Simultaneous determination of aerosol optical thickness and surface reflectance using ASTER visible to near-infrared data over land

An innovative method for the determination of aerosol optical thickness (AOT) and surface reflectance for operational use of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible to near-infrared data is presented. This method is designed to obtain the atmospheric parameters needed in the correction of the image. This method is based on a simplified radiative transfer equation describing the relation between the ground surface reflectance, AOT and top-of-atmosphere reflectance. By exploiting the ASTER dual-angle view capabilities in band 3N (Nadir) and band 3B (Backwards), surface reflectance and AOT can be retrieved synchronously. Thus, it solves the problem of separating atmospheric radiance from the transmitted radiance of the surface to some extent. After applying this new atmospheric correction method to three areas of ASTER images, Beijing urban city, the Heihe River Basin and Hong Kong of China, ASTER surface reflectance products (AST07) were obtained. AOT values from in situ measurements of CIMEL Electronique 318 Sun Photometers or AERONET (AErosol RObotic NETwork) and surface reflectance in situ measured using an Analytical Spectral Device (ASD) Field Spec spectral radiometer are used for validation. AOT derived from the new method is consistent with in situ station measurements from CIMEL Electronique 318 Sun Photometer and level 2.0 data from AERONET, with correlation coefficient (R ²) of 0.98 and root mean square error of 0.05, whereas Multi-angle Imaging Spectroradiometer AOT products underestimate AERONET AOT and Moderate-Resolution Imaging Spectroradiometer AOT products overestimate AERONET AOT in these regions. More encouraging is the comparison between the corrected surface reflectance, AST07 and ASD measurements. Root mean square error of AST07 and retrieved surface reflectance are as follows: band 1 (556 nm) = 0.04 and 0.05; band 2 (661 nm) = 0.036 and 0.035; band 3 (807 nm) = 0.056 and 0.038, which suggests that compared with AST07 in bands 2 and 3, retrieved surface reflectance has better agreement with measured reflectance from ASD.     

Hybrid atmospheric correction algorithms and evaluation on VNIR/SWIR Hyperion satellite data for soil organic carbon prediction

ABSTRACT

Visible near-infrared and shortwave infrared data acquired by spaceborne sensors contain atmospheric noise, along with target reflectance that may affect its end applications, e.g. geological, vegetation, soil surface studies, etc. Several atmospheric correction algorithms have been already developed to remove unwanted atmospheric components of a spectral signature of Earth targets obtained from airborne/spaceborne hyperspectral image. In spite of this, choosing of an appropriate atmospheric correction algorithm is an ongoing research. In this study, two hybrid atmospheric correction (HAC) algorithms incorporating a modified empirical line (EL_m) method were proposed. The first HAC model (named HAC_1) combines (i) a radiative transfer (RT) model based on the concepts of RT equations, which uses real-time in situ atmospheric and climatic data, and (ii) an EL_m technique. The second one (named HAC_2) combines (i) the well-known ATmospheric CORrection (ATCOR) model and (ii) an EL_m technique. Both HAC algorithms and their component single atmospheric correction algorithms (ATCOR, RT, and EL_m) were applied to radiance data acquired by Hyperion satellite sensor over study sites in Australia. The performances of both HAC algorithms were analysed in two ways. First, the Hyperion reflectances obtained by five atmospheric correction algorithms were analysed and compared using spectral metrics. Second, the performance of each atmospheric correction algorithm was analysed for prediction of soil organic carbon (SOC) using Hyperion reflectances obtained from atmospheric correction algorithms. The prediction model of SOC was built using partial least square regression model. The results show that (i) both the hybrid models produce a good spectrum with lower Spectral Angle Mapper and Spectral Information Divergence values and (ii) both hybrid algorithms provided better SOC prediction accuracy, in terms of coefficient of determination (R²), residual prediction deviation (RPD), and ratio of performance to interquartile (RPIQ), with R² ≥ 0.75, RPD ≥ 2, and RPIQ ≥ 2.58 than single algorithms. HAC algorithms, developed using EL_m technique, may be recommended for atmospheric correction of Hyperion radiance data, when archived Hyperion reflectance data have to be used for SOC prediction mapping.     

