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。     

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。     

Total Suspended Sediments Mapping by Using ALOS Imagery Over the Coastal Waters of Langkawi Island, Malaysia

Remote sensing has been extensively used for water delineation and has played an important role in water quality evaluation and environmental management strategies. Suspended sediments are important determinants of water quality in coastal zones. Remote sensing enables the effective monitoring of total suspended sediments (TSS) and the detection of areas with critical water quality issues. This study aims to develop and implement regression models for estimating and mapping TSS concentrations from Advanced Land Observation Satellite (ALOS) images over the coastal waters of Langkawi Island, Malaysia. The algorithm was developed based on the water reflectance model, which is a function of the inherent optical properties of water. Such properties can then be linked to TSS concentration. In this study, an ALOS Advanced Visible and Near Infrared Radiometer type 2 device was used as the imaging sensor system. Concurrent complementary in-situ water samples were collected within the area coverage of the sensor, and digital numbers (DN) for each band corresponding to the sea-truth locations were determined. The extracted DN values were converted into reflectance values and then regressed with their respective sea-truth data. An algorithm was proposed to obtain the regression coefficient. This algorithm can estimate TSS concentrations with a high correlation coefficient (R²?=?0.96) and low root-mean-square error (RMSE?=?1.98 mg/l). Finally, a map of the TSS concentration was generated by using the proposed algorithm. This study found that TSS mapping can be conducted by using ALOS data over the coastal waters of Langkawi Island, Malaysia.     

北太平洋中尺度涡海表温度和叶绿素浓度特征分析

本文利用AVISO卫星高度计资料识别并追踪了北太平洋2007年—2012年的中尺度涡,并利用OSTIA的海表温度SST(Sea Surface Temperature)资料与MODIS的叶绿素a浓度(Chl-a)资料,研究了北太平洋2007年—2012年中尺度涡SST和Chl-a浓度的时空分布特征,并分析北太平洋典型中尺度涡SST与Chl-a浓度的变化特征,主要结论如下:本文共识别出992个中尺度涡,其中442个气旋涡,550个反气旋涡。中尺度涡SST时空分布特征为:气旋涡温度强度(ICE)月变化特征比反气旋涡温度强度(IAE)更强。ICE年际变化显著,IAE则不明显。温度强度较强的气旋涡和反气旋涡集中分布在黑潮延伸区。中尺度涡Chl-a浓度时空分布特征如下:气旋涡和反气旋涡Chl-a浓度月变化特征明显,且二者的变化趋势一致;年际变化则均不明显。Chl-a浓度值高的中尺度涡主要分布在高纬海域。中尺度涡SST与海洋动力参数(振幅、涡度和涡动能(EKE))的相互关系为:反气旋涡SST与振幅的相关性亦正亦负,且在空间上均匀分布。气旋涡SST与振幅的负相关系数主要分布在黑潮延伸区。正相关性强的反气旋涡多于气旋涡。反气旋涡SST与涡度的相关性亦正亦负,气旋涡SST与涡度呈负相关。反气旋涡SST与EKE的相关性亦正亦负;气旋涡的相关性为正。中尺度涡Chl-a浓度与海洋动力参数的相互关系为:反气旋涡Chl-a浓度与振幅的相关性为正,且在空间上均匀分布;气旋涡在黑潮延伸区与阿拉斯加湾呈正相关。反气旋涡和气旋涡Chl-a浓度与涡度均呈正相关。反气旋涡Chl-a浓度与EKE呈正相关;气旋涡Chl-a浓度与EKE相关性亦正亦负。     

Quantitative Inversion of Vegetation Biochemical Components Based on HJ1-A HSI in Coal Mining Area

This study investigates the applicability of estimating chlorophyll and water content at canopy level through empirical models and band combinations. The main goal is to evaluate and compare the accuracy of these two approaches for estimating and mapping canopy chlorophyll and water content through canopy reflectance and spaceborne HJ1-A HSI data acquired over Yanzhou coal mining area. An experiment was carried out. Canopy spectral measurements were acquired in the field using an ASD spectroradiometer along with simultaneous in situ measurements of leaf chlorophyll content. We tested seven variables derived from canopy reflectance for detecting canopy chlorophyll and water content: (1) R, (2) Log(1/R), (3) Log(1/R)′, (4) FDR, (5) SDR, (6) CRR, (7) BD. Stepwise multiple linear regressions were used to select wavelengths from HJ1-A HSI image bands. Correlation analysis was also done between different band combinations and biochemistry. A statistically significant relationship between Log(1/R) and chlorophyll was found at canopy level (R² = 0.516). SDR had the highest correlation with canopy water content (R² = 0.490). In addition, relationship between normalized different band combinations and chlorophyll and water content is also significantly obvious (R² = 0.577 and R² = 0.615). Canopy chlorophyll content was estimated with the intermediate accuracy (R² = 0.4144), while water content was estimated with an acceptable accuracy (R² = 0.4592). Canopy chlorophyll and water content spatial distribution were mapped. Chlorophyll and water stress levels were quantified by comparing different environmental stressors.     

