Ocean Colour Monitor Research Articles

Validation of the Total Suspended Matter (TSM) algorithm using in situ datasets over the Bay of Bengal Coastal Water

TSM (Total suspended matter) is important metric because it influences ocean biogeochemistry and seaweed habitat. Light penetration is influenced by the TSM range, which is linked to primary producer photosynthesis. Many regional and global approaches for measuring various ocean parameters using satellite remote sensing have been developed. The authors developed the linear TSM algorithm in 2022 using in situ data from the Arabian Sea, which has been validated in this work utilising in situ sampling along the Bay of Bengal coast to test its resilience. The algorithm has the remote sensing reflectance band ratio Rrs681/Rrs490 that has been validated against in situ TSM (R2 = 0.88, MAD = 12.28, RMSE = 12.45, NRMSE = 11.60) and satellite validation with OLCI-A (Ocean and Land Colour Instrument-A). OLCI A TSM product discovered poor regression with in situ datasets, suggesting that the algorithm in OLCI A might be modified. The article infers that the validated TSM algorithm in the Bay of Bengal could be useful for different satellite-based synoptic TSM mapping for the Indian Oceansat-3 OCM (Ocean Colour Monitor) mission as TSM could benefit seaweeds and biogeochemistry by improving nutrient flow, trophic interactions, shielding against UV radiation, and adding organic carbon pool.

Operational AOD Retrieval at Subkilometer Resolution Using OceanSat‐2 OCM Over Land: SAER Algorithm, Uncertainties, Validation & Inter‐Sensor Comparison

AbstractThe ocean color monitor (OCM) sensor onboard OceanSat‐2 is providing data in visible and near Infrared (NIR) bands. Due to the limited spectral coverage of OCM, the widely used dark‐target and deep‐blue aerosol algorithms cannot be adapted. Here, a new aerosol optical depth (AOD) retrieval algorithm for OCM (or similar sensors) over land, termed Space Applications Center AErosol Retrieval (SAER) is described. It utilizes two blue bands for the AOD inversion and, Red and NIR band to characterize the surface in visible bands without assuming red and NIR bands transparent to aerosols. Unlike the dark‐target algorithm, the SAER algorithm can retrieve AOD over bright arid and urban areas too. The uncertainty analysis of SAER suggests a theoretically expected error (EE) envelope of ±(0.06 + 0.26 × AOD) for typical retrieval conditions. OCM AOD over land is retrieved operationally for the first time over Indian and neighboring countries' landmass at the finest spatial resolution of 0.007°. The SAER algorithm is validated against in‐situ AOD measurements during the years 2016–2018 at 21 Aerosol Robotic Network stations located in south‐Asia. Overall validation using 1900 match‐up points shows correlations exceeding 0.8 with 74% of retrievals within EE. The retrievals over cropland, grassland, and mixed land cover types show high (low) correlation (bias), while over bright urban areas somewhat low (high) correlation (bias) is observed. Excluding monsoon season, OCM AOD retrievals show good performance over the year. The performance of MODerate Resolution Imaging Spectroradiometer dark‐target and OCM AOD, against common in‐situ, is close to each other. The study shows that OCM AOD can be used for air quality monitoring/modeling at high spatial resolution.

Spectrum recovery from multispectral ocean color data using system spectral shape factor

Data from ocean color monitoring sensors at different spectral channels are available for remote sensing of radiation as seen in the given spectral windows, which is used for deriving information on various atmospheric parameters. However, recent studies have demonstrated the potential of hyperspectral (HS) data over multispectral ocean color (MSOC) data in accurately estimating phytoplankton concentration and in monitoring the coastal dynamics. We propose system spectral shape factor (SSSF)-based approach to recover the embedded HS top-of-atmosphere (TOA) radiance (TOARAD) from the MSOC data. SSSF is defined as convolution of normalized input spectrum and sensor spectral response function (SRF). The advantage of SSSF is that it decouples magnitude and spectral shape part of sensor output and enables recovery of TOARAD. To test this method, the airborne visible/infrared imaging spectrometer-next generation data are used to simulate inputs to MSOC. SRF of ocean color monitor simulated MSOC. SSSF of TOARAD is estimated using SSSF of model-based path radiance spectrum of the pixel, which is similar in spectral shape. Methodology, developed using data from five stations, is validated with data from other five stations. The procedure is successfully repeated using SRFs of sea-viewing wide-field-of-view sensor. The recovered HS data are found to be consistent with the original spectra with very small deviations in spectral angle map (<0.012 rad) spectral information divergence (<5.8 × 10 − 5), mean percentage relative error (MPRE) of TOARAD (<0.7 % ), and MPRE of TOA water leaving radiance (<5.8 % ). This approach possibly opens up research for application of HS analysis on MSOC recovered spectra and for optimization of sensor configurations.

