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针对现有研究在反演叶绿素含量不足的问题,该文基于地面高光谱和实测农学数据,采用PROSAIL模型和连续小波变换并结合偏最小二乘回归、支持向量机和人工神经网络方法反演冬小麦叶绿素。先通过PROSAIL模型模拟作物光谱,再对模拟光谱进行连续小波变换,筛选出敏感波段和尺度并应用于4组实测数据,最后利用小波系数和实测叶绿素构建偏最小二乘回归、支持向量机和人工神经网络反演模型。研究结果表明,利用小波系数构建反演模型的精度相比于植被指数反演有所提高,在基于小波系数反演叶绿素的方法中偏最小二乘法精度略高于其他两种方法。通过将PROSAIL模型、连续小波变换和偏最小二乘回归结合能够实现冬小麦叶绿素遥感估算。 相似文献
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基于高光谱数据的苔草营养成分反演方法研究 总被引：1，自引：0，他引：1
《地理空间信息》2017,(10)
研究基于高光谱数据的苔草营养成分(侧重粗蛋白质、总氮、总磷)反演方法。结果显示,粗蛋白质的最佳反演模型是通过原始光谱反射率(偏最小二乘回归的方法)获得,R2=0.814、RMSE=0.450;总氮的最佳反演模型是通过一阶光谱反射率(偏最小二乘回归的方法)获得,R~2=0.850、RMSE=0.175;总磷的最佳反演模型是通过原始光谱反射率(偏最小二乘回归)获得,R~2=0.882、RMSE=0.025。最佳模型检验结果显示估算值和实测值之间的强相关性:粗蛋白质R2=0.801、RMSE=1.029,总氮R2=0.777、RMSE=0.234,总磷R2=0.756、RMSE=0.043。 相似文献
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基于PLSR的陕北土壤盐分高光谱反演 总被引：3，自引：0，他引：3
选取陕北盐渍土为研究对象,通过采集高光谱数据及土壤样品测定,研究土壤盐分含量与反射率之间相关性,遴选盐分特征波段,利用常规回归分析及偏最小二乘回归分析建立土壤盐分的定量反演模型,并利用检验样点进行对比分析和精度检验。研究结果表明,482 nm,1 365 nm,1 384 nm,2 202 nm及2 353 nm为土壤盐分含量的特征波段,利用高光谱数据进行盐分定量反演具有良好的精度;精度检验结果表明,通过Matlab进行偏最小二乘回归计算的反演模型,实测值与预测值相关性更好,精度较高。 相似文献
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《测绘科学技术学报》2013,(2)
最佳波段选择是高光谱影像降维的常用手段,将本征维数估计与核偏最小二乘法,相结合,提出一种基于核偏最小二乘法的最佳波段选择方法。首先利用自适应最大似然法估计高光谱数据的本征维数;然后将核方法引入到偏最小二乘法中,利用核偏最小二乘法对高光谱影像进行最佳波段选择,所需选择的波段数即为本征维数。实验分析表明,与其他最佳波段选择方法比较,本文方法输出的最佳波段用于地物分类,取得了较高的分类精度。 相似文献
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基于贝叶斯理论的线性与非线性模型反演方法（Fukuda-Johnson，F-J）已广泛应用于地球物理模型的线性-非线性参数反演。但F-J方法的反演结果可能受马尔可夫链蒙特卡洛采样（Markov chain Monte Carlo，MCMC）经验参数选择的影响，而反复调试合适的经验参数需耗费大量计算时间。对线性与非线性模型进行线性化后，也可以利用迭代最小二乘方法反演，但该方法难以选择合适的初始值。为提高参数反演计算效率和避免参数初值选择影响，提出了一种以F-J方法模型解为初始值的迭代最小二乘方法。该方法只需计算一次F-J方法模型解和有限次最小二乘迭代，既提高了F-J方法的反演效率，又能获得迭代最小二乘全局最优解。针对模拟数据实验和实际数据算例，分别采用F-J方法、随机生成初始值的迭代最小二乘方法和以F-J方法结果为初值的迭代最小二乘方法进行参数反演。结果表明，直接使用F-J方法时，MCMC采样参数会影响反演结果；直接进行迭代最小二乘反演时，初始值选取不当会导致迭代无法收敛到正确的结果；以F-J方法的结果作为迭代最小二乘方法的初始值进行反演，可以充分发挥F-J方法的全局最优性和迭代最小二乘方法计算量小、稳定性好的优势。 相似文献
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中国近岸浑浊水体大气修正的迭代与优化算法 总被引：5，自引：0，他引：5
