Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 819-823, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-819-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 819-823, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-819-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

ANALYSIS OF ACCURACY OF MODIS BRDF PRODUCT (MCD43 C6) BASED ON MISR LAND SURFACE BRF PRODUCT – A CASE STUDY OF THE CENTRAL PART OF NORTHEAST ASIA

J. Li1, S. Chen1, W. Qin1, M. Murefu1, Y. Wang2, Y. Yu1, and Z. Zhen1 J. Li et al.
  • 1College of Geo-Exploration Science and Technology, Jilin University, Changchun, China
  • 2Centre for Lightning Protection and Disaster Mitigation of Jilin Province, Changchun, China

Keywords: MODIS BRDF, model accuracy, MISR BRF, vegetation phenology, snow cover

Abstract. EOS/MODIS land surface Bi-directional Reflectance Distribution Function (BRDF) product (MCD43), with the latest version C6, is one of the most important operational BRDF products with global coverage. The core sub-product MCD43A1 stores 3 parameters of the RossThick-LiSparseR semi-empirical kernel-driven BRDF model. It is important for confident use of the product to evaluate the accuracy of bi-directional reflectance factor (BRF) predicted by MCD43A1 BRDF model (mBRF). A typical region in the central part of Northeast Asia is selected as the study area. The performance of MCD43A1 BRDF model is analyzed in various observation geometries and phenological phases, using Multi-angle Imaging SpectroRadiometer (MISR) land-surface reflectance factor product (MILS_BRF) as the reference data. In addition, MODIS products MCD12Q1 and MOD/MYD10A1 are used to evaluate the impacts of land cover types and snow covers on the model accuracy, respectively. The results show an overall excellent performance of MCD43A1 in representing the anisotropic reflectance of land surface, with root mean square error (RMSE) of 0.0262 and correlation coefficient (R) of 0.9537, for all available comparable samples of MILS_BRF and mBRF pairs. The model accuracy varies in different months, which is related to the phenological phases of the study area. The accuracy for pixels labelled as ‘snow’ by MCD43 is obviously low, with RMSE/R of 0.0903/0.8401. Ephemeral snowfall events further decrease the accuracy, with RMSE/R of 0.1001/0.7715. These results provide meaningful information to MCD43 users, especially those, whose study regions are subject to phenological cycles as well as snow cover and change.