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.     

Seasonality and Latitudinal Variation of Water Vapour and Aerosol Optical Thickness and Their relationship Over Tropical Indian Ocean

Aerosol and water vapour are very important element in the Earth’s climate system which has direct role in the Earth’s radiation budget. In this paper the seasonality, latitudinal distribution and the relationship of aerosol optical thickness (AOD) and water vapour (WV) using MODIS Level 3 monthly data from 2001 to 2008 are analysed. The analysis shows that AOD (0.55 μm) values reach maximum during southwest monsoon and remain minimum during northeast monsoon period. The Equatorial Indian Ocean shows minimum AOD (0.115 to 0.153) throughout the year compared to Arabian Sea (0.208 to 0.613) and Bay of Bengal (0.214 to 0.351). Arabian Sea shows high variation and maximum value of AOD compared to Bay of Bengal and Equatorial Indian Ocean. During southwest monsoon WV over Bay of Bengal was found higher in concentration compared to Arabian Sea and Equitorial Indian Ocean throughout the study period. Comparison between Arabian Sea (2.98 cm to 5.07 cm) and Bay of Bengal (3.49 cm to 5.94 cm) shows that WV concentration is less in Arabian Sea throughout the year. The analysis of correlation between WV and AOD was found to be inconsistent. However, AOD and WV shows a strong positive correlation for whole year (Mean R² =0.90) in the Equitorial Indian Ocean region except in the months of January, February and March. In general, the correlation between WV and AOD is found to be strongly positive for oceanic aerosol (sea salt) in low water vapour condition.     

A Novel Approach to Estimate Diffuse Attenuation Coefficients for QuickBird Satellite Images: A Case Study at Kish Island, the Persian Gulf

Diffuse attenuation coefficient (k _d ) is a critical parameter for benthic habitat mapping using remotely sensed data. This research attempted to develop a new approach to estimate k _d in blue and green bands of QuickBird satellite image based on the integration of Lyzenga’s method and updated NASA-k _d ⁴⁹⁰ algorithm. To do this, the Lyzenga’s method was utilized to determine the ratio of k _d in different bands of QuickBird satellite image. Additionally, NASA-k _d ⁴⁹⁰ algorithm was applied to determine k _d ⁴⁹⁰ by using remotely sensed reflectance values of blue (R _rs ^Blue ) and green (R _rs ^Green ) bands in each pixel of QuickBird satellite image. Since the aforementioned algorithm has been developed for other types of sensors, an approach using weighted mean value of parameters for SeaWiFS, MERIS, VIIRS, and OCTS sensors were employed to estimate parameter values for QuickBird image. After determining the k _d ⁴⁹⁰ values as k _d for blue band, the k _d values for green and red bands were subsequently obtained by using Lyzenga’s method. Then, Mumby and Edwards’ method was employed as evidence to evaluate the accuracy of the results achieved from newly developed approach. Eventually, the maximum likelihood classifier was implemented during pre and post correction steps to examine the capability of the proposed approach. The final results proved to be consistent in the areas deeper than 2 m between estimated k _d values using the proposed approach and the results obtained from Mumby and Edwards’ method. On the other hand, the values estimated for extremely shallow areas seem to be overestimated. Furthermore, results demonstrated an increment of ~16 % in the overall accuracy of the classification.     

Medium resolution imaging spectrometer data for monitoring tropical coastal waters: a case study of Berau estuary,East Kalimantan,Indonesia

This article investigates the performance of MERIS reduced resolution data to monitor water quality parameters in the Berau estuary waters, Indonesia. Total suspended matter (TSM), Chlorophyll-a (Chl-a) concentration and diffuse attenuation coefficient (K_d ) were derived from MERIS data using three different algorithms for coastal waters: standard global processor (MERIS L2), C2R and FUB. The outcomes were compared to in situ measurements collected in 2007. MERIS data processed with C2R gave the best retrieval of Chl-a, while MERIS L2 performed the best for TSM retrieval, but large deviations from in situ data were observed, pointing at inversion problems over these tropical waters for all standard processors. Nevertheless, MERIS can be of use for monitoring equatorial coastal waters like the Berau estuary and reef system. Applying a K_d (490) local algorithm to the MERIS RR data over the study area showed a sufficient good correlation to the in situ measurements (R ² = 0.77).     

