Volunteered geographic information (VGI) often contains rich geolocations that are crucial for disaster response and post-disaster assessment. However, existing studies on VGI geolocalization have not fully used the potential of multi-source and multimodal data. In this paper, we constructed a multimodal disaster dataset (MultiIan) and developed two novel methods (i.e. StaGeo and TriGeo) to enhance the cross-view geolocalization accuracy of disaster-related VGI. MultiIan comprised VGI texts and images, street view imagery (SVI) and remote sensing imagery (RSI). Large language models (LLMs) were used to extract the implicit geoinformation from VGI texts for geotagging. StaGeo was developed using staged training with ConvNeXt and vision transformer (ViT), while TriGeo used VGI ↔ SVI ↔ RSI triple-objective joint training of the ViT based on DINOv2. Using SVI to link VGI and RSI, our methods significantly improved the geolocalization accuracy of VGI across various train–test splits in MultiIan. With a typical 8:2 data split, StaGeo achieved Recall@1, Recall@5, Recall@10 and Recall@1% of 54.93%, 71.27%, 77.93% and 80.33%, respectively. TriGeo further improved these metrics, achieving 62.87%, 85.55%, 90.54% and 90.89%, respectively. These findings demonstrate significant advancements in our cross-view geolocalization methods, enabling timely geolocation to support rapid decision-making in emergency response and promoting the broader application of GeoAI in geospatial analysis.

Spontaneous combustion of underground coal seams leads to significant resource loss, causing geological disasters, environmental degradation, and social security problems. This study investigates the dynamic detection and assessment of fires in the Midong Coalfield, Urumqi, from 2013 to 2020, based on time series Landsat-8 time series satellite imagery. The hotspot SFSE method is proposed to map the spatial-temporal distribution of coal fires. Additionally, the CTIR ratio is established to comprehensively evaluate the intensity and spatial variations of the thermal island effect caused by the fires. This study provides for the integrated dynamic interpretation of thermal anomaly intensity, fire area, and fire hazard migration. Furthermore, the TADC is introduced to effectively reflect the central location and clustering of active fires. The analysis reveals that frequent mining activities accelerate fire production and propagation. Conversely, fire suppression efforts demonstrate a significant reduction in fire area (by 60%) and overall CTIR (by 70%).

Water is a fundamental element of urban ecosystems, and water mapping plays an important role in urban water management and disaster prevention and early warning. There are a large number of small water bodies in the urban environment, and there is spectral confusion between water and many complex features in the city, making identifying urban surface water bodies a significant challenge. Spectral mixture analysis (SMA) is widely used in sub-pixel level urban environmental analysis. In order to extract urban water bodies with higher accuracy, this paper first combined scatter plots transformed by minimum noise fraction (MNF) with image-based manual selection to optimize endpoint selection. Secondly, due to the significant variation of spectral curves of urban surface water with spatial location, pure water pixels in the vicinity of the target pixel were retrieved using window algorithms as reference endpoints. Finally, the SMA technique was applied to estimate the abundance of mixed pixels. The results are compared with the extraction accuracy of normalized difference water index (NDWI), improved normalized difference water index (MNDWI), and traditional mixed pixel decomposition method. The experimental results show that this method has a higher recognition accuracy for water pixels, with improvements of 10.8%, 8.7%, and 4.4% compared to the three methods, respectively.

Landslides, as a significant geological hazard widely distributed globally, require precise risk assessment to ensure the utmost protection of human lives and property. Existing studies typically rely on historical landslide inventories and consider factors such as topography, geology, geomorphology, and hydrology for landslide susceptibility evaluation. However, these inventories often need more timely updates and seldom account for the impact of human engineering activities. Therefore, this study focused on Yongping County in Yunnan Province, China, a region prone to frequent landslides. It updated and supplemented the landslide inventory using geolocalization methods. Additionally, the study incorporated various human engineering factors in the evaluation process and employed a convolutional neural network (CNN) model to assess landslide susceptibility. The results align more closely with field survey realities, demonstrating the reliability of this method. This method can potentially be applied to the susceptibility assessment of other geological disasters in the future.

A typical heavy pollution weather in Beijing-Tianjin-Hebei region from October 5 to 12, 2014 was selected for numerical simulation based on WRF-Chem model, using environmental monitoring data, meteorological observation data and reanalysis data to analyze the meteorological impact factors of the pollution process and the spatial and temporal variation characteristics of pollutants.

Methane CH4) is the second largest greenhouse gas in the world after CO2. CH4emissions contribute 16% of the world's greenhouse gases. Although global CH4 emissions are much lower than global carbon dioxide emissions, their global warming potential (GWP) over 100 years is 28-36 times greater than that of carbon dioxide. Using satellites to observe methane is an effective means. This paper is aimed at Hebei Province, using TROPOMI onboard Sentinel-5P. The main research content: 1). The concentration of XCH4in Hebei Province has obvious seasonal trend (autumn > winter > summer > spring). 2). Due to the existence of some stable emission sources in the southwest of Hebei Province, there are obvious anomalous areas of stable high value of methane in these areas.

Single scattering albedo (SSA) is an important parameter affecting the radiative forcing of aerosol. However, current SSA retrieval only relies on several typical aerosol models, limiting the range of SSA variation. This study proposed a new algorithm, and the comparison demonstrates the algorithm has an excellent ability to estimate SSA in pollution over land.

TanSat is crucial for detecting global CO2concentration, solar-induced chlorophyll fluorescence, and CO2flux as China's first atmospheric CO2concentration monitoring satellite. This manuscript presents a preliminary attempt to estimate anthropogenic CO2emissions from large sources with TanSat. We identified XCO2(the column-average dry air-mole fraction of CO2) anomalies and quantified CO2emissions from two TanSat observations in Northeast China. The emission rate estimations of the two XCO2plumes are 11.46 kt CO2/h and 10.09 kt CO2/h respectively, and the emission rates of the Multi-resolution Emission Inventory for China (MEIC) are 6.86 kt CO2/h and 3.10 kt CO2/h respectively. The result shows that TanSat has the ability to quantify anthropogenic CO2emissions.

Aerosol vertical structure (AVS) plays an important role in the Earth's climate system. The spectral radiance matching (SRM) method can effectively reconstruct the aerosol three-dimensional structure, but it is affected by the donor-recipient matching accuracy. To overcome the problem, in this paper, the SRM algorithm is improved by using regional spectral radiation matching instead of single-pixel spectral matching. First, the cost function is constructed by the multispectral radiometric difference between the off-nadir recipient pixel neighborhood and the potential donor neighborhood within the MODIS scan. The best donor-recipient match is selected by minimizing the cost function. Then, the corresponding donor pixel aerosol profile information from CALIPSO observations is assigned to the recipient pixel, which fills the gaps between CALIPSO track and achieves a true AVS global estimate. Finally, the accuracy of the algorithm was verified by using AD-NET station data. The experimental results show that the reconstruction results of the region-based method are consistent with the ground-based lidar measurements and superior to the pixel-based SRM method.

Vegetation parameter, especially the leaf area index (LAI), is a key variable that take part in the land-atmosphere interaction. Many studies have found that by updating LAI in land surface models can improve the simulation ability for land surface variables. This study used the coupled Community Land Model with explicit carbon and nitrogen components (CLM4CN) and Community Atmosphere Model (CAM) as well as Data Assimilation Research Testbed (DART) to update assimilated LAI, to improve the simulation ability for air temperature and precipitation. The Global Land Surface Data Assimilation System (GLDAS) reanalysis data were used to estimate the coupled CLM4CN/CAM/DART performance.

The air quality model can reproduce the processes of atmospheric transport, chemical reaction and removal, and analyze the spatio-temporal evolution law, internal mechanism and origin of air pollution. WRF-Chem model is the third generation of air quality model. Its biggest advantage is that the meteorological model and chemical transmission model are fully coupled in time and spatial resolution to achieve true online feedback, which is the main development direction of the future model. Therefore, this paper selected Xuzhou City, a heavy industry city with serious air pollution, as the research area, and took December when air quality was poor as the research time. WRF-Chem model was used to simulate and analyze meteorological parameters such as wind speed, temperature, relative humidity and concentration of air pollutants including PM2.5and O3in Xuzhou in December 2020. The simulation results were compared with the site data, and the simulation results under different parameter Settings of WRF-Chem mode were studied and compared.

Imported air pollution has a significant impact on urban air quality. Many urban air pollution events are not caused by local emissions, but by the transport of air pollutants from surrounding areas. Therefore, it is very necessary to prevent and control imported air pollution. However, the existing supervision of urban air quality mostly relies on ground monitoring stations, which is extremely limited in time and space. In this paper, MERRA-2 data is used to grasp urban air quality from a more macroscopic perspective, and combined with ground monitoring station data and meteorological data, the transmission route of air pollution is reconstructed. This paper takes Xuzhou City, Jiangsu Province as an example. It is proved that this method is highly feasible and can provide scientific data support for efficient prevention and control of imported air pollution.

Quantifying the surface vegetation growth behavior is crucial to the understanding of the complex response of alpine ecosystems to climate variability. This study investigated the drought-induced phenological shifts of Qinghai Lake Basin (QLB). Phenological metrics including green-up and dormancy were retrieved from satellite vegetation index records from 1982 to 2022. Phenological metrics were characterized using simple linear regression method. The results showed that the long-term trends of QLB phenology were various. Meadow greenup and steppe dormancy were significantly advanced and the earlier trend of meadow dormancy and steppe greenup are insignificant. Drought conditions of the QLB were obtained using multiscale standardized precipitation evapotranspiration index (SPEI). The 6-month SPEI of April explains the most of the interannual shifts in greenup of meadow and steppe ranging from 11.56% to 19.36%. The 12-month SPEI of August and 6-month SPEI of April explains the most of variations in dormancy across QLB.

The GF-7 satellite, launched in November 2019 and put into use in August 2020, operates on a sun-synchronous orbit and is equipped with two linear array stereoscopic cameras capable of obtaining images up to 20 kilometers wide with a spatial resolution better than 0.8 meters. Its mission is to perform stereoscopic mapping at a scale of 1:10,000 using a combined survey mode of stereoscopic cameras and laser altimeters. However, due to the large deviation of the before and after panchromatic images, the accuracy of extracting urban building heights from GF-7 satellite images is limited. In this study, a DSM model of urban buildings will be established, and the accuracy of urban elevation will be improved by combining GF-7's height data with ICESat/ICESat2's height information. Preliminary conclusions will be drawn based on experimental results.

Remote sensing inversion of global PM2.5is an important research topic. In the present study, the Aerosol Optical Depth (AOD) dataset was established by four geostationary satellites to estimate global PM2.5concentrations using improved Geographic Time-Weighted Regression model (IGTWR) models. Then a global hourly PM2.5concentration dataset was obtained in May 2020. The estimated result for PM2.5is verified at ground stations with R of 0.71 and RMSE (Root Mean Square Error) of 26.6 μg/m3. The results indicate that PM2.5has obvious spatial and temporal distribution in the world.

As an important parameter to characterize land surface vegetation, leaf area index (LAI) is not only an important vegetation biophysical parameter that affects surface radiation transfer and material and energy balance, but also an important parameter to connect micro biogeochemical processes such as photosynthesis and respiration of vegetation. Because of the strong inhomogeneity of the lower land surface, correct description of the canopy structure as well as canopy physical and biogeochemical processes is the basis and prerequisite for the correct description of surface radiation characteristics and land-air interaction. In this paper, based on the Community Land Model (CLM4) and Data Assimilation Research Testbed (DART) developed by the National Center for Atmospheric Research (NCAR), the Global LAnd Surface Satellite (GLASS) LAI data are assimilated into the carbon and nitrogen component of CLM4 (CLM4-CN), and the impact of LAI change on climate change on a regional scale is further analyzed in the case of land-atmosphere coupling.

Accurately estimating crop carbon sinks at large regional scales from the perspective of remote sensing is of great significance for carbon neutrality research, crop yield estimation, and scientific agriculture. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate Net primary productivity. The NEP is then further calculated by coupling the soil respiration model to represent the carbon sink at a regional scale. The main research contents: (1) The month-by-month net primary productivity of crops in Jiangsu Province in 2021. (2) The net ecosystem productivity of crops in Jiangsu Province month by month in 2021.

In this paper, surface ozone in China were estimated by the Geographically and Temporally Weighted Regression model with its precursors and AOD data. Based on the GTWR model, the surface ozone was estimated by time, space, ozone precursors and AOD. Taking August 1, 2022 as an example, it was analyzed and confirmed about the feasibility of ozone precursors and AOD data in estimating surface ozone mass concentrations.

Studying evapotranspiration is of great importance for understanding the hydrological cycle and terrestrial ecosystem both at global and regional scales. In this study, the area of Qinghai lake from 1986 to 2022 were obtained by remote sensing technology, and relative hydrological variables including evapotranspiration (ET), transpiration, canopy water evaporation, direct evaporation from bare soil, as well as potential evaporation rate, were obtained from the Global Land Data Assimilation System (GLDAS) products during the same period to quantitatively analyze the impact of lake on the hydrological variables.

