Direct aerosol radiative effect (DARE) is crucial for atmospheric radiation balance and climate. DARE is usually calculated using instantaneous aerosol optical depth (AOD) from satellite overpasses instead of daily mean AOD. Most satellites pass over twice a day at fixed local times, and these high-frequency data may not be an effective substitute for daily averages, thus affecting calculations of the radiative effects of aerosols. This study undertakes a comprehensive global-scale exploration into the disparities between utilizing satellite AOD products as substitutes for time-averaged values in radiative effect simulations. DARE calculated by instantaneous AOD from satellites were compared with the daily DARE from AERONET. The DARE calculated using MISR AOD showed the lowest RMSE of 10.3719 Wm-2. MODIS (Terra/Aqua) performed well in representing daily means, with Terra (RMSE = 11.1417 Wm-2) and Aqua (RMSE = 10.9722 Wm-2) showing similar results. In contrast, using VIIRS instantaneous AOD resulted in a higher RMSE of 14.3992 Wm-2. Since aerosols vary greatly during the day, this paper evaluates the representativeness error at different moments of time by comparing the DARE calculated using the daily mean AOD and instantaneous AOD from AERONET. The results show that the DARE from 8:00 a.m. to 2:00 p.m. has the smallest deviation (less than 0.6 Wm-2), which provides better temporal representativeness for the DARE calculation. The differences in the effect of daily changes in seasonal AOD on DARE calculations were small for all seasons from 10:00 a.m. to 13:00 p.m., and more pronounced in the fall.

In Re-identification (ReID), recent advancements yield noteworthy progress in both unimodal and cross-modal re-trieval tasks. However, the challenge persists in developing a unified framework that could effectively handle varying multimodal data, including RGB, infrared, sketches, and textual information. Additionally, the emergence of large-scale models shows promising performance in various vision tasks but the foundation model in ReID is still blank. In response to these challenges, a novel multimodal learning paradigm for ReID is introduced, referred to as All-in-One (AIO), which harnesses a frozen pre-trained big model as an encoder, enabling effective multimodal re-trieval without additional fine-tuning. The diverse multi-modal data in AIO are seamlessly tokenized into a unified space, allowing the modality-shared frozen encoder to extract identity-consistent features comprehensively across all modalities. Furthermore, a meticulously crafted ensemble of cross-modality heads is designed to guide the learning trajectory. AIO is the first framework to perform all-in-one ReID, encompassing four commonly used modali-ties. Experiments on cross-modal and multimodal ReID reveal that AIO not only adeptly handles various modal data but also excels in challenging contexts, showcasing exceptional performance in zero-shot and domain generalization scenarios. Code will be available at: https://github.com/lihe404/AIO.

Cloud mask (CM) data are indispensable for estimating downward surface shortwave radiation (DSSR). Most DSSR products are generated using CM data derived from passive satellite observations through the threshold methods. Some uncertainty exists in these CM data, yet the impact of CM quality on the DSSR estimates has received limited attention. To address this gap, this study proposed a method to quantify and mitigate errors in DSSR estimates resulting from CM data error, using the Himawari-8 (H8) products as a case study. First, machine learning (ML) models were constructed for CM, DSSR, and needed atmospheric parameters for DSSR estimation. Then, high-reliability CM estimates were utilized to update the H8 CM. The missing atmospheric parameters resulting from the CM updates were filled by the constructed models. Subsequently, DSSR data were estimated based on the updated CM. Results show that the updated CM effectively corrects misclassifications in the H8 CM, and the differences are more than 600 Wm−2 between DSSR estimates and H8 DSSR for some pixels. Cloud-aerosol Lidar and infrared pathfinder satellite observations (CALIPSO) CM and in situ DSSR were used as truth references for validation. The improved accuracy of the updated CM compared to H8 CM is mainly observed for snow/ice, with clear-sky and cloudy-sky hit rates (HRs) increasing by 0.1 and 0.3, respectively. Besides, when the H8 CM is consistent with the updated CM, the estimated DSSR exhibits a slightly lower root mean square error (RMSE) compared to the H8 DSSR, with a difference of no more than 3 Wm−2. However, in cases where the two CM data are inconsistent, the reduction in RMSE for the estimated DSSR compared to the H8 DSSR is more significant, exceeding 9 Wm−2.

