ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 209–214, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-209-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 209–214, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-209-2020

  03 Aug 2020

03 Aug 2020

NEW SOLAR-INDUCED CHLOROPHYLL FLUORESCENCE RETRIEVAL ALGORITHM BASED ON TANSAT SATELLITE DATA

Y. Zhou1,2, X. Lu1, Y. Huang1,2, Z. Gao1,2, and Y. Zheng1,2 Y. Zhou et al.
  • 1Key Laboratory of Spatio-temporal Information and Ecological Restoration of Mines of Natural Resources of the People's Republic of China, Henan Polytechnic University, Jiaozuo 454003, China
  • 2School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China

Keywords: Chlorophyll Fluorescence, Random Sample Consensus, TanSat, KI Fraunhofer Line, Algorithm, Retrieval

Abstract. Solar-induced chlorophyll fluorescence (SIF) is an indicator of plant photosynthesis which could be detected by satellite. However,some existing algorithms are easily affected by the inaccuracy of satellite data which will causing deviation in the retrieval of SIF. To avoid "outliers" with inaccuracy affecting the retrieval results, a random sample consensus algorithm (RANSAC) was introduced to retrieve SIF in this paper. The results show that the chlorophyll fluorescence value obtained by this method is consistent with the OCO-2 SIF product (R2 = 0.81), and also consistent with the MODIS vegetation index (R2 = 0.87 with NDVI, R2 = 0.85 with EVI). Compared with the existing SIF products (OCO-2 SIF), the SIF retrieved in this paper was better in spatial details and outlier distribution.