Remote sensing retrieval of surface suspended sediment concentration in the Yellow River Estuary

Accurate assessment of surface suspended sediment concentration(SSSC) in estuary is essential to address several important issues: erosion, water pollution, human health risks, etc. In this study, an empirical cubic retrieval model was developed for the retrieval of SSSC from Yellow River Estuary. Based on sediments and seawater collected from the Yellow River and southeastern Laizhou Bay, SSSC conditions were reproduced in the laboratory at increasing concentrations within a range common to field observations. Continuous spectrum measurements of the various SSSCs ranging from 1 to 5700 mg/l were carried out using an Ava Field-3 spectrometer. The results indicated the good correlation between water SSSC and spectral reflectance(Rrs) was obtained in the spectral range of 726–900 nm. At SSSC greater than 2700 mg/L, the 740–900 nm spectral range was less susceptible to the effects of spectral reflectance saturation and more suitable for retrieval of high sediment concentrations. The best correlations were obtained for the reflectance ratio of 820 nm to 490 nm. Informed by the correlation between Rrs and SSSC, a retrieval model was developed(R2 = 0.992). The novel cubic model, which used the ratio of a near-infrared(NIR) band(740–900 nm) to a visible band(400–600 nm) as factors, provided robust quantification of high SSSC water samples. Two high SSSC centers, with an order of 103 mg/l, were found in the inversion results around the abandoned Diaokou River mouth, the present Yellow River mouth to the abandoned Qingshuigou River mouth. There was little sediment exchange between the two high SSSC centers due to the directions of the residual currents and vertical mixing.     

Estimation of total suspended matter in the Zhujiang (Pearl) River estuary from Hyperion imagery

Although remote sensing data have been used to estimate total suspended matter (TSM) in coastal waters, it has limitations when applied to estuary waters in low spatial resolution situations. The spatial resolution of ocean color satellites such as SeaWiFS and MODIS is usually ～1 km, and therefore is not adequate for small, local-scale areas such as the Zhujiang (Pearl) River estuary. In contrast, 30 m-resolution EO-1 Hyperion imagery has potential for studying TSM in localized areas. We measured the surface spectral radiance reflectance of the river estuary water in the visible and near infra-red spectral range. Sensitivity analysis indicated that the ratio of remote sensing reflectance at 813 nm (Rrs(813)) to reflectance at 559 nm (Rrs(559)) could be used to estimate TSM concentration, and a linear relationship was established between the ratio and in-situ TSM concentration. We applied the linear relationship to Hyperion imagery to map TSM concentration in the estuary. The Hyperion imagery provided sufficient spatial resolution to detect spatiotemporal changes in TSM concentrations in the estuary small estuary area. This study demonstrated the usefulness of Hyperion imagery for mapping the distribution of TSM in estuary waters.     

Feasibility of using reflectance spectroscopy for the analysis of bio-element concentrations in Antarctic ornithogenic sediments

Compared with traditional chemical analysis methods, reflectance spectroscopy has the advantages of speed, minimal or no sample preparation, non-destruction, and low cost. The present study explored the application of the reflectance spectroscopy within near ultraviolet-visible-near infrared region to predict bio-element compositions in the ornithogenic sediments from the maritime Antarctic. A total of 106 samples were taken from four ornithogenic sediment cores on the Ardley Island of Antarctica, 68 samples were used for building calibration equation, and 38 for prediction of nine bio-elements including P, Ca, Cu, Zn, Se, Sr, Ba, F and S. Three multivariate statistical analysis techniques, including stepwise multiple linear regression (Stepwise-MLR), principal component regression (PCR) and partial least squares regression (PLS) were used to develop mathematical relationships between the spectral data and the chemical reference data. The results showed that the regression models constructed by PCR and PLS models have no significant differences, and obviously supervisor to Stepwise-MLR. The correlations between spectra-predicted and chemically analyzed concentrations of nine bio-elements are statistically significant, and the concentration-versus-depth profiles predicted from reflectance spectra using PLS calibration model are consistent with those from actual chemical analysis. These results demonstrated the feasibility of using reflectance spectroscopy to infer bio-element concentrations in the ornithogenic sediments, and thus it is suggested that the reflectance spectroscopy could provide a rapid and valuable technique to indirectly identify whether the sediments were influenced by penguin droppings in the Antarctic region.     

