10:15 〜 10:30
[ACG41-12] GCOM-C/SGLIとTerra&Aqua/MODIS観測に基づく全球長期蒸発散プロダクトの開発と検証
キーワード：蒸発散、蒸発散指数、衛星、JAXA、GCOM-C/SGLI、Terra and Aqua/MODIS
Evapotranspiration (ET) estimation is essential for understanding the Earth's energy and water budgets, ecosystem dynamics, and effective water resource management. The Evapotranspiration Index (ETindex) estimation algorithm provides global terrestrial ET information using cost-effective input data and a fully automated computational process.
The algorithm primarily relies on satellite-derived surface temperature and global weather data, including temperature, humidity, wind speed, and solar radiation. Its core input is data from the Second-Generation Global Imager (SGLI) sensor aboard the Global Change Observation Mission - Climate (GCOM-C), launched by JAXA in December 2017. This enables the generation of stable land ET maps at a spatial resolution of 250 m.
For the four-year ET dataset (2018–2021) produced by this algorithm, accuracy was evaluated using 12 flux monitoring datasets from the AsiaFlux and AmeriFlux networks, covering five land use categories: cropland, grassland, wetland, broadleaf forest, and coniferous forest. The results indicated minimal bias, with random errors yielding a root mean square error (RMSE) of 1.12 mm/day and a coefficient of variation of 0.51.
Additionally, the algorithm was applied to Terra and Aqua/MODIS data, in combination with SGLI data, to generate a long-term dataset spanning over 20 years (2003–present). These ET and ET index datasets are available on JASMES with a 5 km resolution globally (250 m resolution data around Japan are also provided for SGLI observation period).
The algorithm primarily relies on satellite-derived surface temperature and global weather data, including temperature, humidity, wind speed, and solar radiation. Its core input is data from the Second-Generation Global Imager (SGLI) sensor aboard the Global Change Observation Mission - Climate (GCOM-C), launched by JAXA in December 2017. This enables the generation of stable land ET maps at a spatial resolution of 250 m.
For the four-year ET dataset (2018–2021) produced by this algorithm, accuracy was evaluated using 12 flux monitoring datasets from the AsiaFlux and AmeriFlux networks, covering five land use categories: cropland, grassland, wetland, broadleaf forest, and coniferous forest. The results indicated minimal bias, with random errors yielding a root mean square error (RMSE) of 1.12 mm/day and a coefficient of variation of 0.51.
Additionally, the algorithm was applied to Terra and Aqua/MODIS data, in combination with SGLI data, to generate a long-term dataset spanning over 20 years (2003–present). These ET and ET index datasets are available on JASMES with a 5 km resolution globally (250 m resolution data around Japan are also provided for SGLI observation period).