Uncertainty analysis of BRDF Modeling Using 6S Simulations and Monte-Carlo Method |
Lee, Kyeong-Sang
(Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University)
Seo, Minji (Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University) Choi, Sungwon (Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University) Jin, Donghyun (Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University) Jung, Daeseong (Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University) Sim, Suyoung (Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University) Han, Kyung-Soo (Division of Earth Environmental System Science (Major of Spatial Information Engineering), Pukyong National University) |
1 | Bialek, A., V. Vellucci, B. Gentil, D. Antoine, J. Gorrono, N. Fox, and C. Underwood, 2020. Monte Carlo-based quantification of uncertainties in determining ocean remote sensing reflectance from underwater fixed-depth radiometry measurements, Journal of Atmospheric and Oceanic Technology, 37(2): 177-196. DOI |
2 | Boudina, R., J. Wang, M. Benbouzid, G. Yao, and L. Zhou, 2020. A Review on Stochastic Approach for PHEV Integration Control in a Distribution System with an Optimized Battery Power Demand Model, Electronics, 9(1): 139. DOI |
3 | Calleja, J.F., C. Recondo, J. Peon, S. Fernandez, F. De la Cruz, and J. Gonzalez-Piqueras, 2016.A new method for the estimation of broadband apparent albedo using hyperspectral airborne hemispherical directional reflectance factor values, Remote Sensing, 8(3): 183. DOI |
4 | Che, X., M. Feng, J.O. Sexton, S. Channan, Y. Yang, and Q. Sun, 2017. Assessment of MODIS BRDF/Albedo model parameters (MCD43A1 Collection 6)for directional reflectance retrieval, Remote Sensing, 9(11): 1123. DOI |
5 | Gao, F., C.B. Schaaf, A.H. Strahler, Y. Jin, and X. Li, 2003. Detecting vegetation structure using a kernel-based BRDF model, Remote Sensing of Environment, 86(2): 198-205. DOI |
6 | He, T., S. Liang, D. Wang, H. Wu, Y. Yu, and J. Wang, 2012. Estimation of surface albedo and directional reflectance from Moderate Resolution Imaging Spectroradiometer(MODIS) observations, Remote Sensing of Environment, 119: 286-300. DOI |
7 | He, T., Y. Zhang, S. Liang, Y. Yu, and D. Wang, 2019. Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-Rand Himawari Data: Theoretical Basis, Operational Implementation, and Validation, Remote Sensing, 11(22): 2655. DOI |
8 | Vasilkov, A., W. Qin, N. Krotkov, L. Lamsal, R. Spurr, D. Haffner, J. Joiner, E.S. Yang, and S. Marchenko, 2017. Accounting for the effects of surface BRDF on satellite cloud and trace-gas retrievals: a new approach based on geometry-dependent Lambertian equivalent reflectivity applied to OMI algorithms, Atmospheric Measurement Techniques, 10(1): 333. DOI |
9 | Seong, N.H., D. Jung, J. Kim, and K.S. Han, 2020. Evaluation of NDVI Estimation Considering Atmospheric and BRDF Correction through Himawari-8/AHI, Asia-Pacific Journal of Atmospheric Sciences, 2020: 1-10. |
10 | Su, L., Y. Huang, M.J. Chopping, A. Rango, and J.V. Martonchik, 2009. An empirical study on the utility of BRDF model parameters and topographic parameters for mapping vegetation in a semi-arid region with MISR imagery, International Journal of Remote Sensing, 30(13): 3463-3483. DOI |
11 | Wen, J., Q. Liu, Q. Xiao, Q. Liu, D. You, D. Hao, S. Wu, and X. Lin, 2018. Characterizing land surface anisotropic reflectance over rugged terrain: A review of concepts and recent developments, Remote Sensing, 10(3): 370. DOI |
12 | Yeom, J.M., J.L. Roujean, K.S. Han, K.S. Lee, H.W. Kim, 2020. Thin cloud detection over land using background surface reflectance based on the BRDF model applied to Geostationary Ocean Color Imager(GOCI) satellite data sets, Remote Sensing of Environment, 239: 111610. DOI |
13 | Lorente, A., K. Folkert Boersma, P. Stammes, L. Gijsbert Tilstra, A. Richter, H. Yu, S. Kharbouche, and J. P. Muller, 2018.The importance of surface reflectance anisotropy for cloud and NO2 retrievals from GOME-2 and OMI, Atmospheric Measurement Techniques, 11(7): 4509-4529. DOI |
14 | Lee, C.S., K.S. Han, J.M. Yeom, K.S. Lee, M. Seo, J. Hong, J.W. Hong, K. Lee, J. Shin, I.C. Shin, J. Chun, and J. L. Roujean, 2017. Surface albedo from the geostationary Communication, Ocean and Meteorological Satellite (COMS)/Meteorological Imager (MI) observation system, GIScience & Remote Sensing, 55(1): 38-62. DOI |
15 | Lee, K.S., S.R. Chung, C. Lee, M. Seo, S. Choi, N.H. Seong, D. Jin, M. Kang, J.M. Yeom, J.L. Roujean, D. Jung, S. Sim, and K.S. Han, 2020. Development of Land Surface Albedo Algorithm for the GK-2A/AMI Instrument, Remote Sensing, 12(15): 2500. DOI |
16 | Lobell, D.B., G.P. Asner, J.I. Ortiz-Monasterio, and T.L. Benning, 2003. Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties, Agriculture, Ecosystems & Environment, 94(2): 205-220. DOI |
17 | Roberts, G., 2001. A review of the application of BRDF models to infer land cover parameters at regional and global scales, Progress in Physical Geography, 25(4): 483-511. DOI |
18 | Roujean, J.L., J. Leon-Tavares, B. Smets, P. Claes, F.C. De Coca, and J. Sanchez-Zapero, 2018. Surface albedo and toc-r 300 m products from PROBA-V instrument in the framework of Copernicus Global Land Service, Remote Sensing of Environment, 215: 57-73. DOI |