Atmospheric correction of ENVISAT/MERIS data over case II waters: the use of black pixel assumption in oxygen and water vapour absorption bands

The Medium Resolution Imaging Spectrometer (MERIS) sensor, with its good physical design, can provide excellent data for water colour monitoring. However, owing to the shortage of shortwave-infrared (SWIR) bands, the traditional near-infrared (NIR)–SWIR algorithm for atmospheric correction in inland turbid case II waters cannot be extended to the MERIS data directly, which limits its applications. In this study, we developed a modified NIR black pixel method for atmospheric correction of MERIS data in inland turbid case II waters. In the new method, two special NIR bands provided by MERIS data, an oxygen absorption band (O₂ A-band, 761 nm) and a water vapour absorption band (vapour A-band, 900 nm), were introduced to keep the assumption of zero water-leaving reflectance valid according to the fact that both atmospheric transmittance and water-leaving reflectance are very small at these two bands. After addressing the aerosol wavelength dependence for the cases of single- and multiple-scattering conditions, we further validated the new method in two case lakes (Lake Dianchi in China and Lake Kasumigaura in Japan) by comparing the results with in situ measurements and other atmospheric correction algorithms, including Self-Contained Atmospheric Parameters Estimation for MERIS data (SCAPE-M) and the Basic ERS (European Remote Sensing Satellite) & ENVISAT (Environmental Satellite) (A)ATSR ((Advanced) Along-Track Scanning Radiometer) and MERIS (BEAM) processor. We found that the proposed method had acceptable accuracy in the bands within 560–754 nm (MERIS bands 5–10) (average absolute deviation (AAD) = 0.0081, average deviation (AD) = 0.0074), which are commonly used in the estimation models of chlorophyll-a (chl-a) concentrations. In addition, the performance of the new method was superior to that of the BEAM processor and only slightly worse than that of SCAPE-M in these bands. Considering its acceptable accuracy and simplicity both in principle and at implementation compared with the SCAPE-M method, the new method provides an option for atmospheric correction of MERIS data in inland turbid case II waters with applications aiming for chl-a estimation.     

Mapping inland water carbon content with Landsat 8 data

Landsat 8 is the first Earth observation satellite with sufficient radiometric and spatial resolution to allow global mapping of lake CDOM and DOC (coloured dissolved organic matter and dissolved organic carbon, respectively) content. Landsat 8 is a multispectral sensor however, the number of potentially usable band ratios, or more sophisticated indices, is limited. In order to test the suitability of the ratio most commonly used in lake carbon content mapping, the green–red band ratio, we carried out fieldwork in Estonian and Brazilian lakes. Several atmospheric correction methods were also tested in order to use image data where the image-to-image variability due to illumination conditions would be minimal. None of the four atmospheric correction methods tested, produced reflectance spectra that matched well with in situ measured reflectance. Nevertheless, the green–red band ratio calculated from the reflectance data was in correlation with measured CDOM values. In situ data show that there is a strong correlation between CDOM and DOC concentrations in Estonian and Brazilian lakes. Thus, mapping the global CDOM and DOC content from Landsat 8 is plausible but more data from different parts of the world are needed before decisions can be made about the accuracy of such global estimation.     

Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data

Imaging spectrometers operating in the solar spectrum measure the upwelling reflected solar radiation, and are an important tool in the bio/geochemical characterization of the Earth system. Surface reflectance is usually the starting point for the retrieval of biophysical parameters from remote measurements. Reliable radiometric and spectral calibrations and accurate atmospheric correction are mandatory in the interpretation of the surface reflectance. A complete surface reflectance retrieval scheme specifically designed for ultra-fine spectral resolution (bandwidth from 10 to 2 nm) and spatial resolution (pixel size less than 10 m) imaging spectrometers is presented in this work. The assessment of the spectral calibration is coupled to the removal of the atmospheric distortion so that maps of surface reflectance are derived, as well as columnar water vapor (CWV) maps, estimations of aerosol optical thickness (AOT) and updated sensor gain coefficients and spectral calibration. Radiative transfer calculations are performed by an optimized version of the MODTRAN4 code, which is run before processing each image. The method is tested against Compact Airborne Spectrographic Imager (CASI) 1500 images acquired during the ESA SENtinel-2 and FLuorescence EXperiment (SEN2FLEX) campaigns held in the Barrax (La Mancha, Spain) study site during June and July 2005. A peak-to-peak spectral shift variation of up to 2.3 nm is detected in CASI. Concerning atmospheric products and surface reflectance retrievals, an extensive validation is performed using ground-based measurements. A good correlation between ground measurements and CASI-derived AOT and CWV is found, with a Pearson correlation coefficient r² up to 0.71 and 0.74, respectively. The subsequent surface reflectance retrievals also hold a good correspondence with ground-based measurements.     

Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment

Precise atmospheric correction is important for applications where small differences in surface reflectance (SR) are significant, such as biomass estimation, crop phenology, and retrieval of water quality parameters. It also enables direct comparison between different image dates and different sensors. As a precursor to monitoring different parameters of water quality around the coastline of Hong Kong using medium-resolution sensors Landsat TM/ETM, and HJ-1A/B, this study evaluated the performance of five atmospheric correction methods. The estimated SR of the first four reflective bands of Landsat 7 ETM+ and of the identical bands of the HJ-1A/B satellites was compared with in situ multispectral radiometer (MSR) SR measurements over sand, artificial turf, grass, and water surfaces for the five atmospheric correction methods – second simulation of the satellite signal in the solar spectrum (6S), fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH), atmospheric correction (ATCOR), dark object subtraction (DOS), and the empirical line method (ELM). Among the five methods, 6S was observed to be consistently more precise for SR estimation, with significantly less difference from the in-situ-measured SR, especially over lower reflective water surfaces. Of the two image-based methods, DOS performed well over the darker surfaces of water and artificial turf, although still inferior to 6S, while ELM worked well for grass sites as compared to the DOS and equalled the good performance of 6S over the high reflective sand surfaces.

The study also evaluated the new standard Landsat SR product Landsat ecosystem disturbance adaptive processing system (LEDAPS) using the in situ measured SR data for the three land surface types – sand, artificial turf, and grass. For the highly and moderately reflecting bright sand and artificial turf, LEDAPS performed poorly, while for the darker grass site it performed better, although still inferior to 6S and ELM methods. This is probably due to the variable aerosol types and atmospheric conditions of Hong Kong, as LEDAPS was mainly compiled with reference to larger continental landmass areas.     

Validation of chlorophyll-α concentration maps from Aqua MODIS over the Gulf of Gabes (Tunisia): comparison between MedOC3 and OC3M bio-optical algorithms

Few studies have focused on the use of ocean colour remote sensors in the Gulf of Gabes (southeastern Tunisia). This work is the first study to evaluate the ocean colour chlorophyll-a product in this area. Chlorophyll-a concentrations were measured during oceanographic cruises performed off the Gulf of Gabes. These measurements were used to validate satellite data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite. First, two atmospheric correction procedures (standard and shortwave infrared) were tested to derive the remote-sensing reflectance, and then a comparison between two bio-optical (OC3M and MedOC3) algorithms were realized using the in situ measurements. Both atmospheric correction procedures gave similar results when applied to our study area indicating that most pixels were non-turbid. The comparison between bio-optical algorithms shows that using the regional bio-optical algorithm MedOC3 improves chlorophyll-a estimation in the Gulf of Gabes for the low values of this parameter.     

Atmospheric correction algorithm for MERIS above case‐2 waters

The development and validation of an atmospheric correction algorithm designed for the Medium Resolution Imaging Spectrometer (MERIS) with special emphasis on case‐2 waters is described. The algorithm is based on inverse modelling of radiative transfer (RT) calculations using artificial neural network (ANN) techniques. The presented correction scheme is implemented as a direct inversion of spectral top‐of‐atmosphere (TOA) radiances into spectral remote sensing reflectances at the bottom‐of‐atmosphere (BOA), with additional output of the aerosol optical thickness (AOT) at four wavelengths for validation purposes. The inversion algorithm was applied to 13 MERIS Level1b data tracks of 2002–2003, covering the optically complex waters of the North and Baltic Sea region. A validation of the retrieved AOTs was performed with coincident in situ automatic sun–sky scanning radiometer measurements of the Aerosol Robotic Network (AERONET) from Helgoland Island located in the German Bight. The accuracy of the derived reflectances was validated with concurrent ship‐borne reflectance measurements of the SIMBADA hand‐held field radiometer. Compared to the MERIS Level2 standard reflectance product generated by the processor versions 3.55, 4.06 and 6.3, the results of the proposed algorithm show a significant improvement in accuracy, especially in the blue part of the spectrum, where the MERIS Level2 reflectances result in errors up to 122% compared to only 19% with the proposed algorithm. The overall mean errors within the spectral range of 412.5–708.75 nm are calculated to be 46.2% and 18.9% for the MERIS Level2 product and the presented algorithm, respectively.     

