Effect of the Aerosol Model Assumption on the Atmospheric Correction over Land: Case Studies with CHRIS/PROBA Hyperspectral Images over Benelux

Tirelli, Cecilia; Curci, Gabriele; Manzo, Ciro; Tuccella, Paolo; Bassani, Cristiana

doi:10.3390/rs70708391

Cited by 12 publications

(9 citation statements)

References 57 publications

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“…The OLI@CRI algorithm was developed for the atmospheric correction processing of the OLI images specifically over land following the procedure described in [38,48] adapting the vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) radiative transfer model. This model is the improved version of the 6S used for the new generation of atmospheric correction algorithms, which was successfully adopted by [52].…”

Section: Oli@cri Atmospheric Correction Algorithmmentioning

confidence: 99%

“…The algorithm was implemented with the open-source GNU data language (GDL) [53] and works with the OLI image available in L1T format. As with the previous algorithms [38,39,48], OLI@CRI can be applied by considering the standard aerosol basic components (dust-like, water-soluble, oceanic and soot [25]) provided by default by the 6SV model or the microphysical properties of the aerosol provided by an AERONET station as inverse products. The OLI@CRI algorithm overcomes the limits of software such as Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) [14], where a continental aerosol model is assumed for atmospheric correction processing and an over-correction of the surface reflectance is expected.…”

Section: Oli@cri Atmospheric Correction Algorithmmentioning

confidence: 99%

“…The principal aerosol parameter of the radiative transfer model is the optical thickness of the aerosol at 550 nm, τ 550 , with robust and well-known retrieval algorithms for all of the above-mentioned sensors [24,[34][35][36][37]. Other crucial parameters to simulate the radiative effects of the aerosol are the microphysical properties of the aerosol: the size distribution, dV(r)/dln(r), and the real, Re λ and the imaginary, Ri λ , parts of the refractive index, integrated along the atmospheric column [38,39]. Unlike τ 550 , there is still no robust algorithm for the retrieval of the microphysical properties of the aerosol from space.…”

Section: Introductionmentioning

confidence: 99%

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Effect of the Aerosol Type Selection for the Retrieval of Shortwave Ground Net Radiation: Case Study Using Landsat 8 Data

et al. 2016

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This paper discusses the aerosol radiative effects involved in the accuracy of shortwave net radiation, R n.sw , with sw ∈ (400-900) nm, retrieved by the Operational Land Imager (OLI), the new generation sensor of the Landsat mission. Net radiation is a key parameter for the energy exchange between the land and atmosphere; thus, R n.sw retrieval from space is under investigation by exploiting the increased spatial resolution of the visible and near-infrared OLI data. We adopted the latest version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmospheric radiative transfer model implemented in the atmospheric correction algorithm (OLI Atmospherically-Corrected Reflectance Imagery (OLI@CRI)) developed specifically for OLI data. The values of R n.sw were obtained by varying the microphysical properties of the aerosol during the OLI@CRI retrieval of both the OLI surface reflectance, ρ oli pxl , and the incoming solar irradiance at the surface. The analysis of the aerosol effects on the R n.sw was carried out on a spectrally-homogeneous desert area located in the southwestern Nile Delta. The results reveal that the R n.sw available for energy exchange between the land and atmosphere reduces the accuracy (NRMSE 14%) when the local aerosol microphysical properties are not considered during the processing of space data. Consequently, these findings suggest that the aerosol type should be considered for variables retrieved by satellite observations concerning the energy exchange in the natural ecosystems, such as Photosynthetically-Active Radiation (PAR). This will also improve the accuracy of land monitoring and of solar energy for power generation when space data are used.

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Section: Oli@cri Atmospheric Correction Algorithmmentioning

confidence: 99%

Section: Oli@cri Atmospheric Correction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of the Aerosol Type Selection for the Retrieval of Shortwave Ground Net Radiation: Case Study Using Landsat 8 Data

et al. 2016

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“…Because the microphysical properties of aerosol vary significantly in space and with time, aerosol models are predefined through cluster analysis [11,12]. The assumed aerosol models generally have an impact on aerosol retrieval from ground measurements [52] and on atmospheric correction of satellite images [53]. With regard to MODIS aerosol product, aerosol models include continental (Type 1), moderate absorption fine (Type 2), strong absorption fine (Type 3), weak absorption fine (Type 4), and dust coarse (Type 5).…”

Section: Examination Of the Continental Aerosol Model For Aerosol Retmentioning

confidence: 99%

Exploiting TERRA-AQUA MODIS Relationship in the Reflective Solar Bands for Aerosol Retrieval

Fan

Liu

2016

Remote Sensing

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Satellite remote sensing has been providing aerosol data with ever-increasing accuracy, representative of the MODerate-resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and Deep Blue (DB) aerosol retrievals. These retrievals are generally performed over spectrally dark objects and therefore may struggle over bright surfaces. This study proposed an analytical TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. For the relationship development, the bidirectional reflectance distribution function (BRDF) effects were adjusted using reflectance ratios in the MODIS 2.13 µm band and the path radiance was approximated as an analytical function of aerosol optical thickness (AOT) and scattering phase function. Comparisons with MODIS observation data, MODIS AOT data, and sun photometer measurements demonstrate the validity of the proposed relationship for aerosol retrieval. The synergetic TERRA-AQUA MODIS retrievals are highly correlated with the ground measured AOT at TERRA MODIS overpass time (R 2 = 0.617; RMSE = 0.043) and AQUA overpass time (R 2 = 0.737; RMSE = 0.036). Compared to our retrievals, both the MODIS DT and DB retrievals are subject to severe underestimation. Sensitivity analyses reveal that the proposed method may perform better over non-vegetated than vegetated surfaces, which can offer a complement to MODIS operational algorithms. In an analytical form, the proposed method also has advantages in computational efficiency, and therefore can be employed for fine-scale (relative to operational 10 km MODIS product) MODIS aerosol retrieval. Overall, this study provides insight into aerosol retrievals and other applications regarding TERRA-AQUA MODIS data.

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“…Currently, the determination of aerosol types, including size distribution, refractive index, and aerosol profile, complicates the problem, especially for the aerosol types with strong absorption properties [1,2]. The cloud twilight zone effect [3] is also a challenging task in order to obtain accurate aerosol properties [4].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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Abstract: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 R 2 = 0.72. The comparison between GF4 derived AOD and Aerosol Robotic Network (AERONET) observations has a correlation coefficient of R 2 = 0.86. The algorithm, after comprehensive validation, can be used as an operational running algorithm for creating aerosol product from the Chinese GF4 satellite.

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Effect of the Aerosol Model Assumption on the Atmospheric Correction over Land: Case Studies with CHRIS/PROBA Hyperspectral Images over Benelux

Cited by 12 publications

References 57 publications

Effect of the Aerosol Type Selection for the Retrieval of Shortwave Ground Net Radiation: Case Study Using Landsat 8 Data

Effect of the Aerosol Type Selection for the Retrieval of Shortwave Ground Net Radiation: Case Study Using Landsat 8 Data

Exploiting TERRA-AQUA MODIS Relationship in the Reflective Solar Bands for Aerosol Retrieval

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

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