11:00 AM - 1:00 PM
[ACG39-P02] High temporal retrieval of leaf area index from Himawari-8 data
Keywords:Geostationary Satellite, Himawari-8, Leaf Area Index
High temporal retrieval of leaf area index from Himawari-8 data
Tatsuki Hashimoto, Kazuhito Ichii, Yuhei Yamamoto, Hideki Kobayashi, Wei Li, and Wei Yang
Abstract:
The Leaf Area Index (LAI) is one of the most important physical parameters for understanding surface energy, water, and carbon budgets. LAI is also widely used to monitor temporal changes in vegetation and vegetation responses to climate change and anthropogenic activities. Since satellites observe a wide area with high spatial resolution, various estimation methods of LAI using satellite/sensor observations have been proposed, such as Terra/MODIS and GCOM-C/SGLI. On the other hand, conventional satellite observations are limited to about one observation per day at the same location, and due to the presence of clouds, data is only provided at intervals of eight days or more. Furthermore, deficiencies were more frequent in regions and seasons with more clouds. Himawari 8 is a Japanese geostationary meteorological satellite that started observations in July 2015, and in addition to its high frequency of once-every-10-minutes observations, it has multiple wavelength bands in the visible and near-infrared regions compared to Himawari 7, raising expectations for land surface monitoring. If Himawari-8 can be used, the frequency of observations can be dramatically increased compared to conventional satellite observations, and the time interval for LAI estimation can be shortened. This study aims to develop a method to estimate LAI from Himawari-8 data using a three-dimensional radiative transfer model of vegetation, the FLiES model, which was used as a fundamental radiative transfer model in the construction of FPAR/LAI products for the SGLI sensor onboard the GCOM-C satellite launched by Japan in 2017. The FLiES model is used as the base radiative transfer model for the FPAR/LAI product of the SGLI sensor onboard the GCOM-C satellite launched by Japan in 2017. LUTs (Look-Up-Table) were constructed for each vegetation type using the FLiES model, combining solar and satellite geometric conditions, LAI, and surface reflectance. The LUTs were constructed using a combination of solar and satellite geometric conditions, LAI, and surface reflectance. As a result, we were able to construct a method for LAI estimation using Himawari-8 observation data by using the developed LUT. As a validation of the accuracy, we compared the estimated values with two field observations, and found a tendency of underestimation. As a future task, we will verify the accuracy by comparing with the existing LAI estimation product (SGLI). We are considering expanding the estimation period to reduce the effect of cloud cover.
Tatsuki Hashimoto, Kazuhito Ichii, Yuhei Yamamoto, Hideki Kobayashi, Wei Li, and Wei Yang
Abstract:
The Leaf Area Index (LAI) is one of the most important physical parameters for understanding surface energy, water, and carbon budgets. LAI is also widely used to monitor temporal changes in vegetation and vegetation responses to climate change and anthropogenic activities. Since satellites observe a wide area with high spatial resolution, various estimation methods of LAI using satellite/sensor observations have been proposed, such as Terra/MODIS and GCOM-C/SGLI. On the other hand, conventional satellite observations are limited to about one observation per day at the same location, and due to the presence of clouds, data is only provided at intervals of eight days or more. Furthermore, deficiencies were more frequent in regions and seasons with more clouds. Himawari 8 is a Japanese geostationary meteorological satellite that started observations in July 2015, and in addition to its high frequency of once-every-10-minutes observations, it has multiple wavelength bands in the visible and near-infrared regions compared to Himawari 7, raising expectations for land surface monitoring. If Himawari-8 can be used, the frequency of observations can be dramatically increased compared to conventional satellite observations, and the time interval for LAI estimation can be shortened. This study aims to develop a method to estimate LAI from Himawari-8 data using a three-dimensional radiative transfer model of vegetation, the FLiES model, which was used as a fundamental radiative transfer model in the construction of FPAR/LAI products for the SGLI sensor onboard the GCOM-C satellite launched by Japan in 2017. The FLiES model is used as the base radiative transfer model for the FPAR/LAI product of the SGLI sensor onboard the GCOM-C satellite launched by Japan in 2017. LUTs (Look-Up-Table) were constructed for each vegetation type using the FLiES model, combining solar and satellite geometric conditions, LAI, and surface reflectance. The LUTs were constructed using a combination of solar and satellite geometric conditions, LAI, and surface reflectance. As a result, we were able to construct a method for LAI estimation using Himawari-8 observation data by using the developed LUT. As a validation of the accuracy, we compared the estimated values with two field observations, and found a tendency of underestimation. As a future task, we will verify the accuracy by comparing with the existing LAI estimation product (SGLI). We are considering expanding the estimation period to reduce the effect of cloud cover.