The role of atmospheric correction algorithms in the prediction of soil organic carbon from Hyperion data

In this study, the role of atmospheric correction algorithm in the prediction of soil organic carbon (SOC) from spaceborne hyperspectral sensor (Hyperion) visible near-infrared (vis-NIR, 400–2500 nm) data was analysed in fields located in two different geographical settings, viz. Karnataka in India and Narrabri in Australia. Atmospheric correction algorithms, (1) ATmospheric CORection (ATCOR), (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), (3) 6S, and (4) QUick Atmospheric Correction (QUAC), were employed for retrieving spectral reflectance from radiance image. The results showed that ATCOR corrected spectra coupled with partial least square regression prediction model, produced the best SOC prediction performances, irrespective of the study area. Comparing the results across study areas, Karnataka region gave lower prediction accuracy than Narrabri region. This may be explained due to difference in spatial arrangement of field conditions. A spectral similarity comparison of atmospherically corrected Hyperion spectra of soil samples with field-measured vis-NIR spectra was performed. Among the atmospheric correction algorithms, ATCOR corrected spectra found to capture the pattern in soil reflectance curve near 2200 nm. ATCOR’s finer spectral sampling distance in shortwave infrared wavelength region compared to other models may be the main reason for its better performance. This work would open up a great scope for accurate SOC mapping when future hyperspectral missions are realized.     

MODIS气溶胶光学厚度产品和激光雷达数据在大气颗粒物监测中的应用

遥感反演是区域尺度上近地面颗粒物数据获取的有效手段。利用激光雷达观测的消光系数垂直分布、地面相对湿度、风速等数据,对无锡市MODIS(中分辨率成像光谱仪)气溶胶光学厚度(AOD)产品进行垂直、湿度和风速订正,并用研究区域中7个地面站点的PM_(10)和PM_(2.5)浓度监测数据对订正结果进行评估。结果表明:经过订正的MODIS AOD产品与地面监测数据具有良好的相关性,其中与PM_(10)的决定系数达到0.452,与PM_(2.5)的决定系数达到0.449,说明MODIS AOD产品经相关订正后,可用于无锡及其附近地区地面空气污染的监测。在MODIS AOD产品的垂直订正方面,利用激光雷达数据的订正效果好于利用能见度数据的订正效果。在遥感与实测数据的相关性季节变化方面,夏季相关性最高,秋季次高,春季较低,冬季最低。     

An O2-A Band Atmospheric Correction Algorithm for Tower-based Platform based on Look-up Table

Tower-based spectral observation is an important connecting bridge between flux sites and satellite remote sensing data，and the effect of atmospheric absorption and scattering between horizontal surface and tower-based platform on the atmospheric absorption band such as O2-A is difficult to ignore.Firstly，the influence of atmospheric radiation transfer on the up-welling radiance and down-welling irradiance of the tower-based platform is analyzed，and the atmospheric correction method of based on upward and downward transmittance is established，that is，the influence of the upwelling radiance and down-welling irradiance is corrected by the direct transmittance and the total transmittance.Secondly，using the simulation data of MODTRAN model，the influence of AOD550 and radiative transfer path length on atmospheric transmittance is quantitatively analyzed，and the LUT of AOD550 is established based on the ratio of down-welling irradiance of near-infrared and red bands and solar zenith angle，as well as the upward and downward atmospheric transmittance LUT based on the AOD550 and the radiative transfer path length.Finally，using the canopy spectral data of different growth stages observed by the tower-based platform，the difference of the apparent reflectance between the inside and outside of the O₂-A band absorption line before and after atmospheric correction was analyzed.The results show that the atmospheric correction method based on LUT of AOD550 and radiative transfer path length proposed in this paper can better correct the influence of upwelling radiance and down-welling on the O₂-A absorption band of the tower-based platform，and provides important method support for applications such as SIF observation on the tower platform.     