Evaluation of a three-band model for estimating chlorophyll-a concentration in tidal reaches of the Pearl River Estuary, China

Accurate assessment of phytoplankton chlorophyll-a (Chla) concentration in turbid waters by means of remote sensing was challenging due to the optical complexity of turbid waters. Recently, a conceptual model containing reflectance in three spectral bands in the red and near-infrared range of the spectrum was suggested for retrieving Chla concentrations in turbid productive waters. The objective of this paper was to evaluate the performance of this three-band model to estimate Chla concentration in the Pearl River Estuary (PRE), China. Reflectance spectra of surface water and water samples were collected concurrently. The samples contained variable Chla (4.80-92.60 mg/m³) and total suspended solids (0.4-55.2 mg/L dry wt). Colored dissolved organic matter (CDOM) absorption at 400 nm was 0.40-1.41 m⁻¹; turbidity ranged from 4 to 25 NTU (Nephelometric Turbidity Units). The three-band model was spectrally calibrated by iterative and least-square linear regression methods to select the optimal spectral bands for the most accurate Chla estimation. Strong linear relationships (R²=0.81, RMSE=1.4 mg/m³, N=32) were established between measured Chla and the levels obtained from the calibrated three-band model [R⁻¹(684)-R⁻¹(690)]×R(718), where R(λ) was the reflectance at wavelength λ. The calibrated three-band model was independently validated (R²=0.9521, RMSE=6.44 mg/m³, N=16) and applied to retrieve Chla concentrations from the calibrated EO-1 Hyperion reflectance data in the PRE on December 21, 2006. The EO-1 Hyperion-derived Chla concentrations were further validated using synchronous in situ data collected on the same day (R²=0.64, RMSE=2 mg/m³, N=9). The spatial tendency of Chla distribution mapping by Hyperion showed gradually increased concentrations of Chla farther from the river mouths (although decreasing from east to west), which were disturbed by the combination of river outlets and tidal current in Lingding Bay of the PRE. This observation conformed to previous observations and studies, and could reasonably be explained by geographical changes. Also, results indicated that the slope of the three-band regression line decreased as the Chla concentration increased, resulting in the first sensitive band of the three-band model to move towards short wavelengths. These findings validated the rationale behind the conceptual model and demonstrated the robustness of this algorithm for Chla retrieval from in situ data and the Hyperion satellite sensor in turbid estuarine waters of the PRE, China.     

Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake,China

Based on in situ water sampling and field spectral measurements in Dianshan Lake, a semi-analytical three-band algorithm was used to estimate Chlorophylla (Chla) content in case II waters. The three bands selected to estimate Chla for high concentrations included 653, 691 and 748 nm. An equation, based on the difference in reciprocal reflectance between 653 and 691 nm, multiplied by reflectance at 748 nm as [R_rs⁻¹(653) − R_rs⁻¹ (691)] R_rs(748), explained 85.57% of variance in Chla concentration with a root mean square error (RMSE) of <6.56 mg/m³. In order to test the utility of this model with satellite data, HJ-1A Hyperspectral Imager (HSI) data were analyzed using comparable wavelengths selected from the in situ data [B₆₇⁻¹(656) − B₈₀⁻¹(716)] B₈₇(753). This model accounted for 84.3% of Chla variation, estimating Chla concentrations with an RMSE of <4.23 mg/m³. The results illustrate that, based on the determined wavelengths, the spectrum-based model can achieve a high estimation accuracy and can be applied to hyperspectral satellite imagery especially for higher Chla concentration waters.     

