This PDF file contains the front matter associated with SPIE Proceedings Volume 9878, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Underwater radiometers are generally calibrated in air using a standard source. The immersion factors are required for these radiometers to account for the change in the in-water measurements with respect to in-air due to the different refractive index of the medium. The immersion factors previously determined for RAMSES series of commercial radiometers manufactured by TriOS are applicable to clear oceanic waters. In typical inland and turbid productive coastal waters, these experimentally determined immersion factors yield significantly large errors in water-leaving radiances (L_w) and hence remote sensing reflectances (R_rs). To overcome this limitation, a semi-analytical method with based on the refractive index approximation is proposed in this study, with the aim of obtaining reliable L_w and R_rs from RAMSES radiometers for turbid and productive waters within coastal and inland water environments. We also briefly show the variation of pure water immersion factors (I_fw) and newly derived If on L_w and R_rs for clear and turbid waters. The remnant problems other than the immersion factor coefficients such as transmission, air-water and water-air Fresnel’s reflectances are also discussed.

Despite the importance of the optical properties such as the downwelling diffuse attenuation coefficient for characterizing the upper water column, until recently no in situ optical measurements were published for the Red Sea. Kirby et al. used observations from the Coastal Zone Color Scanner to characterize the spatial and temporal variability of the diffuse attenuation coefficient (K_d(490)) in the Red Sea. To better understand optical variability and its utility in the Red Sea, it is imperative to comprehend the diffuse attenuation coefficient and its relationship with in situ properties. Two apparent optical properties, spectral remote sensing reflectance (R_rs) and the downwelling diffuse attenuation coefficient (K_d), are calculated from vertical profile measurements of downwelling irradiance (E_d) and upwelling radiance (L_u). K_d characterizes light penetration into water column that is important for understanding both the physical and biogeochemical environment, including water quality and the health of ocean environment. Our study tests the performance of the existing K_d(490) algorithms in the Red Sea and compares them against direct in situ measurements within various subdivisions of the Red Sea. Most standard algorithms either overestimated or underestimated with the measured in situ values of K_d. Consequently, these algorithms provided poor retrieval of K_d(490) for the Red Sea. Random errors were high for all algorithms and the correlation coefficients (r²) with in situ measurements were quite low. Hence, these algorithms may not be suitable for the Red Sea. Overall, statistical analyses of the various algorithms indicated that the existing algorithms are inadequate for the Red Sea. The present study suggests that reparameterizing existing algorithms or developing new regional algorithms is required to improve retrieval of K_d(490) for the Red Sea.

The assessment of water clarity of any regional water body is particularly important from ecological and water quality perspectives, especially in the regions which are highly influenced by sediment run-off and seasonal fluctuations in turbidity. The ocean colour remote sensing has played a significant role in monitoring the turbidity level in marine and inland water bodies. However, algorithms to accurately estimate the turbidity in such optically complex waters are scarce or limited by high level of uncertainty due to various issues. The present study proposes a simple, two band algorithm to estimate turbidity in both turbid and clear waters. It was found that the band ratio of remote sensing reflectance (R_rs(670)/R_rs(670)+R_rs(555)) represents the proxy of TSS (Total suspended sediment) and therefore, positively correlates to turbidity. The new algorithm is based on the assumption that light reflected in these two vital bands contains the essential information regarding the total suspended matter in the water column. The statistical results showed that the percent mean relative error between the predicted turbidity and the measured turbidity was within ±20%. To further demonstrate the robustness of the present algorithm, the spatial grid contours for the measured and the predicted turbidity was generated for the month of January 2014, August 2013 and May 2012 for the coastal waters in Bay of Bengal (Point Calimere, located in the southeast coast of India). The close consistency between the predicted and measured turbidity spatial patterns revealed that the present algorithm can be applied with high confidence to predict turbidity in both coastal and inland waters.

Selection of central wavelengths, bandwidths and the number of spectral bands of any sensor to be flown on a remote sensing satellite is important to ensure discriminability of targets and adequate signal-to-noise ratio for the retrieval of parameters. In recent years, a large number of spectral measurements over a wide variety of water types in the Arabian Sea and the Bay of Bengal have been carried out through various ship cruises. It was felt pertinent to use this precious data set to arrive at meaningful selection of spectral bands and their bandwidths of the ocean colour sensor to be flown on the forthcoming Oceansat-3 of ISRO. According to IOOCG reports and studies by Lee and Carder (2002) it is better for a sensor to have ~15 bands in the 400-800 nm range for adequate derivation of major properties (phytoplankton biomass, colored dissolved organic matter, suspended sediments, and bottom properties) in both oceanic and coastal environments from observation of water color.

