曹妃甸近海Ⅱ类水体光谱反射率与悬浮泥沙浓度相关性研究

利用水面以上法对曹妃甸近海Ⅱ类水体进行现场光谱测量,对获取的数据与同步采集的水体悬浮泥沙浓度进行相关分析,初步建立了水体悬浮泥沙浓度与TM数据对应的可见光至近红外波段遥感反射率的经验模型.研究结果表明:该地区近海Ⅱ类水体的反射率随悬浮泥沙浓度的增加而增加,在泥沙浓度小于20 mg/L时,570 nm处的反射率随泥沙浓度增加而增大的特征优于670 nm处的,但当泥沙浓度大于20 mg/L时,后者反射率的增幅明显优于前者;TM 3中心波段处的反射率与表层悬浮泥沙浓度(SPM)呈显著的指数相关,利用模型计算的悬浮泥沙浓度平均相对误差低于±28%.     

近岸水体表层悬浮泥沙平均粒径遥感反演

悬浮泥沙的粒径分布特征不仅体现了悬浮颗粒态物质的存在状态,而且可以指示水动力及再悬浮作用的过程和强度,因此研究悬浮泥沙粒径分布特征具有重要意义。利用Mie理论建立悬浮泥沙平均粒径反演模型,结果表明,悬浮泥沙后向散射系数与其平均粒径的三次方线性关系明显,4个波段(412nm、443nm、555nm、667nm)拟合方程决定系数均在0.93以上,拟合误差最小值为16.6322%(412nm),最大值为20.3143%(667nm)。利用QAA算法从MODIS影像上反演研究区域悬浮泥沙后向散射系数,并结合反演的悬浮泥沙浓度推算研究区域表层悬浮泥沙的平均粒径。对比发现,近岸高悬浮泥沙区域的反演结果与实测数据吻合较好,相关研究可以为深入开展陆海相互作用、海洋生态系统演变和海洋参与全球碳循环等研究提供重要数据支持。     

杭州湾HJ CCD影像悬浮泥沙遥感定量反演

利用环境小卫星CCD(HJ CCD)影像对杭州湾悬浮泥沙浓度(SSC)进行了反演研究。通过对杭州湾水体遥感反射率(Rrs)与SSC进行相关性分析发现,在690nm和830nm左右出现显著的反射峰,分别位于HJ CCD影像的第3和第4波段范围内;大于700nm波长处的Rrs与SSC相关性较好。基于实测Rrs和SSC之间的相关关系,利用第4和第3波段比值作为遥感因子建立SSC反演模型,模型决定系数达到0.90。借鉴近红外-短波红外(NIR-SWIR)结合的大气校正方法反演出的准同步MODIS气溶胶数据,实现了HJ CCD影像的大气校正,第3、第4波段的大气校正结果相对误差分别为5.54%和6.97%。结果显示,HJ CCD影像反演的SSC相对误差为7.12%;杭州湾悬浮泥沙浓度要显著高于长江口,且内部差异明显。研究表明,通过适当的大气校正方法和反演算法,HJ CCD影像可用于杭州湾悬浮泥沙浓度的估计。     

基于MODIS 数据的悬浮泥沙定量遥感方法

以黄海及东海海域为对象,研究用MOD IS数据提取我国海域悬浮泥沙时空分布的定量遥感方法,建立了基于MOD IS数据的悬浮泥沙定量遥感实用模式。研究表明,用250 m和1 000 m分辨率的MOD IS数据进行悬浮泥沙浓度的定量遥感,可以达到实际应用的精度要求。这说明,MOD IS数据是研究近岸水体中悬浮物输运变化规律的一种经济实用数据源。     

江苏近海辐射沙洲水域水深与光谱的相关性研究

通过对实测光谱数据和水深数据的分析研究发现，泥沙浓度在水深遥感中具有重要意义。泥沙含量决定水体光谱反射率与 水深的相关性方向： 泥沙含量较少的清水区，光谱反射率与水深呈负线性相关； 而在泥沙含量较高的浊水区，光谱反射率与水深 数据呈正线性相关。以此为依据，对水体进行清/浊水体的光谱分别处理，可提高水深的光谱反演精度。同时，结合实测水体光谱 进行不同光谱分辨率的模拟分析，发现高光谱分辨率遥感将有助于提高水深反演精度。     