土壤有机质含量估测及其影响因素的光谱分析

 土壤颗粒大小差异使土壤反射光谱产生相应变化,影响土壤有机质含量等属性的光谱预测精度。本研究准备了颗粒粒径分别为2、0.25和0.15mm的土样,测定土壤有机质(Soil Organic Matter,SOM)含量,并于室内模拟条件下测定其反射光谱。通过分析不同粒径土样的原始(Raw)、多次散射校正(Multiple scattering correction, Msc)、一阶微分(First derivative, Fd)、连续统去除(Continuum removal, Cr)光谱与SOM含量之间的关系,筛选出与SOM含量相关性最强的Fd光谱单波段(2250nm, r=0.82, P<0.01),并建立线性回归模型;利用全波段光谱反射率,以偏最小二乘回归(Partial least square regression, PLSR)方法,确立2mm土样Msc处理光谱的PLSR模型为最优模型(RPD=3.56、R²=0.90、RMSEP=1.96g/kg)。土壤颗粒粒径对土壤光谱反射率变化有明显影响,但二者之间并非简单的线性关系,可能存在一个转折点;单变量(单波段光谱反射率)线性回归模型的预测能力,明显低于全波段反射光谱(Msc处理)-PLSR模型;土样样本容量对SOM含量预测精度有显著影响。因此,根据样本容量大小,选择合适的土壤颗粒粒径与光谱预处理方法组合可以提高预测精度。     

Soil and Vegetation Spectral Coupling Difference (SVSCD) for Minerals Extraction from Hyperion Data in Vegetation Covered Area

Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant (SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set’s reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis (PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting (SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.     

Detection of acid rain stress effect on plant using hyperspectral data in Three Gorges region, China

This paper aims to use hyperspectral data to detect the spectral change caused by acid stress to a native forest type in the Three Gorges region of China. For this purpose, a ground-based hyperspectral experiment was conducted at the Three Gorges region to detect acid deposition that caused Masson pine （Pinus massoniana） forest degradation. Continuum removal method was used to isolate wavebands more responsive to stress in wavelengths 450-750nm. The differences in chlorophyll concentrations and needle thickness caused by acidic stress are found to be explicable to the different spectral reflectance patterns in the visible and near-infrared wavelengths. Two new chlorotic indices were utilized to explain the stress-caused leaf chlorosis. The comparison of simulated vegetation indices and principal component analysis （PCA） results suggests that it would be possible to monitor acid rain stress effect on forest ecosystem from some wider spectral regions.     

基于超参数优化CatBoost算法的河流悬浮物浓度遥感反演

悬浮物浓度（TSM）是水生态环境评价的重要参数之一,及时掌握河流悬浮物浓度动态变化信息对于内陆水质监测、水环境治理是十分必要的。本研究基于野外实测光谱和悬浮物浓度数据,筛选与悬浮物浓度高度相关的波段组合反射率作为自变量,基于CatBoost、随机森林和多元线性回归算法构建悬浮物浓度遥感反演模型,采用带交叉验证的网格搜索法分别对CatBoost和随机森林2种机器学习模型进行超参数调优,确定模型最优参数配置,并对比不同模型反演精度,确定最优模型。基于最优模型,利用2019—2020年多时相Sentinel-2 MSI遥感影像,反演闽江下游悬浮物浓度,并分析其时空变化特征。结果表明：(1) b4/b3、(b6-b3)/(b6+b3)、(b4+b8)/b3、(1/b3-1/b4)×b5是MSI反演闽江下游TSM浓度的最佳波段组合反射率;(2)对比其他2种模型,基于超参数优化的CatBoost算法建立的悬浮物反演模型精度最高,其决定系数R2为0.95,均方根误差RMSE和平均绝对百分比误差MAPE分别为15.32 mg/L和19.68%;(3) 2019—2020年闽江下游悬浮物浓度分布“西低东...     

CBERS-02星CCD波段平均太阳辐照度反演及应用

卫星图像波段平均太阳辐照度是计算表观反射率的重要参数。本文采用两种方法对CBERS-02星CCD波段平均太阳辐照度进行了计算,一种方法是利用大气辐射传输软件SBDART模拟大气顶部的太阳光谱曲线,结合CBERS-02星CCD波段响应曲线,计算1～4波段的平均太阳辐照度;另一种方法是用6S模型对波段平均太阳辐照度进行计算。为验证计算结果,用同样的方法,计算了LandsatTM5和ETM的波段平均太阳辐照度,与Landsat公布的数据进行对比,结果表明,用模拟的太阳光谱曲线的计算结果更准确。以2005年8月24日的一景图像为例,计算了图像的表观反射率,经6S模型大气校正后,反演了地表反射率,与地面实测的地表反射率进行了对比,除第四波段误差为4%外,其余1～3波段误差仅为0.01%。     