Validation of Chlorophyll-a Algorithms in the Coastal Waters of Bay of Bengal Initial Validation Results from OCM-2

In this paper we report chlorophyll measurements made during an ocean colour validation cruise in April 2011 of the research vessel, Sagar Paschimi in the coastal waters of Northern Bay of Bengal. The chlorophyll-a concentration in these waters range from 0.2 to 4.0 mg/m³. Chlorophyll-a concentration from OCM-2 was estimated using the global ocean colour algorithms namely, OC2, OC3, OC4 and Chl-a algorithms respectively. OCM data was processed using the global SeaWiFS Data Analysis System (SeaDAS) in which all the above mentioned algorithms are embedded for estimating the chlorophyll-a concentration. A comparative study was made between and in-situ and satellite derived chlorophyll-a concentration. Although the matchups between in-situ and satellite data from OCM-2 were sparse, it indicates that direct application of the standard SeaWiFS algorithm-the OC4-V4 algorithm—in the coastal waters of the Bay of Bengal will underestimate chlorophyll-a by up to 30%. The results show a good correlation with an R value of 0.61 using OC2 algorithm. However, all the other global algorithms over estimate the chlorophyll-a concentration even in low chlorophyll concentration range. The comparison between in-situ and all the existing chlorophyll algorithms shows the efficiency of these algorithms for quantification of chlorophyll in coastal waters and hence the need to develop regional algorithms and fluorescence based algorithms for better quantification.     

Dynamics of chromophoric dissolved organic matter in Mandovi and Zuari estuaries — A study through in situ and satellite data

The spatial and temporal distribution of absorption of chromophoric dissolved organic matter at 440 nm (a_CDOM (440)) in the Mandovi and Zuari estuaries situated along the west coast of India, has been analysed. The study was carried out using remotely sensed data, obtained from the Ocean Colour Monitor (OCM) on board the Indian Remote Sensing satellite — P4, together with in situ data during the period January to December 2005. Satellite retrieval of CDOM absorption was carried out by applying an algorithm developed for the site. A good correlation (R=0.98) was obtained between satellite derived CDOM and in situ data. Time series analysis revealed that spatial distribution of CDOM has a direct link with the seasonal hydrodynamics of the estuaries. The effect of remnant fresh water on CDOM distribution could be analysed by delineating a plume in the offshore region of the Zuari estuary. Though fresh water flux from terrestrial input plays a major role in the distribution of CDOM throughout the Mandovi estuary, its role in the Zuari estuary is significant up to the middle zone. Other processes responsible for feeding CDOM in both the estuaries are coastal advection, in situ production and resuspension of bottom settled sediments. The highest value of a_CDOM(440) was observed in the middle zone of the Mandovi estuary during the post-monsoon season. The relation between a_CDOM(440) and S (spectral slope coefficient of CDOM) could differentiate CDOM introduced in to estuaries through multiple sources. The algorithm developed for the Mandovi estuary is S=0.003 [a_CDOM(440)^−0.7091] while for the Zuari estuary, S=0.0031 [a_CDOM(440)^−0.777], respectively.     

Study on Regional Variations of Aerosol Loading Using Long Term Satellite Data Over Indian Region

Satellite-based measurements of aerosols are one of the most effective ways to understand the role of aerosols in climate in terms of spatial and temporal variability. In the present study, we attempted to analyse spatial and temporal variations of satellite derived aerosol optical depth (AOD) over Indian region using moderate resolution imaging spectrometer over a period of 2001–2011. Due to its vast spatial extent, Indian region and adjacent oceanic regions are divided into different zones for analysis. The land mass is sub divided into five different zones such as Indo Gangetic Plain (IGP), Indian mainland, North Eastern India (NE), South India-1 (SI-1), South India-2 (SI-2). Oceanic areas are divided into Arabian Sea and Bay of Bengal. Arabian Sea is further divided as three zones viz. Northern AS (NAS), Central AS (CAS) and Eastern AS (EAS) zones. Bay of Bengal is divided as North BoB (NBoB), West BoB (WBoB), Central BoB (CBoB), and East BoB (EBoB). The study revealed that among all the land regions, IGP showed the highest peak AOD value (0.52 ± 0.17) while SI-2 showed the lower values of AOD in all the months compared to all India average. The maximum AOD is observed during premonsoon season for all regions. During the winter, average AOD levels were substantially lower than the summer averages. Peak of aerosol loading (0.35 ± 0.159) is observed in March over NE region, whereas in all other regions, peak is observed during May. Frequency distribution of long term AOD (<0.2, 0.3–0.5, >0.5) shows a shift of frequency distribution of AOD from <0.3 to 0.3–0.5 during the study period in all regions except IGP. In IGP shift of frequency of AOD values occurs from 0.3–0.5 to >0.5. Oceanic areas also shows seasonal variation of AOD. Over Arabian Sea, high AOD values with greater variations were observed in summer monsoon season while in Bay of Bengal it is observed during winter monsoon. This is due to the high wind speed prevailing in Arabian Sea during monsoon season which results in production of more sea salt aerosol. Highest AOD values are observed over NAS during monsoon season and over NBOB during winter season. Lowest AOD values with its lower variations observed in both the central region of Arabian Sea and Bay of Bengal.     