琯溪蜜柚叶片氮浓度高光谱遥感监测初探

以福建省平和县琯溪蜜柚为研究对象，利用星载Hyperion高光谱遥感数据对蜜柚叶片进行氮浓度估测。在分析Hyperion数据特征的基础上进行大气校正、几何纠正等预处理，从而得到图像反射率；结合地面光谱测量和蜜柚叶片采样分析，通过逐步回归分析法研究叶片氮浓度与高光谱图像反射率及其衍生量的关系，最终建立其遥感定量监测模型。结果表明，图像反射率的对数变换更有利于氮浓度的定量反演，入选的波段是983 nm、1 245 nm、1 316 nm和1 457 nm，其中1 245 nm波段对氮浓度影响最大，1 457 nm波段最小。利用该模型对氮浓度进行估算的值域与地面调查结果一致，说明利用高光谱进行氮浓度定量反演具有一定的可行性。     

应用高光谱遥感数据估算土壤表层水分的研究

土壤水分是土壤的重要组成部分，它在陆地表层和大气之间的物质和能量交换方面扮演着重要角色，寻求快速而准确的方法估算土壤水分具有重要意义。通常，从可见光一近红外对土壤表层水分的估计多是建立在土壤水分与反射率的关系之上的。而在土壤水分含量不高时，土壤水分的增加使土壤光谱反射率在整个波长范围内降低，尤其在760nm，970nm，1190nm，1450nm，1940nm和2950nm等水分吸收波段，而在土壤水分含量较高时，土壤水分的增加会使土壤光谱反射率在某些光谱波段升高。而土壤水分的估计往往是基于土壤水分与土壤水分吸收波段的吸收强度之间的线性关系上，虽然这些经验的方法对于估算某些土壤的表层水分含量是有效的，但这些关系应用于其它条件(如不同种类土壤、土壤湿度变化范围很大的情况)时却面临很多困难，这与土壤的光谱反射率是由土壤的组成成分(土壤水分、有机质、氧化铁和粘土矿物等)的含量和它们在土壤中的分布密切相关。微分技术处理“连续”的光谱是遥感中常用的数学方法，微分技术能部分消除低频光谱成分的影响。现在微分光谱已广泛地应用于研究植被的生物物理参数、矿物和有机质等。然而利用微分光谱对土壤水分反演的研究却鲜见报道。本文通过对实验室中多种不同类型的土壤进行光谱与土壤表层水分含量进行观测，探讨了通过土壤反射率与微分光谱对土壤表层水分的反演方法。4种类型的土壤光谱数据(反射率(R)，反射率倒数的对数(log(1／R))，反射率的一阶微分光谱(dR／dλ)，反射率倒数的对数的一阶微分光谱(d(log(1／R))／dλ))与土壤表层水分之间的关系在本文中得到分析，R与log(1／R)对于不同土壤类型与土壤表层水分都很敏感，说明通过R与log(1／R)反演土壤表层水分受土壤类型的影响很大，而dR／dλ，d(log(1／R))／dλ)对土壤类型却不敏感，对土壤表层水分较为敏感，说明dR／dλ和d(log(1／R))／dλ)对于反演不同类型土壤具有很大的潜力，微分光谱与土壤水分在某些波段具有显著的相关性。通过随机对9种土壤(各具有4个土壤水分)的数据建立反演土壤水分的模型，并其他9种土壤(各具有4个土壤水分)的数据进行验证模型，结果表明，dR／dλ和d(log(1／R))／dλ)能够显著提高R与log(1／R)对于不同土壤类型土壤表层水分的反演精度，由于吸收过程是非线性的，在四种类型的土壤光谱数据中，总体来说，d(log(1／R))／dλ)具有最好的能力预测不同类型土壤的表层水分含量。     

不同泥沙含量水体光谱特性分析

研究了不同的观测角度下水体光谱反射率与泥沙含量之间的变化关系,提出了差分泥沙光谱指数建立泥沙反演模型,确立了在不同角度下对水体泥沙含量的定量分析模型.     

