Multivariate approach to estimate colour producing agents in Case 2 waters using first-derivative spectrophotometer data

The complex composition and distribution of colour producing agents (CPAs) in turbid aquatic environments such as the Western Basin of Lake Erie (WBLE) presents a challenge to the application of remote sensing data for differentiating among in-water constituents and estimating their concentrations independently. In this study, multivariate procedures are applied to lab-based spectrophotometer data to estimate the concentration of chlorophyll-a and suspended matters in the WBLE. Principal Component Analysis of first-derivative transformed hyper-spectral data from the spectrophotometer extracted three significant spectral components for each cruise, explaining up to 88% of the spectral variability. Spectral matching using reference spectra indicated that two of the extracted patterns represent signatures of in-water constituents that govern the optical properties of the WBLE, namely, cyanobacteria and diatoms associated with green algae. The spectrophotometer data clearly revealed known spectral features associated with phytoplankton, such as the absorption minima near 550 and 700 nm, which can be attributed to the minimum of absorption and fluorescence of chlorophyll-a, respectively. The method also extracted the absorption peaks due to chlorophyll-a, near 670 nm, and due to phycocyanin, near 620 nm. Principal component regression of chlorophyll-a on the PC scores indicated that 63.4% of variation of chlorophyll-a in the WBLE can be explained by two components. Factors 2 and 3 explain 60% of the joint spatiotemporal variability of suspended matters in the WBLE. The results illustrate the potential of multivariate technique applied to remote sensing data in isolating the patterns that represent constituents in turbid Case 2 waters.     

A Four-Band Quasi-Analytical Algorithm With MODIS Bands for Estimating Chlorophyll-a Concentration in Turbid Coastal Waters

Accurate estimation of chlorophyll-a concentration in turbid coastal waters by means of remote sensing is challenging due to the optical complexity of these waters. We have developed a four-band quasi-analytical algorithm for assessment of chlorophyll-a concentration in coastal waters. The objectives of this study are to validate the applicability of three-band semi-analytical algorithm, quasi-analytical algorithm, and four-band quasi-analytical algorithm in estimating chlorophyll-a concentration in turbid coastal waters for MODIS sensor. These three algorithms are calibrated and evaluated against coastal evaluation datasets provided by SeaWiFS Bio-optical Archive and Storage System. The algorithm validation results indicate that the four-band quasi-analytical algorithm produces a superior performance to both three-band semi-analytical algorithm and quasi-analytical algorithm. By comparison, using four-band quasi-analytical algorithm produces 21.61 % uncertainty in estimating chlorophyll-a concentration from turbid coastal waters, lower than 77.90 % for three-band semi-analytical algorithm and 74.31 % for quasi-analytical algorithm, respectively. The significantly reduced uncertainty in chlorophyll-a concentration assessment is due to effectively removal of pigment-package effects and particle overlapping effects on the chlorophyll-a absorption estimation using a optical classification method. These findings imply that, provided that an atmospheric correction scheme for visible and near-infrared bands is available, the database of MODIS imagery could be used for quantitative monitoring of chlorophyll-a concentration in turbid coastal waters by four-band quasi-analytical algorithm.     

Studies on Textural and Compositional Characteristics of Sand and Clay Mixtures Using Hyperspectral Radiometry

This paper examines the hyperspectral signatures (in the Visible Near Infrared (VNIR)-Shortwave Infrared (SWIR) regions) of soil samples with varying colour and minerals. 36 samples of sands (from river and beach) with differing clay contents were examined using a hyperspectral radiometer operating in the 350–2,500 nm range, and the spectral curves were obtained. Analysis of the spectra indicates that there is an overall increase in the reflectance in the VNIR-SWIR region with an increase in the content of kaolinite clay in the sand samples. As regards the red and black clays and sand mixtures, the overall reflectance increases with decreasing clay content. Several spectral parameters such as depth of absorption at 1,400 nm and 1,900 nm regions, radius of curvature of the absorption troughs, slope at a particular wavelength region and the peak reflectance values were derived. There exists a correlation between certain of these spectral parameters (depth, slope, position, peak reflectance, area under the curve and radius of the curve) and the compositional and textural parameters of the soils. Based on these well-defined relations, it is inferred that hyperspectral radiometry in the VNIR and SWIR regions can be used to identify the type of clay and estimate the clay content in a given soil and thus define its geotechnical category.     

