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1.
根据1996-2002年无锡太湖监测站的水质资料分析,太湖悬浮物具有季节性特征,因而分季节的悬浮物估测模型比单一的模型可能更加适合用来估测太湖全年的悬浮物浓度.在分析太湖水体光谱特征的基础上,根据太湖悬浮物的季节性分布特征,使用春夏秋冬四季的Landsat TM/ETM图像和准同步的水质采样数据,建立了太湖分季节的悬浮物估算模型.结果表明:估测因子(B2 B3)/(B2/B3)在春、秋、冬三季都能很好地估测出悬浮物的浓度(R2＞0.52).夏季由于叶绿素的干扰性较大,悬浮物的估测效果不理想.冬季的估测效果最好(R2=0.81),模型为lnSS=14.656×(B2 B3)/(B2/B3) 1.661,其中,ln SS表示悬浮物取自然对数后的值,B2、B3为TM/ETM图像经过6S大气校正、3×3低通滤波后第2、3波段的反射率值. 相似文献
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内陆水体叶绿素a浓度定量反演是水质遥感的热点与难点.本文基于对内陆水体叶绿素a、悬浮物、溶解有机物与水分子的光谱特征分析,从半分析生物光学模型出发,利用太湖实测的水面 ASD 高光谱遥感数据三波段组合,进行迭代优化,得到与叶绿素浓度密切相关而受悬浮物与黄色物质影响小的最优波段组合模型,反演精度较高,其决定系数和均方根误差分别为 0.8358、3.816mg/m3,该方法可以有效地反演高浓度悬浮物主导光学特性的水体叶绿素a浓度. 相似文献
3.
基于反射光谱的太湖北部叶绿素a浓度定量估算 总被引：2，自引：0，他引：2
利用地物光谱仪研究了太湖水体的反射光谱特征与叶绿素a浓度之间的定量关系,结果表明太湖水体的叶绿素a浓度可以用720 nm附近的反射率估算,同时也可以用806 nm和571 nm两个波段的反射率比值来估算,前者建立的估算模型具有较好的通用性,而后者只能较好的估算<10μg/L的叶绿素a浓度;通过对光谱微分的分析,发现叶绿素a浓度与690 nm附近的一阶微分和702 nm附近的二阶微分相关性最好,但基于反射光谱一阶微分的叶绿素a浓度估算模型,并没有显著的提高太湖叶绿素a浓度的估测精度,二阶微分后的估测精度好于一阶微分,但其估测精度仍没有利用720 nm反射光谱的反演模型高．太湖水体的叶绿素a浓度可以利用720 nm附近的反射光谱有效地估算． 相似文献
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利用遥感技术监测太湖蓝藻水华具有重要的现实意义.基于不同遥感数据,包括MODIS/Terra、CBERS-2 CCD、ETM和IRS.P6 LISS3,结合蓝藻水华光谱特征,采用单波段、波段差值、波段比值等方法,提取不同历史时期太湖蓝藻水华.结果表明:MODIS/Terra数据可以利用判别式Band2＞0.1和Band2/Band4＞1提取蓝藻水华;CBERS-2 CCD、ETM和IRS-P6 LISS3数据可以利用Band4大于一定阈值和Band4/Band3＞1提取蓝藻水华;波段比值(近红外,红光＞1)算法稳定,可以发展成为蓝藻水华遥感提取普适模式.同时,本文成功利用ETM和IRS.P6 LISS3数据Band4波段对蓝藻水华空间分布强度进行了五级划分.这为今后利用遥感技术,建立太湖蓝藻水华监测和预警系统莫定了基础. 相似文献
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下行漫衰减系数(K_d)是描述水下光场的重要参数,决定水体真光层深度,影响着浮游藻类初级生产力及其分布特征.基于2008—2013年太湖4次大规模野外试验数据,分析太湖水体漫衰减系数特征及其影响因素,建立适用于多种卫星数据且较高精度的太湖水体490 nm处下行漫衰减系数估算模型.结果表明:无机悬浮物是太湖水体漫衰减系数的主要影响因素;红绿波段比值(674 nm/555 nm)最适合于太湖K_d(490)估算,模型反演精度较高(N=72,R~2=0.72,RMSE=0.89 m~(-1),MAPE=21.58%);利用实测光谱数据,模拟得到MODIS/EOS、OLCI/Sentinel-3、GOCI/COMS和MSI/Sentinel-2等主要传感器波段的信号,构建适用于多种卫星传感器K_d(490)估算的红绿波段模型,建模精度较高(N=72,R~20.7,RMSE0.9 m~(-1),MAPE22.0%),且进行了验证(N=37,R~20.7,RMSE0.9 m~(-1),MAPE22.0%). 相似文献
7.