In this study, ~417 hyper-spectral remote-sensing reflectance spectra (spectral range varies from ~380-800 nm) covering different water types like open, coastal, mid coastal and near coastal waters have been used to identify the suitable spectral bands for OCM-3. Central wavelengths were identified based on the results obtained from hyper-spectral underwater radiometer measurements of Rrs, HPLC pigments and spectrometer analyzed absorption spectra for all the above water types. Derivative analysis has been carried out from 1st to 5th order to identify the inflection and null points for better discrimination / identification of spectral peaks from the in situ R_rs spectra. The results showed that open ocean and coastal ocean waters has spectra peaks mostly in the blue, green region; turbid coastal waters has maximum spectral peaks in the red region. Apart from this, the spectral peaks were identified in the red region for the chlorophyll fluorescence in the open ocean and coastal waters. Based on these results 13 spectral bands were proposed in the VNIR region for the upcoming OCM-3 sensor. In order to obtain water leaving radiances from the measurements at spacecraft platform, it is necessary to do atmospheric correction we need to have spectral bands in the NIR and above regions. Hence, a set of bands 3 bands in the NIR and SWIR region were proposed for OCM-3 to address the atmospheric correction related issues.

The Pre-Aerosol, Cloud, and ocean Ecosystem (PACE) mission will carry into space a spectrometer measuring at 5 nm resolution in the ultraviolet (UV) to near infrared (NIR) and at lower resolution in spectral bands in the NIR and shortwave infrared (SWIR). These observations have great potential for improving estimates of marine reflectance in the post-EOS era. In view of this, we evaluate, using simulations with a coupled radiation transfer code, the gain in marine reflectance accuracy expected by including observations in the UV and SWIR compared with just using observations in the visible to NIR. The study is performed for the PACE threshold aggregate bands with respect to the standard set of bands used to generate ocean color products. The top-of-atmosphere (TOA) signal measured by the PACE spectrometer is simulated for a variety of realistic atmospheric and oceanic conditions. The TOA reflectance and the marine reflectance of the simulated ensemble are decomposed into principal components, and the components of the TOA reflectance sensitive to the ocean signal identified. Inverse models are constructed to retrieve the principal components of the marine reflectance, allowing a reconstruction, therefore an estimation of the marine reflectance. Theoretical performance is quantified as a function of angular geometry, aerosol properties, and water type, showing a significant improvement in retrieval accuracy when using the extended spectral range. On average over all the situations considered (including sun glint), the RMS error is reduced from 0.0037 to 0.0024 at 412 nm, from 0.0013 to 0.0007 at 665 nm, and from 0.0010 to 0.0004 at 865 nm (Case 2 waters are better handled). The performance is degraded at large zenith angles and aerosol optical thickness, is better at scattering angles around 120-130 degrees, and exhibits little dependence on aerosol single scattering albedo and aerosol scale height.

The goal of the Photosynthetically available radiation (PAR) for Primary Production (3P) project is to provide robust, complete, and user-friendly satellite radiation products for ecosystem modeling, carbon cycle investigations, and climate change monitoring. A specific objective is to design and distribute a daily PAR product from MERIS and potentially the recent OLCI. In view of this, a PAR algorithm, based on the NASA Ocean Biology Processing Group (OBPG) operational algorithm, has been developed. The algorithm takes into account statistical diurnal variability of clouds using 3-hourly International Satellite Cloud Climatology (ISCCP) data. The PAR modeling, simplified to accommodate the information available, is evaluated using a Monte Carlo tool that simulates the satellite radiance and corresponding daily PAR. The daily PAR estimates obtained from reduced resolution (i.e., 1 km) MERIS data are evaluated against in situ measurements routinely collected from fixed buoys and platforms, namely BOUSSOLE in the Mediterranean Sea, CCE- 1 and -2 off the West coast of the United States, and COVE in the coastal Atlantic Ocean. The agreement between estimated and measured values is good on a daily time scale and substantially improved on a monthly time scale, with a bias of 2.7 (7.7%) E/m²/day and RMS errors of 8.5 (24.9%) and 4.5 (12.9%) E/m²/day. The bias is reduced significantly (by 1.8%) when using diurnal cloud climatology. Overestimation in cloudy conditions is partly explained by decoupling the clear atmosphere from the cloud/surface layer. Large gaps in regions affected by sun glint (not processed because incorrectly interpreted as cloudy) are adequately filled in the monthly PAR imagery. The statistical performance is satisfactory for long-term studies of aquatic primary production, especially in view of the much larger uncertainties on the fraction of PAR absorbed by live algae and the quantum yield of carbon fixation.

An algorithm to determine the spectral total absorption coefficient of water is presented. The algorithm is based on the Gershun’s equation of α = μK_E. The spectral underwater average cosine, μ and vertical attenuation coefficient of net irradiance, K_E were obtained from radiative transfer simulations using Hydrolight with large in-situ measured data from the coastal and estuarine waters of Goa. A refined algorithm of spectral μ as in Ref. [1] is used to determine the spectral underwater average cosine. The spectral K_E was related to the diffuse attenuation coefficient, K_d. The algorithms to derive absorption were validated using an independent NOMAD optical data at wavelengths 412, 440, 488, 510, 532, 555, 650 and 676 nm. The performance of the algorithm was evident from the high R², low bias and low RMSE. The values of R² at wavelengths 412, 440, 488, 510, 532, 555, 650 and 676 nm were 0.95, 0.95, 0.93, 0.93, 0.88, 0.82, 0.62, and 0.65 respectively. The corresponding bias were -0.0064, 0.0076, 0.0038, 0.0044, 0.0122, 0.0124, 0.0362, and 0.0093 respectively. The algorithms for μ and K_E provide the spectral weighted average within Z₉₀ and have the advantage of deriving the absorption coefficients from the satellite data.

