{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T07:46:33Z","timestamp":1782891993859,"version":"3.54.5"},"reference-count":63,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T00:00:00Z","timestamp":1667520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62001229"],"award-info":[{"award-number":["62001229"]}]},{"name":"National Natural Science Foundation of China","award":["62101264"],"award-info":[{"award-number":["62101264"]}]},{"name":"National Natural Science Foundation of China","award":["62101260"],"award-info":[{"award-number":["62101260"]}]},{"name":"National Natural Science Foundation of China","award":["2020M681604"],"award-info":[{"award-number":["2020M681604"]}]},{"name":"China Postdoctoral Science Foundation","award":["62001229"],"award-info":[{"award-number":["62001229"]}]},{"name":"China Postdoctoral Science Foundation","award":["62101264"],"award-info":[{"award-number":["62101264"]}]},{"name":"China Postdoctoral Science Foundation","award":["62101260"],"award-info":[{"award-number":["62101260"]}]},{"name":"China Postdoctoral Science Foundation","award":["2020M681604"],"award-info":[{"award-number":["2020M681604"]}]},{"name":"Business Finland","award":["62001229"],"award-info":[{"award-number":["62001229"]}]},{"name":"Business Finland","award":["62101264"],"award-info":[{"award-number":["62101264"]}]},{"name":"Business Finland","award":["62101260"],"award-info":[{"award-number":["62101260"]}]},{"name":"Business Finland","award":["2020M681604"],"award-info":[{"award-number":["2020M681604"]}]},{"name":"European Space Agency","award":["62001229"],"award-info":[{"award-number":["62001229"]}]},{"name":"European Space Agency","award":["62101264"],"award-info":[{"award-number":["62101264"]}]},{"name":"European Space Agency","award":["62101260"],"award-info":[{"award-number":["62101260"]}]},{"name":"European Space Agency","award":["2020M681604"],"award-info":[{"award-number":["2020M681604"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Time series of SAR imagery combined with reference ground data can be suitable for producing forest inventories. Copernicus Sentinel-1 imagery is particularly interesting for forest mapping because of its free availability to data users; however, temporal dependencies within SAR time series that can potentially improve mapping accuracy are rarely explored. In this study, we introduce a novel semi-supervised Long Short-Term Memory (LSTM) model, CrsHelix-LSTM, and demonstrate its utility for predicting forest tree height using time series of Sentinel-1 images. The model brings three important modifications to the conventional LSTM model. Firstly, it uses a Helix-Elapse (HE) projection to capture the relationship between forest temporal patterns and Sentinel-1 time series, when time intervals between datatakes are irregular. A skip-link based LSTM block is introduced and a novel backbone network, Helix-LSTM, is proposed to retrieve temporal features at different receptive scales. Finally, a novel semisupervised strategy, Cross-Pseudo Regression, is employed to achieve better model performance when reference training data are limited. CrsHelix-LSTM model is demonstrated over a representative boreal forest site located in Central Finland. A time series of 96 Sentinel-1 images are used in the study. The developed model is compared with basic LSTM model, attention-based bidirectional LSTM and several other established regression approaches used in forest variable mapping, demonstrating consistent improvement of forest height prediction accuracy. At best, the achieved accuracy of forest height mapping was 28.3% relative root mean squared error (rRMSE) for pixel-level predictions and 18.0% rRMSE on stand level. We expect that the developed model can also be used for modeling relationships between other forest variables and satellite image time series.<\/jats:p>","DOI":"10.3390\/rs14215560","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T09:23:37Z","timestamp":1667553817000},"page":"5560","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Improved LSTM Model for Boreal Forest Height Mapping Using Sentinel-1 Time Series"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9655-1207","authenticated-orcid":false,"given":"Shaojia","family":"Ge","sequence":"first","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weimin","family":"Su","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5035-8228","authenticated-orcid":false,"given":"Yrj\u00f6","family":"Rauste","sequence":"additional","affiliation":[{"name":"VTT Technical Research Centre of Finland, P.O. Box 1000, 00076 Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7466-3569","authenticated-orcid":false,"given":"Jaan","family":"Praks","sequence":"additional","affiliation":[{"name":"Department of Electronics and Nanoengineering, Aalto University, P.O. Box 11000, 00076 Aalto, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8576-404X","authenticated-orcid":false,"given":"Oleg","family":"Antropov","sequence":"additional","affiliation":[{"name":"VTT Technical Research Centre of Finland, P.O. Box 1000, 00076 Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s10712-019-09510-6","article-title":"The role and need for space-based forest biomass-related measurements in environmental management and policy","volume":"40","author":"Herold","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.rse.2006.09.034","article-title":"Remote sensing support for national forest inventories","volume":"110","author":"McRoberts","year":"2007","journal-title":"Remote Sens. 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