CDOM吸收特性是湖泊水色遥感的重要研究内容之一,影响着水体的遥感反射率;吸收光谱的形状一般符合波长的负指数关系,但不同水体的形态因子即S值是不同的.实地采集东太湖水样,实验室测量叶绿素、悬浮物质以及黄色物质的组份含量,室内测试计算水样的CDOM吸收光谱,根据光谱曲线形状,把样点分为三组,分别进行考察,结果发现对东太湖春季水体而言,浮游植物的降解对CDOM吸收具有重要甚至主导作用;水体中有机悬浮颗粒占有一定的比例,在测试或计算东太湖总的吸收或散射系数时,必须充分考虑有机悬浮颗粒的吸收与散射特性,否则会带来较大的误差;以500nm为分界点,把300—700nm的波段范围分为两个部分即300—500nm和500—700nm,分别定义CDOM吸收光谱的曲线斜率即S值,可以提高CDOM吸收光谱的估测精度,把S值定义为随波长线性变化的函数,可以进一步提高CDOM吸收光谱的估测精度,对东太湖春季水体而言,当:300≤λ<500nm时,S(μm-1)=-0.0193×λ 20.821,当500≤λ≤700nm时,S(μm-1)=-0.0121×λ 16.003. 相似文献
8.
基于叶绿素荧光峰特征的浑浊水体悬浮物浓度遥感反演 总被引：4，自引：0，他引：4
内陆水体光学特性复杂,其水质参数遥感反演是当前环境遥感研究的热点与难点.2004年10月在太湖实测了67个站点的遥感反射率与相应站点水质参数浓度,通过对水体反射率光谱的分析发现,秋季太湖悬浮物主导了水体光学特性,叶绿素荧光峰的特征主要体现为悬浮物浓度的变化.据此建立了基于水面实测岛光谱遥感反射率数据的叶绿素荧光峰特征与悬浮物浓度之间的拟合关系,发现二者具有很好的响应关系.具体分析了叶绿素荧光峰绝对高度、基线高度、归一化高度(分别归一化到560nm附近最大反射率波段与近红外810nm附近最大反射率波段)及荧光峰积分面积(包括积分总面积、基线以下面积与基线以上面积)等儿种光谱特征与悬浮物浓度之间的关系,其相关系数(R2)分别为0.8822、0.7483、0.8901、0.8547、0.8927、0.8877、0.8632,平均相对误差分别为27.25%、41.03%、27.11%、25.75%、24.91%、25.47%、27.54%.总体反演精度较高,其中总积分面积法效果最好,基线高度法效果最差,而叶绿索荧光峰波段的位移与悬浮物浓度之间不存在明显的相关性.研究结果表明叶绿素荧光峰特征在浑浊内陆水体悬浮物浓度信息提取中具有很好的应用前景,该方法可为浑浊的二类水体悬浮物遥感反演提供了一个新思路. 相似文献
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利用便携式光谱辐射计,采用一定的观测角度获取水体表面的光谱,进而提取水表面下辐照度比R(0-)信息,分析R(0-)光谱特征与叶绿素a浓度之间的相互关系,结果表明太湖夏季水体叶绿素a浓度与R(0-)光谱曲线762 nm、727nm和496nm处的相关系数较大,分别达到了0.85、0.84、-0.80.通过单波段、波段比值模型分析,认为以R(0-)761、R(0-)762/R(0-)496、R(0-)727/R(0-)496为自变量的二次函数模型是利用水表面下辐照度比R(0-)估算太湖夏季水体中叶绿素a浓度的最佳模型,模型的决定系数R2分别达到了0.923、0.919、0.916,回归估计的标准误差S分别为0.012、0.013、0.013,F检验值分别为101.241、96.576、92.925.利用剩余10个样本对估算模型进行精度和误差检验,结果表明以R(0-)762/R(0-)496为自变量的二次函数模型好于另外两个,对太湖夏季水体叶绿素a浓度估算具有一定的实用性.此外,将光谱微分技术应用到R(0-)信息分析太湖夏季水体叶绿素a浓度,结果不能获得较高的预测精度. 相似文献
11.
