Radiometric inter-sensor cross-calibration uncertainty using a traceable high accuracy reference hyperspectral imager

Abstract

Optical earth observation (EO) satellite sensors generally suffer from drifts and biases relative to their pre-launch calibration, caused by launch and/or time in the space environment. This places a severe limitation on the fundamental reliability and accuracy that can be assigned to satellite derived information, and is particularly critical for long time base studies for climate change and enabling interoperability and Analysis Ready Data. The proposed TRUTHS (Traceable Radiometry Underpinning Terrestrial and Helio-Studies) mission is explicitly designed to address this issue through re-calibrating itself directly to a primary standard of the international system of units (SI) in-orbit and then through the extension of this SI-traceability to other sensors through in-flight cross-calibration using a selection of Committee on Earth Observation Satellites (CEOS) recommended test sites. Where the characteristics of the sensor under test allows, this will result in a significant improvement in accuracy. This paper describes a set of tools, algorithms and methodologies that have been developed and used in order to estimate the radiometric uncertainty achievable for an indicative target sensor through in-flight cross-calibration using a well-calibrated hyperspectral SI-traceable reference sensor with observational characteristics such as TRUTHS. In this study, Multi-Spectral Imager (MSI) of Sentinel-2 and Landsat-8 Operational Land Imager (OLI) is evaluated as an example, however the analysis is readily translatable to larger-footprint sensors such as Sentinel-3 Ocean and Land Colour Instrument (OLCI) and Visible Infrared Imaging Radiometer Suite (VIIRS). This study considers the criticality of the instrumental and observational characteristics on pixel level reflectance factors, within a defined spatial region of interest (ROI) within the target site. It quantifies the main uncertainty contributors in the spectral, spatial, and temporal domains. The resultant tool will support existing sensor-to-sensor cross-calibration activities carried out under the auspices of CEOS, and is also being used to inform the design specifications for TRUTHS

Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions

“…This was observed for all three study sites separately and also in the combined adjustment for the Eastern Siberian region. This underlines the expected sensor compatibility and agrees with results from similar studies [33,34,36,38,39]. However, our study went slightly beyond some of the previous ones, which partly based their evaluation on simulated Landsat-8 and/or Sentinel-2 image products [33,34,36], and not on acquired images.…”

“…This underlines the expected sensor compatibility and agrees with results from similar studies [33,34,36,38,39]. However, our study went slightly beyond some of the previous ones, which partly based their evaluation on simulated Landsat-8 and/or Sentinel-2 image products [33,34,36], and not on acquired images. We showed that it is possible to reduce the persistent difference in spectral responses between the measured reflectance values of the spectral bands by deriving and then applying an ordinary least squares regression model, adjusting Sentinel-2 to resemble Landsat-8 reflectance values in Arctic-Boreal permafrost regions.…”

“…Despite similar wavelength ranges covered by the corresponding bands, the spectral response functions per sensor differ, and this impact has to be assessed before a combined time series is applied [33]. Previous comparison studies varied in application region, scale, input datasets, ranging from simulated to acquired image products, and covered temporally close to same-day acquisitions [33][34][35][36][37]. Regardless of these differences, the comparison approaches are always alike, examining corresponding Landsat-8 and Sentinel-2 bands, comparing pixel-pairs, and deriving regression equations for bandpass alignments.…”

Evaluation of Sensor and Environmental Factors Impacting the Use of Multiple Sensor Data for Time-Series Applications

“…Recently, several studies have compared the products from Landsat 8 and Sentinel 2 to characterize and normalize the differences between the two sensors [8][9][10]. While these studies have provided transformation or regression coefficients to generate harmonized data products and have also identified the factors in our study as the source of variation, they do not provide the effect of these factors or their relative magnitude, which is the focus of our study.…”

Rapid Assessment of a Typhoon Disaster Based on NPP-VIIRS DNB Daily Data: The Case of an Urban Agglomeration along Western Taiwan Straits, China

“…Firstly, radiation normalization was applied to the multi-temporal images in the research region before and after the disaster. Relative radiation was normalized to minimize the radiation difference generated by different multi-temporal imaging conditions and sensor radiation differences [52,53]. The goal was to make the same feature ware fond of reflectivity for equal or similar values in the multi-temporal remote sensing images.…”