An historical empirical line method for the retrieval of surface reflectance factor from multi-temporal SPOT HRV,HRVIR and HRG multispectral satellite imagery

SPOT satellites have been imaging Earth's surface since SPOT 1 was launched in 1986. It is argued that absolute atmospheric correction is a prerequisite for quantitative remote sensing. Areas where land cover changes are occurring rapidly are also often areas most lacking in situ data which would allow full use of radiative transfer models for reflectance factor retrieval (RFR). Consequently, this study details the proposed historical empirical line method (HELM) for RFR from multi-temporal SPOT imagery. HELM is designed for use in landscape level studies in circumstances where no detailed overpass concurrent atmospheric or meteorological data are available, but where there is field access to the research site(s) and a goniometer or spectrometer is available. SPOT data are complicated by the ±27° off-nadir cross track viewing. Calibration to nadir only surface reflectance factor (ρ_s) is denoted as HELM-1, whilst calibration to ρ_s modelling imagery illumination and view geometries is termed HELM-2. Comparisons of field measured ρ_s with those derived from HELM corrected SPOT imagery, covering Helsinki, Finland, and Taita Hills, Kenya, indicated HELM-1 RFR absolute accuracy was ±0.02ρ_s in the visible and near infrared (VIS/NIR) bands and ±0.03ρ_s in the shortwave infrared (SWIR), whilst HELM-2 performance was ±0.03ρ_s in the VIS/NIR and ±0.04ρ_s in the SWIR. This represented band specific relative errors of 10–15%. HELM-1 and HELM-2 RFR were significantly better than at-satellite reflectance (ρ_SAT), indicating HELM was effective in reducing atmospheric effects. However, neither HELM approach reduced variability in mean ρ_s between multi-temporal images, compared to ρ_SAT. HELM-1 calibration error is dependent on surface characteristics and scene illumination and view geometry. Based on multiangular ρ_s measurements of vegetation-free ground targets, calibration error was negligible in the forward scattering direction, even at maximum off-nadir view. However, error exceeds 0.02ρ_s where off-nadir viewing was ≥20° in the backscattering direction within ±55° azimuth of the principal plane. Overall, HELM-1 results were commensurate with an identified VIS/NIR 0.02ρ_s accuracy benchmark. HELM thus increases applicability of SPOT data to quantitative remote sensing studies.     

Retrieval of boreal forest LAI using a forest reflectance model and empirical regressions

Spectral invariants provide a novel approach for characterizing canopy structure in forest reflectance models and for mapping biophysical variables using satellite images. We applied a photon recollision probability (p) based forest reflectance model (PARAS) to retrieve leaf area index (LAI) from fine resolution SPOT HRVIR and Landsat ETM+ satellite data. First, PARAS was parameterized using an extensive database of LAI-2000 measurements from five conifer-dominated boreal forest sites in Finland, and mixtures of field-measured forest understory spectra. The selected vegetation indices (e.g. reduced simple ratio, RSR), neural networks and kNN method were used to retrieve effective LAI (L_e) based on reflectance model simulations. For comparison, we established empirical vegetation index-LAI regression models for our study sites. The empirical RSR–L_e regression performed best when applied to an independent test site in southern Finland [RMSE 0.57 (24.2%)]. However, the difference to the best reflectance model based retrievals produced by neural networks was only marginal [RMSE 0.59 (25.1%)]. According to this study, the PARAS model provides a simple and flexible modelling tool for calibrating algorithms for LAI retrieval in conifer-dominated boreal forests. The advantage of PARAS is that it directly uses field measurements to parameterize canopy structure (LAI-2000, hemispherical photographs) and optical properties of foliage and understory.     

SPOT地面场定标与星上定标结果的比较分析

本文研究是在遥感辐射定标场选择的基础。利用６Ｓ大气辐射传输模型进行ＳＰＯＴ遥感数据的定标和地物的光谱反射率反演,即在遥感器飞越辐射定标场上空,在定标场选择若干像元区,测量遥感器对应的各波段地物的光谱反射率和大气光谱参量,并利用大气辐射传输模型给出遥感器人瞳处各光谱带的辐射亮度,最后确定它与遥感器对应输出的数字量化的数量关系,求解定标系数。然后,对相应的研究训练区的遥感数据进行大气辐射校正,进而反演训练区内的地物光谱反射率。最后,通过将反演值与实地测量的地物光谱反射率进行对比分析,来估算定标不确定度,并比较说明两种不同方式定标差异及优势和限制。     

《风云二号》静止气象卫星

《风云二号》静止气象卫星１９９７年６月１０日２０点０１分,从我国西昌卫星发射中心,由长征三号运载火箭成功地将我国自己研制的《风云二号》静止气象卫星发射入轨（封面）。６月１７日,《风云二号》卫星成功地定点在东经１０５°赤道上空。６月２１日１４时０１分,...     