Re-parameterization of a quasi-analytical algorithm for colored dissolved organic matter dominant inland waters

Quasi-Analytical Algorithms (QAAs) are based on radiative transfer equations and have been used to derive inherent optical properties (IOPs) from the above surface remote sensing reflectance (R_rs) in aquatic systems in which phytoplankton is the dominant optically active constituents (OACs). However, Colored Dissolved Organic Matter (CDOM) and Non Algal Particles (NAP) can also be dominant OACs in water bodies and till now a QAA has not been parametrized for these aquatic systems. In this study, we compared the performance of three widely used QAAs in two CDOM dominated aquatic systems which were unsuccessful in retrieving the spectral shape of IOPS and produced minimum errors of 350% for the total absorption coefficient (a), 39% for colored dissolved matter absorption coefficient (a_CDM) and 7566.33% for phytoplankton absorption coefficient (a_phy). We re-parameterized a QAA for CDOM dominated (hereafter QAA_CDOM) waters which was able to not only achieve the spectral shape of the OACs absorption coefficients but also brought the error magnitude to a reasonable level. The average errors found for the 400–750 nm range were 30.71 and 14.51 for a, 14.89 and 8.95 for a_CDM and 25.90 and 29.76 for a_phy in Funil and Itumbiara Reservoirs, Brazil respectively. Although QAA_CDOM showed significant promise for retrieving IOPs in CDOM dominated waters, results indicated further tuning is needed in the estimation of a(λ) and a_phy(λ). Successful retrieval of the absorption coefficients by QAA_CDOM would be very useful in monitoring the spatio-temporal variability of IOPS in CDOM dominated waters.     

Atmospheric Correction Over Coastal Turbid Waters of Bay of Bengal Using OCEANSAT-I Ocean Colour Monitor (OCM) Data

Space-borne ocean-colour remote sensor-detected radiance is heavily contaminated by solar radiation backscattered by the atmospheric air molecules and aerosols. Hence, the first step in ocean-colour data processing is the removal of this atmospheric contribution from the sensor-detected radiance to enable detection of optically active oceanic constituents e.g. chlorophyll-a, suspended sediment etc. In standard atmospheric correction procedure for OCEANSAT-1 Ocean Colour Monitor (OCM) data, NIR bands centered at 765 and 865 nm wavelengths were used for aerosol characterization. Due to high absorption by water molecules, ocean surface in these two wavelengths acts as dark background, therefore, sensor detected radiance can be assumed to have major contribution from atmospheric scattering. For coastal turbid waters this assumption of dark surface fails due to the presence of highly scattering sediments which causes sufficient water-leaving radiance in NIR bands and lead to over-estimation of aerosol radiance resulting in negative water leaving radiance for λ < 700 nm. In the present study, for the turbid coastal waters in the northern Bay of Bengal, the concept of spatial homogeneity of aerosol and water leaving reflectance has been applied to perform atmospheric correction of OCAEANSAT-1 OCM data. The results of the turbid water atmospheric correction have also been validated using in-situ measured water-leaving radiance. Comparison of satellite derived water-leaving radiance for five coastal stations with in-situ measured radiance spectra, indicates an improvement over the standard atmospheric correction algorithm giving physically realistic and positive values. Root Mean Square Error (RMSE) between the in-situ measured and satellite derived water leaving radiance for wavelengths 412 nm, 443 nm, 490 nm, 512 nm and 555 nm was found to be 1.11, 0.718, 0.575, 0.611 and 0.651%, respectively, using standard atmospheric correction procedure. By the use of spatial homogeneity concept, this error was reduced to 0.125, 0.173, 0.176, 0.225, and 0.290 and the correlation coefficient arrived at 0.945, which is an improvement over the standard atmospheric correction procedure.     

