Tropical forest plays a critical role in climate regulation, carbon storage, and biodiversity conservation. Their vulnerability to climate change and human disturbances necessitates accurate, scalable tools for monitoring and predicting forest growth. While recent deep learning models have improved in capturing nonlinear and lagged effects, current remote sensing applications often lack explicit modeling of factor-variant climatic delays and neighborhood interactions. Here we present a remote sensing–oriented deep learning framework named Lag-aware Convolutional Transformer with Error Feedback and Geographic Feature Fusion (LCTEG). LCTEG explicitly models the delayed effects of climatic factors through structured multi-scale convolution, incorporates neighborhood features to learn local conditions, and leverages error-guided attention to improve the stability and interpretability of multi-step predictions. Applied to global tropical forest at 0.1° spatial and 16-day temporal resolution, LCTEG achieved a test R² of 0.966 and reduced MAE, MSE, and RMSE by at least 65%, 86%, and 64% compared to baseline models. Lag analysis confirms delayed climate effects. Perturbation experiments identify precipitation as the strongest driver and show proximity to water bodies as a key spatial factor, highlighting the dominant role of water availability. Furthermore, future projections (2030-2034) show consistent LAI increases under all three SSP scenarios, but with smaller gains under high-emission pathways, suggesting that potential water stress may constrain vegetation growth. Overall, LCTEG serves as a robust, interpretable tool for tropical forest growth prediction and for advancing climate-resilient ecosystem and policy strategies.

A hybrid algorithm based on radiative transfer simulations and machine learning for dust aerosol detection, is developed for the Advanced Himawari Imager (AHI) carried by the geostationary satellite Himawari-8. The sensitivities of the AHI thermal infrared (TIR) channels for dust aerosols are analyzed through radiative transfer simulations. The sensitivity study demonstrates that the simulated clear-sky brightness temperatures (BTs) show an obvious improvement in identifying dust aerosols compared to brightness temperature difference techniques, especially optically thin dust. Therefore, the simulated clear-sky BTs and AHI TIR observed BTs, in addition to ground information, are used as inputs to add physical knowledge in the machine learning model. The performance of an artificial neural network constructed for dust aerosol detection is evaluated by comparing its results with those of active Cloud-Aerosol Lidar with Orthogonal Polarization measurements. The proposed algorithm effectively achieves dust aerosol detection during both daytime and nighttime, with a precision of over 86% and a recall of over 85% on an independent testing dataset. The proposed algorithm is applied to three typical dust events to further illustrate its applicability. Although some thin dust aerosols near the ground are misclassified due to weak signals, most dust aerosols are successfully detected, and the identification is generally not affected by other types of aerosols. The results of the regional classification demonstrate that our algorithm is superior in detecting tenuous dust aerosols compared to Dust RGB images using the AHI TIR channels and physical-based algorithm.

As the successors of the HuanjingJianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HJ-2 A/B satellites have been successfully launched on the September 27 of 2020. The Polarized Scanning Atmospheric Corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric correction instrument requiring high speed and accurate aerosol optical depth (AOD) algorithm. For this purpose, we proposed a neural network based AOD retrieval model (named the AODNet), which takes full advantage of the multispectral measurements of PSAC for AOD retrieval with a high speed. The training of AODNet is conducted by the simulated observation data (currently applicable for the China region) from the forward calculation using the radiative transfer model. In this way, the land surface reflectance (LSR) is no need for our well trained model. It is expected to be one of the effective ways to solve the ill-pose problem in the decoupling of the atmosphere and surface information in AOD retrieval. Either of Sun-sky radiometer Observation NETwork (SONET) AOD or AErosol RObotic NETwork (AERONET) AOD was used to validate the AODNet AOD. The correlation coefficient is higher than 0.85 and more than 60% of the AODNet AOD can fall into the expected error envelope of ±(0.05+20%). The cross-comparison shows that the AODNet has better accuracy than MODIS Dark Target (DT) and Deep Blue (DB) algorithm. The air pollution episode is well characterized by the AODNet AOD using PSAC data.

Surface albedo is closely related to the Earth’s energy budget and is usually estimated by integrating remotely sensed bidirectional reflectance distribution function (BRDF) data based on the widely used Ross–Li kernel-driven models. However, for large solar zenith angles (i.e., SZAs > 70°), albedo estimation using the operational algorithm of the Moderate Resolution Imaging Spectroradiometer (MODIS), i.e., RossThick-LiSparseReciprocal (RTLSR), is not recommended because it is reported to somewhat underestimate the black-sky albedo (BSA) at large SZAs based on ground albedo measurements. Recently, various combinations of the Ross–Li BRDF models with improved capabilities have been developed, and the assessments of these models based on worldwide satellite BRDF data with good spatial sampling, particularly at the large view and solar angles, will be important to improve an understanding of their performance in estimating intrinsic albedos. Following previous studies, the objective of this study is to further assess a series of hotspot-corrected Ross–Li models by demonstrating their ability to fit the POLarization and Directionality of the Earth’s Reflectances (POLDER) data sets and estimate albedo, especially at large SZAs, based on selected concurrent POLDER and MODIS data. The hotspot-corrected RTLSR model obtained by combining the RossThickChen and LiSparseReciprocalChen kernels (RTLSR_C) shows the best fitting ability, with a high cumulative frequency of small root-mean-square errors (RMSEs), thus confirming previous conclusions. Model differences mainly appear in albedo estimates, especially BSA estimates at large SZAs. The BSAs estimated by other models are significantly different from the RTLSR_C estimates in the near-infrared (NIR) and red bands as the SZA increases to approximately 60° and 70°, respectively. In this case, RossThinChen-LiSparseReciprocalChen (RTNLSR_C) yields higher BSA estimates than those of RTLSR_C. Comparisons of the MODIS and POLDER albedos estimated with Ross–Li models show that models with the RossThinChen kernel yield higher BSA estimates than those of the RTLSR_C model as the SZA increases. The results indicate that the retrieved albedo is likely to be more accurate with appropriately selected kernels for BRDF models at large SZAs, providing guidance for selecting suitable combinations of multiple kernels.