Evaluation of four MERIS atmospheric correction algorithms in Lake Kasumigaura,Japan

Accurate atmospheric correction for turbid inland waters remains a significant challenge. Several atmospheric correction algorithms have been proposed to address this issue, but their performance is unclear in regard to Asian lakes, some of which have extremely high turbidity and different inherent optical properties from lakes in other continents. Here, four existing atmospheric correction algorithms were tested in Lake Kasumigaura, Japan (an extremely turbid inland lake), using in situ water-leaving reflectance and concurrently acquired medium resolution imaging spectrometer (MERIS) images. The four algorithms are (1) GWI (the standard Gordon and Wang algorithm with an iterative process and a bio-optical model) (2) MUMM (Management Unit of the North Sea Mathematical Models); (3) SCAPE-M (Self-Contained Atmospheric Parameters Estimation for MERIS Data) and (4) C2WP (Case-2 Water Processor). The results show that all four atmospheric correction algorithms have limitations in Lake Kasumigaura, even though SCAPE-M and MUMM gave acceptable accuracy for atmospheric correction in several cases (relative errors less than 30% for the 2006 and 2008 images). The poor performance occurred because the conditions in Lake Kasumigaura (i.e. the atmospheric state and/or turbidity) did not always meet the assumptions in each atmospheric correction algorithm (e.g. in 2010, the relative errors ranged from 42% to 83%). These results indicate that further improvements are necessary to address the issue of atmospheric correction for turbid inland waters such as Lake Kasumigaura, Japan.     

Retrieval of atmospheric and oceanic properties from MERIS measurements: A new Case‐2 water processor for BEAM

A freely available data processor for the B asic E RS & ENVISAT ( A )ATSR and M ERIS Toolbox (BEAM) was developed to retrieve atmospheric and oceanic properties above and of Case‐2 waters from Medium Resolution Imaging Spectrometer (MERIS) Level1b data. The processor was especially designed for European coastal waters and uses MERIS Level1b Top‐Of‐Atmosphere (TOA) radiances to retrieve atmospherically corrected remote sensing reflectances at the Bottom‐Of‐Atmosphere (BOA), spectral aerosol optical thicknesses (AOT) and the concentration of three water constituents, namely chlorophyll‐a (CHL), total suspended matter (TSM) and the absorption of yellow substance at 443 nm (YEL). The retrieval is based on four separate artificial neural networks which were trained on the basis of the results of extensive radiative transfer (RT) simulations by taking various atmospheric and oceanic conditions into account. The accuracy of the retrievals was acquired by comparisons with concurrent in situ ground measurements and was published in full detail elsewhere. For the remote sensing reflectance product a mean absolute percentage error (MAPE) of 18% was derived within the spectral range 412.5–708.75 nm while the accuracy of the AOT at 550 nm in terms of MAPE was calculated to be 40%. The accuracies for CHL, TSM and YEL were derived from match‐up analysis with MAPEs of 50%, 60% and 71%, respectively.     

Error sources on the land surface temperature retrieved from thermal infrared single channel remote sensing data

In this paper, a theoretical study complementary to others given in the literature about the errors committed on the land surface temperature retrieved from the radiative transfer equation in the thermal infrared region by remote sensing techniques has been analysed. For this purpose, the MODTRAN 3.5 code has been used in order to simulate different conditions and evaluate the influence of several parameters on the land surface temperature accuracy: atmospheric correction, noise of the sensor, land surface emissivity, aerosols and other gaseous absorbers, angular effects, wavelength uncertainty, full‐width half‐maximum of the sensor and band‐pass effects. The results show that the most important error source is due to atmospheric effects, which leads to an error on surface temperature between 0.2 K and 0.7 K, and land surface emissivity uncertainty, which leads to an error on surface temperature between 0.2 and 0.4 K. Hence, assuming typical uncertainties for remote sensing measurements, a total error for land surface temperature between 0.3 K and 0.8 K has been found, so it is difficult to achieve an accuracy lower than these values unless more accurate in situ values for emissivity and atmospheric parameters are available.     

