Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs
This study was undertaken to derive and analyze the advanced microwave scanning radiometer-Earth observing satellite (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 weig...
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| Format: | Article |
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MDPI AG
2019-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/6/715 |
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| author | Brahim Boussidi Peter Cornillon Gavino Puggioni Chelle Gentemann |
| author_facet | Brahim Boussidi Peter Cornillon Gavino Puggioni Chelle Gentemann |
| author_sort | Brahim Boussidi |
| collection | DOAJ |
| description | This study was undertaken to derive and analyze the advanced microwave scanning radiometer-Earth observing satellite (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 <inline-formula> <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mn>6.93</mn> <mspace width="0.166667em"></mspace> <mi>GHz</mi> </mrow> </semantics> </math> </inline-formula> channel aspect ratio, ≈1.74. (The <inline-formula> <math display="inline"> <semantics> <mrow> <mn>6.93</mn> <mspace width="0.166667em"></mspace> <mi>GHz</mi> </mrow> </semantics> </math> </inline-formula> channel is the primary spectral frequency used to determine SST.) The semi-major axis of the estimated footprint is found to be aligned with the instantaneous field-of-view of the sensor as expected from the geometric characteristics of AMSR-E. Footprints were 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 preliminary analysis suggests that use of the derived footprint will reduce the variance between AMSR-E and MODIS fields compared to the results obtained ignoring the shape and size of the footprint as has been the practice in such comparisons to date. |
| first_indexed | 2024-12-19T21:53:48Z |
| format | Article |
| id | doaj.art-fb9cafc5e0ef4703aec66b6398aaf954 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-19T21:53:48Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-fb9cafc5e0ef4703aec66b6398aaf9542022-12-21T20:04:20ZengMDPI AGRemote Sensing2072-42922019-03-0111671510.3390/rs11060715rs11060715Determining the AMSR-E SST Footprint from Co-Located MODIS SSTsBrahim Boussidi0Peter Cornillon1Gavino Puggioni2Chelle Gentemann3Graduate School of Oceanography, University of Rhode Island, 215 South Ferry Road, Narragansett, RI 02882, USAGraduate School of Oceanography, University of Rhode Island, 215 South Ferry Road, Narragansett, RI 02882, USADepartment of Computer Science and Statistics, University of Rhode Island, 9 Greenhouse Road, Suite 2, Kingston, RI 02881, USAEarth and Space Research, Seattle, WA 98121, USAThis study was undertaken to derive and analyze the advanced microwave scanning radiometer-Earth observing satellite (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 <inline-formula> <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics> </math> </inline-formula> 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 <inline-formula> <math display="inline"> <semantics> <mrow> <mn>6.93</mn> <mspace width="0.166667em"></mspace> <mi>GHz</mi> </mrow> </semantics> </math> </inline-formula> channel aspect ratio, ≈1.74. (The <inline-formula> <math display="inline"> <semantics> <mrow> <mn>6.93</mn> <mspace width="0.166667em"></mspace> <mi>GHz</mi> </mrow> </semantics> </math> </inline-formula> channel is the primary spectral frequency used to determine SST.) The semi-major axis of the estimated footprint is found to be aligned with the instantaneous field-of-view of the sensor as expected from the geometric characteristics of AMSR-E. Footprints were 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 preliminary analysis suggests that use of the derived footprint will reduce the variance between AMSR-E and MODIS fields compared to the results obtained ignoring the shape and size of the footprint as has been the practice in such comparisons to date.https://www.mdpi.com/2072-4292/11/6/715SSTAMSR-EMODISfootprintconstrained least squarebootstrap |
| spellingShingle | Brahim Boussidi Peter Cornillon Gavino Puggioni Chelle Gentemann Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs Remote Sensing SST AMSR-E MODIS footprint constrained least square bootstrap |
| title | Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs |
| title_full | Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs |
| title_fullStr | Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs |
| title_full_unstemmed | Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs |
| title_short | Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs |
| title_sort | determining the amsr e sst footprint from co located modis ssts |
| topic | SST AMSR-E MODIS footprint constrained least square bootstrap |
| url | https://www.mdpi.com/2072-4292/11/6/715 |
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