Use of polarimetric features for oil spill characterization is relatively new and have not been used for operational services until now. In the last decade, a number of semi-automatic and automatic techniques have been proposed in order to differentiate oil spill and look-alike spots based on single pol SAR data, however these techniques suffer from a high miss-classification rate which is undesirable for operational services. In addition to that, small operational spillages from offshore platforms are often ignored as it appears insignificant on traditional ScanSAR (wide) images. In order to mitigate this situation a major focus of research in this area is the development of automated algorithms based on polarimetric images to distinguish oil spills from look-alikes. This paper describes the development of an automated Near Real Time (NRT) oil spill detection processing chain based on quad-pol RADARSAT-2 and quad-pol TerraSAR-X images using polarimetric features (e.g. Lexicographic and Pauli Based features). Number TerraSAR-X images acquired over known offshore platforms with same day ascending and descending configuration along with near coincident RADARSAT-2 acquisition. A total number of 10 polarimetric feature parameters were extracted from different types of oil (e.g. crude oil, emulsion etc) and look-alike spots and divided into training and validation dataset. Extracted features were then used for training and validation of a pixel based Artificial Neural Network (ANN) classifier. Initial performance estimation was carried out for the proposed methodology in order to evaluate its suitability for NRT operational service. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spill and look- alike. Polarimetric features such as Scattering diversity, Surface scattering fraction, Entropy and Span proved to be more discriminative than other polarimetric features.

The Indian Space Research Organisation (ISRO) and the Centre National d'etudes Spatiales (CNES) jointly launched SARAL/AltiKa (Satellite with ARgos and ALtiKa) in February 2013. AltiKa is the first mono frequency (Ka-band) radar altimeter with dual frequency radiometer. SARAL/AltiKa promises reliable results on retrieving water level of inland water and coastal bodies, though recognition pattern as well as interpreting and modeling of AltiKa waveforms at land water boundary is still a challenge. Different Retracking methods are widely used for determining the water level more correctly. An altimetry waveform also gives vital information about the reflecting surface. So, waveform classification is many times needed for retrieving surface information or before applying retracking method. In this paper, SARAL/AltiKa 40 Hz waveform dataset (Pass #152) over the Maithon Reservoir, Jharkhand, India were classified using evolutionary minimize indexing function (EMIF) with k-means. A fitness function was used in EMIF to map sampled AltiKa waveforms into single valued scalar. Four waveform groups were identified according to reflection from water, land and land-water boundary. Land-water boundary again divided into two classes viz., land-to-water and water-to-land based on direction of the AltiKa pass over the reservoir. Normalized Differenced Water Index (NDWI) derived from Landsat 8 OLI and Google Earth imagery of nearest date of AltiKa pass was used for accuracy assessment of the proposed method. It was found that the waveforms were classified with 85.7 kappa accuracy. The results of the proposed EMIF will be helpful for identify the SARAL/AltiKa waveforms classes over the inland water bodies.

Oceans are considered as the important source for oil reserves and continuous activities like oil extraction and transportation may sometimes cause the accidental release of oil into the sea surface which causes a major threat to the marine ecosystems, economy and human life. The prime focus of this study is to explore the potential of the fully polarimetric SAR data and analyze the different scattering mechanisms for the oil spilled regions. In this study the fully polarized and orthorectified, L band data of UAVSAR airborne sensor is used which is captured on June 22nd 2010, during which the Deepwater Horizon oil spill occurred in the Gulf of Mexico. For the detection of oil spill different decomposition techniques such as Freeman, Yamaguchi and H/A/α are studied and classified using Wishart classification. Freeman and Yamaguchi decomposition helped in understanding the type of scattering mechanism taking place in slick covered regions, sea surface and in the presence of ships/rig. A set of polarimetric parameters such as magnitude of correlation coefficient, cross product of co polarized channels, anisotropy, alpha ,entropy and the intensity of the coherency matrix are studied which helped in distinguishing the oil spills, sea surface and the look-alikes. The Wishart classification result of Freeman and Yamaguchi decompositions showed more reliable results in comparison to the K-means classification results obtained through segmentation of combined H/A/α decomposition. The entropy, anisotropy and magnitude of correlation coefficient are dependent on the angle of incidence. At low incidence angle the entropy value of oil spills are similar to that of the sea surface whereas the magnitude of correlation coefficient which is a function of dielectric constant, increases for oil spills at low incidence angle. The polarimetric parameter, intensity of the coherency matrix utilizes the whole coherency matrix by calculating its determinant and proven to provide good discrimination between the oil spills and the sea surface.

