{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T05:06:18Z","timestamp":1772600778558,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, we developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation. The target forest is any tropical evergreen forest which accounts for 58% of tropical forests. Firstly, we analyzed the effects of canopy shape on the reflectance simulation based on virtual forests created with different canopy shapes. This result was checked by Tukey\u2019s honestly significant difference (HSD) test. Secondly, the optimal canopy shape was determined by comparing the reflectance from Sentinel-2 Band 4 (red) bottom of atmosphere reflectance with those simulated from virtual forests. Finally, the shadow fraction estimated from the virtual forest was evaluated. Since the focus of this study was to derive the optimal canopy shape, unmanned aerial vehicle (UAV) structure from motion (SfM) was used to obtain the parameters other than canopy shape and to validate the estimated shadow fraction. The results showed that when the Sun zenith angle (SZA) was more than 20\u00b0, significant differences were observed among canopy shapes. The least root mean square error (RMSE) for reflectance simulation was 0.385 from the canopy shape of a half ellipsoid. Moreover, the half ellipsoid also showed the smallest RMSE in estimating shadow fraction (0.032), which indicated the reliability and applicability of CSRS. This study is the first attempt to determine the optimal canopy shape for estimating shadow fraction and is expected to improve the accuracy of GPP estimation in the future.<\/jats:p>","DOI":"10.3390\/rs14164088","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"4088","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5018-1776","authenticated-orcid":false,"given":"Takumi","family":"Fujiwara","sequence":"first","affiliation":[{"name":"Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9138-6601","authenticated-orcid":false,"given":"Wataru","family":"Takeuchi","sequence":"additional","affiliation":[{"name":"Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,21]]},"reference":[{"key":"ref_1","unstructured":"FAO (2021, November 10). Assessment, Global Forest Resources 2020. Available online: https:\/\/www.fao.org\/3\/CA8753EN\/CA8753EN.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1126\/science.1184984","article-title":"Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate","volume":"329","author":"Beer","year":"2010","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e5457","DOI":"10.7717\/peerj.5457","article-title":"Global mapping of potential natural vegetation: An assessment of machine learning algorithms for estimating land potential","volume":"6","author":"Hengl","year":"2018","journal-title":"PeerJ"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15692","DOI":"10.1038\/s41598-019-52076-x","article-title":"Improved Characterisation of Vegetation and Land Surface Seasonal Dynamics in Central Japan with Himawari-8 Hypertemporal Data","volume":"9","author":"Miura","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1038\/s41467-021-20994-y","article-title":"New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests","volume":"12","author":"Hashimoto","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/treephys\/17.1.1","article-title":"Vertical gradients in photosynthetic gas exchange characteristics and refixation of respired CO2 within boreal forest canopies","volume":"17","author":"Brooks","year":"1997","journal-title":"Tree Physiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/BF00317729","article-title":"Canopy structure and vertical patterns of photosynthesis and related leaf traits in a deciduous forest","volume":"96","author":"Ellsworth","year":"1993","journal-title":"Oecologia"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1093\/treephys\/28.6.825","article-title":"Effects of mutual shading of tree crowns on prediction of photosynthetic light-use efficiency in a coastal Douglas-fir forest","volume":"28","author":"Hilker","year":"2008","journal-title":"Tree Physiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3820","DOI":"10.1016\/j.rse.2008.06.001","article-title":"Interpretation and topographic compensation of conifer canopy self-shadowing","volume":"112","author":"Kane","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_10","unstructured":"Chen, M. (2013). Comparison of 3D Tree Parameters. [Master\u2019s Thesis, Wageningen University and Research Centre]."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S0034-4257(98)00071-6","article-title":"Surface Lidar Remote Sensing of Basal Area and Biomass in Deciduous Forests of Eastern Maryland, USA","volume":"67","author":"Lefsky","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112165","DOI":"10.1016\/j.rse.2020.112165","article-title":"Mapping global forest canopy height through integration of GEDI and Landsat data","volume":"253","author":"Potapov","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4021","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. Biogeosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1038\/nature14967","article-title":"Mapping tree density at a global scale","volume":"525","author":"Crowther","year":"2015","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0034-4257(96)00214-3","article-title":"Modeling forest canopy heights: The effects of canopy shape","volume":"60","author":"Nelson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1029\/2009JG000939","article-title":"A satellite-based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest","volume":"115","author":"Kobayashi","year":"2010","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1139\/cjfr-2016-0061","article-title":"Tree light capture and spatial variability of understory light increase with species mixing and tree size heterogeneity","volume":"46","author":"Ligot","year":"2016","journal-title":"Can. J. For. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1016\/j.rse.2009.07.003","article-title":"Mapping forest background reflectivity over North America with Multi-angle Imaging SpectroRadiometer (MISR) data","volume":"113","author":"Pisek","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0034-4257(97)00139-9","article-title":"Ecological Research Needs from Multiangle Remote Sensing Data","volume":"63","author":"Asner","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2011.12.008","article-title":"Global clumping index map derived from the MODIS BRDF product","volume":"119","author":"He","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1109\/36.628798","article-title":"Four-scale bidirectional reflectance model based on canopy architecture","volume":"35","author":"Chen","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.rse.2017.12.031","article-title":"Seasonal change of bidirectional reflectance distribution function in mature Japanese larch forests and their phenology at the foot of Mt. Yatsugatake, central Japan","volume":"209","author":"Hasegawa","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"211","DOI":"10.5194\/isprs-annals-V-3-2021-211-2021","article-title":"Estimation of optimal crown coverage and canopy shape for shadow estimation on tropical moist broadleaf forest","volume":"5","author":"Fujiwara","year":"2021","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2013.01.010","article-title":"Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements","volume":"132","author":"Hmimina","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_25","unstructured":"Myanmar Information Management Unit (2021, January 10). Koppen\u2013Geiger Climate Zones of Myanmar (1986\u20132010). Available online: https:\/\/themimu.info\/sites\/themimu.info\/files\/documents\/Map_Koppen\u2013Geiger_Climate_Zones_of_Myanmar_1986-2010_MIMU1548v01_17Jan2018_A4.pdf."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xu, N., Tian, J., Tian, Q., Xu, K., and Tang, S. (2019). Analysis of Vegetation Red Edge with Different Illuminated\/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index. Remote Sens., 11.","DOI":"10.3390\/rs11101192"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/0034-4257(95)00253-7","article-title":"Modeling radiative transfer in heterogeneous 3-D vegetation canopies","volume":"58","author":"Demarez","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2007.04.010","article-title":"A coupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape","volume":"112","author":"Kobayashi","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0034-4257(00)00129-2","article-title":"3-D Scene Modeling of Semidesert Vegetation Cover and its Radiation Regime","volume":"74","author":"Qin","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fujiwara, T., and Takeuchi, W. (2020). Simulation of Sentinel-2 Bottom of Atmosphere Reflectance Using Shadow Parameters on a Deciduous Forest in Thailand. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9100582"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/S0038-092X(01)00054-8","article-title":"Parameterized transmittance model for direct beam and circumsolar spectral irradiance","volume":"71","author":"Gueymard","year":"2001","journal-title":"Sol. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e06078","DOI":"10.1111\/ecog.06078","article-title":"Improving landscape-scale productivity estimates by integrating trait-based models and remotely-sensed foliar-trait and canopy-structural data","volume":"2022","author":"Wieczynski","year":"2022","journal-title":"Ecography"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ma, X., Huete, A., Tran, N.N., Bi, J., Gao, S., and Zeng, Y. (2020). Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8. Remote Sens., 12.","DOI":"10.3390\/rs12081339"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3701","DOI":"10.1080\/01431160701772500","article-title":"The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared","volume":"29","author":"Seelig","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Purves, D.W., Lichstein, J.W., and Pacala, S.W. (2007). Crown Plasticity and Competition for Canopy Space: A New Spatially Implicit Model Parameterized for 250 North American Tree Species. PLoS ONE, 2.","DOI":"10.1371\/journal.pone.0000870"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1093\/aob\/mcab035","article-title":"Understanding crown shyness from a 3-D perspective","volume":"128","author":"Lau","year":"2021","journal-title":"Ann. Bot."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2326","DOI":"10.1016\/j.rse.2007.10.001","article-title":"Spatially explicit characterization of boreal forest gap dynamics using multi-temporal lidar data","volume":"112","author":"Vepakomma","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/014311600210191","article-title":"Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data","volume":"21","author":"Loveland","year":"2010","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4088\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:13:07Z","timestamp":1760141587000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4088"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,21]]},"references-count":38,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14164088"],"URL":"https:\/\/doi.org\/10.3390\/rs14164088","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,21]]}}}