{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T02:04:47Z","timestamp":1776737087094,"version":"3.51.2"},"reference-count":87,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"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":["31770677"],"award-info":[{"award-number":["31770677"]}],"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":["31760206"],"award-info":[{"award-number":["31760206"]}],"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":["YNWR-QNBJ-2018-184"],"award-info":[{"award-number":["YNWR-QNBJ-2018-184"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ten-Thousand Talents Program of Yunnan Province, China","award":["31770677"],"award-info":[{"award-number":["31770677"]}]},{"name":"Ten-Thousand Talents Program of Yunnan Province, China","award":["31760206"],"award-info":[{"award-number":["31760206"]}]},{"name":"Ten-Thousand Talents Program of Yunnan Province, China","award":["YNWR-QNBJ-2018-184"],"award-info":[{"award-number":["YNWR-QNBJ-2018-184"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The uncertainty from the under-estimation and over-estimation of forest aboveground biomass (AGB) is an urgent problem in optical remote sensing estimation. In order to more accurately estimate the AGB of Pinus densata forests in Shangri-La City, we mainly discuss three non-parametric models\u2014the artificial neural network (ANN), random forests (RFs), and the quantile regression neural network (QRNN) based on 146 sample plots and Sentinel-2 images in Shangri-La City, China. Moreover, we selected the corresponding optical quartile models with the lowest mean error at each AGB segment to combine as the best QRNN (QRNNb). The results showed that: (1) for the whole biomass segment, the QRNNb has the best fitting performance compared with the ANN and RFs, the ANN has the lowest R2 (0.602) and the highest RMSE (48.180 Mg\/ha), and the difference between the QRNNb and RFs is not apparent. (2) For the different biomass segments, the QRNNb has a better performance. Especially when AGB is lower than 40 Mg\/ha, the QRNNb has the highest R2 of 0.961 and the lowest RMSE of 1.733 (Mg\/ha). Meanwhile, when AGB is larger than 160 Mg\/ha, the QRNNb has the highest R2 of 0.867 and the lowest RMSE of 18.203 Mg\/ha. This indicates that the QRNNb is more robust and can improve the over-estimation and under-estimation in AGB estimation. This means that the QRNNb combined with the optimal quantile model of each biomass segment provides a method with more potential for reducing the uncertainties in AGB estimation using optical remote sensing images.<\/jats:p>","DOI":"10.3390\/rs15030559","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T02:31:11Z","timestamp":1674009071000},"page":"559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Reduction in Uncertainty in Forest Aboveground Biomass Estimation Using Sentinel-2 Images: A Case Study of Pinus densata Forests in Shangri-La City, China"],"prefix":"10.3390","volume":"15","author":[{"given":"Lu","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9588-4931","authenticated-orcid":false,"given":"Weiheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"},{"name":"Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650233, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leiguang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"},{"name":"Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650233, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1925-6690","authenticated-orcid":false,"given":"Guanglong","family":"Ou","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650224, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1126\/science.285.5427.574","article-title":"The U.S. Carbon budget: Contributions from land-use change","volume":"285","author":"Houghton","year":"1999","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8675","DOI":"10.1080\/01431161.2021.1984611","article-title":"Estimation of forest aboveground biomass by using a mixed-effects model","volume":"42","author":"Feng","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sun, S., Wang, Y., Song, Z., Chen, C., Zhang, Y., Chen, X., Chen, W., Yuan, W., Wu, X., and Ran, X. (2021). Modelling aboveground biomass carbon stock of the Bohai rim coastal wetlands by integrating remote sensing, terrain, and climate data. Remote Sens., 13.","DOI":"10.3390\/rs13214321"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"113195","DOI":"10.1016\/j.rse.2022.113195","article-title":"Fifty years of Landsat science and impacts","volume":"280","author":"Wulder","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/S0034-4257(03)00039-7","article-title":"Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions","volume":"85","author":"Foody","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Puliti, S., Solberg, S., N\u00e6sset, E., Gobakken, T., Zahabu, E., Mauya, E., and Malimbwi, R. (2017). Modelling aboveground biomass in Tanzanian miombo woodlands using TanDEM-X world DEM and field data. Remote Sens., 9.","DOI":"10.3390\/rs9100984"},{"key":"ref_7","unstructured":"Xue, B.W. (2015). Lidar and Machine Learning Estimation of Hardwood Forest Biomass in Mountainous and Bottomland Environments. [Master\u2019s Thesis, University of Arkansas]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"146816","DOI":"10.1016\/j.scitotenv.2021.146816","article-title":"Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing","volume":"781","author":"Tian","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2014.01.001","article-title":"Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data","volume":"89","author":"Vaglio","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.3390\/rs3071284","article-title":"Portable and airborne small footprint LiDAR: Forest canopy structure estimation of fire managed plots","volume":"3","author":"Listopad","year":"2011","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Spriggs, R., Coomes, D., Jones, T., Caspersen, J., and