基于高频次GOCI数据的太湖悬浮物浓度短期动态和驱动力分析 总被引：2，自引：0，他引：2
总悬浮物是水体中重要的光学敏感物质之一,很大程度上决定了水柱中光的吸收、散射和衰减,同时吸附营养盐、重金属和有毒有害物,对水体物质生物地球化学过程、沉积物埋藏动力和湖泊环境演化具有重要的意义.基于星地同步实验和静止水色成像仪GOCI(Geostationary Ocean Color Imager)构建了太湖悬浮物浓度估算模型,并分析了典型风浪过程中太湖悬浮物浓度短期动态变化过程.研究表明:对太湖水体悬浮物浓度较为敏感的波段为GOCI的第7波段(745nm)和第8波段(865 nm),悬浮物浓度与对应波段遥感反射率线性相关决定系数分别为0.72和0.55;基于GOCI第7波段的悬浮物浓度单波段遥感估算模型能较为准确地估算太湖的悬浮物浓度,模型相对均方根误差和平均绝对百分误差分别为28.3%和24.4%.通过研究典型风浪过程前后太湖悬浮物浓度变化发现其短期动态变化显著,风速、风向是悬浮物浓度短期动态变化的重要驱动因素,悬浮物浓度与风速呈正比,并随着风向扩散;高频连续GOCI影像结果显示悬浮物浓度短期动态变化对风浪扰动的响应有一定的滞后性,滞后时间为数小时到1天,悬浮物沉降与沉积物再悬浮的临界风速约为3.4 m/s. 相似文献
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Landsat 8卫星自2013年2月发射以来,其影像的定标参数经过了不断调整和完善,针对Landsat 8开发的各种算法也相继问世.本文采用最新的参数、算法和引入COST算法建立的大气校正模型,对Landsat 8多光谱和热红外波段进行了处理,反演出它们的反射率和地表温度,并与同日的Landsat 7数据和实测地表温度数据进行了对比.结果表明,现有Landsat 8多光谱数据的定标参数和大气顶部反射率反演算法已有很高的精度,本文引入COST算法建立的Landsat 8大气校正模型也与Landsat 7的COST模型所获得的结果几乎相同,相关系数可高达0.99.但是现有针对Landsat 8提出的地表温度反演算法仍不理想,已提出的劈窗算法误差都较大.鉴于TIRS 11热红外波段的定标参数仍不理想,因此在现阶段建议采用单通道算法单独反演TIRS 10波段来求算地表温度,但要注意根据大气水汽含量的情况选用正确的大气参数计算公式. 相似文献
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Estimation of water clarity in Taihu Lake and surrounding rivers using Landsat imagery 总被引：1，自引：0，他引：1
Dehua ZhaoYing Cai Hao JiangDelin Xu Wenguang ZhangShuqing An 《Advances in water resources》2011,34(2):165-173
In China, the increase in exogenous-source pollutants from rivers is one of the most important causes of lake eutrophication. The application of remote sensing technology to water quality monitoring of rivers connected to these lakes has special significance for lake management at regional scales. Many research studies have estimated water clarity using Landsat imagery. However, most of this work focused on lakes or reservoirs, for which abundant water-only pixels (i.e., pure pixels of water, PPW) were available. Few of these studies have addressed rivers, especially rivers with an average width less than 100 m. In our study, we sought to determine whether water clarity in the rivers connected to Taihu Lake could be estimated using Landsat imagery. We obtained 18 Enhanced Thematic Mapper Plus (ETM+) images from 2009 for 13 rivers ranging from an average of 37.3 to 173.6 m wide. Three field campaigns conducted in May 2009, September 2009, and January 2010 were used to obtain field measurements of Secchi disk depth (SDD). Our results suggested that the widely used model, a(TM1/TM3) + b(TM1) + c, was suitable for the estimation of SDD for Taihu Lake. The brightness of the panchromatic band of ETM+ showed significant correlations with TM1, TM3 and TM1/TM3 (p < 0.001). As a result, SDD in the lake could also be estimated using the Landsat panchromatic band. The multispectral image of ETM+ did not provide adequate PPW for estimation of water clarity in rivers. However, PPW derived from the panchromatic image captured about 93% of the variation in SDD, on average, for the every worst-case scenario in the 13 rivers. Using the PPW in rivers, a significant correlation was found between the brightness of the panchromatic image and SDD (R2 = 0.64, p < 0.001). Our results demonstrate that the panchromatic image of Landsat, but not the multispectral image, can be used to estimate water clarity in rivers with an average width greater than 40 m in the Taihu basin. 相似文献
14.