Based on the data of air quality monitoring stations and high-resolution remote sensing product data in Xuzhou City, the atmospheric pollutants in 2020 and 2021 were analyzed. The modified A-values method and the model simulation method were used to estimate the remaining atmospheric environmental capacity (RAEC) of PM10and PM2.5pollutants in Xuzhou, Jiangsu Province. The results show that the excessive PM10and PM2.5pollutants are the main problems of atmospheric pollution in Xuzhou, and the annual emissions still need to be reduced are 10.80×104t/a and 5.98×104t/a, respectively. Among them, the situation is the most serious in Tongshan District, which still needs to cut annual PM10and PM2.5emissions by 7.41×104t/a and 3.70×104t/a, respectively. Xinyi City has the smallest annual PM10emission reduction, which is 0.89×104t/a. Suining County needs to cut the smallest PM2.5emissions, at 0.67×104t/a. In addition, there are significant quarterly differences in RAEC, with the first quarter > the fourth quarter > the second quarter > the third quarter. Except for the third quarter, the excess atmospheric environmental capacity was the most serious in Xuzhou urban area, the other three quarters were the highest in Tongshan District.

The OCO-2 satellite retrieval of CO2inferred CO2concentrations from atmospheric spectral absorption signals in three bands at 0.76 µm, 1.61 µm, and 2.06 µm. The accuracy of CO2inversion by OCO-2 satellite is directly dependent on the accuracy of the molecular absorption model used in the retrieval algorithm. For gases with low anisotropy and background concentrations like CO2, the accuracy of the molecular absorption model is critical. Therefore, it is essential to establish a fast (usually costly to calculate at line-by-line spectral resolution) and accurate molecular absorption model. In this study, we train an artificial neural network with data from the HITRAN CO2line-by-line absorption database and can build an accurate, precise and effective prediction model for the CO2absorption coefficient. This model only requires the thermodynamic state of CO2as input to obtain an accurate molecular absorption coefficient. Using this prediction model instead of OCO-2 ABSCO V5.2 input to RTRETRIEVALFRAMEWORK MODEL, obtained the retrieval result with the official product R2reaching 0.991. It proves the feasibility of the artificial neural network training model instead of ABSCO.

Quantifying the concentration of absorbing aerosol is essential for pollution tracking and calculation of atmospheric radiative forcing. To quickly obtain absorbing aerosol optical depth (AAOD) with high-resolution and high-accuracy, the gradient boosted regression trees (GBRT) method based on the joint data from Ozone Monitoring Instrument (OMI), Moderate Resolution Imaging Spectro-Radiometer (MODIS), and AErosol RObotic NETwork (AERONET) is used for TROPOspheric Monitoring Instrument (TROPOMI). Compared with the ground-based data, the correlation coefficient of the results is greater than 0.6 and the difference is generally within ±0.04. Compared with OMI data, the underestimation has been greatly improved. By further restricting the impact factors, three valid conclusions can be drawn: 1) the model with more spatial difference information achieves better results than the model with more temporal difference information; 2) the training dataset with a high cloud fraction (0.1–0.4) can partly improve the performance of GBRT results; and 3) when aerosol optical depth (AOD) is less than 0.3, the perform of retrieved AAODs is still good by comparing with ground-based measurements. The novel finding is expected to contribute to regional and even urban anthropogenic pollution research.

Methane is the world's second largest greenhouse gas after carbon dioxide, accounting for about one-sixth of the total greenhouse gas emissions. Due to the development of animal husbandry, carbon dioxide and methane produced in the process of animal husbandry breeding have gradually become one of the important sources of the greenhouse effect. The research of methane gas emissions from animal husbandry is of great significance to sorting out the sources of greenhouse gases. This paper is aimed at Hebei Province, using TROPOMI sensors onboard Sentinel-5P, TANSO-FTS onboard GOSAT observation data, and the related ground station data. The main research content: (1) The temporal and spatial distribution characteristics of methane in Hebei Province from 2009 to 2020; (2) The proportion of methane emissions from ruminants in Hebei Province Estimate using ruminant methane emission formula. The average proportion is 6.10% from 2009 to 2018.

Vegetation net primary productivity (NPP) is an important factor in determining ecosystem quality and carbon sink. It reflects the productive capacity and ecological process of vegetation community, and is of great significance to adjust the global carbon balance and enhance the ecological service function. Using soil respiration model and improved CASA model, combined with MODIS and meteorological data, the net ecosystem productivity (NEP) of vegetation in Qinghai since 2000 was estimated, the spatial and temporal distribution of vegetation NEP and carbon sink, the influences of precipitation and air temperature on vegetation NEP were analyzed in this paper. Meanwhile, global warming has become a hot topic since the 20th century. In this study, the timing of snow-melt is taken as an important factor to study the impact on carbon source/sink of ecosystem in Qinghai province.

Single scattering albedo (SSA) is an important parameter affecting the radiative forcing of aerosol. However, current SSA retrieval only relies on several typical aerosol models, limiting the range of SSA variation. This study proposed a new parameterization scheme of the aerosol model, which optimized the independent SSA value into mixed combination of three basic aerosol components, then constructed lookup table to obtain hourly SSA from the Advanced Himawari Image (AHI) sensor. The comparison is encouraging the retrieved SSA results agree well with AERONET, especially for high aerosol loading (AOD > 0.3 at 470nm), with correlation coefficient of R = 0.50, RMSE = 0.02, and approximately 93% of retrieval results falling within the 5% EE envelope. Under normal conditions, R = 0.46, RMSE = 0.04, and approximately 68% of retrieved SSA fall in within the uncertainty of$\Delta$SSA = ±0.05. The comparison demonstrates the algorithm has the excellent ability to estimate SSA in pollution over land.

The Greenhouse Gases Observing Satellite (GOSAT) is the world's first spacecraft to measure the concentrations of CO2from space and it has high-precision hyperspectral atmospheric CO2monitoring from 2009. Several atmospheric CO2products, such as ACOS, NIES, OCFP and SRFP products from full physics retrieval algorithm, both provide XCO2(the column-average dry-air mole fraction of atmospheric CO2) of GOSAT. These products have different characteristics and advantages and have different performance in different regions. In order to obtain the XCO2data set with high precision, low uncertainty and high coverage, the maximum likelihood estimation (MLE) method is used to fuse GOSAT XCO2products. The algorithm takes into account the uncertainty of each product on each pixel, and is applied from April 2009 to December 2015. The validation result between fusion XCO2and Total Carbon Column Observing Network XCO2shows R = 0.843 and RMSE = 3.248.

This paper calculates the Air Quality Index (AQI) in Xuzhou from 2018 to 2020 and analyzes its annual and monthly changes in air quality to determine the month of severe pollution. Through AQI calculation and interpolation analysis of different locations in Xuzhou, the air quality conditions in different regions are determined. Through the HYSPLIT backward trajectory model, cluster analysis of the air masses in December 2020 is carried out. Combined with the PM2.5 concentration data released every hour by the Xuzhou State Control Station, PSCF and CWT methods are used to determine potential sources of PM2.5 pollution in different regions. This paper also uses the hourly PM2.5 data in Xuzhou area obtained from the retrieval of the Himawari-8/AHI AOD through the IGTWR model for the traceability analysis of pollutants. The results show that there are large potential pollution sources in Henan, northern Anhui, northern Hubei and other places. At the same time, pollutant emissions in parts of northern Jiangsu, southern Shanxi and Shandong province also contribute to PM2.5 in Xuzhou. This paper also traced the source of the PM2.5 heavy pollution days in Xuzhou in December 2020 and determined the location of the specific pollution source based on the actual situation.

Dust as a kind of atmospheric pollutants, its harm are gradually being paid attention to. However, most of the existing supervision of dust relies on ground monitoring sites, which has strong spatial limitations. In this paper, high-resolution remote sensing images are used to identify regional dust sources, which can identify dust at a more macroscopic level from “plane”. At the same time, regional particulate emission inventory was established based on multi-source data, and then the dust emission is quantified. This paper takes Quanshan District, Xuzhou City, Jiangsu Province as an example, and the experimental results show that the method can reflect the actual dust situation well, and provide scientific data support for the supervision of urban dust.

Mining cities are an essential part of China's urban agglomerations, and as mining cities continue to develop, ecological and environmental pollution has become a primary problem. In the present study, the Aerosol Optical Depth (AOD) retrieval of major mining urban agglomerations in China from the Gaofen-1 satellite data. Then a new hybrid model based on CTM (chemical transport model) Transport Model 5 (TM5) and GTWR (Geographic Time-Weighted Regression model) is proposed for PM2.5 and PM10mass concentration estimation. According to the different transformation stages and urban structure of mining cities, the temporal and spatial analysis of particulate matter characteristics is carried out in mining urban agglomerations. The estimated result for PM2.5 is verified at ground stations with R2 of 0.956 and RMSE (Root Mean Square Error) of 10.377 μg/m3, Moreover, the estimated result for PM10is verified at ground stations with R2 of 0.926 and RMSE of 16.669 μg/m3, The results indicate that PM2.5 and PM10have distinct spatial and temporal distribution patterns as Chinese mining cities are undergoing different types of transformation processes.

In this paper, surface-level ozone in China was estimated by using the source-sink analysis and machine learning model. By analyzing the source and sink of surface ozone, it is clear that ozone mass concentration is influenced by background value, regional and local chemical generation, deposition, chemical removal and Interregional transport comprehensively. Then, the light gradient boosting machine (LGBM) model was used to integrate various corresponding satellite-based variables, numerical model-based meteorological variables and land variables to obtain the high spatial resolution surface mass concentration of ozone in China. Taking June, July, August, 2021 as example, the feasibility of the Tropospheric Monitoring Instrument (TROPOMI) data, the European Centre for Medium-Range Weather Forecasts (ECWMF) data and LGBM model in estimating surface-level ozone mass concentration was analyzed and confirmed.

The advent of the era of Remote Sensing Big Data has produced a large number of processing and analysis tasks, which require powerful computing capabilities to support. The computational efficiency of distributed computer clusters which are the most commonly used parallel computing architecture for high performance computing can be significantly improved through an effective task scheduling strategy. In this paper, in order to improve data computing efficiency, we propose a dynamic load balancing strategy for remote sensing data processing workflow tasks based on the Hungarian algorithm for heterogeneous distributed computing clusters. We also compare this strategy with the classic load balancing algorithm. We find that the speed-up effect of the strategy proposed in this paper is better, and the speedups become more pronounced as the number of tasks increases.

The representation of landform element varies over multiple scales, or multiresolution digital elevation model (DEM) and its derivatives. When more details of land surfaces are available to be characterized based on the existing high spatial resolution elevation products, the influence of scale variation might become more significant. This poses a demand for determining a scale-independent approach being competent to support multiscale landform element detection on high-resolution DEMs. Although the practicability of the state-of-the-art scale-independent approaches have been reported on moderate-resolution DEMs, how these approaches perform based on the multiscale data including high and moderate spatial-resolution DEMs is still unexplored. This letter evaluates the performance of four scale-independent techniques including filtering, spatial pyramid, multiscale segmentation, and spatial-contextual approach in landform element detection on different spatial resolution DEMs. The experimental results show that spatial–contextual approach is more effective to support multiscale landform element detection than others.

Global context information is essential for the semantic segmentation of remote sensing (RS) images. However, most existing methods rely on a convolutional neural network (CNN), which is challenging to directly obtain the global context due to the locality of the convolution operation. Inspired by the Swin transformer with powerful global modeling capabilities, we propose a novel semantic segmentation framework for RS images called ST-U-shaped network (UNet), which embeds the Swin transformer into the classical CNN-based UNet. ST-UNet constitutes a novel dual encoder structure of the Swin transformer and CNN in parallel. First, we propose a spatial interaction module (SIM), which encodes spatial information in the Swin transformer block by establishing pixel-level correlation to enhance the feature representation ability of occluded objects. Second, we construct a feature compression module (FCM) to reduce the loss of detailed information and condense more small-scale features in patch token downsampling of the Swin transformer, which improves the segmentation accuracy of small-scale ground objects. Finally, as a bridge between dual encoders, a relational aggregation module (RAM) is designed to integrate global dependencies from the Swin transformer into the features from CNN hierarchically. Our ST-UNet brings significant improvement on the ISPRS-Vaihingen and Potsdam datasets, respectively. The code will be available athttps://github.com/XinnHe/ST-UNet.

Challenges still exist in the task of object detection in remote sensing images with densely distributed objects due to large variation in scale and neglect of the relative position and correlation. To address these issues, a Correlation Learning Detector based on Transformer (CLT-Det) is proposed for detecting dense objects in remote sensing images. A Transformer Attention Module (TAM) is designed to improve the densely packed objects’ model representation ability by learning pixel-wise attention with Transformer. To alleviate the semantic gap caused by variations in scale, a Feature Refinement Module (FRM) is proposed by improving the multi-scale feature pyramid. A Correlation Transformer Module (CTM) is proposed to extract correlation information and encodes position information of dense objects’ features on the classification branch for fully utilizing the position information and correlation among objects. Extensive experiments compared with several state-of-art methods on two challenging remote sensing datasets, namely DOTA and HRSC2016, demonstrate that the proposed CLT-Det achieves promising and competitive performance.

