{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:27:51Z","timestamp":1780486071295,"version":"3.54.1"},"reference-count":73,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,20]],"date-time":"2021-03-20T00:00:00Z","timestamp":1616198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Sichuan Science and Technology Program","award":["2020YFS0058"],"award-info":[{"award-number":["2020YFS0058"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A2090"],"award-info":[{"award-number":["U20A2090"]}],"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":["41801272"],"award-info":[{"award-number":["41801272"]}],"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":["41671361"],"award-info":[{"award-number":["41671361"]}],"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>Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually explored. Optical and SAR data are expected to be sensitive to different types of fuel loads because of their different imaging mechanisms. Optical data mainly captures the characteristics of leaf and forest canopy, while the latter is more sensitive to forest vertical structures due to its strong penetrability. This study aims to explore the performance of Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data as well as their combination on estimating three different types of fuel load\u2014stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We first analyzed the correlation between the three types of fuel load and optical and SAR data. Then, the partial least squares regression (PLSR) was used to build the fuel load estimation models based on the fuel load measurements from Vindeln, Sweden, and variables derived from optical and SAR data. Based on the leave-one-out cross-validation (LOOCV) method, results show that L-band SAR data performed well on all three types of fuel load (R2 = 0.72, 0.70, 0.72). The optical data performed best for FFL estimation (R2 = 0.66), followed by BFL (R2 = 0.56) and SFL (R2 = 0.37). Further improvements were found for the SFL, BFL and FFL estimation when integrating optical and SAR data (R2 = 0.76, 0.81, 0.82), highlighting the importance of data selection and combination for fuel load estimation.<\/jats:p>","DOI":"10.3390\/rs13061189","type":"journal-article","created":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T23:47:41Z","timestamp":1616370461000},"page":"1189","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Yanxi","family":"Li","sequence":"first","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5344-1801","authenticated-orcid":false,"given":"Xingwen","family":"Quan","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"},{"name":"Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanmang","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binbin","family":"He","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1126\/science.1163886","article-title":"Fire in the earth system","volume":"324","author":"Bowman","year":"2009","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1002\/fee.2044","article-title":"Wildfires as an ecosystem service","volume":"17","author":"Pausas","year":"2019","journal-title":"Front. Ecol. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.agrformet.2007.12.005","article-title":"Estimation of live fuel moisture content from MODIS images for fire risk assessment","volume":"148","author":"Yebra","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1007\/s13280-018-1084-1","article-title":"Human\u2013environmental drivers and impacts of the globally extreme 2017 Chilean fires","volume":"48","author":"Bowman","year":"2019","journal-title":"Ambio"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms8537","article-title":"Climate-induced variations in global wildfire danger from 1979 to 2013","volume":"6","author":"Jolly","year":"2015","journal-title":"Nature Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1038\/d41586-018-05840-4","article-title":"Wildfire science is at a loss for comprehensive data","volume":"560","author":"David","year":"2018","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1038\/s41477-019-0485-x","article-title":"Fire, climate and changing forests","volume":"5","author":"Keeley","year":"2019","journal-title":"Nat. Plants"},{"key":"ref_8","unstructured":"Byram, G. (1959). Combustion of forest fuels. Forest Fire Forest Fire: Control Use, McGraw-Hill."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/S0082-0784(63)80091-0","article-title":"The size of flames from natural fires","volume":"9","author":"Thomas","year":"1963","journal-title":"Symp. (Int.) Combust."