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1.
Accurate estimation of chlorophyll-a concentration in turbid coastal waters by means of remote sensing is challenging due to the optical complexity of these waters. We have developed a four-band quasi-analytical algorithm for assessment of chlorophyll-a concentration in coastal waters. The objectives of this study are to validate the applicability of three-band semi-analytical algorithm, quasi-analytical algorithm, and four-band quasi-analytical algorithm in estimating chlorophyll-a concentration in turbid coastal waters for MODIS sensor. These three algorithms are calibrated and evaluated against coastal evaluation datasets provided by SeaWiFS Bio-optical Archive and Storage System. The algorithm validation results indicate that the four-band quasi-analytical algorithm produces a superior performance to both three-band semi-analytical algorithm and quasi-analytical algorithm. By comparison, using four-band quasi-analytical algorithm produces 21.61 % uncertainty in estimating chlorophyll-a concentration from turbid coastal waters, lower than 77.90 % for three-band semi-analytical algorithm and 74.31 % for quasi-analytical algorithm, respectively. The significantly reduced uncertainty in chlorophyll-a concentration assessment is due to effectively removal of pigment-package effects and particle overlapping effects on the chlorophyll-a absorption estimation using a optical classification method. These findings imply that, provided that an atmospheric correction scheme for visible and near-infrared bands is available, the database of MODIS imagery could be used for quantitative monitoring of chlorophyll-a concentration in turbid coastal waters by four-band quasi-analytical algorithm. 相似文献
2.
Wenting Quan 《Journal of the Indian Society of Remote Sensing》2014,42(3):495-503
In this study, a theoretical model for studying the scaling effects on the two-band ratio of red to near-infrared band (TBRRN) is suggested. The model is used to explain the relationship between scaling error and local scale error; the results revealed that a special scale scaling procedure can be divided into a series smaller scale scaling procedures, and the total scaling error is the sum of the scaling error of these series’ smaller scale scaling procedure. Consequently, under the condition that the local scale is adequately fine, the total scale error at the target scale may be estimated accurately. In order to understand the mechanisms associated with scale in practical remote sensing, TBRRN data with 250 m and 1 km resolution is estimated from MODIS data at 645 and 859 nm, retrieved on September 1, 2009, in the Yellow River estuary, China. It is found that the TBRRN estimated from the 1 km resolution MODIS data is ~2.94 % smaller than as estimated from the 250 m MODIS data. The large scaling error distributes neither in the turbid waters, nor in the low suspended sediment regions, but instead in the high-low suspended sediment concentration transitional zone, which may be attributed to the spatial variable of suspended sediment in the transitional zone. This paper also points out that, owing to the importance of total scale error in achieving NASA’s mission in oceanic remote sensing, the way in which to conveniently and precisely estimate the total scale error of remote sensing parameters may potentially be an important topic in the field of oceanic remote sensing, both in present research and in the future. 相似文献
3.
K. Adem Ali Joseph Ortiz Nicholas Bonini Morgan Shuman Catherine Sydow 《地理信息系统科学与遥感》2016,53(4):483-505
There is considerable interest in accurately estimating water quality parameters in turbid (Case 2) and eutrophic waters such as the Western Basin of Lake Erie (WBLE). Lake Erie is a large, open freshwater body that supports diverse ecosystem, and over 12 million people in the mid-western part of the United States depend on it for drinking water, fisheries, navigational, and recreational purposes. The increasing utilization of the freshwater has deteriorated the water severely and currently the lake is experiencing recurring harmful algal blooms (HABs). Improving the water quality of Lake Erie requires the use of robust monitoring tools that help water quality managers understand sources and pathways of influxes that trigger HABs. Satellite-based remote sensing sensor such as the moderate resolution imaging spectroradiometer (MODIS) may provide frequent and synoptic view of the water quality indices. In this study, data set from field measurements was used to evaluate the performance of 14 existing ocean color algorithms. Results indicated that MODIS data consistently underestimated the chlorophyll a concentrations in the WBLE, with the largest source of errors from dissolved organic matter and xanthophyll accessory pigments in this data set. Most of the global algorithms, including OC4v4 and the Baltic model, generated near-identical statistical parameters with an average R2 of ~0.57 and RMSE ~2.9 μg/l. MODIS performed poorly (R2 ~0.18) when its NIR/red bands were used. A slightly improved model was developed using similar band ratio approach generating R2 of ~0.62 and RMSE ~1.8 μg/l. 相似文献
4.