HJ-1A高光谱数据高效大气校正及应用潜力初探

环境与灾害监测预报小卫星于2009年3月30日开始正式交付使用,A星上搭载了我国自主研制的空间调制型干涉高光谱成像仪（HSI）,作为一种新型传感器,HSI数据的应用在我国还处于探索阶段。要充分发挥超光谱数据优势、进行有效的遥感应用,首先需要消除遥感成像过程中的大气影响,获得不同波段的地物真实反射辐射信息。通过使用FLAASH大气辐射传输模型对HSI数据进行大气校正,并与表观反射率进行对比分析,证明了校正后获得的地表光谱反射率的有效性。同时基于校正后得到的光谱反射率图像,进行改良型土壤调整植被指数（MSAVI）与叶面积指数（LAI）的反演,初步展现了HSI数据的实际应用效果。     

The estimation of atmospheric effects for SPOT using AVHRR channel-1 data

The aim of this paper is to estimate the effect of atmospheric contamination on SPOT satellite data for stereoscopic modelling by using data from the AVHRR instrument carried on the NOAA satellite series. This paper includes the development of an atmospheric correction algorithm for the visible spectral channel data from the AVHRR instrument, and an analysis of the atmospherically corrected AVHRR data from many successive days, bearing in mind that the SPOT data for stereoscopic modelling will be from two orbits which will be separated by several days.     

Atmospheric correction of high-spatial-resolution satellite images with adjacency effects: application to EO-1 ALI data

Adjacency effects are an interesting physical phenomenon caused by multiple scattering between the atmosphere and the surface. It is necessary to remove adjacency effects in the surface reflectance retrieved from satellite data at a high spatial resolution. In this study, we propose an atmospheric correction method with adjacency effect correction to derive surface reflectance from Earth Observing-1 (EO-1) Advanced Land Imager (ALI) data. Adjacency effects are corrected using an atmospheric point spread function. An analytical expression of the atmospheric point spread function is presented based on a single scattering approximation. This method was applied to ALI imagery acquired through Watershed Airborne Telemetry Experimental Research (WATER) on 20 May 2008. Compared with the surface reflectance before the adjacency effects were corrected for, the surface reflectance after correction exhibited increased between-pixel contrast. Furthermore, the discrepancies between the surface reflectance before and after corrections decreased from the blue band to the shortwave infrared band.     

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。     

Atmospheric Correction of GF-1/WFV Image in Taihu Lake based on the 6S Model Pixel by Pixel

The spectral information of water is weak, and the commonly used radiation transfer model has poor accuracy in atmospheric correction of water body. Based on the Gaofen-1 WFV image (GF-1/WFV) and the synchronous in situ spectra covering Taihu Lake on 29^th, April, 2016, the sensitivity analysis of the input parameters in 6S model was first performed, and then the image was corrected using 6S model using the observation geometry calculated pixel-by-pixel, the partitioned aerosol type and the Aerosol Optical Depth (AOD) determined by the partitioned dark pixel and Spline interpolation. The experimental results show that the aerosol type has the greatest influence on the 6S atmospheric correction results. Compared with the FLAASH method, the 6S method using the observation geometry and aerosol parameters calculated pixel-by-pixel significantly improved the atmospheric correction accuracy, with the ARE (Average Relative Error) of the four bands reduced by 1.84%，7.78%，4.79%，17%. The 6S atmospheric correction method pixel by pixel with the input of accurate atmospheric parameters can improve the correction accuracy of the remote sensing reflectance above water surface.     

卫星影像大气校正中气溶胶模型的影响分析-以天津地区为例

基于大气校正中常用的大气辐射传输模拟软件6S模型,探讨了不同气溶胶模型对卫星影像大气校正的影响及其适用性问题。选取我国环境小卫星星座HJ\|1的CCD传感器数据,以天津地区大气污染和较清洁条件下的卫星影像为例,基于6S进行大气校正,定量估算了气溶胶模型选取对大气校正精度的影响。与此同时,通过地面太阳-天空辐射计实测,获得研究区域整层大气气溶胶参数进行大气校正,并与基于气溶胶模型的校正结果进行对比,得到以下结论:①6S气溶胶模型中,大陆型与海洋型的校正结果比较接近,而城市型气溶胶模型由于含有较高的煤烟成分比例,其校正结果与前两种相差较大;②天津地区在大气污染情况下,适合应用大陆型气溶胶模型进行大气校正,而在大气较清洁的情况下可选择海洋型气溶胶模型。
     

Radiometric Consistency between Landsat 8 OLIand Sentinel-2 MSI Imagery in Mountainous Terrain