Qiao Wang Xin Jin YunMei Li ChuanQing Wu Heng Lü YanFei Wang Hong Zhang Bin Yin Li Zhu 《中国科学:地球科学(英文版)》2010,53(1):58-66
Synchronization experiment was conducted in June, 2009 to get Inherent Optical Properties (IOP) of water component in Chaohu Lake. Water bio-optical mechanism was studied combined with multispectral data of Environmental Satellite 1 (CCD), and then inversion models of total suspended matter (TSM), inorganic suspended matter (ISM) and organic suspended matter (OSM) concentration were built. The data indicated that: the absorption ratio of suspended particulate matter and CDOM to total were almost no change from band 1 to band 2 with about 85% and 9%, respectively. The ratio of pure water to total increased from 0.4% to 5.6%. Water reflectance in these two bands were influenced by absorption of three kinds of components: algae particles absorption surpassed non-algal particles in band 3, and so played an important role in total absorption with about 35.7%; the proportion of pure water absorption and particles matter backscattering both were 99% in band 4, so these two components decided the main inherent optical properties in band 4. The models of TSM and ISM concentration inversion based on band combination (band 3 + band 4)/(band 1 + band 2) were built, while OSM concentration was estimated by band 4/(band 1 + band 2) index. Inversed by image data, RE of TSM concentration between modeled and measured was 33.4%, and RMSE was 18.68 mg/L. RE of ISM and OSM concentration were 39.9% and 35.2% respectively. The inversion was more accurate when satellite-ground data were just in the same day. At this situation, RE of ISM concentration dropped to 25.4%, and that of TSM and OSM reduced to 26.5% and 26.8% as well. 相似文献
15.
Wang Qiao Jin Xin Li YunMei Wu ChuanQing L&#; Heng Wang YanFei Zhang Hong Yin Bin Zhu Li 《中国科学:地球科学(英文版)》2011,53(1):58-66
Synchronization experiment was conducted in June, 2009 to get Inherent Optical Properties (IOP) of water component in Chaohu Lake. Water bio-optical mechanism was studied combined with multispectral data of Environmental Satellite 1 (CCD), and then inversion models of total suspended matter (TSM), inorganic suspended matter (ISM) and organic suspended matter (OSM) concentration were built. The data indicated that: the absorption ratio of suspended particulate matter and CDOM to total were almost no change from band 1 to band 2 with about 85% and 9%, respectively. The ratio of pure water to total increased from 0.4% to 5.6%. Water reflectance in these two bands were influenced by absorption of three kinds of components: algae particles absorption surpassed non-algal particles in band 3, and so played an important role in total absorption with about 35.7%; the proportion of pure water absorption and particles matter backscattering both were 99% in band 4, so these two components decided the main inherent optical properties in band 4. The models of TSM and ISM concentration inversion based on band combination (band 3 + band 4)/(band 1 + band 2) were built, while OSM concentration was estimated by band 4/(band 1 + band 2) index. Inversed by image data, RE of TSM concentration between modeled and measured was 33.4%, and RMSE was 18.68 mg/L. RE of ISM and OSM concentration were 39.9% and 35.2% respectively. The inversion was more accurate when satellite-ground data were just in the same day. At this situation, RE of ISM concentration dropped to 25.4%, and that of TSM and OSM reduced to 26.5% and 26.8% as well.
