Cloud classification is not only important for weather forecasts, but also for radiation budget studies. Although cloud mask and classification procedures have been proposed for Himawari-8 Advanced Himawari Imager (AHI), their applicability is still limited to daytime imagery. The split window algorithm (SWA), which is a mature algorithm that has long been exploited in the cloud analysis of satellite images, is based on the scatter diagram between the brightness temperature (BT) and BT difference (BTD). The purpose of this research is to examine the usefulness of the SWA for the cloud classification of both daytime and nighttime images from AHI. We apply SWA also to the image data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra to highlight the capability of AHI. We implement the cloud analysis around Japan by employing band 3 (0.469 μm) of MODIS and band 1 (0.47 μm) of AHI for extracting the cloud-covered regions in daytime. In the nighttime case, the bands that are centered at 3.9, 11, 12, and 13 µm are utilized for both MODIS and Himawari-8, with somewhat different combinations for land and sea areas. Thus, different thresholds are used for analyzing summer and winter images. Optimum values for BT and BTD thresholds are determined for the band pairs of band 31 (11.03 µm) and 32 (12.02 µm) of MODIS (SWA31-32) and band 13 (10.4 µm) and 15 (12.4 µm) of AHI (SWA13-15) in the implementation of SWA. The resulting cloud mask and classification are verified while using MODIS standard product (MYD35) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. It is found that MODIS and AHI results both capture the essential characteristics of clouds reasonably well in spite of the relatively simple scheme of SWA based on four threshold values, although a broader spread of BTD obtained with Himawari-8 AHI (SWA13-15) could possibly lead to more consistent results for cloud-type classification than SWA31-32 based on the MODIS sensors.

In Indonesia, there is a need to improve the efficiency of damage assessments of the agricultural insurance system for paddy rice producers affected by floods, droughts, pests, and diseases. In this study, we develop a method to estimate the transplanting date required for damage assessments of paddy rice fields. The study area is the Cihea irrigation district in West Java, Republic of Indonesia. Backscattering coefficients of VH polarization measured by a synthetic aperture radar onboard the Sentinel-1 satellite were used for the estimations. We investigated the accuracy of the estimations of the proposed method by smoothing out the time-series data, applying a speckle filter, and by signal synthesis of the surrounding fields. It was found that these variations effectively improved the estimation accuracy. To further improve the estimation accuracy, the data for all incident angles were used after correcting the incident angle dependence of the backscattering coefficients for three types of data with different incident angles (32°, 41°, and 45°) obtained in the study area. The estimated transplanting date for each field in the test site was compared with the transplanting date obtained through interviews. The standard deviations of the estimation errors for the four cropping periods from March 2018 to February 2020 were found to be ~5–6 days, and the percentages of estimation errors in transplanting dates within 5, 10, and 15 days were estimated to be 69%, 92%, and 97%, respectively. It was confirmed that a sufficiently reliable transplanting date estimation can be obtained ~10–15 days after transplantation.

We have developed a method to monitor the slant column density of nitrogen dioxide in the lower troposphere using a compact hyperspectral camera with a high spectral resolution of 1 nm at the full width half-maximum. Measurements of skylight spectra were conducted in wavelength regions of 460–490 nm and 550–610 nm to retrieve the slant column densities of nitrogen dioxide, in addition to water vapor and oxygen dimer. The results of ground-based measurements are shown for the cases of urban air pollution and aircraft emission near an airport runway.