机载LiDAR点云密度对DEM精度的影响

机载激光雷达技术(LiDAR)作为一项先进的遥感技术,是植被覆盖区DEM获取的重要手段之一,而不同地形坡度条件及点云密度对DEM产品质量有重要影响。本文以辽宁省某市的机载LiDAR数据为基础,选取5种不同地形坡度的点云数据,通过随机、等间距及基于曲率3种不同的点云抽稀方法,按照点云保留率为80%、60%、40%、20%和10%共5个不同梯度的抽稀倍数对原始点云进行抽稀简化处理,生成与之对应的DEM并对其进行精度评价,以此研究地形坡度、点云抽稀方法、抽稀倍数对DEM精度的影响。结果表明,DEM精度与地形坡度呈负相关关系,即RMSE随地形坡度升高不断增加;基于曲率的抽稀方法在地形坡度＞30°时,相较于其他两种方法RMSE较小,具有明显优势;40%的点云保留率是平衡DEM精度与数据存储效率的一个节点,当点云保留率<40%时,DEM的高程RMSE会迅速增大。该研究对于利用机载LiDAR进行大范围DEM生产具有一定的指导和借鉴意义。     

石油类污染水体吸收特性分析

石油类污染物主要以漂浮油、分散油、乳化油和分解油等形式存在于水体中,影响着水体的表观和固有光学特性。利用2008年5月在辽宁省盘锦市辽河油田境内双台子河和绕阳河所测定的水色三要素吸收系数以及对应的水体组分数据,分析了研究区域内Ⅱ类水体水色三要素的吸收光谱特性;探讨了水体中由于石油类污染物的存在,黄色物质和色素吸收光谱的变化特征;采用差值法确定了水体中石油类污染物的吸收系数。研究结果表明:(1)研究区域内,无论是石油类污染水体还是非石油类污染水体,非色素颗粒物和黄色物质的吸收光谱都遵循e指数衰减规律,色素吸收光谱在440nm和675nm处有以叶绿素为主的典型的色素吸收峰,在490nm处有类胡萝卜素的吸收峰;(2)对于石油类污染水体,在测定黄色物质和色素吸收系数时,由于包含了石油类污染物的作用,会增大黄色物质和色素的吸收系数;(3)石油类物质的吸收光谱曲线和非色素颗粒物、黄色物质一样遵循e指数衰减规律,但三者的指数函数斜率有明显的差别,这为将三者区分开来提供了可行性。     

A simple two-band semi-analytical model for retrieval of specific absorption coefficients in coastal waters

A non-linear iterative method is used to replace the traditional spectral slope technique in initializing the total absorption decomposition model. Based on comparison of absorption coefficient by QAA and two-band semi-analytical model (TSAA) models with field measurements collected from the West Florida Shelf waters and Bohai Sea, it is shown that both models are effective in estimating absorption coefficients from the West Florida Shelf waters, but the TSAA model is superior to the QAA model. Use of the TSAA model in estimating absorption coefficient in the West Florida Shelf and Bohai Sea decreases the uncertainty of estimation by 1.3–74.7% from the QAA model. The TSAA model’s sensitivity to the input parameters was evaluated by varying one parameter and keeping the others fixed at their default values. Our results indicate that the TSAA model has quite a strong noise tolerance to addressing the field data of the total absorption coefficient.     

Comparison of Inherent Optical Properties (IOPs) in Open and Coastal Waters of Arabian Sea Using In-Situ Bio-Optical Data

