The grounded question answering in egocentric videos (Ego-GQA) aims to identify the relevant temporal window and generate corresponding responses in natural language given a textual question. Compared with third-person videos, egocentric video understanding requires more advanced human-centric thinking capability. However, existing Ego-GQA approaches often fail to distinguish the inherent limitations of dynamic egocentric context understanding, treating both first-person and third-person perspectives equally. This oversight leads to hallucinations and a lack of proper egocentric reasoning in first-person video understanding. To address this issue, we propose a novel Collaborated with Hallucination (CoHa) framework for the Ego-GQA, which quantifies the hallucinations generated by an Ego-GQA model and further leverages them as error demonstrations to constrain the model's reasoning process, encouraging it to ground predictions in egocentric visual cues instead of relying on biased pretraining priors. Specifically, we first employ Subjective Logic to quantify the degree of uncertainty in unreliable answers. We then generate diffusion-based noisy visual inputs to amplify the hallucinations as error demonstrations, which are used to append appropriate constraints to the model according to the uncertainty. These constraints effectively steer predictions away from the unreliable semantics induced by inherent drawbacks in egocentric thinking. Additionally, we incorporate an interactive refinement module to facilitate the model to explore more fine-grained cues observed from the first-person view. Extensive experiments on two widely used benchmarks demonstrate that our CoHa method outperforms recent state-of-the-art methods. Our code is available at https://github.com/Mrshenshen/CoHa.

Error detection in egocentric procedural task videos aims to identify deviations to support intelligent monitoring and task automation. Despite making significant progress, existing methods that leverage prototypes for egocentric error detection have two drawbacks: (1) The neglect of inherent data traits, i.e., large intra-class variance and minimal inter-class distinction. (2) The absence of causal consistency in temporal modeling. To address these challenges, we introduce a novel framework termed Probabilistic Embeddings with Causal Constraint (PECC) for error detection in egocentric procedural videos. Specifically, we first integrated a causal dilated convolution module in temporal action segmentation model to capture temporal causal consistency. We then train Gaussian Mixture Models (GMMs) for each action class to get frame-level probabilistic embeddings. Finally, We evaluate test frames using log-likelihood values to detect erroneous actions. Extensive experiments conducted on EgoPER and HoloAssist demonstrate that our method achieves state-of-the-art performance, significantly surpassing existing methods in error detection. Our code is available at https://github.com/HouTong-s/PECC-for-Error-Detection-in-Egocentric-Videos.

Recent advancements in Large Vision-Language Models (LVLMs) have attracted considerable attention due to their impressive performance across various downstream tasks. However, these tasks predominantly emphasize third-person perspectives and LVLMs demonstrate inadequate capability in reasoning from a first-person perspective. To mitigate these limitations, we introduce Causal Intervention with Active Learning (CIAL), an innovative approach designed to augment the first-person reasoning capabilities of LVLMs. Specifically, our method first incorporates an Active Learning-driven Knowledge Extraction (ALKE) scheme, which utilizes LVLMs themselves to automatically and autonomously acquire knowledge related to egocentric perspectives. Then, to optimize the model’s response in conjunction with the scene, a Knowledge-guided Causal Intervention (KCI) module is employed, thereby LVLMs can integrate both knowledge and certainty scores for inference. Comprehensive experiments conducted on the EgoThink benchmark demonstrate that our CIAL method significantly improves the models’ ability to understand and reason in egocentric contexts. Our anonymous code is available at https://github.com/running-alpaca/CIAL/.

Text-based person retrieval (TBPR) is a challenging task that aims at retrieving candidate pedestrian images from a gallery, using textual descriptions as queries. Existing methods generally assume that the textual query and the unique candidate image have a certain cross-modal relationship under one-to-one constraint, and optimize their conditional probability via a discriminative paradigm. However, in real scenarios of TBPR, a textual query may associate with multiple candidate images at one time, indicating that the uncertainty resides in the one-to-many cross-modal relationship. Moreover, the learnt conditional probabilities from the discriminative paradigm of existing methods may be less effective in reflecting the joint probabilities of the textual query and candidate images. To tackle these problems, we propose a novel method termed Cross-modal Uncertainty Modeling with Diffusion-based Refinement (CUMDR) for the TBPR task. First, we implicitly model the cross-modal uncertainty to capture richer semantics and complex correlations, thus generating diverse yet plausible retrievals. Additionally, to reasonably mitigate the impact of noisy data with high uncertainty, we quantify the uncertainty to allocate the importance of raw and complement annotations, which are generated from the multi-modal large language model based on the retrieval-augmented template. Finally, we propose a novel diffusion-based denoiser to progressively refine cross-modal alignments by learning joint probabilities. Extensive experiments on three TBPR datasets demonstrate the superior performance and generalizability of our CUMDR approach compared to the latest methods. Our anonymous implementation repository is available athttps://github.com/Shenshen7/CUMDR.

