BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Berlin BEGIN:DAYLIGHT DTSTART:20260329T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20251026T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20251213T104431Z UID:0522B013-84BD-4CB2-B907-57C1523F51BA DTSTART;TZID=Europe/Berlin:20251210T140000 DTEND;TZID=Europe/Berlin:20251210T150000 DESCRIPTION:Please join the next meeting of the German GRSS Chapter (but op en to all who are interested) on Wednesday\, Dec 10\, 2pm Berlin time\, fe aturing a keynote talk of Dr. Milad Asgarimehr\, GFZ Helmholtz Centre for Geosciences\, followed by general updates.\n\nTitle of the presentation:\n \nInnovative remote sensing with GNSS Reflectometry: From hurricanes to fo rests’ water stress\n\nAbstract:\n\nGNSS Reflectometry (GNSS-R) is an pa ssive radar technique that repurposes signals from navigation satellites t o monitor the Earth’s surface at low cost and with high temporal frequen cy. This study presents recent advances showing how GNSS-R\, combined with modern AI\, can deliver improved environmental monitoring across oceans\, land\, and forests. Over oceans\, AI-enhanced GNSS-R provides more accura te wind-speed retrievals\, including during heavy rain and extreme events such as Hurricane Laura (2020). On land\, it yields high-quality soil mois ture estimates. We also show how GNSS-R enables forest water stress monito ring\, capturing sub-daily moisture dynamics that conventional sensors mis s. We further introduce a self-supervised\, generalist GNSS-R framework ca pable of retrieving multiple geophysical variables from a single model wit h reduced training data requirements. These developments demonstrate the g rowing potential of AI-driven GNSS-R as a next-generation Earth observatio n capability for environmental monitoring and early-warning systems.\n\nBi o:\n\nDr. Milad Asgarimehr's research is dedicated to the remote sensing o f the Earth's surface and atmosphere and studying climate trends using Rem ote Sensing data especially from Global Navigation Satellite System (GNSS) signals. He studies the Earth exploiting GNSS signals after reflection of f the Earth's surface\, the technique known as [GNSS Reflectometry](http:/ /www.gfz-potsdam.de/en/section/space-geodetic-techniques/topics/gnss-refle ctometry/). This includes the physics associated with bistatic radar and G NSS concepts\, geophysics\, data analysis\, and geoinformation retrieval a lgorithms and Earth system modeling including those based on Artificial In telligence.\n\nDr. Milad Asgarimehr is the PI of the Helmholtz AI project "[AI4GNSS-R](https://www.gfz-potsdam.de/en/section/space-geodetic-techniqu es/projects/ai4gnss-r)": The project AI for GNSS-R (AI4GNSSR) aims at impl ementing deep learning for novel remote sensing data products based on spa ceborne GNSS-R measurements. These include high-quality ocean surface wind speed data\, especially at extreme conditions and hurricanes\, and potent ially precipitation over calm oceans for the first time using GNSS signals .\n\nSpeaker(s): Milad\, \n\nVirtual: https://events.vtools.ieee.org/m/516 872 LOCATION:Virtual: https://events.vtools.ieee.org/m/516872 ORGANIZER:qian.song@gfz.de SEQUENCE:45 SUMMARY:IEEE GRSS Germany Chapter Webinar URL;VALUE=URI:https://events.vtools.ieee.org/m/516872 X-ALT-DESC:Description: <br /><p style="box-sizing: border-box\; margin: 0p x\; width: 1227.67px\; line-height: 20px\; white-space: pre-wrap\; word-br eak: break-word\; color: rgb(63\, 67\, 80)\; font-family: 'Open Sans'\, sa ns-serif\; font-size: 14px\; font-style: normal\; font-variant-ligatures: normal\; font-variant-caps: normal\; font-weight: 400\; letter-spacing: no rmal\; orphans: 2\; text-align: left\; text-indent: 0px\; text-transform: none\; widows: 2\; word-spacing: 0px\; -webkit-text-stroke-width: 0px\; ba ckground-color: rgba(63\, 67\, 80\, 0.008)\; text-decoration-thickness: in itial\; text-decoration-style: initial\; text-decoration-color: initial\;" >Please join the next meeting of the German GRSS Chapter (but open to all who are interested) on Wednesday\, Dec 10\, 2pm Berlin time\, featuring a keynote talk of Dr. Milad Asgarimehr\, GFZ Helmholtz Centre for Geoscience s\, followed by general updates.