{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T17:39:08Z","timestamp":1777484348941,"version":"3.51.4"},"reference-count":77,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T00:00:00Z","timestamp":1689033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61801221"],"award-info":[{"award-number":["61801221"]}]},{"name":"National Natural Science Foundation of China","award":["62001229"],"award-info":[{"award-number":["62001229"]}]},{"name":"National Natural Science Foundation of China","award":["2020M681604"],"award-info":[{"award-number":["2020M681604"]}]},{"name":"China Postdoctoral Science Foundation","award":["61801221"],"award-info":[{"award-number":["61801221"]}]},{"name":"China Postdoctoral Science Foundation","award":["62001229"],"award-info":[{"award-number":["62001229"]}]},{"name":"China Postdoctoral Science Foundation","award":["2020M681604"],"award-info":[{"award-number":["2020M681604"]}]},{"name":"European Space Agency","award":["61801221"],"award-info":[{"award-number":["61801221"]}]},{"name":"European Space Agency","award":["62001229"],"award-info":[{"award-number":["62001229"]}]},{"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>Copernicus Sentinel-1 images are widely used for forest mapping and predicting forest growing stock volume (GSV) due to their accessibility. However, certain important aspects related to the use of Sentinel-1 time series have not been thoroughly explored in the literature. These include the impact of image time series length on prediction accuracy, the optimal feature selection approaches, and the best prediction methods. In this study, we conduct an in-depth exploration of the potential of long time series of Sentinel-1 SAR data to predict forest GSV and evaluate the temporal dynamics of the predictions using extensive reference data. Our boreal coniferous forests study site is located near the Hyyti\u00e4l\u00e4 forest station in central Finland and covers an area of 2500 km2 with nearly 17,000 stands. We considered several prediction approaches and fine-tuned them to predict GSV in various evaluation scenarios. Our analyses used 96 Sentinel-1 images acquired over three years. Different approaches for aggregating SAR images and choosing feature (predictor) variables were evaluated. Our results demonstrate a considerable decrease in the root mean squared errors (RMSEs) of GSV predictions as the number of images increases. While prediction accuracy using individual Sentinel-1 images varied from 85 to 91 m3\/ha RMSE, prediction accuracy with combined images decreased to 75.6 m3\/ha. Feature extraction and dimension reduction techniques facilitated the achievement of near-optimal prediction accuracy using only 8\u201310 images. Examined methods included radiometric contrast, mutual information, improved k-Nearest Neighbors, random forests selection, Lasso, and Wrapper approaches. Lasso was the most optimal, with RMSE reaching 77.1 m3\/ha. Finally, we found that using assemblages of eight consecutive images resulted in the greatest accuracy in predicting GSV when initial acquisitions started between September and January.<\/jats:p>","DOI":"10.3390\/rs15143489","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T01:01:41Z","timestamp":1689123701000},"page":"3489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Sentinel-1 Time Series for Predicting Growing Stock Volume of Boreal Forest: Multitemporal Analysis and Feature Selection"],"prefix":"10.3390","volume":"15","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":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5715-8912","authenticated-orcid":false,"given":"Erkki","family":"Tomppo","sequence":"additional","affiliation":[{"name":"Department of Forest Sciences, University of Helsinki, 00014 Helsinki, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yrj\u00f6","family":"Rauste","sequence":"additional","affiliation":[{"name":"VTT Technical Research Centre of Finland, 00076 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronald E.","family":"McRoberts","sequence":"additional","affiliation":[{"name":"Department of Forest Resources, University of Minnesota, Saint Paul, MN 55108, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaan","family":"Praks","sequence":"additional","affiliation":[{"name":"Department of Electronics and Nanoengineering, Aalto University, 02150 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weimin","family":"Su","sequence":"additional","affiliation":[{"name":"School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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, 00076 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1080\/014311600210227","article-title":"The global rain forest mapping project a review","volume":"21","author":"Rosenqvist","year":"2000","journal-title":"Int. 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