A downscaling prediction method for traffic source carbon emissions is proposed, which leverages multi-source remote sensing data and deep learning techniques to enhance the spatial resolution of emission estimates. A novel deep learning model, Dilated-CBAM, which integrates dilated convolution and the Convolutional Block Attention Module (CBAM), is developed. This model leverages data on XCO2, NO2, CO concentrations, Aerosol Optical Depth (AOD), and Nighttime Light intensity to refine and predict the 0.1° resolution EDGAR dataset, thereby generating a high-resolution traffic source carbon emissions dataset for Beijing, referred to as HTE-Beijing. The HTE‑Beijing dataset delivers monthly road-traffic emissions through 2023 at 0.01° resolution, substantially enhancing the accuracy and timeliness of emission predictions. Feature importance analysis indicates that XCO2and NO2are the dominant factors influencing the downscaling prediction of carbon emissions across various road types, particularly on highways and urban roads. In the downscaling decomposition experiment conducted on the 2022 test data, the Dilated-CBAM model demonstrates excellent performance, achieving an R2value of 0.974, an RMSE of 747.75 tons (t), and an MAE of 302.52 t. Compared with the EDGAR inventory, the HTE-Beijing dataset offers more accurate carbon emission predictions. It can finely differentiate carbon emission variations among different road types in traffic-dense areas. Furthermore, the HTE-Beijing dataset exhibits a strong correlation with actual observations, with an R value of 0.845. Among all road types, highways exhibit the highest carbon emissions per kilometer, accounting for over 20% of the total emissions across all road types.

Despite demonstrating impressive capabilities in comprehending multi-modal contexts, large vision-language models (LVLMs) are invariably prone to generate unreliable answers, i.e., hallucinations. Existing methods mainly mitigate this hallucination by introducing specific designed datasets or employing contrastive decoding techniques. However, these methods heavily rely on the quality of constructed datasets and negative samples, overlooking the inherent ambiguity in reasoning caused by over-reliance on linguistic priors and data complexity, termed reasoning uncertainty. This oversight hinders the models from effectively identifying the causal relationships behind each token, increasing their susceptibility to hallucinations. To address this issue, we propose a novel framework namedReasoningUncertainty-guidedRefinement (RUR)for mitigating hallucinations in LVLMs from an uncertainty perspective. Specifically, unlike conventional uncertainty quantification methods, we first extract the causal reasoning relationships between tokens by exploiting the link between structural causal models and the Transformer architecture. Based on this relationship, we then employ the Subjective Logic principle to model the reasoning uncertainty at both token and sentence levels, which reflects the unreliability degree of generated tokens and sentences. Finally, guided by reasoning uncertainty, we develop multi-level uncertainty-based adjustment to eliminate deceptive tokens exhibiting severe uncertainty and mitigate potential hallucinations in sentences. Extensive experiments demonstrate that our RUR method consistently achieves state-of-the-art performance on five benchmarks.

