End-to-end performance modeling of passive remote sensing systems

The ultimate goal of end-to-end system modeling is to simulate all known physical effects which determine the content of the data, before flying an instrument system. In remote sensing, one begins with a scene, viewed either statistically or dynamically, computes the radiance in each spectral band, renders the scene, transfers it through representative atmospheres to create the radiance field at an aperture, and integrates over sensor pixels. We have simulated a comprehensive sequence of realistic instrument hardware elements and the transfer of simulated data to an analysis system. This analysis package is the same as that intended for use of data collections from the real system. By comparing the analyzed image to the original scene, the net effect of nonideal system components can be understood. Iteration yields the optimum values of system parameters to achieve performance targets. We have used simulation to develop and test improved multispectral algorithms for (1) the robust retrieval of water surface temperature, water vapor column, and other quantities; (2) the preservation of radiometric accuracy during atmospheric correction and pixel registration on the ground; and (3) exploitation of on-board multispectral measurements to assess the atmosphere between ground and aperture

Design Considerations, Modeling and Analysis for the Multispectral Thermal Imager

The design of remote sensing systems is driven by the need to provide cost-effective, substantive answers to questions posed by our customers. This is especially important for space-based systems, which tend to be expensive, and which generally cannot be changed after they are launched. We report here on the approach we employed in developing the desired attributes of a satellite mission, namely the Multispectral Thermal Imager. After an initial scoping study, we applied a procedure which we call: "End-to-end modeling and analysis (EEM)." We began with target attributes, translated to observable signatures and then propagated the signatures through the atmosphere to the sensor location. We modeled the sensor attributes to yield a simulated data stream, which was then analyzed to retrieve information about the original target. The retrieved signature was then compared to the original to obtain a figure of merit: hence the term "end-to-end modeling and analysis." We base the EEM in physics to ensure high fidelity and to permit scaling. As the actual design of the payload evolves, and as real hardware is tested, we can update the EEM to facilitate trade studies, and to judge, for example, whether components that deviate from specifications are acceptable

Comparing robust and physics-based sea surface temperature retrievals for high resolution, multi-spectral thermal sensors using one or multiple looks

With the advent of multi-spectral thermal imagers such as EOS's ASTER high spatial resolution thermal imagery of the Earth's surface will soon be a reality. Previous high resolution sensors such as Landsat 5 had only one spectral channel in the thermal infrared and its utility to determine absolute sea surface temperatures was limited to 6-8 K for water warmer than 25 deg C. This inaccuracy resulted from insufficient knowledge of the atmospheric temperature and water vapor, inaccurate sensor calibration, and cooling effects of thin high cirrus clouds. The authors will present two studies of algorithms and compare their performance. The first algorithm they call robust since it retrieves sea surface temperatures accurately over a fairly wide range of atmospheric conditions using linear combinations of nadir and off-nadir brightness temperatures. The second they call physics-based because it relies on physics-based models of the atmosphere. It attempts to come up with a unique sea surface temperature which fits one set of atmospheric parameters

<title>Comparing robust and physics-based sea surface temperature retrievals for high-resolution multispectral thermal sensors using one or multiple looks</title>

With the advent of multi-spectral thermal imagers such as EOS's ASTER high spatial resolution thermal imagery of the Earth's surface will soon be a reality. Previous high resolution sensors such as Landsat 5 had only one spectral channel in the thermal infrared and its utility to determine absolute sea surface temperatures was limited to 6-8 K for water warmer than 25 deg C. This inaccuracy resulted from insufficient knowledge of the atmospheric temperature and water vapor, inaccurate sensor calibration, and cooling effects of thin high cirrus clouds. The authors will present two studies of algorithms and compare their performance. The first algorithm they call robust since it retrieves sea surface temperatures accurately over a fairly wide range of atmospheric conditions using linear combinations of nadir and off-nadir brightness temperatures. The second they call physics-based because it relies on physics-based models of the atmosphere. It attempts to come up with a unique sea surface temperature which fits one set of atmospheric parameters