The combined use of multi\|sensor/multi\|temporal images provides more opportunities for long\|term land surface monitoring with high resolution and frequency requirements.However,as sensors differ in their orbital,spatial,or spectral configuration,uncertainty was introduced in the radiometric consistency of multi\|sourse images,and that becomes more outstanding in mountainous terrain with the sharp topographic relief.Therefore,a series of radiometric corrections need to be carry out before further application.The objective of this study was to indicate the radiometric consistency of Landsat\|8 OLI and Sentinel\|2 MSI images.Thus the radiometric differences between the corresponding bands of these two images acquired almost simultaneously by OLI and MSI over 2 areas at different latitude was calculated for the TOA reflectance images first.Then several radiometric corrections(atmospheric correction,BRDF correction and bandpass adjustment) were carried out successively and after each of them the radiometric differences were researched again to assess the performance of each correction method.The results first indicate that there is high radiometric consistency between OLI\|L1T and MSI\|L1C images with the R²greater than 0.9 for each band involved.Then higher consistency was found after the 6S atmospheric correction and C\|factor BRDF correction,while no remarkable improve was found after the fixed\|parameter bandpass adjustment.Furthermore,in area with great topographic relief,the radiometric consistency were higher for hillside facing the sun than hillside in shadow (the MAD of SWIR2 band was 0.010 and RMSD was 0.007 in sun\|light area,while the MAD was 0.005 and RMSD was 0.004 in shadowed area).The results point out that proper atmospheric correction,BRDF correction and bandpass adjustment could be used to improve the radiometric consistency,and topographic correction might also be carried out to balance the radiometric consistency differences between different hillsides.     

Posidonia oceanica genetic and biometry mapping through high-resolution satellite spectral vegetation indices and sea-truth calibration

In the framework of Posidonia oceanica (PO) preservation activities, a small-scale restoration pilot project was implemented in 2005 at a Santa Marinella site to replace the loss of this important species of seagrass in this zone of the central Tyrrhenian coast via an innovative transplantation approach. In this context, taking into account the recent advances in the fields of high-resolution (HR) satellite/airborne remote-sensing and genetics laboratory analysis techniques, we propose this integrated methodology for monitoring changes in transplanted meadows in regard to perspective to provide support in the assessment of the entire local PO and seagrass population dynamic. According to specific information requirements in terms of radiometric and spectral/spatial resolution, the multispectral data currently available from the QuickBird polar satellite’s four-band (red, green, blue visible and near-infrared) HR sensor were exploited for methodology implementation using a practical ‘image-based’ approach to account for atmospheric and water column turbidity typical of this mid-coastal Mediterranean region. First, the extents and types of seagrass cover were suitably mapped, and then also the distributions of specific vegetation parameters related to PO dynamics and health were assessed by exploiting the remotely sensed satellite-derived radiance signals and point sea-truth calibration measurements of the bio-genetic parameters. In particular, we implemented maps of leaf area index, genetic similarity, and density Giraud indices corresponding to distributions of PO patches using multivariate and data-mining models (artificial neural network) based on appropriately preprocessed radiometric and auxiliary (bathymetry) input variables.     

Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon

Detecting and characterizing continuous changes in early forest succession using multi-temporal satellite imagery requires atmospheric correction procedures that are both operationally reliable, and that result in comparable units (e.g., surface reflectance). This paper presents a comparison of five atmospheric correction methods (2 relative, 3 absolute) used to correct a nearly continuous 20-year Landsat TM/ETM+ image data set (19-images) covering western Oregon (path/row 46/29). In theory, full absolute correction of individual images in a time-series should effectively minimize atmospheric effects resulting in a series of images that appears more similar in spectral response than the same set of uncorrected images. Contradicting this theory, evidence is presented that demonstrates how absolute correction methods such as Second Simulation of the Satellite Signal in the Solar Spectrum (6 s), Modified Dense Dark Vegetation (MDDV), and Dark Object Subtraction (DOS) actually make images in a time-series somewhat less spectrally similar to one another. Since the development of meaningful spectral reflectance trajectories is more dependant on consistent measurement of surface reflectance rather than on accurate estimation of true surface reflectance, correction using image pairs is also tested. The relative methods tested are variants of an approach referred to as “absolute-normalization”, which matches images in a time-series to an atmospherically corrected reference image using pseudo-invariant features and reduced major axis (RMA) regression. An advantage of “absolute-normalization” is that all images in the time-series are converted to units of surface reflectance while simultaneously being corrected for atmospheric effects. Of the two relative correction methods used for “absolute-normalization”, the first employed an automated ordination algorithm called multivariate alteration detection (MAD) to statistically locate pseudo-invariant pixels between each subject and reference image, while the second used analyst selected pseudo-invariant features (PIF) common to the entire image set. Overall, relative correction employed in the “absolute-normalization” context produced the most consistent temporal reflectance response, with the automated MAD algorithm performing equally as well as the handpicked PIFs. Although both relative methods performed nearly equally in terms of observed errors, several reasons emerged for preferring the MAD algorithm. The paper concludes by demonstrating how “absolute-normalization” improves (i.e., reduces scatter in) spectral reflectance trajectory models used for characterizing patterns of early forest succession.     