Text-based person retrieval aims at retrieving a specific pedestrian image from a gallery based on textual descriptions. The primary challenge is how to overcome the inherent heterogeneous modality gap in the situation of significant intra-class variation and minimal inter-class variation. Existing approaches commonly employ vision-language pre-training or attention mechanisms to learn appropriate cross-modal alignments from noise inputs. Despite commendable progress, current methods inevitably suffer from two defects: 1) Matching ambiguity, which mainly derives from unreliable matching pairs; 2) One-sided cross-modal alignments, stemming from the absence of exploring one-to-many correspondence, i.e., coarse-grained semantic alignment. These critical issues significantly deteriorate retrieval performance. To this end, we propose a novel framework termed Adaptive Uncertainty-based Learning (AUL) for text-based person retrieval from the uncertainty perspective. Specifically, our AUL framework consists of three key components: 1) Uncertainty-aware Matching Filtration that leverages Subjective Logic to effectively mitigate the disturbance of unreliable matching pairs and select high-confidence cross-modal matches for training; 2) Uncertainty-based Alignment Refinement, which not only simulates coarse-grained alignments by constructing uncertainty representations but also performs progressive learning to incorporate coarse- and fine-grained alignments properly; 3) Cross-modal Masked Modeling that aims at exploring more comprehensive relations between vision and language. Extensive experiments demonstrate that our AUL method consistently achieves state-of-the-art performance on three benchmark datasets in supervised, weakly supervised, and domain generalization settings. Our code is available at https://github.com/CFM-MSG/Code-AUL.

Text-based person retrieval (TBPR) involves retrieving pedestrian images from a gallery using textual queries. Existing methods assume that all the training pairs are correct and the textual query only corresponds to one image. However, in practical scenarios of TBPR, there indeed exists data noise and many-to-many matching relationships between semantically similar images and textual queries. To address these problems, we propose a novel approach termed Diverse Embedding Modeling (DEM) with Adaptive Noise Filter for the TBPR task. Firstly, we propose a dynamic margin to measure the degree of noise, which can adaptively reduce the weights of image-text pairs with severe noise during training, thereby effectively mitigating the impact of noisy pairs. Moreover, we model diverse visual and textual embeddings from learnable parameterized distributions, which aim to simulate the many-to-many matching scenarios. Extensive experiments conducted on three TBPR datasets demonstrate the superior performance of our DEM method compared to recent state-of-the-art methods.

Temporal Sentence Grounding (TSG), which aims to localize events in untrimmed videos with a given language query, has been widely studied in the last decades. However, recently researchers have demonstrated that previous approaches are severely limited in out-of-distribution generalization, thus proposing the De-biased TSG challenge which requires models to overcome weakness towards outlier test samples. In this paper, we design a novel framework, termed Counterfactually-Augmented Event Matching (CAEM), which incorporates counterfactual data augmentation to learn event-query joint representations to resist the training bias. Specifically, it consists of three components: (1) A Temporal Counterfactual Augmentation module that generates counterfactual video-text pairs by temporally delaying events in the untrimmed video, enhancing the model's capacity for counterfactual thinking. (2) An Event-Query Matching model that is used to learn joint representations and predict corresponding matching scores for each event candidate. (3) A Counterfact-Adaptive Framework (CAF) that incorporates the counterfactual consistency rules on the matching process of the same event-query pairs, furtherly mitigating the bias learned from training sets. We conduct thorough experiments on two widely used DTSG datasets, i.e., Charades-CD and ActivityNet-CD, to evaluate our proposed CAEM method. Extensive experimental results show our proposed CAEM method outperforms recent state-of-the-art methods on all datasets. Our implementation code is available at https://github.com/CFM-MSG/CAEM_Code.

TheComposed Query-Based Image Retrieval (CQBIR)task aims to precisely obtain the preserved and modified parts, based on the multi-grained semantics learned from the composed query. Since the composed query includes a reference image and the modification text, not just a single modality, this task is more challenging than the general image retrieval tasks. Most previous methods attempt to learn preserved and modified parts via different attention modules and fuse them as a unified representation. However, these methods have two intrinsic drawbacks: 1) The different granular semantic information of the composed query is neglected, which results in the fact that learned preserved and modified parts are irrelevant to correct semantics. 2) The preserved and modified parts learned by previous methods have obvious overlaps, which may lead the model to obtain sub-optimal preserved and modified regions. To this end, we propose a novel method termedMulti-Grained Attention Network with Mutual Exclusion (MANME)to address the above problems. Our MANME method mainly consists of two components: 1) A multi-grained semantic construction for obtaining various textual and visual semantic information. 2) An attention with mutual exclusion constraint for reducing the degree of overlap between preserved and modified parts. It adequately utilizes the various granular semantic information and effectively refines the learned preserved and modified parts. Extensive experiments and further analyses on three widely used CQBIR datasets demonstrate that our proposed MANME method achieves new state-of-the-art performance on the CQBIR task.

