An Analysis of Improved Ross-Li Models on the Ability of Estimationg Albedo under Large Solar Zenith Angle by Polder Datasets

Yaxuan Chang, Ziti Jiao, Xiaoning Zhang, Yadong Dong, Siyang Yin, Lei Cui, Anxin DIng, Jing Guo, Rui Xie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Surface albedo is a key parameter controlling the earth energy budget, which can be estimated by integrating the Bidirectional Reflectance Distribution Function (BRDF). The semi-empirical kernel-driven BRDF models has been widely used in BRDF/Albedo products, MODIS products for instance (Schaaf et al., 2002). However, these albedo products are suspect under large SZA (Liu et al., 2009). With the hotspot improved kernel-driven models, it is necessary to assess the property of these models. In this study, two POLDER datasets are utilized to access these models by root-mean-square error (RMSE) and relative RMSE (RMSE r). Then, cross-comparison between albedos estimated by improved models under several SZAs is analyzed by POLDER and the concurrence MODIS pixels. This study is aimed at choosing suitable models for albedo estimation under different angular situations to retrieve more accurate albedo.

Original languageEnglish
Title of host publication2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6708-6711
Number of pages4
ISBN (Electronic)9781538691540
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: 28 Jul 20192 Aug 2019

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/TerritoryJapan
CityYokohama
Period28/07/192/08/19

Keywords

  • BRDF
  • albedo
  • hotspot correction
  • kernel-driven model

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