Sun Glint Characterization in an Ocean Color Sensor with Different Payload Tilts and Correction Algorithms for Glint: A Study Using Oceansat-II Ocean Colour Monitor

Sun Glint Characterization in an Ocean Color Sensor with Different Payload Tilts and Correction Algorithms for Glint: A Study Using Oceansat-II Ocean Colour Monitor

A chlorophyll-a retrieval algorithm for the Coastal Zone Imager (CZI) onboard the HY-1C satellite in the Pearl River Estuary, China

ABSTRACT Accurate estimation of the near-surface chlorophyll-a (Chl-a) concentration in turbid estuaries via remote sensing is an ongoing challenge. Recently, with the successful launch of the third satellite of the China Ocean Colour Satellite Series, HY-1 C, equipped with the Coastal Zone Imager (CZI) with a 3-day revisit period and 50-m resolution in four bands, this study constructed a Chl-a inversion model via symbolic regression (SR) in the Pearl River Estuary (PRE) considering 60 in situ samples to explore the current ocean colour monitoring potential of the CZI. Compared to five state-of-the-art algorithms, the SR model attained a lower mean absolute percentage error (MAPE, 35.51%), mean absolute error (MAE, 1.56) and root-mean-square error (RMSE, 0.24). Additionally, the Chl-a spatial distributions determined with the SR model also better coincided with those determined with synchronous satellite products. The above results indicate that the SR model performed better than the five state-of-the-art models and could be effectively applied to turbid PRE waters. This research may provide a reference regarding the practicality and feasibility of the CZI in water environment monitoring in coastal and estuarial waters at a high temporal-spatial resolution.

Distribution and oceanography characteristic of light fishing in the Java Sea

Light fishing location obtained from visible infrared imaging radiometer suite (viirs) boat detection (VBD) and satellite-based oceanographic data of chlorophyll-a concentration (chl-a), sea-surface temperature (SST), salinity, and sea-surface height (SSH) were used to evaluate the distribution and characteristic of oceanographic conditions on the light fishing location in the Java Sea. The objectives of this study were to monitor the distribution of light fishing vessels from VBD and to evaluate the oceanographic characteristic on light fishing position. VBD data were prepared by national oceanic and atmospheric administration website and oceanographic data was provided by ocean color and copernicus marine environment monitoring service website. Overlaid process between light fishing position and oceanographic data was used to extract the oceanographic value in the light fishing location. The result showed most of light fishing occurred during southeast monsoon. In general, most of light fishing appeared in position of 107 - 114 °E and 4 - 5.30 °S. In addition, light fishing located in oceanographic condition for SST of 28.1 - 31.1 °C, chl-a of 0.26 - 0.60 mg/m3, salinity of 32 - 35.5 psu and SSH of 0. 5 - 0.62 m. The VBD data opens up a number of potential future applications for more computable fisheries data analysis.