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1139\/x04-054","article-title":"Crown fire behaviour in a northern jack pine\u2013black spruce forest","volume":"34","author":"Stocks","year":"2004","journal-title":"Can. J. Forest Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1071\/WF01045","article-title":"Effect of thinning and prescribed burning on crown fire severity in ponderosa pine forests","volume":"11","author":"Pollet","year":"2002","journal-title":"Int. J. Wildland Fire"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.rse.2004.10.013","article-title":"Estimating forest canopy fuel parameters using LIDAR data","volume":"94","author":"Andersen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1071\/WF03032","article-title":"Estimation of vegetative fuel loads using Landsat TM imagery in New South Wales, Australia","volume":"12","author":"Brandis","year":"2003","journal-title":"Int. J. Wildland Fire"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1071\/WF06038","article-title":"Allometric equations for crown fuel biomass of Aleppo pine (Pinus halepensis Mill.) in Greece","volume":"16","author":"Mitsopoulos","year":"2007","journal-title":"Int. J. Wildland Fire"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1071\/WF01028","article-title":"Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling","volume":"10","author":"Keane","year":"2001","journal-title":"Int. J. Wildland Fire"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6461","DOI":"10.3390\/rs5126461","article-title":"Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation","volume":"5","author":"Mallinis","year":"2013","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111543","DOI":"10.1016\/j.rse.2019.111543","article-title":"Structural characterisation of mangrove forests achieved through combining multiple sources of remote sensing data","volume":"237","author":"Lucas","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kganyago, M., and Shikwambana, L. (2020). Assessment of the Characteristics of Recent Major Wildfires in the USA, Australia and Brazil in 2018\u20132019 Using Multi-Source Satellite Products. Remote Sens., 12.","DOI":"10.3390\/rs12111803"},{"key":"ref_19","first-page":"351","article-title":"Estimation of Crown Fuel Load of Suppressed Trees in Non-treated Young Calabrian Pine (Pinus brutia Ten.) Plantation Areas","volume":"19","author":"Baysal","year":"2019","journal-title":"Kast. \u00dcniversitesi Orman Fak\u00fcltesi Derg."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1071\/WF06092","article-title":"Estimating crown fuel loading for calabrian pine and Anatolian black pine","volume":"17","author":"Kucuk","year":"2008","journal-title":"Int. J. Wildland Fire"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1080\/13102818.2007.10817452","article-title":"Canopy Fuel Characteristics and Fuel Load in Young Black Pine Trees","volume":"21","author":"Kucuk","year":"2007","journal-title":"Biotechnol. Biotechnol. Equip."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Arellano-P\u00e9rez, S., Castedo-Dorado, F., L\u00f3pez-S\u00e1nchez, C.A., Gonz\u00e1lez-Ferreiro, E., Yang, Z., D\u00edaz-Varela, R.A., \u00c1lvarez-Gonz\u00e1lez, J.G., Vega, J.A., and Ruiz-Gonz\u00e1lez, A.D. (2018). Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard. Remote Sens., 10.","DOI":"10.3390\/rs10101645"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Scott, J.H., Reinhardt, E.D., and Station, R.M.R. (2001). Assessing Crown Fire Potential by Linking Models of Surface and Crown Fire Behavior.","DOI":"10.2737\/RMRS-RP-29"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"610","DOI":"10.31298\/sl.142.11-12.4","article-title":"Predicting crown fuel biomass of Turkish red pine (Pinus brutia Ten.) for the Mediterranean regions of Turkey","volume":"142","author":"Sari","year":"2018","journal-title":"\u0160umarski List"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1071\/WF02049","article-title":"Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA","volume":"13","author":"Reich","year":"2004","journal-title":"Int. J. Wildland Fire"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/0169-2046(93)90090-Z","article-title":"Deriving dynamic information on fire fuel distributions in southern Californian chaparral from remotely sensed data","volume":"24","author":"Stow","year":"1993","journal-title":"Landsc. Urban. Plan."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.envsoft.2017.07.007","article-title":"Development of a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data","volume":"97","author":"Chen","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Ferreiro, E., Arellano-P\u00e9rez, S., Castedo-Dorado, F., Hevia, A., Vega, J.A., Vega-Nieva, D.J., \u00c1lvarez-Gonz\u00e1lez, J.G., and Ruiz-Gonz\u00e1lez, A.D. (2017). Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airbone laser scanning data. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176114"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Michel, U., Schulz, K., Ehlers, M., Nikolakopoulos, K.G., Civco, D., Chen, Y., Zhu, X., Yebra, M., Harris, S., and Tapper, N. (2016, January 27\u201329). Estimation of forest surface fuel load using airborne lidar data. Proceedings of the Earth Resources and Environmental Remote Sensing\/GIS Applications VII, Edinburgh, UK.","DOI":"10.1117\/12.2239715"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2006.09.032","article-title":"Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey","volume":"108","author":"Skowronski","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1071\/WF11018","article-title":"Application of QuickBird imagery in fuel load estimation in the Daxinganling region, China","volume":"21","author":"Jin","year":"2012","journal-title":"Int. J. Wildland Fire"},{"key":"ref_32","first-page":"1","article-title":"Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method","volume":"99","author":"Lai","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1109\/TGRS.2006.887002","article-title":"Estimation of Forest Fuel Load From Radar Remote Sensing","volume":"45","author":"Saatchi","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.rse.2012.07.010","article-title":"Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system","volume":"125","author":"Cho","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.rse.2005.01.010","article-title":"Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest","volume":"95","author":"Lefsky","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.rse.2012.05.029","article-title":"Mapping forest aboveground biomass in the Northeastern United States with ALOS PALSAR dual-polarization L-band","volume":"124","author":"Cartus","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1038\/nclimate2581","article-title":"Recent reversal in global terrestrial biomass loss","volume":"5","author":"Evans","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.rse.2019.02.004","article-title":"The Vegetation Structure Perpendicular Index (VSPI): A forest condition index for wildfire predictions","volume":"224","author":"Massetti","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bai, X., He, B., Li, X., Zeng, J., Wang, X., Wang, Z., Zeng, Y., and Su, Z. (2017). First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau. Remote Sens., 9.","DOI":"10.3390\/rs9070714"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, L., Quan, X., He, B., Yebra, M., Xing, M., and Liu, X. (2019). Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sens., 11.","DOI":"10.3390\/rs11131568"},{"key":"ref_41","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_42","first-page":"109","article-title":"Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest","volume":"62","author":"Debastiani","year":"2019","journal-title":"Ann. For. Res."},{"key":"ref_43","first-page":"39","article-title":"Collaborative inversion heavy metal stress in rice by using two-dimensional spectral feature space based on HJ-1 A HSI and radarsat-2 SAR remote sensing data","volume":"78","author":"Li","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","first-page":"1","article-title":"Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation","volume":"10","author":"Nuthammachot","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.jhydrol.2007.07.010","article-title":"The role of catchment scale and landscape characteristics for runoff generation of boreal streams","volume":"344","author":"Laudon","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_46","unstructured":"Petersson, H. (1999). Biomassafunktioner for tr\u00e4dfraktioner av tall, gran och bj\u00f6rk i sverige. SLU Inst. Skoglig Resur. Och Geomatik Arbetsrapport, 59."},{"key":"ref_47","first-page":"22052","article-title":"BIOSAR 2008: Final Report","volume":"8","author":"Hajnsek","year":"2009","journal-title":"ESA-ESTEC Noordwijk Neth. Tech. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2015.01.007","article-title":"Potential of high-resolution ALOS\u2013PALSAR mosaic texture for aboveground forest carbon tracking in tropical region","volume":"160","author":"Thapa","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JSTARS.2010.2077619","article-title":"Generating Large-Scale High-Quality SAR Mosaic Datasets: Application to PALSAR Data for Global Monitoring","volume":"3","author":"Shimada","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2014.04.014","article-title":"New global forest\/non-forest maps from ALOS PALSAR data (2007\u20132010)","volume":"155","author":"Shimada","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_52","first-page":"23","article-title":"Landsat 7 scan line corrector-off gap-filled product developme","volume":"16","author":"Scaramuzza","year":"2005","journal-title":"Proc. Pecora"},{"key":"ref_53","first-page":"77","article-title":"The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina Alterniflora canopies","volume":"49","author":"Hardisky","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1080\/01431161.2015.1024893","article-title":"Predicting