Sentinel-1 A now routinely acquires data over the ocean since 2014. Data are processed by ESA through the Payload Data Ground Segment up to Level-2 for Copernicus users. Level-2 products consist of geo-located geophysical parameters related to wind, waves and ocean current. In particular, Sentinel-1A wave measurements provide 2D ocean swell spectra (2D wave energy distribution as a function of wavelength and direction) as well as integrated parameters such as significant wave height, dominant wavelength and direction for each partition. In 2016, Sentinel-1 B will be launched by ESA and GF-3 by CNSA. Then in 2018, CFOSAT (China France Oceanography Satellite project), a joint mission from the Chinese and French Space Agencies, will be launched. They will also provide 2D Ocean waves spectra. This paper focuses on the techniques used to validate 2D-ocean waves as measured by satellite and the challenges and opportunities of such a program for ocean waves measurements from space.

Water level was retrieved, using AltiKa radar altimeter onboard the SARAL satellite, over Ukai reservoir using modified retrieval algorithms specifically for inland water bodies. The methodology was based on waveform classification, waveform retracking and dedicated inland range corrections algorithms. The 40 Hz waveforms were classified based on the linear discriminant analysis (LDA) and Bayesian classifier. Waveforms were retracked using Brown, Threshold, and Offset Centre of Gravity methods. Retracking algorithms were implemented on full waveform and sub-waveforms (only one leading edge) for estimating the improvement in the estimated range. ECMWF operational, ERA reanalysis pressure fields and global ionosphere maps were used to exactly estimate the range corrections. The microwave and optical images were used for estimating the extent of the water body and altimeter track location. Four GPS field trips were conducted, same day on the SARAL pass, using two Dual frequency GPS. One GPS was mounted close to Dam as static mode and the other was used on a moving vehicle within the reservoir in Kinematic mode. Tide gauge dataset was provided by the flood cell, Ukai dam authority for the time period 1972-2015. The altimeter retrieved water level results were then validated with the GPS survey and in-situ tide gauge dataset. With good selection of virtual station (waveform classification, back scattering coefficient), Ice-2 retracker and subwavefom retracker both works better with overall RMSE better than 15 cm. The results supports that AltiKa dataset, due to smaller foot-print and sharp trailing edge of Ka band waveform, can be utilized for more accurate water level information over inland water bodies.

A larger part of the heat supplied by the tropical oceans, through evaporation is utilized for development of large-scale weather systems. The knowledge of evaporation rates/Latent Heat Flux (LHF) over the ocean is essential for parameterizing Ocean–atmospheric coupled predictive models. There are several methods in estimating evaporation rates/LHF over the ocean. Among them, the prominent are (1) eddy correlation or direct method, (2) profile or gradient method and (3) bulk aerodynamic method. Here bulk-aerodynamic method is conceived, since the implementation of this method is easy and spatial and temporal coverage is very high. To calculate evaporation rate/LHF using bulk aerodynamic formulae the parameters required are Wind speed, saturated vapour pressure at sea surface temperature and vapour pressure at air temperature.

We estimated LHF using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data for a period of 5 years (2001-2005) during monsoon over North Indian Ocean (NIO). The LHF values found to be high in Somali region during onset phase of summer monsoon and slowly become less, though the winds become stronger. This could be due to sudden fall of SST with the onset and intense upwelling. The variations found to be larger from year to year and these variations are discussed in relation to the intensity of monsoon activity. The LHF estimates are found to be useful in studying the large-scale weather systems. The results pertaining to the study period over NIO are presented.

India has the fifth largest installed wind power capacity in the world, mainly from onshore wind farms. As on today, there are no offshore wind power farms installed in the country. However, with the utilization of onshore and the proposed offshore wind farms, it is expected to reach 60,000 MW generation capacities by 2022. A large amount of data is necessary to assess the wind potential for these future wind farms. Offshore buoys and meteorological masts are both scarce and expensive. In the present study, we have utilized QuikSCAT (2000–2009), OSCAT (2010–2014), ASCAT (2012-2015) scatterometer and RISAT-1 SAR (2012–2014) data to evaluate the possible wind energy resources along the Indian coast. Orbit wise scatterometer wind products have been processed to generate long-term synoptic monthly means along the entire coast. The monthly average wind energy density (in W/m²) has been computed and extended up to 80 m height (standard wind turbine height) using power law. As scatterometer data are relatively coarser and unavailable near the coast, high resolution winds have been retrieved using RISAT-1 SAR data. However, due to inherent limitations of having lesser swath and data availability of SAR, presently the study has been conducted along Gujarat coast. Then, unit capacity of wind power was computed and potential sites are identified for the wind farms. The data is very useful in identifying potential sites of wind energy in the coastal and offshore regions. We are planning to extend this study for the entire Indian coast in the near future.