Distributed Computing Model of Multispectral Time Series Data Analysis for Chlorophyll Concentration Determination Using Ocean Color Monitor-2 Data

Abstract In general, the ocean color monitor sensor (OCM) is used to identify and monitor the phytoplankton bloom and fishing zones. In the ocean, there is a food chain among plants and fish, and by calculating the normalized difference vegetation index (NDVI), we can identify chlorophyll concentration of the particular ocean area. NDVI is an indication of the presence of chlorophyll concentration. In this study, importance is given to the NDVI of ocean area, because our Earth is mostly occupied by the ocean in the sense of water. OCM sensor data are used to identify the chlorophyll, which in turn indicates the presence of phytoplankton. This is the primary production in the basic food chain, and also for the fishes. Remote sensing methodology is used to find and understand the spatial allocation of oceanwater constituents. Usually, the phytoplankton pigment emits a greenish color in the seawater, which is the visible region in the ocean. It enables plant objects to be identified from among the other suspended matter on the oceanwater. NDVI is mainly used in land applications to identify vegetation and forestation, and it is used to identify the chlorophyll pigment concentration of the ocean surface. Therefore, NDVI can be used to map chlorophyll-determined zones through which possible fishing zones can be generated. This study also shows that the NDVI generation technique is used to discover the resources of seawater for mapping the fishing zones. However, the limitation on large-scale computation for the entire earth surface leaves challenges toward raising the technological solution. Hence, an attempt is made to integrate a distributed computational model to cover the larger spatial data. Therefore, a grid-based satellite image processing system is designed to discover the chlorophyll pigment concentration on the ocean through NDVI generation. With multiple computing nodes of the configured grid, the spatial coverage on the oceanic surface is widened and computational speed is also improved to yield a promising outcome.

Accuracy Assessment of Bio-optical Models to Retrieve Backscattering Coefficients in Case 1 Waters of the Bay of Bengal

The backscattering coefficient (bb) has been obtained either in situ observations/measurements or semi-analytical/analytical and empirical algorithms that depend on the relationship between ‘bb’ and the remote sensing reflectance (Rrs). Several models were developed to estimate backscattering coefficient from the satellite imagery. The present study aims to assess the accuracy of bio-optical models and infer the best suitable model for Ocean Colour Monitoring (OCM-2) and for the upcoming OCM-3 sensors of Indian Space Research Organization. For this purpose, the bio-optical algorithms/models such as quasi-analytical algorithm (QAA), QAA version 5, Generalized Inherent Optical Properties (GIOP) and optimization of semi-analytical models in case 1 waters of the Bay of Bengal (BoB) were considered. For this analysis, three satellite sensors Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua, Visible Infrared Imaging Radiometer Suite (VIIRS), and OCM-2 datasets has been used to assess the model’s accuracy. The results showed that the optimization technique performs better (bias = 0.002, 0.179 and root-mean-square error (RMSE) = 8%, 20%) for MODIS-A and OCM-2, respectively, whereas GIOP performs better (bias = 0.08, RMSE = 14%) for VIIRS in case 1 waters of the BoB. From the statistical analysis of each model for all stations, it is recommended to use the optimization technique rather than GIOP technique for estimation of satellite based ‘bb’ in Indian waters.

Remote sensing and in situ platform based study on impact of Bay of Bengal cyclones (Phailin, Helen, Lehar, and Madi) on ocean chlorophyll and associated physical parameters

Impact of four major cyclones: Phailin, Helen, Lehar, and Madi have been studied in terms of changes in ocean productivity utilizing Indian satellite Oceansat-2 ocean color monitor and MODIS-Aqua data during October–December 2013. The 8-day and monthly composite chlorophyll images have been generated owing to the pre-, during, and post-stages of four cyclones; out of which, three were very severe cyclonic storms and the Helen was severe cyclonic storm. The chlorophyll concentration increased two- to threefolds due to the aftereffect of cyclone in the coastal water, and the concentration was very high (around 2–3 mg/m3). To understand the possible links of cyclones to cooling and upwelling, MODIS-Aqua sea surface temperature (SST) images have been processed. The SST images have shown the cooling effect near the cyclone track pass, over Bay of Bengal. The cooling was observed by 2–3 degree Celsius during the above cyclones. To understand the in situ surface water and water column salinity and temperature variability, the Argo float data are plotted and interpreted. The surface contour shows the lowering of salinity and temperature with effects of cyclones. In addition, the Argo depth profile data have shown possible mixed layer deepening in few profiles around halocline and thermocline depths. This study will be useful for mapping potential fishing zone, ocean biogeochemistry and other useful applications and research.