The two main inherent optical properties (IOPs) namely absorption and back scattering coefficients were estimated using a quasi analytical algorithm (QAA) for open and coastal ocean waters of Arabian Sea. Absorption due to gelbstoff and back scattering due to the particulate matter were calculated using the quasi analytical algorithm for all the in-situ measured reflectance spectra collected in the Arabian Sea. A comparative study was made to study the spectral variability of reflectance spectra in open as well as coastal waters of Arabian Sea. Spectral analysis was made for the absorption and back scattering coefficients calculated using the QAA for both open and coastal waters. The absorption coefficient in the open ocean waters vary from a minimum value of 0.029 to a maximum value of 0.445 and it varies from a minimum value of 0.081 to a maximum value of 4.000 for the coastal waters of Arabian Sea. Absorption due to gelbstoff or the CDOM a_g(λ), calculated for the Arabian Sea waters show a variation of 0.000202 to 0.112437 for open ocean waters and it varies from 0.002848 to 2.8936 for coastal waters of Arabian Sea. Particulate back scattering coefficient for open ocean waters vary from 0.0000307 to 0.006575 whereas b_bp(λ) vary from 0.000167 to 0.026014 for coastal ocean waters. The minimum slope for the open ocean waters is 0.989 and maximum value of 2.147 (average value of 1.7) was observed; whereas a minimum value of 0.046 and a maximum value of 1.201 (average value of 0.6) were observed from the in-situ spectra for coastal waters of Veraval. The slope ‘Y’ estimated from the model is 1.957 for open ocean waters and 0.515 for coastal waters collected in the Arabian Sea.     

Multiple statistical approaches for the discrimination of mangrove species of Rhizophoraceae using transformed field and laboratory hyperspectral data

This study aims at discriminating eight mangrove species of Rhizophoraceae family of Indian east coast using field and laboratory spectra in spectral range (350–2500 nm). Parametric and non-parametric statistical analyses were applied on spectral data in four spectral modes: (i) reflectance (ii) continuum removed, (iii) additive inverse and (iv) continuum removed additive inverse. We introduced continuum removal of inverse spectra to utilize the advantage of continuum removal in reflectance region. Non-parametric test gave better separability than parametric test. Principal component analysis and stepwise discriminant analysis were applied for feature reduction and to identify optimal wavelengths for species discrimination. To quantify the separability, Jeffries–Matusita distance measure was derived. Green (550 nm), red edge (680–720 nm) and water absorption region (1470 and 1850 nm) were found to be optimal wavelengths for species discrimination. The continuum removal of additive inverse spectra gave better separability than the continuum removed spectra.     

Using spectral information from the NIR water absorption features for the retrieval of canopy water content

Canopy water content (CWC) is important for mapping and monitoring the condition of the terrestrial ecosystem. Spectral information related to the water absorption features at 970 nm and 1200 nm offers possibilities for deriving information on CWC. In this study, we compare the use of derivative spectra, spectral indices and continuum removal techniques for these regions. Hyperspectral reflectance data representing a range of canopies were simulated using the combined PROSPECT + SAILH model. Best results in estimating CWC were obtained by using spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features. Real data from two different test sites were analysed. Spectral information at both test sites was obtained with an ASD FieldSpec spectrometer, whereas at the second site HyMap airborne imaging spectrometer data were also acquired. Best results were obtained for the derivative spectra. In order to avoid the potential influence of atmospheric water vapour absorption bands the derivative of the reflectance on the right slope of the canopy water absorption feature at 970 nm can best be used for estimating CWC.     

Retrieval of Brown Carbon based on the aerosol complex refractive indices in the winter of Wuhan

Abstract

The impact of Brown Carbon (BrC) to aerosol light absorption has been paid more attention recently and there are a large number of studies showing that the influence of BrC on radiative forcing should not be ignored. BrC also acts as an important component of haze pollution which is occurring frequently in Wuhan, China. Therefore, it is essential to estimate their optical properties, composition, and mass concentration. Considering most haze pollution happens during the coldest time, we retrieved BrC columnar content during winter in Wuhan for the first time. Our method bases on the fact that BrC showed the strong spectral dependence on UV-light absorption. Using this method, we found that BrC makes up the small proportions of total aerosol volume (less than 10%). In the winter of 2011, we retrieved the daily-averaged columnar-integrated mass concentration of BrC on clear day is 4.353 mg/m² while that of haze day is 12.750 mg/m². According to the sensitivity study, we found that the results highly relied on the assumed aerosol refractive index. To reduce the uncertainty of this approach, we need to gain a better understanding of the temporal variability of the radiation absorbing components of these aerosols in the future.     

