{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T03:33:34Z","timestamp":1775792014656,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"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":["42201343"],"award-info":[{"award-number":["42201343"]}],"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":["41771359"],"award-info":[{"award-number":["41771359"]}],"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>Topography significantly affects remotely sensed reflectance data and subsequently impacts the retrieval of the leaf area index (LAI) from surface reflectance data over rugged terrains. However, most LAI inversion algorithms ignore the influence of terrain. This paper quantitatively analyzes the topographic effects on LAI values retrieved from remote sensing data at various spatial scales (30, 90, 270, 540, 1080, and 5400 m) over rugged terrains. The PRO4SAILT (PROSPECT + 4SAILT) model and the Proy algorithm were used to simulate multiscale surface reflectance for different LAI values over rugged terrains. Based on Gaussian process regression (GPR), an LAI inversion algorithm that ignores terrain effects was first developed. The simulated multiscale reflectance data were subsequently input into the inversion algorithm to retrieve LAI values. Finally, the retrieved LAI values were compared with the corresponding reference LAI values. The results demonstrate that the finer the spatial resolution is, the more significant the topographic effects on the retrieved LAI values are. When the reference LAI is five, as the spatial resolution increases from 5400 m to 30 m, the mean percentage error (MPE) of the retrieved LAI increases from 10.46% to 13.72%, and the root mean square error (RMSE) increases from 0.5376 to 1.005. Regardless of the spatial resolution, the error in the retrieved LAI values increases with an increasing terrain slope. When the reference LAI is five and the spatial resolution is 30 m, the MPE at a slope of 15\u00b0\u201330\u00b0 is close to 5%, and the RMSE is close to 0.3. The MPE at a slope of 30\u00b0\u201345\u00b0 is close to 20%, and the RMSE is close to one. In addition, the accuracy of the retrieved LAI values is closely related to the sky view factor (SVF). In general, the larger the SVF is, the smaller the error in the retrieved LAI values. In addition, the conversion relationships between the retrieved LAI values using the algorithm that ignores terrain effects and the true LAI values are provided in this study.<\/jats:p>","DOI":"10.3390\/rs16081404","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T09:16:33Z","timestamp":1713258993000},"page":"1404","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploring the Effects of Topography on Leaf Area Index Retrieved from Remote Sensing Data at Various Spatial Scales over Rugged Terrains"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9209-6390","authenticated-orcid":false,"given":"Yajie","family":"Zheng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"The China Urban Construction Design & Research Institute Co., Ltd., Beijing 100120, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8245-6762","authenticated-orcid":false,"given":"Zhiqiang","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9954-7062","authenticated-orcid":false,"given":"Hanyu","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Southwest University, Chongqing 400715, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0812-7164","authenticated-orcid":false,"given":"Jinling","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eja.2016.04.007","article-title":"Improvement of Spatially and Temporally Continuous Crop Leaf Area Index by Integration of CERES-Maize Model and MODIS Data","volume":"78","author":"Jin","year":"2016","journal-title":"Eur. J. Agron."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Martin, C., Jessica, M., Eric, V., and Christopher, J. (2016). A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation. Remote Sens., 8.","DOI":"10.3390\/rs8030263"},{"key":"ref_3","first-page":"421","article-title":"Defining Leaf Area Index for Non-flat Leaves","volume":"15","author":"Chen","year":"1992","journal-title":"Agric. For. Meteorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rse.2007.02.018","article-title":"LAI, fAPAR and fCover CYCLOPES Global Products Derived from VEGETATION Part 1: Principles of the Algorithm","volume":"110","author":"Baret","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TGRS.2013.2237780","article-title":"Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product from Time-Series MODIS Surface Reflectance","volume":"52","author":"Xiao","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.rse.2012.12.027","article-title":"GEOV1: LAI and FAPAR Essential Climate Variables and FCOVER Global Time Series Capitalizing over Existing Products. Part1: Principles of Development and Production","volume":"137","author":"Baret","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"927","DOI":"10.3390\/rs5020927","article-title":"Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011","volume":"5","author":"Zhu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.rse.2004.06.016","article-title":"Object-Based Retrieval of Biophysical Canopy Variables Using Artificial Neural Nets and Radiative Transfer Models","volume":"93","author":"Atzberger","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MGRS.2015.2510084","article-title":"A Survey on Gaussian Processes for Earth-Observation Data Analysis: A Comprehensive Investigation","volume":"4","author":"Verrelst","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gewali, U.B., Monteiro, S.T., and Saber, E. (2019). Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth. Remote Sens., 11.","DOI":"10.3390\/rs11131614"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.isprsjprs.2020.07.004","article-title":"Gaussian Processes Retrieval of LAI from Sentinel-2 Top-of-Atmosphere Radiance Data","volume":"167","author":"Vicent","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","first-page":"102454","article-title":"Estimating the Phenological Dynamics of Irrigated Rice Leaf Area Index Using the Combination of PROSAIL and Gaussian Process Regression","volume":"102","author":"Adeluyi","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/S0034-4257(02)00074-3","article-title":"Global Products of Vegetation Leaf Area and Fraction Absorbed PAR from Year One of MODIS Data","volume":"83","author":"Myneni","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1126\/science.1160787","article-title":"Atmospheric Warming and the Amplification of Precipitation Extremes","volume":"321","author":"Allan","year":"2008","journal-title":"Science"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"39-1","DOI":"10.1029\/2001GL013833","article-title":"New Observational Evidence for Global Warming from Satellite","volume":"29","author":"Jin","year":"2002","journal-title":"Geophys. Res. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1126\/science.1087910","article-title":"Global Warming Trend of Mean Tropospheric Temperature Observed by Satellites","volume":"302","author":"Vinnikov","year":"2003","journal-title":"Science"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"769","DOI":"10.2307\/1313568","article-title":"Terrestrial Biomass and the Effects of Deforestation on the Global Carbon Cycle","volume":"49","author":"Potter","year":"1999","journal-title":"BioScience"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jung, M. (2011). Global Patterns of Land-Atmosphere Fluxes of Carbon Dioxide, Latent Heat, and Sensible Heat Derived from Eddy Covariance, Satellite, and Meteorological Observations. J. Geophys. Res. Biogeosci., 116.","DOI":"10.1029\/2010JG001566"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.1080\/01431160802562255","article-title":"Global Land Surface Phenology Trends from GIMMS Database","volume":"30","author":"Julien","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring Vegetation Phenology Using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/0034-4257(91)90016-Y","article-title":"Global Land Cover Classification by Remote Sensing: Present Capabilities and Future Possibilities","volume":"35","author":"Townshend","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_22","first-page":"52","article-title":"Intercomparison and Validation of MODIS and GLASS Leaf Area Index (LAI) Products over Mountain Areas: A Case Study in Southwestern China","volume":"55","author":"Jin","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2637","DOI":"10.1016\/j.rse.2010.06.001","article-title":"A Conceptual Framework to Define the Spatial Resolution Requirements for Agricultural Monitoring Using Remote Sensing","volume":"114","author":"Duveiller","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty Five Years of Remote Sensing in Precision Agriculture: Key Advances and Remaining Knowledge Gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.rse.2003.04.007","article-title":"Remote Sensing Applications for Precision Agriculture: A Learning Community Approach","volume":"88","author":"Seelan","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"16738","DOI":"10.1073\/pnas.1004875107","article-title":"High-Resolution Forest Carbon Stocks and Emissions in the Amazon","volume":"107","author":"Asner","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2011.04.042","article-title":"Impact of Spatial Resolution and Satellite Overpass Time on Evaluation of the Surface Urban Heat Island Effects","volume":"117","author":"Sobrino","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/01431168208948387","article-title":"Spatial Resolution Requirements for Urban Studies","volume":"3","author":"Welch","year":"1982","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8019","DOI":"10.3390\/rs70608019","article-title":"Modeling Top of Atmosphere Radiance over Heterogeneous Non-Lambertian Rugged Terrain","volume":"7","author":"Mousivand","year":"2015","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4597","DOI":"10.1109\/TGRS.2017.2694483","article-title":"Modeling Canopy Reflectance over Sloping Terrain Based on Path Length Correction","volume":"55","author":"Yin","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TGRS.2013.2247405","article-title":"Improved LAI Algorithm Implementation to MODIS Data by Incorporating Background, Topography, and Foliage Clumping Information","volume":"52","author":"Gonsamo","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1109\/JSTARS.2020.2970999","article-title":"A Simulation-Based Analysis of Topographic Effects on LAI Inversion over Sloped Terrain","volume":"13","author":"Yu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wen, J., Liu, Q., Xiao, Q., Liu, Q., You, D., Hao, D., Wu, S., and Lin, X. (2018). Characterizing Land Surface Anisotropic Reflectance over Rugged Terrain: A Review of Concepts and Recent Developments. Remote Sens., 10.","DOI":"10.3390\/rs10030370"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1080\/01431160410001735111","article-title":"Estimating Surface Solar Radiation over Complex Terrain Using Moderate-resolution Satellite Sensor Data","volume":"26","author":"Wang","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5397","DOI":"10.1080\/01431160903130903","article-title":"Scale Effect and Scale Correction of Land-Surface Albedo in Rugged