Retrieval and Validation of Chlorophyll-a Concentrations in the Coastal Waters Off Yanam and Kakinada (Godavari) Basin Along East Coast of India

In this study chlorophyll measurements were made during March 2012 in the estuarine waters of Off Kakinada and Yanam coast, Bay of Bengal onboard a coastal vessel. In-situ water samples and optical data was collected at 21 stations (surface to 150 m depth) using Underwater radiometer (Hyperpro-II). In-vivo chlorophyll profiles were collected using wet labs fluorometer integrated with underwater Hyperspectral radiometer. Chlorophyll-a concentrations were estimated using HPLC by collecting the water samples at each sampling location. And also chlorophyll-a concentrations were retrieved from the OCM-2 data of OCEANSAT-2 satellite, processed using SeaDAS v.6.2 with the available global ocean colour algorithms namely, OC2 and OC4V4. A total of 33 samples used covering all the stations for chlorophyll-a estimation, and surface water samples of all the stations only being used for direct comparison among chlorophyll concentrations of HPLC, in-situ (fluorometrically integrated to Hyperpro-II) and retrieved from OCM-2. A good correlation found between the Fluorometer derived and HPLC measured chlorophyll-a concentration with an R² value of 0.78. The relation between Chlorophyll-a concentration measured from HPLC and retrieved from OCM-2 (OC2 and OC4V4 algorithms) using SeaDASv.6.2 for 10 samples has been compared for validation and obtained an R² value of 0.6. Also comparisons done with the in-situ measured (fluorometer) Chlorophyll-a concentration with OCM-2 chlorophyll data (OC4-V4 and OC2 algorithms) and validation with 10 concurrent in-situ surface measurements showed a significant overestimation by OCM-2 at low chlorophyll-a concentrations and underestimation at high chlorophyll-a concentrations.     

The Gauss–Listing geopotential value W 0 and its rate from altimetric mean sea level and GRACE

In geopotential space, the fundamental geodetic parameter W ₀ defines the Gauss–Listing geoid which can be used to best represent the Earth’s mean sea level (MSL) and hence specifies a conventional zero height level to unify vertical datums employed by mapping agencies throughout the world. Further, W ₀ cannot be considered invariant as the parameter varies temporally as a direct response to sea level change and mass redistributions. This study determines W ₀ and its rate, dW ₀/dt, by utilizing altimetric MSL models and an independent mean dynamic topography (MDT) model to define points on the geoid. W ₀ and dW ₀/dt are estimated by two approaches: (i) by means of a global gravity field model (GGM) and (ii) within normal gravity field space as the geopotential value of the best fitting reference ellipsoid. The study shows that uncertainty in W ₀ is mainly influenced by MDT while the choice of methodology, GGM and MSL data coverage are not significant within reason. Our estimate W ₀ =?62636854.2 ± 0.2 m²?s^?2 at epoch 2005.0 differs by 1.8?m²s^?2 from the International Astronomical Union reference value. This study shows that, at a sub-decadal time scale, the time variation dW ₀/dt stems mainly from sea level change with negligible effect from gravity field variations. dW ₀/dt =?(?2.70 ± 0.03)?×?10^?2?m²?s^?2?year^?1, corresponding to a MSL rise of 2.9?mm?year^?1, is evaluated from sea level change based on 16?years of TOPEX and Jason-1 data.     