相似文献16.
Up-scaling of SEBAL derived evapotranspiration maps from Landsat (30 m) to MODIS (250 m) scale 总被引：2，自引：0，他引：2
Remotely sensed imagery of the Earth’s surface via satellite sensors provides information to estimate the spatial distribution of evapotranspiration (ET). The spatial resolution of ET predictions depends on the sensor type and varies from the 30–60 m Landsat scale to the 250–1000 m MODIS scale. Therefore, for an accurate characterization of the regional distribution of ET, scaling transfer between images of different resolutions is important. Scaling transfer includes both up-scaling (aggregation) and down-scaling (disaggregation). In this paper, we address the up-scaling problem.The Surface Energy Balance Algorithm for Land (SEBAL) was used to derive ET maps from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. Landsat 7 bands have spatial resolutions of 30–60 m, while MODIS bands have resolutions of 250, 500 and 1000 m. Evaluations were conducted for both “output” and “input” up-scaling procedures, with aggregation accomplished by both simple averaging and nearest neighboring resampling techniques. Output up-scaling consisted of first applying SEBAL and then aggregating the output variable (daily ET). Input up-scaling consisted of aggregating 30 m Landsat pixels of the input variable (radiance) to obtain pixels at 60, 120, 250, 500 and 1000 m before SEBAL was applied. The objectives of this study were first to test the consistency of SEBAL algorithm for Landsat and MODIS satellite images and second to investigate the effect of the four different up-scaling processes on the spatial distribution of ET.We conclude that good agreement exists between SEBAL estimated ET maps directly derived from Landsat 7 and MODIS images. Among the four up-scaling methods the output simple averaging method produced aggregated data and aggregated differences with the most statistically and spatially predictable behavior. The input nearest neighbor method was the least predictable but was still acceptable. Overall, the daily ET maps over the Middle Rio Grande Basin aggregated from Landsat images were in good agreement with ET maps directly derived from MODIS images. 相似文献
17.
Suspended sediment concentration (SSC) is a critical parameter in the study of river sediment transport and water quality variation, but traditional measurement methods are costly and time‐consuming. This paper is focused on presenting a methodology that may be useful in estimating SSC which is of key importance in process geomorphology and hydrology. In previous studies, remote sensing has been applied to estimate the SSC of sea waters as well as low turbid inland waters like lakes, reservoirs and short river reaches visible within a single Landsat satellite image coverage. Rivers, especially highly turbid large rivers, have largely been ignored. The dataset used in this paper includes measured SSC and multi‐temporal Landsat ETM+ images covering most part of the Yangtze River. Using an effective easy‐to‐use atmospheric correction method that does not require in situ atmospheric conditions, retrieved water reflectance of Band 4 was found to be a good SSC indicator within the large SSC range 22–2610 mg l–1. The newly developed regression relation between SSC and water reflectance of Band 4 appears to be able to provide a relatively accurate SSC estimate directly from Landsat ETM+ images for the Yangtze River from the upper, the middle to the lower reaches. With the relation it is possible to estimate or map out SSC dynamics of large rivers which lack SSC data due to constraints of conventional measurements. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
18.