A novel method for destriping of OCM-2 data and radiometric performance analysis for improved ocean color data products

Despite the capability of Ocean Color Monitor aboard Oceansat-2 satellite to provide frequent, high-spatial resolution, visible and near-infrared images for scientific research on coastal zones and climate data records over the global ocean, the generation of science quality ocean color products from OCM-2 data has been hampered by serious vertical striping artifacts and poor calibration of detectors. These along-track stripes are the results of variations in the relative response of the individual detectors of the OCM-2 CCD array. The random unsystematic stripes and bandings on the scene edges affect both visual interpretation and radiometric integrity of remotely sensed data, contribute to confusion in the aerosol correction process, and multiply and propagate into higher level ocean color products generated by atmospheric correction and bio-optical algorithms. Despite a number of destriping algorithms reported in the literature, complete removal of stripes without residual effects and signal distortion in both low- and high-level products is still challenging. Here, a new operational algorithm has been developed that employs an inverted gaussian function to estimate error fraction parameters, which are uncorrelated and vary in spatial, spectral and temporal domains. The algorithm is tested on a large number of OCM-2 scenes from Arabian Sea and Bay of Bengal waters contaminated with severe stripes. The destriping effectiveness of this approach is then evaluated by means of various qualitative and quantitative analyses, and by comparison with the results of the previously reported method. Clearly, the present method is more effective in terms of removing the stripe noise while preserving the radiometric integrity of the destriped OCM-2 data. Furthermore, a preliminary time-dependent calibration of the OCM-2 sensor is performed with several match-up in-situ data to evaluate its radiometric performance for ocean color applications. OCM-2 derived water-leaving radiance products obtained after calibration show a good consistency with in-situ and MODIS-Aqua observations, with errors less than the validated uncertainties of ±5% and ±35% endorsed for the remote-sensing measurements of water-leaving radiance and retrieval of chlorophyll concentrations respectively. The calibration results show a declining trend in detector sensitivity of the OCM-2 sensor, with a maximum effect in the shortwave spectrum, which provides evidence of sensor degradation and its profound effect on the striping artifacts in the OCM-2 data products.

Retrieval of Aerosol Optical Depth Over Land at 0.490 μm from Oceansat-2 Data

In the present work a hybrid approach is implemented to retrieve aerosol optical depth (AOD) from ocean colour monitor sensor on board Oceansat-2. The retrieved AOD (490 nm) has been validated with the ground observed AOD (500 nm) made at different locations of the study area for different months observed by AERONET sites and hand held sun photometer. The Oceasat 2 retrieved AOD (490 nm) is also compared with the MODIS retrieved AOD (470 nm). It is observed that Oceansat 2 retrieved AOD is in good agreement with ground observed AOD with a significant correlation of 0.839 and with a RMSE value of 0.273. The comparison between Oceansat 2 retrieved AOD and MODIS retried AOD are also found to be encouraging with a correlation coefficient of 0.852 with RMSE 0.1243.

Optical Detection of Diatom Bloom in the Coastal Waters of Bay of Bengal Using Oceansat-2 OCM

Phytoplankton blooms are the indicators of marine ecosystem health status and their monitoring is a key component in the effective management of coastal and oceanic resources. It remained as a challenge in the coastal waters due to the presence of suspended sediments and dissolved organic matter which interferes with the phytoplankton signal. Several methods and techniques were developed to detect, map and monitor the phytoplankton blooms in the coastal waters using in situ and satellite data. To address this issue ISRO took an initiative to monitor and identify the phytoplankton blooms in the North Indian Ocean to be specific in the coastal waters. As part of this program, the blooms are monitored using Oceansat-2 Ocean Colour Monitor (OCM-2) data all along the East coast of India. This paper presents a case study in the bloom dominated coastal waters along Off Godavari delta covering Kakinada and Yanam in the East coast of India. In-situ measurements collected onboard during a bloom event are optical parameters like Remote Sensing reflectance, Rrs(λ), Water leaving radiance Lw(λ) from the Hyperpro-II under water radiometer, absorption due to phytoplankton, aph(λ), from the spectrophotometer, biological parameters of phytoplankton cell counts and HPLC pigments. From the in situ biological measurements, it is confirmed that the bloom was dominated by with diatom species of Skeletonema costatum with a high concentration of Fucoxanthin as the biomarker for diatoms from the HPLC pigment analysis. Based on these results obtained, optical detection of the bloom species using spectral discrimination of Rrs for different cell enumerations was considered and further spectral derivative analysis (first to fifth order) has been carried out to identify the peaks to detect the bloom forming species. The criteria considered as the minima in the second order derivative (dλ2Rrs) for identifying the peak to discriminate the spectra of the bloom forming species. From the analysis, a unique peak at 483 nm which is identified in particular to S. costatum species in the coastal waters which are identified from the chlorophyll product derived from OCM-2. From the results observed, it can ensure that diatom blooms can be detected from OCM-2 using 490 nm spectral band.

