Xingwei He

Validation and analysis of aerosol optical thickness retrieval over land

Aerosol optical thickness (AOT) retrieval from Moderate Resolution Imaging Spectroradiometer (MODIS) data has been well established over oceans, but this is not the case over land. In this article, the AOT data sets retrieved by exploiting the synergy of TERRA and AQUA MODIS data (SYNTAM) over land are validated with ground-based measurements from Aerosol Robotic Network (AERONET) data, as well as from the National Aeronautics and Space Administration (NASA) AOT products, amended with a DeepBlue algorithm in Asian (15–60° N and 35–150° E) and American areas (30–40° N and 100–120° W). Overall, AOT retrieval errors of around 10–20% against AERONET data are found at both 1 and 10 km resolutions. The spectral and spatial sensitivities of the AOT correlation are explicitly addressed at both 1 and 10 km resolutions. Three window sizes, 1 × 1, 3 × 3 and 5 × 5, are tested for SYNTAM to evaluate the effect of window size on parameter statistics, and it is found that the accuracy of the SYNTAM method decreases with increasing window size. The validations at three spectral bands of 0.47, 0.55 and 0.66 μm show that the accuracies of different bands are 80–90% similar, and that the band at 0.47 μm has the highest accuracy most of the time. Comparisons between AOT data sets derived from the SYNTAM and AOT products from the NASA Dark Dense Vegetation (DDV) and the DeepBlue algorithms are also conducted using data from the USA. More pixels with AOT values for the area could be retrieved using the SYNTAM method with the NASA DeepBlue algorithm. The AOT values of more than 90% of pixels derived by both methods are very close. This clearly shows that AOT data from SYNTAM are very close to the AOT data set from the NASA DeepBlue algorithm in cloud-free areas. The synergic use of both the SYNTAM and DeepBlue algorithms could produce AOT values over much greater land areas.

Observation of an agricultural biomass burning in central and east China using merged aerosol optical depth data from multiple satellite missions

Agricultural biomass burning (ABB) in central and east China occurs every year from May to October and peaks in June. During the period from 26 May to 16 June 2007, one strong ABB procedure happened mainly in Anhui, Henan, Jiangsu and Shandong provinces. This article focuses on analysis of this ABB procedure using a comprehensive set of aerosol optical depth (AOD) data merged by using the optimal interpolation method from the Moderate Resolution Imaging Spectroradiometer, the Multi-angle Imaging Spectroradiometer (MIRS) as well as Sea-viewing Wide Field-of-view Sensor (SeaWiFS)-derived AOD products. In addition, the following additional data are used: fire data from the National Satellite Meteorological Centre of China Meteorological Administration, the mass trajectory analyses from hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model and ground-based AOD and Ångström data derived from the Aerosol Robotic Network and China Aerosol Remote Sensing Network. The results show that merged satellite AOD values can expand the spatial coverage of agricultural biomass aerosol distributions with good accuracy (R = 0.93, root mean square error = 0.37). Based on the merged AOD images, the highest AOD values were found concentrated in central China as well as in eastern China before 6 June and further extended to northeast China after 12 June. AODs from ground measurement show that eastern China always keeps high AOD values (>1.0), with a maximum exceeding 3.0 and extending as high as nearly 5.0 during this ABB event. With the help of the HYSPLIT model, we analysed the ABB sources and examined how transport paths affect the concentrations of air pollutants in some sites. The results show that Henan, Jiangsu and Anhui provinces are the three main sources in this ABB.