面向土壤分类的高光谱反射特征参数模型 总被引：2，自引：0，他引：2
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。 相似文献
5.
Kumar Arun Prasad 《国际地球制图》2016,31(8):891-912
This study aims at discriminating eight mangrove species of Rhizophoraceae family of Indian east coast using field and laboratory spectra in spectral range (350–2500 nm). Parametric and non-parametric statistical analyses were applied on spectral data in four spectral modes: (i) reflectance (ii) continuum removed, (iii) additive inverse and (iv) continuum removed additive inverse. We introduced continuum removal of inverse spectra to utilize the advantage of continuum removal in reflectance region. Non-parametric test gave better separability than parametric test. Principal component analysis and stepwise discriminant analysis were applied for feature reduction and to identify optimal wavelengths for species discrimination. To quantify the separability, Jeffries–Matusita distance measure was derived. Green (550 nm), red edge (680–720 nm) and water absorption region (1470 and 1850 nm) were found to be optimal wavelengths for species discrimination. The continuum removal of additive inverse spectra gave better separability than the continuum removed spectra. 相似文献
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7.
The accurate assessment of total suspended sediment (TSM) concentration in coastal waters by means of remote sensing is quite challenging, due to the optical complexity and significant variability of these waters. In this study, three-band semi-analytical TSM retrieval (TSTM) model with HJ-1A/CCD spectral bands was developed for the retrieval of TSM concentration from turbid coastal waters. This model was calibrated and validated by means of one calibration dataset and three independent validation datasets obtained from three different turbid waters. It was found that the TSTM model may be used to retrieve accurate TSM concentration data from highly turbid waters without the spectral slope of the model requiring further optimization. Finally, the TSM concentration data were quantified from the HJ-1A/CCD images after atmospheric correction using the dark-object subtraction technique. Upon comparing the model-derived and field-measured TSM concentration data, it was observed that the TSTM model produced <29% uncertainty in deriving TSM concentration from the HJ-1A/CCD data. These findings imply that the TSTM model may be used for the quantitative monitoring of TSM concentration in coastal waters, provided that the atmospheric correction scheme for the HJ-1A/CCD imagery is available. 相似文献
8.
Study of hyper-spectral behaviour of snow is important to interpret, analyse and validate optical remote sensing observations. To map and understand response of snow-mixed pixels in RS data, field experiments were conducted for linear mixing of external materials (i.e. Vegetation, Soil) with snow, using spectral-radiometer (350–2500 nm). Further, systematic non-linear mixing of snow contaminants (soil, coal, ash) in terms of size and concentration of contaminants is analysed to imitate and understand spectral response of actual field scenarios. Sensitivity of band indices along with absorption peak characteristics provide clues to discriminate the type of contaminants. SWIR region is found to be useful for discriminating size of external contaminants in snow e.g. Avalanche deposited snow from light contaminated forms. Present research provide inputs for mapping snow-mixed pixels in medium/coarse resolution remote sensing RS data (in terms of linear mixing) and suitable wavelength selections for identification and discriminating type/size of snow contaminants (in terms of non-linear mixing). 相似文献
9.