In this article, we study the challenging cross-modal image retrieval task,Composed Query-Based Image Retrieval (CQBIR), in which the query is not a single text query but a composed query, i.e., a reference image, and a modification text. Compared with the conventional cross-modal image-text retrieval task, the CQBIR is more challenging as it requires properly preserving and modifying the specific image region according to the multi-level semantic information learned from the multi-modal query. Most recent works focus on extracting preserved and modified information and compositing it into a unified representation. However, we observe that the preserved regions learned by the existing methods contain redundant modified information, inevitably degrading the overall retrieval performance. To this end, we propose a novel method termedCross-ModalAttentionPreservation (CMAP). Specifically, we first leverage the cross-level interaction to fully account for multi-granular semantic information, which aims to supplement the high-level semantics for effective image retrieval. Furthermore, different from conventional contrastive learning, our method introduces self-contrastive learning into learning preserved information, to prevent the model from confusing the attention for the preserved part with the modified part. Extensive experiments on three widely used CQBIR datasets, i.e., FashionIQ, Shoes, and Fashion200k, demonstrate that our proposed CMAP method significantly outperforms the current state-of-the-art methods on all the datasets. The anonymous implementation code of our CMAP method is available at https://github.com/CFM-MSG/Code_CMAP.

Composed Query-Based Image Retrieval (CQBIR) aims to retrieve the most relevant image from all the candidates according to the composed query. However, the multi-model query brings more challenges to learning the proper semantics, which include the traits mentioned in the text and resemblance with reference images. The improper learned semantics reduced the performance of existing CQBIR methods. To this end, we propose a novel framework termed Dual-Path Semantic Construction Network for Composed Query-Based Image Retrieval (DSCN). It consists of three components: (1) Multi-level Feature Extraction obtains the textual and visual features of various hierarchies for learning multi-level semantics. (2) Visual-to-Textual Semantic Construction module refines the learned semantics at the textual level. (3) Textual-to-Visual Semantic Construction module performs semantic guidance in the visual semantic space. Extensive experiments on three benchmarks, i.e., FashionIQ, Shoes, and Fashion200k show that our DSCN method outperforms recent state-of-the-art methods.

Text-based person retrieval is one of the fundamental tasks in the field of computer vision, which aims to retrieve the most relevant pedestrian image from all the candidates according to textual descriptions. Such a cross-modal retrieval task could be challenging since it requires one to properly select distinguishing clues and perform cross-modal alignments. To achieve cross-modal alignments, most previous works focus on different inter-modal constraints while overlooking the influence of intra-modal noise, yielding sub-optimal retrieved results in certain cases. To this end, we propose a novel framework termed Multi-granularity Separation Network with Bidirectional Refinement Regularization (MSN-BRR) to tackle the problem. The framework consists of two components: (1) Multi-granularity Separation Network, which extracts the multi-grained discriminative textual and visual representations at local and global semantic levels. (2) Bidirectional Refinement Regularization, which alleviates the influence of intra-modal noise and facilitates the proper alignments between the visual and textual representations. Extensive experiments on two widely used benchmarks, i.e., CUHK-PEDES and ICFG-PEDES show that our MSN-BRR method outperforms current state-of-the-art methods.

Text-based person retrieval aims at searching for a pedestrian image from multiple candidates with textual descriptions. It is challenging due to uncertain cross-modal alignments caused by the large intra-class variations. To address the challenge, most existing approaches rely on various attention mechanisms and auxiliary information, yet still struggle with the uncertain cross-modal alignments arising from significant intra-class variation, leading to coarse retrieval results. To this end, we propose a novel framework termed Deep Cross-modal Evidential Learning (DCEL), which deploys evidential deep learning to consider the cross-modal alignment uncertainty. Our DCEL model comprises three components: (1) Bidirectional Evidential Learning, which models alignment uncertainty to measure and mitigate the influence of large intra-class variation; (2) Multi-level Semantic Alignment, which leverages a proposed Semantic Filtration module and image-text similarity distribution to facilitate cross-modal alignments; (3) Cross-modal Relation Learning, which reasons about latent correspondences between multi-level tokens of image and text. Finally, we integrate the advantages of the three proposed components to enhance the model to achieve reliable cross-modal alignments. Our DCEL method consistently outperforms more than ten state-of-the-art methods in supervised, weakly supervised, and domain generalization settings on three benchmarks: CUHK-PEDES, ICFG-PEDES, and RSTPReid.