Currently, no aerosol optical thickness (AOT) data set over the Arctic snow/ice-covered regions derived from space-borne passive remote sensing is available. The challenge is to develop an accurate and robust technique to derive AOT above highly variable and bright snow/ice surfaces. To extend data coverage of the eXtensible Bremen Aerosol/cloud and surfacE Retrieval (XBAER) AOT data product in the future, we propose a new algorithm for the retrieval of AOT and surface properties over snow/ice simultaneously. The algorithm utilizes the linear perturbation theory and does not use any simplified atmospheric correction techniques. Key issues like the selection of a proper aerosol type and optimal surface parameterization method for the retrieval of AOT over the Arctic have been investigated. The aerosol type is investigated using the aerosol climatology microphysical properties derived from four Aerosol Robotic Network (AERONET) sites (Barrow, Hornsund, Kangerlussuaq, and Tiksi). The three-parametric Ross-Li linear kernel model is used to describe the snow bidirectional reflectance distribution function (BRDF). The a priori knowledge of wavelength-dependent features of the coefficients in the Ross-Li linear kernel model is derived from Polarization and Directionality of the Earth's Reflectances (POLDER) measurements over the Arctic and utilized as constraints in the retrieval. The studies show that the combination of Ross-Li surface model and weakly absorbing aerosol parameterization provides an optimal way to derive AOT over the Arctic snow/ice-covered regions from passive remote sensing observations. The retrieved AOTs using POLDER show good agreement with AERONET observations.

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.

The retrieval of cloud optical properties for aerosol contaminated water cloud is challenging because of the complexity of physical processes in such situations. Conventionally, cloud optical data products are typically derived, ignoring the aerosol impacts on radiative transfer in the retrieval process. This is potentially a significant source of error. In this paper, the eXtensible Bremen Aerosol Retrieval (XBAER) algorithm has been optimized for the retrieval of aerosol/cloud properties for the aerosol contaminated cloud (ACC) scenarios. This version of XBAER delivers cloud optical thickness (COT) and cloud effective radius (CER) for ACCs and simultaneously retrieves aerosol optical thickness (AOT). The surface parameterization and aerosol types used in the standard XBAER algorithm have been adapted in this retrieval to account for the ACC conditions. Aerosol types in XBAER for the retrieval of ACC scenarios have been parameterized to comprise weak and strong absorptions. The comparisons of COT, CER, and AOT retrieved using this adapted XBAER algorithm and two new NASA algorithms show good agreements, especially for biomass burning aerosol. The correlation coefficients are >0.9 for AOT, ~0.8 for CER, and ~0.7 for COT. There is also a good agreement for dust plume contaminated cloud scene. The XBAER derived AOT values for dust aerosols are systematically smaller than the NASA retrieval, but both products have the same spatial distribution patterns. The comparison of COT retrieved using the adapted XBAER algorithm and that retrieved from ground-based microwave radiometer (MWR) measurements shows much better agreement for ACC conditions with high AOT.

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.

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.

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].

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.

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.

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 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.

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.

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.

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.

This paper proposes a method for the classification of land cover in remote sensing imagery using evolutionary artificial neural networks (EANN) compared against multilayer perceptrons (MLP) with backpropagation algorithm. Evolutionary neural networks have combined the features of artificial neural networks (ANN) and evolutionary algorithms (EA) in the way that simultaneously evolving ANN architecture and weights. The parsimony of evolved ANN is encouraged by preferring node mutation and connection mutation. This enables consistent reductions of mean square errors of spectral classification with respect to sample pixels. Land-cover classification experiments were carried out by EANN-based classifiers and MLP-based classifiers in a 300times300 pixels Landsat-7 Enhanced Thematic Mapper plus (ETM+) high-resolution image of Zhaoyuan in Shandong province in eastern China. We found that the use of evolutionary algorithms for finding the optimal ANN results mainly in improvements in overall accuracy of an ANN with backpropagation algorithm and produce more compact ANN with good generalization ability in comparison with MLP. It is observed that classification accuracy of up to 90% is achievable for Landsat data produced by EANN.