M. R. Patrick J. Dehn K. R. Papp Z. Lu K. Dean L. Moxey P. Izbekov R. Guritz 《Journal of Volcanology and Geothermal Research》2003,127(1-2):87-105
Okmok Volcano, in the eastern Aleutian Islands, erupted in February and March of 1997 producing a 6-km-long lava flow and low-level ash plumes. This caldera is one of the most active in the Aleutian Arc, and is now the focus of international multidisciplinary studies. A synthesis of remotely sensed data (AirSAR, derived DEMs, Landsat MSS and ETM+ data, AVHRR, ERS, JERS, Radarsat) has given a sequence of events for the virtually unobserved 1997 eruption. Elevation data from the AirSAR sensor acquired in October 2000 over Okmok were used to create a 5-m resolution DEM mosaic of Okmok Volcano. AVHRR nighttime imagery has been analyzed between February 13 and April 11, 1997. Landsat imagery and SAR data recorded prior to and after the eruption allowed us to accurately determine the extent of the new flow. The flow was first observed on February 13 without precursory thermal anomalies. At this time, the flow was a large single lobe flowing north. According to AVHRR Band 3 and 4 radiance data and ground observations, the first lobe continued growing until mid to late March, while a second, smaller lobe began to form sometime between March 11 and 12. This is based on a jump in the thermal and volumetric flux determined from the imagery, and the physical size of the thermal anomalies. Total radiance values waned after March 26, indicating lava effusion had ended and a cooling crust was growing. The total area (8.9 km2), thickness (up to 50 m) and volume (1.54×108 m3) of the new lava flow were determined by combining observations from SAR, Landsat ETM+, and AirSAR DEM data. While the first lobe of the flow ponded in a pre-eruption depression, our data suggest the second lobe was volume-limited. Remote sensing has become an integral part of the Alaska Volcano Observatory’s monitoring and hazard mitigation efforts. Studies like this allow access to remote volcanoes, and provide methods to monitor potentially dangerous ones. 相似文献
19.
MODIS Terra Collection 6 fractional snow cover validation in mountainous terrain during spring snowmelt using Landsat TM and ETM+ 
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下载免费PDF全文 Christopher J. Crawford 《水文研究》2015,29(1):128-138
Daily swath MODIS Terra Collection 6 fractional snow cover (MOD10_L2) estimates were validated with two‐day Landsat TM/ETM + snow‐covered area estimates across central Idaho and southwestern Montana, USA. Snow cover maps during spring snowmelt for 2000, 2001, 2002, 2003, 2005, 2007, and 2009 were compared between MODIS Terra and Landsat TM/ETM + using least‐squared regression. Strong spatial and temporal map agreement was found between MODIS Terra fractional snow cover and Landsat TM/ETM + snow‐covered area, although map disagreement was observed for two validation dates. High‐altitude cirrus cloud contamination during low snow conditions as well as late season transient snowfall resulted in map disagreement. MODIS Terra's spatial resolution limits retrieval of thin‐patchy snow cover, especially during partially cloudy conditions. Landsat's image acquisition frequency can introduce difficulty when discriminating between transient and resident mountain snow cover. Furthermore, transient snowfall later in the snowmelt season, which is a stochastic accumulation event that does not usually persist beyond the daily timescale, will skew decadal snow‐covered area variability if bi‐monthly climate data record development is the objective. As a quality control step, ground‐based daily snow telemetry snow‐water‐equivalent measurements can be used to verify transient snowfall events. Users of daily MODIS Terra fractional snow products should be aware that local solar illumination and sensor viewing geometry might influence fractional snow cover estimation in mountainous terrain. Cross‐sensor interoperability has been confirmed between MODIS Terra and Landsat TM/ETM + when mapping snow from the visible/infrared spectrum. This relationship is strong and supports operational multi‐sensor snow cover mapping, specifically climate data record development to expand cryosphere, climate, and hydrological science applications. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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M. C. W. Baker 《Surveys in Geophysics》1982,5(3):281-293
The principal uses of Landsat imagery in volcanological studies are for regional reconnaisance, for the interpretation of large volcanic structures and to facilitate the comparison of structures in different parts of the world. Standard black and white single band prints and standard false colour composites are the cheapest and most readily available forms of Landsat imagery. However, standard Landsat images have a poorer resolution and lower information content than enhanced images. The most generally useful enhancement techniques for volcanic studies have proved to be destriping. contrast stretching and edge enhancement. Enhancement techniques are illustrated with examples of young volcanic structures from the Central Andes. The next few years should see significant advances in satellite remote sensing technology with higher resolution imagery (down to 10–30m) and imagery in a wider range of spectral bands becoming available.Paper presented at the Symposium Volcanoes of the Earth and Planets, held at the University of Lancaster, March 17, 1981. 相似文献