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

Abstract The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid – the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

Dust aerosol optical depth retrieval and dust storm detection for Xinjiang Region using Indian National Satellite Observations

The Xinjiang Uyghur Autonomous Region (Xinjiang) is located near the western border of China. Xinjiang has a high frequency of dust storms, especially in late winter and early spring. Geostationary satellite remote sensing offers an ideal way to monitor the regional distribution and intensity of dust storms, which can impact the regional climate. In this study observations from the Indian National Satellite (INSAT) 3D are used for dust storm detection in Xinjiang because of the frequent 30-min observations with six bands. An analysis of the optical properties of dust and its quantitative relationship with dust storms in Xinjiang is presented for dust events in April 2014. The Aerosol Optical Depth (AOD) derived using six predefined aerosol types shows great potential to identify dust events. Cross validation between INSAT-3D retrieved AOD and MODIS AOD shows a high coefficient of determination (R2 = 0.92). Ground validation using AERONET (Aerosol Robotic Network) AOD also shows a good correlation with R2 of 0.77. We combined the apparent reflectance (top-of-atmospheric reflectance) of visible and shortwave infrared bands, brightness temperature of infrared bands and retrieved AOD into a new Enhanced Dust Index (EDI). EDI reveals not only dust extent but also the intensity. EDI performed very well in measuring the intensity of dust storms between 22 and 24 April 2014. A visual comparison between EDI and Feng Yun-2E (FY-2E) Infrared Difference Dust Index (IDDI) also shows a high level of similarity. A good linear correlation (R2 of 0.78) between EDI and visibility on the ground demonstrates good performance of EDI in estimating dust intensity. A simple threshold method was found to have a good performance in delineating the extent of the dust plumes but inadequate for providing information on dust plume intensity.

A Method for Retrieving Land Surface Reflectance Using MODIS Data

Surface reflectance retrieval is an important step in the data processing chain for the extraction of quantitative information in many applications. The aim of this paper is to develop a method for retrieving surface reflectance and aerosol optical depth simultaneously over both dark vegetated surfaces and bright land surfaces. After applying this method 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, with R-squared (R2) greater than 0.84 and root mean square error (RMSE) of 0.027 at band 1 (0.66 μm), 0.015 at band 3 (0.47 μm), and 0.017 at band 4 (0.55 μm). The R2 of MOD09 with ASD measured surface reflectance is around 0.60, and RMSE are 0.049 at band 1, 0.024 at band 3, and 0.036 at band 4 .

Multi-scale aerosol retrieval over land from satellite data and its application on haze monitoring

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.

Comparison of two methods for aerosol optical depth retrieval over North Africa from MSG/SEVIRI data

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 R2 between retrieved AOD from the LABITS algorithm and AERONET AOD is 0.80 and RMSE is 0.037.

High-Throughput Geocomputational Workflows in a Grid Environment

A grid-computing platform facilitates geocomputational workflow composition to process big geosciences data while fully using idle resources to accelerate processing speed. An experiment with aerosol optical depth retrieval from satellite data shows a 25 percent improvement in runtime over a single high-performance computer.

The analysis of the haze event in the North China plain in 2013

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 PM2.5 were analyzed and the concentration of PM2.5 was found up to 300 μg/m3 during the haze event.

China Collection 1.1: an aerosol optical depth dataset at 1km resolution over mainland China retrieved from satellite data

NASA’s Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors have been observing the Earth from polar orbit, from Terra since early 2000 and from Aqua since mid 2002. MODIS is uniquely suited for characterization of aerosols, combining broad swath size, multi-band spectral coverage and moderately high spatial resolution imaging. By using MODIS data, many algorithms have showed excellent competence at the aerosol distribution and properties retrieval. However, in China, many regions are not satisfied with the dark density pixel condition. In this paper, aerosol optical depth (AOD) datasets (China Collection 1.1) at 1 km resolutions have been derived from the MODIS data using the Synergetic Retrieval of Aerosol Properties (SRAP) method over mainland China for the period from August 2002 to now, comprising AODs at 470, 550, and 660 nm. We compared the China Collection 1.1 AOD datasets for 2010 with AERONET data. From those 2460 collocations, representing mutually cloud-free conditions, we find that 62% of China Collection 1.1 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 1.1 AOD datasets have a more complete coverage with fewer data gaps over the study region.