Overview of Intercalibration of Satellite Instruments

Abstract

Intercalibration of satellite instruments is critical for detection and quantification of changes in the Earth's environment, weather forecasting, understanding climate processes, and monitoring climate and land cover change. These applications use data from many satellites; for the data to be interoperable, the instruments must be cross-calibrated. To meet the stringent needs of such applications, instruments must provide reliable, accurate, and consistent measurements over time. Robust techniques are required to ensure that observations from different instruments can be normalized to a common scale that the community agrees on. The long-term reliability of this process needs to be sustained in accordance with established reference standards and best practices. Furthermore, establishing physical meaning to the information through robust Système International d'unités traceable calibration and validation (Cal/Val) is essential to fully understand the parameters under observation. The processes of calibration, correction, stability monitoring, and quality assurance need to be underpinned and evidenced by comparison with "peer instruments" and, ideally, highly calibrated in-orbit reference instruments. Intercalibration between instruments is a central pillar of the Cal/Val strategies of many national and international satellite remote sensing organizations. Intercalibration techniques as outlined in this paper not only provide a practical means of identifying and correcting relative biases in radiometric calibration between instruments but also enable potential data gaps between measurement records in a critical time series to be bridged. Use of a robust set of internationally agreed upon and coordinated intercalibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth's climate at uncertainty levels needed to detect and attribute the mechanisms of change. This paper summarizes the state-of-the-art of postlaunch radiometric calibration of remote sensing satellite instruments through intercalibration.

Calibration of the DSCOVR EPIC visible and NIR channels using MODIS Terra and Aqua data and EPIC lunar observations

“…The differences in the position and spectral width of the corresponding EPIC and MODIS channels may result in discrepancies when scenes with different spectral signatures are observed by the two instruments (Chander, 2013). In Version 2 calibration, to compensate for these differences we employed spectral band adjustment factors (SBAFs) which convert MODIS reflectance values to equivalent EPIC reflectance for various surface types.…”

Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements

“…As summarized in [27], the methodologies involved in sensor inter-calibration activities can be classified into three general groups: (1) the simultaneous nadir overpass (SNO) approach; (2) statistical inter-calibration; and (3) double differencing (DD) methods. The statistical inter-calibration approach compares sensor observations directly, and calibrates the target instrument data using empirical models developed from multiple (e.g., thousands) collocated observations; this approach is best suited for two instruments having collocated and near simultaneous observations.…”

RapidScat Cross-Calibration Using the Double Difference Technique

“…To calculate the double difference, the observed single differences for both sensors are first evaluated as differences between the observed σ 0 values and the corresponding modeled values obtained from the GMF. The double difference is then calculated by subtracting the single difference values for each instrument [1, 2,6]. Before engaging in any cross-calibration effort, instruments are calibrated individually to ensure σ 0 consistency.…”

“…Demand for improved calibration accuracy has been steadily increasing. A common approach has been to combine data from multiple instruments [1,2]. A typical scenario involves comparing measurements from a pair of instruments already individually calibrated before cross-calibration.…”