Use of Field Spectroscopy for Exploring the Impact of Atmospheric Effects on Landsat 5 TM/7 ETM+ Satellite Images Intended for Hydrological Purposes in Cyprus

The spectroradiometric retrieved reflectance of a local crop, namely, beans (Phaseolus vulgaris), is directly compared to the reflectance of Landsat 5TM and 7ETM+ atmospherically corrected and uncorrected satellite images. Also, vegetation indices from the same satellite images—atmospherically corrected and uncorrected—are compared with the corresponding vegetation indices produced from field measurements using a spectroradiometer. Vegetation Indices are vital in the estimation of crop evapotransiration under standard conditions (ET_c) because they are used in stochastic or empirical models for describing crop canopy parameters such as the Leaf Area Index (LAI) or crop height. ET_c is finally determined using the FAO Penman-Monteith method adapted to satellite data, and is used to examine the impact of atmospheric effects. Regarding the reflectance comparison, the main problem was observed in Band 4 of Landsat 5TM and 7ETM+, where the difference, for uncorrected images, was more than 20% and statistically significant. Results regarding ET_c show that omission or ineffective atmospheric corrections in Landsat 5TM,/7ETM+ satellite images always results in a water deficit when estimating crop water demand. Diminished estimated crop water requirements can result in a reduction in output or, if critical, crop failure. The paper seeks to illustrate the importance of removing atmospheric effects from satellite images designated for hydrological purposes.     

Calibration of ASTER and ETM + imagery using empirical line method—A case study of north-east of Hajjah, Yemen

This study is aimed at using the Empirical Line Method (ELM) to eliminate atmospheric effects with respect to visible and near infrared bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) and enhanced thematic mapper plus (ETM+) data. Two targets (Amran limestone as light target and quartz-biotite-sericite-graphite schists as dark target), which were widely exposed and easy to identify in the imagery were selected. The accuracy of the atmospheric correction method was evaluated from three targets (vegetation cover, Amran limestone and Akbra shale) of the surface reflectance. Analytical spectral devices (ASD) FieldSpec3 was used to measure the spectra of target samples. ETM+ data were less influenced by the atmospheric effect when compared to ASTER data. Normalized differences vegetation indices (NDVI) displayed good results with reflectance data when compared with digital number (DN) data because it is highly sensitive to ground truth reflectance (GTR). Most of the differences observed before and after calibration of satellite images (ASTER and ETM+) were absorbed in the SWIR region.      

Atmospheric correction to IRS-P6 AWiFS data and its validation with ground measurements: A study over the semi-arid region

In this study, we have implemented a fast atmospheric correction algorithm to IRS-P6 advanced wide field sensor (AWiFS) satellite data for retrieving surface reflectance under different atmospheric and surface conditions. The algorithm is based on MODIS climatology products and simplified use of Second Simulation of Satellite Signal in Solar Spectrum (6S) radiative transfer code. The algorithm requires information on aerosol optical depth (AOD) for correcting the satellite dataset. The atmospheric correction algorithm has been tested for IRS-P6 AWiFS False colour composites covering the International Crops Research Institute for the Semi-Arid Tropics Farm, Patancheru, Hyderabad, India, under varying atmospheric conditions. Ground measurements of surface reflectance representing different land use/land cover, i.e. red soil, chick pea, groundnut and pigeon pea crops were conducted to validate the algorithm. Terra MODIS AOD₅₅₀ validated with Microtops-II sun photometer–derived AOD₅₀₀ over the urban region of Hyderabad exhibited very good correlation of ～0.92, suggesting possible use of satellite-derived AOD for atmospheric correction.     