Uncertainty analysis of remote sensing of colored dissolved organic matter: Evaluations and comparisons for three rivers in North America

The uncertainties involved in remote sensing inversion of CDOM (Colored Dissolved Organic Matter) were analyzed in estuarine and coastal regions of three North American rivers: Mississippi, Hudson, and Neponset. Water optical and biogeochemical properties, including CDOM absorption and above-surface spectra, were collected in very high resolution. CDOM’s concentrations (a_g(440), absorption coefficient at 440 nm) were inverted from EO-1 Hyperion images, using a quasi-analytical algorithm for CDOM (QAA-CDOM). Uncertainties are classified to five levels, in which the underwater measurement uncertainty (level 1), image preprocessing uncertainty (level 4) and inverse model uncertainty (level 5) were evaluated. Results indicate that at level 1, in situ CDOM measurement is significant with 0.1 in the unit of QSU and 0.01 in the unit of a_g(440) (m⁻¹). At level 4, surface wave is a potential uncertainty source for high-resolution images in estuarine and coastal regions. The remote sensing reflectance of wavy water is about 10 times of the truth. At level 5, the overall uncertainty of QAA-CDOM inversion is 0.006 m⁻¹, with accuracy R² = 0.77, k = 1.1 and RMSE_log = 0.33 m⁻¹. The correlations between uncertainties and other water properties indicate that the large uncertainty in some rivers, such as the Neponset and Atchafalaya, might be caused by high-concentration chlorophyll or sediments. The relationships among the three level uncertainties show that the level 1 uncertainty generally does not propagate into level 4 and 5, but the large uncertainty at level 4 usually introduce large uncertainty at level 5.     

Variability of Altimetric Range Correction Parameters over Indian Tropical Region using JASON-1 & JASON-2 Radar Altimeters

Region-specific atmospheric range correction maps are generated over the Indian tropical region from Jason-1 & Jason-2 radar altimeters data. Seasonal and spatial variability of wet tropospheric correction (WTC), ionospheric correction (IC), dry tropospheric correction (DTC), and sea state bias (SSB) correction are analyzed over the Bay of Bengal and the Arabian Sea. Two year atmospheric range correction data from JASON-1 (2008) & JASON-2 altimeters (2009) has been analyzed where each Jason cycle is exactly 9.9156?days repeat. The monthly and yearly mean variation of the range correction parameters has been studied over the Indian continent. For precise study, four different regions were selected as the Region of Interest in the North & South of the Arabian Sea and Bay of Bengal. WTC, Significant Wave Height (SWH), Wind Speed (WS) and SSB show the higher values during monsoon months. The yearly mean WTC over Indian Tropical region was 26.22?cm in 2008 and 26.20?cm in 2009. SSB Correction values mainly depend on the SWH and wind speed. The yearly mean SSB correction over Indian Tropical region was 6.87?cm in 2008 and 7.02?cm in 2009. DTC values are less during monsoon season and it shows a high value in the month of January. The yearly mean DTC over Indian Tropical region was 230.42?cm in 2008 and 230.43?cm in 2009.The IC values mainly depend on frequency and total electron content (TEC) in the ionosphere which further depends on the solar activity. The yearly mean IC over Indian Tropical region was higher in 2008 (2.98?cm) in comparison to mean IC in 2009 (2.29?cm). This study is useful to understand the variability of atmospheric correction parameters especially over Indian continent.     

Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient

An approach for estimating soil moisture is presented and tested by using surface-temperature-based soil evaporation transfer coefficient (h_a), a coefficient recently proposed through the equation h_a = (T_s − T_a)/(T_sd − T_a), where T_s, T_sd, and T_a are land surface temperature (LST), reference soil (dry soil without evaporation) surface temperature, and air temperature respectively. Our analysis and controllable experiment indicated that h_a closely related to soil moisture, and therefore, a relationship between field soil moisture and h_a could be developed for soil moisture estimation. Field experiments were carried out to test the relationship between h_a and soil moisture. Time series Aqua-MODIS images were acquired between 11 Sep. 2006 and 1 Nov. 2007. Then, MODIS derived h_a and simultaneous measured soil moisture for different soil depths were used to establish the relations between the two variables. Results showed that there was a logarithmic relationship between soil moisture and h_a (P < 0.01). These logarithmic models were further validated by introducing another ground-truth data gathered from 46 meteorological stations in Hebei Province. Good agreement was observed between the measured and estimated soil moisture with RMSE of 0.0374 cm³/cm³ and 0.0503 cm³/cm³ for surface energy balance method at two soil depths (10 cm and 20 cm), with RMSE of 0.0467 cm³/cm³ and 0.0581 cm³/cm³ for maximum temperature method at two soil depths. For vegetated surfaces, the ratio of h_a and NDVI suggested to be considered. The proposed approach has a great potential for soil moisture and drought evaluation by remote sensing.     