中国近岸水体泥沙含量较高，且变化梯度大；泥沙浓度可从几千mg／l变化到1mg／l以下。针对上述浑浊二类水体的大气修正一直是水色遥感应用的难点之一。利用水体近红外固有光学特性的Arnone光谱迭代算法在泥沙含量较低时适用，其近红外光谱迭代关系与根据2003年春季黄东海水色试验数据得出的现场光谱关系大致相当。当泥沙含量达到某一程度时，该算法失效，导致蓝光等较短波段的离水辐亮度为负。根据现场数据分析结果，这一分界点大概在泥沙浓度10—20mg／l。因此，本文首次提出将中国近岸浑浊水体进一步区分为中低和中高浑浊水体，并给出初步的划分标准，采用光谱优化方法对中高浑浊水体进行水色大气修正。优化误差函数的选取以现场试验获取的可见光波段光谱关系式为基础。结果表明，优化算法在近岸高浑浊水体可给出满足光谱分布规律的反演结果。与其他大气校正方法一样，优化方法也需要进一步的微调。将Gordon标准算法、Arnone光谱迭代和优化方法结合，对SeaWiFS图像进行处理，分别得出归一化离水辐亮度和总悬浮物（TSM）浓度分布图像，结果令人满意。 相似文献
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内陆湖泊颗粒有机碳反演及日变化初步研究 总被引：1，自引：0，他引：1
以太湖及洞庭湖为例,检验海洋一类水体颗粒有机碳(particulate organic carbon, POC)浓度遥感估算方法在内陆湖泊二类水体中的适用性,结果表明,一类水体POC反演模型并不适用于二类水体。针对二类水体建立了以近红外波段(834 nm)为因子的单波段POC反演模型以563及834 nm波段组合为因子的两种双波段反演模型,模型验证结果显示,单波段模型的均方根误差(RMSE)为1.12 mg/L,平均相对误差(MAPE)为35.8%,两个双波段反演模型的RMSE分别为1.09 mg/L及1.11 mg/L,MAPE分别为37.3%及37.8%,三种模型均可用于太湖及洞庭湖水体的POC浓度遥感估算。在此基础上,以太湖为例,建立了基于静止轨道卫星海洋水色(GOCI)卫星数据的太湖POC反演模型,反演模型的MAPE为35%。利用5月13日8景GOCI影像,研究了太湖POC浓度日变化,发现POC浓度日变化存在两个阶段:上午至中午的递减阶段和中午至傍晚的递增阶段。 相似文献
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水体光谱测量与分析Ⅰ：水面以上测量法 总被引：152，自引：9，他引：152
水色遥感的基础之一是水体光谱特征测量与分析。水体光谱特性包括两个方面 :表观光学特性(AOPs)和固有光学特征 (IOPs)。该文主要讨论与水色遥感基本参数直接相关的表观光学量的测量与分析。现场表观光谱的测量从方法上可分为两类 :剖面测量法和水表面以上测量法。两种方法相对独立 ,适用范围上具有互补性。该文主要针对水面以上测量法进行讨论。水色遥感一般要求表观光谱特性的测量必须能够导出离水辐射率LW、归一化离水辐射率LWN、遥感反射率Rrs、和刚好处于水面以下 0 -深度的辐照度比R等。国际上有关水色的测量规范中 ,仅有针对大洋一类水体的。中国的近岸水体和湖水绝大部分是二类水体 ,因此该文也将对二类水体光谱测量的有关参数化问题进行讨论。同时 ,对数据分析处理中的一些关键参数的确定 ,提出新方法 相似文献
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R K Sarangi Prakash Chauhan Shailesh Nayak 《Journal of the Indian Society of Remote Sensing》2001,29(4):197-201
Remote sensing of ocean colour yields information on the constituents of sea water, such as the concentration of phytoplankton
pigments, suspended sediments and yellow substances. It is well understood that the study of ocean colour is significantly
related with the primary production and zonation of potential fishing sites in coastal and oceanic waters. The major pigment
constituent is predominated by chlorophyll-a (ocean colour pigment of phytoplankton). The chlorophyll mapping on regular basis
plays a major role in assessing water quality and classifying different water types. IRS P-3 MOS-B satellite data for three
consecutive passes of path 94, during the period of January-February 1997 have been used to derive chlorophyll-a concentration.