The validation of satellite ocean-color products is an important task of ocean-color missions. The uncertainties of these products are poorly quantified in the Yellow Sea (YS) and East China Sea (ECS), which are well known for their optical complexity and turbidity in terms of both oceanic and atmospheric optical properties. The objective of this paper is to evaluate the primary ocean-color products from three major ocean-color satellites, namely the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Through match-up analysis with in situ data, it is found that satellite retrievals of the spectral remote sensing reflectance Rrs(λ) at the blue-green and green bands from MERIS, MODIS and SeaWiFS have the lowest uncertainties with a median of the absolute percentage of difference (APDm) of 15–27% and root-mean-square-error (RMS) of 0.0021–0.0039 sr−1, whereas the Rrs(λ) uncertainty at 412 nm is the highest (APDm 47–62%, RMS 0.0027–0.0041 sr−1). The uncertainties of the aerosol optical thickness (AOT) τa, diffuse attenuation coefficient for downward irradiance at 490 nm Kd(490), concentrations of suspended particulate sediment concentration (SPM) and Chlorophyll a (Chl-a) were also quantified. It is demonstrated that with appropriate in-water algorithms specifically developed for turbid waters rather than the standard ones adopted in the operational satellite data processing chain, the uncertainties of satellite-derived properties of Kd(490), SPM, and Chl-a may decrease significantly to the level of 20–30%, which is true for the majority of the study area. This validation activity advocates for (1) the improvement of the atmosphere correction algorithms with the regional aerosol optical model, (2) switching to regional in-water algorithms over turbid coastal waters, and (3) continuous support of the dedicated in situ data collection effort for the validation task. 相似文献
10.
Rimjhim Bhatnagar Singh S. S. Ray S. K. Bal B. S. Sekhon G. S. Gill Sushma Panigrahy 《Journal of the Indian Society of Remote Sensing》2013,41(2):301-308
Crop residue has become an increasingly important factor in agriculture management. It assists in the reduction of soil erosion and is an important source of soil organic carbon (soil carbon sequestration). In recent past, remote sensing, especially narrowband, data have been explored for crop residue assessment. In this context, a study was carried out to identify different narrow-bands and evaluate the performance of SWIR region based spectral indices for crop residue discrimination. Ground based hyperspectral data collected for wheat crop residue was analyzed using Stepwise Discriminant Analysis (SDA) technique to select significant bands for discrimination. Out of the seven best bands selected to discriminate between matured crop, straw heap, combine-harvested field with stubbles and soil, four bands were from SWIR (1980, 2030, 2200, 2440 nm) region. Six spectral indices were computed, namely CAI, LCA, SINDRI, NDSVI, NDI5 and hSINDRI for crop residue discrimination. LCA and CAI showed to be best (F?>?115) in discriminating above classes, while LCA and SINDRI were best (F?>?100) among all indices in discriminating crop residue under different harvesting methods. Comparison of different spectral resolution (from 1 nm to 150 nm) showed that for crop residue discrimination a resolution of 100 nm at 2100–2300 m region would be sufficient to discriminate crop residue from other co-existing classes. 相似文献
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12.
Particulate organic carbon (POC) plays an important role in the carbon cycle in water due to its biological pump process. In the open ocean, algorithms can accurately estimate the surface POC concentration. However, no suitable POC-estimation algorithm based on MERIS bands is available for inland turbid eutrophic water. A total of 228 field samples were collected from Lake Taihu in different seasons between 2013 and 2015. At each site, the optical parameters and water quality were analyzed. Using in situ data, it was found that POC-estimation algorithms developed for the open ocean and coastal waters using remote sensing reflectance were not suitable for inland turbid eutrophic water. The organic suspended matter (OSM) concentration was found to be the best indicator of the POC concentration, and POC has an exponential relationship with the OSM concentration. Through an analysis of the POC concentration and optical parameters, it was found that the absorption peak of total suspended matter (TSM) at 665 nm was the optimum parameter to estimate POC. As a result, MERIS band 7, MERIS band 10 and MERIS band 12 were used to derive the absorption coefficient of TSM at 665 nm, and then, a semi-analytical algorithm was used to estimate the POC concentration for inland turbid eutrophic water. An accuracy assessment showed that the developed semi-analytical algorithm could be successfully applied with a MAPE of 31.82% and RMSE of 2.68 mg/L. The developed algorithm was successfully applied to a MERIS image, and two full-resolution MERIS images, acquired on August 13, 2010, and December 7, 2010, were used to map the POC spatial distribution in Lake Taihu in summer and winter. 相似文献
13.
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
14.
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。 相似文献
15.