星载高光谱Hyperion数据在海滩涂调查应用中的分析

以江苏苏北地区为研究区,较系统地分析了目前世界上星载传感器中光谱分辨率最高的EO-1卫星Hyperion数据的基本特点和处理方法,研究了将图像的灰度值转变为辐射能量值和反射率的方法和步骤,并选取了15类典型地物,进行了样本采集、辐射亮度和反射率反演,最后对典型地物的光谱曲线进行综合分析研究,并概括了苏北沿海滩涂典型地物的基本光谱特征。     

土壤粒径的光谱响应特性研究

以实验室制备的5个不同粒径水平的土壤样本和室内高光谱数据为基础,通过对光谱数据进行重采样、数学变换等预处理并进行单因素方差分析、相关性分析和回归分析,探讨土壤粒径的高光谱特性,建立了光谱数据预测土壤粒径的校正模型。结果表明,土壤粒径对反射光谱有显著的影响,波长越长影响越大;在全波段范围内土壤粒径和光谱数据都呈负相关关系,对原始光谱数据进行微分变换能增加其与土壤粒径的相关性;以反射率一阶微分建立的回归模型为反演土壤粒径的最佳模型,其建模决定系数■、预测决定系数■、预测相对偏差RPD分别为0.666,0.653,2.043,预测均方根误差RMSE为0.175。     

黑臭水体水面阴影提取的自适应阈值算法研究

黑臭水体水面阴影对水面光谱信息产生干扰,严重地影响了利用高空间分辨率遥感数据进行水质状况监测的精度,因此,在数据预处理中必须进行阴影剔除。本研究基于无人机高光谱遥感数据,通过分析各种波段组合下黑臭水体水面的阴影像元和水体像元的光谱特征空间,选择以492、666和792 nm处的反射率建立黑臭水体的河面阴影指数（RSSI）,并利用最大类间方差法（OTSU）自动确定划分本影、半影以及水体的阈值。利用南京金川河和龙江河的无人机高光谱遥感影像对算法进行测试,结果表明：RSSI阴影指数能突出显示阴影与水体的差异;OTSU自适应确定的阈值能较好地区分本影、半影和水体,阴影的总体识别精度达到85%以上。该算法能够有效地识别黑臭水体水面阴影,为后续开展水体的定性、定量遥感监测提供数据预处理的技术支持。     

基于高分辨率遥感影像的湿地互花米草-芦苇混合交错带提取方法

互花米草是中国东部河口滩涂湿地主要入侵物种之一,其与本地物种芦苇竞争生长,形成了大范围的混合交错带。该交错带是研究湿地生态系统动态变化的重要信息,但因2种物种光谱的相似性及其在交错带的组成复杂性,使利用遥感技术提取交错带难度较大。因此,本文提出了一种将二者生长物候差异与其光谱特征相结合,考虑二者海陆位置分布差异,运用实测剖面观测数据确定光谱指标和阈值的综合提取方法。运用高分一号多光谱遥感数据,通过分析不同时相互花米草与芦苇冠层光谱差异,确定用来提取混合交错带的高分遥感影像,实现了研究区互花米草-芦苇混合交错带的提取。结果表明：不同时相宜选用不同的提取指标,本研究中在春季选择了近红外波段反射率,而秋季则选择了红波段反射率;2个时相的混合交错带范围存在明显差异,客观反映了互花米草与芦苇在不同季节的竞争状况。     

高分六号影像在内陆水体叶绿素a反演中的应用潜力分析

GF-6 WFV影像具有宽覆盖、高时空分辨率、高光谱分辨率等特点,目前在农业和林业遥感领域都有一定应用,但是在水质遥感中的应用潜力还缺乏系统的评估。本文以潘家口和大黑汀水库为研究区,采用2019年9月24—25日获取的潘家口和大黑汀水库叶绿素a浓度、实测遥感反射率和准同步GF-6 WFV影像,构建了潘家口和大黑汀水库叶绿素a浓度经验反演模型,探索GF-6 WFV在内陆水体叶绿素a浓度遥感监测中的应用潜力。研究结果表明,基于GF-6 WFV模拟光谱构建的潘家口和大黑汀水库叶绿素a浓度经验模型决定系数均在0.90以上,GF-6 WFV影像在水体叶绿素a遥感监测中具有应用潜力,尤其是新增的黄波段和红边波段1,有助于提高GF-6 WFV影像叶绿素a浓度遥感监测能力;GF-6 WFV影像大气校正误差降低了叶绿素a浓度遥感监测精度,GF-6 WFV影像水体大气校正精度有待改进,以提升GF-6 WFV影像水质遥感监测能力。     