Vanderwel, M. (2017). An alternative approach to using LiDAR remote sensing data to predict stem diameter distributions across a temperate forest landscape. Remote Sens., 9.","DOI":"10.3390\/rs9090944"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.isprsjprs.2012.03.011","article-title":"Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions","volume":"70","author":"Cutler","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"El Hage, M., Villard, L., Huang, Y., Ferro-Famil, L., Koleck, T., Le Toan, T., and Polidori, L. (2022). Multicriteria accuracy assessment of digital elevation models (DEMs) produced by airborne P-band polarimetric SAR tomography in tropical rainforests. Remote Sens., 14.","DOI":"10.3390\/rs14174173"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rse.2019.03.032","article-title":"The European space agency BIOMASS Mission: Measuring forest above-ground biomass from space","volume":"227","author":"Quegan","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_15","first-page":"1","article-title":"Long-term trends of P-Band temporal decorrelation over a tropical dense forest-experimental results for the BIOMASS mission","volume":"60","author":"Villard","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.1016\/j.rse.2011.03.020","article-title":"The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle","volume":"115","author":"Quegan","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hajnsek, I., and Desnos, Y.-L. (2021). Polarimetric Synthetic Aperture Radar: Principles and Application, Springer International Publishing. Remote Sensing and Digital Image, Processing.","DOI":"10.1007\/978-3-030-56504-6"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Serrano, P.M., C\u00e1rdenas, D., Jos\u00e9, L., Corral-Rivas, J.J., Jim\u00e9nez, E., L\u00f3pez-S\u00e1nchez, C.A., and Vega-Nieva, D.J. (2019). Modeling of aboveground biomass with Landsat 8 OLI and machine learning in temperate forests. Forests, 11.","DOI":"10.3390\/f11010011"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.ecolind.2019.02.023","article-title":"Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm","volume":"102","author":"Zeng","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sagang, L.B.T., Ploton, P., Sonk\u00e9, B., Poilv\u00e9, H., Couteron, P., and Barbier, N. (2020). Airborne Lidar sampling pivotal for accurate regional AGB predictions from multispectral images in forest-Savanna landscapes. Remote Sens., 12.","DOI":"10.3390\/rs12101637"},{"key":"ref_21","unstructured":"SUHET (2013). Sentinel-2 User Handbook, European Space Agency."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, Y., Guerschman, J., Shendryk, Y., Henry, D., and Harrison, M.T. (2021). Estimating pasture biomass using Sentinel-2 imagery and machine learning. Remote Sens., 13.","DOI":"10.3390\/rs13040603"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1002\/rse2.93","article-title":"Sentinel-2 accurately maps green-attack stage of European spruce bark beetle (Ips typographus L.) compared with Landsat-8","volume":"5","author":"Abdullah","year":"2018","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A comparison of vegetation indices over a global set of TM images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_26","unstructured":"Kalaitzidis, C., Zianis, D., and Heinzel, V. (2009). Proceedings of the 29th Symposium of the European Association of Remote Sensing Laboratories, Chania, Greece, IOS Press Ebook."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8069","DOI":"10.1080\/01431161.2020.1771789","article-title":"Integration of synthetic aperture radar and multispectral data for aboveground biomass retrieval in Zagros oak forests, Iran: An attempt on Sentinel imagery","volume":"41","author":"Safari","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106155","DOI":"10.1016\/j.agwat.2020.106155","article-title":"Potential of using spectral vegetation indices for corn green biomass estimation based on their relationship with the photosynthetic vegetation sub-pixel fraction","volume":"236","author":"Luan","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"113232","DOI":"10.1016\/j.rse.2022.113232","article-title":"Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery","volume":"282","author":"David","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4829","DOI":"10.1080\/01431160500239107","article-title":"Relating SAR image texture to the biomass of regenerating tropical forests","volume":"26","author":"Kuplich","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","unstructured":"Wang, X.Q., Pang, Y., Zhang, Z.J., and Yuan, Y. (2014, January 13\u201318). Forest Aboveground Biomass Estimation Using SPOT-5 Texture Indices and Spectral Derivatives. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec, QC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apgeog.2018.05.011","article-title":"Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest","volume":"96","author":"Ghosh","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_34","first-page":"20150202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. Ser. A Math. Phys. Eng. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/s40663-020-00276-7","article-title":"Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests","volume":"7","author":"Su","year":"2020","journal-title":"For. Ecosyst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"34","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Arsalan, G., Soheil, Z., Reza, M.A., Meisam, A., Mohammadzadeh, A., and Sadegh, J. (2021). Ghrobanian-Mangrove ecosystem mapping using Sentinel-1 and Sentinel-2 Satellite images and random forest algorithm in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13132565"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"147335","DOI":"10.1016\/j.scitotenv.2021.147335","article-title":"Estimating the aboveground biomass of coniferous forest in Northeast China using spectral variables, land surface temperature and soil moisture","volume":"785","author":"Jiang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zeng, P., Zhang, W., Li, Y., Shi, J., and Wang, Z. (2022). Forest total and component above-ground biomass (AGB) estimation through C- and L-band polarimetric SAR data. Forests, 13.","DOI":"10.3390\/f13030442"},{"key":"ref_41","first-page":"100560","article-title":"Estimating tree aboveground biomass using multispectral satellite-based data in Mediterranean agroforestry system using random forest algorithm","volume":"23","author":"Godinho","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ou, G.L., Lv, Y.Y., Xu, H., and Wang, G.X. (2019). Improving forest aboveground biomass estimation of Pinus densata forest in Yunnan of southwest China by spatial regression using Landsat 8 images. Remote Sens., 11.","DOI":"10.3390\/rs11232750"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1080\/01431161.2012.725958","article-title":"Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression","volume":"34","author":"Axelsson","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, S., Wang, D., and Sun, J. (2022). Artificial neural network-based ionospheric delay correction method for satellite-based augmentation systems. Remote Sens., 14.","DOI":"10.3390\/rs14030676"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Beaudoin, A., Hall, R.J., Castilla, G., Filiatrault, M., Villemaire, P., Skakun, R., and Guindon, L. (2022). Improved k-NN mapping of forest attributes in northern Canada using spaceborne L-Band SAR, multispectral and LiDAR data. Remote Sens., 14.","DOI":"10.3390\/rs14051181"},{"key":"ref_46","first-page":"100462","article-title":"Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest","volume":"21","author":"Yadav","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1007\/s40808-021-01276-4","article-title":"Multi-layer perceptron artificial neural network (MLP-ANN) prediction of biomass higher heating value (HHV) using combined biomass proximate and ultimate analysis data","volume":"8","author":"Joshua","year":"2022","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.biombioe.2016.03.020","article-title":"Artificial neural network application in comparison with modeling allometric equations for predicting above-ground biomass in the Hyrcanian mixed-beech forests of Iran","volume":"88","author":"Vahedi","year":"2016","journal-title":"Biomass Bioenergy"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1016\/j.ecolmodel.2009.04.025","article-title":"A comparison of two models with Landsat data for estimating above-ground grassland biomass in Inner Mongolia, China","volume":"220","author":"Xie","year":"2009","journal-title":"Ecol. Model."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.rse.2017.10.011","article-title":"Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region","volume":"204","author":"Yang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1002\/1099-131X(200007)19:4<299::AID-FOR775>3.0.CO;2-V","article-title":"A quantile regression neural network approach to estimating the conditional density of multiperiod returns","volume":"19","author":"Taylor","year":"2000","journal-title":"J. Forecast."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"33","DOI":"10.2307\/1913643","article-title":"Regression Quantiles","volume":"46","author":"Koenker","year":"1978","journal-title":"Econometrica"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1890\/1540-9295(2003)001[0412:AGITQR]2.0.CO;2","article-title":"A gentle introduction to quantile regression for ecologists","volume":"1","author":"Cade","year":"2003","journal-title":"Front. Ecol. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1137\/12S01174X","article-title":"A quantile regression study of climate change in Chicago, 1960\u20132010","volume":"5","author":"Julien","year":"2012","journal-title":"SIAM Undergrad. Res. Online"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1016\/j.cageo.2010.07.005","article-title":"Quantile regression neural networks: Implementation in R and application to precipitation downscaling","volume":"37","author":"Cannon","year":"2011","journal-title":"Comput. Geosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3207","DOI":"10.1007\/s00477-018-1573-6","article-title":"Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes","volume":"32","author":"Cannon","year":"2018","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.enconman.2018.03.010","article-title":"Probability density forecasting of wind power using quantile regression neural network and kernel density estimation","volume":"164","author":"He","year":"2018","journal-title":"Energy Convers. Manag."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1080\/13504509.2014.943330","article-title":"Regional-scale analysis on the strengths, weaknesses, opportunities, and threats in sustainable development of Shangri-La County","volume":"22","author":"Dong","year":"2014","journal-title":"Int. J. Sustain. Dev. World Ecol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1007\/s10722-011-9783-5","article-title":"Genetic diversity analysis of hulless barley from Shangri-la region revealed by SSR and AFLP markers","volume":"59","author":"Guo","year":"2011","journal-title":"Genet. Resour. Crop Evol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3796","DOI":"10.1111\/j.1365-294X.2011.05157.x","article-title":"Colonization of the Tibetan plateau by the homoploid hybrid pine Pinus densata","volume":"20","author":"Wang","year":"2011","journal-title":"Mol. Ecol."},{"key":"ref_61","unstructured":"Compilation Committee of Yunnan Forest (1986). Yunnan Forest, China Forestry Publishing House."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1007\/s11676-018-0713-7","article-title":"Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data","volume":"30","author":"Zhang","year":"2019","journal-title":"J. For. Res."