The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 553–557, 2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 553–557, 2020

  21 Aug 2020

21 Aug 2020


X. Yang1,2, X. Xi1, C. Wang1,2, J. Shi3, and Y. Huang4 X. Yang et al.
  • 1Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3School of Tourism and Geographical Science, Yunnan Normal University, Kunming 650500 , China
  • 4Guangxi Zhuang Autonomous Region Institute of Natural Resources Remote Sensing Sensing, Nanning 530029 530029, China

Keywords: Fraction of Photosynthetically Active Radiation (FPAR), Airborne LiDAR, Inversion, Direct radiation, Diffuse radiation

Abstract. Fraction of absorbed Photosynthetically Active Radiation (FPAR) is one of the pivotal parameters in terrestrial ecosystem modelling and crop growth monitoring. Airborne LiDAR is an advanced active remote sensing technology which can acquire fine three-dimensional canopy structural information quickly and accurately. Although some previous studies have shown that LiDAR-derived metrics had strong relationships with canopy FPARs, these estimation models without physical meaning are hard to be extended to various vegetation canopies and different growth periods. This study proposed a physical FPAR inversion method based on airborne LiDAR data and field measurements. The method considered direct and diffuse radiations separately based on the SAIL model and energy budget balance principle. The canopy FPAR was inversed from the structural information provided by LiDAR point cloud data and the spectral information provided by ground measurements. The estimated FPAR was validated with the field-measured FPAR over 39 maize plots. Results showed that the proposed method had a good performance in estimating the total FPAR of maize canopy (R2 = 0.76, RMSE = 0.062, n = 39). This study provides the potential to estimate the total, direct, and diffuse FPARs of vegetation canopy from airborne LiDAR data.