{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T04:45:28Z","timestamp":1773549928953,"version":"3.50.1"},"reference-count":87,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"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":["42171439"],"award-info":[{"award-number":["42171439"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22-3-7-cspz-1-nsh"],"award-info":[{"award-number":["22-3-7-cspz-1-nsh"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021B03"],"award-info":[{"award-number":["2021B03"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Qingdao Science and Technology Benefit the People Demonstration and Guidance Program, China","award":["42171439"],"award-info":[{"award-number":["42171439"]}]},{"name":"Qingdao Science and Technology Benefit the People Demonstration and Guidance Program, China","award":["22-3-7-cspz-1-nsh"],"award-info":[{"award-number":["22-3-7-cspz-1-nsh"]}]},{"name":"Qingdao Science and Technology Benefit the People Demonstration and Guidance Program, China","award":["2021B03"],"award-info":[{"award-number":["2021B03"]}]},{"name":"Open Research Fund Program of Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, China","award":["42171439"],"award-info":[{"award-number":["42171439"]}]},{"name":"Open Research Fund Program of Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, China","award":["22-3-7-cspz-1-nsh"],"award-info":[{"award-number":["22-3-7-cspz-1-nsh"]}]},{"name":"Open Research Fund Program of Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, China","award":["2021B03"],"award-info":[{"award-number":["2021B03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aboveground biomass (AGB) is a vital indicator for studying carbon sinks in forest ecosystems. Semiarid forests harbor substantial carbon storage but received little attention due to the high spatial\u2013temporal heterogeneity that complicates the modeling of AGB in this environment. This study assessed the performance of different data sources (annual monthly time-series radar was Sentinel-1 [S1]; annual monthly time series optical was Sentinel-2 [S2]; and single-temporal airborne light detection and ranging [LiDAR]) and seven prediction approaches to map AGB in the semiarid forests on the border between Gansu and Qinghai Provinces in China. Five experiments were conducted using different data configurations from synthetic aperture radar backscatter, multispectral reflectance, LiDAR point cloud, and their derivatives (polarimetric combination indices, texture information, vegetation indices, biophysical features, and tree height- and canopy-related indices). The results showed that S2 acquired better prediction (coefficient of determination [R2]: 0.62\u20130.75; root mean square error [RMSE]: 30.08\u201338.83 Mg\/ha) than S1 (R2: 0.24\u20130.45; RMSE: 47.36\u201356.51 Mg\/ha). However, their integration further improved the results (R2: 0.65\u20130.78; RMSE: 28.68\u201335.92 Mg\/ha). The addition of single-temporal LiDAR highlighted its structural importance in semiarid forests. The best mapping accuracy was achieved by XGBoost, with the metrics from the S2 and S1 time series and the LiDAR-based canopy height information being combined (R2: 0.87; RMSE: 21.63 Mg\/ha; relative RMSE: 14.45%). Images obtained during the dry season were effective for AGB prediction. Tree-based models generally outperformed other models in semiarid forests. Sequential variable importance analysis indicated that the most important S1 metric to estimate AGB was the polarimetric combination indices sum, and the S2 metrics were associated with red-edge spectral regions. Meanwhile, the most important LiDAR metrics were related to height percentiles. Our methodology advocates for an economical, extensive, and precise AGB retrieval tailored for semiarid forests.<\/jats:p>","DOI":"10.3390\/rs16173241","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T07:59:40Z","timestamp":1725263980000},"page":"3241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Aboveground Biomass Mapping in SemiArid Forests by Integrating Airborne LiDAR with Sentinel-1 and Sentinel-2 Time-Series Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Linjing","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, 579 Qianwangang Road, Qingdao 266590, China"},{"name":"Key Laboratory of Ocean Geomatics, Ministry of Natural Resources, 579 Qianwangang Road, Qingdao 266590, China"}]},{"given":"Xinran","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, 579 Qianwangang Road, Qingdao 266590, China"}]},{"given":"Yaru","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, 579 Qianwangang Road, Qingdao 266590, China"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, 579 Qianwangang Road, Qingdao 266590, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhao, H., Li, Z., Zhou, G., Qiu, Z., and Wu, Z. (2019). Site-Specific Allometric Models for Prediction of Above- and Belowground Biomass of Subtropical Forests in Guangzhou, Southern China. Forests, 10.","DOI":"10.3390\/f10100862"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/1750-0680-6-13","article-title":"Options for monitoring and estimating historical carbon emissions from forest degradation in the context of REDD+","volume":"6","author":"Herold","year":"2011","journal-title":"Carbon Balance Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102333","DOI":"10.1016\/j.forpol.2020.102333","article-title":"Research trends: Tropical dry forests: The neglected research agenda?","volume":"122","year":"2021","journal-title":"For. Policy Econ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1007\/s10021-017-0168-2","article-title":"Carbon