Satellite-based aerosol optical property retrieval over land, especially size-related parameters, is challenging. This study proposed a novel two-stage machine learning (ML) algorithm for retrieving aerosol optical depth (AOD), Ångström exponent (AE), fine mode fraction (FMF), and fine mode AOD (FAOD)) over land using MODIS observed reflectance. The new ML algorithm consists of three steps: (1) first, all samples extracted from AERONET measurements were used to train the ML model, (2) then, to reduce the extreme estimation bias of the model, divided low-value and high-value samples were used to train low-value and high-value ML models, respectively, and (3) finally, the three ML models were integrated into the final retrieval based on the weight interpolation. Independent site network validation results show that the new ML algorithm has a Pearson correlation coefficient (R) of 0.894 (0.638, 0.661, 0.865) and root mean square error (RMSE) of 0.146 (0.258, 0.245, 0.153) for the AOD (AE, FMF, FAOD) retrieval, which significantly outperforms the validation metrics of MODIS operational products, with AOD (AE, FMF, FAOD) RMSE of 0.130-0.156 (0.536-0.569, 0.313, 0.191). The inter-comparison of aerosol products shows that the spatial patterns of AOD, AE, FMF, and FAOD of the new ML algorithm are in good agreement with those of the MODIS and POLDER products. These results illustrate that the new ML algorithm has good performance and transferability and indicate the ability of ML methods to be applied to multispectral instruments (such as MODIS) to retrieve multiple aerosol properties.

Mainstream satellite cloud masking algorithms are prone to mis-masking in haze-polluted areas, which may cause errors in aerosol radiative effect calculations and attribution of surface solar radiance changes; thereby, distinguishing between clouds and haze is critical to obtaining accurate land and atmospheric data products. Existing cloud and haze mask algorithms based on the threshold method may require us to spend a lot of manpower to perform multiple threshold tests; in addition, the obtained thresholds are only applicable to particular sensors, which limits the generality of the threshold-based cloud and haze mask algorithms. In this study, a new cloud and haze mask algorithm based on a combination of radiative transfer simulations and machine learning text simulation-based cloud and haze masking (SCHM) is proposed and applied to MODIS images. When we simulated the apparent reflectance of the first seven visible and text near-infrared channels of MODIS, the CALIOP and AERONET data verification results showed that the SCHM algorithm achieved 85.16% and 90.08% hit rates for cloud and haze recognition, respectively. When we added three thermal infrared channels (20, 31, and 35 bands) for simulation, the cloud and haze hit rates were improved to approximately 85.72% and 90.62%, respectively. This indicates that the SCHM algorithm can improve the accuracy of detection results by improving the radiative transfer simulation parameters. Compared with existing threshold-based methods, the SCHM algorithm has the advantages of simple logic, convenient modification, and flexible configuration.

The Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite has high spatiotemporal resolution and provides useful spectral information that can be used to monitor aerosols and air pollution. The objective of this study is to propose the Land General Aerosol (LaGA) algorithm for retrieving aerosol information using AGRI data in the Asia region. First, the sensitivity analysis indicated that the AGRI blue band is more suitable for aerosol retrieval, and its red band is sensitive under high aerosol loading. Then, a real-time surface reflectance (SR) database was established using the atmosphere-corrected technique based on the background AOD library and regional aerosol model parameters. By comparing the AGRI observed reflectance with that calculated using a lookup table, the AGRI aerosol optical depth (AOD) with a 1-h resolution was obtained. The validation results indicated that the AGRI AOD, both at all moments (data volume: 12,102) and the daily mean (data volume: 1,766), exhibit a good agreement with AERONET AOD (R > 0.830). Its performance was comparable to that of the MOdIs dark target (DT) AOD (expected error (EE), ± (0.05 + 20%τAERONET): AGRI = 0.673 vs. DT = 0.666) and Himawari-8 (H8) AOD (EE: AGRI = 0.698 vs. H8 = 0.658). The pixel-by-pixel comparison demonstrated that the R between the AGRI and MODIS AODs was >0.6, and the mean bias between them was within ±0.05 in most of the study area. These results suggest the robustness of the proposed algorithm, and it has great potential for application in the follow-up Fengyun 4 series satellites.

Satellite observation is an effective way of obtaining global aerosol information. The study focuses on developing a new scheme to apply the traditional dark target (DT) method to the advanced Medium Resolution Spectral Imager (MERSI II), which is a part of the Chinese Fengyun-3-D satellite. Compared with the Moderate Resolution Imaging Spectroradiometer (MODIS), MERSI II shows higher ratios between red (0.65$\mu \text{m}$) and near-infrared ($2.13~\mu \text{m}$) bands in surface reflectance estimation and the green band ($0.55~\mu \text{m}$) that is more sensitive to cloud screening. Aerosol optical depth (AOD) is retrieved from earlier MERSI II observations by following the adapted DT method over land in Asia in 2018. Overall, AOD from MERSI II has a good performance compared with ground-based measurements with an expected error (EE%) of 66.38% and$R^{2}$of 0.834, which is close to the MODIS EE% of 70.59% and$R^{2}$of 0.829. Both sensors slightly overestimate the AOD over heavy aerosol loading regions, but MERSI-II has larger retrieval area covering a wider swath than MODIS in heavy hazy areas. On a spatial scale, the MERSI II effectively reflects the AOD distribution pattern but tends to overestimate and underestimate AOD at low and high latitudes, respectively, when compared with MODIS. The MERSI II sensor shows good aerosol detection potential, and the DT algorithm can be applied. MERSI II will provide important observation data on climate change and atmospheric pollution for the investigations in the future.