Aerosol optical depth (AOD) is an important factor to estimate the effect of aerosol on light, and an accurate retrieval of it can make great contribution to monitor atmosphere. Therefore, retrieval of AOD has been a frontier topic and attracted much attention from researchers at home and abroad. In 2013, China launched Gaofen-1 satellite, improving the scale and timeliness of remote sensing data acquisition and making up for the shortcomings of lacking multi-spectral satellite with medium and high spatial resolution. In this paper, we calculated AOD at 100m from Gaofen-1 and AQUA data based on the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm over Beijing, China. The experimental results are compared with the Aerosol Robotic Network (AERONET) for preliminary validation. The correlation coefficient is about 0.9 and a root-mean-square error (RMSE) of about 0.13. The experimental results show that the method have higher accuracy, and further validation work is continuing.

Aerosols playa significant role in earth-atmospheric radiant balance and global climate changes. FengYun-4A(FY-4A) is the first three-axis stabilized geostationary satellite in China, the Advanced Geosynchronous Radiation Imager (AGRI) is one of the four payloads onboard the satellite. In fact, FY-4A AOD products is not officially available so far, and there are few researches on AOD retrieval of FY-4A. Therefore, AGRI data was used to develop a new algorithm for retrieval of AOD over land in this paper. We used MCD43C2 datasets to obtain the band surface reflectance, established the surface reflectance ratio database. Next used 6SV model to build lookup table (LUT), calculated hourly AOD eventually. To validate our algorithm, the AGRI -derived AOD was quantitatively compared with AERONET ground-based measurements. It shows the better accuracy and coverage than JMA-AOD.

The global long-term aerosol optical depth (AOD) dataset has a great significance for the study of global climate change. Advanced Very High Resolution Radiometer (AVHRR) on National Oceanic and Atmospheric Administration (NOAA) satellites can provide global observation from 1978 to present. In this paper, we have improved the algorithm for the retrieval of the AOD over land proposed by Xue et al in 2017 [1]. We obtain 0.64μm band surface reflectance by utilizing a linear relationship between the surface reflectance at the wavelength of3.75μm and 0.64μm, which has been verified in the Moderate Resolution Imaging Spectroradiometer (MODIS). Considering difference of spectral response between AVHRR's bands and MODIS's bands, we calibrate this empirical relationship by fitting the simulated surface reflectance of the relative AVHRR and MODIS bands. A radiative transfer model for Lambertian surface and the look-up table (LUT) method are applied to NOAA-7, 9,11,14, 18 over China mainland (15° - 60° N, 70° - 140° E) from 1982 to 2011. Comparison of retrieval AVHRR AOD against AErosol RObotic NETwork (AERONET) data shows good consistency with more than 60% points within uncertainty of$\pm$(0.05+0.25xAOD).

In this paper, PM2.5 and PM10 in mainland China were estimated by using the Geographically and Temporally Weighted Regression model and FY-4 AOD data. Based on the GTWR model, the PM was estimated by BLH, RH, time, space and AOD. Taking June 2, 2019 as an example, the feasibility of FY-4 data in estimating PM2.5 and PM10 mass concentrations was analyzed and confirmed.

The impact of permafrost zones on the global climate is estimated using a global climate model that parameterizes the interactive fluctuations of climate and greenhouse gases {GHGs). This study develops a new coupled model of CO2 and CH4 global cycles (CMCCGC) for the parameterization of environmental processes in the Siberian permafrost zone based on remote sensing data, allowing us to describe in detail a structure of radio-heat fields in the tundra and adjoin permafrost zones. The results of this study show that a reliable understanding of the role of Siberian permafrost in global climate change requires addressing many specific tasks.

Remote sensing has played a key role on the effects of ozone depletion on global biogeochemical cycles, through the increase in the biological effective solar ultraviolet radiation in B region (UV-B) (290–320 nm) reaching the Earth's surface. This work aims to analyze data derived from remote sensing instruments and techniques that are closely related to the UV-B effects on ecology, biodiversity and conservation (EBC) due to atmospheric ozone depletion and climate change. As a case-study of the association of ozone and UV-B is selected, Athens, Greece, which is characterized by high sunshine. The results obtained reveals that the interplay between ozone and UV- B is complex with high uncertainties and therefore their effects on the EBC mechanisms require much longer periods of data with greater and better spatial coverage. Undoubtedly, the optimized combination of ground-based network and advanced remote sensing instrumentation may help a lot in reducing the above-mentioned uncertainties.

In this paper, through the analysis and comparison of three commonly used atmospheric environmental capacity estimation methods, we find that the existing methods have many limitations in the aspects of data base and the factors considered. Taking Xuzhou City in Jiangsu Province as an example, based on the air pollution multi-source model, environmental impact assessment and primary pollutant simulation are carried out by using remote sensing data, ground monitoring station data, pollution emission inventory data and meteorological data. In addition, the iterative algorithm of multi-pollutant environmental capacity with the joint constraint of$\text{PM}_{2.5}$and$\mathrm{O}_{3}$is established to recalculate the urban atmospheric environmental capacity.

Due to the limitations in the number of satellites and the swath width of satellites (determined by the field of view and height of satellites), it is impossible to monitor global aerosol distribution using polar orbiting satellites at a high frequency. This limits the applicability of aerosol optical depth (AOD) data sets in many fields, such as atmospheric pollutant monitoring and climate change research, where a high-temporal data resolution may be required. Although geostationary satellites have a high–temporal resolution and an extensive observation range, three or more satellites are required to achieve global monitoring of aerosols. In this article, we obtain an hourly and global AOD data set by integrating AOD data sets from four geostationary weather satellites [Geostationary Operational Environmental Satellite (GOES-16), Meteosat Second Generation (MSG-1), MSG-4, and Himawari-8]. The integrated data set will expand the application range beyond the four individual AOD data sets. The integrated geostationary satellite AOD data sets from April to August 2018 were validated using Aerosol Robotic Network (AERONET) data. The data set results were validated against: the mean absolute error, mean bias error, relative mean bias, and root-mean-square error, and values obtained were 0.07, 0.01, 1.08, and 0.11, respectively. The ratio of the error of satellite retrieval within ±($0.05+ 0.2\times $AODAERONET) is 0.69. The spatial coverage and accuracy of the MODIS/C61/AOD product released by NASA were also analyzed as a representative of polar orbit satellites. The analysis results show that the integrated AOD data set has similar accuracy to that of the MODIS/AOD data set and has higher temporal resolution and spatial coverage than the MODIS/AOD data set.

Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper. With a newly defined channel-wise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels.

This paper presents a novel unsupervised polarimetric synthetic aperture radar (PolSAR) image classification method, which incorporates polarimetric image factorization and deep convolutional networks into a principled framework. To implement this idea, we design a convolutional neural network (CNN) with a newly defined loss function which measures the probability distribution distance between the initial distribution maps and CNN predictions. In the proposed method, we firstly execute polarimetric image factorization to generate a dictionary of meaningful atom scatters and their corresponding distribution maps, where the strongest scatters are selected as training samples for CNN. Next, we train the CNN by iteratively optimizing the defined energy function, producing the final distribution maps and classification result. The proposed approach is applied on a real UAVSAR image. Experimental results justify that our approach can effectively classify the PolSAR image in an unsupervised way and produce favorable classification results.

Aiming at improving the classification performance with greatly reduced annotation cost, this paper presents an active deep learning approach for minimally-supervised PolSAR image classification, which integrates active learning and fine-tuning convolutional neural network (CNN) into a principled framework. Starting from a CNN trained using a very limited number of labeled pixels, we iteratively and actively select the most informative candidates for annotation, and incrementally fine-tune the CNN by incorporating the newly annotated pixels. Moreover, to boost the performance and robustness of the proposed method, we employ Markov random field to enforce label smoothness, and data augmentation technique to enlarge the training set. Extensive experiments demonstrated that our approach achieved state-of-the-art classification results with significantly reduced annotation cost.

Particulate matters (PMs) have substantial influences on environmental system, climate change and public health. PM monitoring method with high temporal resolution and wide coverage is necessarily for the warning of air pollution. In this study, we used Geostationary satellite Himawari-8 data and other meteorological parameters such as relative humidity (RH) and planetary boundary layer height (PBLH) to estimate hourly PM2.5concentrations over the southeast coastal region of China. The estimated PM2.5shows good correlation with the ground-based observation data with the correlation coefficient of 0.66. The retrieval results also show hourly spatial variation of PM2.5clearly.

The Arctic has a unique geographical environment. In recent years, the Arctic has undergone major changes, including an increase in temperature, a decrease in the extent and thickness of sea ice, and the reasons for these changes have yet to be further studied. In the Arctic, aerosol is an important factor that causes the temperature and environmental change. The lack of ground-based observation data in the Arctic makes satellite remote sensing an effective means for aerosol monitoring.We first selected the C61 version of the MODIS Level2 10 KM aerosol product from 2000 to 2018 as the Arctic's aerosol monitoring data. Then the products Aerosol Optical Depth (AOD) was evaluated by 20 ground-based AERONET (AErosol RObotic NETwork) sites in the Arctic. Based on the data with high quality control, the monthly averaged AOD in the Arctic were analyzed. Results show that: 1. MODIS AOD is overestimated in the Arctic and requires quality control to obtain more reliable results; 2. The monthly averaged AOD in the Arctic region does not exceed 0.3 with maximum 0.25 and minimum 0.027, and the average monthly AOD value of 18 years is 0.111. Usually, AOD peaks in summer and has a valley in autumn and winter, but it rises in spring with the Arctic haze events.

Recently, deep neural networks have received intense interests in polarimetric synthetic aperture radar (PolSAR) image classification. However, its success is subject to the availability of large amounts of annotated data which require great efforts of experienced human annotators. Aiming at improving the classification performance with greatly reduced annotation cost, this paper presents an active deep learning approach for minimally supervised PolSAR image classification, which integrates active learning and fine-tuned convolutional neural network (CNN) into a principled framework. Starting from a CNN trained using a very limited number of labeled pixels, we iteratively and actively select the most informative candidates for annotation, and incrementally fine-tune the CNN by incorporating the newly annotated pixels. Moreover, to boost the performance and robustness of the proposed method, we employ Markov random field (MRF) to enforce class label smoothness, and data augmentation technique to enlarge the training set. We conducted extensive experiments on four real benchmark PolSAR images, and experiments demonstrated that our approach achieved state-of-the-art classification results with significantly reduced annotation cost.

The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: R = 0.88, root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of ±0.2·AODAERONET±0.05. A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land.

Within the European Space Agency Climate Change Initiative (CCI) project Aerosol_cci, there are three aerosol optical depth (AOD) data sets of Advanced Along-Track Scanning Radiometer (AATSR) data. These are obtained using the ATSR-2/ATSR dual-view aerosol retrieval algorithm (ADV) by the Finnish Meteorological Institute, the Oxford-Rutherford Appleton Laboratory (RAL) Retrieval of Aerosol and Cloud (ORAC) algorithm by the University of Oxford/RAL, and the Swansea algorithm (SU) by the University of Swansea. The three AOD data sets vary widely. Each has unique characteristics: the spatial coverage of ORAC is greater, but the accuracy of ADV and SU is higher, so none is significantly better than the others, and each has shortcomings that limit the scope of its application. To address this, we propose a method for converging these three products to create a single data set with higher spatial coverage and better accuracy. The fusion algorithm consists of three parts: the first part is to remove the systematic errors; the second part is to calculate the uncertainty and fusion of data sets using the maximum likelihood estimate method; and the third part is to mask outliers with a threshold of 0.12. The ensemble AOD results show that the spatial coverage of fused data set after mask is 148%, 13%, and 181% higher than those of ADV, ORAC, and SU, respectively, and the root-mean-square error, mean absolute error, mean bias error, and relative mean bias are superior to those of the three original data sets. Thus, the accuracy and spatial coverage of the fused AOD data set masked with a threshold of 0.12 are improved compared to the original data set. Finally, we discuss the selection of mask thresholds.

Over the past few years, regional air pollution has frequently occurred in Mid-Eastern China, especially in Beijing. As the primary pollutants in urban air, atmospheric particulate matter (PM) not only leads to the decrease of atmospheric visibility, but also increases the mortality and morbidity of respiratory system diseases. In order to estimate the concentration, distribution and other properties of PM2.5, a physical model is used. In building model, AOD at 550nm, the Effective Radius, ground-based PM2.5data, the relative humidity (RH) and boundary layer height (PBL) data are used, pollutant concentration and PM characteristics are taken into account comprehensively to build the retrieval model for near surface concentration of PM2.5. The result shows that the correlation coefficient (R2) of retrieval and ground-based PM2.5is greater than 0.60, showing this method is possible for retrieving PM2.5in Beijing area.

In recent years, the demand for the understanding of the earth has been raised to a new height. The surface parameters calculated from satellite data is becoming more and more precise. However, it is still difficult to make sure the temporal consistency and spatial continuity for large scale geoscience phenomena. A new earth observation platform is necessary in order to improve the consistency and the continuity. The Moon, as the only natural satellite of the Earth, has special advantages as a platform for earth observation. This paper mainly discusses the advantages and the potential applications of the moon-based occultation observation for atmospheric phenomena.

Particulate matters (PM) have substantial influences on environmental system, climate change and public health. Ground based PM2.5concentration measurement is insufficient in many circumstances. In this study, we using satellite retrieved AOD and other meteorological parameters such as the planetary boundary layer height (PBLH), temperature (TEMP), relative humidity (RH), U wind component (U), V wind component (V), surface pressure (SP), and large-scale precipitation (LSP), to establish GA-BP ANN AOD-PM2.5retrieve model. The test R reached 0.83. This model is seasonally and regionally stable. The satellite AOD and ANN retrieved PM2.5has the similar trend and distribution, and the trained model have practical as well as theoretical value.