Terrain","volume":"30","author":"Wen","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/0034-4257(95)00155-7","article-title":"Topographic Effects in AVHRR NDVI Data","volume":"54","author":"Burgess","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1016\/j.agrformet.2009.06.001","article-title":"The Computation of Foliage Clumping Index Using Hemispherical Photography","volume":"149","author":"Gonsamo","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1016\/j.agrformet.2008.05.005","article-title":"Slope Correction for LAI Estimation from Gap Fraction Measurements","volume":"148","author":"Marie","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.agrformet.2018.11.033","article-title":"Review of Indirect Optical Measurements of Leaf Area Index: Recent Advances, Challenges, and Perspectives","volume":"265","author":"Yan","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5515","DOI":"10.1109\/TGRS.2020.3022874","article-title":"The 4SAILT Model: An Improved 4SAIL Canopy Radiative Transfer Model for Sloping Terrain","volume":"59","author":"Shi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/0034-4257(89)90044-8","article-title":"Evaluation of Topographic Effects in Remotely Sensed Data","volume":"30","author":"Proy","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3725","DOI":"10.1109\/TGRS.2012.2187300","article-title":"Technical Methodology for ASTER Global DEM","volume":"50","author":"Fujisada","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1016\/j.rse.2008.02.012","article-title":"PROSPECT-4 and 5: Advances in the Leaf Optical Properties Model Separating Photosynthetic Pigments","volume":"112","author":"Feret","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1364\/AO.24.000383","article-title":"Simple Equation to Approximate the Bidirectional Reflectance from Vegetative Canopies and Bare Soil Surfaces","volume":"24","author":"Walthall","year":"1985","journal-title":"Appl. Opt."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1109\/TGRS.2007.895844","article-title":"Unified Optical-Thermal Four-Stream Radiative Transfer Theory for Homogeneous Vegetation Canopies","volume":"45","author":"Verhoef","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.cpc.2009.09.018","article-title":"Variance Based Sensitivity Analysis of Model Output. Design and Estimator for the Total Sensitivity Index","volume":"181","author":"Saltelli","year":"2010","journal-title":"Comput. Phys. Commun."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.rse.2014.01.023","article-title":"Global Sensitivity Analysis of the Spectral Radiance of a Soil\u2013Vegetation System","volume":"145","author":"Mousivand","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.rse.2015.06.002","article-title":"Global Sensitivity Analysis of the SCOPE Model: What Drives Simulated Canopy-Leaving Sun-Induced Fluorescence?","volume":"166","author":"Verrelst","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Prikaziuk, E., and van der Tol, C. (2019). Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus. Remote Sens., 11.","DOI":"10.3390\/rs11202424"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Berger, K., Atzberger, C., Danner, M., Wocher, M., Mauser, W., and Hank, T. (2018). Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model. Remote Sens., 10.","DOI":"10.3390\/rs10122063"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2015.10.026","article-title":"Topographic Radiation Modeling and Spatial Scaling of Clear-Sky Land Surface Longwave Radiation over Rugged Terrain","volume":"172","author":"Yan","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111556","DOI":"10.1016\/j.rse.2019.111556","article-title":"Modeling Surface Longwave Radiation over High-Relief Terrain","volume":"237","author":"Yan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1109\/JSTARS.2018.2855192","article-title":"Modeling of Land Surface Thermal Anisotropy Based on Directional and Equivalent Brightness Temperatures over Complex Terrain","volume":"12","author":"Jiao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","first-page":"4404616","article-title":"Exploring Topographic Effects on Surface Parameters over Rugged Terrains at Various Spatial Scales","volume":"60","author":"Shi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K.I. (2006). Gaussian Processes for Machine Learning, MIT Press. Adaptive computation and machine learning.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_56","first-page":"23","article-title":"Index That Quantifies Topographic Heterogeneity","volume":"5","author":"Riley","year":"1999","journal-title":"Intermt. J. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5085","DOI":"10.1029\/94JD03249","article-title":"An Analytic Radiative Transfer Model for a Coupled Atmosphere and Leaf Canopy","volume":"100","author":"Liang","year":"1995","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_58","first-page":"4400312","article-title":"An Optical-Thermal Surface-Atmosphere Radiative Transfer Model Coupling Framework with Topographic Effects","volume":"60","author":"Shi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1109\/TGRS.2005.852480","article-title":"SCS+C: A Modified Sun-Canopy-Sensor Topographic Correction in Forested Terrain","volume":"43","author":"Soenen","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1404\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:28:42Z","timestamp":1760106522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1404"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,16]]},"references-count":59,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16081404"],"URL":"https:\/\/doi.org\/10.3390\/rs16081404","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,16]]}}}