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ou, G.L., Li, C., Lv, Y.Y., Wei, A.C., Xiong, H.X., Xu, H., and Wang, G.X. (2019). Improving aboveground biomass estimation of Pinus densata forests in Yunnan using Landsat 8 imagery by incorporating age dummy variable and method comparison. Remote Sens., 11.","DOI":"10.3390\/rs11070738"},{"key":"ref_64","first-page":"49","article-title":"A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"108412","DOI":"10.1016\/j.agrformet.2021.108412","article-title":"Disentangling the effects of stand and climatic variables on forest productivity of Chinese fir plantations in subtropical China using a random forest algorithm","volume":"304\u2013305","author":"Wang","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_66","first-page":"31","article-title":"Applications of artificial intelligence in machine learning: Review and Prospect","volume":"115","author":"Roy","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1080\/01431160802549278","article-title":"How many hidden layers and nodes?","volume":"30","author":"Stathakis","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.conbuildmat.2014.03.041","article-title":"An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model","volume":"62","author":"Tiryaki","year":"2014","journal-title":"Constr. Build. Mater."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.rse.2015.07.028","article-title":"LiDAR-based prediction of forest biomass using hierarchical models with spatially varying coefficients","volume":"169","author":"Babcock","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"6940","DOI":"10.1038\/s41598-017-07197-6","article-title":"Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm","volume":"7","author":"Wang","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sun, X., Li, G., Wang, M., and Fan, Z. (2019). Analyzing the uncertainty of estimating forest aboveground biomass using optical imagery and spaceborne LiDAR. Remote Sens., 11.","DOI":"10.3390\/rs11060722"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.isprsjprs.2014.08.014","article-title":"Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series","volume":"102","author":"Zhu","year":"2015","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2015.10.005","article-title":"Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above-ground biomass across different fertilizer treatments","volume":"110","author":"Sibanda","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_74","first-page":"399","article-title":"High-density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm","volume":"18","author":"Mutanga","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1080\/10106049.2019.1588390","article-title":"Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal","volume":"35","author":"Pandit","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_76","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_77","unstructured":"Xu, H., and Yue, C. (2014). Study on Forest Landscape Change and Forest Biomass Estimation in Shangri-La Based on Remote Sensing Technology, Yunnan Science and Technology Press."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.rse.2009.12.018","article-title":"Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches","volume":"114","author":"Powell","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_79","first-page":"743","article-title":"Remote sensing estimation of Pinus densata aboveground biomass based on k-NN nonparametric model","volume":"40","author":"Xie","year":"2018","journal-title":"Acta Agric. Univ. Jiangxiensis"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Li, L., Zhou, X.S., Chen, L., Chen, L., Zhang, Y., and Liu, Y. (2020). Estimating urban vegetation biomass from Sentinel-2A image data. Forests, 11.","DOI":"10.3390\/f11020125"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"967","DOI":"10.14358\/PERS.71.8.967","article-title":"Satellite estimation of aboveground biomass and impacts of forest stand structure","volume":"71","author":"Lu","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Chang, J.S., and Shoshany, M. (2016, January 10\u201315). Mediterranean Shrublands Biomass Estimation Using Sentinel-1 and Sentinel-2. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730380"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Masjedi, A., Zhao, J.Q., Thompson, A.M., Yang, K.W., Flatt, J.E., Crawford, M.M., Ebert, D.S., Tuinstra, M.R., Hammer, G., and Chapman, S. (2018, January 22\u201327). Sorghum Biomass Prediction Using UAV-Based Remote Sensing Data and Crop Model Simulation. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519034"},{"key":"ref_84","unstructured":"Freeman, E.A., and Moisen, G.G. (2015, January 8\u201310). An Application of Quantile Random Forests for Predictive Mapping of Forest Attributes. Proceedings of the New Directions in Inventory Techniques & Applications Forest Inventory & Analysis (FIA) Symposium, Portland, OR, USA."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Le, N.N., Ha, N.T., Nguyen, L.V., Xia, J., Yokoya, N., To, T.T., Trinh, H.X., Kieu, L.Q., and Takeuchi, W. (2020). Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam. Remote Sens., 12.","DOI":"10.3390\/rs12050777"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in vegetation remote sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.rse.2019.01.039","article-title":"Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach","volume":"224","author":"Xu","year":"2019","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/559\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:08:36Z","timestamp":1760119716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,17]]},"references-count":87,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030559"],"URL":"https:\/\/doi.org\/10.3390\/rs15030559","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,17]]}}}