Development of a global LAI estimation algorithm for JAXA's new earth observation satellite sensor, GCOM-C/SGLI

Wednesday, 17 December 2014
Yuhsaku Ono1, Hiroshi Murakami1, Hideki Kobayashi2, Kenlo Nishida Nasahara3, Koji Kajiwara4 and Yoshiaki Honda4, (1)JAXA Japan Aerospace Exploration Agency, Sagamihara, Japan, (2)JAMSTEC, Yokohama, Japan, (3)University of Tsukuba, Tsukuba, Japan, (4)Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
Leaf Area Index (LAI) is defined as the one-side green leaf area per unit ground surface area. Global LAI products, such as MOD15 (Terra&Aqua/MODIS) and CYCLOPES (SPOT/VEGETATION) are used for many global terrestrial carbon models. Japan Aerospace eXploration Agency (JAXA) is planning to launch GCOM-C (Global Change Observation Mission-Climate) which carries SGLI (Second-generation GLobal Imager) in the Japanese Fiscal Year 2017. SGLI has the features, such as 17-channel from near ultraviolet to thermal infrared, 250-m spatial resolution, polarization, and multi-angle (nadir and ±45-deg. along-track slant) observation. In the GCOM-C/SGLI land science team, LAI is scheduled to be generated from GCOM-C/SGLI observation data as a standard product (daily 250-m). In extisting algorithms, LAI is estimated by the reverse analysis of vegetation radiative transfer models (RTMs) using multi-spectral and mono-angle observation data. Here, understory layer in vegetation RTMs is assumed by plane parallel (green leaves + soil) which set up arbitrary understroy LAI. However, actual understory consists of various elements, such as green leaves, dead leaves, branches, soil, and snow. Therefore, if understory in vegetation RTMs differs from reality, it will cause an error of LAI to estimate. This report describes an algorithm which estimates LAI in consideration of the influence of understory using GCOM-C/SGLI multi-spectral and multi-angle observation data.