Mapping sediment-laden sea ice in the Arctic using AVHRR remote-sensing data: Atmospheric correction and determination of reflectances as a function of ice type and sediment load

Exploiting the fact that the spectral characteristics of light backscattered from sediment-laden ice differ substantially from those of clean ice and that sediment tends to accumulate at the ice surface during the first melt season, remote-sensing techniques provide a valuable tool for mapping the extent of particle-laden ice in the Arctic basin and assessing its particulate loading. This study considers two fundamental problems that still need to be addressed in order to make full use of satellite observations for this type of assessment: (i) the effects of the atmosphere on surface reflectances derived from radiances measured by the satellite sensor need to be quantified and ultimately corrected for, and (ii) the spectral reflectance of the ice surface as a function of particle loading and sub-pixel distribution needs to be determined in order to derive quantitative estimates from the at-sensor satellite signal. Here, spectral albedos have been computed for different ice surfaces of variable sediment load with a radiative transfer model for sea ice coupled with an optical model for particulates included in sea ice. In a second step, the role of the atmosphere in modulating the surface reflectance signal is assessed with the aid of an atmospheric radiative transfer model applied to a “standard” Arctic atmosphere and surface boundary conditions as prescribed by the sea ice radiative transfer model. A series of sensitivity studies helps assess differences between top-of-the-atmosphere and true surface reflectance and has been utilized to derive a look-up table for atmospheric correction of Advanced Very High Resolution Radiometer (AVHRR) data over sediment-laden sea ice surfaces. In particular, the effects of solar elevation, viewing geometry, and atmospheric properties are considered. The atmospheric corrections are necessary for certain geometries and surface types. Large discrepancies between raw and corrected data are particularly evident in the derived coverage of clean ice and ice with small sediment loading.     

Comparing Sentinel-2 MSI and Landsat 8 OLI in soil salinity detection: a case study of agricultural lands in coastal North Carolina

This study presents the first comparison of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) in identifying soil salinity using soil physiochemical, spectral, statistical, and image analysis techniques. By the end of the century, intermediate sea level rise scenarios project approximately 1.3 meters of sea level rise along the coast of the southeastern United States. One of the most vulnerable areas is Hyde County, North Carolina, where 1140 km² of agricultural lands are being salinized, endangering 4,200 people and $40 million USD of property. To determine the best multispectral sensor to map the extent of salinization, this study compared the feasibility of OLI and MSI to estimate electrical conductivity (EC). The EC of field samples were correlated with handheld spectrometer spectra resampled into multispectral sensor bands. Using an iterative ordinary least squares regression, it was found that EC was sensitive to OLI bands 2 (452 nm – 512 nm) and 4 (636 nm – 673 nm) and MSI bands 2 (457.5 nm – 522.5 nm) and 4 (650 nm – 680 nm). Respectively, the R²_Adj and Root Mean Square Error (RMSE) of 0.04–0.54 and 1.15 for OLI, and 0.05–0.67 and 1.17 for MSI, suggests that the two sensors have similar salinity modelling skill. The extracted saline soils make up approximately 1,703 hectares for OLI and 118 hectares for MSI, indicating overestimation from the OLI image due to its coarser spatial resolution. Additionally, field samples indicate that nearby vegetated land is saline, indicating an underestimation of total impacted land. As sea levels rise, accurately monitoring soil salinization will be critical to protecting coastal agricultural lands. MSI’s spatial and temporal resolution makes it superior to OLI for salinity tracking though they have roughly equivalent spectral resolutions. This study demonstrates that visible spectral bands are sensitive to soil salinity with the Blue and Red spectral ranges producing the highest model accuracy; however, the low accuracies for both sensors indicate the need of narrowband sensors. The HyspIRI to be launched in the early 2020s by NASA may provide ideal data source in soil salinity studies.     

