Inversion of Aerosol Optical Depth Based on the CCD and IRS Sensors on the HJ-1 Satellites

Zhang, Yang; Liu, Zhihong; Wang, Yongqian; Ye, Zhixiang; Leng, Lu

doi:10.3390/rs6098760

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…HJ-1B carries two charge-coupled device (CCD) cameras and an infrared camera sensor (IRS). The sensor parameters are shown in Table 1 [17,18]. The source data used in this paper are IRS infrared data, i.e., the Near Infrared band (NIR, band 5), Shortwave Infrared band (SWIR, band 6), MWIR band (band 7) and LWIR band (band 8).…”

Section: Hj-1b Satellite Datamentioning

confidence: 99%

A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data

et al. 2016

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Abstract:Fire detection based on multi-temporal remote sensing data is an active research field. However, multi-temporal detection processes are usually complicated because of the spatial and temporal variability of remote sensing imagery. This paper presents a spatio-temporal model (STM) based forest fire detection method that uses multiple images of the inspected scene. In STM, the strong correlation between an inspected pixel and its neighboring pixels is considered, which can mitigate adverse impacts of spatial heterogeneity on background intensity predictions. The integration of spatial contextual information and temporal information makes it a more robust model for anomaly detection. The proposed algorithm was applied to a forest fire in 2009 in the Yinanhe forest, Heilongjiang province, China, using two-month HJ-1B infrared camera sensor (IRS) images. A comparison of detection results demonstrate that the proposed algorithm described in this paper are useful to represent the spatio-temporal information contained in multi-temporal remotely sensed data, and the STM detection method can be used to obtain a higher detection accuracy than the optimized contextual algorithm.

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Section: Hj-1b Satellite Datamentioning

confidence: 99%

A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data

et al. 2016

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“…The HJ-1B satellite was launched at 11:25 a.m. on September 6, 2008, which carries two CCD cameras and one IRS camera (Table 1). With a high spatial resolution of 30 m and a two day revisit cycle (jointly used with the HJ-1A satellite), 28 25,2013) were chosen. To perform a landscape analysis, this study took a subset of a 50 Â 50 km sample plot, which was carefully selected to represent the city landscape structure (Fig.…”

Section: Study Area and Data Collectionmentioning

confidence: 99%

Exploring the effects of landscape structure on aerosol optical depth (AOD) patterns using GIS and HJ-1B images

Fang

Tan

et al. 2016

Environ. Sci.: Processes Impacts

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A GIS approach and HJ-1B images were employed to determine the effect of landscape structure on aerosol optical depth (AOD) patterns. Landscape metrics, fractal analysis and contribution analysis were proposed to quantitatively illustrate the impact of land use on AOD patterns. The high correlation between the mean AOD and landscape metrics indicates that both the landscape composition and spatial structure affect the AOD pattern. Additionally, the fractal analysis demonstrated that the densities of built-up areas and bare land decreased from the high AOD centers to the outer boundary, but those of water and forest increased. These results reveal that the built-up area is the main positive contributor to air pollution, followed by bare land. Although bare land had a high AOD, it made a limited contribution to regional air pollution due to its small spatial extent. The contribution analysis further elucidated that built-up areas and bare land can increase air pollution more strongly in spring than in autumn, whereas forest and water have a completely opposite effect. Based on fractal and contribution analyses, the different effects of cropland are ascribed to the greater vegetation coverage from farming activity in spring than in autumn. The opposite effect of cropland on air pollution reveals that green coverage and human activity also influence AOD patterns. Given that serious concerns have been raised regarding the effects of built-up areas, bare land and agricultural air pollutant emissions, this study will add fundamental knowledge of the understanding of the key factors influencing urban air quality.

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“…The terms ρ 0 pµ s , µ v , ϕq, Tpµ s q, Tpµ v q and S are functions of the total AOD, which can be extracted from the radiative transfer code. This model can also be applied to many other satellite platforms besides MODIS, such as PARASOL and HJ-1 [19,20]. For the polarization measurement, the partially-polarized light is assumed to be linearly polarized, and the polarized radiance can be expressed as…”

Section: Retrieval Methodsmentioning

confidence: 99%

Retrieval of Aerosol Fine-Mode Fraction from Intensity and Polarization Measurements by PARASOL over East Asia

Zhang

Qie

et al. 2016

Remote Sensing

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Abstract:The fine-mode fraction (FMF) of aerosol optical depth (AOD) is a key optical parameter that represents the proportion of fine particles relative to total aerosols in the atmosphere. However, in comparison to ground-based measurements, the FMF is still difficult to retrieve from satellite observations, as attempted by a Moderate-resolution Imaging Spectroradiometer (MODIS) algorithm. In this paper, we introduce the retrieval of FMF based on Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) data. This method takes advantage of the coincident multi-angle intensity and polarization measurements from a single satellite platform. In our method, we use intensity measurements to retrieve the total AOD and polarization measurements to retrieve the fine-mode AOD. The FMF is then calculated as the ratio of the retrieved fine-mode AOD to the total AOD. The important processes in our method include the estimation of the surface intensity and polarized reflectance by using two semi-empirical models, and the building of two sets of aerosol retrieval lookup tables for the intensity and polarized measurements via the 6SV radiative transfer code. We apply this method to East Asia, and comparisons of the retrieved FMFs for the Beijing, Xianghe and Seoul_SNU sites with those of the Aerosol Robotic Network (AERONET) ground-based observations produce correlation coefficients (R 2 ) of 0.838, 0.818, and 0.877, respectively. However, the comparison results are relatively poor (R 2 = 0.537) in low-AOD areas, such as the Osaka site, due to the low signal-to-noise ratio of the satellite observations.

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Inversion of Aerosol Optical Depth Based on the CCD and IRS Sensors on the HJ-1 Satellites

Cited by 11 publications

References 18 publications

A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data

A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data

Exploring the effects of landscape structure on aerosol optical depth (AOD) patterns using GIS and HJ-1B images

Retrieval of Aerosol Fine-Mode Fraction from Intensity and Polarization Measurements by PARASOL over East Asia

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