Accumulation in Neotropical Dry Secondary Forests: The Roles of Forest Age and Tree Dominance and Diversity","volume":"21","author":"Mora","year":"2018","journal-title":"Ecosystems"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.foreco.2006.01.022","article-title":"Development of tree volume equations for common timber species in the tropical rain forest area of Nigeria","volume":"226","author":"Akindele","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1111\/gcb.12060","article-title":"The imprint of humans on landscape patterns and vegetation functioning in the dry subtropics","volume":"19","author":"Baldi","year":"2013","journal-title":"Glob. Chang. Biol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"197","DOI":"10.15446\/caldasia.v38n1.57838","article-title":"Composition of insect assemblage canopy of subtropical dry forests of Semiarid Chaco, Argentina","volume":"38","author":"Diodato","year":"2016","journal-title":"Caldasia"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/j.jaridenv.2010.04.007","article-title":"Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina","volume":"74","author":"Gasparri","year":"2010","journal-title":"J. Arid Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1007\/s12665-020-09158-1","article-title":"Analysis of forest cover changes and trends in the Brazilian semiarid region between 2000 and 2018","volume":"79","author":"Santos","year":"2020","journal-title":"Environ. Earth Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1023\/A:1005342932178","article-title":"Carbon sequestration and turnover in semiarid savannas and dry forest","volume":"40","author":"Tiessen","year":"1998","journal-title":"Clim. Chang."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.agrformet.2013.12.008","article-title":"Spatial variability of canopy interception in a spruce forest of the semiarid mountain regions of China","volume":"188","author":"He","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2016.05.019","article-title":"Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry","volume":"183","author":"Cunliffe","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_14","first-page":"e02455","article-title":"Seasonal variation of environment and conspecific density-dependence effects on early seedling growth of a tropical tree in semi-arid savannahs","volume":"43","author":"Mensah","year":"2023","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2015.08.001","article-title":"Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest","volume":"169","author":"Tymen","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"113543","DOI":"10.1016\/j.rse.2023.113543","article-title":"A LiDAR biomass index-based approach for tree- and plot-level biomass mapping over forest farms using 3D point clouds","volume":"290","author":"Du","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Naik, P., Dalponte, M., and Bruzzone, L. (2021). Prediction of Forest Aboveground Biomass Using Multitemporal Multispectral Remote Sensing Data. Remote Sens., 13.","DOI":"10.3390\/rs13071282"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2014.11.001","article-title":"Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa","volume":"101","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1080\/07038992.2017.1330143","article-title":"Above Ground Biomass Estimation of Dalbergia sissoo Forest Plantation from Dual-Polarized ALOS-2 PALSAR Data","volume":"43","author":"Baig","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5167","DOI":"10.1109\/JSTARS.2019.2957549","article-title":"Aboveground Biomass Mapping Using ALOS-2\/PALSAR-2 Time-Series Images for Borneo\u2019s Forest","volume":"12","author":"Hayashi","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dlamini, M., Chirima, G., Sibanda, M., Adam, E., and Dube, T. (2021). Characterizing Leaf Nutrients ofWetland Plants and Agricultural Crops with Nonparametric Approach Using Sentinel-2 Imagery Data. Remote Sens., 13.","DOI":"10.3390\/rs13214249"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1080\/01431161.2017.1294781","article-title":"Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle","volume":"38","author":"Ahmed","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.isprsjprs.2017.04.005","article-title":"Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating","volume":"128","author":"Matikainen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6005605","DOI":"10.1109\/LGRS.2021.3125429","article-title":"A Deep Transfer Learning Method for Estimating Fractional Vegetation Cover of Sentinel-2 Multispectral Images","volume":"19","author":"Yu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1029\/2006JG000217","article-title":"Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements","volume":"111","author":"Zhang","year":"2006","journal-title":"J. Geophys. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"127521","DOI":"10.1016\/j.ufug.2022.127521","article-title":"Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds","volume":"69","author":"Lin","year":"2022","journal-title":"Urban For. Urban Green."