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1.
应用MODIS监测太湖水体叶绿素a浓度季节变化研究   总被引:1,自引:0,他引:1  
以太湖作为实验区,利用波段比值、差值和组合算法讨论了非成像及成像高光谱数据和叶绿素浓度相关性差异和敏感波段分布,在此基础上将不同时段的MODIS影像,不同空间分辨率的波段反射率与叶绿素a浓度实测值进行相关分析,通过回归拟合建立并验证了不同季节的叶绿素a浓度遥感监测模型,并应用模型计算出太湖水体叶绿素a浓度的分布情况,对太湖水质变化进行了评价.研究结果表明,MODIS影像在太湖的水质动态变化监测中是可用的.  相似文献   

2.
应用MODIS数据监测巢湖蓝藻水华的研究   总被引:6,自引:1,他引:5       下载免费PDF全文
以巢湖为研究区域,以MODIS 卫星影像为数据源,结合准同步的地面水质监测数据,将MODIS 250 m分辨率的波段反射率与叶绿素a浓度实测值进行相关分析。在此基础上通过回归拟合,构建基于中分辨率成像光谱仪(MODIS) 的叶绿素遥感提取模型。应用模型成功提取出蓝藻爆发水域chl-a的分布。从MODIS遥感图像上可以清晰地反映出巢湖这次蓝藻爆发的强度、地点和分布范围 。研究结果表明:用MODIS影像监测巢湖蓝藻水华是可行的,其中250m分辨率波段1 、2的比值组合r2/r1与叶绿素a浓度实测值高度相关(R=0.909 3),适于反演叶绿素a浓度。  相似文献   

3.
本文旨在寻找悬浮泥沙浓度的MODIS遥感影像估算模型,并利用实测的高光谱数据对其敏感波段和反演模型进行测试和验证。以鄱阳湖为研究区域,利用光谱数据进行分析,为利用遥感影像建模提供依据。进一步利用同步进行的鄱阳湖水质采样分析与MODIS影像中等分辨率各个波段反射率及其组合进行相关分析,寻找反演悬浮泥沙浓度的敏感波段。实验表明,MODIS的第一波段反射率对于悬浮泥沙浓度有很好的匹配(R2 = 0.91; n = 25),进而建立了鄱阳湖地区的悬浮泥沙浓度遥感定量估算模型。利用估算模型和鄱阳湖地区历史MODIS影像,得到了鄱阳湖悬浮泥沙浓度分布图。基于对汛期鄱阳湖悬浮泥沙浓度的连续监测,可对长江倒灌入鄱阳湖现象的形态进行观测。  相似文献   

4.
水体叶绿素a浓度不仅是水质状况的重要指标,也是制定水环境保护和水资源开发利用方案的重要依据。以2004年8月19日太湖水质浓度实验数据和同步的Hyperion影像为数据基础,研究适用于Hyperion影像的四波段半分析算法。由模型参数标定数据集(37组)对四波段半分析算法参数的拟合分析和模型检验数据集(5组)对算法精度的评估可知,基于指数拟合方法获取的四波段半分析算法具有较高的叶绿素a浓度估算精度(相关系数为0.8913,平均绝对误差为1.1109μg/L,对应的平均相对误差为5.69%,其对应的4个波段波长分别为671.02nm、701.55nm、711.72nm和742.25nm)。用以上四波段半分析算法从Hyperion影像中提取的叶绿素a浓度呈湖心低、沿湖区域高的格局。与22.23 μg/L的年均叶绿素a浓度相比较,2004年8月19日的叶绿素a浓度处于年际较高水平。  相似文献   

