance measurements due to imaging acquisition parameters

Shihyan Lee; Gerhard Meister; Frederick S. Patt; Robert E. Eplee Jr.

doi:10.1117/1.JRS.13.014508

4 February 2019 Estimating residual uncertainties for lunar irradiance measurements due to imaging acquisition parameters

Shihyan Lee, Gerhard Meister, Frederick S. Patt, Robert E. Eplee Jr.

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 1, 014508 (February 2019). https://doi.org/10.1117/1.JRS.13.014508

Abstract

Lunar calibration is a commonly used method to track a climate satellite sensor’s long-term radiometric stability. We present a modeling approach to examine the satellite sensor lunar observation uncertainties due to several important aspects related to the lunar image acquisition by the satellite sensor: lunar pixel shift, point spread function (PSF), lunar orientation, pitch, and oversampling rates. Our analyses can be summarized as follows. (1) The sensor observed lunar irradiance can vary due to small lunar pixel shift if the PSF is less than ideal. (2) During lunar calibration, an unstable oversampling rate due to spacecraft control will result in errors in observed lunar irradiance. A drift in oversampling rate would result in a bias in observed lunar irradiance and a random variation in oversampling rate would cause random error in lunar irradiance. Increasing the overall oversampling rates can reduce random error in observed lunar irradiance but would not change the biases in the observation. (3) Furthermore, the biases can vary when the Moon is observed at different orientations. Our results show impacts on observed lunar irradiance are on the order of 0.1%, which is a significant part of the overall uncertainty for a lunar irradiance measurement of a climate satellite sensor.

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Shihyan Lee, Gerhard Meister, Frederick S. Patt, and Robert E. Eplee Jr. "Estimating residual uncertainties for lunar irradiance measurements due to imaging acquisition parameters," Journal of Applied Remote Sensing 13(1), 014508 (4 February 2019). https://doi.org/10.1117/1.JRS.13.014508

Received: 22 March 2018; Accepted: 9 January 2019; Published: 4 February 2019

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