Development of a scheme for atmospheric correction of Resourcesat-2 AWiFS data

This paper presents a method called SACRS2, a scheme for atmospheric correction of RS2-AWiFS (Resourcesat2-Advanced Wide Field Sensor) data. The SACRS2 is a computationally fast scheme developed from a physics-based detailed radiative transfer model 6SV for correcting large amount of data from the high-repetivity AWiFS sensor. The method is based on deriving a set of equations with coefficients which depend on the spectral bands of the RS2-AWiFS sensor through forward signal simulations by 6SV. Semi-empirical formulations provided in the SMAC method with a few improvements have been used to describe various atmospheric interactions. A total of 112 coefficients for different equations are determined using the best fit equations against the computations of the 6SV. After the specific coefficients for the RS2-AWiFS spectral bands are determined, the major inputs of the scheme are raw digital numbers recorded by RS2-AWiFS sensor, atmospheric columnar water vapour content, ozone content, aerosol optical thickness at 550 nm and viewing-illumination conditions. Results showed a good performance of the SACRS2 with a maximum relative error in the SACRS2 simulations ranged between 1% for a reflectance of 0.5 and 8.6% for reflectance of 0.05 with respect to 6SV computations. Validation of retrieved surface reflectance using the SACRS2 scheme with respect to in-situ measurements at two sites indicated a capability of this scheme to determine the surface reflectance within 10%. This is a first of its kind scheme developed for the atmospheric correction of any Indian Remote Sensing satellite data. A package containing the SACRS2 software is available on the MOSDAC website for the researchers.     

Retrieval of leaf area index in alpine wetlands using a two-layer canopy reflectance model

In this paper, we focused on the retrieval of the LAI in an alpine wetland located in western part of China in late August and early July 2011. A two-layer canopy reflectance model (ACRM) was used to establish the relationships between the LAI and the reflectance of near-infrared (NIR) and red (RED) wavebands. The reflectance data were derived from Landsat TM L1T product and the Terra and Aqua MODIS 16-day and 8-day composite reflectance products (MOD/MYD09) at 250 m resolution. Due to the lack of the information about some major input parameters for ACRM, which are sensitive to model outputs in the reflectance of NIR and RED wavebands, the inverse problem was ill-posed. To overcome this problem, a method of increasing the sensitivity of the LAI while reducing the influence of other model free parameters based on the study of free parameters’ sensitivity to the ACRM outputs and the region’s features was studied. The area of interest was divided into two parts using the approximately statistic normalized difference vegetation index (NDVI) value around 0.5. One part was sparse vegetation (0.1 < NDVI < 0.5), which is more sensitive to soil background effects and less sensitive to the canopy biophysical and biochemical variables. The other part was dense vegetation (0.5 ≤ NDVI < 1.0), which is less sensitive to soil background effects and more sensitive to plant canopies and leaf parameters. Then, the relationships of ρ_nir–LAI and ρ_red–LAI were established using a look-up table algorithm for the two parts. Furthermore, a regularization technique for fast pixel-wise retrieval was introduced to reduce the elements of LUT sets while maintaining a relatively high accuracy. The results were very promising compared to the field measured LAI values that the correlation (R²) of the measured LAI values and retrieved LAI values reached 0.95, and the root-mean-square deviation (RMSD) was 0.33 for late August, 2011, while the R² reached 0.82 and RMSD was 0.25 for early July 2011.     

高分辨率光学遥感卫星在轨几何定标现状与展望

光学遥感卫星影像在轨几何定标是确保影像几何质量的必要环节。首先介绍了光学卫星在轨几何定标方法的研究现状;然后,对于当前主流的基于地面几何检校场的在轨几何定标方法,结合光学卫星地面分辨率不断提高的发展趋势,阐述了其受限于对参考影像的依赖而存在的问题;最后,论述了无需地面定标场的在轨自主几何定标的发展趋势,并提出了多种基于多角度交叉约束成像的在轨自主几何定标方法构想。     

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.     

Assessment of satellite ocean color products of MERIS,MODIS and SeaWiFS along the East China Coast (in the Yellow Sea and East China Sea)

The validation of satellite ocean-color products is an important task of ocean-color missions. The uncertainties of these products are poorly quantified in the Yellow Sea (YS) and East China Sea (ECS), which are well known for their optical complexity and turbidity in terms of both oceanic and atmospheric optical properties. The objective of this paper is to evaluate the primary ocean-color products from three major ocean-color satellites, namely the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Through match-up analysis with in situ data, it is found that satellite retrievals of the spectral remote sensing reflectance R_rs(λ) at the blue-green and green bands from MERIS, MODIS and SeaWiFS have the lowest uncertainties with a median of the absolute percentage of difference (APD_m) of 15–27% and root-mean-square-error (RMS) of 0.0021–0.0039 sr⁻¹, whereas the R_rs(λ) uncertainty at 412 nm is the highest (APD_m 47–62%, RMS 0.0027–0.0041 sr⁻¹). The uncertainties of the aerosol optical thickness (AOT) τ_a, diffuse attenuation coefficient for downward irradiance at 490 nm K_d(490), concentrations of suspended particulate sediment concentration (SPM) and Chlorophyll a (Chl-a) were also quantified. It is demonstrated that with appropriate in-water algorithms specifically developed for turbid waters rather than the standard ones adopted in the operational satellite data processing chain, the uncertainties of satellite-derived properties of K_d(490), SPM, and Chl-a may decrease significantly to the level of 20–30%, which is true for the majority of the study area. This validation activity advocates for (1) the improvement of the atmosphere correction algorithms with the regional aerosol optical model, (2) switching to regional in-water algorithms over turbid coastal waters, and (3) continuous support of the dedicated in situ data collection effort for the validation task.     

Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake,China

Based on in situ water sampling and field spectral measurements in Dianshan Lake, a semi-analytical three-band algorithm was used to estimate Chlorophylla (Chla) content in case II waters. The three bands selected to estimate Chla for high concentrations included 653, 691 and 748 nm. An equation, based on the difference in reciprocal reflectance between 653 and 691 nm, multiplied by reflectance at 748 nm as [R_rs⁻¹(653) − R_rs⁻¹ (691)] R_rs(748), explained 85.57% of variance in Chla concentration with a root mean square error (RMSE) of <6.56 mg/m³. In order to test the utility of this model with satellite data, HJ-1A Hyperspectral Imager (HSI) data were analyzed using comparable wavelengths selected from the in situ data [B₆₇⁻¹(656) − B₈₀⁻¹(716)] B₈₇(753). This model accounted for 84.3% of Chla variation, estimating Chla concentrations with an RMSE of <4.23 mg/m³. The results illustrate that, based on the determined wavelengths, the spectrum-based model can achieve a high estimation accuracy and can be applied to hyperspectral satellite imagery especially for higher Chla concentration waters.     

A soft-classification-based chlorophyll-a estimation method using MERIS data in the highly turbid and eutrophic Taihu Lake

Soft-classification-based methods for estimating chlorophyll-a concentration (C_chla) by satellite remote sensing have shown great potential in turbid coastal and inland waters. However, one of the most important water color sensors, the MEdium Resolution Imaging Spectrometer (MERIS), has not been applied to the study of turbid or eutrophic lakes. In this study, we developed a new soft-classification-based C_chla estimation method using MERIS data for the highly turbid and eutrophic Taihu Lake. We first developed a decision tree to classify Taihu Lake into three optical water types (OWTs) using MERIS reflectance data, which were quasi-synchronous (±3 h) with in situ measured C_chla data from 91 sample stations. Secondly, we used MERIS reflectance and in situ measured C_chla data in each OWT to calibrate the optimal C_chla estimation model for each OWT. We then developed a soft-classification-based C_chla estimation method, which blends the C_chla estimation results in each OWT by a weighted average, where the weight for each MERIS spectra in each OWT is the reciprocal value of the spectral angle distance between the MERIS spectra and the centroid spectra of the OWT. Finally, the soft-classification based C_chla estimation algorithm was validated and compared with no-classification and hard-classification-based methods by the leave-one-out cross-validation (LOOCV) method. The soft-classification-based method exhibited the best performance, with a correlation coefficient (R²), average relative error (ARE), and root-mean-square error (RMSE) of 0.81, 33.8%, and 7.0 μg/L, respectively. Furthermore, the soft-classification-based method displayed smooth values at the edges of OWT boundaries, which resolved the main problem with the hard-classification-based method. The seasonal and annual variations of C_chla were computed in Taihu Lake from 2003 to 2011, and agreed with the results of previous studies, further indicating the stability of the algorithm. We therefore propose that the soft-classification-based method can be effectively used in Taihu Lake, and that it has the potential for use in other optically-similar turbid and eutrophic lakes, and using spectrally-similar satellite sensors.     

Characterising invasive non-native Rhododendron ponticum spectra signatures with spectroradiometry in the laboratory and field: Potential for remote mapping

