Assessment of Long-Term Sensor Radiometric Degradation Using Time Series Analysis

The monitoring of top-of-atmosphere (TOA) reflectance time series provides useful information regarding the long-term degradation of satellite sensors. For a precise assessment of sensor degradation, the TOA reflectance time series is usually corrected for surface and atmospheric anisotropy by using bidirectional reflectance models so that the angular effects do not compromise the trend estimates. However, the models sometimes fail to correct the angular effects, particularly for spectral bands that exhibit a large seasonal oscillation due to atmospheric variability. This paper investigates the use of time series algorithms to identify both the angular effects and the atmospheric variability simultaneously in the time domain using their periodical patterns within the time series. Two nonstationary time series algorithms were tested with the Landsat 5 Thematic Mapper time series data acquired over two pseudoinvariant desert sites, the Sonoran and Libyan Deserts, to compute a precise long-term trend of the time series by removing the seasonal variability. The trending results of the time series algorithms were compared to those of the original TOA reflectance time series and those normalized by a widely used bidirectional-reflectance-distribution-function model. The time series results showed an effective removal of seasonal oscillation, caused by angular and atmospheric effects, producing trending results that have a higher statistical significance than other approaches.

I. Introduction

Satellite sensors are subject to the postlaunch degradation and aging of calibration devices [1]–[3] owing to their exposure to the harsh environment of space. Vicarious calibration, which refers to calibration techniques that do not depend on onboard calibration devices, is critical for quantitative remote sensing for which high measurement accuracy and long-term stability are necessary. Recently, many studies have investigated the long-term stability of satellite sensors in the context of relative calibration by using top of atmosphere (TOA) reflectance time series data acquired for pseudoinvariant calibration sites (PICSs). The satellite sensors validated with such methods include the Advanced Very High Resolution Radiometer (AVHRR) Metop-A [4], Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua [5]–[7], Landsat 5 Thematic Mapper (TM) [6], Landsat 7 Enhanced TM [6], [7], and Along-Track Scanning Radiometer-2 [8]. The assessment of sensor degradation is usually performed after correcting the TOA reflectance for the angular variation caused by changes in sun–sensor–target geometry since the angular effects are not directly related to the sensor performance. In many studies [4], [5], [7], [9], the angular effects have been treated with semiempirical bidirectional reflectance distribution function (BRDF) models.