Type: Poster presentation
Venue: 14th Conference on Satellite Meteorology and Oceanography
Citation:
Lynch, R.J., E. Kennelly, H.E. Snell, D.B. Hogan and J.-L. Moncet, Advanced Radiative Transfer Model for Real-Time Remote Sensing Applications, Presented at American Meteorological Society 14th Conference on Satellite Meteorology and Oceanography, paper 6.29, January 2006.
Resource Link: http://ams.confex.com/ams/Annual2006/techprogram/paper_103726.htm
We have developed the Integrated Algorithm Testbed (IATB) to address the development and validation needs of current and future remote sensing platforms. The core of the IATB is the Optimal Spectral Sampling (OSS) radiative transfer model. OSS is a robust approach to radiative transfer modeling which addresses the need for algorithm speed, accuracy, and flexibility. The OSS technique allows for the rapid calculation of radiance for any class of multispectral, hyperspectral, or ultraspectral sensors at any spectral resolution operating in any region from microwave through UV wavelengths by selecting and appropriately weighting the monochromatic points that contribute over the sensor bandwidth. This allows for the calculation to be performed at a small number of spectral points while retaining the advantages of a monochromatic calculation such as exact treatment of multiple scattering and/or polarization. The OSS method is well suited for remote sensing applications which require extremely fast and accurate radiative transfer calculations: atmospheric compensation, spectral and spatial feature extraction, multi-sensor data fusion, sub-pixel spectral analysis, qualitative and quantitative spectral analysis, sensor design and data assimilation. The OSS is currently used as part of the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) VIIRS, CrIS, CMIS, and OMPS-IR environmental parameter retrieval algorithms. This paper describes the application of OSS within the IATB and provides examples of how we have applied the IATB approach to the development of retrieval algorithms for current and future generation satellite sensors.