{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T08:40:02Z","timestamp":1735548002787,"version":"3.32.0"},"reference-count":64,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2017YFB0503004","2017YFC0210100"],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of\u00a0Hunan Province","doi-asserted-by":"publisher","award":["2019JJ50047"],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801227","31870531"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Accurate estimation of polarized reflectance (Rp) of land surfaces is critical for remote sensing of aerosol optical properties. In the last two decades, many data-driven bidirectional polarization distribution function (BPDF) models have been proposed for accurate estimation of Rp, among which the generalized regression neural network (GRNN) based BPDF model has been reported to perform the best. GRNN is just a simple machine learning (ML) technique that can solve non-linear problems. Many ML techniques were reported to work well in solving non-linear problems and consequently may provide better performance in BPDF modeling. However, incorporating various ML techniques with BPDF modeling and comparing their performances have never been well documented. In this study, three widely used ML algorithms\u2014i.e., support vector regression (SVR), K-nearest-neighbor (KNN), and random forest (RF)\u2014were applied for BPDF modeling. Using measurements collected by the Polarization and Directionality of the Earth\u2019s Reflectance onboard PARASOL satellite (POLDER\/PARASOL), non-linear relationships between Rp and the input variables, i.e., Fresnel factor (Fp), scattering angle (SA), reflectance at 670 nm (R670) and 865 nm (R865), were built using these ML algorithms. Results showed that taking Fp, SA, R670, and R865 as input variables, the performance of the four ML-based BPDF models was quite similar. The KNN-based BPDF model provided slightly better results, and improved the accuracy of the semi-empirical BPDF models by 9.55% in terms of the overall root mean square error (RMSE). Experiments of different configuration of input variables suggested that using multi-band reflectance as input variables provided better results than using vegetation indices. The RF-based BPDF model using all reflectances at six bands as input variables produced the best results, improving the overall accuracy by 6.62% compared with the GRNN-based BPDF model. Among all the input variables, reflectance at absorbing spectral bands\u2014e.g., 490 nm and 670 nm\u2014played more significant roles in RF-based BPDF modeling due to the domination of polarized partition in total reflectance. Fresnel factor and scattering angle were also important for BPDF modeling. This study confirmed the feasibility of applying ML techniques to more accurate BPDF modeling, and the RF-based BPDF model proposed in this study can be used to increase the accuracy of remote sensing of the complete aerosol properties.<\/jats:p>","DOI":"10.3390\/rs12233891","type":"journal-article","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T14:16:49Z","timestamp":1606486609000},"page":"3891","source":"Crossref","is-referenced-by-count":2,"title":["Modeling Bidirectional Polarization Distribution Function of Land Surfaces Using Machine Learning Techniques"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8638-8434","authenticated-orcid":false,"given":"Siyuan","family":"Liu","sequence":"first","affiliation":[{"name":"Beijing Key Lab of Spatial Information Integration and 3S Application, Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3396-3327","authenticated-orcid":false,"given":"Yi","family":"Lin","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Spatial Information Integration and 3S Application, Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}]},{"given":"Lei","family":"Yan","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Spatial Information Integration and 3S Application, Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Guangxi Key Laboratory of Remote Measuring System, Guiling University of Aerospace Technology, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6127-3385","authenticated-orcid":false,"given":"Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 410082, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/PROC.1985.13232","article-title":"Polarization of Light Scattered by Vegetation","volume":"73","author":"Vanderbilt","year":"1985","journal-title":"P IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/0034-4257(81)90011-0","article-title":"The Relationship between Polarized Visible-Light and Vegetation Amount","volume":"11","author":"Curran","year":"1981","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8191","DOI":"10.1109\/TGRS.2019.2918927","article-title":"Spectral Invariance Hypothesis Study of Polarized