{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:31:41Z","timestamp":1775230301817,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601368 and 41861144026"],"award-info":[{"award-number":["41601368 and 41861144026"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest canopy height is a basic metric characterizing forest growth and carbon sink capacity. Based on full-polarized Advanced Land Observing Satellite\/Phased Array type L-band Synthetic Aperture Radar (ALOS\/PALSAR) data, this study used Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technology to estimate forest canopy height. In total the four methods of differential DEM (digital elevation model) algorithm, coherent amplitude algorithm, coherent phase-amplitude algorithm and three-stage random volume over ground algorithm (RVoG_3) were proposed to obtain canopy height and their accuracy was compared in consideration of the impacts of coherence coefficient and range slope levels. The influence of the statistical window size on the coherence coefficient was analyzed to improve the estimation accuracy. On the basis of traditional algorithms, time decoherence was performed on ALOS\/PALSAR data by introducing the change rate of Landsat NDVI (Normalized Difference Vegetation Index). The slope in range direction was calculated based on SRTM (Shuttle Radar Topography Mission) DEM data and then introduced into the s-RVoG (sloped-Random Volume over Ground) model to optimize the canopy height estimation model and improve the accuracy. The results indicated that the differential DEM algorithm underestimated the canopy height significantly, while the coherent amplitude algorithm overestimated the canopy height. After removing the systematic coherence, the overestimation of the RVoG_3 model was restrained, and the absolute error decreased from 23.68 m to 4.86 m. With further time decoherence, the determination coefficient increased to 0.2439. With the introduction of range slope, the s-RVoG model shows improvement compared to the RVoG model. Our results will provide a reference for the appropriate algorithm selection and optimization for forest canopy height estimation using full-polarized L-band synthetic aperture radar (SAR) data for forest ecosystem monitoring and management.<\/jats:p>","DOI":"10.3390\/rs13020174","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T20:45:42Z","timestamp":1609965942000},"page":"174","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Forest Canopy Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) Technology Based on Full-Polarized ALOS\/PALSAR Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0303-3978","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China"}]},{"given":"Qihui","family":"Zheng","sequence":"additional","affiliation":[{"name":"Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China"}]},{"given":"Haibing","family":"Xiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aperture Array and Space Application, No. 38 Research Institute of CETC, Hefei 230088, China"},{"name":"Key Laboratory of Intelligent Information Processing, No. 38 Research Institute of CETC, Hefei 230088, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1122-1661","authenticated-orcid":false,"given":"Xu","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Environment and Resources, Fuzhou University, Fuzhou 350108, China"}]},{"given":"Tetsuro","family":"Sakai","sequence":"additional","affiliation":[{"name":"Biosphere Informatics Laboratory, Department of Social Informatics, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/416679a","article-title":"Ecosystem dynamics of the boreal forest: The Kluane project","volume":"416","author":"Stenseth","year":"2002","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s11056-017-9608-2","article-title":"Monitoring of post-fire forest regeneration under different restoration treatments based on ALOS\/PALSAR data","volume":"49","author":"Chen","year":"2018","journal-title":"New For."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/nature10415","article-title":"Increased forest ecosystem carbon and nitrogen storage from nitrogen rich bedrock","volume":"477","author":"Morford","year":"2011","journal-title":"Nature"},{"key":"ref_4","first-page":"e00479","article-title":"Shrub biomass estimation in semi-arid sandland ecosystem based on remote sensing technology","volume":"16","author":"Chen","year":"2018","journal-title":"Glob. 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