Abstract

This article aims to evaluate impact of image saturation on radiometric intercalibration of Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) stable nighttime light (NTL) images. We simulated two sets of stable NTL images, one with saturation and the other without saturation, by using DMSP/OLS radiance calibrated images without saturation. For each dataset, we intercalibrated the simulated images of seven years using a quadratic regression model. We found that the saturation-derived difference of the radiometric intercalibration is more obvious in areas where the digital number value is higher than a certain value (e.g., ∼30). By comparing the calibrated images of the two datasets, we found that the absolute value of the saturation-derived relative bias of the intercalibration tends to be positively correlated to the ratio of saturated pixels in the calibration field, with Pearson correlation of 0.4610 ( $p$ ${{\boldsymbol{R}}^2}$ of quadratic regression and linear regression), with Pearson correlation of 0.6398 ( $p$ < 0.01). These findings indicate that the image saturation impacts the intercalibration of the DMSP/OLS stable NTL images, and the impact is affected by the image characteristics.

Highlights

  • T HE Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) was originally designed to detect the moonlit clouds, which can detect the nighttime light at the Earth’s surface as well [1,2,3]

  • We found that the absolute value of the saturation-derived relative bias of the intercalibration tends to increase as the ratio of saturated pixels increases, of which the Pearson correlation is 0.4610 (p0.01)

  • The results indicate the ratio of saturated pixels in the calibration field is one of the factors that affects the saturationderived impacts of the intercalibration, and we speculated that the correlation will be stronger if the range of saturated pixel ratios extends in Sicily

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Summary

Introduction

T HE Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) was originally designed to detect the moonlit clouds, which can detect the nighttime light at the Earth’s surface as well [1,2,3]. In order to calibrate the images of different years, the quadratic regression model [2, 27], the power regression model [28], the sixth order polynomial quantile regression model [29] have been proposed. Among these methods, the quadratic regression intercalibration model is the most widely used method [30, 31]

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