The scanning pencil-beam Scatterometer configuration is pretty effective in covering a large ground-swath by rotating a moderately sized paraboloid dish at a moderate speed. For example, Oscat (Oceansat-II Scatterometer) did cover a ground-swath of 1550km using a 1m diameter reflector that was rotated at 20.5 rpm. The decade-long service (1999-2009) provided by the Seawinds instrument onboard the Quikscat mission followed by an almost half-a-decade (2009-2014) service of Oscat has made this configuration tremendously popular with the global user community. A major drawback of conventional pencil-beam systems like Seawinds and Oscat is the relatively poor spatial resolution. The ground-resolution is beamwidth-limited azimuthally while, in elevation, the resolution is improved by engaging pulse-compression and range-binning. Oscat’s Instantaneous Field of View (IFOV) was 25km wide in azimuth (az) and 50km in elevation (el) at 49° incidence angle. The range-compressed resolution bins had dimensions of 6km (el) x 25km (az). Therefore, qualified wind products could be generated upon square grids no finer than 25km x 25km resolution. According to recommendations of International Ocean Vector Wind Science Team (IOVWST) and Oscat user community, high-resolution scatterometry is the requirement of the day with wind-vector cell-size dimension of 5km or better. One way to improve the resolution is to adopt the SAR principle of Range-Doppler discrimination in the scanning pencil-beam configuration. The footprint can be resolved simultaneously in range as well as in azimuth, thus significantly improving the size of the combined Range-Doppler resolution bin (~ 1km). However, the addition of Doppler filtering to conically scanning radar brings with it its own disadvantages e.g. the limitations of dwell time and the constant change in orientation of isodop lines. This paper presents the constraints in system design of high-resolution scanning systems, the design trade-offs, the methods of handling high PRF, the radar pulsing scheme and the achievable resolution.

The present remote sensing techniques have imposed limitations in the applications of LIDAR Technology. The fundamental sampling inadequacy of the remote sensing data obtained from satellites is that they cannot resolve in the third spatial dimension, the vertical. This limits our possibilities of measuring any vertical variability in the water column. Also the interaction between the physical and biological process in the oceans and their effects at subsequent depths cannot be modeled with present techniques. The idea behind this paper is to introduce underwater LIDAR measurement system by using a LIDAR mounted on an Autonomous Underwater Vehicle (AUV). The paper introduces working principles and design parameters for the LIDAR mounted AUV (AUV-LIDAR). Among several applications the papers discusses the possible use and advantages of AUV-LIDAR in water pollution detection through profiling of Dissolved Organic Matter (DOM) in water bodies.

CFOSAT (the China France Oceanography Satellite) is a joint mission from the Chinese and French Space Agencies, devoted to the observation ocean surface wind and waves so as to improve wind and wave forecast for marine meteorology, ocean dynamics modeling and prediction, climate variability knowledge, fundamental knowledge of surface processes. Currently under Phase D (manufacturing phase), the launch is now planned for mid-2018 the later. The CFOSAT will carry two payloads, both Ku-Band radar: the wave scatterometer (SWIM) and the wind scatterometer (SCAT). Both instruments are based on new concepts with respect to existing satellite-borne wind and wave sensors. Indeed, one of the originalities of CFOSAT is that it will provide simultaneously and in the same zone, the directional spectra of ocean waves and the wind vector. The concept used to measure the directional spectra of ocean waves has never been used from space until now: it is based on a near-nadir incidence pointing, rotating fan-beam radar, used in a real-aperture mode. In this paper we present the CFOSAT mission, its objectives and main characteristics. We then focus on the SWIM instrument, the expected geophysical products and performances. Finally, we present ongoing studies based on existing satellite data of directional spectra of ocean waves (Sentinel-1, ..) and carried out in preparation to CAL/VAL activities and to future data exploitation.

Coastal eddies, frontal zones and microscale oceanographic features are now easily observable from satellite measurements of SST and Chl a. Enhancing the utility of these space-borne measurements for biological productivity, biogeochemical cycling and fisheries investigations will require novel bio-optical methods capable of providing information on the community structure, biomass and photo-physiology of phytoplankton associated on spatial scales that match these features. This study showcases high-resolution in-situ measurements of sea water hydrography (SeaBird CTD®), CDOM (WetLabs ALF®), phytoplankton functional types (PFTs, FlowCAM®), biomass (bbe Moldaenke AlgaeOnlineAnalyzer® and WetLabs ALF®) and phytoplankton photosynthetic competency (mini-FIRe) across microscale features encountered during a recent (Nov. 2014) cruise in support of NOAA's VIIRS ocean color satellite calibration and validation activities. When mapped against binned daily, Level 2 satellite images of Chl a, K_d490 and SST over the cruise period, these high-resolution in-situ data showed great correspondence with the satellite data, but more importantly allowed for identification of PFTs and water types associated with microscale features. Large assemblages of phytoplankton communities comprising of diatoms and diatom-diazotroph associations (DDAs), were found in mesohaline frontal zones. Despite their high biomass, these populations were characterized by low photosynthetic competency, indicative of a bloom at the end of its active growth possibly due to nitrogen depletion in the water. Other prominent PFTs such as Trichodesmium spp., Synechococcus spp. and cryptophytes, were also associated with specific water masses offering the promise and potential that ocean remote sensing reflectance bands when examined in the context of water types also measurable from space, could greatly enhance the utility of satellite measurements for biological oceanographic, carbon cycling and fisheries studies.