Spatial variability of the atmosphere over southern England,and its effect on scene-based atmospheric corrections

Earth observation data acquired in the optical region require atmospheric correction before they can be used quantitatively. Most operational methods of atmospheric correction assume that the atmospheric properties are uniform across the image, but this assumption is unlikely to be valid for large images. This study aims to characterize the spatial variation in atmospheric properties over a typical mid-latitude area (southern England), and to assess the errors that would result from applying a scene-based atmospheric correction to data collected under this variable atmosphere. Two key atmospheric properties – aerosol optical thickness (AOT) and precipitable water content (PWC) – are assessed over two clear days in June 2006, and results show an AOT range of approximately 0.1–0.5 and a PWC range of 1.5–3.0 cm. Radiative transfer modelling shows that errors in reflectance of up to 1.7 percentage points, and up to a 5% change in normalized difference vegetation index, can be caused by AOT variability, but that PWC variability has minimal effects. Sensitivity analyses also show that the high uncertainty of many data sources used to provide AOT values for atmospheric correction may also lead to significant errors in the resulting products. The spatial variability of the atmosphere cannot be ignored, and we are in need of operational, generic methods to perform a spatially variable atmospheric correction.     

Landsat8卫星影像大气校正及其植被指数与SEVI性能比较

遥感影像受大气的吸收散射以及地形起伏变化的影响，使得传感器接收到的辐射信号既包含了地物的信息，同时也包含了大气以及地形的信息。为了提高地表反射率的反演精度，需要去除遥感影像中大气和地形的影响。提出了一种基于查找表的Landsat8-OLI遥感影像的大气校正方法，该方法由6S辐射传输模型生成查找表，其中输入的参数包括大气水蒸汽含量、臭氧浓度和气溶胶光学厚度等MODIS大气参数产品。利用传统方法建立的大气参数查找表通常只考虑一部分因素，这对于以MODIS产品为输入参数的大气校正是不适用的。本文建立了一个包括大部分输入参数的高维大气校正查找表，对于Landsat-8 OLI传感器具有很高的通用性，通过进行光谱分析、与USGS地表反射率产品交叉验证等方式来验证模型的精度。验证结果表明该方法能有效地反演精确可靠的地表反射率。最后，采用目视解译、统计分析将校正结果与SEVI做对比分析，比较地形影响消减的效果。结果表明该模型与SEVI在地形消减的效果上作用相当。     

Retrieval of aerosol optical thickness from HJ-1 CCD data based on MODIS-derived surface reflectance

The successfully launched Huanjing-1 (HJ-1) satellite by China in 2008 provides a new source of data for monitoring the environment. In this article, we develop a new algorithm for retrieving the aerosol optical thickness (AOT) using HJ-1 charge-coupled device (CCD) data with the assistance of the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and the bidirectional reflectance distribution function (BRDF) data products. This algorithm is then used to retrieve AOT in a delta region of the Yangtze River. The retrieved results are assessed for their accuracy by comparison with ground-measured data using sun photometers. Comparison of such derived AOT with in situ AOT measured using sun photometers indicates a root mean squared error (RMSE) of 0.123, and their regression relation has a correlation coefficient of 0.896 that is statistically significant at the 0.01 level. Such a relatively high level of retrieval accuracy suggests that HJ-1 CCD data can be used competently and effectively to retrieve AOT with the assistance of MODIS products that are used to construct the surface reflectance model. This study successfully demonstrates the feasibility of synergistically retrieving AOT from data acquired by different sensors. The lower dependence on data from a sole source means that the retrieval is less restrictive by data availability.     

Landsat8卫星影像大气校正及其植被指数与SEVI性能比较

遥感影像受大气的吸收散射以及地形起伏变化的影响,使得传感器接收到的辐射信号既包含了地物的信息,同时也包含了大气以及地形的信息。为了提高地表反射率的反演精度,需要去除遥感影像中大气和地形的影响。提出了一种基于查找表的Landsat8-OLI遥感影像的大气校正方法,该方法由6S辐射传输模型生成查找表,其中输入的参数包括大气水蒸汽含量、臭氧浓度和气溶胶光学厚度等MODIS大气参数产品。利用传统方法建立的大气参数查找表通常只考虑一部分因素,这对于以MODIS产品为输入参数的大气校正是不适用的。本文建立了一个包括大部分输入参数的高维大气校正查找表,对于Landsat-8 OLI传感器具有很高的通用性,通过进行光谱分析、与USGS地表反射率产品交叉验证等方式来验证模型的精度。验证结果表明该方法能有效地反演精确可靠的地表反射率。最后,采用目视解译、统计分析将校正结果与SEVI做对比分析,比较地形影响消减的效果。结果表明该模型与SEVI在地形消减的效果上作用相当。     

Processing and accuracy of Landsat Thematic Mapper data for lake surface temperature measurement

Processing of Landsat-5 TM thermal images for lake surface temperature determination is addressed. A specific preprocessing algorithm to reduce sensor noise is presented and calibration and atmospheric correction is discussed. The atmospheric impact on thermal radiation measurements is modelled using Lowtran-7 utilizing radiosonde data. Comparing ground truth measurements acquired for 21 images between 1987 and 1994 with satellite derived temperatures yielded a mean square error of 0.53 deg K. A systematic overestimation or underestimation of Landsat derived temperatures was not found. The emissivity effect upon the accuracy of the derived surface temperature is discussed as well as effects of using alternate atmospheric profile data.     

