Imaging and Field Spectrometer Data's Role in Calibrating Climate-quality Sensors

The role of hyperspectral data and imaging spectrometry in the calibration of terrestrial remote sensing sensors has been a key to the development of long-term data sets. Most of these data sets are developed from multispectral imagers with tens of spectral bands rather than hundreds to thousands of bands available in spectrometry. Typical lamp-based calibration in the laboratory benefits from inclusion of spectrometer characterization of the source output. The advantage of spectrometer-based calibrations is it permits convolution of the data to arbitrary band shapes that mimic the spectral responses of the sensors being calibrated. Lamp-based calibration, while convenient to operate and control, does not simulate the solar spectrum that is the basic energy source for many of the imaging systems. Using the sun as a source for preflight radiometric calibration reduces many of these uncertainties, but introduces its own difficulties caused by the spectrally-varying effects due to the solar spectrum and atmospheric absorption. Spectrometry provides the spectral knowledge necessary to permit calibration of these sensors at a level sufficient to create climate-quality data sets. In-flight calibration has relied on imaging spectrometers on aircraft and satellites for direct comparison to other sensors to removes biases from their preflight calibrations. Similar approaches relying on ground-based, portable spectrometers have also been widely used. The current work describes the typical approaches used for preflight and in-flight calibration of multispectral imagers using spectrometer-based data. Sample results are presented to demonstrate the current state of the art. Such data sets play a critical role in the development of climatic data sets such as those expected from the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission and application of these approaches to NASA’s upcoming CLARREO mission are discussed including proposed methods for significantly reducing the uncertainties to allow CLARREO data to be used for climate data records.


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