Document Type
Article
Date of Original Version
12-24-2018
Department
Oceanography
Abstract
This study was undertaken to derive and analyze the Advanced Microwave Scanning Radiometer - EOS (AMSR-E) sea surface temperature (SST) footprint associated with the Remote Sensing Systems (RSS) Level-2 (L2) product. The footprint, in this case, is characterized by the weight attributed to each 4x4 km square contributing to the SST value of a given AMSR-E pixel. High-resolution L2 SST fields obtained from the MODerate-resolution Imaging Spectroradiometer (MODIS), carried on the same spacecraft as AMSR-E, are used as the sub-resolution “ground truth“ from which the AMSR-E footprint is determined. Mathematically, the approach is equivalent to a linear inversion problem, and its solution is pursued by means of a constrained least square approximation based on the bootstrap sampling procedure. The method yielded an elliptic-like Gaussian kernel with an aspect ratio ≈ 1.58, very close to the AMSR-E 6.93GHz channel aspect ratio, ≈ 1.7. (The 6.93GHz channel is the primary spectral frequency used to determine SST.) The semi-major axis of the estimated footprint is found to be alignedwith the instantaneous field-of-view of the sensor as expected fromthe geometric characteristics of AMSR-E. Footprintswere also analyzed year-by-year and as a function of latitude and found to be stable – no dependence on latitude or on time. Precise knowledge of the footprint is central for any satellite-derived product characterization and, in particular, for efforts to deconvolve the heavily oversampled AMSR-E SST fields and for studies devoted to product validation and comparison. A preliminarly analysis suggests that use of the derived footprint will reduce the variance between AMSR-E and MODIS fields compared to the results obtained.
Citation/Publisher Attribution
Boussidi, B.; Cornillon, P.; Puggioni, G.; Gentemann, C. Determining the AMSR-E SST Footprint from Co-located MODIS SSTs. Preprints 2018, 2018120291 (doi: 10.20944/preprints201812.0291.v1).
Available at: http://dx.doi.org/10.20944/preprints201812.0291.v1
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comment
Dr. Gavino Puggioni is an Associate Professor in the Department of Computer Science and Statistics.