C3 and C4 grass nutrient variability using in situ canopy reflectance and partial least squares regression","volume":"36","author":"Adjorlolo","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.isprsjprs.2015.09.003","article-title":"Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment","volume":"109","author":"Rapaport","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"036015","DOI":"10.1117\/1.JRS.10.036015","article-title":"Comparison of partial least squares and support vector regressions for predicting leaf area index on a tropical grassland using hyperspectral data","volume":"10","author":"Kiala","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s12601-016-0018-8","article-title":"Application of a partial least-squares regression model to retrieve chlorophyll-a concentrations in coastal waters using hyper-spectral data","volume":"51","author":"Ryan","year":"2016","journal-title":"Ocean. Sci. J."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Guo, Y.-J., Han, J.-J., Zhao, X., Dai, X.-Y., and Zhang, H. (2020). Understanding the Role of Optimized Land Use\/Land Cover Components in Mitigating Summertime Intra-Surface Urban Heat Island Effect: A Study on Downtown Shanghai, China. Energies, 13.","DOI":"10.3390\/en13071678"},{"key":"ref_60","first-page":"73","article-title":"Avalia\u00e7\u00e3o Indirecta da Carga de Combust\u00edvel em Pinhal Bravo","volume":"10","author":"Fernandes","year":"2002","journal-title":"Silva Lusit."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1139\/cjfr-2012-0374","article-title":"Allometric equations for estimating canopy fuel load and distribution of pole-size maritime pine trees in five Iberian provenances","volume":"43","author":"Vega","year":"2013","journal-title":"Can. J. For. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"4020","DOI":"10.1109\/TGRS.2009.2034464","article-title":"Employing a Method on SAR and Optical Images for Forest Biomass Estimation","volume":"47","author":"Amini","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1109\/TGRS.2003.813351","article-title":"Herbaceous biomass retrieval in habitats of complex composition: A model merging sar images with unmixed landsat tm data","volume":"41","author":"Svoray","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4957","DOI":"10.1080\/01431160903022985","article-title":"Comparison of Resourcesat-1 AWiFS and SPOT-5 data over managed boreal forest stands","volume":"30","author":"Reese","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"110959","DOI":"10.1016\/j.rse.2018.11.002","article-title":"Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning","volume":"231","author":"Jay","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/LGRS.2018.2819884","article-title":"Forest Biomass Retrieval From L-Band SAR Using Tomographic Ground Backscatter Removal","volume":"15","author":"Blomberg","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.rse.2017.05.010","article-title":"Biomass estimation in a boreal forest from TanDEM-X data, lidar DTM, and the interferometric water cloud model","volume":"196","author":"Askne","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, C., Zhu, J., Fu, H., Xie, Q., and Shen, P. (2018). Forest Above-Ground Biomass Estimation Using Single-Baseline Polarization Coherence Tomography with P-Band PolInSAR Data. Forests, 9.","DOI":"10.3390\/f9040163"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Huang, X., Ziniti, B., Torbick, N., and Ducey, M.J. (2018). Assessment of Forest above Ground Biomass Estimation Using Multi-Temporal C-band Sentinel-1 and Polarimetric L-band PALSAR-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10091424"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.rse.2011.10.030","article-title":"Continuous monitoring of forest disturbance using all available Landsat imagery","volume":"122","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"112167","DOI":"10.1016\/j.rse.2020.112167","article-title":"A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection","volume":"252","author":"Ye","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.rse.2018.11.027","article-title":"Biomass estimation in dense tropical forest using multiple information from single-baseline P-band PolInSAR data","volume":"221","author":"Liao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_73","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1189\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:38:36Z","timestamp":1760161116000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1189"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,20]]},"references-count":73,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13061189"],"URL":"https:\/\/doi.org\/10.3390\/rs13061189","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,20]]}}}