ALBERT

Description: A novel geostationary satellite, the H8/AHI (Himawari-8/Advanced Himawari Imager), greatly improved the scan times per day covering East Asia, and the operational products have been stably provided for a period of time. Currently, atmospheric aerosol pollution is a major concern in China. H8/AHI aerosol products with a high temporal resolution are helpful for real-time monitoring of subtle aerosol variation. However, the H8/AHI aerosol optical thickness (AOT) product has been updated three times since its launch, and the evaluation of this dataset is currently rare. In order to validate its accuracy, this study compared the H8/AHI Level-3 (L3) hourly AOT products of all versions with measurements obtained from eleven sunphotometer sites located in eastern China from 2015 to 2018. Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOT products from the same period were also used for inter-comparison. Although the H8/AHI AOT retrievals in version 010 show a moderate agreement with ground-based observations (correlation coefficient (R): 0.66–0.85), and the time series analysis shows that it can effectively monitor hourly variation, it suffers from an obvious underestimation of 0.3 compared to ground-based and MODIS observations. After the retrieval algorithm updated the predefined aerosol model, the overall underestimation of AHI AOTs was solved (version 010 slope: 0.43–0.62, version 030 slope: 0.75–1.02), and the AOTs in version 030 show a high agreement with observations from ten sites (R: 0.73–0.91). In addition, the surface reflectance dataset derived from the minimum reflectivity model in version 010 is inaccurate in parts of eastern China, for both “bright” and “dark” land surfaces, which leads to the overestimation of the AOT values under low aerosol loads at the Beijing and Xianghe sites. After the update of the surface dataset in version 030, this phenomenon was alleviated, resulting in no significant difference in scatterplots under different surface conditions. The AOTs of H8/AHI version 030 show a significant improvement compared to the previous two versions, but the spatial distribution of AHI is still different from MODIS AOT products due to the differences in sensors and algorithms. Therefore, although the evaluation in this study demonstrates the effectiveness of H8/AHI AOT products for aerosol monitoring at fine temporal resolutions, the performance of H8/AHI AOT products needs further study by considering more conditions.

Description: With the development and the improvement of meteorological satellites, different instruments have significantly enhanced the ability to observe clouds over large spatial regions. Recent geostationary satellite radiometers, e.g., Advanced Himawari Imager (AHI) and Advanced Geosynchronous Radiation Imager (AGRI) onboard the Himawari-8 and the Fengyun-4A satellite, respectively, provide observations over similar regions at higher spatial and temporal resolutions for cloud and atmosphere studies. To better understand the reliability of AHI and AGRI retrieval products, we compare their cloud products with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, especially in terms of the cloud optical thickness (COT) and cloud effective radius (CER). Our comparison indicates that cloud mask and cloud phase of these instruments are reasonably consistent, while clear differences are noticed for COT and CER results. The average relative differences (RDs) between AHI and AGRI ice COT and that of MODIS are both over 40%, and the RDs of ice CER are less than 20%. The consistency between AHI and MODIS water cloud results is much better, with the RDs of COT and CER being 29% and 9%, respectively, whereas the RDs of AGRI COT and CER are still larger than 30%. Many factors such as observation geometry, cloud horizontal homogeneity, and retrieval system (e.g., retrieval algorithm, forward model, and assumptions) may contribute to these differences. The RDs of COTs from different instruments for homogeneous clouds are about one-third smaller than the corresponding RDs for inhomogeneous clouds. By applying unified retrieval systems based on the forward radiative transfer models designed for each particular band, we find that 30% to 70% of the differences among the results from different instruments are caused by the retrieval system (e.g., different treatments or assumptions for the retrievals), and the rest may be due to sub-pixel inhomogeneity, parallax errors, and calibration.

Description: Abstract Clouds and shadows pose a significant barrier for land surface optical and infrared remote sensing image processing and their various applications. The detection and removal of clouds and shadows from satellite images have always been critical preprocessing steps. To date, a variety of methods have been designed to solve this problem. Some require particular channels, while others are heavily dependent on the availability of temporally adjacent images (reference images). Moreover, many methods are too complex to use by common users. For those reasons, in this paper an alternative scheme for detecting clouds and shadows is proposed based on simulated TOA radiance fields. At the same time, a simple approach to remove clouds and shadows is also provided. The results indicate that the new method can properly identify both clouds and shadows in satellite images. Especially, it shows obvious advantage over the MODIS cloud product (MOD35) for shadow detection. Although the proposed cloud removal method is simple, the radiances of a contaminated image can be reasonably reconstructed with RMSE 〈3.0 W/m2 ⋅ sr ⋅ μm and MBE (mean bias) 〈1.0 W/m2 ⋅ sr ⋅ μm for all seven MODIS reflective bands for our case studies. These results prove the effectiveness of the proposed scheme in identifying and removing clouds and shadows from remotely sensed images. Meanwhile, these findings provide some new ideas for the remote sensing community, especially in the fields of cloud detection and image processing.