Evaluation of a three-band model for estimating chlorophyll-a concentration in tidal reaches of the Pearl River Estuary, China 总被引：1，自引：0，他引：1
Shuisen Chen Ligang Fang Hongli Li Weiqi Chen Wenrui Huang 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(3):356-364
Accurate assessment of phytoplankton chlorophyll-a (Chla) concentration in turbid waters by means of remote sensing was challenging due to the optical complexity of turbid waters. Recently, a conceptual model containing reflectance in three spectral bands in the red and near-infrared range of the spectrum was suggested for retrieving Chla concentrations in turbid productive waters. The objective of this paper was to evaluate the performance of this three-band model to estimate Chla concentration in the Pearl River Estuary (PRE), China. Reflectance spectra of surface water and water samples were collected concurrently. The samples contained variable Chla (4.80-92.60 mg/m3) and total suspended solids (0.4-55.2 mg/L dry wt). Colored dissolved organic matter (CDOM) absorption at 400 nm was 0.40-1.41 m−1; turbidity ranged from 4 to 25 NTU (Nephelometric Turbidity Units). The three-band model was spectrally calibrated by iterative and least-square linear regression methods to select the optimal spectral bands for the most accurate Chla estimation. Strong linear relationships (R2=0.81, RMSE=1.4 mg/m3, N=32) were established between measured Chla and the levels obtained from the calibrated three-band model [R−1(684)-R−1(690)]×R(718), where R(λ) was the reflectance at wavelength λ. The calibrated three-band model was independently validated (R2=0.9521, RMSE=6.44 mg/m3, N=16) and applied to retrieve Chla concentrations from the calibrated EO-1 Hyperion reflectance data in the PRE on December 21, 2006. The EO-1 Hyperion-derived Chla concentrations were further validated using synchronous in situ data collected on the same day (R2=0.64, RMSE=2 mg/m3, N=9). The spatial tendency of Chla distribution mapping by Hyperion showed gradually increased concentrations of Chla farther from the river mouths (although decreasing from east to west), which were disturbed by the combination of river outlets and tidal current in Lingding Bay of the PRE. This observation conformed to previous observations and studies, and could reasonably be explained by geographical changes. Also, results indicated that the slope of the three-band regression line decreased as the Chla concentration increased, resulting in the first sensitive band of the three-band model to move towards short wavelengths. These findings validated the rationale behind the conceptual model and demonstrated the robustness of this algorithm for Chla retrieval from in situ data and the Hyperion satellite sensor in turbid estuarine waters of the PRE, China. 相似文献
16.
17.
金属溶解物的吸收光谱测量是水体重金属遥感反演的关键。本文使用水体透射光测量装置,利用ASD光谱仪测量相同厚度不同浓度铜离子溶液的透射光辐亮度,运用比值法计算水体铜离子消光系数和吸收系数,最终得到400—900 nm波长范围内的水体铜离子单位浓度吸收系数光谱。该方法可以较好地消除实验装置和水中悬浮物的影响。结果表明,水体铜离子在蓝、绿光波段吸收作用极小,红光至红外波段的吸收系数快速增大,与铜离子溶液颜色相吻合;吸收峰位于810 nm。多次独立实验测量所得结果的标准差很小,说明测量结果稳定。与Jancsò测量结果的对比分析表明,本文结果与国际上著名测量结果接近,且部分波段的测量值更为合理。 相似文献
18.