State of the art Ocean color algorithms are proven for retrieving the ocean constituents (chlorophyll-a, CDOM and Suspended Sediments) in case-I waters. However, these algorithms could not perform well at case-II waters because of the optical complexity. Hyperspectral data is found to be promising to classify the case-II waters. The aim of this study is to propose the spectral bands for future Ocean color sensors to classify the case-II waters. Study has been performed with Rrs’s of HICO at estuaries of the river Indus and GBM of North Indian Ocean. Appropriate field samples are not available to validate and propose empirical models to retrieve concentrations. The sensor HICO is not currently operational to plan validation exercise. Aqua MODIS data at case-I and Case-II waters are used as complementary to in- situ. Analysis of Spectral reflectance curves suggests the band ratios of Rrs 484 nm and Rrs 581 nm, Rrs 490 nm and Rrs 426 nm to classify the Chlorophyll –a and CDOM respectively. Rrs 610 nm gives the best scope for suspended sediment retrieval. The work suggests the need for ocean color sensors with central wavelength’s of 426, 484, 490, 581 and 610 nm to estimate the concentrations of Chl-a, Suspended Sediments and CDOM in case-II waters.

Hyperspectral remote sensing has shown great promise in characterizing and monitoring of optical properties of water. This study aims at characterizing the spectral reflectance and to develop hyperspectral algorithms for retrieval of bio-optical properties of Bhindawas wetland, a man-made lake in Haryana, India. The spectral reflectance of the lake was measured using SVC GER 1500 Spectroradiometer and water samples were collected from different sampling sites within the lake during three different field surveys in 2014. Characterization of spectral responses was carried out using principal component analysis and Canonical Correspondence Analysis (CCA). It revealed that the dataset was typical of Case II waters by extracting two principal components that explained around 99% of the variation, and CCA identified that different optical parameters such as TSS, TOC, Chla and turbidity delineate its spectral response. Water quality results were correlated with reflectance to determine their relationships. Furthermore, multiple linear regression was used to derive the two and three band model for retrieval of TSS, Chla and Turbidity concentration for lake. Retrieval algorithms with significant accuracy were developed for Chla (R²=0.80, RMSE=0.12μg/l), TSS (R2=0.86, RMSE=59.1mg/l) and Turbidity (R²=0.84, RMSE=13NTU).

Argo floats equipped with sensors to measure Dissolved Oxygen, Chlorophyll-a and backscattering are deployed in the Arabian Sea, Bay of Bengal and Southern Indian Ocean as part of Indian Argo program. In this study, abnormal chlorophyll-a bloom observed by a float with WMO ID 2902086 deployed in the south central Bay of Bengal is analyzed. High concentration of chlorophyll > 0.8 mg/l is observed during December 2013. This period is also associated with drop in temperature and increase in salinity. Analysis of data from the bio-Argo float has shown the impact of many cyclones and depressions that occurred during the period. Of particular importance is cyclone ‘Madi’, which passed very near to the position of mentioned float, during December 2013. This is also evident from the satellite based wind observations from OSCAT through curl of wind stress and Ekman pumping. The sub-surface chlorophyll bloom is substantiated by the surface chlorophyll-a values of MODIS during the period. Intense mixing caused due to the passage of cyclone might have resulted in mixing of subsurface waters thereby breaking the stratification of otherwise stable surface waters of Bay of Bengal, enhancing the nutrient supply, which resulted in strong chlorophyll bloom. The subsurface chlorophyll structure of Bay of Bengal and its variability during the passage of cyclone is for the first time revealed by the floats equipped with biological sensors. This work reveals the synergistic application of in-situ (Bio- Argo) and satellite data to monitor the changes in subsurface structure during the passage of cyclones.

The Japanese Meteorological Agency (JMA) geostational meteorological satellite, Himawari-8, carrying Advanced Himawari Imager (AHI) has been in regular operation since July 7, 2015. Before the AHI, geostational meteorological sensors hardly observed ocean color due to only one broad band in visible wavelengths and relatively large radiometric noise. However, AHI has six spectral bands from visible to shortwave infrared wavelengths (470 nm, 510 nm, 640 nm, 856 nm, 1610 nm, and 2257 nm) in addition to ten thermal infrared bands, and their radiometric noise can be reduced by temporal average since AHI observes the full disk by every 10 minutes. This study investigates the possibility of ocean color estimation (remote sensing reflectance, R_rs, and chlorophyll-a concentration, Chla) from AHI observations. The estimation sensitivity was tested using in-situ data, a simple in-water optical model, and the spectral response of AHI bands. We found the error characteristics and limitation of the estimation by AHI as follows. Chla can be estimated by the traditional scheme, blue (470 nm) green (510 nm) ratio in Chla <3 mg/m³. The estimation in Chla > 5 mg/m³ can be improved by using the green (510 nm) red (640 nm) bands. The random noise can be reduced, by averaging original 10-minute images over an hour. Good estimates are obtained in the summer hemisphere; however, retrieved imagery becomes noisy in the mid and high latitudes (e.g., > 35°) of winter hemisphere due to a long path of the solar light.

