Leiku Yang

Improved Aerosol Optical Depth and Ångstrom Exponent Retrieval Over Land From MODIS Based on the Non-Lambertian Forward Model

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.

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 .

Satellite aerosol retrieval using dark target algorithm by coupling BRDF effect over AERONET site

For most satellite aerosol retrieval algorithms even for multi-angle instrument, the simple forward model (FM) based on Lambertian surface assumption is employed to simulate top of the atmosphere (TOA) spectral reflectance, which does not fully consider the surface bi-directional reflectance functions (BRDF) effect. The approximating forward model largely simplifies the radiative transfer model, reduces the size of the look-up tables, and creates faster algorithm. At the same time, it creates systematic biases in the aerosol optical depth (AOD) retrieval. AOD product from the Moderate Resolution Imaging Spectro-radiometer (MODIS) data based on the dark target algorithm is considered as one of accurate satellite aerosol products at present. Though it performs well at a global scale, uncertainties are still found on regional in a lot of studies. The Lambertian surface assumpiton employed in the retrieving algorithm may be one of the uncertain factors. In this study, we first use radiative transfer simulations over dark target to assess the uncertainty to what extent is introduced from the Lambertian surface assumption. The result shows that the uncertainties of AOD retrieval could reach up to ±0.3. Then the Lambertian FM (L_FM) and the BRDF FM (BRDF_FM) are respectively employed in AOD retrieval using dark target algorithm from MODARNSS (MODIS/Terra and MODIS/Aqua Atmosphere Aeronet Subsetting Product) data over Beijing AERONET site. The validation shows that accuracy in AOD retrieval has been improved by employing the BRDF_FM accounting for the surface BRDF effect, the regression slope of scatter plots with retrieved AOD against AEROENET AOD increases from 0.7163 (for L_FM) to 0.7776 (for BRDF_FM) and the intercept decreases from 0.0778 (for L_FM) to 0.0627 (for BRDF_FM).

The improved synergetic retrieval of aerosol properties algorithm

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 and BRDF/albedo inversion over land from MSG/SEVIRI data

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 R2 is 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 aerosols local spatio-temporal variation from the geostationary satellite data.

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.

An improved method for the retrieval of surface reflectance from EOS/MODIS data

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.

Air qulity analysis based on PM 2.5 distribution over China

Since the year of 2011, PM2.5 have 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 PM2.5 over China, These findings indicate a positive annual variation trend before Beijing 2008 Olympic Games, however the air qulity of Beijing still needs improving.

Aerosol optical depth and surface reflectance retrieval over land using geostationary satellite data

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 AODs spatial consistence and Rs 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 retrival of North China using NOAA AVHRR data

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.