{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:29:47Z","timestamp":1765232987017,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T00:00:00Z","timestamp":1604275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Planned Project of Sichuan Province, China","award":["2020YFS0058"],"award-info":[{"award-number":["2020YFS0058"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671361 & 41801272"],"award-info":[{"award-number":["41671361 & 41801272"]}],"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>Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R2 = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management.<\/jats:p>","DOI":"10.3390\/rs12213590","type":"journal-article","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T09:04:46Z","timestamp":1604307886000},"page":"3590","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires"],"prefix":"10.3390","volume":"12","author":[{"given":"Changming","family":"Yin","sequence":"first","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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":[{"role":"author","vocabulary":"crossref"}]},{"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-9315","authenticated-orcid":false,"given":"Marta","family":"Yebra","sequence":"additional","affiliation":[{"name":"Fenner School of Environment and Society, Australian National University, Canberra, ACT 2601, Australia"},{"name":"Bushfire and Natural Hazards Cooperative Research Centre, East Melbourne, VIC 3002, Australia"},{"name":"Research School of Aerospace, Mechanical, and Environmental Engineering, Australian National University, Canberra, ACT 2601, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gengke","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1126\/science.1155121","article-title":"Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests","volume":"320","author":"Bonan","year":"2008","journal-title":"Science"},{"key":"ref_2","first-page":"1","article-title":"Biophysical feedback of global forest fires on surface temperature","volume":"10","author":"Liu","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1126\/science.aac6759","article-title":"Forest health and global change","volume":"349","author":"Trumbore","year":"2015","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1029\/2005JG000143","article-title":"Use of a radiative transfer model to simulate the postfire spectral response to burn severity","volume":"111","author":"Chuvieco","year":"2006","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.rse.2017.12.038","article-title":"A comparison and validation of satellite-derived fire severity mapping techniques in fire prone north Australian savannas: Extreme fires and tree stem mortality","volume":"206","author":"Edwards","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1029\/2008JG000911","article-title":"Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure","volume":"114","author":"Frolking","year":"2009","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_7","unstructured":"Lutes, D.C. (2006). Landscape assessment: Ground measure of severity, the Composite Burn Index. FIREMON: Fire Effects Monitoring and Inventory System, USDA Forest Service, Rocky Mountain Research Station."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.rse.2008.10.011","article-title":"GeoCBI: A modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data","volume":"113","author":"Chuvieco","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yin, C., He, B., Yebra, M., Quan, X., Edwards, A.C., Liu, X., and Liao, Z. (2020). Improving burn severity retrieval by integrating tree canopy cover into radiative transfer model simulation. Remote Sens. Environ., 236.","DOI":"10.1016\/j.rse.2019.111454"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.rse.2013.04.013","article-title":"Spectral analysis of fire severity in north Australian tropical savannas","volume":"136","author":"Edwards","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chuvieco, E. (2009). Earth Observation of Wildland Fires in Mediterranean Ecosystems, Springer.","DOI":"10.1007\/978-3-642-01754-4"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.rse.2008.11.009","article-title":"Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA","volume":"113","author":"Miller","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2006.12.006","article-title":"Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)","volume":"109","author":"Miller","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/LGRS.2005.858485","article-title":"Remote Sensing of Fire Severity: Assessing the Performance of the Normalized Burn Ratio","volume":"3","author":"Roy","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"296","DOI":"10.5424\/fs\/2009183-01070","article-title":"Combining spectral mixture analysis and object-based classification for fire severity mapping","volume":"18","author":"Quintano","year":"2009","journal-title":"For. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.rse.2013.04.017","article-title":"Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries","volume":"136","author":"Quintano","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2016.12.009","article-title":"Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature","volume":"190","author":"Quintano","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.rse.2018.07.005","article-title":"The utility of Random Forests for wildfire severity mapping","volume":"216","author":"Collins","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Collins, L., McCarthy, G., Mellor, A., Newell, G., and Smith, L. (2020). Training data requirements for fire severity mapping using Landsat imagery and random forest. Remote Sens. Environ., 245.","DOI":"10.1016\/j.rse.2020.111839"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rse.2016.08.023","article-title":"Detecting unburned areas within wildfire perimeters using Landsat and ancillary data across the northwestern United States","volume":"186","author":"Meddens","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ramo, R., and Chuvieco, E. (2017). Developing a Random Forest Algorithm for MODIS Global Burned Area Classification. Remote Sens., 9.","DOI":"10.3390\/rs9111193"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1080\/2150704X.2014.963733","article-title":"A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests","volume":"5","author":"Hultquist","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.rse.2006.11.022","article-title":"Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models","volume":"108","author":"Chuvieco","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.rse.2008.08.008","article-title":"Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models","volume":"113","author":"Chuvieco","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1016\/j.rse.2010.02.008","article-title":"Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery","volume":"114","author":"Asner","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"129","DOI":"10.4996\/fireecology.0301129","article-title":"Simulation Approaches for Burn Severity Estimation Using Remotely Sensed Images","volume":"3","author":"Chuvieco","year":"2007","journal-title":"Fire Ecol."