The use of selected pseudo-invariant targets for the application of atmospheric correction in multi-temporal studies using satellite remotely sensed imagery

Because atmospheric effects can have a significant impact on the data obtained from multi-spectral satellite remote sensing, it is frequently necessary to make corrections before any other image processing can be started. This paper describes a robust and relatively simple atmospheric correction method that uses pseudo-invariant targets (PITs) in conjunction with the empirical line method. The method is based on the selection of a number of suitable generic PITs, on the basis that they are large, distinctive in shape, and occur in many geographical areas. Whereas the multi-temporal normalization method corrects all images to a selected reference image, in this method images are simultaneously corrected using targets with a range of estimated surface reflectance values. The paper describes some applications of the method for a range of environmental studies involving water quality and air pollution monitoring, and mapping land-cover changes.     

The selection of appropriate spectrally bright pseudo-invariant ground targets for use in empirical line calibration of SPOT satellite imagery

The appropriate utilization of multi-temporal SPOT multispectral satellite imagery in quantitative remote sensing studies requires the removal of atmospheric effects. One widely used and potentially very accurate way of achieving absolute atmospheric correction is the calibration of at-satellite radiance data to field measures of the surface reflectance factor (ρ_s). There are a number of variations in this technique, which are known collectively as empirical line (EL) approaches. However, the successful application of an EL spectral calibration requires the presence and careful selection of appropriate pseudo-invariant ground targets within each scene area. Real surfaces, even those that are man-made and vegetation-free, display non-Lambertian reflectance behaviour to some extent. Because of the ±31° off-nadir incidence angle range of the SPOT sensors, this is a crucial consideration. In favourable circumstances, it may be possible to utilize a goniometer to collect multiangular ρ_s measurements, but for widespread lower cost application of EL approaches currently, the use of a handheld spectrometer to measure nadir only ρ_s is a more realistic proposition. In either case, the selection of targets that have more limited and stable multiangular reflectance behaviour is preferable. Details are given of the reflectance properties of a variety of spectrally bright potential calibration surface types, encompassing sands, gravel, asphalts, and managed and artificial grass turf surfaces, measured in the field using the Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO). Bright calibration site selection requirements for SPOT data are discussed and the physical mechanisms behind the varying reflectance characteristics of the surfaces are considered. The most desirable properties for useful calibration targets are identified. The results of this study will assist other workers in the identification of likely suitable EL calibration sites for medium and high resolution optical satellite data, and therefore help optimize efforts in the time consuming and costly process of measuring ρ_s in the field.     

HJ-1B卫星三种反演地表温度模型的分析与验证

由于对不同的传感器,单通道算法中的经验公式必须根据相应的热红外波段特征重新进行拟合,该文以覆盖广州市的卫星影像HJ-1B/CCD和HJ-1B/IRS为数据源,利用基于同步面上像元尺度的多源遥感数据MOD021KM,并结合上下行辐射和地表比辐射率来反演大气水汽含量以及大气透射率。在此基础上,针对环境卫星,提出修正的普适性单通道算法、大气辐射传输方程算法和单窗算法3种模型,进行广州市地表温度反演,最后利用野外观测数据进行验证。结果表明,相比普适性单通道算法和单窗算法,辐射传输方程模型精度比较高。     

Leaf area index retrieval using gap fractions obtained from high resolution satellite data: Comparisons of approaches,scales and atmospheric effects

This study is aimed at demonstrating the feasibility of the large scale LAI inversion algorithms using red and near infrared reflectance obtained from high resolution satellite imagery. Radiances in digital counts were obtained in 10 m resolution acquired on cloud free day of August 23, 2007, by the SPOT 5 high resolution geometric (HRG) instrument on mostly temperate hardwood forest located in the Great Lakes – St. Lawrence forest in Southern Quebec. Normalized difference vegetation index (NDVI), scaled difference vegetation index (SDVI) and modified soil-adjusted vegetation index (MSAVI) were applied to calculate gap fractions. LAI was inverted from the gap fraction using the common Beer–Lambert's law of light extinction under forest canopy. The robustness of the algorithm was evaluated using the ground-based LAI measurements and by applying the methods for the independently simulated reflectance data using PROSPECT + SAIL coupled radiative transfer models. Furthermore, the high resolution LAI was compared with MODIS LAI product. The effects of atmospheric corrections and scales were investigated for all of the LAI retrieval methods. NDVI was found to be not suitable index for large scale LAI inversion due to the sensitivity to scale and atmospheric effects. SDVI was virtually scale and atmospheric correction invariant. MSAVI was also scale invariant. Considering all sensitivity analysis, MSAVI performed best followed by SDVI for robust LAI inversion from high resolution imagery.     

Spectral sensitivity of ALOS,ASTER, IKONOS,LANDSAT and SPOT satellite imagery intended for the detection of archaeological crop marks

This study compares the spectral sensitivity of remotely sensed satellite images, used for the detection of archaeological remains. This comparison was based on the relative spectral response (RSR) Filters of each sensor. Spectral signatures profiles were obtained using the GER-1500 field spectroradiometer under clear sky conditions for eight different targets. These field spectral signature curves were simulated to ALOS, ASTER, IKONOS, Landsat 7-ETM+, Landsat 4-TM, Landsat 5-TM and SPOT 5. Red and near infrared (NIR) bandwidth reflectance were re-calculated to each one of these sensors using appropriate RSR Filters. Moreover, the normalised difference vegetation index (NDVI) and simple ratio (SR) vegetation profiles were analysed in order to evaluate their sensitivity to sensors spectral filters. The results have shown that IKONOS RSR filters can better distinguish buried archaeological remains as a result of difference in healthy and stress vegetation (approximately 1–8% difference in reflectance of the red and NIR band and nearly 0.07 to the NDVI profile). In comparison, all the other sensors showed similar results and sensitivities. This difference of IKONOS sensor might be a result of its spectral characteristics (bandwidths and RSR filters) since they are different from the rest of sensors compared in this study.     

Development of an image based integrated method for determining and mapping aerosol optical thickness (AOT) over urban areas using the darkest pixel atmospheric correction method,RT equation and GIS: A case study of the Limassol area in Cyprus

This paper presents the development of an image-based integrated method for determining and mapping aerosol optical thickness (AOT). Using the radiative transfer (RT) equation, a methodology was developed to create a Geographical Information System (GIS) model that can visually display the AOT distribution over urban areas. In this paper, the model was applied to eleven Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) satellite images of Limassol, Cyprus during 2010 and 2011 to determine the AOT levels in Limassol Cyprus during satellite overpass. The study is innovative and unique in that the RT equation, satellite images, the darkest pixel (DP) method of atmospheric correction and GIS were integrated to derive AOT from satellite images and display the AOT distribution over an urban area without the input of any meteorological or atmospheric parameters. The accuracy of the algorithm was verified through statistical analysis by the strong agreement between the AOT values derived using the algorithm and the in situ AOT values from the ground-based sensors.     

L&R海空重力仪测量误差综合补偿方法

为了削弱各类误差源的影响,提出了一种两阶段误差综合补偿方法:第一阶段采用相关分析法对仪器厂家标定的交叉耦合改正(CC改正)的不足进行修正;第二阶段采用测线网平差对各类剩余误差的综合影响进行补偿。实际观测数据处理结果验证了该方法的有效性和可靠性。