For the accurate derivation of biophysical parameters based on surface reflectance, atmospheric correction is a necessary step to remove scattering and absorption effects by multiple atmospheric components. However, the huge amount of data and complex algorithms pose great computing challenges for massive operational tasks. Towards the global atmospheric correction for Medium Resolution Spectral Imager (MERSI) data onboard FY-3A and FY-3B, this paper describes an atmospheric correction algorithm considering the directional properties of the observed surface, and exploits its parallel implementations on multi-core and many-core architectures. The algorithm was developed with Open Multiprocessing (OpenMP) for multi-core processors and Compute Unified Device Architecture (CUDA) for Graphics Processing Units (GPU). Experimental results show the runtime was reduced from 187.19s to 42.63s and 10.11s when implemented on a multi-core processor and NVIDIA Tesla K80 respectively.

The AERONET level 2.0 data at Beijing site from 2001 to 2016 were analyzed to investigate the aerosol properties and explore the aerosol mixtures. The aerosol optical depths (AOD) values over Beijing are high throughout the years and show distinct seasonal variation. The annual means for AOD440nmand Ångström exponent were 0.76 ± 0.16 and 1.08 ± 0.35, respectively. The aerosol volume size distributions indicate that Beijing are affected by both fine and coarse particles, the distributions show obvious seasonal difference, with more coarse particles in spring and dominant fine particles in summer. The relationship between Ångström exponent and Ångström exponent difference were analyzed to explore the aerosol absorption and aerosol mixtures. The high extinction in Beijing are strong linked with the hygroscopic and coagulation growth of fine mode particle, as well as the dust aerosol. Aerosol properties in Beijing were deeply affected by the industry emission, as well as the dust transported from north and west China.

In order to improve the accuracy and spatial coverage of AOD datasets, we proposed a method to obtain a consistent dataset with higher spatial coverage and better accuracy from Deep Blue (DB) AOD and Dark Target (DT) AOD products. The fusion algorithm consists of three parts: the first part is to remove the system errors, the second part is to calculate the uncertainty and fusion of datasets using the maximum likelihood estimate method, and the third part is to mask outliers. The MBE, MAE, RMB and RMSE of DB AOD in 2015 are 0.04, 0.13, 1.10 and 0.20 respectively, the MBE, MAE, RMB and RMSE of DT AOD in 2015 are 0.07, 0.12, 1.18 and 0.17 respectively, the MBE, MAE, RMB and RMSE of combined AOD provided by MODIS in 2015 are 0.05, 0.11, 1.12 and 0.16 respectively, and the MBE, MAE, RMB and RMSE of fusion data after mask with a threshold of 0.20 in 2015 are 0.03, 0.10, 1.08 and 0.15 respectively. The accuracy of fusion data after mask is obviously superior to the original data and the combined data provided by MODIS. In addition, the spatial coverage of the data has also been significantly improved.

Taklimakan Desert, located in southwest Xinjiang Uyghur Autonomous Region, is one of the predominant dust origin in China. Dust is one of the main types of atmospheric aerosol in this region. Emerging remote sensing imagery from geostationary meteorological satellite undeniably becomes an ideal mean for monitoring large regional distribution and intensity of dust storms. Among them, Indian National Satellite (INSAT 3A) is suitable for dust aerosol retrieval and dust storm detection for Xinjiang region as it can provide high spatiotemporal earth observation with a Charge Couple Device (CCD) camera. The camera contains three bands with a spatial resolution of 1km, which are very applicable for AOD retrieval. However, there is still no mature algorithm for the retrieval of AOD over land using INSAT 3A data, though some work have been done with other geostationary satellites [1-4].

Feng-Yun (FY-3) is the second generation of the Chinese Polar Orbiting Meteorological Satellites with global, three-dimensional, quantitative, and multispectral capabilities. Medium Resolution Spectral Imager (MERSI) has 20 channels onboard the FY-3A and FY-3B satellites, including five channels (four VIS and one thermal IR) with a spatial resolution of 250m. The top of the atmosphere signal are necessary to be radiometrically calibrated and corrected for atmospheric effects based on surface reflectance, especially in land surface remote sensing and applications. This paper presents an atmospheric correction algorithm for FY3/MERSI data over land in China, taking into account the directional properties of the observed surface by a kernel-based Bi-directional Reflectance Distribution Function (BRDF) model. The comparison with MODGA and ASD reflectance showed that there is a good agreement. Therefore, FY3/MERSI can serve a reliable and new data source for quantifying global environment change.

Atmospheric particulate matter estimated by using satellite data is gaining more attention due to their wide spatial coverage advantages. Here, instead of empirical statistical approach, we describe a physical-based approach that reduces the uncertainty of surface PM10estimation from satellite data. In our approach, particulate matter mass concentration retrievals require the inclusion of optical properties of aerosol particles and meteorological parameters. We use one year of MODIS aerosol optical depth data at 550 nm and meteorological data to estimate surface level PM10over China. As compared to regression coefficients obtained through simple correlation (R = 0.44) or multiple regression (R = 0.53) techniques, the physical-based approach derives hourly PM10data that compared with ground-based measurements with R = 0.74. Although the degree of improvement varies over different sites and seasons in China, this study demonstrates the potential for using physical-based approach for operational air quality monitoring.

The retrieval of the distribution of aerosol properties and determination of trends in their temporal variation can only be achieved by the use of long-term satellite measurements. The Advanced Very High Resolution Radiometer (AVHRR) carried on board the National Oceanic and Atmospheric Administration (NOAA) and the EUMETSAT Meteorological Operational Satellite (Metop) polar orbiting satellites offers more than 35 years of satellite data since 1978 which makes it worthwhile to explore their use for the analysis of aerosols on a daily basis. So in this paper we applied the algorithm for the land aerosol and bidirectional reflectance inversion by times series technique (LABITS) to data from AVHRR Channel 1 on the NOAA-12 and NOAA-14 satellites using a model for the earth-atmosphere system which couples an atmospheric radiative transfer model with the Ross - Thick - Li-sparse bidirectional reflectance factor (BRF) model. Preliminary results show that LABITS provides good results over China for different surface type. Comparing our results with the with SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) aerosol product AOD products shows good agreement. The algorithm has the potential to retrieve global AOD over land for long time series of NOAA AVHRR data going back to the 1980s, which are urgently needed for studies on aerosol climatology and global climate change.

Anthropogenic reasons such as biomass burning smoke and industrial pollution generated with industrialization can change the property of aerosols. In order to analyze the air quality of countries in different development levels we select four countries (Brazil, China, Australia, Canada) and four city groups (Sao Paulo, Beijing-Tianjin-Tangshan, Sydney, and Ottawa) in each country as our research region and the 2000-2012 monthly mean AOD 550, Dust AOD 550, FM AOD 550, ANG 550_870 of these regions are also displayed. The result shows that high AOD area is located in Southeast China, North Brazil, and Northeast Canada and AOD has gradually increased in this area from 2000 to 2012. ANG 550_870 in China, Brazil, and Australia is increased as a whole, where the particle diameter is decreased. AOD 550, Dust AOD 550, FM AOD 550, ANG 550_870 of Sao Paulo, Beijing-Tianjin-Tangshan, Sydney and Ottawa city groups have seasonal and yearly law and the air quality order of this four regions is Sydney > Sao Paulo > Ottawa > Beijing-Tianjin-Tangshan region.

With the rapid development of remote sensing technology, remote sensing data and processing methods are both growing exponentially. But current remote sensing information services and the knowledge sharing have the obvious deficiencies of the remote sensing application service capabilities. The current services can not fully meet the requirements of remote sensing users for remote sensing information service sharing. In this paper, using ontology and workflow service technology, we carried on the research of intelligent remote sensing information service and knowledge sharing technology. We also designed and produced a prototype of remotely-sensed information service integration and sharing framework using Ontology. This prototype system provides the possibility of intelligent remote sensing information services and remote sensing product generation, promotes a wide range of information sharing and intelligent, value-added services in the field of remote sensing.

With the spatial, spectral and temporal resolutions of remote sensing data increasing, the computing efficiency becomes one of bottlenecks for remote sensing image data processing, especially for that with time response requirements. In this paper, towards the aerosol optical depth retrieval application from moderate resolution imaging spectroradiometer data, taking the time-consuming interpolation pre-processing as the study case which includes the inverse distance weighted and the bilinear interpolation methods, parallel computing methods were designed and implemented based on OpenMP programming model. The parallel runtime were measured and analyzed from the aspects of different image data size and threads. Experimental results show that the multi-core parallelization based on OpenMP programming model can efficiently reduce runtime, and offer suggestions for efficient desktop solutions with the evolving multicore architectures.

The project aerosol-CCI as part of European Space Agency (ESA) Climate Change Initiative (CCI) has provided three aerosol retrieval algorithms for the Advanced Along-Track Scanning Radiometer (AATSR) aboard on ENVISAT. For the purpose of estimating different performance of these three algorithms in Asia, in this paper we compared the Aerosol Optical Depth (AOD) of L2 data (10km×10km) including FMI AATSR Dual-view ADV algorithm, the Oxford RAL Aerosol and Cloud retrieval (ORAC) algorithm and the Swansea University AATSR retrieval (SU) algorithm with the AErosol RObotic NETwork (AERONET) and the China Aerosol Remote Sensing Network (CARSNET) data separately. The result shows that the algorithms of ADV and SU have good performance on the retrieval of AOD, and the ORAC algorithm has relative lower precision than other two algorithms.

A wide range of data products have been published since the operation of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's TERRA and AQUA satellites. Based on DarkTarget and DeepBlue method, NASA has published Aerosol Optical Depth (AOD) products Collection 6.0 with spatial resolution of 3km. Although validated globally, regional and systematic errors are still found in the MODIS-retrieved AOD products. This is especially remarkable for bright heterogeneous land surface, such as mainland China. In order to solve the aerosol retrieval problem over heterogeneous bright land surface, the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) has been developed based on the synergetic use of the MODIS data of TERRA and AQUA satellites. Using the SRAP algorithm, we produced AOD dataset-China Collection 2.1 at 1km spatial resolution, dated from August 2002 to 2012. We compared the China Collection 2.1 AOD datasets for 2010 with AERONET data. From those 2460 collocations, representing mutually cloud-free conditions, we find that 62% of China Collection 2.1 AOD values comparing with AERONET-observed values within an expected error envelop of 20% and 55% within an expected error envelop of 15%. Compared with MODIS Level 2 aerosol products, China Collection 2.1 AOD datasets have a more complete coverage with fewer data gaps over the study region.

The Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) data from 1997 to 2001 are adopted to monitor the radiometric stability of six pseudo-invariant calibration sites (PICSs) at the top of atmosphere (TOA). Cloud-free and homogeneous observations of the spectral TOA reflectance ρTOAat eight SeaWiFS channels over these sites are fitted to the Ross-Li bidirectional reflectance distribution function (BRDF) model, and the time series of BRDF-normalized spectral TOA reflectance RTOAis presented and analyzed afterward. Overall, good stability during the evaluated period is exhibited as more than half of the derived trends are statistically insignificant, whereas root mean square (RMS) of the BRDF modeling residuals reveal spectral dependence of the PICSs' stability at TOA, i.e., the uncertainty of RTOAappears to be larger at shortwave visible (SV) channels (~2.5%) compared with that of red/NIR bands (~1%). In addition, the early mission data adopted in our study shows favorable reliability thus is recommended to be applied for similar purposes.

A comparison between the algorithm for Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS) and a daily estimation of aerosol optical depth (AOD) algorithm (AERUS-GEO) over land surface using MSG/SEVIRI data over North Africa is presented. To obtain indications about the quantitative performance of two AOD retrieval methods mentioned above, daily SEVIRI AOD values is considered with respect to those measured from the global aerosol-monitoring Aerosol Robotic Network (AERONET) data. The correlation coefficient (R2) between retrieved SEVIRI AOD at 650 nm from the AERUS-GEO algorithm and the AERONET Level 2.0 daily average AOD at 675 nm is 0.80 and root mean square error (RMSE) is 0.044, and R2between retrieved AOD from the LABITS algorithm and AERONET AOD is 0.80 and RMSE is 0.037.

In recent years, regional haze weather appears frequently in China. The North China Plain is one of the four main regions in China heavily afflicted by haze. Since 2013, there have been many times of haze event in the North China Plain, and the quite severe haze pollution incidents took place in May 5-7, June 26-29 and September 28-30. This paper, investigates into these three severe haze episodes and their optical properties of aerosol. This research uses the Synergetic Retrieval of Aerosol Properties (SRAP) method to retrieve the Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) data and also the AOD over Beijing areas with a 100 m × 100 m resolution by the synergetic use of small satellite data from the China HJ-1A/1B Charge-Coupled Device camera and Terra MODIS data. Beside the ground measurements of PM2.5were analyzed and the concentration of PM2.5was found up to 300 μg/m3during the haze event.