Pigments in the host tissues of corals can make a significant contribution to their spectral signature and can affect their apparent color as perceived by a human observer. The aim of this study is classifying the spectral reflectance of corals base on different color. It is expected that they can be used as references in discriminating between live corals, dead coral covered with algae Spectral reflectance data was collected in three small islands, Spermonde Archipelago, Indonesia by using a hyperspectral radiometer underwater. First and second derivative analysis resolved the wavelength locations of dominant features contributing to reflectance in corals and support the distinct differences in spectra among colour existed. Spectral derivative analysis was used to determine the specific wavelength regions ideal for remote identification of substrate type. The analysis results shown that yellow, green, brown and violet live corals are spectrally separable from each other, but they are similar with dead coral covered with algae spectral.

North Arabian Sea experiences massive proliferation of variable algal species. The study presents variability of Noctiluca and its association with hydrographic parameters such as sea surface temperature (SST) and water column stability using ten years of satellite data. The area was categorized into three regions, North (23 to 26°N and 56 to 70°E), West (18 to 23°N and 56 to 62°E) and East (18 to 23°N and 62 to 74°E). The Noctiluca dominated area was extracted following approach of Dwivedi et. al. (2015) based on slope of Remote Sensing Reflectance (Rrs) between 488 to 443nm and 488 to 531nm. The data used in the present study depicted two distinct clusters based on regression between difference of R_rs(488) and R_rs(443) with R_rs(488) and R_rs(531). The major clusters representing Noctiluca falls within the range of 0.0004 to 0.0015 (R_rs488-R_rs443) and -0.0012 to -0.0004 (R_rs488-R_rs531). The occurrence of Noctiluca showed bi-modal distribution at an annual scale with the dominance in the northern region during winter monsoon (February- March). In western and eastern region higher frequency of Nuctiluca was during post monsoon having lag of one month from western (September) to eastern (October) region. The periodicity of Noctiluca, carried out using Fourier analysis, showed predominance at annual scale in Northern and semi-annual scale in Western and Eastern region. This indicates that the Noctiluca bloom in the northern region is primarily triggered by winter mixing whereas in western and eastern part of northern Arabian Sea it has combined effect of summer upwelling as well as winter mixing.

Asia's largest brackish water ecosystem, Chilika lagoon, supports livelihood of millions of inhabitants and also known to be bio-geo-chemically dynamic. This demands continuous monitoring of lagoon for which optical remote sensing may be crucial. The in situ bio-optical parameters were analyzed in two sectors (Outer Channel: OC and Southern Sector: SS) of Asia's largest brackish water ecosystem, Chilika lagoon. The spectral Remote Sensing Reflectance (R_rs) ranged from 0.003 to 0.02sr^-1 in OC whereas in SS it was between 0.003 and 0.028sr^-1. The minimum R_rs was at 400nm that gradually increased with a peak at 580nm and subsequently decreased towards longer wavelength. R_rs exhibited similar pattern in both the sectors from 400 to 600nm. Beyond this wavelength, R_rs was relatively higher in SS. The shifting of peak towards longer wavelength could be attributed to high absorption due to chlorophyll-a (chl-a) and chromophoric dissolved organic matter (CDOM) that varied largely between sectors with relatively higher concentration of chl-a in OC and CDOM in SS. Further, we modelled chl-a by seven ocean colour algorithms (OC4, OC4E, OC4O, OC3M, OC3V, OC3C and OCMO2) using in situ R_rs. The modelled chl-a was overestimating in situ at all stations due to high concentration of CDOM contaminating chl-a signals. However in OC, in situ and modelled chl-a followed the same trend (R²=0.88 to 0.90) probably due to strong co-variance of chl-a with CDOM. The analysis of this study points out towards the requirement for sector specific bio-optical algorithm for accurate chl-a retrieval for synoptic monitoring of lagoon health.

The flow of marine debris in the Sea of Japan causes extensive damage to coastal environments. It is important to understand the debris flow in the ocean for environmental research. The small size of most marine debris in the Sea of Japan makes it impossible to be confirmed directly, even when using high-spatial-resolution satellite imagery. Thus, to extract candidate pixels containing possible marine debris, pixels with spectra that differ from those of the surrounding ocean and wave crests were identified. As a first step towards monitoring marine debris, a previously proposed method for identifying marine debris floating in the Sea of Japan uses a histogram showing the distance from the regression line of the scatter diagram of satellite spectral bands. In this paper, a new method using a spectral angle mapper (SAM) in four- or eight-dimensional space corresponding to satellite spectral bands is proposed. The validity of the method using SAM is also discussed.

