Studying near-surface aerosol properties is of importance for a better assessment of the aerosol effect on radiative forcing. We employ the data from a near-horizontal lidar to investigate the diurnal behavior of aerosol extinction and single scattering albedo (SSA) at 349 nm. The response of these parameters to ambient relative humidity (RH) is examined for the data from a one-month campaign conducted in Chiba, Japan, during November 2017, a transition period from fall to winter. The Klett method and adaptive slope method are used in deriving the aerosol extinction coefficient from the lidar data, while the SSA values are retrieved using an aethalometer. Also, a visibility-meter is used to examine the aerosol loading inside the atmospheric boundary layer. It is found that the aerosol growth during the deliquescence phase is more readily observed than the contraction in the efflorescence phase. The decrease of SSA before the deliquescence RH is found for approximately 46% of the deliquescence cases, presumably representing the particle shrinkage of soot particles.

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.

An efficient assessment of rice varieties in tropical regions is crucial for selecting cultivars suited to unique environmental conditions. This study explores machine learning algorithms that leverage multispectral sensor data from UAVs to evaluate rice varieties. It focuses on three paddy rice types at different ages (six, nine, and twelve weeks after planting), analyzing data from four spectral bands and vegetation indices using various algorithms for classification. The results show that the neural network (NN) algorithm is superior, achieving an area under the curve value of 0.804. The twelfth week post-planting yielded the most accurate results, with green reflectance the dominant predictor, surpassing the traditional vegetation indices. This study demonstrates the rapid and effective classification of rice varieties using UAV-based multispectral sensors and NN algorithms to enhance agricultural practices and global food security.

Comparing cloud cover (CC) products from different satellites with the same ground-based CC dataset provides information on the similarities or differences of values among satellite products. For this reason, 42-month CC products from Moderate Resolution Imaging Spectrometer’s (MODIS) Collection 6.1 daily cloud cover products (MOD06_L2, MYD06_L2, MOD08_D3, and MYD08_D3) and Himawari-8 are compared with the ground-based camera datasets. The comparison shows that CC from MODIS differs from ground measurement CC by as much as 57% over Chiba, Japan, when low CC is observed by the camera. This indicates MODIS’s ability to capture high-level clouds that are not effectively seen from the ground. When the camera detects high CC, an indication of the presence of low-level clouds, CC from MODIS is relatively higher than the CC from the camera. In the case of Himawari-8 data, when the camera observes low CC, this difference is around 0.7%. This result indicates that high-level clouds are not effectively observed, but the Himawari-8 data correlates well with camera observations. When the camera observes high CC, Himawari-8-derived CC is lower by around 10% than CC from the camera. These results show the potential of continuous observations of nighttime clouds using the camera to provide a dataset that can be used for intercomparison among nighttime satellite CC products.

The retrieval of the aerosol optical thickness (AOT) from remotely-sensed data relies on the adopted aerosol model. However, the method of this technique has been rather limited because of the high variability of the surface albedo, in addition to the spatial variability in the aerosol properties over the land surfaces. To overcome unsolved problems, we proposed a method for the visibility-derived AOT estimation from SKYNET-based measurement and daytime satellite images with a custom aerosol model over the Chiba area (35.62° N, 140.10° E), which is located in the greater Tokyo metropolitan area in Japan. Different from conventionally-used aerosol models for the boundary layer, we created a custom aerosol model by using sky-radiometer observation data of aerosol volume size distribution and refractive indices, coupled with spectral response functions (SPFs) of satellite visible bands to alleviate the wide range of path-scattered radiance. We utilized the radiative transfer code 6S to implement the radiative transfer calculation based on the created custom aerosol model. The concurrent data from ground-based measurement are used in the radiative analysis, namely the temporal variation of AOT from SKYNET. The radiative estimation conducted under clear-sky conditions with minimum aerosol loading is used for the determination of the surface albedo, so that the 6S simulation yields a well-defined relation between total radiance and surface albedo. We made look-up tables (LUTs) pixel-by-pixel over the Chiba area for the custom aerosol model to retrieve the satellite AOT distribution based on the surface albedo. Therefore, such a reference of surface albedo generated from clear-sky conditions, in turn, can be employed to retrieve the spatial distribution of AOT on both clear and relatively turbid days. The value for the AOTs retrieved using the custom aerosol model is found to be stable than conventionally-used typical aerosol models, indicating that our method yields substantially better performance.