Remote estimation of water quality parameters of Himalayan lake (Kashmir) using Landsat 8 OLI imagery

The overarching aim of this study was to derive simple and accurate algorithms for the retrieval of water quality parameters for the Wular Lake using Landsat 8 OLI satellite data. The water quality parameters include pH, COD, DO, alkalinity, hardness, chloride, TDS, total suspended solids (TSS), turbidity, electric conductivity and phosphate. Regression analysis was performed using atmospherically corrected true reflectance values of original OLI bands, images after applying enhancement techniques (NDVI, principal components) and the values of the water quality parameters at different sample locations to obtain the empirical relationship. Most of the parameters were well correlated with single OLI bands with R² greater than 0.5, whereas phosphate showed a good correlation with NDVI image. The parameters like pH and DO showed a good relation with the principal component I and IV, respectively. The high concentration of pH, COD, turbidity and TSS and low concentration of DO infers the anthropogenic impact on lake.     

Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM

Suspended sediment yield is a very important environmental indicator within Amazonian fluvial systems, especially for rivers dominated by inorganic particles, referred to as white water rivers. For vast portions of Amazonian rivers, suspended sediment concentration (SSC) is measured infrequently or not at all. However, remote sensing techniques have been used to estimate water quality parameters worldwide, from which data for suspended matter is the most successfully retrieved. This paper presents empirical models for SSC retrieval in Amazonian white water rivers using reflectance data derived from Landsat 5/TM. The models use multiple regression for both the entire dataset (global model, N = 504) and for five segmented datasets (regional models) defined by general geological features of drainage basins. The models use VNIR bands, band ratios, and the SWIR band 5 as input. For the global model, the adjusted R² is 0.76, while the adjusted R² values for regional models vary from 0.77 to 0.89, all significant (p-value < 0.0001). The regional models are subject to the leave-one-out cross validation technique, which presents robust results. The findings show that both the average error of estimation and the standard deviation increase as the SSC range increases. Regional models were more accurate when compared with the global model, suggesting changes in optical proprieties of water sampled at different sampling stations. Results confirm the potential for the estimation of SSC from Landsat/TM historical series data for the 1980s and 1990s, for which the in situ database is scarce. Such estimates supplement the SSC temporal series, providing a more comprehensive SSC temporal series which may show environmental dynamics yet unknown.     

中国海洋卫星及应用进展

中国十分重视海洋遥感及其监测技术的发展,初步形成了具有优势互补的海洋遥感观测体系,并发挥了显著的经济和社会效益。其中,海洋一号(HY-1A/B)卫星已经广泛应用于中国海温预报业务系统、冬季海冰业务监测、夏季赤潮和绿潮监测、海岸带动态变化监测、近岸海水水质监测和渔业遥感监测等方面。海洋二号(HY-2A)卫星不仅填补了中国海洋动力环境卫星遥感的空白,也是目前国际上唯一在轨运行的集主被动微波遥感器于一身的综合型海洋动力环境卫星,具备同时获取风场、有效波高、海面高度和海面温度的能力。通过卫星获得的数据提高了中国海洋环境监测与灾害性海况预报的水平,为国民经济建设和国防建设、海洋科学研究、全球变化研究等提供了可靠的遥感数据,同时还在国际对地观测体系中发挥了重要作用,受到国内外用户的高度认可。海洋一号和海洋二号卫星系列为中国建立完善的海洋环境立体监测体系奠定了坚实基础。根据国家发展和"一带一路"建设的实施,在加快建设海洋强国、维护海洋权益和加快发展海洋经济的进程中对海洋遥感的发展也进一步提出了更高的要求和更紧迫的需求。为此,紧紧围绕国家海洋强国战略需求,在《国家民用空间基础设施中长期发展规划(2015年—2025年)》中专门规划了海洋观测卫星系列,服务于中国的海洋资源开发、环境保护、防灾减灾、权益维护、海域使用管理、海岛海岸带调查和极地大洋考察等方面,同时兼顾陆地和大气观测领域的需求。在充分继承已有HY-1A/B、HY-2A、高分三号(GF-3)和中法海洋卫星(CFOSAT)成功研制经验和应用成果的基础上,发展多种光学和微波遥感技术,建设新一代的海洋水色卫星和海洋动力环境卫星,具备卫星组网观测能力;发展海洋监视监测卫星,构建优势互补的海洋卫星综合观测体系。通过空间基础设施的建设,海洋遥感卫星必将在建设海洋强国的进程中发挥出重要作用。     