In the autumn and winter, aerosol is the important atmospheric pollutant over the Beijing-Tianjin-Hebei region. For monitoring aerosol in the autumn and winter, the lack of vegetation and the aging of MODIS sensor are two problems that needed to be solved. In this paper, after analyzing the characteristics of aerosol radiance in the red and shortwave infrared (2.12 μm) bands of MODIS, we develop a new algorithm for terrestrial aerosol with the assumption that the reflectance ratio between the red and 2.12 μm bands is invariant. With MODIS data over the Beijing-Tianjin-Hebei region from September 2016 to February 2017, the algorithm is applied to aerosol retrieval. The retrieved aerosol optical depth images show that our algorithm can retrieve aerosol over sparse vegetation, and the validation with the AERONET/PHOTONS Beijing site shows that the correlation is greater than 0.9% and 77% of the retrievals fall within the expected error. An error analysis shows that a 2% error in the proportion of the soot component can lead to 15% retrieval error, and over more than 60% of the surface area, the error from the changes in the ratio between the red and 2.12 μm bands can lead to retrieved errors less than 0.1.

Global climate change is one of the most challenging issues facing the world today. Atmospheric aerosols and carbon dioxide (CO2), as two key factors driving the global climate change, have earned enormous attention from scientist around the world [1]. One challenge for the satellite measurements of CO2using this SWIR wavelength range (∼1.6µm) is the impact of multiple scattering by aerosols and cirrus [2]. Since the rapid economic growth and associated increase in fossil fuel consumption have caused serious particulate pollution in many regions of China [3], remote sensing of CO2using SWIR band in China needs to pay more attention to the scattering properties of aerosol particles and the multiple scattering. Considering the complexity of morphological and chemical properties, aerosol particles are grouped based on a large number of TEM/SEM images, and then their scattering properties at 1.6µm band are calculated by the T-matrix method [4] and GMM method [5]. In this study, the Monte Carlo method is used to solve the multiple scattering problem by simulating photons transport in the scattering media. We combined this multiple scattering model with the LBLRTM [6] as a forward radiative transfer model for studying the impact of aerosol scattering on the satellite observations of CO2using SWIR band. Finally, based on the GOCART aerosol component products, AERONET aerosol size distribution products, CALIPSO aerosol profile products, and MODIS aerosol optical depth and surface albedo products, the monthly variability of errors in CO2concentrations over China were calculated and analyzed. The results indicate that AOD and surface albedo are two of most important factors for the satellite observations of CO2. For low surface albedo, the retrieved CO2columns are undervalued when aerosol scattering is neglected. While for moderate and high surface albedos, the retrieved CO2columns are overvalued. As shown in Fighre 1, CO2concentrations are overestimated in western regions of China, especially in desert areas (a maximum of ∼7.08% in September), and those are underestimated in eastern regions (a minimum of ∼−6.9% in June).

The cloudbow structure is directly related to the retrieval of cloud droplet size distribution (droplet effective radius and effective variance). This study investigated the effect of the cloud optical thickness, ground surface albedo and the above-cloud absorbing dust layer on the cloudbow structure based on the modeled airborne directional polarimetric camera (DPC) measurements, which are simulated in 670 nm using Mie scattering theory and the vector radiative transfer mode. It is found that the polarized reflectance increase as the increase of the cloud optical thickness (COT) and saturate when COT=10. The absorbing dust layer's signal would cover the signal from the cloud layer as the aerosol optical thickness increased to 1. Additionally, the surface albedo has negligible effect on the cloudbow structure.

Nitrogen dioxide (NO2), sulfur dioxide (SO2), and smoke are major pollutants, which are used to evaluate the air quality. This study developed a dual-phase air quality monitoring system to monitor the air quality, which based on the Shuffled Complex Evolution algorithm (SCE-UA), ground-based AQI data and satellite observations of NO2, SO2, and Aerosol Optical Depth (AOD). The system is implemented in two phases: the optimization of model coefficients and the air quality index (AQI) simulation. A comprehensive evaluation system of the air quality was then established. The model coefficients of the AQI regression model are optimized by the SCE-UA algorithm in the optimization phase, and the optimized coefficients are used as the final model coefficients in the AQI simulation phase. The experimental results indicate that the SCE-UA algorithm can effectively optimize the coefficients of the AQI regression model. It provides a promising solution to monitor the air quality through using the satellite observations and optimizing model coefficients.

