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The advent of ICESat-2 and GEDI, two advanced lidar datasets, offers new opportunities for improving canopy height estimation. In this study, we used footprint-level canopy height products from ICESat-2 and GEDI, combined with features extracted from Landsat-8, PALSAR-2, and FABDEM products. The AutoGluon stacking ensemble learning algorithm was employed to construct inversion models, generating 30 m resolution continuous canopy height maps for the tropical forests of Puerto Rico. Accuracy validation was performed using the high-resolution G-LiHT airborne lidar products. Results show that tropical forest canopy height inversion remains challenging, with all models yielding relative root mean square errors (rRMSE) exceeding 0.30. The stacking ensemble model outperformed all base learners, and the GEDI-based map had slightly higher accuracy than the ICESat-2-based map, with RMSE values of 4.81 and 4.99 m, respectively. Both models showed systematic biases, but the GEDI-based model exhibited less underestimation for taller canopies, making it more suitable for biomass estimation. The proposed approach can be applied to other forest ecosystems, enabling fine-resolution canopy height mapping and enhancing forest conservation efforts.<\/jats:p>","DOI":"10.3390\/rs16203798","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T07:47:05Z","timestamp":1728892025000},"page":"3798","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Evaluating ICESat-2 and GEDI with Integrated Landsat-8 and PALSAR-2 for Mapping Tropical Forest Canopy Height"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8912-8729","authenticated-orcid":false,"given":"Aobo","family":"Liu","sequence":"first","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250014, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6710-0434","authenticated-orcid":false,"given":"Yating","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250014, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6910-6565","authenticated-orcid":false,"given":"Xiao","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"Key Laboratory of Comprehensive Observation of Polar Environment, Sun Yat-sen University, Ministry of Education, Zhuhai 519082, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shit, P.K., Pourghasemi, H.R., Das, P., and Bhunia, G.S. 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