Performance evaluation of normalized difference chlorophyll index in northern Gulf of Mexico estuaries using the Hyperspectral Imager for the Coastal Ocean

The Hyperspectral Imager for the Coastal Ocean (HICO) was used to derive chlorophyll-a (chl-a) based on the normalized difference chlorophyll index (NDCI) in two Gulf of Mexico coastal estuaries. Chl-a data were acquired from discrete in situ water sample analysis and above-water hyperspectral surface acquisition system (HyperSAS) remote sensing reflectance in Pensacola Bay (PB) and Choctawhatchee Bay (CB). NDCI algorithm calibrations and validations were completed on HICO data. Linear and best-fit (polynomial) calibrations performed strongly with R² of 0.90 and 0.96, respectively. The best validation of NDCI resulted with an R² of 0.74 and root-mean-square error (RMSE) of 1.64 µg/L. A strong spatial correspondence was observed between NDCI and chl-a, with higher NDCI associated with higher chl-a and these areas were primarily located in the northern PB and eastern CB at the river mouths. NDCI could be effectively used as a qualitative chl-a monitoring tool with a reduced need for site-specific calibration.     

Estimating dissolved organic carbon concentration in turbid coastal waters using optical remote sensing observations

Dissolved Organic Carbon (DOC) is an important component in the global carbon cycle. It also plays an important role in influencing the coastal ocean biogeochemical (BGC) cycles and light environment. Studies focussing on DOC dynamics in coastal waters are data constrained due to the high costs associated with in situ water sampling campaigns. Satellite optical remote sensing has the potential to provide continuous, cost-effective DOC estimates. In this study we used a bio-optics dataset collected in turbid coastal waters of Moreton Bay (MB), Australia, during 2011 to develop a remote sensing algorithm to estimate DOC. This dataset includes data from flood and non-flood conditions. In MB, DOC concentration varied over a wide range (20–520 μM C) and had a good correlation (R² = 0.78) with absorption due to coloured dissolved organic matter (CDOM) and remote sensing reflectance. Using this data set we developed an empirical algorithm to derive DOC concentrations from the ratio of R_rs(412)/R_rs(488) and tested it with independent datasets. In this study, we demonstrate the ability to estimate DOC using remotely sensed optical observations in turbid coastal waters.     

Machine learning approaches to coastal water quality monitoring using GOCI satellite data

Since coastal waters are one of the most vulnerable marine systems to environmental pollution, it is very important to operationally monitor coastal water quality. This study attempts to estimate two major water quality indicators, chlorophyll-a (chl-a) and suspended particulate matter (SPM) concentrations, in coastal environments on the west coast of South Korea using Geostationary Ocean Color Imager (GOCI) satellite data. Three machine learning approaches including random forest, Cubist, and support vector regression (SVR) were evaluated for coastal water quality estimation. In situ measurements (63 samples) collected during four days in 2011 and 2012 were used as reference data. Due to the limited number of samples, leave-one-out cross validation (CV) was used to assess the performance of the water quality estimation models. Results show that SVR outperformed the other two machine learning approaches, yielding calibration R² of 0.91 and CV root-mean-squared-error (RMSE) of 1.74 mg/m³ (40.7%) for chl-a, and calibration R² of 0.98 and CV RMSE of 11.42 g/m³ (63.1%) for SPM when using GOCI-derived radiance data. Relative importance of the predictor variables was examined. When GOCI-derived radiance data were used, the ratio of band 2 to band 4 and bands 6 and 5 were the most influential input variables in predicting chl-a and SPM concentrations, respectively. Hourly available GOCI images were useful to discuss spatiotemporal distributions of the water quality parameters with tidal phases in the west coast of Korea.     