5.
航天成像光谱仪CHRIS在内陆水质监测中的应用   总被引:2,自引:0,他引:2  
欧空局2001年10月22日成功发射的PROBA卫星上搭载了紧密型高分辨率成像光谱仪(CHRIS),它可以提供高光谱分辨率、高空间分辨率和多角度的遥感数据,它代表了新一代的地球观测数据源。CHRIS有5种工作模式,其中模式2是专门为水体研究而设计的,它在400~1 050 nm的可见光至近红外有18个波段,每个波段数据的空间分辨率是17 m。CHRIS数据的高光谱分辨率、高空间分辨率和多时相覆盖的特点为内陆水质监测提供了有利条件。为了验证CHRIS在内陆水质监测中的具体应用,在太湖梅梁湾开展了水面综合试验,在梅梁湾均匀分布的14个水面采样点分别测量了水面光谱和水质参数。利用这些数据,同时结合CHRIS数据的光谱特征,建立了叶绿素浓度反演半经验模型,应用于CHRIS图像反演了太湖梅梁湾的叶绿素浓度分布图,并取得了较好的结果。最后指出CHRIS数据不但在内陆水质监测中具有巨大潜力,而且CHRIS遥感器是今后内陆水质监测卫星遥感器的典范。  相似文献   

6.
城乡化发展与基础设施建设滞后之间矛盾的深入导致面源污染和工业废水排放对于闽江水质造成了一定影响,因而对闽江叶绿素a进行实时监测及污染物迁移动态监测,是闽江水质治理的关键步骤。文章基于四年实测光谱及水质数据,通过闽江干流实测水体光谱特征分析以及遥感影像敏感波段分析,确定了闽江干流丰、枯水期叶绿素a光谱特征存在差异,并利用多元回归及机器学习分别构建了丰、枯水期闽江干流叶绿素a浓度反演模型,通过精度验证确定了丰、枯水期叶绿素a的最佳遥感反演模型。  相似文献   

7.
2011年3月27日于太湖梅梁湾和湖心区域进行光谱数据采集,同步水质理化分析数据得到叶绿素a浓度区间为4.99μg/L~31.06μg/L。基于较低叶绿素a浓度水平的实测光谱数据及同步的理化分析数据分别采用二波段模型、光谱反射率一阶微分模型、反射峰位置模型、三波段模型和四波段模型对梅梁湾和湖心区域的叶绿素a浓度进行建模遥感估算。5个模型的回归分析结果对应R2分别为0.775,0.811,0.786,0.826和0.846,RMSE分别为4.02μg/L,3.52μg/L,3.82μg/L,3.44μg/L和3.24μg/L。并针对春季较低叶绿素a浓度水平下的光谱估算模型在应用价值和精度方面做了比较评价。  相似文献   

8.
面向土地覆盖分类的MODIS影像融合研究   总被引:1,自引:0,他引:1  
MODIS影像的多波段及其1、2波段的250 m中等分辨率为大区域中空间分辨率的土地覆盖制图提供了可能。为了有效利用MODIS影像的空间和光谱信息,使用SFIM、HPF和PCA变换等遥感影像融合方法,分别采用MODIS影像的波段1(b1)和波段2(b2)对3~7(b3~b7)波段进行融合,并就融合影像的光谱保真度和分类精度对6种不同融合结果进行评价。结果表明不同的融合结果得到的分类精度均有不同程度的提高;3种融合方法中使用b2的融合效果均优于b1;SFIM变换在光谱失真较小的情况下能够较大程度地提高分类精度。因此使用b2的SFIM变换可以用于提高MODIS土地覆盖图的空间分辨率和精度。
  相似文献   

9.
基于TM影像的太湖夏季悬浮物和叶绿素a浓度反演   总被引:4,自引:0,他引:4  
利用2006年8月1日陆地卫星TM数据与7月29日~8月1目的同步湖面采样数据,分析了典型站点反射率光谱特征,建立悬浮物和叶绿素a的反演模型。结果表明悬浮物的变化主要影响TM第2和第4波段,叶绿素a则主要影响TM第3、第4波段。TM4波段与悬浮物具有较高的相关度,利用TM4波段、3×3像元窗口能较好反演出悬浮物浓度。植被指数NDVI((TM4-TM3)/(TM4+TM3))与叶绿素a浓度有较高的相关度,基于5×5像元窗口的植被指数NDVI能很好的反演出太湖叶绿素a浓度,但不能很好地区分出水华和水草。2006年夏季,太湖悬浮物的分布特点为河口区、梅梁湾、西北近岸区最高,其次是湖心区,东太湖、胥口湾和南面的湖州附近湖区浓度最低。叶绿素a分布情况为最高的点出现在梅梁湾口、竺山湾西北部沿岸区以及西太湖近岸区;湖的大部分边缘区即与陆地交接处,叶绿素a的浓度也偏高;南面的湖州附近以及西山岛北面部分湖区叶绿素a浓度最低。而东太湖、胥口湾附近叶绿素a偏高则主要是由该水域沉水植被的发育造成的。  相似文献   