The present study emphasizes on the chlorophyll mapping using IRS-P3 MOS-B data for the coastal and offshore water of Maharashtra
coast, India. 相似文献
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Prakash Chauhan M Mohan S R Nayak R R Navalgund 《Journal of the Indian Society of Remote Sensing》2002,30(1-2):87-94
In-situ chlorophyll concentration data and remote sensing reflectance (Rrs) measurements collected in six different ship campaigns in the Arabian Sea were used to evaluate the accuracy, precision, and suitability of different ocean color chlorophyll algorithms for the Arabian Sea. The bio-optical data sets represent the typical range of biooptical conditions expected in this region and are composed of 47 stations encompassing chlorophyll concentration, between 0.072 and 5.90 mg m-3, with 43 observations in case I water and 4 observations in case II water. Six empirical chlorophyll algorithms [i.e. Aiken-C, POLDER-C, OCTS-C, Morel-3, Ocean Chlorophyll-2 (OC2) and Ocean Chlorophyll-4 (OC4)] were selected for analysis on the Arabian Sea data set. Numerous statistical and graphical criterions were used to evaluate the performance of these algorithms. Among these six chlorophyll algorithms two chlorophyll algorithms (i.e. OC2 and OC4) performed well in the case I waters of the Arabian Sea. The OC2 algorithm, a modified cubic polynomial function which uses ratio of Rrs490 nm and Rrs555 nm (where, Rrs is remote sensing reflectance), performed well with r2=0.85; rms =0.15. The OC4 algorithm, a four-band (443, 490, 510, 555 nm), maximum band ratio formulation was found best on the basis of statistical analysis results with r2=0.85 and rms=0.14. Both OC2 and OC4 algorithms failed to estimate chlorophyll inTrichodesmium dominated waters. The OC2 algorithm was preferred over OC4 algorithm for routine processing of the OCM data to generate chlorophyll-a images, as it uses a band ratio of 490/555 nm and atmospheric correction is more accurate in 490 nm compared to 443 nm band, which is used by OC4 algorithm. 相似文献
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Total suspended sediment (TSS) data concentrations are retrieved from two sets of satellite ocean color data (the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Korean Geostationary Ocean Color Imager (GOCI)) using an existing regional model to characterize spatial and temporal variation of TSS in the Yellow and East China Seas. MODIS-derived TSS maps show that TSS concentrations are, in general, high along the Korean and Chinese coasts including the Bohai Sea and the Yangtz River estuary, and lower in the middle of the Yellow Sea and the southeastern area of the East China Sea. The monthly average of 10-year MODIS data reveals that TSS values are highest during winter (January to February) and lowest in summer (July to August). Short-term TSS concentrations retrieved from GOCI data showed the dominant influence of semi-diurnal tidal changes on sediment dynamics through temporal (hourly) and spatial distribution in coastal zones of the Yellow sea. The results presented here demonstrate that the satellite-derived TSS products can be utilized as an application tool for future studies on long- and short-term sediment dynamics of turbid coastal waters. In particular, GOCI observations provide unique important capabilities to characterize and quantify the water properties at high temporal (hourly) and spatial (0.5 km) resolutions in the turbid coastal waters of the Yellow Sea and its vicinities. 相似文献
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利用验潮站资料的中国近岸海潮模型精度评估 总被引：1，自引：0，他引：1
针对全球海潮模型在我国近海海域精度较差的问题,该文采用中国沿岸30个长期验潮站的调和常数,对比了3种全球海潮模型DTU10、TPXO7.2、NAO.99b和1个区域海潮模型NAO.99Jb在中国沿岸的准确度。通过海潮模型与验潮站分潮的振幅中误差、迟角中误差以及8个主要分潮的预报误差,对渤海、黄海、东海和南海北部进行了详细的分析。结果表明,NAO.99Jb在中国海域精度最高,NAO.99b次之。在渤海海域,DTU10在Q_1分潮精度最高,NAO.99b在K2分潮精度最高,其余分潮均是NAO.99Jb精度最高;在黄海海域,NAO.99b在Q_1分潮精度最高,其余分潮均是NAO.99Jb精度最高;在东海海域,DTU10在Q_1分潮精度最高,TPXO7.2在P1精度最高,NAO.99Jb在O1、M2分潮精度最高,其余分潮均是NAO.99b精度最高;在南海北部海域,DTU10在N2、S2分潮精度最高,其余分潮均是NAO.99Jb精度最高。 相似文献