</p>\n<p style="box-sizing: border-box\; m argin: 0.5em 0px 0px\; width: 1227.67px\; line-height: 20px\; white-space: pre-wrap\; word-break: break-word\; color: rgb(63\, 67\, 80)\; font-famil y: 'Open Sans'\, sans-serif\; font-size: 14px\; font-style: normal\; font- variant-ligatures: normal\; font-variant-caps: normal\; font-weight: 400\; letter-spacing: normal\; orphans: 2\; text-align: left\; text-indent: 0px \; text-transform: none\; widows: 2\; word-spacing: 0px\; -webkit-text-str oke-width: 0px\; background-color: rgba(63\, 67\, 80\, 0.008)\; text-decor ation-thickness: initial\; text-decoration-style: initial\; text-decoratio n-color: initial\;">Title of the presentation:</p>\n<p style="box-sizing: border-box\; margin: 0.5em 0px 0px\; width: 1227.67px\; line-height: 20px\ ; white-space: pre-wrap\; word-break: break-word\; color: rgb(63\, 67\, 80 )\; font-family: 'Open Sans'\, sans-serif\; font-size: 14px\; font-style: normal\; font-variant-ligatures: normal\; font-variant-caps: normal\; font -weight: 400\; letter-spacing: normal\; orphans: 2\; text-align: left\; te xt-indent: 0px\; text-transform: none\; widows: 2\; word-spacing: 0px\; -w ebkit-text-stroke-width: 0px\; background-color: rgba(63\, 67\, 80\, 0.008 )\; text-decoration-thickness: initial\; text-decoration-style: initial\; text-decoration-color: initial\;"><strong><span lang="EN-US" style="mso-fa reast-language: EN-US\;">Innovative remote sensing with GNSS Reflectometry : From hurricanes to forests&rsquo\; water stress</span></strong></p>\n<p style="box-sizing: border-box\; margin: 0.5em 0px 0px\; width: 1227.67px\; line-height: 20px\; white-space: pre-wrap\; word-break: break-word\; colo r: rgb(63\, 67\, 80)\; font-family: 'Open Sans'\, sans-serif\; font-size: 14px\; font-style: normal\; font-variant-ligatures: normal\; font-variant- caps: normal\; font-weight: 400\; letter-spacing: normal\; orphans: 2\; te xt-align: left\; text-indent: 0px\; text-transform: none\; widows: 2\; wor d-spacing: 0px\; -webkit-text-stroke-width: 0px\; background-color: rgba(6 3\, 67\, 80\, 0.008)\; text-decoration-thickness: initial\; text-decoratio n-style: initial\; text-decoration-color: initial\;">Abstract:</p>\n<p sty le="box-sizing: border-box\; margin: 0.5em 0px 0px\; width: 1227.67px\; li ne-height: 20px\; white-space: pre-wrap\; word-break: break-word\; color: rgb(63\, 67\, 80)\; font-family: 'Open Sans'\, sans-serif\; font-size: 14p x\; font-style: normal\; font-variant-ligatures: normal\; font-variant-cap s: normal\; font-weight: 400\; letter-spacing: normal\; orphans: 2\; text- align: left\; text-indent: 0px\; text-transform: none\; widows: 2\; word-s pacing: 0px\; -webkit-text-stroke-width: 0px\; background-color: rgba(63\, 67\, 80\, 0.008)\; text-decoration-thickness: initial\; text-decoration-s tyle: initial\; text-decoration-color: initial\;">GNSS Reflectometry (GNSS -R) is an passive radar technique that repurposes signals from navigation satellites to monitor the Earth&rsquo\;s surface at low cost and with high temporal frequency. This study presents recent advances showing how GNSS- R\, combined with modern AI\, can deliver improved environmental monitorin g across oceans\, land\, and forests. Over oceans\, AI-enhanced GNSS-R pro vides more accurate wind-speed retrievals\, including during heavy rain an d extreme events such as Hurricane Laura (2020). On land\, it yields high- quality soil moisture estimates<span lang="EN-US">. We also show how GNSS- R </span>enables forest water stress<span lang="EN-US"> monitoring</span>\ , capturing sub-daily moisture dynamics that conventional sensors miss. We further introduce a self-supervised\, generalist GNSS-R framework capable of retrieving multiple geophysical variables from a single model with red uced training data requirements. These developments demonstrate the growin g potential of AI-driven GNSS-R as a next-generation Earth observation cap ability for environmental monitoring and early-warning systems.