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1111\/j.1365-2486.2011.02551.x","article-title":"Quantifying small-scale deforestation and forest degradation in African woodlands using radar imagery","volume":"18","author":"Ryan","year":"2012","journal-title":"Glob. Chang. Biol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rse.2011.07.023","article-title":"ESA\u2019s sentinel missions in support of Earth system science","volume":"120","author":"Berger","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2017.04.016","article-title":"Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species","volume":"129","author":"Shoko","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., Wang, R., Sun, F., and Wu, X. (2018). Evaluating the Performance of Sentinel-2, Landsat 8 and Pl\u00e9iades-1 in Mapping Mangrove Extent and Species. Remote Sens., 10.","DOI":"10.3390\/rs10091468"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2012.02.001","article-title":"Lidar sampling for large-area forest characterization: A review","volume":"121","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1111\/nph.15517","article-title":"Terrestrial LiDAR: A three-dimensional revolution in how we look at trees","volume":"222","author":"Disney","year":"2019","journal-title":"New Phytol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2017.08.014","article-title":"Influence of footprint size and geolocation error on the precision of forest biomass estimates from space-borne waveform LiDAR","volume":"200","author":"Milenkovic","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2019.03.016","article-title":"Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery","volume":"151","author":"Liu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","first-page":"101986","article-title":"Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery","volume":"85","author":"Wang","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1080\/17538947.2017.1301581","article-title":"Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon","volume":"10","author":"Feng","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_38","first-page":"160","article-title":"Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area","volume":"14","author":"Tian","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, L., Ren, C., Zhang, B., Wang, Z., and Xi, Y.J.F. (2018). Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery. Forests, 9.","DOI":"10.3390\/f9100582"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, L., Wang, Y., Ren, C., Zhang, B., and Wang, Z. (2019). Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data. Remote Sens., 11.","DOI":"10.3390\/rs11040414"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"111496","DOI":"10.1016\/j.rse.2019.111496","article-title":"Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets\u2014A case study","volume":"236","author":"Forkuor","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.foreco.2019.02.027","article-title":"Diversity-carbon stock relationship across vegetation types in W National park in Burkina Faso","volume":"438","author":"Dimobe","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_43","first-page":"497","article-title":"Biomass and net production of forest vegetation in China","volume":"16","author":"Fang","year":"1996","journal-title":"Acta Ecol. Sin."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1080\/17538947.2023.2165180","article-title":"Integrating Sentinel-1 and 2 with LiDAR data to estimate aboveground biomass of subtropical forests in northeast Guangdong, China","volume":"16","author":"Zhang","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5569","DOI":"10.1109\/JSTARS.2017.2748341","article-title":"Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass","volume":"10","author":"Shao","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2016.03.016","article-title":"Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas","volume":"117","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.3390\/rs4051190","article-title":"Advances in Forest Inventory Using Airborne Laser Scanning","volume":"4","author":"Yu","year":"2012","journal-title":"Remote Sens."},{"key":"ref_48","unstructured":"Agency, E.S. (2013). Sentinel-1 User Handbook."},{"key":"ref_49","first-page":"13","article-title":"Despeckling of multitemporal sentinel SAR images and its impact on agricultural area classification","volume":"11","author":"Lukin","year":"2018","journal-title":"Remote Sens."},{"key":"ref_50","unstructured":"Small, D., and Schubert, A. (2008). Guide to ASAR Geocoding, ESA-ESRIN Technical Note RSL-ASAR-GC-AD."},{"key":"ref_51","unstructured":"Sentinel-2_Team (2015). Sentinel-2 User Handbook."},{"key":"ref_52","unstructured":"B\u00f6nisch, H., and Sitte, H.H. (2016). Immunohistochemical Methods for the Study of the Expression of Low-Affinity Monoamine Transporters in the Brain. Neurotransmitter Transporters: Investigative Methods, Springer."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cao, L., and She, G. (2017). Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests. Remote Sens., 9.","DOI":"10.3390\/rs9090940"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"111323","DOI":"10.1016\/j.rse.2019.111323","article-title":"Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms","volume":"232","author":"Galvao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1139\/x05-230","article-title":"Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: An examination of scanning density","volume":"36","author":"Thomas","year":"2006","journal-title":"Can. J. For. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"6407","DOI":"10.3390\/rs6076407","article-title":"Estimates of Aboveground Biomass from Texture Analysis of Landsat Imagery","volume":"6","author":"Kelsey","year":"2014","journal-title":"Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.isprsjprs.2015.06.002","article-title":"Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas","volume":"108","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1080\/07038992.2021.1968811","article-title":"Multi-Sensor Aboveground Biomass Estimation in the Broadleaved Hyrcanian Forest of Iran","volume":"47","author":"Ronoud","year":"2021","journal-title":"Can. J. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1127\/pfg\/2016\/0303","article-title":"Important Variables of a RapidEye Time Series for Modelling Biophysical Parameters of Winter Wheat","volume":"2016","author":"Dahms","year":"2016","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.isprsjprs.2017.10.016","article-title":"Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery","volume":"134","author":"Castillo","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","unstructured":"Chen, T., He, T., and Benesty, M. (2016). xgboost: Extreme Gradient Boosting. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2015.04.013","article-title":"Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods\u2014A comparison","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_68","first-page":"102027","article-title":"Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India","volume":"86","author":"Sinha","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2011.11.002","article-title":"Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3","volume":"118","author":"Verrelst","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinlcage and Selection via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1080\/15481603.2016.1269869","article-title":"Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks","volume":"54","author":"Pham","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T.D., and Tien Bui, D. (2018). Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran). Remote Sens., 10.","DOI":"10.3390\/rs10020172"},{"key":"ref_74","first-page":"1","article-title":"Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data","volume":"53","author":"Zhao","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2017.08.001","article-title":"Toward a general tropical forest biomass prediction model from very high resolution optical satellite images","volume":"200","author":"Ploton","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"109286","DOI":"10.1016\/j.ecolind.2022.109286","article-title":"Improved estimation of aboveground biomass in rubber plantations by fusing spectral and textural information from UAV-based RGB imagery","volume":"142","author":"Liang","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Tamga, D.K., Latifi, H., Ullmann, T., Baumhauer, R., Bayala, J., and Thiel, M. (2023). Estimation of Aboveground Biomass in Agroforestry Systems over Three Climatic Regions in West Africa Using Sentinel-1, Sentinel-2, ALOS, and GEDI Data. Sensors, 23.","DOI":"10.3390\/s23010349"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"112582","DOI":"10.1016\/j.rse.2021.112582","article-title":"Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion","volume":"264","author":"Almeida","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1002\/2016GB005465","article-title":"Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon","volume":"30","author":"Longo","year":"2016","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Gao, L.H., Chai, G.Q., and Zhang, X.L. (2022). Above-Ground Biomass Estimation of Plantation with Different Tree Species Using Airborne LiDAR and Hyperspectral Data. Remote Sens., 14.","DOI":"10.3390\/rs14112568"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.rse.2012.01.015","article-title":"Forest structure modeling with combined airborne hyperspectral and LiDAR data","volume":"121","author":"Latifi","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Krofcheck, D.J., Litvak, M.E., Lippitt, C.D., and Neuenschwander, A. (2016). Woody Biomass Estimation in a Southwestern US Juniper Savanna Using LiDAR-Derived Clumped Tree Segmentation and Existing Allometries. Remote Sens., 8.","DOI":"10.3390\/rs8060453"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s13007-023-01043-9","article-title":"Improved estimation of aboveground biomass of regional coniferous forests integrating UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries","volume":"19","author":"Wang","year":"2023","journal-title":"Plant Methods"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Li, C.H., Zhou, L.Z., and Xu, W.B. (2021). Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China. Remote Sens., 13.","DOI":"10.3390\/rs13081595"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"127445","DOI":"10.1016\/j.ufug.2021.127445","article-title":"Quantification of carbon sequestration by urban forest using Landsat 8 OLI and machine learning algorithms in Jodhpur, India","volume":"67","author":"Uniyal","year":"2022","journal-title":"Urban For. Urban Green."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"016008","DOI":"10.1117\/1.JRS.12.016008","article-title":"Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data","volume":"12","author":"Laurin","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_87","first-page":"47","article-title":"Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia. American journal of pathology","volume":"2","author":"Mundava","year":"2014","journal-title":"Am. J. Pathol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3241\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:46:43Z","timestamp":1760111203000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,1]]},"references-count":87,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173241"],"URL":"https:\/\/doi.org\/10.3390\/rs16173241","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,1]]}}}