Development of land surface reflectances and related products using Himawari-8 AHI

Abstract:

New generation geostationary satellites provide very high temporal earth observation with multiple visible, near-infrared, and shortwave-infrared wavelength regions. Thus, these datasets can provide us unexpectedly hyper-temporal resolution to monitor land surface. However, since geostationary satellite data are provided with top-of-atmosphere status, complex data pre-processing such as atmospheric correction, cloud masking are required. Furthermore, observation geometries of the satellites are completely different between polar-orbiting satellites and geostationary satellites, we need a careful intercomparison.

First, cloud mask data are generated based on Yamamoto et al. (2018). We updated and simplified the algorithm, and conducted an intercomparison with high-spatial resolution satellites, such as Landsat-series. Intercomparison with MODIS products has also conducted. Our approach of cloud mask generation is basically consistent with Landsat-based ones. If we tested at tropical forest sites, we found that cloud cover ratio of land surface is overall consistent between our products and MODIS ones.

Second, we estimated land surface reflectances using AHI. Our approach uses a commonly used radiative transfer code, 6SV, Himawari-8 aerosol products as atmospheric data. The estimated surface reflectances were evaluated with MODIS products and ground observation data. In the comparison with MODIS, we used two surface reflectance products, MOD09/MYD09 and MCD43. MCD43 is a BRDF corrected surface reflectance and provides its kernel function paramters. As a comparison of AHI and MODIS, we found that MODIS-based surface reflectances with adjustment to AHI observation geometry show the better agreement with those of AHI. Thus, matching the observation geometry is essential to evaluating surface reflectances. Furthermore, we confirmed that the effects of observation condition differences are much smaller when intercomparison is conducted using vegetation index.