{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:04:06Z","timestamp":1730264646746,"version":"3.28.0"},"reference-count":9,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T00:00:00Z","timestamp":1658016000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T00:00:00Z","timestamp":1658016000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,17]]},"DOI":"10.1109\/igarss46834.2022.9883245","type":"proceedings-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T20:12:24Z","timestamp":1664395944000},"page":"6112-6114","source":"Crossref","is-referenced-by-count":2,"title":["Forecasting Soil Moisture Using a Deep Learning Model Integrated with Passive Microwave Retrieval"],"prefix":"10.1109","author":[{"given":"Archana","family":"Kannan","sequence":"first","affiliation":[{"name":"University of Southern California,Los Angeles,CA,United States"}]},{"given":"Grigorios","family":"Tsagkatakis","sequence":"additional","affiliation":[{"name":"Institute of Computer Science,Foundation for Research and Technology - Hellas (FORTH),Heraklion,Greece"}]},{"given":"Ruzbeh","family":"Akbar","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology,Cambridge,MA,United States"}]},{"given":"Daniel","family":"Selva","sequence":"additional","affiliation":[{"name":"Texas A&#x0026;M University,College Station,TX,United States"}]},{"given":"Vinay","family":"Ravindra","sequence":"additional","affiliation":[{"name":"NASA Ames Research Center,Moffett Field,CA,United States"}]},{"given":"Richard","family":"Levinson","sequence":"additional","affiliation":[{"name":"NASA Ames Research Center,Moffett Field,CA,United States"}]},{"given":"Sreeja","family":"Nag","sequence":"additional","affiliation":[{"name":"NASA Ames Research Center,Moffett Field,CA,United States"}]},{"given":"Mahta","family":"Moghaddam","sequence":"additional","affiliation":[{"name":"University of Southern California,Los Angeles,CA,United States"}]}],"member":"263","reference":[{"key":"ref4","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/6184713"},{"key":"ref6","first-page":"1","article-title":"Soil Moisture Active Passive (SMAP) Algorithm Theoretical Basis Document L2 & L3 Radar\/Radiometer Soil Moisture (Passive) Data Products","author":"entekhabi","year":"2014","journal-title":"JPL"},{"key":"ref5","first-page":"1022","article-title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","volume":"48","author":"gal","year":"2016","journal-title":"Proceedings of the 33rd International Conference on Machine Learning"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2008.2011631"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS39084.2020.9323248"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1175\/JHM-D-19-0169.1"},{"journal-title":"SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update Version 5 Boulder Colorado USA NASA National Snow and Ice Data Center Distributed Active Archive Center","year":"2020","author":"reichle","key":"ref9"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0214508"}],"event":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","start":{"date-parts":[[2022,7,17]]},"location":"Kuala Lumpur, Malaysia","end":{"date-parts":[[2022,7,22]]}},"container-title":["IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9883023\/9883024\/09883245.pdf?arnumber=9883245","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T21:02:39Z","timestamp":1665781359000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9883245\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,17]]},"references-count":9,"URL":"https:\/\/doi.org\/10.1109\/igarss46834.2022.9883245","relation":{},"subject":[],"published":{"date-parts":[[2022,7,17]]}}}