Ocean color satellite determinations of phytoplankton size class in the Arabian Sea during the winter monsoon

A regionally tuned three component “abundance” model of Brewin et al. (2012) has been used to discriminate satellite ocean color derived fields of phytoplankton biomass observable as Chlorophyll-a (Chl-a), into three size classes, i.e. microplankton (>20μm), nanoplankton (>2 to <20μm) and picoplankton (<2μm). The model has been applied to MODIS-Aqua and Oceansat-2, Ocean Color Monitor (OCM) derived fields of Chl-a data between Nov. and Mar. In the Arabian Sea, during the evolution of blooms of the large (>800μm sized) green mixotrophic dinoflagellate Noctiluca scintillans. A comparison of shipboard measured and model derived values of phytoplankton size classes (PSCs) show the superiority of the regionally tuned model over parameterizations used in the original model of Brewin et al. (2012). A total number of 39 in situ data points have been used for the tuning of the regional model and 5 different in-situ data points have been used for the comparison with in situ data in this remote region of data paucity. The absolute mean and the maximum absolute errors for all size fractions are 4.7% and 17.2% respectively, as compared to the values of 9.6% and 26% respectively obtained using Brewin et al. (2012).When applied to a weekly time series of Chl-a images, the regionally tuned model is able to capture the seasonal cycle of PSC in the Arabian Sea associated with the tail end of the fall inter-monsoon (Nov.), the winter monsoon (Dec. to Feb.) and the transition to the spring inter-monsoon Although ocean color remote sensing is a useful tool for studying phytoplankton processes in regions like the Arabian Sea that suffer from a paucity of in-situ observations, enhancing the validity and confidence in satellite ocean color derived products such as PSC, will require additional shipboard datasets.

Geospatial approach towards enumerative analysis of suspended sediment concentration for Ganges–Brahmaputra Bay

This study presents an easy, modular, user-friendly, and flexible software package for processing of Landsat 7 ETM and Landsat 8 OLI-TIRS data for estimating suspended particulate matter concentrations in the coastal waters. This package includes 1) algorithm developed using freely downloadable SCILAB package, 2) ERDAS Models for iterative processing of Landsat images and 3) ArcMAP tool for plotting and map making. Utilizing SCILAB package, a module is written for geometric corrections, radiometric corrections and obtaining normalized water-leaving reflectance by incorporating Landsat 8 OLI-TIRS and Landsat 7 ETM+ data. Using ERDAS models, a sequence of modules are developed for iterative processing of Landsat images and estimating suspended particulate matter concentrations. Processed images are used for preparing suspended sediment concentration maps. The applicability of this software package is demonstrated by estimating and plotting seasonal suspended sediment concentration maps off the Bengal delta. The software is flexible enough to accommodate other remotely sensed data like Ocean Color monitor (OCM) data, Indian Remote Sensing data (IRS), MODIS data etc. by replacing a few parameters in the algorithm, for estimating suspended sediment concentration in coastal waters.