随机森林回归模型的悬浮泥沙浓度遥感估算

三峡工程蓄水以来,清水下泄,坝下游河段发生了长时间、长距离的沿程冲刷,河流悬浮泥沙浓度发生改变,给沿岸生态系统带来了不利影响。随机森林算法灵活、稳健,已被广泛应用于各类生态环境变量的回归预测分析,但其在水体悬浮泥沙浓度估算方面的能力尚未得到充分认识。基于泥沙站点监测数据和MODIS卫星遥感反射率数据,通过构建随机森林非参数回归预测模型,对三峡工程坝下游宜昌至城陵矶河段在建坝前后14年间(2002年—2015年)各月的悬浮泥沙浓度进行遥感估算。研究表明:(1)基于随机森林的悬浮泥沙浓度估算模型表现较好,模型预测值与实测值间相关性好、预测精度高,优于其他模型(线性回归、支持向量机、人工神经网络模型)。(2)在参与模型构建的MODIS波段变量中,红波段被认为是最重要的预测变量,但不能单独使用它进行预测,悬浮泥沙遥感预测需要多变量共同参与。(3)将悬浮泥沙数据按季节分类所构建的随机森林模型,其平均误差为0.46 mg/L,平均相对均方根误差为12.33%,估算效果最优,能够满足较高精度下悬浮泥沙浓度估算的需求。综上,可以考虑以季节为划分依据,用随机森林回归模型估算悬浮泥沙浓度,并用于后期坝下游河道悬浮泥沙浓度时空反演。     

耕层土壤有机质高光谱间接估测模型

针对光学遥感技术只能获取表层土壤光谱信息而无法直接估测耕层土壤有机质含量的问题,探索建立基于表层土壤高光谱信息的耕层土壤有机质间接估测模型。以山东省济南市章丘区采集的76个表层、耕层土壤样本数据为基础,首先分析原始光谱反射率的光谱特征;然后利用反射率的一阶微分、平方根的一阶微分和对数倒数的一阶微分等方法对原始光谱反射率进行变换,并根据极大相关性原则选取估测因子;最后根据表层土壤有机质含量与耕层有机质含量间的内在关系,建立耕层土壤有机质含量的间接估测模型。结果表明,以557、1 621、2 107和2 316 nm波段对数倒数的一阶微分变换值和864 nm波段反射率平方根倒数一阶微分的变换值为估测因子,使用二次函数关系模型对耕层土壤有机质含量间接估测的精度最高,其决定系数R~2为0.784,平均相对误差为10.7%。研究表明,利用表层土壤高光谱信息间接估测耕层有机质含量可行有效。     

鄱阳湖富营养化高光谱遥感监测模型初探

基于对光谱反射率与水质参数的相关分析,分别选取特征波长建立水质参数高光谱估测模型。结合修正营养状态指数,对湖泊的富营养化程度进行了监测和评价。结果表明:①总氮、总磷含量和透明度值的高光谱估测模型效果较理想;②单项指数评价水体富营养化水平其结果存在较大差异,综合考虑多个指标,计算营养指数的平均值,可以对富营养化程度进行正确的评价;③由于悬浮物浓度变化较大,掩盖了水体的叶绿素a信息,以致叶绿素a估测模型不具有通用性,为了完善叶绿素a浓度估测模型需要获得大范围、多季节的光谱数据,以便建立更有代表性和通用性的模型;④评价结果显示,鄱阳湖呈现轻度到中度富营养化状态,需要采取有力的保护措施防止进一步恶化。     

Spectral Reflectance Characteristics of the Soils on Basaltic Terrain of Central Indian Plateau

