{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:33:49Z","timestamp":1775745229738,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T00:00:00Z","timestamp":1640131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"Scientific and Technological Research Council of Turkey","doi-asserted-by":"publisher","award":["118E258"],"award-info":[{"award-number":["118E258"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This article addresses the scarcity of labeled data in multitemporal remote sensing image analysis, and especially in the context of Chlorophyll-a (Chl-a) estimation for inland water quality assessment. We propose a multitask CNN architecture that can exploit unlabeled satellite imagery and that can be generalized to other multitemporal remote sensing image analysis contexts where the target parameter exhibits seasonal fluctuations. Specifically, Chl-a estimation is set as the main task, and an unlabeled sample\u2019s month classification is set as an auxiliary network task. The proposed approach is validated with multitemporal\/spectral Sentinel-2 images of Lake Balik in Turkey using in situ measurements acquired during 2017\u20132019. We show that harnessing unlabeled data through multitask learning improves water quality estimation performance.<\/jats:p>","DOI":"10.3390\/rs14010018","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T02:02:57Z","timestamp":1640224977000},"page":"18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2685-914X","authenticated-orcid":false,"given":"Melike","family":"Ilteralp","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gebze Technical University, Kocaeli 41400, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7201-9243","authenticated-orcid":false,"given":"Sema","family":"Ariman","sequence":"additional","affiliation":[{"name":"Department of Meteorological Engineering, Samsun University, Samsun 55070, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6168-2883","authenticated-orcid":false,"given":"Erchan","family":"Aptoula","sequence":"additional","affiliation":[{"name":"Institute of Information Technologies, Gebze Technical University, Kocaeli 41400, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Caballero, I., Steinmetz, F., and Navarro, G. 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