In order to produce long-term aerosol optical depth (AOD) dataset over land from the Advanced Very High Resolution Radiometer (AVHRR), AVHRR data quality in terms of radiometric calibration must be maintained. A vicarious calibration method have been developed by incorporating well calibrated Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) radiance data over several pseudo-invariant targets to inter-calibrate the channel 1 of AVHRR based on Bidirectional Reflectance Distribution Functions (BRDFs) and spectral band adjustment factor (SBAF) models for different targets. Comparison of our calibration coefficients with those of Pathfinder Atmospheres Extended (PATMOS-x) indicate the calibration accuracy to be within 2.5%. The operational L1B and recalibrated AVHRR radiance are applied to derive AOD maps over East America (dark surface) and West Africa (bright surface) using the land aerosol and bidirectional reflectance inversion by times series technique (LABITS) algorithm. Preliminary comparisons show that significant difference in the retrieved AOD from the two different calibration is expected, while the spatial distribut ion of AOD difference is complicated due to different surface brightness and deficiencies of numeric solutions.

Aerosol Optical Depth (AOD) is an significant parameter of aerosol optical properties. Operational production of AOD datasets over long time series, large-scale coverage puts on a severe challenge to computing technologies due to both the complexity of retrieval algorithm and the huge data amounts. The Grid computing solution-Remote Sensing Service Node (RSSN) was constructed as a high-throughput platform for remote sensing applications. Taking the sub-workflow level characteristics of some remote sensing retrieval applications into consideration, a sub-workflow parallel implementation for the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data was taken on the RSSN, and an initial experiment result proved that the subworkflow parallel could further reduce the runtime of data parallel solutions commonly used.

Along with the unprecedented data-collecting capability, the higher algorithm accuracy and real-time application requirements, redundant spatial computing model had been implemented. Traditionally these spatial computing models are stored in different application centers. To avoid waste of resource, Grid workflow provides a powerful tool for sharing both remote sensing data and processing middleware. In order to enhance the interoperability of the heterogeneous quantitative remote sensing retrieval model in the Grid workflow environment, we propose a web service based Grid workflow framework to improve this situation. According to the Open Geospatial Consortium (OGC) and web service standards, we implement a prototype of this framework. Through the experiment, we can find that web service can work well with Grid workflow and provide a management ability of remote sensing model. Also this approach can separate the application logic and process logic, providing the interoperability ability both in application and process layers.

The inversion of atmospheric aerosol optical depth (AOD) using satellite data has always been a challenge topic in atmospheric research. In order to solve the aerosol retrieval problem over bright land surface, the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm has been developed based on the synergetic using of the MODIS data of TERRA and AQUA satellites [1, 2]. In this paper we describe, in details, the quality assessment or quality assurance (QA) plan for AOD products derived using the SRAP algorithm. The pixel-based QA plan is to give a QA flag to every step of the process in the AOD retrieval. The quality assessment procedures include three common aspects: 1) input data resource flags, 2) retrieval processing flags, 3) product quality flags [3]. Besides, all AOD products are assigned a QA `confidence' flag (QAC) that represents the aggregation of all the individual QA flags. This QAC value ranges from 3 to 0, with QA = 3 indicating the retrievals of highest confidence and QA = 2/QA = 1 progressively lower confidence [4], and 0 means `bad' quality. These QA (QAC) flags indicate how the particular retrieval process should be considered. It is also used as a filter for expected quantitative value of the retrieval, or to provide weighting for aggregating/averaging computations [5]. All of the QA flags are stored as a "bit flag" scientific dataset array in which QA flags of each step are stored in particular bit positions.

A large number of quantitative retrieval models have been proposed in recent years, and there is continuous momentum in proposing new ones. Building a model, from design through to implementation stages, involves a process of knowledge collection, organization and transmission. In this paper we introduce the SECI model to manage the conversion of qualitative remote sensing knowledge and propose a mode of knowledge representation on the basis of the ontology for geospatial modeling. We develop a platform based on the above research and demonstrate the efficiency of the knowledge representation mode using this platform.

In this letter, an improved algorithm for aerosol retrieval is presented by employing the non-Lambertian forward model (forward model) (NL_FM) in the Moderate Resolution Imaging Spectroradiometer (MODIS) dark target (DT) algorithm to reduce the uncertainties induced when using the Lambertian FM (L_FM). This new algorithm was applied to MODIS measurements of the whole year of 2008 over Eastern China. By comparing the results with that of AERONET, we found that the accuracy of the aerosol optical depth (AOD) retrieval was improved with the regression plots concentrating around the 1 : 1 line and two-thirds falling within the expected error (EE) envelope EE = ±0.05±0.1τ (from 53.6% with L_FM to 68.7% with NL_FM at band 0.55 μm). Surprisingly, more accurate retrieval of the AOD demonstrated significantly improved the Ångstrom exponent (AE) retrieval, which is related to particle size parameters. The regression plots tended to concentrate around the 1 : 1 line, and many more fell within the EE = ±0.4 from 53.6% with L_FM to 80.9% with NL_FM. These results demonstrate that including the NL_FM in the MODIS DT algorithm has the potential to significantly improve both AOD and AE retrievals with respect to AERONET in comparison to the L_FM used in the current MODIS operational retrievals.

In recent years the satellite monitoring capabilities in particular to derive maps of aerosol optical depth (AOD) have increased tremendously. There are many aerosol retrieval algorithms for different satellites and sensors such as Dark-Target method (DT), Deep Blue, etc. In this paper, we used an improved approach called the Synergetic Retrieval of Aerosol Properties (SRAP) method to retrieve aerosol properties over land surfaces by using the MODIS data. The improvement of the SRAP method include the following respects: 1) Considering the importance of gas absorption correction, we use ancillary data acquired from National Center for Environmental Prediction (NCEP) analyses to correct the effect of gas absorption. 2) A new cloud mask based on a spatial variability test as well as the absolute value at the 0.47 µm and the 1.38 µm bands were implemented in the SRAP algorithm.

Aerosol Optical Thickness (AOT) retrieval over very bright surface is a great challenge because the surface contribution dominates the Top Of Atmosphere (TOA) signal. In this paper, we presented a method for AOT retrieval over snow-covered surface. For the first step, the surface is assumed to be a mixture between snow and ice. The main idea is that the ratio between nadir and forward observation was used for AOT retrieval. For different snow-covered surface type, the surface reflectance is estimated by a mixing model of snow and the other item (ice), tuned by the normalized Differential Snow Index (NDSI) of the satellite observation. Then the AOT can be obtained using Look-Up-Table (LUT) method. In the paper, we mainly focus on the Arctic region, which is a very nice study area for mixture between snow and ice. Validation between ground-based measurements and satellite-derived results shows good agreement. Thus the method provides a promising method for global-covered and whole season AOT retrieval. In the next step, three different regions will be used for validation of the method, for the Arctic region, Canada, Beijing, was chosen for pure snow-ice mixture, snow-vegetation mixture and snow-solid mixture surface.

A new algorithm for Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS) is presented and applied to National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) data over China. Based on the assumptions that the surface bidirectional reflective property are not varying during one day and aerosol characteristics are constant in 0.1° × 0.1° window, we inverse the aerosol optical depth (AOD) and bidirectional reflectance distribution function (BRDF) parameters. Preliminary AOD validation with Aerosol Robotic Network (AERONET) data shows that the correlation coefficient, R2, is 0.79, the root-mean-square error, RMSE, is 0.13 and the uncertainty is Δτ= ±0.05 ± 0.20Δ. Comparing with MODIS AOD product, it is found that both the AOD results are consistent very well. The R2is 0.80 and RMSE is 0.10. The algorithm is flexible and appropriate for aerosol retrieval over both dark and bright land surface. It is potential to retrieve long term global AOD over land from NOAA AVHRR data since 1980s and to study aerosol climatology and global climate change well.

A new algorithm for Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS) is presented and applied to Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) data. Based on the assumptions that the surface bidirectional reflective property are not varying during one day and aerosol characteristics are constant in 2 × 2 window, we inverse the aerosol optical depth (AOD) and bidirectional reflectance distribution function (BRDF) parameters. Preliminary validation shows good accuracy. The correlation coefficient R2is 0.84, the root-mean-square error is about 0.05, and the uncertainty is found to be Δτ= ± 0.05 ± 0.15τ. Comparing with MODIS products, our inversion are consistent very well. The algorithm is flexible and appropriate for aerosol retrieval over both dark and bright land surface. It is potential to retrieve AOD with a high-frequency over land and to monitor aerosol's local spatio-temporal variation from the geostationary satellite data.

The Remote Sensing Quantitative Retrieval is not only a Compute-intensive problem, but also a Data-intensive problem. One of the most effective solutions is Grid Computing platform built on the basis of network, which is a scalable virtual unified platform with unlimited computing power and storage capacity. The RSSN (Remote Sensing Information Service Grid Node) is a PC cluster for Remote Sensing Quantitative Retrieval. It was used for producing aerosol optical depth (AOD) production covered the land area of Asia. We designed the cluster as a hierarchical one, and achieved a Global Task Scheduler for the workflow process, improved the system performance to some extent.

A grid workflow is a type of high-level grid middleware to support modeling, redesign and execution of large-scale intricate scientific and business processes in many complex e-science applications. In this paper, we make improvement on existing grid workflow to meet the application request of remote sensing in two ways: expand the interface of models and design map of remote sensing tasks on grid platform. A case of remote sensing quantitative retrieval grid workflow is shown in section 3.

Surface reflectance retrieval is an important step in the data processing chain for the extraction of quantitative information in many applications areas. The aim of this paper is to develop a new method for retrieving surface reflectance and aerosol optical depth simultaneously over both dark vegetated surfaces and bright land surfaces. After applying this new model to the Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Heihe River Basin of China, aerosol optical depth and surface reflectance values of these regions are calculated. The retrieved surface reflectance from MODIS is consistent with measured reflectance from Analytical Spectral Device (ASD) Field Spec spectral radiometer, and the root mean square error (RMSE) are; Band 1 (0.66μm): 0.027; Band 3 (0.47μm): 0.015; Band 4 (0.55μm): 0.017. The R-squared (R2) value reveals a good agreement between MOD09 and retrieved surface reflectance at band 1. The RMSE of the reflectance value differences are quite small; Band 1: 0.031; Band 3: 0.026; Band 4: 0.029.

Since the year of 2011, PM2.5have become a heated topic in China. Particulate matter (PM), also known as aerosol, is one of the major pollutants that affect air quality. Exposure to particular matter with aerodynamic diameters less than 2.5 μm (PM2.5) can cause lung and respiratory diseases and even premature deaths. In this paper we use the aerosol optical depth (AOD) retrieved by the Synergetic Retrieval of Aerosol Properties (SRAP) method from MODIS data to calculate PM2.5., then estimate number of days with good air quality (PM2.5≤0.075 mg/m3) at each pixel over China in 2008. The result is applied to examine the air quality, From which we can see that there are about 200 days with good air quality in Beijing, in agreement with Official Reports. Throng analyzing the calculated days with good air quality in August from 2005 to 2008, we examined the temporal variations of PM2.5over China, These findings indicate a positive annual variation trend before Beijing 2008 Olympic Games, however the air qulity of Beijing still needs improving.

In this paper, a new algorithm, Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS), is presented and applied to Advanced Very High Resolution Radiometer (AVHRR) data in North China. In this algorithm, we couple the Ross Thick-Li Sparse Bidirectional Reflectance Distribution Function (BRDF) model and the atmospheric radiative transfer model. Assuming that the surface bidirectional reflective property is unchanged during a short period, usually 2-4 days and aerosol characteristics has a high temporal variation but is consistent spatially, then we can obtain AOD and BRDF parameters jointly by numerical iterative technique. The data used to test our algorithm is Global Area Coverage (GAC) 4KM Level 1B from AVHRR/3 on board NOAA-18 and NOAA-19 from 8 July to 9 July, 2011 in North China (110°E-130°E, 25°N-45°N). Synchronous Aerosol Robotic Network (AERONET) level 1.5 data and field measured data during the Ministry Of Science and Technology Aerosol Project (MOSTap) in Beijing-Tianjin-Tangshan region in 2011 was adopted to validate our retrieved result. The correlation coefficient R is about 0.72. Also, in the area both retrieved AOD and MODIS aerosol product have an effective value, the consistency between them is quite good.

In this paper, an analytical strategy is presented to retrieve jointly aerosol optical depth (AOD) and surface reflectance (R) from geostationary satellites. The new algorithm is based on a parameterization of the atmospheric radiative transfer model. Taking AOD and R as unknown parameters and based on some reasonable assumptions of AOD's spatial consistence and R's temporal invariance, both parameters of each pixel can be derived. Applying this algorithm to data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations on board Meteosat Second Generation (MSG), we obtain regional maps of AOD and R from two adjacent observations. Preliminary validation results by comparing our retrieved AOD with Aerosol Robotic Network (AERONET) data show good accuracy, and retrieved R is also reasonable. This method is potential to be applied in instantaneously monitoring aerosol spatio-temporal variation using geostationary satellite sensors with only one single visible channel and high-frequency observations.

Aerosol Optical Depth (AOD) is an important index that indicates the aerosol condition. This article summarizes the efforts to explore discrepancies in the AOD retrieval process, mainly focusing on consistency analysis among multiple instruments and algorithms, and we conduct the experiments of consistency in a single instrument and algorithm of AOD retrieval based on the Synergetic Retrieval of Aerosol Properties (SRAP) model from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In this article we propose the discrepancy problem, and provide some possible solutions.