10.
MODIS影像因其共享性和时间序列的完整性而成为大区域积雪监测研究广泛使用的数据源,进行MODIS影像波段间融合,能够为积雪研究提供较高分辨率的影像数据源。为了充分利用MODIS影像250 m分辨率波段的空间和光谱信息,提取亚像元级的积雪面积,使用两种具有高光谱保真度的影像融合方法:基于SFIM变换和基于小波变换的融合方法,采取不同的波段组合策略,对MODIS影像bands 1~2和bands 3~7进行融合,并以Landsat TM影像的积雪分类图作为“真值”,对融合后影像进行混合像元分解得到的积雪丰度图的精度进行评价。结果表明:利用基于SFIM变换和小波变换方法融合后影像提取的积雪分类图精度较高,数量精度为75%,比未融合影像积雪分类图的精度提高了6%,表明MODIS影像波段融合是一种提取高精度积雪信息的有效方法。  相似文献   

11.
基于2007年11~12月太湖全湖实测水质参数和光谱数据,首先利用高斯方程对遥感反射率进行过滤分解,找出叶绿素a(chl\|a)吸收峰675 nm以后的荧光反射峰(Fluorescence Peak:FP),再以662 nm处的反射率为基准,采用归一化荧光高度法进行叶绿素a浓度(C chl-a)反演,得到chl-a反演模型。基于高斯分解获取的chl-a的荧光反射峰值R(FP)与662 nm处的反射率R (662)比值[R(FP)/R(662)]与C chl-a之间存在显著的相关性,该模型为秋季太湖水体C chl-a的最佳反演模型。在高悬浮泥沙条件下,该模型能够较好地表示出叶绿素荧光高度与叶绿素浓度之间的关系,为C chl-a反演提供新的方法和依据,并为传感器敏感波段的选取和设置提供参考。  相似文献   

12.
We provide results of quantitative measurements and characterization for inland freshwater Lake Taihu from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. China's Lake Taihu, which is located in the Yangtze River delta in one of the world's most urbanized and heavily populated areas, contains consistently highly turbid waters in addition to frequent large seasonal algae blooms in various lake regions. Thus, satellite data processing requires use of the shortwave infrared (SWIR) atmospheric correction algorithm. Specifically for Lake Taihu, an iterative SWIR-based atmospheric correction algorithm has been developed and proven to provide reasonably accurate water-leaving radiance spectra data. Using MODIS-Aqua measurements, the blue-green algae bloom in Lake Taihu in 2007 has been studied in detail, demonstrating the importance and usefulness of satellite water color remote sensing for effectively monitoring and managing a bloom event.Seasonal and interannual variability, as well as spatial distributions, of lake water properties were studied and assessed using the MODIS-Aqua measurements from 2002 to 2008. Results show that overall waters in Lake Taihu are consistently highly turbid all year round, with the winter and summer as the most and least turbid seasons in the lake, respectively. Extremely turbid waters in the winter are primarily attributed to strong winter winds that lead to significant amounts of total suspended sediment (TSS) in the water column. In addition, MODIS-Aqua-measured water-leaving radiance at the blue band is consistently low in various bay regions in Lake Taihu, indicating high algae concentration in these regions. Climatological water property maps, including normalized water-leaving radiance spectra nLw(λ), chlorophyll-a concentration, and water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), are derived from all MODIS-Aqua data from 2002 to 2008 for Lake Taihu, showing overall spatial distribution features for the lake water property.  相似文献   

13.
Remote sensing techniques can be used to estimate and map the concentrations of suspended matter in inland water, providing both spatial and temporal information. Although an empirical approach to remote sensing of inland waters has been carried out frequently, satellite imagery has not been incorporated into routine lake monitoring programmes due in part to the lack of a standard prediction equation with multi‐temporal capacity for suspended matter. Empirical and physical models must be developed for each lake and its corresponding turbidity composition if they are to be compared over time, or with other bodies of water.