OCEAN COLOR RETRIEVAL USING LANDSAT-8 IMAGERY IN COASTAL CASE 2 WATERS (CASE STUDY PERSIAN AND OMAN GULF)

Abstract. Ocean color (OC) monitoring using satellite imageries provides an appropriate tool for a better understanding of marine processes and changes in the coastal environment. Radiance measurements in the range of visible light of the electromagnetic spectrum provides information of ocean color that is associated with the water constituents. This measurements are used to monitor the level of biological activity and the presence of particles in the water. Ocean features such as the concentration of chlorophyll, suspended sediment concentration and sea surface temperature have a significant impact on the dynamics of the ocean. The concentration of chlorophyll (chla), active pigments of phytoplankton photosynthesis, as a key indicator applied for assessment of water quality and biochemistry. Experimental algorithms chla related to internal communication various optical components in the water that may be change in space and time in the water with different optical characteristics. Therefore, the algorithms have been developed for one area may not work for other places and each region according to its specific characteristics needs that determined by an algorithm may be appropriate to local. We have tried treatment several algorithms for determination of chlorophyll, including experimental algorithms with a simple band ratio of blue-green band (i.e. OCx) and algorithms includes two bands ratio with variable 𝑅𝑟𝑠(λ2)/𝑅𝑟𝑠(λ1), the three bands ratio with variable [𝑅𝑟𝑠(λ1)−1−𝑅𝑟𝑠(λ2)−1]×𝑅𝑟𝑠(λ3) and four bands ratio with variable [𝑅𝑟𝑠(λ1)−1−𝑅𝑟𝑠(λ2)−1]/[𝑅𝑟𝑠(λ4)−1−𝑅𝑟𝑠(λ3)−1] that desired wavelength (i.e. λ1, λ2, λ3 and λ4) in the range of red and near-infrared wavelengths of the electromagnetic spectrum are in the region of the Persian Gulf and Oman Sea look. Despite the high importance of the Persian Gulf and Oman Sea which can have up basin countries, to now few studies have been done in this area. The focus of this article on the northern part of Oman Sea and Persian Gulf, the shores of neighboring Iran (case 2 water). In this paper, by using Landsat 8 satellite imageries, we have discussed chla concentrations and customizing different OC algorithms for this new dataset (Landsat-8 imagery). This satellite was launched in 2013 and its data using two sensors continuously are provided operating one sensor imager land (OLI: Operational Land Imager) and the Thermal Infrared Sensor (TIRS: Thermal InfraRed Sensor) and are available. This sensors collect image data, respectively, for the nine-band short wavelength in the range of 433-2300 nm and dual-band long wavelength thermal. Seven band of the nine band picked up by the sensor information of OLI to deal with sensors TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) in previous satellite Landsat compatible and two other band, the band of coastal water (433 to 453 nm) and Cirrus band (1360 to 1390 nm), short wave infrared provides to measure water quality and high thin clouds. Since OLI sensor in Landsat satellite 8 compared with other sensors to study OC have been allocated a much better spatial resolution can be more accurate to determine changes in OC. To evaluate the results of the image sensor MODIS (Moderate Resolution Imaging Spectroradiometer) at the same time satellite images Landsat 8 is used. The statistical parameters used in order to evaluate the performance of different algorithms, including root mean square error (RMSE) and coefficient of determination (R2), and on the basis of these parameters we choose the most appropriate algorithm for the area. Extracted results for implementing different OC algorithms clearly shows superiority of utilized method by R2=0.71 and RMSE=0.07.

OCEAN COLOR RETRIEVAL USING LANDSAT-8 IMAGERY IN COASTAL CASE 2 WATERS (CASE STUDY PERSIAN AND OMAN GULF)

