17:15 〜 19:15
[ACG44-P03] Retrieval of the Vegetation Clumping Index (CI) from Geostationary Satellite Observations Using an Improved BRDF Model
キーワード：Clumping Index (CI)、Himawari静止軌道衛星、双方向反射分布関数（BRDF）
The Foliage Clumping Index (CI), which accounts for the nonrandom spatial distribution of foliage elements within a canopy, is crucial for accurate estimations of Leaf Area Index (LAI) and Gross Primary Productivi (GPP). CI is defined as the ratio of the effective leaf area index (LAIe) to LAI. Traditional remote sensing methods for CI calculation, primarily relying on low-earth-orbit (LEO) satellites and the Normalized Difference Hot-spot and Dark-spot (NDHD) index, often overlook the potential of geostationary satellites.
This study utilizes data from the Himawari geostationary satellite, which allows for multiple hot-spot occurrences for each pixel throughout the year, facilitating the precise validation of the bidirectional reflectance distribution function (BRDF) model. Compared to previous LEO satellites, Himawari can provides more cloud-free, high-quality data, filling gaps in MODIS CI data and serving as a supplementary dataset for global CI products. Additionally, its high-frequency observations enable the monitoring of seasonal and long-term changes in CI.
In terms of methodology, this study employs the Enhanced Rahman-Pinty-Verstraete (E-RPV) BRDF model to fit Himawari reflectance. Furthermore, to ensure the accuracy of this study, the proposed method has been cross-validated using datasets from other sensors, such as SCAR-B and MISR.
In conclusion, comparisons with in-situ observation sites indicate that the CI derived from Himawari offers improved accuracy relative to the MODIS CI product, demonstrating better agreement with ground-based measurements. Moreover, cross-validation using data from SCAR-B and MISR further confirms the reliability and accuracy of the proposed algorithm, providing robust support for CI estimation using geostationary satellite observations.
This study utilizes data from the Himawari geostationary satellite, which allows for multiple hot-spot occurrences for each pixel throughout the year, facilitating the precise validation of the bidirectional reflectance distribution function (BRDF) model. Compared to previous LEO satellites, Himawari can provides more cloud-free, high-quality data, filling gaps in MODIS CI data and serving as a supplementary dataset for global CI products. Additionally, its high-frequency observations enable the monitoring of seasonal and long-term changes in CI.
In terms of methodology, this study employs the Enhanced Rahman-Pinty-Verstraete (E-RPV) BRDF model to fit Himawari reflectance. Furthermore, to ensure the accuracy of this study, the proposed method has been cross-validated using datasets from other sensors, such as SCAR-B and MISR.
In conclusion, comparisons with in-situ observation sites indicate that the CI derived from Himawari offers improved accuracy relative to the MODIS CI product, demonstrating better agreement with ground-based measurements. Moreover, cross-validation using data from SCAR-B and MISR further confirms the reliability and accuracy of the proposed algorithm, providing robust support for CI estimation using geostationary satellite observations.