This study aimed to develop and apply multi‐temporal models to estimate and map the concentrations of total suspended matter (TSM) in Lake Taihu, China. Two Landsat‐5 Thematic Mapper (TM) images and nearly contemporaneous in situ measurements of TSM were used. A modified Dark‐Object Subtraction (DOS) method was used, and appeared to be adequate for atmospheric correction. The relationships were examined between TSM concentrations and atmospherically corrected TM band and band ratios. Results of this study show that the ratio TM4/TM1 has a strong relationship with TSM concentrations for lake waters with relatively low concentrations of phytoplankton algae. However, TM3 provided a strong predictive relationship with TSM concentrations despite varied water quality conditions. Different prediction models were developed and compared using multiple regression analysis. The Akaike Information Criteria (AIC) approach was used to choose the best models. The validation of the multi‐temporal capability of the best models indicated that it is feasible to apply the linear regression model using TM3 to estimate TSM concentrations across time in Lake Taihu, even if no in situ data were available.  相似文献   

14.
Retrieval of satellite remotely sensed chlorophyll-a (chl-a) concentrations in coastal regions such as the Bohai and Yellow Seas (BYS) is challenging due to their complex oceanic and atmospheric optical properties. The standard OC3M (ocean chl-a three-band algorithm for MODIS (moderate-resolution imaging spectroradiometer)) algorithm has been widely used in the BYS, despite well-known uncertainties about its accuracy in terms of absolute magnitude. This was based on the belief that OC3M chl-a is capable of representing reliable relative spatial and temporal patterns of sea surface chl-a concentrations. In this study, the ability of the standard OC3M chl-a algorithm to reproduce accurate seasonality patterns was evaluated, based on comparisons with in situ chl-a measurements in the BYS. The results quantified the overestimation by the standard OC3M algorithm with a median absolute percentage difference of 98.48% and a median relative difference of 1.13 mg m?3.More importantly, the seasonality from OC3M chl-a was significantly biased relative to the seasonal patterns of in situ chl-a. In addition, a regional GAM (generalized additive model)-based satellite chl-a algorithm was evaluated and compared with OC3M chl-a. The results showed the GAM chl-a improved accuracy in both magnitude and seasonality when compared with in situ chl-a, relative to that from OC3M chl-a.  相似文献   

15.
This paper presents an algorithm to retrieve land surface temperature (LST) and emissivity by integrating MODIS (Moderate Resolution Imaging Spectroradiometer) data onboard Terra and Aqua satellites. For a study area, there will be four pairs of day and night observations by MODIS onboard two satellites every day. Solar zenith angle, view zenith angle, and atmospheric water vapour have first been taken as independent variables to analyse their sensitivities to the same infrared channel measurements of MODIS on both Terra and Aqua satellites. Owing to their similar influences on the same MODIS band from Terra and Aqua satellites, four pairs of MODIS data from Terra and Aqua satellites can be thought of as MODIS measurement on a satellite at different viewing angles and viewing time. Comparisons between the retrieved results and in-situ measurements at three test sites (Qinghai Lake, Poyang Lake and Luancheng in China) indicate that the root mean square (rms) error is 0.66 K, except for the sand in Poyang Lake area. The rms error is less than 0.7 K when the retrieved results are compared with Earth Observing System (EOS) MODIS LST data products using the physics-based day/night algorithm. Emissivities retrieved by this algorithm are well compared to EOS MODIS emissivity data products (V5). The proposed algorithm can therefore be regarded as complementary and an extension to the EOS physics-based day/night algorithm.  相似文献   