Aerosol optical depth (AOD) is a key parameter reflecting aerosol properties, and it is inevitable to mosaic multi-orbit data while making AOD datasets in some situations due to the limitation in scanning width of various instruments. This article summarizes some conventional methods eliminating seams of mosaic images, and introduces the idea of combining the Gaussian dispersion model into mosaic process in the AOD retrieval case. In this article, we elaborate the principle of the Gauss plume model and put forward the improved method. Based on the AOD products from the Synergetic Retrieval of Aerosol Properties (SRAP) model from the Moderate Resolution Imaging Spectroradiometer (MODIS) data, we conduct experiments on improved methods, and evaluate the processing effect.

Istomina (2011) presented a dual-view approach using Advanced Along-Track Scanning Radiometer (AATSR) onboard ENVISAT for Arctic AOD retrieval. In this paper, some improvements have been done. One improvement is that we use more physical-based dual-view estimation without assumption. Another improvement is to include the more appropriate snow Bidirectional Reflectance Distribution Function (BRDF) ratio estimation from pure snow BRDF model given by Kokhanovsky et al (2005) together with Snow Cover Fraction (SFC), in order to get more appropriate surface properties during retrieval. Simple compare between Istomina's algorithm and the algorithm described in the paper show the approach described in the paper also provides reasonable results without correction from Raditive Transfer Model (RTM) like SCIATRAN.

MODIS and MISR are two main satellites provide aerosol observations. However, AOD products generated from these two sensors by different retrieval algorithms are inconsistent. In this paper, a semi-empirical optical fusion method was proposed to produce consistent AOD with different derived AOD datasets form MODIS and MISR. Using the semi-empirical optical algorithm, new merged AOD data sets were generated over China for 2010. We used level 2 cloud screened quality assured AERONET measurements to evaluate the merged AOD results. Our results showed that the combination of MODIS and MISR with this method could produce a more consistent, reliable AOD data with great improvement in spatial AOD coverage.

The retrieval of aerosol properties over land is more complicated due to the relatively strong contribution of the land surface reflectance to the radiation measured at the top-of-atmosphere (TOA). Another problem caused by the anisotropic earth surface which is a second order effect, can create systematic biases in the aerosol retrieval. But the simple Lambertian surface assumption is still widely used in most aerosol retrieval algorithms for the single satellite view. In this paper, radiative transfer simulations with coupling surface-atmosphere are employed to assess how much uncertainties are introduced from the Lambertian surface assumption in satellite aerosol optical depth (AOD) retrieval. The result shows that it has great impacts on aerosol retrieval especially at lower aerosol loading. The uncertainties mainly depend on the anisotropy of the target. The more difference lies between the reflectance along observing direction and the average ' reflectance ρ̅t, ρ̅t, ρtof the whole BRDF surface, the larger error happens in aerosol retrieval.

Cloud computing, a long-held dream of computing as a utility, has prosperous potential of transforming a large part of the information technology (IT) industry. It has successfully become a new business model in a short time. Now, many companies have launched their own products. Nevertheless, the current commercial mode does not meet the request of applications of remote sensing quantitative inversion.

Atmospheric aerosols play an important role in climate change research. It was found that different algorithms and instruments produce somewhat different results for aerosol optical depth (AOD) even if the same location at the same time is observed. Therefore, it is critical to integrate data from multiple platforms and techniques to derive a consistent AOD product. This paper introduced an approach to combine MODIS and MISR AOD data. One-month AOD data derived over Asian land with two different retrieval algorithms applied to MODIS and one retrieval algorithm applied to MISR are compared. Results show that the correlation coefficient between combined AOD product and AOD measured by CE318 is 0.70, and the root mean square error (RMSE) is 0.023. Moreover, it provided more details about the aerosols over land than either of the individual satellite measurements by mutually compensating for each other.

In recent years the satellite monitoring capabilities in particular to derive maps of aerosol optical depth (AOD) have increased tremendously. There are many aerosol retrieval algorithms for different satellites and sensors. In 2005, a new algorithm for AOD retrieval by synergetic use of of Terra and Aqua MODIS data (SYNTAM) was proposed by Tang et al. With this algorithm, surface reflectance and AOD can be simultaneously retrieved. Now we attempt to provide multi-scale AOD, using SYNTAM algorithm. We calculated AODs at 10km, 1km, 500m and 100m spatial resolution from MODIS and HJ-1A/1B CCD (the China HJ-1A/1B of the Environment and Disasters Monitoring Microsatellite Constellation Charge-Coupled Device) data over East China on June 25, 2009. The retrieval results were compared to the result of ground-based aerosol measurements by CE318 automatic sun tracking photometer at the AErosol RObotic NETwork (AERONET) sites. The validation results show that the results retrieved by SYNTAM have good precision.

In this paper, an advanced synergetic algorithm for aerosol retrieval from small satellite data is presented and applied on MODerate resolution Imaging Spectroradiometer (MODIS) and the Hyper-Spectral Imager (HSI) data from China HJ-1A satellite of the Environment and Disasters Monitoring Micro satellite Constellation. Using this algorithm, 500m MODIS data are downscaled to 100m based on maximal mutual information. By synergy of MODIS and HJ-1A HSI data, we obtained 100m × 100m aerosol optical depth (AOD) at 550nm over Beijing City, on April 5, 2009. Comparison with Aerosol Robotic Network (AERONET) measurement data, our results have good precision. The correlation coefficient is about 0.86 and the uncertainty is found to be Δτ = ±0.01 ± 0.23τ. From 100m AOD map, we can see more details of aerosols' spatial distribution. It is very useful and powerful for urban air quality monitoring.

The algorithm is based on the assumption that TOA reflectance increase with the aerosol load as well as the surface reflectance at same time gradually changes on different days within 14 days. Then the surface reflectance is derived from FengYun-2D (FY2D) measurements every 1 hour as the second darkest of reflectance for each time of day to minimize the effect of geometry change and cloud. The “true surface reflectance” of each time was calculated from the composite reflectance and their weighs. The weigh of each time, contribution of the surface and aerosol background were determined using the prior information, and both of them were various in different time. The AOD retrieval based on a Look-Up Table (LUT) using composite background (CB) method and improved composite background (ICB) algorithm were compared with AERONET sites, it was found that the ICB provides larger coverage and higher accuracy AOD product compared with CB.

The determination of aerosol's effect contains much uncertainty. During quantification of aerosols via remote sensing, surface reflectance error of 0.01 could bring Aerosol Optical Depth (AOD) error of 0.1. In order to avoid this, simultaneous retrieval of AOD and surface properties would be a promising method. In this paper, we present a novel analytic solution to atmospheric radiative transfer equation and utilize this solution to retrieve AOD and surface albedo simultaneously from bi-temporal geostationary FY-2 remote sensing data.

As a result of increasing attention paid to aerosols in climate studies, numerous global satellite aerosol products have been generated. There exists, however, an outstanding problem that these satellite products have substantial discrepancies, that must be lowered substantially for narrowing the range of the estimates of aerosol's climate effects. In this paper, three different data assimilation methods were used to produce consistent aerosol optical depth (AOD) with four different derived AOD products. The results illustrate that the data assimilation method can produce comprehensive AOD fields with reasonably good data values and acceptable errors. Through comparing, the Kalman filter method is more preferable to the optimal interpolation and three-dimensional variation method.

Abstract graphical Grid workflow can adapt the dynamic nature of Grid environment. It is more intuitive and convenient for user to apply Grid services solving the remote sensing distributable computing problems than concrete Grid workflows. This paper firstly introduces the feasibilities and advantages of abstract Grid workflow applying for remote sensing quantitative retrieval services. And then it gives the relative research status about science workflow applied to geosciences' domain. In the design of Grid workflow data structure, the authors illustrate the abstract Grid workflow's data structure for remote sensing quantitative retrieval. And then the authors give the key operation algorithms of the abstract Grid workflow data structure. In the implementation part, the authors have completed the abstract graphical Grid workflow composition system for remote sensing quantitative retrieval service. Using it, users can construct a workflow based on remote sensing application just by dragging and clicking the components of interest provided by the system.

This paper describes a case study of satellite data integrated with HYSPLIT forward-trajectory model to forecast air quality in regional scale. During mid-October 2009, a severe haze event occurs in eastern China. With the help of HYSPLIT Trajectory Model, a 24-hr, 48-hr and 72-hr forward trajectory from three regions with high aerosol optical depth (AOD) values were created. Forward trajectories were mapped on geospatial MODIS Terra AOD data to apply the integrate method. The spread direction described by trajectory lines matches the AOD maps quite well. Haze from Hebei province rapidly spread to Shangdong province, then spread to Bohai Bay. Haze from Hubei province rapidly spread to the Anhui province, then spread to Jiangsu province, and finally spread to Yellow Sea. Haze from Guangdong province spread to southwest just as the trajectory lines show. Results show that this integrated satellite data with forward trajectory analysis is a promising technique for improving air quality forecasts.

On Oct. 28th, 2009, a heavy fog hit East China. From the MODIS RGB composite image, it can be seen clearly that, much of land of the area (110°-123°E, 30°-42°N) was covered by the heavy fog or cloud. Using MODIS multi-satellite algorithm for aerosol optical depth (AOD) retrieval, we get the AOD Map at 470, 550 and 660 nm from MODIS data at 1km × 1km resolution. By Validating with the AERONET data, the results have good precision. The average relative error is about 10% and the correlation coefficient is as high as 0.88. Comparing with 10km MODIS aerosol products, our results can show more details because of the high resolution. From the AOD maps we can see the fog scope and the distribution as well as the relative thickness. Therefore, it is an effective method to monitor the fog.

Aerosol optical depth (AOD) is a significant indicator of dust episode. However, AOD retrieval over land still remains a difficult task because the measured signal is a composite of reflectance of sunlight by the variable surface covers and back scattering by the semitransparent aerosol layer. In this paper, an approach using bi-angle with Moderate Resolution Imaging Spectroradiometer (MODIS) data was presented. The derived AOD is compared to AERONET observations in the Washington State area and a retrieval error within 18% is found. Moreover, a dust episode in Washington State on October 4, 2009 was presented; we have analyzed the advection and dispersion of this event to get the possible source areas for the episode and its influence in the next day under the meteorological conditions.

In addition to climate-related processes, aerosol effects are also enormous in regional/local atmospheric phenomena. Fine resolution satellite data can be used to provide a macro-view of extreme aerosol events. The Aerosol Optical Thickness (AOT) for two extreme aerosol events were retrieved with spatial resolution 500m over East of China and Washington state, USA, respectively. Validation confirms that the retrieval would be trustable to some extent. Retrieved AOT at 500m resolution, NASA's AOT products from MODIS data at 10km resolution and NASA's snapshot views are utilized to analyze these two extreme aerosol events. Retrieved AOT maps at 500m resolution reveal much of the tendency and are smoother, which would be complementary to NASA products.

The terabytes of data processed and the tight-coupling remote sensing algorithms can't be scheduled by grid platform directly. Workflow for Grid computing is an application way to integrate distributed data and algorithms in a Grid computing environment. This paper firstly introduces the advantages and shortage of Grid computing applying for remote sensing quantitative retrieval service. And then it give the necessary to design a remote-sensing-oriented Grid workflow GUI for users after analyzing the status of the existing Grid workflow applied to relative domain. During the design, the authors illustrate the relation of system among processing steps. Finally, the authors design a concrete case for remote sensing quantitative retrieval service in atmospheric aerosol.

Aerosol quantitative retrieval from remote sensing over land surface is still a challenging task, especially for bright land areas such as desert, urban, coast, arid and semi-arid regions. A new aerosol optical depth (AOD) and surface reflectance remote sensing retrieval model is developed by exploiting a kernel-driven BRDF (Bidirectional Reflectance Distribution Function) model and the SYNTAM (Synergy of TERRA and AQUA MODIS) model, which considered the surface BRDF effect while retrieving AOD. After applying this new model to Terra and Aqua MODIS data in the Heihe River Basin of China, AOD and surface reflectance of this region are retrieved. Results show that the multiple correlation coefficient (R2) between retrieved AOD from MODIS and in situ measurements of CIMEL CE318 Sun-photometers is 0.92 at 0.55/zm. Using ASD Field Spec spectral radiometer measurements to validate retrieved surface reflectance, the RMSE values for band 1~3 are lower than 0.06.

A retrieval algorithm for AOD from MODIS multi-spatial scale data with 1km × 1km, 500m × 500m and 250m × 250m resolution based on mutual information (MI) is proposed. The concept of mutual information represents a measure of relative entropy between two sets. In this algorithm, an interpolation formula based on the maximal MI which is used to transform low resolution image into high resolution image is constructed. Then, the AOD with 500m × 500m and 250m × 250m resolution which are over Heihe Watershed, in Gansu province, China, on 5thJuly, 2008 are retrieved from TERRA and AQUA MODIS data. Compared with the 500m × 500m AOD retrieved by multi-satellite algorithm directly, the AOD with 500m × 500m resolution got by the MI algorithm is coincident with absolute error between 0.02–0.03. Preliminary validation result comparing with AERONET measured data shows good accuracy and promising potential. The Further research work is ongoing.

