{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T08:07:39Z","timestamp":1769155659411,"version":"3.49.0"},"reference-count":62,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42074029"],"award-info":[{"award-number":["42074029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41825009"],"award-info":[{"award-number":["41825009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42064002"],"award-info":[{"award-number":["42064002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB0503402"],"award-info":[{"award-number":["2017YFB0503402"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An improved method for retrieving Above-ground Biomass (AGB) and Canopy Height (CH) based on an observable from Cyclone Global Navigation Satellite System (CYGNSS), soil moisture from Soil Moisture Active Passive (SMAP) and location is proposed. The observable derived from CYGNSS is more sensitive to vegetation. The CYGNSS observable, soil moisture and the location are used as the input features of an Artificial Neural Network (ANN) to retrieve AGB and CH. The sensitivity analysis of the CYGNSS observable to target parameters shows that the proposed observable is more sensitive to AGB\/CH than the conventional observable. The AGB\/CH retrievals of the improved method show that it has better performance than that of the traditional method, especially in the areas with AGB in the range of 0 to100 Mg\/ha and CH in the range of 0 to10 m. For AGB retrievals, the root mean square error (RMSE) and correlation coefficient are 64.84 Mg\/ha and 0.80 in the range of 0 to 550 Mg\/ha. Compared with the traditional method, the RMSE is decreased by 11.63%, while the correlation coefficient is increased by 5.26%. For CH retrievals, the RMSE and correlation coefficient are 5.97 m and 0.83 in the range of 0 to 45 m. The RMSE is decreased by 12.59%, while the correlation coefficient is increased by 5.06%. The analysis of the improved method in different areas shows that the performance of the improved method over the area with high vegetation is better than the area with low vegetation. The results obtained here further strengthens the capability of GNSS-R for global AGB\/CH retrievals as well as different land cover areas.<\/jats:p>","DOI":"10.3390\/rs13132491","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T11:07:40Z","timestamp":1624619260000},"page":"2491","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["An Improved Method for Pan-Tropical Above-Ground Biomass and Canopy Height Retrieval Using CYGNSS"],"prefix":"10.3390","volume":"13","author":[{"given":"Fade","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}]},{"given":"Fei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}]},{"given":"Lilong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9101-4007","authenticated-orcid":false,"given":"Yang","family":"Nan","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1126\/science.1217962","article-title":"Baseline Map of Carbon Emissions from Deforestation in Tropical Regions","volume":"336","author":"Harris","year":"2012","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2540","DOI":"10.1111\/gcb.12605","article-title":"Determination of tropical deforestation rates and related carbon losses from 1990 to 2010","volume":"20","author":"Achard","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Houghton, R.A., Hall, F., and Goetz, S. (2009). Importance of biomass in the global carbon cycle. J. Geophys. Res. Space Phys., 114.","DOI":"10.1029\/2009JG000935"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"014002","DOI":"10.1088\/1748-9326\/6\/1\/014002","article-title":"Monitoring, reporting and verification for national REDD + programmes: Two proposals","volume":"6","author":"Herold","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1038\/ngeo671","article-title":"CO2 emissions from forest loss","volume":"2","author":"Morton","year":"2009","journal-title":"Nat. Geosci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"18866","DOI":"10.1073\/pnas.0702737104","article-title":"Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks","volume":"104","author":"Canadell","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1111\/j.1365-2486.2005.00955.x","article-title":"Aboveground Forest Biomass and the Global Carbon Balance","volume":"11","author":"Oughton","year":"2005","journal-title":"Glob. Chang. Biol."},{"key":"ref_8","first-page":"13","article-title":"Fusion of pan-tropical biomass maps using weighted averaging and regional calibration data","volume":"31","author":"Ge","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00442-005-0100-x","article-title":"Tree allometry and improved estimation of carbon stocks and balance in tropical forests","volume":"145","author":"Chave","year":"2005","journal-title":"Oecologia"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1016\/j.foreco.2009.12.003","article-title":"Increasing wood production through old age in tall trees","volume":"259","author":"Sillett","year":"2010","journal-title":"Forest Ecol. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1111\/j.1654-1103.2012.01471.x","article-title":"Tropical forest biomass estimation and the fallacy of misplaced concreteness","volume":"23","author":"Clark","year":"2012","journal-title":"J. Veg. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1111\/2041-210X.12904","article-title":"Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR","volume":"9","author":"Lau","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1111\/2041-210X.12301","article-title":"Nondestructive estimates of above-ground biomass using terrestrial laser scanning","volume":"6","author":"Calders","year":"2014","journal-title":"Methods Ecol. Evol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1111\/1365-2745.12543","article-title":"Conservative species drive biomass productivity in tropical dry forests","volume":"104","author":"Schiavini","year":"2016","journal-title":"J. Ecol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1111\/j.1365-2745.2011.01829.x","article-title":"Environmental filtering of dense-wooded species controls above-ground biomass stored in African moist forests","volume":"99","author":"Rossi","year":"2011","journal-title":"J. Ecol."},{"key":"ref_16","unstructured":"Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, Guilford Publications. [5th ed.]."},{"key":"ref_17","first-page":"031537","article-title":"Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms","volume":"1","author":"Lefsky","year":"2007","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3802","DOI":"10.1109\/JSTARS.2014.2353661","article-title":"Evaluation of ALOS\/PALSAR L-Band Data for the Estimation of Eucalyptus Plantations Aboveground Biomass in Brazil","volume":"8","author":"Baghdadi","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2005.03.009","article-title":"Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales","volume":"96","author":"Clark","year":"2005","journal-title":"Remote. Sens. Environ."},{"key":"ref_21","unstructured":"Healey, S.P., Hernandez, M.W., Edwards, D.P., Lefsky, M.A., Freeman, E., Patterson, P.L., Lindquist, E.J., and Lister, A.J. (2015). CMS: GLAS LiDAR-derived Global Estimates of Forest Canopy Height, 2004\u20132008, ORNL DAAC."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111303","DOI":"10.1016\/j.rse.2019.111303","article-title":"Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: A comparison to higher frequencies and optical indices","volume":"232","author":"Chaparroa","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lefsky, M.A., Harding, D.J., Keller, M., Cohen, W.B., Carabajal, C.C., Espirito-Santo, F.D.B., Hunter, M.O., and De Oliveira, R. (2005). Estimates of forest canopy height and aboveground biomass using ICESat. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL023971"},{"key":"ref_24","first-page":"331","article-title":"A Passive Reflectometry and Interferometry System (PARIS): Application to ocean altimetry","volume":"17","year":"1993","journal-title":"ESA J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.optlastec.2014.11.001","article-title":"Long term performance of the High Output Maximum Efficiency Resonator (HOMER) laser for NASA\u05f3s Global Ecosystem Dynamics Investigation (GEDI) lidar","volume":"68","author":"Stysley","year":"2015","journal-title":"Opt. Laser Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2014.2374220","article-title":"Tutorial on Remote Sensing Using GNSS Bistatic Radar of Opportunity","volume":"2","author":"Zavorotny","year":"2014","journal-title":"IEEE Geosci. Remote. Sens. Mag."},{"key":"ref_27","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1981). Microwave Remote Sensing: Active and Passive, Addison-Wesley Reading."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.1029\/98GL51615","article-title":"Effect of sea roughness on bistatically scattered range coded signals from the Global Positioning System","volume":"25","author":"Garrison","year":"1998","journal-title":"Geophys. Res. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gleason, S. (2006). Remote Sensing of Ocean, Ice and Land Surfaces Using Bistatically Scattered GNSS Signals from Low Earth Orbit. [Ph.D. Thesis, University of Surrey].","DOI":"10.1109\/IGARSS.2006.792"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5435","DOI":"10.1002\/2015GL064204","article-title":"Spaceborne GNSS-Reflectometry for ocean winds: First results from the UK TechDemoSat-1 mission: Spaceborne GNSS-R: First TDS-1 results","volume":"42","author":"Foti","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6829","DOI":"10.1109\/TGRS.2014.2303831","article-title":"Spaceborne GNSS-R Minimum Variance Wind Speed Estimator","volume":"52","author":"Clarizia","year":"2014","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.1002\/2016GL068189","article-title":"Demonstrating soil moisture remote sensing with observations from the UK TechDemoSat-1 satellite mission","volume":"43","author":"Chew","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1002\/2016EA000194","article-title":"Wetland monitoring with Global Navigation Satellite System reflectometry","volume":"4","author":"Nghiem","year":"2017","journal-title":"Earth Space Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111417","DOI":"10.1016\/j.rse.2019.111417","article-title":"A novel approach to monitoring wetland dynamics using CYGNSS: Everglades case study","volume":"233","author":"Morris","year":"2019","journal-title":"Remote. Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111869","DOI":"10.1016\/j.rse.2020.111869","article-title":"Estimating inundation extent using CYGNSS data: A conceptual modeling study","volume":"245","author":"Chewa","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Stilla, D., Zribi, M., Pierdicca, N., Baghdadi, N., and Huc, M. (2020). Desert Roughness Retrieval Using CYGNSS GNSS-R Data. Remote. Sens., 12.","DOI":"10.3390\/rs12040743"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1016\/j.asr.2010.04.025","article-title":"Forest biomass monitoring with GNSS-R: Theoretical simulations","volume":"47","author":"Ferrazzoli","year":"2011","journal-title":"Adv. Space Res."