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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. 相似文献
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T. Preethi Latha P. V. Nagamani K. H. Rao S. K. Dash S. B. Choudhury Aziz Rehman V. V. S. S. Sarma M. Naveen Babu P. Amarendra B. Srinivasa Rao T. D. V. Prasad 《Journal of the Indian Society of Remote Sensing》2014,42(1):129-138
In this study chlorophyll measurements were made during March 2012 in the estuarine waters of Off Kakinada and Yanam coast, Bay of Bengal onboard a coastal vessel. In-situ water samples and optical data was collected at 21 stations (surface to 150 m depth) using Underwater radiometer (Hyperpro-II). In-vivo chlorophyll profiles were collected using wet labs fluorometer integrated with underwater Hyperspectral radiometer. Chlorophyll-a concentrations were estimated using HPLC by collecting the water samples at each sampling location. And also chlorophyll-a concentrations were retrieved from the OCM-2 data of OCEANSAT-2 satellite, processed using SeaDAS v.6.2 with the available global ocean colour algorithms namely, OC2 and OC4V4. A total of 33 samples used covering all the stations for chlorophyll-a estimation, and surface water samples of all the stations only being used for direct comparison among chlorophyll concentrations of HPLC, in-situ (fluorometrically integrated to Hyperpro-II) and retrieved from OCM-2. A good correlation found between the Fluorometer derived and HPLC measured chlorophyll-a concentration with an R2 value of 0.78. The relation between Chlorophyll-a concentration measured from HPLC and retrieved from OCM-2 (OC2 and OC4V4 algorithms) using SeaDASv.6.2 for 10 samples has been compared for validation and obtained an R2 value of 0.6. Also comparisons done with the in-situ measured (fluorometer) Chlorophyll-a concentration with OCM-2 chlorophyll data (OC4-V4 and OC2 algorithms) and validation with 10 concurrent in-situ surface measurements showed a significant overestimation by OCM-2 at low chlorophyll-a concentrations and underestimation at high chlorophyll-a concentrations. 相似文献
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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。 相似文献
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P. V. Nagamani Prakash Chauhan R. M. Dwivedi 《Journal of the Indian Society of Remote Sensing》2007,35(3):201-207
An artificial neural network (ANN) based chlorophyll-a algorithm was developed to estimate chlorophyll-a concentration using OCEANSAT-I Ocean Colour Monitor (OCM) satellite-data.
A multi-layer perceptron (MLP) type neural network was trained using simulated reflectances (~60,000 spectra) with known chlorophyll-a concentration, corresponding to the first five spectral bands of OCM. The correlation coefficient(r
2) andRMSE for the log transformed training data was found to be 0.99 and 0.07, respectively. The performance of the developed ANN-based
algorithm was tested with the global SeaWiFS Bio-optical Algorithm Mini Workshop (SeaBAM) data (~919 spectra), 0.86 and 0.13
were observed asr
2 andRMSE for the test data set. The algorithm was further validated with thein-situ bio-optical data collected in the northeastern Arabian Sea (~215 spectra), ther
2 andRMSE were observed as 0.87 and 0.12 for this regional data set. Chlorophyll-a images were generated by applying the weight and bias matrices obtained during the training, on the normalized water leaving
radiances (nL
W) obtained from the OCM data after atmospheric correction. The chlorophyll-a image generated using ANN based algorithm and global Ocean Chlorophyll-4 (OC4) algorithm was compared. Chlorophyll-a estimated using both the algorithms showed a good correlation for the open ocean regions. However, in the coastal waters
the ANN algorithm estimated relatively smaller concentrations, when compared to OC4 estimated chlorophyll-a. 相似文献