16.
Total suspended solid (TSS) concentration is an important water quality parameter. Mapping its varying distribution using satellite images with high temporal resolution is valuable for studying suspended sediment transportation and diffusion patterns in inland lakes. A total of 255 sites were used to make remote-sensing reflectance measurements and surface water sampling at four Chinese inland lakes, i.e. Taihu Lake, Chaohu Lake, Dianchi Lake, and the Three Gorges Reservoir, at different seasons. A two-step retrieval method was then developed to estimate TSS concentration for contrasting Chinese inland lakes, which is described in this article. In the first step, a cluster method was applied for water classification using eight Geostationary Ocean Colour Imager (GOCI) channel reflectance spectra simulated by spectral reflectance measured by an Analytical Spectral Devices (ASD) Inc. spectrometer. This led to the classification of the water into three classes (1, 2, and 3), each with distinct optical characteristics. Based on the water quality, spectral absorption, and reflectance, the optical features in Class 1 were dominated by TSS, while Class 3 was dominated by chl-a and the optical characteristics of Class 2 were dominated jointly by TSS and chl-a. In the second step, class-specific TSS concentration retrieval algorithms were built. We found that the band ratio Band 8/Band 4 was suitable for Class 1, while the band ratio of Band 7/Band 4 was suitable for both Class 2 and Class 3. A comprehensive determination value, combining the spectral angle mapper and Euclidean distance, was adopted to identify the classes of image pixels when the method was applied to a GOCI image. Then, based on the pixel’s class, the class-specific retrieval algorithm was selected for each pixel. The accuracy analysis showed that the performance of this two-step method was improved significantly compared to the unclassed method: the mean absolute percentage error decreased from 38.9% to 24.3% and the root mean square error decreased from 22.1 to 16.5 mg l–1. Finally, the GOCI image acquired on 13 May 2013 was used as a demonstration to map the TSS concentration in Taihu Lake with a reasonably good accuracy and highly resolved spatial structure pattern.  相似文献   

17.
The absolute radiometric accuracy of Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) data was evaluated with in situ data collected in a vicarious calibration field campaign conducted in Lake Titicaca, Bolivia during May 26 and June 17, 2000. The comparison between MODIS TIR data produced by the version 2.5.4 Level-1B code and the band radiances calculated with atmospheric radiative transfer code MODTRAN4.0 based on lake surface kinetic temperatures measured by five IR radiometers deployed in the high-elevation Lake Titicaca and the atmospheric temperature and water vapor profiles measured by radiosondes launched on the lake shore on June, 15 2000, a calm clear-sky day, shows good agreements in bands 31 and 32 (within an accuracy of 0.4%) in the daytime overpass case. Sensitivity analysis indicates that the changes on the measured atmospheric temperature and water vapor profiles result in negligible or small effects on the calculated radiances in the atmospheric window bands (bands 20–23, 29, and 31–32). Therefore, comparisons for these bands were made for cases when lake surface temperature measurements were available but no radiosonde data were available and in subareas of 10×16 pixels where there was no in situ measurement but MODIS brightness temperatures in band 31 vary within ±0.15 K by using the validated band 31 to determine lake surface temperatures through the MODTRAN4.0 code. Comparisons and error analysis show that the specified absolute radiometric accuracies are reached or nearly reached in MODIS bands 21, 29, and 31–33 and that there is a calibration bias of 2–3% in bands 20, 22, and 23. The error analysis also shows that the radiosondes cannot provide accurate atmospheric temperature and water vapor profiles to estimate the calibration accuracies in the atmospheric sounding bands (bands 24–25, 27–28, and 34–36) at the specified 1% level and that the calibration accuracy in the ozone band 30 cannot be estimated without in situ measurements of ozone.  相似文献   

18.
The spatial distribution of the sum of chlorophyll a and phaeophytin a concentrations (chl-a) under light wind (0–2 m s?1) conditions was studied in two lakes with an AISA airborne imaging spectrometer. Chl-a was interpreted from AISA radiance data using an algorithm based on the near-infrared (700–710 nm) to red (660–665 nm) ratio. The results of Lake Lohjanjärvi demonstrate that the use of one monitoring station can result in over- or underestimation by 29–34% of the overall chl-a compared with an AISA-based estimation. In Lake Hiidenvesi, the AISA-based estimation for the mean chl-a with 95% confidence limits was 25.19±2.18 µg l?1. The use of AISA data together with chl-a measured at 15 in situ sampling stations decreased the relative standard error of the mean chl-a estimation from 20.2% to 4.0% compared with the use of 15 discrete samples only. The relative standard error of the mean chl-a using concentrations at the three routine monitoring stations was 15.9 µg l?1 (63.1%). The minimum and maximum chl-a in Lake Hiidenvesi were 2 and 101 µg l?1, 6 and 70 µg l?1 and 11 and 66 µmg l?1, estimated using AISA data, data from 15 in situ stations and data from three routine in situ stations, respectively.  相似文献   

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