An improved Optimal Estimation (OE) method is presented for methane (CH4) column density retrieval from satellite observations in short-wave infrared band (SWIR), to avoid non-convergence of iteration process for CH4retrieval caused by the singularity or non-positivity of the Hessian matrix. We add a constraining factor γ and a step factor α to the OE iteration algorithm. Then, total column averaged CH4dry air mole fraction, XCH4is retrieved using GOSAT Level 1b data. Retrievals are validated by comparisons with ground-based FTIR measurements from TCCON stations. Comparison shows good agreement and the correlation coefficient is more than 0.55. Preliminary validations approve the utility of proposed retrieval algorithm.

China's industrialized regions have seen increasing occurrence of heavy haze caused by severe particle pollution. However, aerosol retrieval under these circumstances is often excluded from NASA's Moderate Resolution Imaging Spectrometer (MODIS) aerosol products due to cloud mask and suspected high surface reflectance. An algorithm to retrieve the haze aerosol optical thickness (HAOT) is developed using MODIS data to supplement the current MODIS retrieval algorithm. This method includes 1) haze identification, 2) the generation of a surface reflectance database using MODIS data in hazy conditions, and 3) the development of haze aerosol models with four aerosol components simulated by a global 3-D atmospheric chemical transport model (GEOS-Chem). This algorithm was used in combination with the MODIS dense dark vegetation algorithm to retrieve 1 km HAOT over North China Plain from March to September of 2008. The values of the retrieved HAOT values are mostly between 0.7–3, with a correlation coefficient of 0.82 with the Aerosol Robotic NETwork observations and a 19% mean relative difference. Retrieval uncertainties associated with the errors in haze detection, surface reflectance, and haze models were analyzed using ground measurements.

There are four CCD cameras with spatial resolution of 30 m in Environment Satellite 1 (HJ-1), the new satellite developed by China. In the paper, deep blue algorithm for CCD/HJ-1 is applied to Ning-bo. Based on the database of surface reflectance from MODerate-resolution Imaging Spectroradiometer (MODIS) spectral reflectance product and look-up table (LUT), aerosol optical depth (AOD) over cloud-free land pixel is retrieved from apparent reflectance in the first band of CCD/HJ-1. AODs over Ning-bo area are retrieved from January to September in 2011, and the results are validated by ground-based measurements of CE318 in the center of Ning-bo city. The validation shows that the retrieved AODs are larger than that of ground-based measurements, but correlation coefficient (R) is greater than 0.7. Further improvements of overestimated AODs is the focus of our future research.

In China, haze days is occurring more and more frequently. By remote sensing, haze area can be monitored quickly over large region. In this paper, from level 1B data of Moderate Resolution Imaging Spectrometer (MODIS) and meteorologic data, the haze distribution is monitored by the threshold of apparent reflectance and brightness temperature in eight bands, and haze optical depth (HOD) are monitored by deep blue algorithm. From October 2 to 14 in the year of 2011, we monitor haze occurrence from MODIS data over North China Plain. The results show: (1) the areas with dense industries, large population, and a mass of vehicles have the most haze days; (2) when the haze occurs, PM10 will increase obvious.

In this paper, we explored the changes of air quality over Jing-Jin-Tang (Beijing-Tianjin-Tangshan) district during the period from 2002 to 2009. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, Dense Dark Vegetation (DDV) algorithm is employed to retrieve the aerosol optical thickness (AOT) with 1-km resolution. Comparison of the satellite inferred AOT and the values from ground-based Aerosol Robotic Network (AERONET) sun/sky radiometer measurements indicates a good agreement (R2=0.786) in Beijing site. We compared the spatial, monthly and annual variation over Jing-Jin-Tang district and analyzed the main factors of these changes. Our study indicates that there is a decreasing trend in the annual variation of AOT since 2004. The averages of AOT were commonly higher in spring and summer than those in autumn and winter, and the retrieved AOT over cities and southern areas is obviously larger than that over rural and northern areas respectively.