Reflectance With the Ground-Based Multiangle SpectroPolarimetric Imager","volume":"57","author":"Bradley","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4865","DOI":"10.1080\/01431161.2019.1672219","article-title":"Spectropolarimetric characterization of pure and polluted land surfaces","volume":"41","author":"Peltoniemi","year":"2020","journal-title":"Int. J. Remote Sens"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1016\/j.jqsrt.2009.02.017","article-title":"Polarised bidirectional reflectance factor measurements from vegetated land surfaces","volume":"110","author":"Suomalainen","year":"2009","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4913","DOI":"10.1029\/2000JD900364","article-title":"Remote sensing of aerosols over land surfaces from POLDER-ADEOS-1 polarized measurements","volume":"106","author":"Deuze","year":"2001","journal-title":"J. Geophys Res. Atmos"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jqsrt.2013.05.028","article-title":"Aerosol type over east Asian retrieval using total and polarized remote Sensing","volume":"129","author":"Xie","year":"2013","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1029\/2019EA000593","article-title":"The Normalized Difference Vegetation Index and Angular Variation of Surface Spectral Polarized Reflectance Relationships: Improvements on Aerosol Remote Sensing Over Land","volume":"6","author":"Wang","year":"2019","journal-title":"Earth Space Sci"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/36.763292","article-title":"Parameterization of surface polarized reflectance derived from POLDER spaceborne measurements","volume":"37","author":"Nadal","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2642","DOI":"10.1016\/j.rse.2009.07.022","article-title":"Polarized reflectances of natural surfaces: Spaceborne measurements and analytical modeling","volume":"113","author":"Maignan","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106578","DOI":"10.1016\/j.jqsrt.2019.106578","article-title":"Modeling polarized reflectance of snow and ice surface using POLDER measurements","volume":"236","author":"Yang","year":"2019","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2444","DOI":"10.1016\/j.jqsrt.2010.07.001","article-title":"Polarized optical scattering signatures from biological materials","volume":"111","author":"Martin","year":"2010","journal-title":"J. Quant. Spectrosc. Radiat."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4388","DOI":"10.1109\/TGRS.2019.2890998","article-title":"Optical Properties of Reflected Light From Leaves: A Case Study From One Species","volume":"57","author":"Sun","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/0034-4257(89)90015-1","article-title":"A Reflectance Model for the Homogeneous Plant Canopy and Its Inversion","volume":"27","author":"Nilson","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/0034-4257(91)90072-E","article-title":"Polarization of Light Reflected by Crop Canopies","volume":"38","author":"Rondeaux","year":"1991","journal-title":"Remote Sens Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4008","DOI":"10.1109\/TGRS.2017.2686485","article-title":"Polarized Remote Sensing: A Note on the Stokes Parameters Measurements From Natural and Man-Made Targets Using a Spectrometer","volume":"55","author":"Sun","year":"2017","journal-title":"IEEE T Geosci. Remote"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, B., Knyazikhin, Y., Lin, Y., Yan, K., Chen, C., Park, T., Choi, S.H., Mottus, M., Rautiainen, M., and Myneni, R.B. (2016). Analyses of Impact of Needle Surface Properties on Estimation of Needle Absorption Spectrum: Case Study with Coniferous Needle and Shoot Samples. Remote Sens., 8.","DOI":"10.3390\/rs8070563"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1399-3054.1993.tb01753.x","article-title":"Polarized and Specular Reflectance Variation with Leaf Surface-Features","volume":"88","author":"Grant","year":"1993","journal-title":"Physiol. Plant."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/TGRS.1995.8746030","article-title":"Polarized Reflectance of Bare Soils and Vegetation\u2014Measurements and Models","volume":"33","author":"Breon","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.jqsrt.2009.11.001","article-title":"Reflection models for soil and vegetation surfaces from multiple-viewing angle photopolarimetric measurements","volume":"111","author":"Litvinov","year":"2010","journal-title":"J. Quant. Spectrosc. Radiat."