On the significance of periodic signals in noise analysis of GPS station coordinates time series

Each of the GPS-derived time series consists of the deterministic (functional) and stochastic part. We propose that the deterministic part includes all periodicities from 1st to 9th harmonics of residual Chandler, tropical and draconitic periods and compare it with commonly used calculations of the annual and semi-annual tropical curve. Then, we address the issues of whether all residual periodicities, as proposed here, need to be taken into consideration when performing noise analysis. We use the position time series from 180 International GNSS Service stations obtained at the Jet Propulsion Laboratory using the GIPSY-OASIS software in a Precise Point Positioning mode. The longest series has 22.1 years of GPS daily solutions. The spectral indices range from –0.12 to –0.92, while the median values of “global” spectral indices are equal to: –0.41 ± 0.15, –0.38 ± 0.12 and –0.33 ± 0.18 for North, East and Up components, respectively. All non-modelled geophysical processes or non-included artificial effects in time series lead to an underestimation of errors of velocities, but also to changes in the velocity values themselves. The proposed assumption of seasonals subtraction caused the Akaike information criterion values to show a decrease in the median value of 30 %, which in fact means that all the seasonals mentioned here must be taken into account when analyzing noises. Finally, we noticed that there are some of the GPS stations that improved their velocity uncertainty even of 56 %.     

Tree Diversity Detection with Mid-Resolution Images and Environmental Data in a Neural Network

Biodiversity maps are crucial to conservation management. The present study assesses the accuracy of detecting tree diversity in an Italian forest site by combining mid-resolution images from Landsat-TM or Advanced Land Observation Satellite (ALOS)’s Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) sensors with environmental data namely elevation, slope, aspect and solar radiation in an artificial Neural Network (NN) classifier. The map accuracies obtained for Landsat-TM and ALOS images are 60 % and 53 % respectively. Use of environmental data increases accuracies to 91 % and 81 % respectively. Landsat-TM detects tree diversity more accurately than ALOS. Both the coarser pixel size and finer spectral resolution of Landsat-TM contributed to its higher accuracy.     

面向土壤分类的高光谱反射特征参数模型

提出了一种无损、快速、成本低的土壤分类方法,选取松嫩平原4种典型土壤(黑土、黑钙土、风砂土和草甸土)耕层(0—20 cm)土样的实验室反射光谱数据作为研究对象,采用重采样、包络线消除法处理光谱数据,提取反映反射光谱特征的光谱特征参数,利用K均值聚类(K-means clustering)和决策树(decision tree)分别进行聚类分析和分类模型构建,实现土壤的快速分类。结果表明,利用表层土壤反射光谱特征参数构建的决策树分类模型可以对研究区土壤进行分类。研究成果有望加快土壤制图,为土壤理化性质的时空变化研究提供技术支持。     

An improved atmospheric correction algorithm for applying MERIS data to very turbid inland waters

Atmospheric correction (AC) is a necessary process when quantitatively monitoring water quality parameters from satellite data. However, it is still a major challenge to carry out AC for turbid coastal and inland waters. In this study, we propose an improved AC algorithm named N-GWI (new standard Gordon and Wang’s algorithms with an iterative process and a bio-optical model) for applying MERIS data to very turbid inland waters (i.e., waters with a water-leaving reflectance at 864.8 nm between 0.001 and 0.01). The N-GWI algorithm incorporates three improvements to avoid certain invalid assumptions that limit the applicability of the existing algorithms in very turbid inland waters. First, the N-GWI uses a fixed aerosol type (coastal aerosol) but permits aerosol concentration to vary at each pixel; this improvement omits a complicated requirement for aerosol model selection based only on satellite data. Second, it shifts the reference band from 670 nm to 754 nm to validate the assumption that the total absorption coefficient at the reference band can be replaced by that of pure water, and thus can avoid the uncorrected estimation of the total absorption coefficient at the reference band in very turbid waters. Third, the N-GWI generates a semi-analytical relationship instead of an empirical one for estimation of the spectral slope of particle backscattering. Our analysis showed that the N-GWI improved the accuracy of atmospheric correction in two very turbid Asian lakes (Lake Kasumigaura, Japan and Lake Dianchi, China), with a normalized mean absolute error (NMAE) of less than 22% for wavelengths longer than 620 nm. However, the N-GWI exhibited poor performance in moderately turbid waters (the NMAE values were larger than 83.6% in the four American coastal waters). The applicability of the N-GWI, which includes both advantages and limitations, was discussed.     