Assessment of satellite ocean color products of MERIS,MODIS and SeaWiFS along the East China Coast (in the Yellow Sea and East China Sea)

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 R_rs(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 (APD_m) of 15–27% and root-mean-square-error (RMS) of 0.0021–0.0039 sr⁻¹, whereas the R_rs(λ) uncertainty at 412 nm is the highest (APD_m 47–62%, RMS 0.0027–0.0041 sr⁻¹). The uncertainties of the aerosol optical thickness (AOT) τ_a, diffuse attenuation coefficient for downward irradiance at 490 nm K_d(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 K_d(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.     

Validations of suspended sediment concentration (ssc) derived using ocean colour monitor (ocm) data off Chennai coast, India

Indian Remote Sensing Satellite (IRS) — P4 Ocean Colour Monitor (OCM) data were used to estimate Suspended Sediment Concentration (SSC) in the coastal waters off Chennai and to study the distribution along the coast. Surface water samples were collected during May and October 2000 synchronized with satellite overpass, and quantitative estimates of Suspended Sediment Concentration (SSC) were done. OCM — Data Analysis System (DAS) software developed by the Space Applications Centre, Ahmedabad was used for OCM data processing and analysis. The field data and OCM derived SSC showed a correlation of r = 0.85 and r = 0.95 for the months of May and October respectively.     

叶片化学组分成像光谱遥感探测分析

利用地面光谱仪的测量数据,进行了成像光谱遥感探测叶片学组分的机理性研究。采用多元逐步回归方法,分析了鲜叶片７种化学组分含量与其光谱特性的关系,分别建立的反射率ρ及其变化式１／ρ、ｌｏｇρ和ρ的一阶导数Ｋρ与化学组分含量的统计方程,并对这４个指标的性能进行了比较了评价。结果表明,在９５％的置信水平下,可以由叶片的精细光谱特征较好地反映出化学组分含量;特别是利用Ｋρ作为因子,使置信水平提高到９９％,尤     

Can suspended sediment concentrations be estimated from multispectral imagery using only image-derived information?

Sampling for suspended sediment concentrations (SSC) in inland waters is traditionally based on collecting samples at sparse locations and in limited intervals. A number of investigators explored the utility of earth-observing satellites and air-borne sensors for monitoring of SSC over vast areas. Two approaches are commonly deployed: (1) empirical relationships between a chosen remotely sensed quantity and the actual in-situ SSC; and (2) bio-optical models founded on radiative transfer modeling. Unfortunately, in-situ measurements are often unavailable for direct image calibration, and inherent optical properties of optically active constituents (specific scattering and absorption coefficients) are usually unknown. This paper examines the possibility to retrieve SSC from multispectral satellite imagery without any in-situ data, i.e. using only image-derived information. The fundamental principle of image selfcalibration relies on the fact that in the visual domain of wavelengths (∼400–700 nm) the at-sensor reflectance becomes “saturated“ at high SSC, whereas the near-infrared domain (∼700–900 nm) remains almost perfectly linearly related to sediment concentrations. The core idea of the self-calibrating procedure is rather simple and is based on fitting an exponential function between reflectance and SSC, with SSC replaced by a linear relationship between SSC and reflectance in the near-infrared domain. As a first approximation of the non-linearity between reflectance and SSC levels in the 400–700 nm range, we used the equation proposed by Schiebe et al. (1992), although other equations, especially those arising from optical theory could be used as well. The technique is illustrated on a moderately sediment-laden reservoir and two scenes acquired from Landsat ETM+. The standard error of the estimated SSC was below 15 mg/L (i.e. ∼25 % relative error for the observed range of SSC). Although the proposed algorithm does not yield better results than other models mentioned in the literature, the primary advantage of the outlined methodology is that no in-situ measurements (water sampling nor spectral profiling) are needed — i.e. only image-derived information is used.     