Atmospheric remote sensing offers us a view to estimate air quality in describing the aerosol distribution either for a local or global coverage because aerosol parameters, such as aerosol optical depth (AOD) are significant indicators of the air quality. However, AOD retrieval over land still remains a difficult task because the measured signal is a composite of reflectance of sunlight by the variable surface covers and back scattering by the semitransparent aerosol layer. In this paper, an approach using bi-angle with Moderate Resolution Imaging Spectroradiometer (MODIS) data was presented. The derived AOD is compared to AERONET observations in the Asia area and a retrieval error within 16% is found. Moreover, a biomass burning episode in North China between June 7, 2007 was presented, it is demonstrated that AOD increased up to 2.0 during the burning phase and then returned to normal values (0.2-0.5), which fully in line with the observation result.

In this paper, we focus on making desktop Grids adapted to remote sensing application. The initial efforts were made to build a remote sensing retrieval application on the RSIN framework for dealing with climate change, named High Performance Aerosol property Retrieval Software (HiPARS). HiPARS uses integration of applications that access the backend of desktop Grid comprising of office PCs with a client. The HiPARS remote sensing application was built on RSIN framework, which uses the loose-coupled architecture of a desktop client and high throughput computing (HTC) and Grid backend. It allows for fast development by enabling existing code and new algorithms, and provides a familiar graphical environment for remote sensing users. The proposed solution is to transition the current PCs to a desktop Grid that can be cost-effectively sustained. It will be accessible for processing satellite data in quasi real-time, for parameter retrieving or for parallel processing efforts.

The paper uses the approach of NDWI model to extract the water bodies of the Tangjiashan barrier lake from the multi-source satellite images of ALOS AVNIR-2 and CBERS-02B CCD and detects the changes of the barrier lake pre- and post-earthquake. The result shows that the river widen through Zhangping area increased from 92 m on Mar. 31, 2007 for per-earthquake to 420 m on May 16, 2008 and 474 m on May 18, 2008 for post-earthquake. And the area of barrier lake in the study area increased from an surface area of 0.6938 km2on Mar. 31, 2007 for pre-earthquake to 1.2842 km2on May 16, 2008 and 1.3658 km2on May 18, 2008 for post-earthquake.

Aerosol Optical Depth (AOD) is a vitally important physical parameter of aerosol which can be retrieved from satellite remote sensing data. The traditional algorithm such as MODIS aerosol algorithm is only appropriate for the dark pixels which have low reflectance. If the surface reflectance is high, the algorithm is ineffective. In this paper we proposed an advanced quantitative retrieval algorithm for AOD over land from MODIS data. In our new algorithm, MODIS L1B reflectance values are corrected for water vapor, ozone and carbon dioxide before used for further AOT calculation. Then we applied our algorithm to retrieve AOD of Beijing area in May and June, 2007. Preliminary validation result comparing with AERONET measured data shows good accuracy and promising potential with relative errors reduced from around 10% to below 10% (around 5%). Further research work is ongoing.

Remote Sensing data processing and analysis involve multiple algorithm process procedures. Traditional Remote Sensing algorithms are tight-coupling. It is possible for the Grid Workflow to integrate distributed data and algorithms among idle computational resources in a Grid computing environment. This paper firstly introduces the workflow description language and the features of remote sensing algorithms. Then, it gives the status of workflow research and analyses the related research of Grid workflow applied to remote sensing domain. It presents the necessity to implement an GUI of dynamic Grid Workflow web portal applied to Remote Sensing information service. During the design, the authors use an 8-tuples to illustrate the relationships of the different parts of such a workflow. Finally, the authors implements the GUI of Grid workflow described.

With the launches of the MODIS sensors on the Terra and Aqua spacecrafts, new data sets of the Earth surface are obtained, and a aerosol retrieval model by exploiting the synergy of Terra and Aqua MODIS data was promoted. Thereafter, its aerosol optical depth products have been continuously evaluated and validated. Most of these validation studies have shown that although MODIS generally derived aerosol optical depth to within the expected error, MODIS tended to poor quality when there is a visible difference in aerosol size distributions between the two pass observations time. To solve this problem, the model was improved, one visible channel was import, and the model was reconstructed. In order to validate what were mentioned above, a case study over Beijing was provided, and preliminary validations have been carried out by comparing with AERONET measured data. This algorithm has been tested. Compared with co-located AERONET site, the new MODIS algorithm retrieves aerosol optical depth more accurately than the previous. MODIS/AERONET tau regression has an equation of y = 1.0496x - 0.0106, R2= 0.9931.

More and more problems were emerged with the development of Beijing's urbanization. One of them is urban heat island (UHI) effect. Our study area is located in the central urban area of Beijing which mainly the area surrounded by the fifth ring road. During our monitoring of the UHI effect using Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, an urban heat sink was found. This paper focuses on the analysis of this urban heat sink developed in the winter morning of Beijing by detailedly analyzing the subsurface's thermal characteristics. Thermal inertia was used in this paper to identify the subsurface's thermal characteristics. Thermal inertia is a physical parameter representing the ability of a material to conduct and store heat, and in the context of planetary science, it is a measure of the subsurface's ability to store heat during the day and reradiate it during the night. After getting the surface albedo and the land surface temperatures at day and night respectively, the thermal inertia was calculated using a real thermal inertia model. The result shows that the urban area has a bigger thermal inertia than that of the rural area. Which makes the materials in rural area have a rapidly increase in surface temperature then those in the urban area in winter morning which caused the formation of the urban heat sink.

This paper describes an approach to assess air quality over urban area using high spatial resolution remote sensing data. As an example, we report here the results of the air quality assessment in Hong Kong area on April 17, 2006. An aerosol optical thickness (AOT) retrieval method with 15m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data was developed by integration of urban BRDF model and ASTER dual-angle view capabilities in 3N and 3B bands, which was used for calculation the AOT values over Hong Kong. Ground-level PM2.5 values in Hong Kong area was obtained from a linear regression equation describing the relationship between AOT and PM2.5. Results showed that the detailed information of air quality in Hong Kong area could be obtained from the high-resolution retrieval results and indicated that satellite remote sensing of aerosol could serve as an efficient tool for monitoring the spatial distribution of particulate pollutants on the ground-level. Further validation with in situ land surface AOT and pollution concentration data should be carried out in the future.

Aerosol optical depth (hearafter called AOD) is an important geophysical parameter, and its retrieval from satellite data are both data and computationally intensive. This paper presents the implementation of parallel AOD retrieval from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, based on IBM System Cluster 1600 deployed in Chinese Meteorological Administration (CMA), with focuses on the design of parallel algorithm through Single Program Multiple Data (SPMD) model. In order to demonstrate the parallel performance of the proposed parallel implementation, experiment of parallel AOD retrieval is given, and results show that the implementation of AOD parallel computing, as a viable cost-effective method, has great scalability on clusters machines, which can obtain optimum performance at 128 processors. Meanwhile, the parallel algorithm can be operationally used in AOD retrieval in the near future, which is an important input parameter for the air quality model.

In this paper, a method using multi-resource remotely sensed data and ground base data for quantitative determination of aerosol optical properties was demonstrated. The model exploits the synergy of TERRA and AQUA MODerate resolution Imaging Spectrometer (MODIS) data, to simultaneously retrieve both Aerosol Optical Thickness (AOT) and surface reflectance. The ground station data routinely coming from AERosol Robotic NETwork (AERONET) of ground-based sun- and sky-scanning radiometers were assimilated as variables describing model initial states. To meet the increased computational needs caused by the complex and computational intensity, the algorithm was migrated to run as parallel processing on a Grid platform. Experimental results were presented with a realistic application, using data collected by MODIS over China mainland. The results show that Grid-enabled model allowed on-demand large volume of ground-based data assimilation with parameters, and achieved improvement both in retrieval accuracy and computing performance.

Biomass burning is a distinct biogeochemical process and plays a major role in the global carbon cycle impacting both regional and global climate change. The extent of burning and the impact of these activities on the global environment are not well understood. Remote sensing offers the most cost effective means for long-term monitoring of associated aerosols. Broadband aerosol optical thickness (AOT) ranging from 550 nm to 900 nm derived using the multiresource Terra and Aqua MODIS data were used to analyze the affection of biomass burning for atmosphere The authors have collected of such typical straw of wheat burning events during May and June 2007 for the study.

Drought in sprint is one of the natural disasters that threatens our agriculture development and causes series environment, social and economical consequences. Remote sensing technology becomes possible to rapidly monitor soil moisture in regional scale by combining remote sensing and Geophysical Information Systems technology. In this paper, a thermal inertia map was obtained from MODIS 1B data on April 16, 2004 using thermal inertia model in Northern China Plain covering Latitude from 37degN to 39.5degN and Longitude from 115degE to 117.5degE. A soil moisture map was obtained based on the relationship between thermal inertia and soil moisture. And a distributive map of drought was obtained by dense slicing and some analyses based on the relationship between ranks of drought and soil moisture were performed. The drought map shows that most of the studied area was in a normal condition except some small part of the northern and western eastern was in a slightly drought. The actual drought information obtained from the official Website of Beijing Meteorological Bureau indicates that it was in a normal condition of soil moisture in Northern China Plain in the first ten days of April. The result derived from MODIS data using thermal inertia model is in consistent with the actual situation. It indicates that it is an effective way to monitor regional drought condition by means of deriving soil moisture using MODIS data.

A project to design and construct an airborne 8mm band 2-Dimensional (2D) aperture synthesis radiometer BHU-2D was launched in the Electromagnetics Laboratory of BeiHang University (BHU). BHU-2D consists of 48 antenna/receiver elements, an FPGA-based 1bit/2levels digital cross-correlation subsystem, and a calibration subsystem. A ground based laboratory prototype of BHU-2D has been completed, which consists of 10 antenna elements arrayed in a T- shape configuration, 10 receivers, and a digital correlation and data processing subsystem. The testing and imaging experiments have been carried out. Initial results are successful; the images of some scenes and the person indoors are obtained. The instrument overview, calibration method, and preliminary imaging results of the prototype are presented in this paper.

The detection of oil spill is very important for the oil exploration. Remote sensing technology is one of the methods to detect the potential oil basin, especially in a large region. High resolution satellite images can be used as an approach of early oil exploration because its visualized, continuous and macroscopical characteristics. In this paper, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used to detect the possible oil regions in the Yinggehai area of Hainan Province, China from thermal anomaly including sea surface temperature and apparent thermal inertia. By comparing with the drilling materials, our results show that the area with oil spill has a lower temperature and higher thermal inertia values than the surrounding areas. It indicates that our derived sea surface temperature and apparent thermal inertia anomaly just reflect the above mentioned status and the method can be used to detect the potential oil basin.

Multi-angle remote sensing is proposed on the anisotropy characteristic of ground objects. Compared with the traditional unidirectional remote sensing, it is provided with the capability of obtaining 3D structure of ground objects and represents the new aspect of quantitative remote sensing. This paper introduced the latest achievements in multi-angle remote sensing in China. Firstly, some field and laboratory BRDF experiments for both vegetated and non-vegetated surfaces and major goniometry laboratories in China were introduced. Furthermore, this paper briefly reviewed different BRDF models and their applications in China, including the applications in vegetation canopy, rock et al. In this part, Thermal BRDF models and field measurements in thermal BRDF data were also discussed here. What is more, some key questions in BRDF model research were discussed. Finally, the present difficulties and future prospects of multi-angle remote sensing are analyzed.

In this paper, an innovative method for simultaneous retrieval of AOT at 0.55 and 0.66μm from Advanced Along- Track Scanning Radiometer (AATSR) TOA radiance is presented which exploits multi-spectral and dual-angle view capabilities, based on the fact that the consistent angular dependence of reflectance exists in a certain spectrum and is invariable with wavelength. No assumptions are required about the land surface spectral properties, thus allowing for quantitative regional mapping of aerosol over virtually arbitrary land surface. This study uses the dual-angle 0.55−, 0.66−, and 1.6μm channel data from the AATSR on board ENVISAT to retrieve AOT over bright land like acrid, semi-acrid areas, and urban areas. Validation was performed over Beijing of China against AErosol RObotic NETwork (AERONET) ground-based Sun-photometer data, which shows good agreement between the AATSR-derived estimates of AOT and the sun-photometer measurements. Pearson’s correlation coefficient, r2, are 0.81 and 0.83 at nadir and forward view, respectively for 0.55μm and 0.88 and 0.81 for 0.66μm for 22 cases combined. Moreover, the algorithm is applied at region scales, thus resulting in AOT maps covering Beijing, demonstrating the great potential over bright land surface at global scales for atmosphere correction and as important climate model input parameters.

As is known that multi–angle sensor can provide more information to characterize the angular spectral reflectance, which is important for the inversion of most geophysical parameters, including aerosol optical thickness (AOT). In this paper, a synergetic approach for simultaneous retrieval of AOT over land at a 1 km spatial resolution for 0.55, 0.66 and 0.87 μm from Advanced Along–Track Scanning Radiometer (AATSR) TOA radiance is presented which exploits the synergy of AATSR dual–angle view capabilities and semi–empirical linear kernel–driven bi-directional reflectance distribution function (BRDF) model product from NASA. In this approach, the BRDF characteristics of land surface are used to retrieve AOT by combining the geometrical information extracted from AATSR and linear kernel–driven BRDF parameter product. No assumptions are required about the land surface spectral properties, thus allowing for quantitative retrieval of aerosol particles over virtually arbitrary land surface. A retrieval example is given by validating against AERONET Beijing in–situ AOT measurements with the maximum standard deviation of 0.06, which suggests that the retrieval precision of AOT be favorable by accounting for BRDF effects through this synergetic approach developed in this paper.