Evaluation of spectral scale effects in estimation of vegetation leaf area index using spectral indices methods

Spectral index methodology has been widely used in Leaf Area Index (LAI) retrieval at different spatial scales. There are differences in the spectral response of different remote sensors and thus spectral scale effect generated during the use of spectral indices to retrieve LAI. In this study, PROSPECT, leaf optical properties model and Scattering by Arbitrarily Inclined Layers (SAIL) model, were used to simulate canopy spectral reflectance with a bandwidth of 5 nm and a Gaussian spectral response function was employed to simulate the spectral data at six bandwidths ranging from 10 to 35 nm. Additionally, for bandwidths from 5 to 35 nm, the correlation between the spectral index and LAI, and the sensitivities of the spectral index to changes in LAI and bandwidth were analyzed. Finally, the reflectance data at six bandwidths ranging from 40 to 65 nm were used to verify the spectral scale effect generated during the use of the spectral index to retrieve LAI. Results indicate that Vegetation Index of the Universal Pattern Decomposition (VIUPD) had the highest accuracy during LAI retrieval. Followed by Normalized Difference Vegetation Index (NDVI), Modified Simple Ratio Indices (MSRI) and Triangle Vegetation Index (TVI), although the coefficient of determination R ² was higher than 0.96, the retrieved LAI values were less than the actual value and thus lacked validity. Other spectral indices were significantly affected by the spectral scale effect with poor retrieval results. In this study, VIUPD, which exhibited a relatively good correlation and sensitivity to LAI, was less affected by the spectral scale effect and had a relatively good retrieval capability. This conclusion supports a purported feature independent of the sensor of this model and also confirms the great potential of VIUPD for retrieval of physicochemical parameters of vegetation using multi-source remote sensing data.     

基于反射峰面积的水体叶绿素遥感反演模拟研究

叶绿素浓度是水体富营养化状态的重要指标,也是水色遥感反演的水质参数之一。水体中叶绿素浓度的遥感反演主要是建立实测光谱和实测水质参数二者之间的关系模型,利用遥感影像进行叶绿素浓度的信息提取。传统的叶绿素浓度遥感反演受区域性和季节性的影响,反演精度不高,而且反演模型不具普适性,需对叶绿素光谱特征进行分析,建立高精度的反演模型。本文采用Hydrolight数据模拟了不同叶绿素浓度(1~200 µg·L^-1)的水体在可见光近红外的反射波谱曲线,通过分析叶绿素的光谱特征选取了特征波段或波段组合,并建立了叶绿素浓度反演模型。研究表明,除反射峰波长模型外,反射峰面积模型、三波段模型、红光线高度模型等均能较好地反演叶绿素浓度。在不同叶绿素反演模型中,除红光线模型外,最优的是反射峰面积模型,其决定系数为0.9689,反演误差为25.25 µg·L^-1;其次是三波段模型,其决定系数为0.9637,反演误差为10.66 µg·L^-1。究其原因,三波段模型考虑了水体中非色素悬浮物、黄色物质及水体后向散射对叶绿素浓度反演造成的影响;反射峰面积模型除此之外还综合考虑了叶绿素散射效率的影响。     

Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions

Soil surface roughness, denoted by the root mean square height(RMSH), and soil moisture(SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance(SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m × 3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red(675 nm), green(555 nm), blue(485 nm), near infrared(845 nm), shortwave infrared 1(1600 nm), and shortwave infrared 2(2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R~2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands(i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R~2 and root mean square error(RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands(green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R~2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle(UAV) optical imagery.     

一种考虑雪粒径变化的积雪面积反演算法

光谱混合分析能够提取亚像元信息,被广泛地应用于遥感影像目标探测之中。本文针对MODIS积雪遥感影像,基于光谱混合分析框架,利用渐进辐射传输模型建立不同粒径大小的雪反射率光谱库,提出了一种考虑端元变化及二次辐射的雪盖面积反演算法。此算法首先利用渐进辐射传输模型建立不同粒径大小积雪的反射率光谱库,然后使用序贯最大角凸锥方法获取植被、土壤与岩石、阴影的光谱库。在建立各种地物反射率光谱库之后,利用均方根误差最小的方法获取最优端元组合。在此基础上,考虑端元独立辐射以及积雪与其它地物的二次辐射过程,利用稀疏光谱混合模型获取积雪面积与雪粒径大小。实验结果表明：此方法能够同时反演雪粒径与积雪面积,反演的雪粒径相比单波段的渐进辐射传输模型小,反演的积雪面积相比MOD10A1产品精度略微提高。     