In-situ spectral reflectance characteristics of soils were studied under field conditions with Multiband Ground Truth Radiometer covering 0.45?C0.52, 0.52?C0.59, 0.62?C0.68, and 0.77?C0.86 ??m spectral bands. Twenty-two surface soil samples were studied in laboratory for their spectral reflectance characteristics using ISCO Model S.R. Spectroradiometer in visible wavelength (450?C725 nm), with 25 nm bandwidth, and in infrared wavelength (750?C1550 nm), with 50 nm bandwidth. The Bidirectional reflectance factor representative of spectral reflectance varied from 3.78 to 11.3???m in band 1, 6.09 to 15.41???m in band 2, 8.05 to 19.41???m in band 3, and 12.18 to 31.2???m in band 4. In-situ spectral reflectance in general increases with the wavelength from visible to infrared bands for all the soils. Black soils have relatively lower reflectance as compared to red soils, which is attributed to the variation in the physicochemical properties of soils. Spectral reflectance, under laboratory conditions, for all the soils increases with wavelength from visible to infrared region except at 950 nm and 1200 nm, where reflectance decreased in all soils, due to weaker water absorption bands and also at 1350 nm, due to strong water absorption at this band. The spectral reflectance of red soils were higher, in-situ as well as under laboratory conditions, as compared to black soils, which is attributed to variation in soil colour, organic matter and clay content of soils. It is observed that the spectral reflectance decrease due to moisture content in soils in all the spectral bands because of darker appearance of soils at moist conditions. Laboratory reflectance measurements serve to define the extent to which intrinsic spectral information is available from soils as a consequence of their composition.     

Quantification of suspended solids in Dal lake,Srinagar using remote sensing technology

Indian Remote Sensing satellite (IRS)-1B, Linear Imaging Self Scanner (LISS)-II spectral digital data was analysed to determine the feasibility of quantifying the concentration of suspended solids in the surface water of inland water body, Dal lake, in Srinagar, India. The water samples collected in concurrent with IRS-1B overpass, were analysed to determine the concentration of suspended solids. The results indicate that a positive functional relationship exist between the concentration of suspended solids and the visible wave length bands 1 and 3 and near infrared band 4. It has been observed that as the concentration of suspended solids increase, the spectral response also increases. It is concluded that IRS LISS-H data can be effectively used to quantify suspended sediment concentration in the Dal lake surface water.     

黄河口水体光谱特性及悬沙浓度遥感估测

通过黄河口含沙水体野外遥感光谱反射率的观测实验，探讨了黄河口水体表观光谱特性，分析了悬浮体中有机颗粒含量和悬沙粒度对光谱特性的影响。针对Landsat TM／ETM^＋影像波段特性，对黄河口含沙水体在其可见光至近红外4个波段的光谱特性进行了模拟分析，并结合表观光谱观测数据建立了经验回归函数，以估测不同时相黄河口水体表层悬沙的浓度。     

Ground validation of an algorithm for estimating surface suspended sediment concentrations from multi-spectral reflectance data

Pollution of water resources by sediments eroded from degraded watersheds is a critical concern around the world. Current methods for locating these eroding areas and off-site damage to water resources through visual observations and field sampling with subsequent laboratory analysis are time consuming and expensive. There is thus, a justified interest in developing algorithms for quick estimation of suspended sediment concentrations in large water-bodies from remotely sensed data. This paper presents the results of a ground validation study on characterization and quantification of surface suspended sediment concentrations (SSC) in sediment laden water bodies through an n-waveband specific numerical index, total information content. A comparison of SSC-predictive potential of the proposed new index, derived from four broad (100–300 nm) Landsat MSS, five broad (40–300 nm) Landsat TM and eight narrow (20–40 nm) IRS-P4 OCM spectral bands, with that of the conventional (NIR-Red and NIR+Red) indices, computed from the same spectral band data, is also presented. The study reveaied that at SSCs 250 mg/1, the proposed index (derived from either broad / narrow landsat MSS/TM or IRS-P4 OCM spectral data) could lead to SSC predictions (with mean errors within 20%) comparable with those obtained with the conventional indices (derived from the same spectral band data). It could further be observed that, in general, lower sediment concentrations (i.e. SSCs 150 mg/1) were associated with higher prediction inaccuracies. A comparison of the mean errors of predictions associated with the proposed and the conventional (NIR-Red and NIR+Red) indices computed from broad and narrow band data for SSCs 150 mg/I, revealed that an increase in number of wavebands (from 4 MSS to 5 TM or 8 OCM bands) and a decrease in the bandwidth of these wavebands (from broad MSS/ TM bands to narrow OCM bands) led to a significant increase in the prediction accuracy of the proposed new index. These prediction accuracies were observed to be the highest with the proposed index calculated from narrow OCM-P4 spectral data. However this could not be observed with the conventional indices at any of the SSC ranges and with the proposed index at SSCs 250 mg/l. This shows that the lower SSC-predictive potential of proposed index was a significant function of both the number and the bandwidth of spectral bands used for its computation. In fact in one of the cases, lower SSC (150 mg/l) -predictive accuracy of the proposed index was found to be significantly higher than that of the conventional (NIR+R) index. The proposed algorithm could thus compress the information contained in the entire reflectance spectrum of the sediment laden water bodies to their sediment type and concentration specific characteristic values. This characteristic of the proposed index was not shared by any of the conventional indices, based on only two waveband data. In fact the proposed index appears to be the only mean of completely compressing and quantifying the information contained in all the information channels of a narrow band spectrometer (consisting of 200 wavebands) to be shortly launched by ISRO for satellite based inventory of natural resources.     