Aerosol optical thickness retrieval (AOT) from remotely sensed data over land is still a difficult task because the solar light reflected by the Earth-atmospheric system mainly comes from the ground surface. A novel aerosol remote sensing model SYNTAM by exploiting the synergy of TERRA and AQUA MODIS data could be used to accomplish part of the task of aerosol retrieval over land, especially over higher reflective surface. This paper addressed the issue how the SYNTAM method is applied to the national wide MODIS data for retrieval of aerosol optical thickness over China. The retrieval process is time consuming and the EMS (enhanced memory systems) memory required is very high. To solve the problem, we adapted the SYTAM model to the Grid computing environment. First retrieval process is laid out. The process is decomposed into small steps in pipeline. Decomposing strategy does not consider the difference computability among the computing elements. A coordinator is in charge of load balance. And then the detailed data query, data pre-processing, job monitoring and post-processing are discussed. We have developed one middleware by which the retrieval algorithm can be parallel processed. Preliminary experiment of aerosol retrieval of nationwide range was conducted. The experimental results indicated the computational process had been accelerated in an effective way. Using Grid computing platform, wide range and real-time aerosol information can be retrieved.

This paper focuses on the estimation of heat energy exchange between land and air using MODIS data in Poyang Lake area of Jiangxi Province. Based on energy balance equation, net radiation including net longwave and shortwave radiation, sensible heat flux, latent heat flux and ground heat flux are the major components of energy balance. In this paper, the net longwave radiation, net shortwave radiation, sensible heat flux, and latent heat flux were derived from MODIS data and the in situ data obtained from local meteorological stations because the ground heat flux is very small and can be neglected. By means of comparing our derived parameters with the in situ data collected from the local observation station, the longwave radiation, shortwave radiation and net radiation made a good accuracy, but the latent heat flux and sensible heat flux were not agreement with the in situ data which might be caused by the unstable atmosphere status. Research on the heat energy exchange is helpful for the people working on environmental, hydrology and ecology research in Poyang Lake area of Jiangxi Province.

Remote sensing and geographic information system, combined with methods of spatial statistics, provide powerful new tools for understanding the epidemiology of diseases and for improving disease prevention and control, the same holds true in high pathogenic avian influenza (HPAI) epidemic. The study is to determine such risk factors as distance from HPAI outbreak sites to highway using GIS, and land surface temperature around HPAI outbreak sites retrieved from RS image by means of the self-iterative algorithm developed by us. Finally, A diagnostic study is made to investigate the relationship between water vapor above infected area of HPAI H5N1 and possibility of HPAI. We find that about 75.5 percent of HPAI outbreak between 0.5-2.0 g/cm2 of water vapor. Ultimately we would like to provide detailed and precise support information for governments to prevent and control HPAI epidemic.

Aerosol particles, water vapor and clouds are the three important factors that affect the signals received by sensors. To remove the aerosol effect, some methods have been developed. MODIS aerosol products provided by NASA are based on the Dark Dense Vegetation (DDV) algorithm. The Synergy of Terra and Aqua MODIS (SYNTAM) method is developed recently that can be used to retrieval aerosol optical thickness over land from MODIS data, no matter whether the land is dark or bright. The experiments prove that the DDV method is better for dark targets and the SYNTAM method is better for bright surfaces. By combining the DDV method and SYNTAM method, it will provide more details about the aerosols over land by mutually compensating for each other.

Soil moisture plays an important role in surface energy balances, regional runoff, potential drought and crop yield. Early detection of potential drought or flood is important for the local governm...

The pandemic disease of avian influenza, which was first recognized in Italy in 1878, outbreaks in most temperate regions such as Southeast Asian, resulting in the death of a paramount number of birds and chicken, even probably infecting the human being, so it is of particular concern. Because the virus of HPAI, i.e. H5N1 is largely dependent on the habitat or the surrounding environment, analysis of the correlation between the land surface temperature (LST) in the infected area of HPAI epidemic and the outbreak of HPAI is becoming more and more pressing. In this paper we successfully verify that there are strong correlation between LST and HPAI. Except that, it is true that the outbreak of HPAI is characterized by the vicinity to the river. This paper mainly addressed the concern about the connection between LST and the infected area associated with the outbreak of HPAI, moreover, It also expounded on the vicinity of river to affected area played a important role in the prevalence of HPAI.

A huge amount of remotely sensed data is acquired daily and many algorithms for processing the data are complex. Grid computing, which is different from conventional distributed computing, can harness the power of many computers in a network to solve problems. Grid computing has become an exciting way of solving the large-scale or complex problems. To apply the grid computing to remote sensing information, a remote sensing information grid analysis and service node (RSIGN) has been established in Telegeoprocessing Group, Institute of Remote Sensing Applications, Chinese Academy of Sciences. The management and distribution of the tasks are key problems in RSIGN node. How to distribute and manage the tasks has significant influence on the efficiency of the whole grid system. In this paper, we will discuss two main strategies for RSIGN node: geometric parallel and algorithm parallel. Different problems in the grid node need different strategies. On one hand, the data to be processed and analyzed can be divided into sub-datasets and processed on different computers. On the other hand, some algorithms can be rewritten to fit the parallel rules. In the latter case, different steps of processing the whole dataset can be executed on different computers. Through the experience of applied the both strategies on RSIGN node, we compared and evaluated the two strategies, and figured out the merits and disadvantages of two different strategies when applied on remotely sensed information analysis. At the end of the paper, we discuss how to choose the best task managing strategy for different problems in remote sensing information processing and analysis.

Abstract -GIS software provides quite powerful analysis tools for contemporary spatial epidemiology research. When dealing with epidemiological data, some space/time analysis involved in can be done using correlated tools from GIS software. This paper first briefly introduces several space/time statistical analysis tools in ArcGIS and their applications in spatial epidemiology, and then illustrates the space/time analysis procedures of Hepatitis B in China. We think that while current main GIS software provides powerful tools for spatial epidemiological research, the analysis techniques provided by these tools are still limited. It is promising to develop epidemiological analysis module based on a common GIS platform. Keywords: spatial epidemiology, GIS, space/time analysis I. INTRODUCTION Spatial epidemiology concerns the study of the occurrence of disease in spatial locations and time points and its explanatory factors [1]. So space/time data handling is an important issue in spatial epidemiology. In classical epidemiology, statistical analysis has played key role. However, in spatial epidemiology spatial/time statistical analysis techniques become important approaches. GIS (Geographical Information Systems) is a computer system relevant to administration and analysis of spatial data. Current GIS software contains several space/time data statistical analysis tools, which are supposed to be quite useful for the study of spatial epidemiology. This paper first briefly introduces several space/time statistical analysis tools in ArcGIS [5] and their applications in spatial epidemiology, and then illustrates the space/time analysis procedures of Hepatitis B in China. The examples deal with spatial analysis and spatiotemporal analysis of Hepatitis B in China during several years. We conclude that these tools are powerful in the study of spatial epidemiology. They provide spatial epidemiological study with a variety of effective techniques. Whereas, the analysis techniques provided by these tools are still limited. It is hard to meet more complex need for research of spatial epidemiology. And we think that it is necessary, feasible and efficient to develop epidemiological analysis module based on a common GIS platform. II. DOMAIN OF SPATIAL EPIDEMIOLOGY AND THE SPACE/TIME ANALYSIS TOOLS OF GIS Spatial epidemiology is the description and analysis of geographically indexed health data with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors [1]. *Corresponding author

Hyperspectral remote sensing exploits the fact that all material reflects, absorb, and emit electromagnetic energy, at specific wavelengths, in distinctive patterns related to their molecular composition. Hyperspectral algorithms for the estimation of the concentrations of chlorophyll A and carotenoids can be developed using statistical approaches. Some algorithms for the estimation of the concentrations of chlorophyll A and carotenoids in rice leaves from airborne hyperspectral data were developed in this research. Algorithms based on reflectance band ratios and first derivative have been developed for the estimation of chlorophyll A and carotenoid content of rice leaves by using airborne hyperspectral data acquainted by Pushbroom Hyperspectral Imager (PHI). There was a strong R680/R825 and chlorophyll A relationship with a linear relationship between the ratio of reflectance at 680 nm and 825 nm. The first derivative at 686 nm and 601 nm correlated best with carotenoid. The relationship between the ratio of R680/R825 and chlorophyll A relationship, the first derivative at 686 nm and carotenoid concentration were used to develop predictive regression equations for the estimation of canopy chlorophyll A and carotenoid concentration respectively. The relationship was applied to the imagery and a chlorophyll A concentration map was generated.

In order to provide relevant data and decision support information to the Beijing Olympic Organizing Committee and International Olympic Organizing Committee, we are building up an environmental remote sensing monitoring system for Beijing 2008 Olympic Game using multiresolution, multiband, multitemporal remote sensing method and virtual reality technique. This system has four main features: 1) a 10 years long term monitoring from 1999 to 2008; 2) a stereo observation from spaceborne, airborne and ground remote sensing means; 3) a large area monitoring from Olympic game sites, Beijing area to its surrounding areas; 4) comprehensive monitoring for ecological environment, engineering construction, environmental pollution, traffic situation etc. This system serves as a huge platform for dynamic Earth observing monitoring and virtual Olympic environment with annual report and seasonal report forms. This work presents a preliminary result of constructing the system for Beijing 2008 Olympic Game.

Buffer analyzing is widely used for identifying areas surrounding geographic features. As the volume of spatial data increases rapidly, a high computational power is required. Over the past decade, grid has become a powerful tool to provide huge computational capabilities. It allocates the resource efficiently to applications. The objective of This work is to investigate the performance of buffer analysis in grid computing environments. In this paper, we discuss the parallel generation algorithm of line buffer zone. Our experiments were performed on a grid-computing environment, which is in the High-Throughput Spatial Information Processing Prototype System based on Grid platform in Institute of Remote Sensing Applications, Chinese Academy of Sciences. The computing nodes are heterogeneous. Performance results are also reported in This work.

Grid computing seeks to aggregate computing resources which are geographically distributed or heterogeneous and leverage on resources one don't own for oneself computational intensive applications. The procedure to generate the look-up table (LUT), which is very commonly used for aerosol remote sensing retrieval, is computational intensive even though the aims to take it are mainly to speedup the retrieval computation. This work focuses on realization of the compute-intensive look-up table generation on GCP-ARS (Grid Computation Platform for Aerosol Remote Sensing), which is one grid middleware we are developing based on Condor system. We discuss our approach to parameterization, task partitioning, generated methodology, and the collection of result. Experimental results obtained using Condor-pool consisted of commodity PCs are discussed.

Aerosol retrieval over land remains a difficult task because the solar light reflected by the Earth-Atmospheric system mainly comes from the ground surface. Dark Dense Vegetation (DDV) for MODIS data has showed excellent competence at the aerosol distribution and properties retrieval, which is, however, restrictedly used for lower reflectance ground surface such as water body and dense vegetation. We attempt to derive Aerosol Optical Thickness (AOT) by exploiting the synergy of TERRA and AQUA MODIS data (SYNTAM), which can be used for various ground surfaces, including high reflective surface. Preliminary validation result compared with AERONET data shows good accuracy and a promising potential.

With the tremendous advances in hand-held computing and communication capabilities, rapid proliferation of mobile computing environments, and decreasing device costs, we are seeing a growth in mobile e-Geoscience in various consumer and business markets. The trend is also picking up in large enterprises where dependency on mobile e-business environments has become routine for thousands of employees conducting daily business activities. Grid is a new technology. With corresponding middleware it can give strong computing power. We mainly discuss our experience on the middleware development and architecture used in remote sensing data analysis using Mobile handheld devices. It clearly shows that the possibilities of using Grid computing technology to real-time update spatial databases regularly by means of telecommunications systems in order to support problem solving and decision-making at any time and any place.

We propose a new algorithm for remotely sensed image texture classification and segmentation in this paper. We observe that the traditional method LSE is unstable in practical applications. This motivates us to develop more stable method. We have proposed the regularization technique to suppress the instability of LSE in previous research. Our contribution in this paper is that we propose a new stable method, which is based on the total variation, abbreviated TV, for reducing instability in texture analysis, and apply which to remotely sensed image texture classification and segmentation. Experiment results on remotely sensed images demonstrate our new algorithm is superior to LSE and seems promising in applications.

In the recent past there have been several technological developments which people have proceeded to use separately in isolation from one another. What we aim to discuss in this presentation is an attempt at synthesis. In this paper, we describe our pilot telegeoprocessing programme which is currently being carried out in the Institute of Remote Sensing Applications, Chinese Academy of Science, China.

Remote sensing data, DEM, land use and land cover GIS data are used to monitor soil erosion in Mingjing River valley. Instead of using the field sample sites parameters for the USLE model calculation we extracted the parameters from satellite data. Landsat TM and ETM temporal data were used to investigate the changes in between 1993 and 2000. The results prove that there was /spl plusmn/5% soil erosion difference between our calculation results from ETM data in 2000 and field statistical results in 1999.