Compared with traditional ground surveys, remote sensing has the potential to map the spatial extent of non-native invasive species rapidly and reliably. This paper assesses the potential of spectroradiometry to distinguish and characterise the status of invasive non-native rhododendron (Rhododendron ponticum). Absolute reflectance of target plant material was measured with an ASD Fieldspec Pro System under standardised laboratory conditions and in the field to characterise spectral signatures in the winter, during leaf-off conditions for woodland overstory, and in the summer when mature rhododendrons are flowering. A logistic regression model of absolute reflectance at key wavelengths (490, 550, 610, 1040 and 1490 nm) was used to determine the success of discriminating rhododendron from three other shrubby species likely to be encountered in woodlands during the winter. The logistic regression model was highly significant (p < 0.001), with 93.5% of 246 leaf sets correctly identified as rhododendron or non-rhododendron (i.e. cherry laurel (Prunus laurocerasus), holly (Ilex aquifolium), and beech (Fagus sylvatica)). Rescaling the data to emulate the spectral resolution of airborne and satellite acquired data decreased the total success rate of correctly identifying rhododendron by only 0.4%; although this error rate will likely increase for airborne or satellite data as a result of atmospheric attenuation and reduced spatial resolution. This demonstrates the potential to map bush presence using hyperspectral data and indicates the optimum spectral wavelengths required. Such information is critical to the development of successful strategic management plans to eradicate rhododendron (and the associated Phytophthora ramorum pathogen) effectively from a site.     

Advanced vegetation indices for sensing paddy growth via hyperspectral measurements

In this study, field-based spectroradiometer measurements were carried out in 72 plots with eight rice varieties along with three nitrogen applications. The critical wavelengths for the studied rice varieties at which the effects of nitrogen applications on spectral reflectance response were found significant, are ρ₅₅₀, ρ₅₆₀, ρ₆₅₅, ρ₇₅₀, ρ₇₅₅, ρ₇₈₀, ρ₈₁₀, ρ₈₄₀, ρ₉₀₀, ρ₉₂₀, ρ₁₀₀₀, ρ₁₀₁₀, ρ₁₀₂₀. Using the critical wavelengths, three derived vegetation indices were compared with established indices (VI and GRI) sensitive to nitrogen. VI₁ and VI₂ were efficiently able to discriminate the level of nitrogen treatments for most of the rice genotypes whereas VI and GRI were unable to find significant difference in nitrogen treatments for rice genotypes except two. Furthermore, nitrogen difference plots exhibited that the paddy crop growth has started earlier with N₃ (150 kg/ha) than N₂ (100 kg/ha) application. Quantitative analysis of rice genotypes revealed that VI₂ can be applied functionally for the assessment of nitrogen stress in rice agriculture system.     

高分辨率卫星影像GPS像控点数据库建设研究

本通过对SPOT2.5m高分辨率卫星影像数据校正采用的各类控制资料的分析,阐述了GPS像控点数据库建设的必要性,通过对像控点的选取、外业施测、精度评价及GPS像控点数据库建设等论述,提出了选用GPS控制点作为SPOT2.5m高分辨率卫星影像数据校正控制资料,可保证影像校正精度、节省时间和减少投资。     

GF-1星WFV相机的快速大气校正

高分一号卫星(GF-1)WFV相机是中国新型高分辨率传感器,为了更好地进行定量应用,需完成高精度大气校正,但需要解决数量大,辅助数据不足等关键问题。针对WFV相机构建了快速大气校正模型,(1)采用交叉定标方法借助Landsat 8数据完成辐射定标;(2)从WFV相机的辅助数据出发,计算得到太阳天顶角、观测天顶角等辅助信息;(3)考虑不同海拔大气分子散射的不同,完成基于海拔数据的分子散射校正;(4)采用深蓝算法,从第一波段(蓝光)反演得到气溶胶信息;(5)计算每个像元的大气校正参数,进而获取地表反射率,完成大气校正。在此基础上,利用IDL语言建立相应的大气校正模块,以过境华北地区的3景WFV数据为例进行大气校正实验。结果表明,模型能够快速完成大气校正,并能较好的去除大气分子与气溶胶影响,较好地还原植被、裸土等典型地表类型的光谱反射曲线,校正后的NDVI更好地反映了各地物的特征。     

大气校正对SPOT卫星遥测水质的影响

藉由卫星遥测进行河川水质监测，目前尚没有较明确可行之方法，如何利用较为简单且适当的SPOT卫星遥测大气校正方法，正确辨识水体水质，是本研究的主要目的。利用SPOT卫星作两阶段非监督式及监督式自动分类，确认卫星影像中水质测站对应之水体样本，并将所有样本依季节分群，俾让卫星监测水体水质样本较为均质。模拟方式采用多变量回归、类神经网络及判别分析3种模式，并比较4种不同之大气校正程序。结果发现。以水质及其指标整体预测来看，类神经网络预测结果较优于多变量回归及判别分析的结果，大气校正方法以直接采用灰度值并消除最暗像元灰度值之校正方法，即可达到不错之预测结果。综合而言，以SPOT或分辨率更高之卫星光谱遥测水质是简单可行，但仍需更多数据以验证其精确度。