This work presents various normalized difference water indices (NDWI) to delineate lakes from Schirmacher Oasis, East Antarctica, by using a very high resolution WorldView-2 (WV-2) satellite imagery. Schirmacher oasis region hosts a number of fresh as well as saline water lakes, such as epishelf lakes, ice-free or landlocked lakes, which are completely frozen or semi-frozen and in a ice-free state. Hence, detecting all these types of lakes distinctly on satellite imagery was the major challenge, as the spectral characteristics of various types of lakes were identical to the other land cover targets. Multiband spectral index pixel-based approach is most experimented and recently growing technique because of its unbeatable advantages such as its simplicity and comparatively lesser amount of processing-time. In present study, semiautomatic extraction of lakes in cryospheric region was carried out by designing specific spectral indices. The study utilized number of existing spectral indices to extract lakes but none could deliver satisfactory results and hence we modified NDWI. The potentials of newly added bands in WV-2 satellite imagery was explored by developing spectral indices comprising of Yellow (585 – 625 nm) band, in combination with Blue (450 – 510 nm), Coastal (400 – 450 nm) and Green (510 – 580 nm) bands. For extraction of frozen lakes, use of Yellow (585 – 625 nm) and near-infrared 2 (NIR2) band pair, and Yellow and Green band pair worked well, whereas for ice-free lakes extraction, a combination of Blue and Coastal band yielded appreciable results, when compared with manually digitized data. The results suggest that the modified NDWI approach rendered bias error varying from ~1 to ~34 m².

High-resolution pansharpened images from WorldView-2 were used for bathymetric mapping around Larsemann Hills and Schirmacher oasis, east Antarctica. We digitized the lake features in which all the lakes from both the study areas were manually extracted. In order to extract the bathymetry values from multispectral imagery we used two different models: (a) Stumpf model and (b) Lyzenga model. Multiband image combinations were used to improve the results of bathymetric information extraction. The derived depths were validated against the in-situ measurements and root mean square error (RMSE) was computed. We also quantified the error between in-situ and satellite-estimated lake depth values. Our results indicated a high correlation (R = 0.60~0.80) between estimated depth and in-situ depth measurements, with RMSE ranging from 0.10 to 1.30 m. This study suggests that the coastal blue band in the WV-2 imagery could retrieve accurate bathymetry information compared to other bands. To test the effect of size and dimension of lake on bathymetry retrieval, we distributed all the lakes on the basis of size and depth (reference data), as some of the lakes were open, some were semi frozen and others were completely frozen. Several tests were performed on open lakes on the basis of size and depth. Based on depth, very shallow lakes provided better correlation (≈ 0.89) compared to shallow (≈ 0.67) and deep lakes (≈ 0.48). Based on size, large lakes yielded better correlation in comparison to medium and small lakes.

The principles of sea surface monitoring by optical images are discussed. The methods for retrieval of sea roughness characteristics and near surface winds by its manifestations on sea surface under grazing angles are given. The optical multi-channel set consisted from synchronized optical receivers with sensibility in various parts of optical spectra was developed. The developed principles of optical monitoring were tested in natural experiments with the optical set.

A experiments were performed in the shelf zone of the Black Sea in 2015 to study variability of the current fields and other characteristics of sea bulk, wind waves, and the near-surface atmospheric layer. Region with the secluded underwater hill streamlined with currents was selected. Measurements were carried out from the onboard of vessels on move and in drift by optical, radar, acoustic equipment, and STD probe.

The complex different structure of waters, which was formed under the influence of shelf waters and water of the open sea interaction, was observed during the experiment. The analysis of measurements in the water column showed that that the flow around underwater elevation forms the hydro-physical disturbances of marine environment. Maximum flow observed above the slopes of underwater elevation and reach 50 cm/s. Wind speed varied from 0 to 10 m/s.

On radar panoramas in the region of underwater elevation is observed the appearance of the wave structure, different from the background wind waves. This anomaly on the sea surface is connected with non-uniform current in the neighborhood underwater elevation.

The response of debris covered glaciers to climate change is complex. Presence of debris cover influence the glaciers in two different ways depends on its composition. Thick debris cover retards the melting of underlying ice whereas the thin layer of debris accelerates the melting. Also, the debris cover alters the surface energy balance, affects the sediment transportation capacity and may also lead to the glacier fragmentation, if stagnant at terminus. Therefore, evaluating the changes in supra-glacial debris cover is needed. In this study, ~185 glaciers were selected from Chandra- Bhaga basins (Chenab) to study the changes in supra-glacial debris cover from 1994 to 2009. A semi-automatic approach is used along with Landsat TM imagery to evaluate the changes. Further, depending on the size of glaciers, they were classified into four classes i.e., 2-5, 5-10, 10-20 and more than 20 km². The significant increase in debris cover between 1994 and 2009 is 1.83 ± 1.6 km², respectively.

Algorithms developed for underwater horizontal and vertical visibilities are presented. The algorithms have been developed to derive the underwater visibilities based on the contrast theory using the in-situ and Hydrolight derived optical parameters. Unlike many other algorithms on vertical visibilities, these algorithms have been developed using the photopic optical parameters. One of the important components in the contrast visibility relation is the coupling parameter, which has been modeled using the underwater average cosine. These algorithms for vertical and horizontal visibilities have been validated for the coastal waters of Goa with the measured and those derived from the ocean color data of OCM-2 and MODIS.