[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-139014-105":59,"doc-detail-139014-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","global-long-term-microwave-vegetation-optical-depth-climate-archive-vodca","Global Long-term Microwave Vegetation Optical Depth Climate Archive (VODCA)","","Since the late 1970s, spaceborne microwave radiometers have enabled retrievals of vegetation optical depth (VOD), a vegetation-structure and water-content indicator. Long-term VOD studies have been limited by short sensor lifetimes, which motivates creating a unified climate data record by merging multiple VOD products. This work introduces VOD Climate Archive (VODCA), multi-sensor VOD series for Ku-, X- and C-bands, co-calibrated and aggregated to reduce random error and capture consistent spatio-temporal trends and anomalies.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/global-long-term-microwave-vegetation-optical-depth-climate-archive-vodca/139014/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/global-long-term-microwave-vegetation-optical-depth-climate-archive-vodca/139014.png","ImageObject",300,407,{"name":92,"@type":93},"Mason","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-21","2026-08-23",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is vegetation optical depth (VOD) and what does it represent?","Question",{"text":112,"@type":113},"VOD describes how vegetation attenuates microwave radiation measured from satellite observations. It is related to vegetation density, biomass, and water content.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why is long-term VOD dynamics difficult to study directly?",{"text":117,"@type":113},"Individual microwave sensors cover only limited time spans. This short coverage hampers analysis of long-term VOD variability unless multiple products are merged into a single climate record.",{"name":119,"@type":110,"acceptedAnswer":120},"How does VODCA combine multiple microwave sensors into long-term products?",{"text":121,"@type":113},"VODCA merges retrievals from several sensors and spectral bands. The approach co-calibrates datasets using cumulative distribution function matching (with AMSR-E as reference) and aggregates temporally overlapping observations using an arithmetic mean of the scaled data.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},139014,1787494098,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},5909887256941,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA  \nLeander Moesinger 1 , Wouter Dorigo 1 , Richard de Jeu2 , Robin van der Schalie2 , Tracy Scanlon 1 , Irene Teubner 1 , and Matthias Forkel 1  \n1Technische Universität Wien, Department of Geodesy and Geoinformation, Gußhausstraße 27-29, 1040 Vienna, Austria  \n2VanderSat, Wilhelminastraat 43A, 2011 VK Haarlem, The Netherlands  \nCorrespondence: Leander Moesinger ([Leander.Moesinger@geo.tuwien.ac.at](Leander.Moesinger@geo.tuwien.ac.at), [vodca@geo.tuwien.ac.at](vodca@geo.tuwien.ac.at))  \nAbstract. Since the late 1970s, spaceborne microwave radiometers have been providing measurements of radiation emitted by the Earth's surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to the density, biomass, and water content of vegetation. Because of its high temporal resolution and long availability, VOD can be used to monitor short-to long-term changes in vegetation. However, studying long-term VOD dynamics is gener-  \n5 ally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. However, combining multiple sensors into a single product is challenging as systematic differences between input products like biases, different temporal and spatial resolutions and coverage need to be overcome.  \nHere, we present a new series of long-term VOD products, the VOD Climate Archive (VODCA) . VODCA combines VOD re-  \n10 trievals that have been derived from multiple sensors (SSM/I, TMI, AMSR-E, Windsat and AMSR-2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely Ku-band (period 1987-2017), X-band (1997-2018) and C-band (2002-2018) . In this way, our multi-band VOD products preserve the unique characteristics of each frequency with respect to the structural elements of the canopy. Our merging approach builds on an existing approach that is used to merge satellite products of surface soil moisture: First, the data sets are co-calibrated via  \n15 cumulative distribution function matching using AMSR-E as scaling reference. To do so, we apply a new matching technique that scales outliers more robustly than ordinary piece-wise linear interpolation. Second, we aggregate the data sets by taking the arithmetic mean between temporally overlapping observations of the scaled data.  \nThe characteristics of VODCA are assessed for self-consistency and against other products. Using an autocorrelation analysis, we show that the merging of the multiple data sets successfully reduces the random error compared to the input data sets.  \n20 Spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and with Leaf Area Index observations from the MODIS instrument as well as with Vegetation Continuous Fields from the AVHRR instruments. Long-term trends in Ku-Band VODCA shows that since 1987 there has been a decline in VOD in the tropics and in large parts of east-central and north Asia, while a substantial increase is observed in India, large parts of Australia, south Africa, southeastern China and central north America. In summary, VODCA shows vast potential for monitoring spatial-temporal  \n25 ecosystem changes as it is sensitive to vegetation water content and unaffected by cloud cover or high sun zenith angles. As  \nsuch it complements existing long-term optical indices of greenness and leaf area.  \nThe VODCA products (Moesinger et al., 2019) are open access and available under Attribution 4.0 International at [https:](https:)//[doi.org/10.5281/zenodo.2575599](doi.org/10.5281/zenodo.2575599)  \n5 Copyright statement. COPYRIGHSTATEMENTTEXT  \n1 Introduction  \nVegetation attenuates microwave radiation that is emitted or reflected by the Earth surface. The degree of attenuation","cbCaibTMnBoRiJKW","https://ap.wps.com/l/cbCaibTMnBoRiJKW","pdf",8514296,33,"English","# Abstract\n# Introduction\n## Vegetation attenuation and VOD concepts\n## Applications of satellite-derived VOD","[{\"question\":\"What is vegetation optical depth (VOD) and what does it represent?\",\"answer\":\"VOD describes how vegetation attenuates microwave radiation measured from satellite observations. It is related to vegetation density, biomass, and water content.\"},{\"question\":\"Why is long-term VOD dynamics difficult to study directly?\",\"answer\":\"Individual microwave sensors cover only limited time spans. This short coverage hampers analysis of long-term VOD variability unless multiple products are merged into a single climate record.\"},{\"question\":\"How does VODCA combine multiple microwave sensors into long-term products?\",\"answer\":\"VODCA merges retrievals from several sensors and spectral bands. The approach co-calibrates datasets using cumulative distribution function matching (with AMSR-E as reference) and aggregates temporally overlapping observations using an arithmetic mean of the scaled data.\"}]","Global Long-term Microwave Vegetation Optical Depth Climate Archive (VODCA) | PDF",83]