An Accuracy Accessing Approach for Chlorophyll-a Concentration Retrieval Results

In this study, a MDC3A algorithm (Multi-Data Crossing Algorithm for Accuracy Accessing) was developed for accessing the accuracy of chl-a (chlorophyll-a) retrieval model in case of no sufficient available in situ measurements. Three simple estimation algorithms of chl-a concentration, i.e., two-band algorithm, three-band algorithm and four-band algorithm, were used as input dataset of MDC3A algorithm to illuminate its performance. These three simple algorithms were calibrated and validated by calibration and validation dataset collected on October 27–28, 2003. According to model calibration and validation results, it was found that the four-band algorithm (R ²?=?0.8676) had a superior performance to the two-band (R ²?=?0.5061) and three-band (R ²?=?0.5142) algorithm. The uncertainties in modeling prediction of these three simple algorithms were underestimated as 0.07 %, 0.04 % and 8.07 % for calibration dataset and 8.38 %, 9.33 % and 9.37 % for validation dataset by MDC3A algorithm through comparison with in situ measurements. Because the MDC3A algorithm was able to detect the random errors from measured values, but had an inadequate ability to detect systematical errors and gross errors from measured values. The uncertainty estimated by MDC3A algorithm was usually lower than that estimated by in situ measurements.     

Spatial distribution of altered minerals in the Gadag Schist Belt (GSB) of Karnataka,Southern India using hyperspectral remote sensing data

Spatial distribution of altered minerals in rocks and soils in the Gadag Schist Belt (GSB) is carried out using Hyperion data of March 2013. The entire spectral range is processed with emphasis on VNIR (0.4–1.0 μm) and SWIR regions (2.0–2.4 μm). Processing methodology includes Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes correction, minimum noise fraction transformation, spectral feature fitting (SFF) and spectral angle mapper (SAM) in conjunction with spectra collected, using an analytical spectral device spectroradiometer. A total of 155 bands were analysed to identify and map the major altered minerals by studying the absorption bands between the 0.4–1.0-μm and 2.0–2.3-μm wavelength regions. The most important and diagnostic spectral absorption features occur at 0.6–0.7 μm, 0.86 and at 0.9 μm in the VNIR region due to charge transfer of crystal field effect in the transition elements, whereas absorption near 2.1, 2.2, 2.25 and 2.33 μm in the SWIR region is related to the bending and stretching of the bonds in hydrous minerals (Al-OH, Fe-OH and Mg-OH), particularly in clay minerals. SAM and SFF techniques are implemented to identify the minerals present. A score of 0.33–1 was assigned for both SAM and SFF, where a value of 1 indicates the exact mineral type. However, endmember spectra were compared with United States Geological Survey and John Hopkins University spectral libraries for minerals and soils. Five minerals, i.e. kaolinite-5, kaolinite-2, muscovite, haematite, kaosmec and one soil, i.e. greyish brown loam have been identified. Greyish brown loam and kaosmec have been mapped as the major weathering/altered products present in soils and rocks of the GSB. This was followed by haematite and kaolinite. The SAM classifier was then applied on a Hyperion image to produce a mineral map. The dominant lithology of the area included greywacke, argillite and granite gneiss.     

Age-based classification of arecanut crops: a case study of Channagiri,Karnataka, India

Arecanut is one of the predominant plantation crop grown in India. Yield of this crop depends upon age of the crop and there is no information on the spectral behaviour of arecanut crops across its ages. In this study popular supervised classification algorithms were utilized for age discrimination of arecanut crops using Hyperion imagery. Arecanut plantations selected for the study are located in Channagiri Taluk, Davanagere district of Karnataka state, India. Ground truth information collected involves: (i) GPS coordinates of selected plots, (ii) spectral reflectance of arecanut crops with age ranging from 1 to 50 years, using handheld spectroradiometer with 1 nm spectral resolution. These spectral measurements were made close in time to the acquisition of Hyperion imagery to build age-based spectral library. It is observed from the analysis that crops of ages below 3, 3–7, 8–15 and above 15 years were showing distinct spectral behaviour. Accordingly, crops age ranging from 1 to 50 were grouped into four classes. Classification of arecanut crops based on age groups was performed using methods like spectral angle mapper, support vector machine and minimum distance classifier, and were compared to find the most suitable method. Among the classification methods adopted, support vector machine with linear kernel function resulted in most accurate classification method with overall accuracy of 72% for within class seperability. Individual age group classification producer’s accuracy varied minimum of 12.5% for 3–7 years age group and maximum of 86.25% for above 15 years age group. It may be concluded that, not only age- based arecanut crop classification is possible, but also it is possible to develop age-based spectral library for plantation crop like arecanut.     

