{"id":437,"date":"2011-08-25T08:46:44","date_gmt":"2011-08-25T08:46:44","guid":{"rendered":"http:\/\/bluemarble.ch\/wordpress\/?p=437"},"modified":"2025-11-21T13:29:26","modified_gmt":"2025-11-21T13:29:26","slug":"global-re-analysis-of-vegetation","status":"publish","type":"post","link":"https:\/\/bluemarble.ch\/wordpress\/2011\/08\/25\/global-re-analysis-of-vegetation\/","title":{"rendered":"Global Re-Analysis of Vegetation"},"content":{"rendered":"<p>Our <a title=\"Remote sensing data assimilation\" href=\"https:\/\/bluemarble.ch\/wordpress\/2008\/11\/26\/remote-sensing-data-assimilation\/\">previous work<\/a>\u00a0on the remote-sensing based data assimilation framework has been extended to regional and global scales. It employs\u00a0a subgrid\u2010scale representation of plant functional types (PFTs) and elevation classes to generate a globally applicable phenological parameter set. We are able to predict (or hindcast) a\u00a050 year long (1960\u20132009) daily 1\u00b0 \u00d7 1\u00b0 global phenology climate data record with a mean FPAR and LAI prediction error of 0.065 (\u2212) and 0.34 (m^2 m^\u22122).<\/p>\n<div id=\"attachment_438\" style=\"width: 300px\" class=\"wp-caption alignnone\"><a href=\"https:\/\/bluemarble.ch\/wordpress\/2011\/08\/25\/global-re-analysis-of-vegetation\/laimean\/\" rel=\"attachment wp-att-438\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-438\" class=\"size-medium wp-image-438\" title=\"Laimean\" src=\"https:\/\/bluemarble.ch\/wordpress\/wp-content\/uploads\/2012\/04\/Laimean-300x136.png\" alt=\"\" width=\"300\" height=\"136\" srcset=\"https:\/\/bluemarble.ch\/wordpress\/wp-content\/uploads\/2012\/04\/Laimean-300x136.png 300w, https:\/\/bluemarble.ch\/wordpress\/wp-content\/uploads\/2012\/04\/Laimean.png 550w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><p id=\"caption-attachment-438\" class=\"wp-caption-text\">Mean annual leaf area index (LAI) from our satellite-model reanalysis project<\/p><\/div>\n<p>The data set as well as the underlying data assimilation and prediction code with all parameters are available publicly as open source:<\/p>\n<p><a href=\"http:\/\/phenoanalysis.sourceforge.net\" target=\"_blank\">http:\/\/phenoanalysis.sourceforge.net<\/a><\/p>\n<p>R. Stockli, T. Rutishauser, I. Baker, M. Liniger, and A. S. Denning. A global reanalysis of vegetation phenology. J. Geophys. Res. &#8211; Biogeo- sciences, 116(G03020), 2011. doi: 10.1029\/2010JG001545.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Our previous work\u00a0on the remote-sensing based data assimilation framework has been extended to regional and global scales. It employs\u00a0a subgrid\u2010scale representation of plant functional types (PFTs) and elevation classes to generate a globally applicable phenological parameter set. We are able to predict (or hindcast) a\u00a050 year long (1960\u20132009) daily 1\u00b0 \u00d7 1\u00b0 global phenology climate &hellip; <\/p>\n<p><a class=\"more-link btn\" href=\"https:\/\/bluemarble.ch\/wordpress\/2011\/08\/25\/global-re-analysis-of-vegetation\/\">Continue reading<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[54,5],"tags":[72,77,78,79,75,76,81,74,73,16,82,83,80],"class_list":["post-437","post","type-post","status-publish","format-standard","hentry","category-colorado-state-university","category-nasa","tag-climatology","tag-data-assimilation","tag-enkf","tag-ensemble-kalman-filter","tag-fpar","tag-fraction-of-photosynthetically-active-radiation","tag-global","tag-lai","tag-leaf-area-index","tag-modis","tag-prediction","tag-reanalysis","tag-supercomputer","item-wrap"],"_links":{"self":[{"href":"https:\/\/bluemarble.ch\/wordpress\/wp-json\/wp\/v2\/posts\/437","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bluemarble.ch\/wordpress\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bluemarble.ch\/wordpress\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bluemarble.ch\/wordpress\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/bluemarble.ch\/wordpress\/wp-json\/wp\/v2\/comments?post=437"}],"version-history":[{"count":7,"href":"https:\/\/bluemarble.ch\/wordpress\/wp-json\/wp\/v2\/posts\/437\/revisions"}],"predecessor-version":[{"id":489,"href":"https:\/\/bluemarble.ch\/wordpress\/wp-json\/wp\/v2\/posts\/437\/revisions\/489"}],"wp:attachment":[{"href":"https:\/\/bluemarble.ch\/wordpress\/wp-json\/wp\/v2\/media?parent=437"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bluemarble.ch\/wordpress\/wp-json\/wp\/v2\/categories?post=437"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bluemarble.ch\/wordpress\/wp-json\/wp\/v2\/tags?post=437"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}