Emissivity errors in the vegetation cover method caused by the lack of atmospheric correction

The influence of the lack of atmospheric correction of the optical images used to calculate land surface emissivity (LSE) was assessed. When thermal emissivity is determined by the vegetation cover method (VCM), information from the solar spectrum is required to calculate the vegetation cover fraction. The atmospheric correction was obtained in this study by using a combination of the dark dense vegetation (DDV) method and the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) code. The methodology was applied to a Landsat Thematic Mapper (TM) image of Tomelloso, Spain. We determined that the emissivity between 10 and 12 µm only increases by 0.4% (which represents a systematic error of approximately +0.2 K) when atmospherically corrected reflectances are used in relation to non‐corrected Tomelloso scenes. Nevertheless, other test areas could yield larger differences.     

Determination of chlorophyll concentration changes in Lake Garda using an image-based radiative transfer code for Landsat TM images

The distribution of phytoplankton chlorophyll concentration in Lake Garda (Italy) was estimated using Landsat Thematic Mapper (TM) data acquired at two different times, February 1992 and March 1993. To investigate the waterleaving radiance adequately, the contribution of the atmospheric path radiance reaching the sensor should be removed. In this work a completely image-based atmospheric correction method was applied by means of an inversion technique based on a simplified radiative transfer code (RTC). A semi-empirical approach of relating atmospherically corrected TM spectral reflectances to in situ measurements through regression analysis was used. Limnological parameters were measured near to the TM images dates; some of the in situ measurements were used to define algorithms relating chlorophyll concentration measurements to water surface reflectance and the others too were used to validate the results of the predictive model. The models developed, which performed better (r² = 0.818) when concentrations were higher than > 3.0 mg m³, were used to map chlorophyll concentration throughout the lake. Spatial distribution maps of chlorophyll concentration and concentration changes were produced with contour intervals of 1 mg m³.     

Lidar and spectroradiometer measurements of atmospheric aerosol optical characteristics over an urban area in Sofia,Bulgaria

A series of campaigns involving a systematic investigation of the atmosphere over an urban area of Sofia city were carried out. A European Aerosol Research Lidar Network (EARLINET) scanning aerosol lidar, a spectroradiometer, a standard sun photometer and a ground meteorological station were used in the observations. Multiple aerosol layers of variable thickness (200–600 m) were observed systematically in the planetary boundary layer (PBL) over the study area and the experimental data were compared with theoretical data. A study of the optical characteristics of the atmospheric aerosol, including the extinction coefficient, aerosol optical depth (AOD) and Angstrom parameters α and β, was performed and their variations followed during the convective boundary layer (CBL) formation. Values of the AOD obtained using the different instruments during simultaneous measurements were compared. Preliminary results show that the AOD values recorded by the sun photometer and those calculated on the basis of the spectroradiometer data are higher than those retrieved from the lidar data. Determination of the atmospheric optical depth and extinction coefficient using a ground-based spectral instrument is a relatively simple and inexpensive method of monitoring the total aerosol content in the atmosphere as well as the air quality over the region.     

Temperature and emissivity separation from ASTER data for low spectral contrast surfaces