Hyperspectral radiometric observation of the northeast Arabians Sea during April 2006

Parameters were retrieved from the hyperspectral radiometer like upwelling and downwelling radiance (Lu and Ed) upwelling and downwelling attenuation (K-Lu and K-Ed) for 9 stations in the northeast Arabian Sea between 16–26 April 2006. Data was analyzed for 5 offshore and 4 coastal stations of the cruise SS-244, on board FORV “Sagar Sampada” between latitude 9-22oN and longitude 68–74°E. The peak for all parameters was observed to be different respectively for depths 1, 5, 10, 20, 30, 40, 50 meters in coastal and offshore stations. Each peak in the respective wavelength is due to a particular composition; phytoplankton pigments have spectral peaks at 443, 490, 555, 670 nm, suspended matter, sediments have peaks at 630 and 670 nm. Detailed analysis with High Performance Liquid Chromatography (HPLC) data and comparison with the water composition of our hyperspectral radiometer results show that the marine cyanophyte, Trichodesmium bloom produces high pigment concentrations of chlorophyll-a, zeaxanthin, β-carotene and pheophytin and their absorptions are interpreted at wavelengths 443, 490, 515 and 536 nanometers, respectively. A dip around 515 nm was seen in the Ed and Lu profiles in our study.     

Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura,Japan) from Landsat TM data

The remote sensing of Case 2 water has been far less successful than that of Case 1 water, due mainly to the complex interactions among optically active substances (e.g., phytoplankton, suspended sediments, colored dissolved organic matter, and water) in the former. To address this problem, we developed a spectral decomposition algorithm (SDA), based on a spectral linear mixture modeling approach. Through a tank experiment, we found that the SDA-based models were superior to conventional empirical models (e.g. using single band, band ratio, or arithmetic calculation of band) for accurate estimates of water quality parameters. In this paper, we develop a method for applying the SDA to Landsat-5 TM data on Lake Kasumigaura, a eutrophic lake in Japan characterized by high concentrations of suspended sediment, for mapping chlorophyll-a (Chl-a) and non-phytoplankton suspended sediment (NPSS) distributions. The results show that the SDA-based estimation model can be obtained by a tank experiment. Moreover, by combining this estimation model with satellite-SRSs (standard reflectance spectra: i.e., spectral end-members) derived from bio-optical modeling, we can directly apply the model to a satellite image. The same SDA-based estimation model for Chl-a concentration was applied to two Landsat-5 TM images, one acquired in April 1994 and the other in February 2006. The average Chl-a estimation error between the two was 9.9%, a result that indicates the potential robustness of the SDA-based estimation model. The average estimation error of NPSS concentration from the 2006 Landsat-5 TM image was 15.9%. The key point for successfully applying the SDA-based estimation model to satellite data is the method used to obtain a suitable satellite-SRS for each end-member.     

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。