},{"key":"ref_27","first-page":"580","article-title":"Forest reflectance and transmittance FRT user guide","volume":"41","author":"Kuusk","year":"2002","journal-title":"Sci. Chin. D"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2014.03.035","article-title":"Modeling directional forest reflectance with the hybrid type forest reflectance model FRT","volume":"149","author":"Kuusk","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.rse.2006.06.025","article-title":"Validation of the forest radiative transfer model FRT","volume":"112","author":"Kuusk","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.envsoft.2017.06.006","article-title":"Retrieval of forest fuel moisture content using a coupled radiative transfer model","volume":"95","author":"Quan","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1016\/j.jaridenv.2009.04.011","article-title":"Relationships between stand density and canopy structure in a dryland forest as estimated by ground-based measurements and multi-spectral spaceborne images","volume":"73","author":"Sprintsin","year":"2009","journal-title":"J. Arid. Environ."},{"key":"ref_32","first-page":"73","article-title":"Correlations between stand structure and surface potential fire behavior of Pinus tabuliformis forests in Miaofeng Mountain of Beijing","volume":"1","author":"Lianqiang","year":"2019","journal-title":"J. Beijing For. Univ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gascon, F., Bouzinac, C., Th\u00e9paut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., and Gaudel-Vacaresse, A. (2017). Copernicus Sentinel-2A Calibration and Products Validation Status. Remote Sens., 9.","DOI":"10.3390\/rs9060584"},{"key":"ref_34","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016, January 9\u201313). Sentinel-2 Sen2Cor: L2A processor for users. Proceedings of the Living Planet Symposium 2016, Prague, Czech Republic."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., M\u00fcller-Wilms, U., and Gascon, F. (2017, January 11\u201314). Sen2Cor for Sentinel-2. Proceedings of the Image and Signal Processing for Remote Sensing XXIII, Warsaw, Poland.","DOI":"10.1117\/12.2278218"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.5194\/acp-5-1855-2005","article-title":"Technical note: The libRadtran software package for radiative transfer calculations\u2014Description and examples of use","volume":"5","author":"Mayer","year":"2005","journal-title":"Atmos. Chem. Phys. Discuss."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/17538947.2013.786146","article-title":"Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error","volume":"6","author":"Sexton","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2018.04.053","article-title":"A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing","volume":"212","author":"Yebra","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0034-4257(90)90100-Z","article-title":"PROSPECT: A model of leaf optical properties spectra","volume":"34","author":"Jacquemoud","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/S0034-4257(99)00111-X","article-title":"A Directional Multispectral Forest Reflectance Model","volume":"72","author":"Kuusk","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0022-4073(01)00007-3","article-title":"A two-layer canopy reflectance model","volume":"71","author":"Kuusk","year":"2001","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/S0034-4257(00)00184-X","article-title":"The GeoSail model: A simple addition to the SAIL model to describe discontinuous canopy reflectance","volume":"75","author":"Huemmrich","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/S0034-4257(98)00007-8","article-title":"LIBERTY\u2014Modeling the Effects of Leaf Biochemical Concentration on Reflectance Spectra","volume":"65","author":"Dawson","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.rse.2006.07.005","article-title":"Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes","volume":"105","author":"Cheng","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.rse.2002.06.002","article-title":"Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies","volume":"89","author":"Miller","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0034-4257(94)90035-3","article-title":"A multispectral canopy reflectance model","volume":"50","author":"Kuusk","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/0168-1923(94)02216-7","article-title":"A Markov chain model of canopy reflectance","volume":"76","author":"Kuusk","year":"1995","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.05.015","article-title":"Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties","volume":"92","author":"Schaepman","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_49","unstructured":"Lang, M., Nilson, T., Kuusk, A., Kiviste, A., and Hordo, M. (June, January 31). The performance of different leaf mass and crown diameter models in forming the input of a forest reflectance model: A test on forest growth sampleplots and Landsat ETM images. Proceedings of the ForestSat 2005, Boras, Switzerland."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.rse.2004.07.013","article-title":"A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper","volume":"93","author":"Dennison","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014Interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Parks, S.A., Holsinger, L.M., Koontz, M.J., Collins, L., Whitman, E., Parisien, M.-A., Loehman, R., Barnes, J.L., Bourdon, J.-F., and Boucher, J. (2019). Giving Ecological Meaning to Satellite-Derived Fire Severity Metrics across North American Forests. Remote Sens., 11.","DOI":"10.3390\/rs11141735"},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2018.03.019","article-title":"Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques","volume":"210","author":"Meng","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Liu, Z. (2016). Effects of climate and fire on short-term vegetation recovery in the boreal larch forests of Northeastern China. Sci. Rep., 6.","DOI":"10.1038\/srep37572"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3590\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:28:03Z","timestamp":1760178483000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3590"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,2]]},"references-count":55,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12213590"],"URL":"https:\/\/doi.org\/10.3390\/rs12213590","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,11,2]]}}}