Sea salt aerosols were assumed to be homogeneous spheres in most climate models. However, observations show that sea salt particles are inhomogeneous during the deliquesce and crystallization processes. Using a two‐layer sphere model, we found that backscattering of solar radiation associated with sea salts is underestimated in homogeneous sea salt models. The Community Earth System Model is used to assess the inhomogeneity effect on direct radiative forcing. For global climate model simulation, the inhomogeneity effect on radiative transfer is found to be small as high RHs over widespread oceans suppress the impact of inhomogeneity. On the other hand, in coastal regions, the inhomogeneity effect can cause up to 10% radiative forcing difference of sea salt aerosols. The inhomogeneity effect of sea salt aerosols has to be considered over coastal regions, especially in the Mediterranean, Australia, and the eastern coast of South America.

The accurate identification of the location, intensity, and spread of wildfires is an essential early-stage precaution for reducing wildfire damage. Satellite imaging platforms, particularly those with high revisiting frequencies and fine spatial resolutions, represent the most efficient possible means of monitoring wildfires dynamically. However, the extraction of accurate fire-related information from satellite images remains challenging, and few studies have investigated the use of remote sensing data from satellites with geostationary orbits. The present work addresses these issues by applying over 5,000 images obtained from the geostationary Himawari-8 satellite of a severe Australian wildfire occurring from November 2019 to February 2020 to train and test a fully connected convolutional neural network (CNN) for identifying the location and intensity of wildfires. The proposed CNN model obtains a detection accuracy greater than 80%, which greatly exceeds that of other machine learning algorithms, such as support vector machine and k-means clustering. Moreover, the CNN model can be trained in a relatively short period, even when employing large training datasets, and predictions can be made in just one or two minutes. The proposed model provides insight into the application of deep learning methodologies for wildfire monitoring based on the imagery provided by geostationary satellites, and support for developing similar satellite missions.

The accurate extraction of an aquaculture area is significant in aquaculture management, post disaster evaluation, and aquatic environment protection. However, little attention has been paid to the aquaculture area extraction in coastal water with high turbidity. In this study, based on the spectral and geospatial features of aquaculture cages in complex coastal water with varying turbidity, we proposed a new aquaculture area extraction method using a Gaofen-2(GF-2) satellite image with 0.8m spatial resolution. The water was classified into clear, medium, and high turbidity categories according to the suspended sediment concentration (SSC) derived from the inversion of the GF-2 image. Different rules of extraction were developed with respect to those three categories of water body: (1) The Normalized Difference Water Index(NDWI) threshold was set for the clear water, (2) a ratio index (R=Green/NIR) was established for the medium turbid water body, and (3) for the turbid water body, feature analysis with a specified classification rule was established. The experimental results demonstrated that our proposed method worked well, with the high accuracies of 87.3300% for the overall accuracy, even for the high turbidity water. The Kappa coefficient was 0.7375, which was much better than the Kappa coefficient values of the three conventional classification methods represented in this paper. This study provides effective information support and auxiliary decision analysis for management departments to scientifically plan and environmentally manage coastal aquaculture areas.