三江源区毒杂草型退化草地植被光谱特征分析

毒草型退化草地具有群落演替特点,通过高光谱遥感技术反演毒杂草分布与退化草地群落结构能对该类退化草地进行有效监测,而光谱特征分析是毒杂草与优良牧草遥感识别的基础。本文选取了三江源区毒草型退化草地的8种典型毒杂草和4种优良牧草的地面实测高光谱数据作为研究样本,经过SG平滑、包络线去除、导数变换和光谱参量化对毒杂草种和优良牧草种的光谱特征进行了分析,并通过马氏距离法提取其特征识别波段。结果表明：① 8种典型毒杂草和4种优良牧草的 “近红外峰值”差异较大,其中鹅绒萎陵菜的“近红外峰值”达到60.07%,而最小者早熟禾仅为17.53%;② 经包络线去除处理后,植被光谱曲线中吸收谷和反射峰光谱差异更加明显,且可减少环境背景对植被光谱的影响,如沼泽草甸的鹅绒委陵菜和驴蹄草,其“绿峰幅值”分别为6.46%和6.89%,经处理后其“绿峰指数”分别为0.2866和0.3671,而在2种环境下生长的同一草种（狼毒草1和狼毒草2）的峰谷特征差异不明显;③ 基于马氏距离法提取的毒杂草与优良牧草的敏感识别波段主要分布在680~750 nm和900~1000 nm波长范围内,以醉马草与矮嵩草为例,其基于反射率的敏感识别波段为713.1~737.1 nm和934.6~965.6 nm。该研究可为利用高光谱遥感进行大面积毒杂草草种识别和植被群落生长监测提供重要科学依据,对于三江源区毒杂草的监测防治和畜牧业的可持续发展具有重要意义。     

Estimating fraction of photosynthetically active radiation of corn with vegetation indices and neural network from hyperspectral data

The fraction of photosynthetically active radiation(FPAR) is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles.Based on ground-measured corn hyperspectral reflectance and FPAR data over Northeast China,the correlations between corn-canopy FPAR and hyperspectral reflectance were analyzed,and the FPAR estimation performances using vegetation index(VI) and neural network(NN) methods with different two-band-combination hyperspectral reflectance were investigated.The results indicated that the corncanopy FPAR retained almost a constant value in an entire day.The negative correlations between FPAR and visible and shortwave infrared reflectance(SWIR) bands are stronger than the positive correlations between FPAR and near-infrared band reflectance(NIR).For the six VIs,the normalized difference vegetation index(NDVI) and simple ratio(SR) performed best for estimating corn FPAR(the maximum R2 of 0.8849 and 0.8852,respectively).However,the NN method esti-mated results(the maximum R2 is 0.9417) were obviously better than all of the VIs.For NN method,the two-band combinations showing the best corn FPAR estimation performances were from the NIR and visible bands;for VIs,however,they were from the SWIR and NIR bands.As for both the methods,the SWIR band performed exceptionally well for corn FPAR estimation.This may be attributable to the fact that the reflectance of the SWIR band were strongly controlled by leaf water content,which is a key component of corn photosynthesis and greatly affects the absorption of photosynthetically active radiation(APAR),and makes further impact on corn-canopy FPAR.     

铅污染水稻的冠层高光谱特征研究

本研究通过铅污染土壤中的水稻盆钵栽培试验,考察了水稻对土壤重金属铅的吸收以及铅对水稻生长的胁迫,并借助地面高光谱辐射仪器获取多个生育期（苗期、分蘖前期、分蘖盛期、拔节期和孕穗期）的水稻冠层高光谱反射数据。在进行光谱测量的同时测定了水稻植株体内的铅含量与冠层叶片叶绿素含量。分析结果表明:铅污染胁迫下水稻冠层叶片叶绿素含量与叶绿素a、b组成变化明显,可见光区间520nm～560nm和630nm～690nm处是铅污染水稻对冠层反射高光谱敏感的特征波段。通过模拟高光谱分辨率遥感传感器MODIS的相应波段（第4通道:545nm～564nm、第1通道附近:620nm～670nm）以及考虑叶绿素的荧光特征（760nm）,本研究分别选择敏感波段中的552nm,672nm与760nm构造了复合归一化污染指数CNDPI（Compos-ite Normalized Difference Pollution Index）,分析发现CNDPI能够明显地区分不同铅污染水平的水稻。在分蘖前期采用适当的冠层光谱反射率形式（敏感波段、CNDPI）可以实现水稻铅污染的遥感监测。