Storey, Jim | Engineering Papers

NASA NTRS2018

Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable.

calibration; geometric; radiometric; Landsat; Sent↗

NASA NTRS2003

Parameters Describing Earth Observing Remote Sensing Systems

The Earth science community needs to generate consistent and standard definitions for spatial, spectral, radiometric, and geometric properties describing passive electro-optical Earth observing sensors and their products. The parameters used to describe sensors and to describe their products are often confused. In some cases, parameters for a sensor and for its products are identical; in other cases, these parameters vary widely. Sensor parameters are bound by the fundamental performance of a system, while product parameters describe what is available to the end user. Products are often resampled, edge sharpened, pan-sharpened, or compressed, and can differ drastically from the intrinsic data acquired by the sensor. Because detailed sensor performance information may not be readily available to an international science community, standardization of product parameters is of primary performance. Spatial product parameters described include Modulation Transfer Function (MTF), point spread function, line spread function, edge response, stray light, edge sharpening, aliasing, ringing, and compression effects. Spectral product parameters discussed include full width half maximum, ripple, slope edge, and out-of-band rejection. Radiometric product properties discussed include relative and absolute radiometry, noise equivalent spectral radiance, noise equivalent temperature diffenence, and signal-to-noise ratio. Geometric product properties discussed include geopositional accuracy expressed as CE90, LE90, and root mean square error. Correlated properties discussed include such parameters as band-to-band registration, which is both a spectral and a spatial property. In addition, the proliferation of staring and pushbroom sensor architectures requires new parameters to describe artifacts that are different from traditional cross-track system artifacts. A better understanding of how various system parameters affect product performance is also needed to better ascertain the utility of existing datasets and products as well as to specify the performance of new sensors and products. Examples of simulations performed for the Landsat Data Continuity Mission illustrate how various parameters affect system and product performance. Specific examples include the effects of ground sample distance, MTF, and band-to-band registration on various products.

Zanoni, Vicki↗