Description: Using 15‐year MODIS observations, this study statistically analyses the cloud characteristics over China along with five regions of N, S, NW, NE, and TP. It is found that multi‐year averaged CF is ~61% over China, with ~64–65% in summer and winter, and ~58% in spring and autumn; and for most seasons, CF and COT are larger in S than N, CTT and CTP are lowest over TP, cloud r e is larger in NW and TP than other regions. Using 15‐year observations obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board both NASA Terra and Aqua from March 2003 to February 2018, this study investigated the spatio‐temporal variations of both macro‐ and micro‐physical cloud properties over China, including cloud fraction (CF), cloud top pressure (CTP), cloud top temperature (CTT), cloud optical thickness (COT), and effective radius (r e) of both liquid water and ice clouds. Multi‐year averaged CF is around 61% over whole China region. However, CF varies with both regions and seasons. The CFs are about 6–8% larger in summer and winter (~64–65%) than in spring and autumn (~58%). By classifying China into five regimes, which are northwestern China (NW), northeastern China (NE), Tibetan Plateau (TP), northern China (N), and southern China (S), there is a clear CF regional distribution pattern. In general, there are large amount of clouds in S and southeast of TP, and small amount in NE, N, NW and most TP. Moreover, there are generally more clouds over ocean than over land, and much more clouds over S than over N. The CFs are larger (smaller) in the afternoon than in the morning over most land (ocean) regions. Furthermore, the largest CF differences between afternoon and morning occur over the TP region in China. COT demonstrates almost the same regional distribution pattern as the CF for all four seasons. Specifically, COT is higher in S than in N, which is most likely associated with the type of clouds and the availability of water vapour. Cloud r e shows larger values in NW and TP than in eastern China regions in all seasons except for summer, which could be related to the heavy aerosol pollution in eastern China regions. Accompanying with the cold cloud tops over TP, a low CTP centre is often located there.

Description: Abstract The detection of supercooled water clouds (SWCs) is essential for artificial rain enhancement, the prevention of aircraft ice accretion and better understanding of radiative energy balance. However, it is challenging to identify SWCs using only passive satellite measurements. We adopt measurements from the Advanced Himawari Imager (AHI), which is onboard the new‐generation, high temporal‐, spatial‐, and spectral‐resolution geostationary Himawari‐8 satellite, to develop a time‐continuous Himawari‐8 SWC (HSWC) algorithm. The HSWC algorithm includes a group of tests using comprehensive cloud properties (e.g., cloud phase (CPH), cloud top temperature (CTT), cloud optical thickness (COT) and cloud effective radius (CER)). Unlike previous SWC detection algorithms, which are based on CTT and COT properties, we introduce CER and CPH information into the HSWC algorithm because the distribution of SWCs is sensitive to CER values, and SWCs may appear in mixed‐phase clouds identified by satellites. Our analyses indicate that the additions of the CER and CPH tests could improve the performance of SWC detection by 15.07% and 4.75%, respectively. The full disk SWC detection results identified by the HSWC algorithm in January, May, August, and October of 2017 are validated using lidar measurements. The hit rate (HR) and false alarm rate (FAR) are 93.52% and 25.27%, respectively. Our study provides potential SWC regions for the implementation of artificial rain enhancement.

Description: Microwave vegetation index (MVI) is a vegetation index defined in microwave bands. It has been developed based on observations from AMSR-E and widely used to monitor global vegetation. Recently, our study found that MVI was influenced by the atmosphere, although it was calculated from microwave bands. Ignoring the atmospheric influence might bring obvious uncertainty to the study of global vegetation. In this study, an atmospheric effect sensitivity analysis for MVI was carried out, and an atmospheric correction algorithm was developed to reduce the influence of the atmosphere. The sensitivity analysis showed that water vapor, clouds and precipitation were main parameters that had an influence on MVI. The result of the atmospheric correction on MVI was validated at both temporal and spatial scales. The validation showed that the atmospheric correction algorithm developed in this study could obviously improve the underestimation of MVI on most land surfaces. Seasonal patterns in the uncorrected MVI were obviously related to atmospheric water content besides vegetation changes. In addition, global maps of MVI showed significant differences before and after atmospheric correction in the northern hemisphere in the northern summer. The atmospheric correction will make the MVI more reliable and improve its performance in calculating vegetation biomass.