Remote Sensing of suspended sediments along the Tamil Nadu coastal waters

Indian Remote Sensing satellite (IRS) 1A & 1B digital data in combination with field measurement were used to map distribution and concentration of suspended sediments along the Tamil Nadu coastal waters for monsoon and non-monsoon periods. Qualitative suspended sediment mapping was done for entire Tamil Nadu coast while quantitative studies were taken at two selected sites (eg. Tuticorin and Ennore). For qualitative mapping both monsoon (17-12-90) and non-monsoon (18-4-90) season data were analysed by level slicing technique and a qualitative scale was assigned to different sediment classes based on tonal variations. The suspended sediment concentration (SSC) samples were collected on April 15, 1992 and March 10, 1992 around Ennore and Tuticorin coastal waters respectively, synchronous to IRS-1A satellite overpass. This data was used for quantitative estimation of SSC using digital chromaticity technique. The study shows that the plumes of high suspended sediment concentration are contributed from the nearshore wetlands and river mouths and were finally dispersing towards Jaffna coast. Different classes of high to low SSC values ranging from less than 5 mg/L in offshore areas to 21 mg/L in nearshore of Tuticorin were also delineated. The dispersal pattern of the sediments on different is discussed.     

Examining the Phenological Cycle of Barley (Hordeum vulgare) Using Satellite and in situ Spectroradiometer Measurements for the Detection of Buried Archaeological Remains

The detection of buried archaeological remains using satellite remote sensing is still an open question in archaeological research. This research investigates how the phenological stages of crops can be used support the detection of buried archaeological remains. Ground remote sensing data using the GER-1500 spectroradiometer were obtained from two sites. One site was the Neolithic settlements in central Greece and the other was in Alampra village in Cyprus. For the latter, an archaeological environment was simulated and ground spectroradiometric measurements were systematically acquired over the different phases of the phenological cycle of barley crops. The acquired in situ reflectance measurements have been converted to "in-band" reflectance values of the Landsat TM/ETM+ using the satellite relative spectral responses filters (RSR). Based on the proposed methodology, 97 Landsat MSS, TM, and ETM+ satellite images were acquired (covering a period from 1983 to 2011), for the Thessalian (Greek) site. It has been found that phenological-cycle observations can provide valuable information for identifying buried archaeological remains. Such observations may be used in cases where the spatial resolution of satellite imagery is not high and therefore cannot help support the detection of archaeological remains using standard interpretation techniques.     

Evaluation of EO-1 hyperion data for agricultural applications

The present study was carried out to evaluate the satellite-based hyperspectral data available from Hyperion onboard EO-1 of NASA for agricultural applications. The study was carried out for Daurala block of Meerut district, using data of March 2005. The preliminary data analysis showed that there are 196 usable bands out of a total of 242 bands. Principal component (PC) analysis showed that about 99% of the information explained in 10 PCs. The atmospherically corrected reflectance, derived from satellite data had good agreement with the ground reflectance, observed using handheld spectroradiometer, with r² ranging from 0.85 to 0.98. A set of twenty most usable bands was selected by the criteria of maximum contribution to first five PCs and the band combinations with least inter-band correlations.     

Calibration of an optical equation to analyse the atmospheric turbidity and water quality of an estuarine environment

Mapping of oceanic productive zones through an optical sensor on board a satellite requires a detailed knowledge about the interaction of solar flux with the atmosphere and water column. Hence the radiative transfer on February 14, 2002 over estuaries of Goa has been examined through a couple radiative transfer model (CRTM). This has been carried out in the context of an optical sensor when it looks down at nadir. The forcing of extraterrestrial solar irradiance with atmosphere results in its absorption and scattering due to aerosol particles and gaseous constituents and rayleigh particles. The optical spectrum has been subjected to atmospheric forcing and radiation has been calculated for every 1 nm. Special reference is given to two wavelengths (443 and 510 nm), which are important for ocean optics. The atmospheric components pertaining to these wavelengths are 0.04193 w/m²/nm/sr and 0.0225 w/m²/nm/sr (rayleigh) and 0.0121 w/m²/nm/sr and 0.015 w/m²/nm/sr (aerosol). Within the optical frame work (400 –700 nm), the effect of aerosol overrides that of rayleigh from 545 nm onwards. The specular reflectance at the sea surface contributes 1.725 x l0^-12 w/m²/nm/sr and 1.947x 10^-12 w/m²/nm/sr while the respective signature from water column is 0.418x 10^-3w/m²/nm/sr and 0.536x 10^-3 w/m²/nm/sr. The simulated water signature has been compared with radiances measured by a radiometer (Satlantic radiometer). The correlation (R²) is found to be around 0.97. The sensitivity analysis further reveals the respective wavelengths of the optical spectrum affected by atmospheric and oceanic constituents. Moreover, the effect of change in solar zenith angle on water signature has also been carried out. This could be applied to invert satellite data so as to retrieve coastal water constituents and productivity.