</p>\n<p st yle="box-sizing: border-box\; margin: 0.5em 0px 0px\; width: 1227.67px\; l ine-height: 20px\; white-space: pre-wrap\; word-break: break-word\; color: rgb(63\, 67\, 80)\; font-family: 'Open Sans'\, sans-serif\; font-size: 14 px\; font-style: normal\; font-variant-ligatures: normal\; font-variant-ca ps: normal\; font-weight: 400\; letter-spacing: normal\; orphans: 2\; text -align: left\; text-indent: 0px\; text-transform: none\; widows: 2\; word- spacing: 0px\; -webkit-text-stroke-width: 0px\; background-color: rgba(63\ , 67\, 80\, 0.008)\; text-decoration-thickness: initial\; text-decoration- style: initial\; text-decoration-color: initial\;">Bio:</p>\n<p style="box -sizing: border-box\; margin: 0.5em 0px 0px\; width: 1227.67px\; line-heig ht: 20px\; white-space: pre-wrap\; word-break: break-word\; color: rgb(63\ , 67\, 80)\; font-family: 'Open Sans'\, sans-serif\; font-size: 14px\; fon t-style: normal\; font-variant-ligatures: normal\; font-variant-caps: norm al\; font-weight: 400\; letter-spacing: normal\; orphans: 2\; text-align: left\; text-indent: 0px\; text-transform: none\; widows: 2\; word-spacing: 0px\; -webkit-text-stroke-width: 0px\; background-color: rgba(63\, 67\, 8 0\, 0.008)\; text-decoration-thickness: initial\; text-decoration-style: i nitial\; text-decoration-color: initial\;">Dr. Milad Asgarimehr's research is dedicated to the remote sensing of the Earth's surface and atmosphere and studying climate trends using Remote Sensing data especially from Glob al Navigation Satellite System (GNSS) signals. He studies the Earth exploi ting GNSS signals after reflection off the Earth's surface\, the technique known as <a href="http://www.gfz-potsdam.de/en/section/space-geodetic-tec hniques/topics/gnss-reflectometry/" rel="nofollow">GNSS Reflectometry</a>. &nbsp\; This includes the physics associated with bistatic radar and GNSS concepts\, geophysics\, data analysis\, and geoinformation retrieval algor ithms and Earth system modeling including those based on Artificial Intell igence.</p>\n<p style="box-sizing: border-box\; margin: 0.5em 0px 0px\; wi dth: 1227.67px\; line-height: 20px\; white-space: pre-wrap\; word-break: b reak-word\; color: rgb(63\, 67\, 80)\; font-family: 'Open Sans'\, sans-ser if\; font-size: 14px\; font-style: normal\; font-variant-ligatures: normal \; font-variant-caps: normal\; font-weight: 400\; letter-spacing: normal\; orphans: 2\; text-align: left\; text-indent: 0px\; text-transform: none\; widows: 2\; word-spacing: 0px\; -webkit-text-stroke-width: 0px\; backgrou nd-color: rgba(63\, 67\, 80\, 0.008)\; text-decoration-thickness: initial\ ; text-decoration-style: initial\; text-decoration-color: initial\;">Dr. M ilad Asgarimehr is the PI of the Helmholtz AI project "<a href="https://ww w.gfz-potsdam.de/en/section/space-geodetic-techniques/projects/ai4gnss-r" target="_blank" rel="nofollow noopener">AI4GNSS-R</a>": The project AI for GNSS-R (AI4GNSSR) aims at implementing deep learning for novel remote sen sing data products based on spaceborne GNSS-R measurements. These include high-quality ocean surface wind speed data\, especially at extreme conditi ons and hurricanes\, and potentially precipitation over calm oceans for th e first time using GNSS signals.</p> END:VEVENT END:VCALENDAR