},{"key":"ref_38","first-page":"150","article-title":"Performance of GNSS-R GLORI data for biomass estimation over the Landes forest","volume":"74","author":"Zribi","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4743","DOI":"10.1109\/JSTARS.2015.2496661","article-title":"First Dual-Band Multiconstellation GNSS-R Scatterometry Experiment Over Boreal Forests from a Stratospheric Balloon","volume":"9","author":"Camps","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Carreno-Luengo, H., Luzi, G., and Crosetto, M. (2020). Above-Ground Biomass Retrieval over Tropical Forests: A Novel GNSS-R Ap-proach with CyGNSS. Remote Sens., 12.","DOI":"10.3390\/rs12091368"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/JSTARS.2020.2982993","article-title":"Remote Sensing of Forest Biomass Using GNSS Reflectometry","volume":"13","author":"Santi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4322","DOI":"10.1109\/TGRS.2018.2890646","article-title":"Time-Series Retrieval of Soil Moisture Using CYGNSS","volume":"57","author":"Johnson","year":"2019","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Comite, D., and Pierdicca, N. (2021). Decorrelation of the Near-Specular Land Scattering in Bistatic Radar Systems. IEEE Trans. Geosci. Remote. Sens., 1\u201313.","DOI":"10.1109\/TGRS.2021.3072864"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1218","DOI":"10.1109\/JSTARS.2020.2975187","article-title":"Space-borne GNSS-R signal over a complex topography: Modeling and validation","volume":"13","author":"Dente","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1109\/JSTARS.2020.2981570","article-title":"Modeling the Effects of Topography on Delay-Doppler Maps","volume":"13","author":"Campbell","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_46","unstructured":"Entekhabi, D., Yueh, S., O\u2019Neill, P.E., Kellogg, K.H., Allen, A., Bindlish, R., Brown, M., Chan, S., Colliander, A., and Crow, W.T. (2021, January 07). SMAP Handbook. Soil Moisture Active Passive. Available online: https:\/\/nsidc.org\/data\/SPL3SMP_E\/versions\/1."},{"key":"ref_47","unstructured":"O\u2019Neill, P.E., Chan, S., Njoku, E.G., Jackson, T., Bindlish, R., and Chaubell, J. (2020). SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 4."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"9899","DOI":"10.1073\/pnas.1019576108","article-title":"Benchmark map of forest carbon stocks in tropical regions across three continents","volume":"108","author":"Saatchi","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1038\/nclimate1354","article-title":"Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps","volume":"2","author":"Baccini","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1111\/gcb.13139","article-title":"An integrated pan-tropical biomass map using multiple reference datasets","volume":"22","author":"Avitabile","year":"2016","journal-title":"Glob. Chang. Biol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"04021","DOI":"10.1029\/2011JG001708","article-title":"Mapping forest canopy height globally with spaceborne lidar","volume":"116","author":"Simard","year":"2011","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1109\/36.841977","article-title":"Scattering of GPS signals from the ocean with wind remote sensing application","volume":"38","author":"Zavorotny","year":"2000","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1109\/TGRS.2017.2771253","article-title":"Bistatic Radar Equation for Signals of Opportunity Revisited","volume":"56","author":"Voronovich","year":"2017","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4454","DOI":"10.1109\/TGRS.2020.3009784","article-title":"An Algorithm for Detecting Coherence in Cyclone Global Navigation Satellite System Mission Level-1 Delay-Doppler Maps","volume":"59","author":"Johnson","year":"2021","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"10426","DOI":"10.1109\/TGRS.2019.2935257","article-title":"On the Coherency of Ocean and Land Surface Specular Scattering for GNSS-R and Signals of Opportunity Systems","volume":"57","author":"Balakhder","year":"2019","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/LGRS.2019.2916164","article-title":"Spatial Resolution in GNSS-R Under Coherent Scattering","volume":"17","author":"Camps","year":"2019","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1109\/36.387598","article-title":"Dielectric properties of soils in the 0.3\u20131.3-GHz range","volume":"33","author":"Peplinski","year":"1995","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"221","DOI":"10.3390\/rs71215841","article-title":"Review of Machine Learning Approaches for Biomass and Soil Moisture Re-trievals from Remote Sensing Data","volume":"7","author":"Ali","year":"2015","journal-title":"Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"9756","DOI":"10.1109\/TGRS.2019.2929002","article-title":"Application of Neural Network to GNSS-R Wind Speed Retrieval","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Eroglu, O., Kurum, M., Boyd, D., and Gurbuz, A.C. (2019). High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks. Remote. Sens., 11.","DOI":"10.3390\/rs11192272"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/LGRS.2019.2948566","article-title":"A GNSS-R Geophysical Model Function: Machine Learning for Wind Speed Retrievals","volume":"17","author":"Asgarimehr","year":"2019","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_62","unstructured":"ESA (2021, January 28). Land Cover CCI Product User Guide Version 2. Techical Report. Available online: Maps.elie.ucl.ac.be\/CCI\/viewer\/download\/ESACCI-LC-Ph2-PUGv22.0.pdf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2491\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:24:08Z","timestamp":1760163848000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2491"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,25]]},"references-count":62,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13132491"],"URL":"https:\/\/doi.org\/10.3390\/rs13132491","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,25]]}}}