Ocean color (OC) monitoring using satellite imageries provides an appropriate tool for a better understanding of marine processes and changes in the coastal environment. Radiance measurements in the range of visible light of the electromagnetic spectrum provides information of ocean color that is associated with the water constituents. This measurements are used to monitor the level of biological activity and the presence of particles in the water. Ocean features such as the concentration of chlorophyll, suspended sediment concentration and sea surface temperature have a significant impact on the dynamics of the ocean. The concentration of chlorophyll (&amp;lt;i&amp;gt;chla&amp;lt;/i&amp;gt;), active pigments of phytoplankton photosynthesis, as a key indicator applied for assessment of water quality and biochemistry. Experimental algorithms &amp;lt;i&amp;gt;chla&amp;lt;/i&amp;gt; related to internal communication various optical components in the water that may be change in space and time in the water with different optical characteristics. Therefore, the algorithms have been developed for one area may not work for other places and each region according to its specific characteristics needs that determined by an algorithm may be appropriate to local. We have tried treatment several algorithms for determination of chlorophyll, including experimental algorithms with a simple band ratio of blue-green band (i.e. OCx) and algorithms includes two bands ratio with variable 𝑅&amp;lt;sub&amp;gt;𝑟𝑠&amp;lt;/sub&amp;gt;(λ&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;)/𝑅&amp;lt;sub&amp;gt;𝑟𝑠&amp;lt;/sub&amp;gt;(λ&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt;), the three bands ratio with variable [𝑅&amp;lt;sub&amp;gt;𝑟𝑠&amp;lt;/sub&amp;gt;(λ&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt;)−1−𝑅&amp;lt;sub&amp;gt;𝑟𝑠&amp;lt;/sub&amp;gt;(λ&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;)−1]×𝑅&amp;lt;sub&amp;gt;𝑟𝑠&amp;lt;/sub&amp;gt;(λ&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;) and four bands ratio with variable [𝑅&amp;lt;sub&amp;gt;𝑟𝑠&amp;lt;/sub&amp;gt;(λ&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt;)−1−𝑅&amp;lt;sub&amp;gt;𝑟𝑠&amp;lt;/sub&amp;gt;(λ&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;)−1]/[𝑅&amp;lt;sub&amp;gt;𝑟𝑠&amp;lt;/sub&amp;gt;(λ&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;)−1−𝑅&amp;lt;sub&amp;gt;𝑟𝑠&amp;lt;/sub&amp;gt;(λ&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;)−1] that desired wavelength (i.e. λ&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt;, λ&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, λ&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; and λ&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;) in the range of red and near-infrared wavelengths of the electromagnetic spectrum are in the region of the Persian Gulf and Oman Sea look. Despite the high importance of the Persian Gulf and Oman Sea which can have up basin countries, to now few studies have been done in this area. The focus of this article on the northern part of Oman Sea and Persian Gulf, the shores of neighboring Iran (case 2 water). In this paper, by using Landsat 8 satellite imageries, we have discussed chla concentrations and customizing different OC algorithms for this new dataset (Landsat-8 imagery). This satellite was launched in 2013 and its data using two sensors continuously are provided operating one sensor imager land (OLI: Operational Land Imager) and the Thermal Infrared Sensor (TIRS: Thermal InfraRed Sensor) and are available. This sensors collect image data, respectively, for the nine-band short wavelength in the range of 433-2300 nm and dual-band long wavelength thermal. Seven band of the nine band picked up by the sensor information of OLI to deal with sensors TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) in previous satellite Landsat compatible and two other band, the band of coastal water (433 to 453 nm) and Cirrus band (1360 to 1390 nm), short wave infrared provides to measure water quality and high thin clouds. Since OLI sensor in Landsat satellite 8 compared with other sensors to study OC have been allocated a much better spatial resolution can be more accurate to determine changes in OC. To evaluate the results of the image sensor MODIS (Moderate Resolution Imaging Spectroradiometer) at the same time satellite images Landsat 8 is used. The statistical parameters used in order to evaluate the performance of different algorithms, including root mean square error (RMSE) and coefficient of determination (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;), and on the basis of these parameters we choose the most appropriate algorithm for the area. Extracted results for implementing different OC algorithms clearly shows superiority of utilized method by R2=0.71 and RMSE=0.07.