The Ring effect refers to the filling in of Fraunhofer lines, which is known as solar absorption lines, caused almost entirely by Rotational Raman scattering. The Rotational Raman scattering by N2and O2in the atmosphere is the main factor that leads to Ring effect. Basically, the Ring effect is considered as a pseudo-absorption process in retrieval of trace gas constituents in atmosphere. The solar spectrum measured by OMI/AURA is convolved with rotational Raman cross sections of N2and O2, divided by the original solar spectrum, with a cubic polynomial subtracted off, to create a differential Ring spectrum. This method has been suggested in order to obtain an effective differential Ring cross-section for the DOAS fitting process. The differential Ring spectrum could be used to improve the accuracy of the retrieval of the trace gases concentration. The results have been in basic agreement with the corresponding results calculated with RTM, and the R2statistic is 0.9663.Next, the differential Ring spectrum calculated with rotational Raman cross sections of atmosphere in the fixed wavelength of 410nm and 488nm are derived. The results with the fixed wavelength have been also in basic agreement with the corresponding results calculated with RTM, and the R2statistics are 0.9624 and 0.9639 respectively. At last but not the least, the computational complexity calculated at fixed wavelength of 410nm or of 488nm is 0.128% of that calculated with wavelengths from 410nm to 488nm.

Nitrogen dioxide (NO2) plays a very important role among the anthropogenic trace gases. The tropospheric NO2vertical column density (VCD) maps derived have been used to study many scientific applications, pollution emissions and pollutant distribution. During the 2008 Summer Olympic Games in Beijing, NO2is one main air pollutant which should be monitored. This paper presents the NO2inverse algorithm, the Differential Optical Absorption Spectroscopy (DOAS), from satellite measurements and the results using this method. The results show 1) the tropospheric NO2VCD in Beijing is about the same as that in other cities nearby in June, 2008; 2) from July 1, the tropospheric NO2VCD in Beijing decreases significantly, however, it changes little in other cities nearby; 3) the tropospheric NO2VCD in Beijing increases a little in August, 2008, which is much lower than that in other cities around, such as Tianjin, Tangshan.

Operational global quantitative retrievals of aerosol have been made from Moderate Resolution Imaging Spectrometer (MODIS) data for several years by NASA EOS teams. Dark Dense Vegetation (DDV) algorithm has shown excellent competence at aerosol distribution and properties over land. In Sep. 2008, China successfully launched environmental satellite and received Charge Coupled Device (CCD) sensor data, it will provide a new way to monitor aerosol optical thickness (AOT) and distribution at a higher resolution (30 m * 30 m). According to DDV algorithm and HJ-1-CCD camera characters, we measured different surface reflectance spectra in Beijing and Pearl River Delta areas, then ascertained NDVI value and the surface reflectance radio between HJ-1-CCD red and blue band. This paper introduces the aerosol retrieval process including lookup table establishing, cloud detection and so on; Finally, the retrieved AOTs were validated by ground measurement and compared by MODIS aerosol products.

On the eve of Beijing Olympic Games (BOG), frequent haze days had been extensively concerned by home and abroad. Ground-based and satellite remote sensing project was carried out to monitor haze distribution and intension by Chinese Academy of Sciences. Based on the assumption that surface reflectance vary slowly in a relative short period, the Haze Optical Thickness (HOT) retrieval model using MODIS data is established. Aerosol type is selected according to the ground experiment of haze particle composing in Central North China Plain. This model avoid that NASA Dense Dark Vegetation (DDV) algorithm couldn't determine the radio of surface reflectance between middle-IR and visible channel on haze day. From Jun. 1st to Sep. 30th, 2008, AOT observed by sun photometer (CE318) on the ground was used to validate HOT, and it had shown good coherence.

In this paper, the retrieval of aerosol over land surfaces from CCD data of China Brazil Earth Resources Satellite (CBERS) 02B was studied. The method is dark dense vegetation (DDV) algorithm: 1) dense vegetation (dark pixel) was recognized by the NDVI threshold; 2) the look up table (LUT) of atmosphere was computed from the Satellite Signal in the Solar Spectrum (6S) model; 3) the aerosol was retrieved through interpolating the LUT by CCD data. The method was applied to Beijing area, and the retrieved aerosol was validated by ground-based measurements of CE318. The result shows that from CBERS02B data, the aerosol can be retrieved well.