Lu She

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.

An atmospheric correction algorithm for FY3/MERSI data over land in China

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.

The inter-comparison of AATSR aerosol optical depth retrievals from various algorithms

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.

Dust Detection and Intensity Estimation Using Himawari-8/AHI Observation

In this study, simple dust detection and intensity estimation methods using Himawari-8 Advanced Himawari Imager (AHI) data are developed. Based on the differences of thermal radiation characteristics between dust and other typical objects, brightness temperature difference (BTD) among four channels (BT11–BT12, BT8–BT11, and BT3–BT11) are used together for dust detection. When considering the thermal radiation variation of dust particles over different land cover types, a dynamic threshold scheme for dust detection is adopted. An enhanced dust intensity index (EDII) is developed based on the reflectance of visible/near-infrared bands, BT of thermal-infrared bands, and aerosol optical depth (AOD), and is applied to the detected dust area. The AOD is retrieved using multiple temporal AHI observations by assuming little surface change in a short time period (i.e., 1–2 days) and proved with high accuracy using the Aerosol Robotic Network (AERONET) and cross-compared with MODIS AOD products. The dust detection results agree qualitatively with the dust locations that were revealed by AHI true color images. The results were also compared quantitatively with dust identification results from the AERONET AOD and Angstrom exponent, achieving a total dust detection accuracy of 84%. A good agreement is obtained between EDII and the visibility data from National Climatic Data Center ground measurements, with a correlation coefficient of 0.81, indicating the effectiveness of EDII in dust monitoring.

Ensemble of ESA/AATSR Aerosol Optical Depth Products Based on the Likelihood Estimate Method With Uncertainties

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.

Aerosol optical and physical properties over beijing

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 AOD440nm and 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.

Estimating ground-level PM 2.5 concentration in beijing using BP ANN model from satellite data

Particulate matters (PM) have substantial influences on environmental system, climate change and public health. Ground based PM2.5 concentration 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-PM2.5 retrieve model. The test R reached 0.83. This model is seasonally and regionally stable. The satellite AOD and ANN retrieved PM2.5 has the similar trend and distribution, and the trained model have practical as well as theoretical value.

Moon-based occultation observation for atmospheric phenomena

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.

Image fusion of MODIS AOD (collection 6) in China based on uncertainty

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.

SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm

The recently launched Chinese GaoFen-4 (GF4) satellite provides valuable information to obtain geophysical parameters describing conditions in the atmosphere and at the Earth’s surface. The surface reflectance is an important parameter for the estimation of other remote sensing parameters linked to the eco-environment, atmosphere environment and energy balance. One of the key issues to achieve atmospheric corrected surface reflectance is to precisely retrieve the aerosol optical properties, especially Aerosol Optical Depth (AOD). The retrieval of AOD and corresponding atmospheric correction procedure normally use the full radiative transfer calculation or Look-Up-Table (LUT) methods, which is very time-consuming. In this paper, a Simplified AtmospHeric correction AlgoRithm for gAofen data (SAHARA) is presented for the retrieval of AOD and corresponding atmospheric correction procedure. This paper is the first part of the algorithm, which describes the aerosol retrieval algorithm. In order to achieve high-accuracy analytical form for both LUT and surface parameterization, the MODIS Dark-Target (DT) aerosol types and Deep Blue (DB) similar surface parameterization have been proposed for GF4 data. Limited Gaofen observations (i.e., all that were available) have been tested and validated. The retrieval results agree quite well with MODIS Collection 6.0 aerosol product, with a correlation coefficient of R2 = 0.72. The comparison between GF4 derived AOD and Aerosol Robotic Network (AERONET) observations has a correlation coefficient of R2 = 0.86. The algorithm, after comprehensive validation, can be used as an operational running algorithm for creating aerosol product from the Chinese GF4 satellite.