• Ensemble of ESA/AATSR aerosol optical depth products based on the likelihood estimate method with uncertainties

      Xie, Yanqing; Xue, Yong; Che, Yahui; Guang, Jie; Mei, Linlu; Voorhis, Dave; Fan, Cheng; She, Lu; Xu, Hui; University of Chinese Academy of Sciences; et al. (IEEE, 2017-10-20)
      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.
    • Internet-of-Things-based smart cities: Recent advances and challenges.

      Mehmood, Yasir; Ahmad, Farhan; Yaqoob, Ibrar; Adnane, Asma; Imran, Muhammad; Guizani, Sghaier; University of Bremen; University of Malaya; University of Derby; King Saud University; et al. (IEEE, 2017-09-08)
      The Internet of Things (IoT) is a revolutionary communication paradigm that aims to bring forth an invisible and innovative framework to connect a plethora of digital devices with the Internet. Thus, it intends to make the Internet more immersive and pervasive [1]. The emerging IoT market is continously gaining momentum as operators, vendors, manufacturers, and enterprises begin to recognize the opportunities it offers. According to the latest IDC forecast.1 https://www.telecompaper.com/news/global-iot-market-to-reach-usd-17-tln-in-2020-idc-1085269, accessed October 20, 2016. the worldwide IoT market will reach US 1.7trillionin2020upfromUS655.8 billion in 2014 with a compound annual growth rate of 16.9 percent. The devices alone are expected to represent 31.8 percent of the total worldwide IoT market in 2020. This greater percentage of the revenue in 2020 is expected through building IoT platforms, application softwares, and service-related offerings.
    • Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe

      Xue, Yong; He, Xingwei; de Leeuw, Gerrit; Mei, Linlu; Che, Yahui; Rippin, Wayne; Guang, Jie; Hu, Yincui; University of Chinese Academy of Sciences; University of Derby; et al. (Elsevier, 2017-07-06)
      An algorithm for the retrieval of the aerosol optical depth over land (ADL) using radiances at the top of the atmosphere (TOA) measured by the Advanced Very High Resolution Radiometer (AVHRR) is proposed. AVHRR is the only satellite sensor providing nearly continuous global coverage since June 1979, which could generate the longest aerosol climate data records currently available from operational satellites. In the implementation of the ADL algorithm, an analytical model is used which couples an atmospheric radiative transfer model and a land surface reflectance parameterization. The radiation field can be separated into three parts: direct radiance, single-scattered radiance, and multiple-scattered. Each of these parts is individually parameterized. To obtain the surface reflectance in an automatic retrieval procedure over land for AVHRR, the aerosol scattering effect at 3.75 μm was assumed to be negligible and relationships between the surface reflectances at 0.64 μm and 3.75 μm were evaluated for different surface types and the authors propose to use these to obtain the surface reflectance at the shorter wavelength. The 0.64 μm surface reflectance was then used in a radiative transfer model to compute AOD at that wavelength using six different aerosol types, where optimal estimation (OE) theory is applied to minimize the difference between modeled and measured radiances. The ADL algorithm is applied to re-calibrated Level 1B radiances from the AVHRRs on-board the TIROS-N and the Metop-B satellites to retrieve the AOD over North China and Central Europe. The results show that the AOD retrieved from these two instruments are in agreement with co-located AOD values from ground-based reference networks. Over North China, using AERONET sites, 58% of the ADL AOD values are within an expected error (EE) range of ±(0.05 + 20%) and 53% are within the EE range of ±(0.05 + 15%). For GAW-PFR (World Meteorological Organization, WMO, Global Atmosphere Watch, GAW) sites, part of the European ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure) sites, 79% of the ADL AOD values are within the EE range of ±(0.05 + 20%) and 75% are within the EE range of ±(0.05 + 15%). Not surprisingly, the agreement is better over Europe with generally lower AOD values. An additional cross comparison of the AOD results with MODIS (MODerate-resolution Imaging Spectroradiometer) DeepBlue aerosol products shows that the spatial distributions of the two AOD datasets are similar, but with generally lower values for ADL and lower coverage. The temporal variation of the annual mean AOD over selected AERONET sites shows that ADL values are generally between 0.2 and 0.5 over North-Eastern China and trace the MODIS and AERONET data for the overlapping years quite well.
    • SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm.

      She, Lu; Mei, Linlu; Xue, Yong; Che, Yahui; Guang, Jie; University of Derby; Chinese Academy of Sciences; University of Bremen (2017-03-09)
      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.
    • Validation of aerosol products from AATSR and MERIS/AATSR synergy algorithms—Part 1: Global Evaluation.

      Che, Yahui; Mei, Linlu; Xue, Yong; Guang, Jie; She, Lu; Li, Ying; University of Derby; Chinese Academy of Sciences; University of Chinese Academy of Sciences; University of Bremen; et al. (MPDI, 2018-09-06)
      The European Space Agency’s (ESA’s) Aerosol Climate Change Initiative (CCI) project intends to exploit the robust, long-term, global aerosol optical thickness (AOT) dataset from Europe’s satellite observations. Newly released Swansea University (SU) aerosol products include AATSR retrieval and synergy between AATSR and MERIS with a spatial resolution of 10 km. In this study, both AATSR retrieval (SU/AATSR) and AATSR/MERIS synergy retrieval (SU/synergy) products are validated globally using Aerosol Robotic Network (AERONET) observations for March, June, September, and December 2008, as suggested by the Aerosol-CCI project. The analysis includes the impacts of cloud screening, surface parameterization, and aerosol type selections for two products under different surface and atmospheric conditions. The comparison between SU/AATSR and SU/synergy shows very accurate and consistent global patterns. The global evaluation using AERONET shows that the SU/AATSR product exhibits slightly better agreement with AERONET than the SU/synergy product. SU/synergy retrieval overestimates AOT for all surface and aerosol conditions. SU/AATSR data is much more stable and has better quality; it slightly underestimates fine-mode dominated and absorbing AOTs yet slightly overestimates coarse-mode dominated and non-absorbing AOTs.