Description: Mapping the components, size, and absorbing/scattering properties of particle pollution is of great interest in the environmental and public health fields. Although the Multi-angle Imaging SpectroRadiometer (MISR) can detect a greater number of aerosol microphysical properties than most other spaceborne sensors, the Angstrom exponent (AE) and single-scattering albedo (SSA) products are not widely utilized or as robust as the aerosol optical depth (AOD) product. This study focused on validating MISR AE and SSA data using AErosol RObotic NETwork (AERONET) data for China from 2004 to 2014. The national mean value of the MISR data (1.08) was 0.095 lower than that of the AERONET data. However, the MISR SSA average (0.99) was significantly higher than that of AERONET (0.89). In this study, we developed a method to improve the AE and SSA by narrowing the selection of MISR mixtures via the introduction of the following group thresholds obtained from an 11-year AERONET dataset: minimum and maximum values (for the method of MISR_Imp_All) and the top 10% and bottom 10% of the averaged values (for MISR_Imp_10%). Overall, our improved AE values were closer to the AERONET AE values, and additional samples (MISR_Imp_All: 28.04% and 64.72%, MISR_Imp_10%: 34.11% and 73.13%) had absolute differences of less than 0.1 and 0.3 (defined by the expected error tests, e.g., EE_0.1) compared with the original MISR product (18.46% and 50.23%). For the SSA product, our method also improved the mean, EE_0.05, and EE_0.1 from 0.99, 16.13%, and 56.45% (MISR original product) to 0.96, 40.32%, and 70.97% (MISR_Imp_All), and 0.94, 54.84%, and 90.32% (MISR_Imp_10%), respectively.

Description: Remote Sensing, Vol. 10, Pages 518: Haze Optical Properties from Long-Term Ground-Based Remote Sensing over Beijing and Xuzhou, China Remote Sensing doi: 10.3390/rs10040518 Authors: Kai Qin Luyao Wang Jian Xu Husi Letu Kefei Zhang Ding Li Jiaheng Zou Wenzhi Fan Aerosol haze pollution has had a significant impact on both global climate and the regional air quality of Eastern China, which has a high proportion of high level pollution days. Statistical analyses of aerosol optical properties and direct radiative forcing at two AERONET sites (Beijing and Xuzhou) were conducted from 2013 to 2016. Results indicate: (1) Haze pollution days accounted for 26% and 20% of days from 2013 to 2016 in Beijing and Xuzhou, respectively, with the highest proportions in winter; (2) The averaged aerosol optical depth (AOD) at 550 nm on haze days were about 3.7 and 1.6 times greater than those on clean days in Beijing and Xuzhou, respectively. At both sites, the maximum AOD occurred in summer; (3) Hazes were dominated by fine particles at both sites. However, as compared to Xuzhou, Beijing had larger coarse mode AOD and higher percentage of small α. This data, together with an analysis of size distribution, suggests that the hazes in Beijing were more susceptible to coarse dust particles than Xuzhou; (4) During hazes in Beijing, the single scattering albedo (SSA) is significantly higher when compared to clean conditions (0.874 vs. 0.843 in SSA440 nm), an increase much less evident in Xuzhou. The most noticeable differences in both SSA and the imaginary part of the complex refractive index between Beijing and Xuzhou were found in winter; (5) In Beijing, the haze radiative forcing produced an averaged cooling effect of −113.6 ± 63.7 W/m2 at the surface, whereas the averaged heating effect of 77.5 ± 49.7 W/m2 within the atmosphere was at least twice as strong as clean days. In Xuzhou, such a radiative forcing effect appeared to be much smaller and the difference between haze and clean days was insignificant. Derived from long-term observation, these findings are more significant for the improvement of our understanding of haze formation in China and the assessment of its impacts on radiative forcing of climate change than previous short-term case studies.

Description: Cloud detection by passive satellite sensors is very challenging in hazy weather over China because the reflective characteristics of haze and clouds are very similar. Consequently, hazy areas tend to be mistaken as cloudy or clear areas by current cloud mask algorithms. The Advanced Himawari Imager (AHI) aboard Himawari-8 is a multispectral earth observation sensor with high temporal and spatial resolutions. A cloud and haze detection algorithm for AHI measurements is urgently needed for monitoring atmospheric pollution and its transport over China. This study presents the new Himawari-8 Cloud and Haze Mask (HCHM) algorithm that classifies image pixels from central and eastern China into one of three categories: clear, cloudy or hazy. Based on the observations that haze occurs near the ground and accumulates in low-elevation plains and basins while clouds form at high altitudes, the proposed HCHM algorithm incorporates altitude information to adjust the thresholds used in the selected threshold tests to separate haze and cloud pixels. We find that combining auxiliary digital elevation model (DEM) data with traditional indicators, such as the R 0.86 /R 0.64 , R 0.86 /R 1.6 and BT 11 -BT 3.9 , improves the accuracy of cloud and haze discrimination. The HCHM algorithm is applied to Himawari-8 observations from Aug. 2015, Nov. 2015, Jan. 2016 and May 2016 and validated against the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM) results. The validation shows that the average leakage rate (LR), false alarm rate (FAR) and haze missing rate (HMR) of the HCHM algorithm are 3.95%, 5.88% and 4.17%, respectively.