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.rse.2017.02.026","article-title":"Polarized reflectances of urban areas: Analysis and models","volume":"193","author":"Xie","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.5194\/tc-9-2323-2015","article-title":"Soot on Snow experiment: Bidirectional reflectance factor measurements of contaminated snow","volume":"9","author":"Peltoniemi","year":"2015","journal-title":"Cryosphere"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jqsrt.2017.07.014","article-title":"Semi-empirical models for polarized reflectance of land surfaces: Intercomparison using space-borne POLDER measurements","volume":"202","author":"Yang","year":"2017","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.1985.289390","article-title":"Plant Canopy Specular Reflectance Model","volume":"23","author":"Vanderbilt","year":"1985","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jqsrt.2018.09.033","article-title":"Leaf polarized polarized BRDF simulation based on Monte Carlo 3-D vector RT modeling","volume":"221","author":"Kallel","year":"2018","journal-title":"J. Quant. Spectrosc Radiat."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106815","DOI":"10.1016\/j.jqsrt.2019.106815","article-title":"Two-scale Monte Carlo ray tracing for canopy-leaf vector radiative transfer coupling","volume":"243","author":"Kallel","year":"2020","journal-title":"J. Quant. Spectrosc Radiat."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.jqsrt.2016.11.006","article-title":"Canopy polarized BRDF simulation based on non-stationary Monte Carlo 3-D vector RT modeling","volume":"189","author":"Kallel","year":"2017","journal-title":"J. Quant. Spectrosc Radiat."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"591","DOI":"10.3390\/atmos3040591","article-title":"Exploration of a Polarized Surface Bidirectional Reflectance Model Using the Ground-Based Multiangle SpectroPolarimetric Imager","volume":"3","author":"Diner","year":"2012","journal-title":"Atmosphere"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1364\/AO.48.001228","article-title":"Analysis of the spectral and angular response of the vegetated surface polarization for the purpose of aerosol remote sensing over land","volume":"48","author":"Waquet","year":"2009","journal-title":"Appl. Opt."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1002\/2016JG003640","article-title":"New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression","volume":"122","author":"Ichii","year":"2017","journal-title":"J. Geophys Res. Biogeosci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Forkuor, G., Hounkpatin, O.K.L., Welp, G., and Thiel, M. (2017). High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0170478"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kotta, J., Kutser, T., Teeveer, K., Vahtmae, E., and Parnoja, M. (2013). Predicting Species Cover of Marine Macrophyte and Invertebrate Species Combining Hyperspectral Remote Sensing, Machine Learning and Regression Techniques. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0063946"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cui, Y.K., Chen, X., Xiong, W.T., He, L., Lv, F., Fan, W.J., Luo, Z.L., and Hong, Y. (2020). A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model. Remote Sens., 12.","DOI":"10.3390\/rs12030455"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, Y.H., Yang, B., Lin, H., and Zhang, J.Q. (2020). Modeling Polarized Reflectance of Natural Land Surfaces Using Generalized Regression Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12020248"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7117","DOI":"10.1080\/01431161.2012.700134","article-title":"Histogram matching for the calibration of kNN stem volume estimates","volume":"33","author":"Gilichinsky","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shah, S.H., Angel, Y., Houborg, R., Ali, S., and McCabe, M.F. (2019). A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat. Remote Sens., 11.","DOI":"10.3390\/rs11080920"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5843","DOI":"10.1109\/JSEN.2019.2904137","article-title":"A Machine Learning-Based Approach for Land Cover Change Detection Using Remote Sensing and Radiometric Measurements","volume":"19","author":"Zerrouki","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2015.04.032","article-title":"Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method","volume":"165","author":"Liang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Loozen, Y., Rebel, K.T., de Jong, S.M., Lu, M., Ollinger, S.V., Wassen, M.J., and Karssenberg, D. (2020). Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method. Remote Sens. Environ., 247.","DOI":"10.1016\/j.rse.2020.111933"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"31","DOI":"10.5194\/essd-9-31-2017","article-title":"A BRDF-BPDF database for the analysis of Earth target reflectances","volume":"9","author":"Breon","year":"2017","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ding, A.X., Jiao, Z.T., Dong, Y.D., Zhang, X.N., Peltoniemi, J.I., Mei, L.L., Guo, J., Yin, S.Y., Cui, L., and Chang, Y.X. (2019). Evaluation of the Snow Albedo Retrieved from the Snow Kernel Improved the Ross-Roujean BRDF Model. Remote Sens., 11.","DOI":"10.3390\/rs11131611"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.3390\/rs9121325","article-title":"Adjustment of Sentinel-2 Multi-Spectral Instrument (MSI) Red-Edge Band Reflectance to Nadir BRDF Adjusted Reflectance (NBAR) and Quantification of Red-Edge Band BRDF Effects","volume":"9","author":"Roy","year":"2017","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/72.97934","article-title":"A General Regression Neural Network","volume":"2","author":"Specht","year":"1991","journal-title":"IEEE T Neural Networ"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1109\/LGRS.2018.2845698","article-title":"Wind Speed Estimation From X-Band Marine Radar Images Using Support Vector Regression Method","volume":"15","author":"Chen","year":"2018","journal-title":"IEEE Geosci Remote S"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1080\/01431161.2012.725958","article-title":"Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression","volume":"34","author":"Axelsson","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","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":"Feret","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A Library for Support Vector Machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/S0034-4257(01)00209-7","article-title":"Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method","volume":"77","author":"Ek","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/S0034-4257(00)00188-7","article-title":"Selecting estimation parameters for the Finnish multisource National Forest Inventory","volume":"76","author":"Katila","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Sun, H., Wang, Q., Wang, G.X., Lin, H., Luo, P., Li, J.P., Zeng, S.Q., Xu, X.Y., and Ren, L.X. (2018). Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat Images. Remote Sens., 10.","DOI":"10.3390\/rs10081248"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4879","DOI":"10.1080\/01431161.2020.1718242","article-title":"Influence of polarized reflection on airborne remote sensing of canopy foliar nitrogen content","volume":"41","author":"Liu","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"975","DOI":"10.5194\/amt-4-975-2011","article-title":"Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations","volume":"4","author":"Dubovik","year":"2011","journal-title":"Atmos. Meas. Tech."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.rse.2010.11.005","article-title":"Models for surface reflection of radiance and polarized radiance: Comparison with airborne multi-angle photopolarimetric measurements and implications for modeling top-of-atmosphere measurements","volume":"115","author":"Litvinov","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhou, R.K., Wu, D.S., Zhou, R.Y., Fang, L.M., Zheng, X.Y., and Lou, X.W. (2019). Estimation of DBH at Forest Stand Level Based on Multi-Parameters and Generalized Regression Neural Network. Forests, 10.","DOI":"10.3390\/f10090778"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Liang, L., Di, L.P., Huang, T., Wang, J.H., Lin, L., Wang, L.J., and Yang, M.H. (2018). Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm. Remote Sens., 10.","DOI":"10.3390\/rs10121940"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.5194\/amt-6-2007-2013","article-title":"The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI): A new tool for aerosol and cloud remote sensing","volume":"6","author":"Diner","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1029\/2000GL011783","article-title":"Retrieval of aerosol properties over the ocean using multispectral and multiangle photopolarimetric measurements from the Research Scanning Polarimeter","volume":"28","author":"Chowdhary","year":"2001","journal-title":"Geophys. Res. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.jqsrt.2018.07.003","article-title":"Directional Polarimetric Camera (DPC): Monitoring aerosol spectral optical properties over land from satellite observation","volume":"218","author":"Li","year":"2018","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.jqsrt.2018.07.008","article-title":"The multi-viewing multi-channel multi-polarisation imager\u2014Overview of the 3MI polarimetric mission for aerosol and cloud characterization","volume":"219","author":"Fougnie","year":"2018","journal-title":"J. Quant. Spectrosc. Radiat. Transf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3891\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T08:04:20Z","timestamp":1735545860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3891"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,27]]},"references-count":64,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12233891"],"URL":"https:\/\/doi.org\/10.3390\/rs12233891","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,11,27]]}}}