The performance of Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) thermal infrared (TIR) data product algorithms was evaluated for low spectral contrast surfaces (such as vegetation and water) in a test site close to Valencia, Spain. Concurrent ground measurements of surface temperature, emissivity, and atmospheric radiosonde profiles were collected at the test site, which is a thermally homogeneous area of rice crops with nearly full vegetation cover in summer. Using the ground data and the local radiosonde profiles, at-sensor radiances were simulated for the ASTER TIR channels and compared with L1B data (calibrated at-sensor radiances) showing discrepancies up to 3% in radiance for channel 10 at 8.3 μm (equivalently, 2.5 °C in temperature or 7% in emissivity), whereas channel 13 (10.7 μm) yielded a closer agreement (maximum difference of 0.5% in radiance or 0.4 °C in temperature). We also tested the ASTER standard products of land surface temperature (LST) and spectral emissivity generated with the Temperature-Emissivity Separation (TES) algorithm with standard atmospheric correction from both global data assimilation system profiles and climatology profiles. These products showed anomalous emissivity spectra with lower emissivity values and larger spectral contrast (or maximum-minimum emissivity difference, MMD) than expected, and as a result, overestimated LSTs. In this work, a scene-based procedure is proposed to obtain more accurate MMD estimates for low spectral contrast materials (vegetation and water) and therefore a better retrieval of LST and emissivity with the TES algorithm. The method uses various gray-bodies or near gray-bodies with known emissivities and assumes that the calibration and atmospheric correction performed with local radiosonde data are accurate for channel 13. Taking the channel 13 temperature (atmospherically and emissivity corrected) as the true LST, the radiances for the other channels were simulated and used to derive linear relationships between ASTER digital numbers and at-ground radiances for each channel. The TES algorithm was applied to the adjusted radiances and the resulting products showed a closer agreement with the ground measurements (differences lower than 1% in channel 13 emissivities and within ± 0.3 °C in temperature for rice and sea pixels).     

Evaluating the impact of adjusting surface temperature derived from Landsat 7 ETM+ in crop evapotranspiration assessment using high-resolution airborne data

Surface temperature (T_s) is an essential parameter in many land surface processes. When T_s is obtained from remotely sensed satellite data the consideration of atmospheric correction may be needed to obtain accurate surface temperature estimates. Most atmospheric correction methods adjust atmospheric transmissivity, path radiance and downward thermal radiation coefficients. Following a standardized atmospheric correction of Landsat 7 thermal data, some differences were found between these corrected data and surface temperature derived from very-high resolution airborne thermal data. Five different methods for determining atmospheric correction were evaluated comparing atmospherically corrected Landsat 7 data with airborne data for an area of olive orchards located at Southern Spain. When using standard default Landsat 7 calibration coefficients T_s differences between satellite and airborne observations ranged from 1 to 6 K, highlighting the need to perform more robust atmospheric correction. When applying the customized values for semi-arid temperate climate in Idaho, USA, and the values based on the National Centers for Environmental Prediction (NCEP) T_s differences ranged from 1 to 4 K, indicating that additional local calibration may be appropriate. Optimal coefficients were determined using the Generalized Reduced Gradient (GRG) approach, a nonlinear algorithm included in Solver tool, obtaining T_s differences around 1–3 K. In order to evaluate the impact of considering the proposed correction approaches, assessment of the evapotranspiration and crop coefficient values derived from the Mapping Evapotranspiration with Internalized Calibration (METRIC) energy balance model provided maximum errors of around 4%, indicating that the METRIC model does not require a robust atmospheric correction. However, the localized calibration approaches are proposed as useful alternatives when absolute land surface temperatures values are required, as in the case of the determination of crop water stress based on differences between canopy (T_c) and air temperature (T_air).     

Atmospheric correction based on inherent optical properties of sea water at NIR wavelengths combined with an automated aerosol spectra determination (ASD) technique

Atmospheric correction is the process whereby atmospheric effects on sensor-recorded radiance are removed and the surface radiance is estimated. Atmospheric effects due to gaseous absorption, molecular scattering – and their seasonal as well as latitudinal variations – can be adequately accounted for by using pre-computed look-up tables. However, scattering by aerosol particles is difficult to correct. At-sensor radiance at near-infrared (NIR) wavelengths, after being corrected for gaseous absorption and molecular scattering, was assumed (in standard atmospheric correction) to have been entirely due to aerosol scattering and was used to calculate the aerosol parameters. This assumption, although valid for open ocean clear waters, is not valid for turbid waters due to scattering by suspended particles in the water, which results in an appreciable amount of water-leaving radiance in the NIR region. A new turbid water atmospheric correction scheme is described here for Oceansat-2 Ocean Colour Monitor (OCM-2) data based on inherent optical properties (IOPs) of sea water at NIR, and obtaining an accurate spectral profile of aerosol radiance.