{"id":749,"date":"2019-07-12T11:21:24","date_gmt":"2019-07-12T10:21:24","guid":{"rendered":"http:\/\/35.193.178.118\/?page_id=749"},"modified":"2019-07-30T12:15:51","modified_gmt":"2019-07-30T11:15:51","slug":"evaluating-error-correlation-pt5","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/fiduceo\/archive\/tutorials\/evaluating-error-correlation\/evaluating-error-correlation-pt5\/","title":{"rendered":"Evaluating error correlation"},"content":{"rendered":"\r\n<h2 class=\"wp-block-heading\">Spectral correlation due to common temperature<\/h2>\r\n\r\n\r\n\r\n<p>On the previous page we looked at Type B methods of error correlation evaluation using the example of a rolling average. On this page, we will continue our examination of this topic by looking a common and important example of correlation between spectral channels by considering an onboard calibration target used to calibrate more than one spectral channel.<\/p>\r\n\r\n\r\n\r\n<p>In a thermal infrared or microwave sensor, it is common to use an internal warm calibration target as a reference. The radiance of the target is given by calculating Planck\u2019s Law for the ICWT temperature. Thus, an uncertainty associated with temperature in the ICWT affects all channels, but not equally since shorter wavelengths are most sensitive to changes in temperature than longer wavelengths. There is therefore a full correlation, because there is a common error, the error in temperature, but the sensitivity to this error is different from one channel to another. To deal with this we consider Planck\u2019s Law, here simplifying the situation to assume that each channel is at a single central wavelength:<\/p>\r\n\r\n\r\n\r\n<p><span class=\"wp-block-katex-display-block katex-eq\" data-katex-display=\"true\"> {L_{{\\rm{ICWT}},{\\rm{A}}}} = \\frac{{{\\varepsilon _{\\rm{A}}}{c_{1,L}}}}{{\\lambda _{\\rm{A}}^5\\left( {\\exp [{c_2}\/{\\lambda _{\\rm{A}}}T} \\right] &#8211; 1)}}<\/span><\/p>\r\n\r\n\r\n\r\n<p><span class=\"wp-block-katex-display-block katex-eq\" data-katex-display=\"true\"> {L_{{\\rm{ICWT}},{\\rm{B}}}} = \\frac{{{\\varepsilon _{\\rm{B}}}{c_{1,L}}}}{{\\lambda _{\\rm{B}}^5\\left( {\\exp [{c_2}\/{\\lambda _{\\rm{B}}}T} \\right] &#8211; 1)}}<\/span><\/p>\r\n\r\n\r\n\r\n<p>We are interested in the correlation associated with these due to the common error in temperature. Using the same formulation as we used in the rolling average example on the previous page, the covariance is given by:<\/p>\r\n\r\n\r\n\r\n<p><span class=\"wp-block-katex-display-block katex-eq\" data-katex-display=\"true\"> u\\left( {{L_{{\\rm{ICWT}},{\\rm{A}}}},{L_{{\\rm{ICWT}},{\\rm{B}}}}} \\right) = \\frac{{\\partial {L_{{\\rm{ICWT}},{\\rm{A}}}}}}{{\\partial T}}\\frac{{\\partial {L_{{\\rm{ICWT}},{\\rm{B}}}}}}{{\\partial T}}{u^2}\\left( T \\right)<\/span><\/p>\r\n\r\n\r\n\r\n<p>where:<\/p>\r\n\r\n\r\n\r\n<p><span class=\"wp-block-katex-display-block katex-eq\" data-katex-display=\"true\">{\\left. {\\frac{{\\partial L}}{{\\partial T}}} \\right|_{\\left( {{\\lambda _i},T} \\right)}} = \\frac{{{\\varepsilon _1}L\\left( {{\\lambda _i},T} \\right)hc}}{{{\\lambda _i}{k_{\\rm{B}}}{T^2}(1 &#8211; {\\rm{exp}}\\left[ { &#8211; hc\/\\left( {{\\lambda _i}{K_{\\rm{B}}}T} \\right)} \\right]}}<\/span><\/p>\r\n\r\n\r\n\r\n<p>We can convert this covariance into correlation coefficient using the expression given on the previous page.<\/p>\r\n","protected":false},"excerpt":{"rendered":"<p>Spectral correlation due to common temperature On the previous page we looked at Type B methods of error correlation evaluation using the example of a rolling average. On this page,&#8230;<a class=\"read-more\" href=\"&#104;&#116;&#116;&#112;&#115;&#58;&#47;&#47;&#114;&#101;&#115;&#101;&#97;&#114;&#99;&#104;&#46;&#114;&#101;&#97;&#100;&#105;&#110;&#103;&#46;&#97;&#99;&#46;&#117;&#107;&#47;&#102;&#105;&#100;&#117;&#99;&#101;&#111;&#47;&#97;&#114;&#99;&#104;&#105;&#118;&#101;&#47;&#116;&#117;&#116;&#111;&#114;&#105;&#97;&#108;&#115;&#47;&#101;&#118;&#97;&#108;&#117;&#97;&#116;&#105;&#110;&#103;&#45;&#101;&#114;&#114;&#111;&#114;&#45;&#99;&#111;&#114;&#114;&#101;&#108;&#97;&#116;&#105;&#111;&#110;&#47;&#101;&#118;&#97;&#108;&#117;&#97;&#116;&#105;&#110;&#103;&#45;&#101;&#114;&#114;&#111;&#114;&#45;&#99;&#111;&#114;&#114;&#101;&#108;&#97;&#116;&#105;&#111;&#110;&#45;&#112;&#116;&#53;&#47;\">Read More ><\/a><\/p>\n","protected":false},"author":219,"featured_media":0,"parent":732,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"__cvm_playback_settings":[],"__cvm_video_id":"","footnotes":""},"coauthors":[6],"class_list":["post-749","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.8.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Evaluating error correlation - Fiduceo<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/research.reading.ac.uk\/fiduceo\/archive\/tutorials\/evaluating-error-correlation\/evaluating-error-correlation-pt5\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Evaluating error correlation - Fiduceo\" \/>\n<meta property=\"og:description\" content=\"Spectral correlation due to common temperature On the previous page we looked at Type B methods of error correlation evaluation using the example of a rolling average. 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