Aggregation of Hyperion Hyperspectral Bands to ALI and ETM+ Bands Using Spectral Response Information and the Weighted Sum Method

Author(s):  
DaeSung Kim ◽  
MuWook Pyeon
2019 ◽  
Vol 141 (10) ◽  
Author(s):  
George A. Hazelrigg

2010 ◽  
Vol 27 (8) ◽  
pp. 1331-1342 ◽  
Author(s):  
M. M. Schreier ◽  
B. H. Kahn ◽  
A. Eldering ◽  
D. A. Elliott ◽  
E. Fishbein ◽  
...  

Abstract The combination of multiple satellite instruments on a pixel-by-pixel basis is a difficult task, even for instruments collocated in space and time, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) on board the Earth Observing System (EOS) Aqua. Toward the goal of an improved collocation methodology, the channel- and scan angle–dependent spatial response functions of AIRS that were obtained from prelaunch measurements and calculated impacts from scan geometry are shown within the context of radiance comparisons. The AIRS spatial response functions are used to improve the averaging of MODIS radiances to the AIRS footprint, and the variability of brightness temperature differences (ΔTb) between MODIS and AIRS are quantified on a channel-by-channel basis. To test possible connections between ΔTb and the derived level 2 (L2) datasets, cloud characteristics derived from MODIS are used to highlight correlations between these quantities and ΔTb, especially for ice clouds in H2O and CO2 bands. Furthermore, correlations are quantified for temperature lapse rate (dT/dp) and the magnitude of water vapor mixing ratio (q) obtained from AIRS L2 retrievals. Larger values of dT/dp and q correlate well to larger values of ΔTb in the H2O and CO2 bands. These correlations were largely eliminated or reduced after the MODIS spectral response functions were shifted by recommended values. While this investigation shows that the AIRS spatial response functions are necessary to reduce the variability and skewness of ΔTb within heterogeneous scenes, improved knowledge about MODIS spectral response functions is necessary to reduce biases in ΔTb.


2016 ◽  
Vol 825 ◽  
pp. 153-160
Author(s):  
Adéla Hlobilová ◽  
Matěj Lepš

This paper deals with a reconstruction of random media via multi-objective optimization. Two statistical descriptors, namely a two-point probability function and a two-point lineal path function, are repetitively evaluated for the original medium and the reconstructed image to appreciate the improvement in the optimization progress. Because of doubts of the weights setting in the weighted-sum method, purely multi-objective optimization routine Non-dominated Sorting Genetic Algorithm~II is utilized. Three operators are compared for creating new offspring populations that satisfy a prescribed volume fraction constraint. The main contribution is in the testing of the proposed methodology on several benchmark images.


Sign in / Sign up

Export Citation Format

Share Document