HR: 1340h
AN: SF43A-0776    [Abstracts]
TI: Remote Sensing Opportunities Beyond Exploration of the Photon's State Variables, with Examples
AU: * Davis, A B
EM: adavis@lanl.gov
AF: Los Alamos National Laboratory, Space & Remote Sensing Sciences Group, Los Alamos, NM 87545
AU: Cox, L
EM: larrycox@lanl.gov
AF: Los Alamos National Laboratory, Space & Remote Sensing Sciences Group, Los Alamos, NM 87545
AU: Funsten, H
EM: hfunsten@lanl.gov
AF: Los Alamos National Laboratory, Center for Space Science and Exploration, Los Alamos, NM 87545
AB: The evolution of Earth system observation by satellite remote sensing can be seen as a systematic exploration and exploitation of the photon or EM wave's state variables: emission (or last scattering) point and direction, wavelength, and now polarization, plus travel time in active techniques. Wavelength spawns the spectral dimension of the data, itself evolving from broadband to multi- to hyper-spectral sampling and to ever more exotic wavelengths (e.g., microwaves and sub-mm wavelengths). We are thus constantly pushing the technology to open new spectral ``windows'' or to examine the available ones in increasing detail. Polarization, a statistical property of the photon beam, offers promising applications. Position and direction are geometrical beam characteristics that simply determine the pixel of interest on the focal plane; no more is expected. Similarly the temporal dimension in active techniques is interpreted just as return-travel time, hence position along the source beam. Conventional exploitation algorithms (i.e., retrieval schemes) look for known patterns, trends, correlations, etc. between radiances at different wavelengths and/or polarizations. This strategy seems natural and enables independent pixel-by-pixel exploitation, a significant advantage for the data processing as the numbers of pixels and spectral/polarization channels increases. However, at this level, we are overlooking all the information that could be reaped from the complex spatial/directional relations that are so obvious when we examine satellite imagery visually. At best, this is left for projects in post-processing that tend to be limited to case-studies published in the research literature. We will argue that this strategy of extensive photon-state sampling and the resulting ---or is it driven by?--- pixel-by-pixel exploitation paradigm in remote sensing is suboptimal for a number of reasons. For one, it comes hand-in-hand with making horizontal homogeneity assumptions inside the pixel footprint that may not be realistic enough to approximate the actual radiative transfer processes at the accuracy of the instrument's SNR level. Furthermore, as spatial resolution increases, there will be increasing amounts of radiometric cross-talk between pixels (a.k.a. adjacency effects) at scattering/reflecting wavelengths; this impact of spatial variability will also be ignored. A cost-effective remedy for the ills caused by these internal and external variability effects is to engage the three-dimensional radiative transfer modeling community in improved algorithm development. Last but not least, there is an emerging class of instruments that are designed from the onset using the rich phenomenology of 3D radiative transfer, often with time dependence; the theory that supports these instruments is not so much about individual photon beams but about the complex interaction of the photon population as it flows within the 3D atmosphere-surface medium. The examples we will use include high-resolution O$_2$ spectrometry as a cloud-scene probe and off-beam cloud lidar systems. Because of the radically different kind of theory involved, these instrumental developments are not evolutionary but revolutionary. This poses a challenge for mission and program management as well as for the developer. How does one promote a promising but unconventional instrument design into an operational setting?
UR: http://nis-www.lanl.gov/~adavis
DE: 5494 Instruments and techniques
DE: 6919 Electro-optics
DE: 6944 Nonlinear phenomena
DE: 6969 Remote sensing
DE: 3360 Remote sensing
SC: Special Focus: Advances in Data Acquisition, Management, Analysis and Display [SF]
MN: 2004 AGU Fall Meeting