Local-scale validation of the Surface Observation Gridding System with in situ weather observations in a semi-arid environment

Although the Surface Observation Gridding System (SOGS) provides spatially continuous models of meteorological conditions, little work has been done to validate SOGS data independently for site-specific research and, as a result, a single nearby weather station is commonly selected instead. This study sought to determine local-scale accuracy of SOGS data (1) by correlation with independent, in situ weather station measurements and (2) relative to a nearby weather station. Correlations between SOGS data and in situ weather observations and between in situ weather observations and a nearby weather station were examined in a semi-arid environment of southeastern Idaho over the 2006 growing season. The results indicate that both SOGS and nearby weather station data were significantly correlated with in situ weather station measurements. Although temperature correlations between in situ and the nearby weather station were slightly greater compared to SOGS, SOGS data were a better predictor of precipitation. This suggests that the use of a nearby weather station is appropriate for local temperature parameters but precipitation parameters are better estimated using SOGS data. Overall, the validation of the SOGS weather models agreed closely with independent, in situ weather measurements and, as a result, greater confidence can be placed in the accuracy of the productivity, biomass and global climate change models derived from these data.     

Atmospheric corrections of single broadband channel and multidirectional airborne thermal infrared data: Application to the ReSeDA experiment

This study focused on atmospheric corrections of airborne thermal infrared remote sensing data acquired with a multidirectional and single broadband channel sensor during the ReSeDA experiment. For single channel sensors, atmospheric corrections are generally performed using atmospheric radiative transfer models such as MODTRAN 3.5 along with radiosoundings. A sensitivity study was performed using MODTRAN 3.5 simulations to assess the accuracy of the processing regarding the experimental context. It was shown that the local topography and the atmosphere spatial variability could affect significantly the radiosounding representativeness, whereas the fluctuations of the flight altitude around the nominal value induced non-negligible inaccuracies. Moreover, using a broadband sensor induced non-linear effects that required a second order correction and also the use of look-up tables to reduce computation time. A theoretical error was next proposed to account for both the sensor accuracy and the experimental uncertainties considered when performing the sensitivity study. This theoretical error was about 1°C, and agreed well with the results obtained when validating against field measurements.     

基于高程或气溶胶厚度与6S模型校正参数回归方程的遥感图像大气校正模型

为了消除影像中的大气影响,反演地表真实反射率,需要进行大气校正。6S是通用的大气校正模型之一,但是对于覆盖面积较广、下垫面较复杂的影像,若用平均的气溶胶厚度和高程参数来对影像进行校正,将出现较大误差。提出了建立一种基于6S模型,以高程或气溶胶厚度为参数的大气校正模型与方法,并以广州Landsat ETM+为例,对校正前后的反射率直方图、NDVI灰度直方图以及各类地物的光谱特征进行分析。结果证明了该方法简单实用,校正精度高,可直接用于该地区相似气候条件下影像的大气校正,对类似的研究有借鉴意义。&     

太湖水体GF-1/WFV影像的6S逐像元大气校正

水体光谱信息微弱,常用的基于辐射传输模型的大气校正方法在水体中校正精度较差。基于覆盖太湖水体的2016年4月29日的高分一号宽幅相机影像（GF-1/WFV）和同步的实测光谱数据,对6S辐射传输模型的输入参数进行敏感性分析,逐像元计算观测几何,使用分区气溶胶类型、分区暗像元和Spline插值确定的气溶胶光学厚度（Aerosol Optical Depth,AOD）进行6S逐像元大气校正。实验结果表明:气溶胶模式对6S大气校正结果的影响最大,与FLAASH方法相比,逐像元计算观测几何和气溶胶参数的校正方法对大气校正精度有改进作用,4个波段的平均相对误差分别降低了1.84%、7.78%、4.79%和17%。结合精确大气参数输入的6S逐像元大气校正方法可以改进水体表面遥感反射率的大气校正精度。