利用中心差分法进行GPS定速时最佳点数的选取

分析了利用中心差分法进行GPS定速时的主要误差来源,证明了当数据采样率一定时,增加中心差分法的点数可减少微分过程中的截断误差,但同时会放大导出相位率的观测误差,得出中心差分法的最佳点数应使这两种误差之和最小的结论。实验结果表明,当数据采样间隔为1s、载体平均速度和加速度为20m.s-1和0.2m.s-2时,9个点的中心差分法定速精度最高。     

Improved method for estimating the inter-frequency satellite clock bias of triple-frequency GPS

Considering the contribution of the hardware biases to the estimated clock errors, an improved method for estimating the satellite inter-frequency clock bias (IFCB) is presented, i.e., the difference in the satellite clock error as computed from ionospheric-free pseudorange and carrier phase observations using L1/L2 and P1/P2 versus L1/L5 and P1/P5. The IFCB is composed of a constant and a variable part. The constant part is the inter-frequency hardware bias (IFHB). It contains the satellite and receiver hardware delays and can be expressed as a function of the DCBs [DCB (P1 ? P2) and DCB (P1 ? P5)]. When a reference satellite is selected, the satellite IFHB can be computed but is biased by a reference satellite IFHB. This bias will not affect the utilization of IFCB in positioning since it can be absorbed by the receiver clock error. Triple-frequency observations of 30 IGS stations between June 1, 2013, and May 31, 2014, were processed to show the variations of the IFHB. The IFHB values show a long-term variation with time. When a linear and a fourth-order harmonic function are used to model the estimated IFCB, which contains contributions of the hardware delays and clock errors, the results show that 89 % of the IFCB can be corrected given the current five triple-frequency GPS satellites with the averaged fitting RMS of 1.35 cm. Five days of data are processed to test the estimated satellite clock errors using the strategy presented. The residuals of P1/P5 and L1/L5 have a STD of <0.27 m and 0.97 cm, respectively. In addition, most predicted satellite IFCBs reach an accuracy of centimeter level and its mean accuracy of 5 days is better than 7 cm.     

Re-parameterization of a quasi-analytical algorithm for colored dissolved organic matter dominant inland waters

Quasi-Analytical Algorithms (QAAs) are based on radiative transfer equations and have been used to derive inherent optical properties (IOPs) from the above surface remote sensing reflectance (R_rs) in aquatic systems in which phytoplankton is the dominant optically active constituents (OACs). However, Colored Dissolved Organic Matter (CDOM) and Non Algal Particles (NAP) can also be dominant OACs in water bodies and till now a QAA has not been parametrized for these aquatic systems. In this study, we compared the performance of three widely used QAAs in two CDOM dominated aquatic systems which were unsuccessful in retrieving the spectral shape of IOPS and produced minimum errors of 350% for the total absorption coefficient (a), 39% for colored dissolved matter absorption coefficient (a_CDM) and 7566.33% for phytoplankton absorption coefficient (a_phy). We re-parameterized a QAA for CDOM dominated (hereafter QAA_CDOM) waters which was able to not only achieve the spectral shape of the OACs absorption coefficients but also brought the error magnitude to a reasonable level. The average errors found for the 400–750 nm range were 30.71 and 14.51 for a, 14.89 and 8.95 for a_CDM and 25.90 and 29.76 for a_phy in Funil and Itumbiara Reservoirs, Brazil respectively. Although QAA_CDOM showed significant promise for retrieving IOPs in CDOM dominated waters, results indicated further tuning is needed in the estimation of a(λ) and a_phy(λ). Successful retrieval of the absorption coefficients by QAA_CDOM would be very useful in monitoring the spatio-temporal variability of IOPS in CDOM dominated waters.