Performance of operational satellite bio-optical algorithms in different water types in the southeastern Arabian Sea

Summary The in situ remote sensing reflectance (Rrs) and optically active substances (OAS) measured using hyperspectral radiometer, were used for optical classification of coastal waters in the southeastern Arabian Sea. The spectral Rrs showed three distinct water types, that were associated with the variability in OAS such as chlorophyll-a (chl-a), chromophoric dissolved organic matter (CDOM) and volume scattering function at 650 nm (β650). The water types were classified as Type-I, Type-II and Type-III respectively for the three Rrs spectra. The Type-I waters showed the peak Rrs in the blue band (470 nm), whereas in the case of Type-II and III waters the peak Rrs was at 560 and 570 nm respectively. The shifting of the peak Rrs at the longer wavelength was due to an increase in concentration of OAS. Further, we evaluated six bio-optical algorithms (OC3C, OC4O, OC4, OC4E, OC3M and OC4O2) used operationally to retrieve chl-a from Coastal Zone Colour Scanner (CZCS), Ocean Colour Temperature Scanner (OCTS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean Colour Monitor (OCM2). For chl-a concentration greater than 1.0 mg m−3, algorithms based on the reference band ratios 488/510/520 nm to 547/550/555/560/565 nm have to be considered. The assessment of algorithms showed better performance of OC3M and OC4. All the algorithms exhibited better performance in Type-I waters. However, the performance was poor in Type-II and Type-III waters which could be attributed to the significant co-variance of chl-a with CDOM.

Use of IRS-P4 Ocean Color Monitor (OCM) images for tracing the red edge of the terrestrial vegetation reflectance spectrum

A methodology is put forward to retrieve the red edge for terrestrial vegetated regions of IRS P4 Ocean Color Monitor (OCM) images. The objective is to utilize land-related portions of the archived OCM images that contain a significant amount of digital information on land cover. OCM band data were simulated from spectroradiometric reflectance of fresh green leaves and hyperspectral reflectance of vegetated regions derived from EO-1 Hyperion images. The red edge recovered from these model data using numerical techniques of Lagrange interpolation and inverted Gaussian was compared with the original one and reasonable accuracy was obtained. The technique was then applied to the actual red and near-infrared bands of OCM images, and red edge reflectance curves were computed for evergreen, deciduous and mangrove forest regions of the images for winter and spring seasons. Consistent results were obtained for seasonal changes, and vegetated and non-vegetated areas could be distinguished.

SNR Improvement by Photon Noise Filtering in Ocean Color Monitor Satellite Images

In high radiometric resolution electro optical image payloads of remote sensing satellites, photon noise dominates SNR performance. Photon noise is input signal dependent and difficult to filter. This paper proposes a photon noise filtering technique for Ocean Color Monitor (OCM) images. Existing filtering techniques are meant for object detection and handles images with poor SNR. As OCM SNR is on higher side, custom sigma filter based denoising technique is developed. Proposed technique first converts photon noise to signal independent Gaussian noise. For this variance stabilization, Anscombe transform is used. Simulations are carried on various images. Proposed technique provides 2050% reduction in overall as well count-wise RMSE. FFT analysis shows significant reduction in noise. Proposed technique is of low complexity.

Estimation of photosynthetically available radiation (PAR) from OCEANSAT-I OCM using a simple atmospheric radiative transfer model

Photosynthetically available radiation (PAR) is an important variable for radiation budget, marine and terrestrial ecosystem models. OCEANSAT-1 Ocean Color Monitor (OCM) PAR was estimated using two different methods under both clear and cloudy sky conditions. In the first approach, aerosol optical depth (AOD) and cloud optical depth (COD) were estimated from OCEANSAT-1 OCM TOA (top-of-atmosphere) radiance data on a pixel by pixel basis and PAR was estimated from extraterrestrial solar flux for fifteen spectral bands using a radiative transfer model. The second approach used TOA radiances measured by OCM in the PAR spectral range to compute PAR. This approach also included surface albedo and cloud albedo as inputs. Comparison between OCEANSAT-1 OCM PAR at noon with in situ measured PAR shows that root mean square difference was 5.82% for the method I and 7.24% for the method II in daily time scales. Results indicate that methodology adopted to estimate PAR from OCEANSAT-1 OCM can produce reasonably accurate PAR estimates over the tropical Indian Ocean region. This approach can be extended to OCEANSAT-2 OCM and future OCEANSAT-3 OCM data for operational estimation of PAR for regional marine ecosystem applications.