The bidirectional reflectance distribution function (BRDF) is a physical quantity that represents the change of surface reflection with the Sun and the direction of observation, which is of great significance to the study of surface anisotropic reflection characteristics. In this paper, based on MODIS (Moderate Resolution Imaging Spectroradiometer) BRDF model parameters products (MCD43A1), we utilize the Ross-Li model to simulate the surface reflectance of the four land surface types in North China: vegetation, bare soil, cropland, and urban, and comparatively analyze the seasonal variation of their surface anisotropic reflection characteristics. Therefore, this study can provide reliable scientific basis for improving land surface process model, promoting surface-atmosphere interaction and global climate change research. The results show that: (1) The backscattering of the four land surface types is greater than the forward scattering, and the larger the scattering angle is, the larger the bidirectional reflectance will be. The distribution trend of bidirectional reflectance of different surface types is quite different in different bands and seasons. (2) The bidirectional reflectance of the four land surface types varies with the wavelength roughly the same in spring, summer, and autumn. In winter, due to snow covering the ground, the bidirectional reflectance of vegetation, cropland, and urban is higher in visible and near-infrared bands. Due to the fixed simulation angle, the distribution trend of the bidirectional reflectance of bare soil in four seasons is multipeak in the multi-band range.
GaoFen-4 (GF-4) is China’s first optical remote sensing geostationary satellite, which observes the Earth’s surface every 20 s. GF-4 has a gaze camera with a ground resolution of 50 m in the visible spectral bands that are sensitive to aerosol optical depth (AOD). AOD retrieval was completed using initial prior surface reflectance in ground-atmosphere decoupling. Surface reflectance was then updated using satellite measurements. The algorithm used in this study was based on two time-adjacent images of GF-4 (which have a constant viewing angle in the same area) and on the following two assumptions: (1) AOD varies quickly with time, but slowly with location and (2) surface reflectance varies quickly with location, but slowly with time. AOD retrieval was then accomplished using a lookup table strategy. The data from June 2016, in the North China Plain were selected for the AOD retrieval test. GF-4-derived AOD was validated using ground measurements from the aerosol robotic network and Sun–Sky radiometer network with a correlation coefficient of R = 0.794. The derived results agreed reasonably well with the moderate resolution imaging spectroradiometer collection 6.0 aerosol product, with a correlation coefficient of R = 0.893. The atmospheric distribution and changes in some parts of the North China Plain were analyzed using the aerosol products of GF-4. The results showed that it is feasible to use the time-series imaging method to conduct high spatiotemporal resolution aerosol inversion using GF-4’s data, which can be used for detecting changes in air pollution.
The remote sensing image is usually polluted by atmosphere components especially like aerosol particles. For the quantitative remote sensing applications, the radiative transfer model based atmospheric correction is used to get the reflectance with decoupling the atmosphere and surface by consuming a long computational time. The parallel computing is a solution method for the temporal acceleration. The parallel strategy which uses multi-CPU to work simultaneously is designed to do atmospheric correction for a multispectral remote sensing image. The parallel framework’s flow and the main parallel body of atmospheric correction are described. Then, the multispectral remote sensing image of the Chinese Gaofen-2 satellite is used to test the acceleration efficiency. When the CPU number is increasing from 1 to 8, the computational speed is also increasing. The biggest acceleration rate is 6.5. Under the 8 CPU working mode, the whole image atmospheric correction costs 4 minutes.
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 Arctic region is especially sensitive to climate change; meanwhile atmospheric aerosol is one of the largest
uncertainties geophysical factors in climate modeling, calling for aerosol database in Arctic regions with sufficient
temporal and spatial coverage. Satellite remote sensing is the best approach to obtain the aerosol information over the
Arctic region, for which appropriate aerosol models are required. In this study, five distinctive aerosol models are
classified using cluster analysis from Level 1.5 data collected in Aerosol Robotic Network (AERONET) sites. More
than 14,000 cases are collected over 17 AERONET sites in Arctic region from 1995 to 2012. For each case, 23
parameters, representing either optical properties or size distribution patterns are input into cluster analysis after
abnormal records and outliers are discarded and data of different attributes are standardized. Averaged properties in
each cluster are obtained then and we extensively study the absorptive, scattering, and size distributive characteristics
along with the temporal and spatial distributions for each model. Aerosol optical properties are carried out for each
model using Second Simulation of a Satellite Signal in the Solar Spectrum - Vector (6SV) code and we conclude that
our models are representative of the major aerosol properties in the Arctic region and can be utilized in the retrieval
algorithms designed for this area.
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.
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