The coastal zone represents a unique interface between land and sea, and addressing the ecological crisis it faces is of global significance. One of the most fundamental and effective measures is to extract the coastline’s location on a large scale, dynamically, and accurately. Remote sensing technology has been widely employed in coastline extraction due to its temporal, spatial, and sensor diversity advantages. Substantial progress has been made in coastline extraction with diversifying data types and information extraction methods. This paper focuses on discussing the research progress related to data sources and extraction methods for remote sensing-based coastline extraction. We summarize the suitability of data and some extraction algorithms for several specific coastline types, including rocky coastlines, sandy coastlines, muddy coastlines, biological coastlines, and artificial coastlines. We also discuss the significant challenges and prospects of coastline dataset construction, remotely sensed data selection, and the applicability of the extraction method. In particular, we propose the idea of extracting coastlines based on the coastline scene knowledge map (CSKG) semantic segmentation method. This review serves as a comprehensive reference for future development and research pertaining to coastal exploitation and management.

This paper presents a new algorithm to retrieve the aerosol optical depth (AOD) from a Himawari-8 Advanced Himawari Imager (AHI). Six typical aerosol models that derived from the long-term ground-based observations of East Asia are used in AOD retrieval. To accurately determine the surface reflectance, improved channel relationships between red, blue, and shortwave infrared (SWIR) are built up according to the infrared Normalized Difference Vegetation Index (NDVI_SWIR). Based on the new derived aerosol models and improved channel relationships, AOD over East Asian is retrieved by using the AHI data. The results are compared with Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MOD04 and MYD04) and yielded a correlation coefficient lager than 0.8 (R = 0.87 and 0.92, respectively). In addition, the retrieved AOD values are also validated by ground-based measurements at 12 Aerosol Robotic Network (AERONET) locations and revealed a good agreement between them (R = 0.86).

Sulfur dioxide (SO₂) in the planetary boundary layer (PBL) as a kind of gaseous pollutant has a strong effect regarding atmospheric environment, air quality, and climate change. As one of the most polluted regions in China, air quality in Beijing-Tianjin-Hebei (BTH) region has attracted more attention. This paper aims to study the characteristics of SO₂ distribution and variation over BTH. Spatial and temporal variations for a long term (2006–2017) over BTH derived from OMI PBL SO₂ products were discussed. The temporal trends confirm that the SO₂ loading falls from average 0.88 DU to 0.16 DU in the past 12 years. Two ascending fluctuations in 2007 and 2011 appeared to be closely related to the economic stimulus of each five-year plan (FYP). The spatial analysis indicates an imbalanced spatial distribution pattern, with higher SO₂ level in the southern BTH and lower in the northern. This is a result of both natural and human factors. Meanwhile, the SO₂ concentration demonstrates a decreasing trend with 14.92%, 28.57%, and 27.43% compared with 2006, during the events of 2008 Olympic Games, 2014 Asia-Pacific Economic Cooperation (APEC) summit, and 2015 Military Parade, respectively. The improvement indicates that the direct effect is attributed to a series of long-term and short-term control measures, which have been implemented by the government. The findings of this study are desirable to assist local policy makers in the BTH for drawing up control strategies regarding the mitigation of environmental pollution in the future.

Geostationary satellites observe the earth surface and atmosphere with a short repeat time, thus, providing aerosol parameters with high temporal resolution, which contributes to the air quality monitoring. Due to the limited information content in satellite data, and the coupling between the signals received from the surface and the atmosphere, the accurate retrieval of multiple aerosol parameters over land is difficult. With the strategy of taking full advantage of satellite measurement information, here we propose a neural network AEROsol retrieval framework for geostationary satellite (NNAeroG), which can potentially be applied to different instruments to obtain various aerosol parameters. NNAeroG was applied to the Advanced Himawari Imager on Himawari-8 and the results were evaluated versus independent ground-based sun photometer reference data. The aerosol optical depth, Ångström exponent and fine mode fraction produced by the NNAeroG method are significantly better than the official JAXA aerosol products. With spectral bands selection, the use of thermal infrared bands is meaningful for aerosol retrieval.

Satellite remote sensing is of considerable importance for estimating ground-level PM_2.5 concentrations to support environmental agencies monitoring air quality. However, most current studies have focused mainly on the application of MODIS aerosol optical depth (AOD) to predict PM_2.5 concentrations, while PARASOL AOD, which is sensitive to fine-mode aerosols over land surfaces, has received little attention. In this study, we compared a linear regression model, a quadratic regression model, a power regression model and a logarithmic regression model, which were developed using PARASOL level 2 AOD collected in China from 18 January 2013 to 10 October 2013. We obtained R (correlation coefficient) values of 0.64, 0.63, 0.62, and 0.57 for the four models when they were cross validated with the observed values. Furthermore, after all the data were classified into six levels according to the Air Quality Index (AQI), a low level of statistical significance between the four empirical models was found when the ground-level PM_2.5 concentrations were greater than 75 μg/m³. The maximum R value was 0.44 (for the logarithmic regression model and the power model), and the minimum R value was 0.28 (for the logarithmic regression model and the power model) when the PM_2.5 concentrations were less than 75 μg/m³. We also discussed uncertainty sources and possible improvements.