{"id":295,"date":"2026-08-08T21:19:29","date_gmt":"2026-08-09T02:19:29","guid":{"rendered":"https:\/\/research.ece.ncsu.edu\/impress\/?page_id=295"},"modified":"2026-08-08T21:19:29","modified_gmt":"2026-08-09T02:19:29","slug":"smap-radiometer","status":"publish","type":"page","link":"https:\/\/research.ece.ncsu.edu\/impress\/research\/smap-radiometer\/","title":{"rendered":"Enhancing SMAP Radiometer Performance with Deep Learning"},"content":{"rendered":"\n<div class=\"wp-block-group has-background\" style=\"border-left-color:#CC0000;border-left-width:4px;background-color:#f7f8f9;margin-top:0px;margin-bottom:26px;padding-top:16px;padding-right:20px;padding-bottom:16px;padding-left:20px\"><div class=\"wp-block-group__inner-container is-layout-flow wp-block-group-is-layout-flow\">\n<p class=\"wp-block-paragraph\" style=\"font-size:15px\"><strong>Sponsor:<\/strong> NASA \u2014 Soil Moisture Active Passive (SMAP) Mission Science Team<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-size:15px\"><strong>Award:<\/strong> 80NSSC25K7061<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-size:15px\"><strong>Role:<\/strong> Co-Investigator (PI: Mehmet Kurum, University of Georgia)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-size:15px\"><strong>Period:<\/strong> January 2025 \u2013 December 2027<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"font-size:15px\"><strong>Team:<\/strong> Ahmed Manavi Alam and IMPRESS Lab researchers at NC State<\/p>\n<\/div><\/div>\n\n<h3 class=\"wp-block-heading has-text-color\" style=\"color:#CC0000\">Motivation<\/h3>\n\n<p class=\"wp-block-paragraph\">NASA\u2019s SMAP mission has produced a decade of global soil moisture observations from an L-band radiometer. Two effects limit the quality of that record: radio frequency interference from ground-based transmitters in and near the protected band, and slow drifts in radiometer calibration. Both are difficult to handle with fixed, threshold-based algorithms, because interference appears in many forms and calibration references are limited.<\/p>\n\n<h3 class=\"wp-block-heading has-text-color\" style=\"color:#CC0000\">Approach<\/h3>\n\n<p class=\"wp-block-paragraph\">This project applies deep learning to both problems, working directly with SMAP mission data:<\/p>\n\n<ul class=\"wp-block-list\">\n<li><strong>High-resolution RFI detection<\/strong> \u2014 convolutional architectures that flag interference at finer time-frequency resolution than conventional detectors, recovering observations that would otherwise be discarded.<\/li>\n\n<li><strong>Learning-based calibration<\/strong> \u2014 calibration models that use reduced reference information together with two-dimensional spectral features to track instrument behavior over time.<\/li>\n\n<li><strong>Validation against mission products<\/strong> \u2014 assessing the effect of improved RFI handling and calibration on downstream soil moisture retrievals.<\/li>\n<\/ul>\n\n<h3 class=\"wp-block-heading has-text-color\" style=\"color:#CC0000\">Selected outcomes<\/h3>\n\n<ul class=\"wp-block-list\">\n<li><em>Radio Frequency Interference Detection for SMAP Radiometer Using Convolutional Neural Networks<\/em>, IEEE JSTARS, 2022.<\/li>\n\n<li><em>Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features<\/em>, IEEE JSTARS, 2024.<\/li>\n\n<li><em>High-Resolution Radio Frequency Interference Detection in Microwave Radiometry Using Deep Learning<\/em>, IGARSS, 2023.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Sponsor: NASA \u2014 Soil Moisture Active Passive (SMAP) Mission Science Team Award: 80NSSC25K7061 Role: Co-Investigator (PI: Mehmet Kurum, University of Georgia) Period: January 2025 \u2013&#8230;<\/p>\n","protected":false},"author":152,"featured_media":0,"parent":7,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-295","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/research.ece.ncsu.edu\/impress\/wp-json\/wp\/v2\/pages\/295","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.ece.ncsu.edu\/impress\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/research.ece.ncsu.edu\/impress\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/research.ece.ncsu.edu\/impress\/wp-json\/wp\/v2\/users\/152"}],"replies":[{"embeddable":true,"href":"https:\/\/research.ece.ncsu.edu\/impress\/wp-json\/wp\/v2\/comments?post=295"}],"version-history":[{"count":1,"href":"https:\/\/research.ece.ncsu.edu\/impress\/wp-json\/wp\/v2\/pages\/295\/revisions"}],"predecessor-version":[{"id":300,"href":"https:\/\/research.ece.ncsu.edu\/impress\/wp-json\/wp\/v2\/pages\/295\/revisions\/300"}],"up":[{"embeddable":true,"href":"https:\/\/research.ece.ncsu.edu\/impress\/wp-json\/wp\/v2\/pages\/7"}],"wp:attachment":[{"href":"https:\/\/research.ece.ncsu.edu\/impress\/wp-json\/wp\/v2\/media?parent=295"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}