Tiantian Shao Kaishan Song Jia Du Ying Zhao Zhiming Liu Bai Zhang 《Journal of the Indian Society of Remote Sensing》2016,44(1):77-89
Chromophoric dissolved organic matter (CDOM), the light absorbing fraction of dissolved organic carbon (DOC), together with phytoplankton and total suspended matter are the main optically active components could be retrieved by remote sensing data. Generally, different composition of DOC and CDOM corresponds to different water surface reflectance. Absorption properties of CDOM and retrieval models for CDOM and DOC were investigated with data from potable reservoirs located in the central of Jilin Province. Water sampling field surveys were conducted on 15, 16 and 19 of September 2012 across the Shitoukoumen, Erlonghu and Xilicheng reservoirs, respectively. Both empirical regression (single band model and band ratio model) and partial least squares coupled with back-propagation artificial neural models (PLSBPNN) were established to estimate CDOM absorption coefficient at 355 nm [aCDOM(355)] and DOC concentration with in situ measured remote sensing reflectance. It was found that the band ratio models and PLSBPNN model performed well for estimating DOC concentration while the band ratio models yielded the best result in retrieval CDOM. Moreover, all the three models performed better on the DOC concentration estimation than the performance on aCDOM(355). Band ratio models outperformed (R 2 ?=?0.55) other models for estimating CDOM absorption coefficient, while PLSBPNN model outperformed other models with respect to DOC estimation (R 2 ?=?0.93). High spectral slope values indicated that CDOM in the potable waters primarily comprised low molecular weight organic substances; while sources of DOC were mainly coming from exogenous input, which was the main reason lead to the difference of model performances on DOC and aCDOM(355) estimation. The algorithms developed in this study is needed to be tested and refined with more in situ spectral data, also future work is still needed to be undertaken for characterizing the dynamic of the potable water quality with remotely sensed imagery. 相似文献
19.
Nivedita Sanwlani Prakash Chauhan Ranganath R. Navalgund 《Journal of the Indian Society of Remote Sensing》2010,38(4):617-626
Space-borne ocean-colour remote sensor-detected radiance is heavily contaminated by solar radiation backscattered by the atmospheric
air molecules and aerosols. Hence, the first step in ocean-colour data processing is the removal of this atmospheric contribution
from the sensor-detected radiance to enable detection of optically active oceanic constituents e.g. chlorophyll-a, suspended sediment etc. In standard atmospheric correction procedure for OCEANSAT-1 Ocean Colour Monitor (OCM) data, NIR
bands centered at 765 and 865 nm wavelengths were used for aerosol characterization. Due to high absorption by water molecules,
ocean surface in these two wavelengths acts as dark background, therefore, sensor detected radiance can be assumed to have
major contribution from atmospheric scattering. For coastal turbid waters this assumption of dark surface fails due to the
presence of highly scattering sediments which causes sufficient water-leaving radiance in NIR bands and lead to over-estimation
of aerosol radiance resulting in negative water leaving radiance for λ < 700 nm. In the present study, for the turbid coastal
waters in the northern Bay of Bengal, the concept of spatial homogeneity of aerosol and water leaving reflectance has been
applied to perform atmospheric correction of OCAEANSAT-1 OCM data. The results of the turbid water atmospheric correction
have also been validated using in-situ measured water-leaving radiance. Comparison of satellite derived water-leaving radiance for five coastal stations with in-situ measured radiance spectra, indicates an improvement over the standard atmospheric correction algorithm giving physically
realistic and positive values. Root Mean Square Error (RMSE) between the in-situ measured and satellite derived water leaving radiance for wavelengths 412 nm, 443 nm, 490 nm, 512 nm and 555 nm was found
to be 1.11, 0.718, 0.575, 0.611 and 0.651%, respectively, using standard atmospheric correction procedure. By the use of spatial
homogeneity concept, this error was reduced to 0.125, 0.173, 0.176, 0.225, and 0.290 and the correlation coefficient arrived
at 0.945, which is an improvement over the standard atmospheric correction procedure. 相似文献
20.
基于高光谱遥感反射比的太湖水体叶绿素a含量估算模型 总被引：19，自引：1，他引：19
旨在寻找叶绿素a的高光谱遥感敏感波段并建立其定量估算模型。通过对太湖水体的连续监测，获得了从2004年6月到8月3个月的太湖水体高光谱数据和水质化学分析数据。利用实测的高光谱数据分析计算太湖水体的离水辐亮度和遥感反射比；然后，通过相关分析寻找反演叶绿素a浓度的高光谱敏感波段，进而建立反演太湖水体叶绿素a浓度的高光谱遥感定量估算模型，并用相关数据对模型进行精度分析。研究发现，水体的遥感反射比光谱在719nm和725nm存在两个峰，其中719nm处的峰更明显且稳定。通过模型的对比分析，发现用这两个峰值处的遥感反射比参与建模可以提高叶绿素a的估算精度；并且认为由反射比比值变量R719／R670所建立的线性模型对叶绿素a浓度的估算精度最理想。 相似文献