Summary of the Final Report of Research Results1) Title of the proposed research
Evaluation and improvement of the phenology monitoring algorithm of terrestrial vegetation
2) Principal Investigator (PI) and Co-Investigators (Co-Is)
PI:
Dr. Hideki Kobayashi
Co-I:
Dr. Rikie Suzuki
Dr. Tsuneo Matsunaga
Dr. Nicolas Delbart
3) PI’s affiliation
Japan Agency for Marine-Earth Science and Technology4) Summary of the Final Report of Research Results
Reliable monitoring of vegetation seasonality (leaf appearance date, growing length, maximum
leaf area index etc.) is important to estimate the terrestrial carbon budget. GOSAT Cloud AerosolImager (CAI) provides not only the optical properties of the atmospheric particles but also provides
phenological information; spectral channels of CAI are similar to those of MODIS andSPOT-VEGETATION. Our research goals include 1) the synergistic use of satellite data using
SPOT-VEGETATION and MODIS data, 2) evaluation of CAI, and 3) estimation of phenologicalinformation. We conducted this study in three difference locations in the world: a grassland site in
Qinghai Tibetan plateau, a deciduous needleleaf forest (larch) site in Yakutsk, Russia, and anevergreen needleleaf forest (black spruce) site in Poker Flat Research Range near Fairbanks, Alaska,USA. In these sites, we first collected SPOT-VEGETATION S10 and Terra-MODIS MOD09 surface
reflectance datasets as well as TANSO/CAI. SPOT-VEGETATION S10 is an atmosphericallycorrected surface reflectance data sets produced at 10-day interval since April 1998. The spatial
resolution is 1/112 degree. MODIS MOD09 is also atmospherically corrected surface reflectancedata sets produced at 8-day interval since the beginning of 2000. The spatial resolution of the data
product is 500m. The CAI data used are the level 1B top of atmosphere radiance and the level 3
NDVI data sets. For CAI L1B data sets, we applied atmospheric correction using the 6S radiativetransfer model assuming a mid-latitude summer atmospheric model and continental aerosol withaerosol optical thickness at 550m = 0.05. Before using the actual CAI data, we compared NDVI andNDWI from Terra-MODIS and SPOT-VEGETATION to understand whether the VI seasonalpatterns can yield consistent seasonal patterns. We found a good agreement in terms of their seasonaltimings and absolute values in Yakutsk and Qinghai sites. Then, we compared NDWI and NDVI

from CAI L1B data with SPOT-VEGETAION data. The NDVI has similar seasonal patterns in theQinghai site, although only a limited number of data was retrieved due to cloud contamination. TheNDWI from CAI was substantially lower than that of SPOT-VEGETATION. To investigate thecause of this large difference in NDWI, we also compared reflectance data sets in red, NIR, andSWIR in the Qinghai site. The biases in red and NIR reflectances are small in the vegetation growing
season, however in SWIR, the biases was as large as 0.1 in a reflectance unit. Therefore, the NDWI
difference was mainly caused by the SWIR reflectance difference between CAI andSPOT-VEGETATION. From these analyses, we concluded that the use of NDVI for phenology
monitoring is more robust than NDWI method when using multiple satellite data sets. Indeed, theseasonal timing and the maximum of the CAI L3 NDVI agree well with SPOT-VEGETATION atthree study sites. One limitation using CAI data is that CAI L3 NDVI does not contain the actual
date of measurements when computing a 15-day composite. As a surrogate of NDWI-based
phenology measurements, which we initially proposed, NDVI with an appropriate threshold could
work for CAI-based vegetation phenology monitoring.
5) List of publications relating to the proposed research
No publication has been published yet.