Variability of Total Suspended Matter in the Northern Coastal Bay of Bengal as Observed from Satellite Data

The coastal regions of northern Bay of Bengal (BoB) are one of the most turbid areas owing to the large freshwater discharge from the three major river systems. This study is conducted to investigate the seasonal and interannual variability of total suspended matter (TSM) in the northern BoB. TSM concentration derived from medium resolution imaging spectrometer for the period 2002–2011 is used for this study. TSM concentration is observed to be the highest during summer monsoon season along the north-eastern region, off the Ganges–Brahmaputra river mouth. The variability of the TSM concentration depicts the role of river runoff associated with the summer monsoon in influencing the same. It is observed that the sediment concentration tapers away towards the offshore regions with the maximum extent observed up to 21.5°N latitude. Interannual variability is also observed with highest TSM concentrations occurring in the years 2003, 2008 and 2011 and least during 2004. Time series analysis performed at three major river discharge regions illustrated the distinct and highly variable nature of TSM dynamics prevailing in the northern BoB.     

Understanding the Spatial Variability of Chlorophyll a and Total Suspended Matter Distribution Along the Southwest Bay of Bengal Using In-Situ and OCM-2 & MODIS-Aqua Measurements

Spatial and temporal distribution of chlorophyll a (chl a) and Total Suspended Matter (TSM) and inter comparison of Ocean Color Monitor-2 (OCM-2) and Moderate Resolution Imaging Spectro-radiometer (MODIS-Aqua) derived chlorophyll a and TSM was made along the southwest Bay of Bengal (BoB). The in-situ chl a and TSM concentration measured during different seasons were ranged from 0.09 to 10.63 μgl^?1 and 11.04–43.75 mgl^?1 respectively. OCM-2 and MODIS derived chl a showed the maximum (6–8 μgl^?1) at nearshore waters and the minimum (0–1 μgl^?1) along the offshore waters. OCM-2 derived TSM imageries showed the maximum (50–60 mgl^?1) along the nearshore waters of Palk Strait and the moderate concentration (2–5 mgl^?1) was observed in the offshore waters. MODIS derived minimum TSM concentration (13.244 mgl^?1) was recorded along the offshore waters, while the maximum concentration of 15.78 mgl^?1 was found along the Kodiakarai region. The inter-comparison of OCM-2 and MODIS chl a data (R ² ?=?0.549, n?=?49, p?<?0.001, SEE?=?±0.117) indicate that MODIS data overestimates chl a concentration in the nearshore waters of the southern BoB compared to the OCM-2. The correlation between OCM-2 and MODIS-Aqua TSM data (R ² ?=?0.508, N?=?53, P?<?0.001 and SEE?=?±0.024) confirms that variation in the range of values measured by OCM-2 (2–60 mgl^?1) and the MODIS (13–16 mgl^?1) derived TSM values. Despite problems in range of measurements, persistent cloud cover etc., the launch of satellites like OCM-2 with relatively high spatial resolutions makes job easier and possible to monitor chl a distribution and sediment discharges on day to day basis in the southwest BoB.     

Comparison of ocean color chlorophyll algorithms for IRS-P4 OCM sensor usingin-situ data

In-situ chlorophyll concentration data and remote sensing reflectance (R_rs) measurements collected in six different ship campaigns in the Arabian Sea were used to evaluate the accuracy, precision, and suitability of different ocean color chlorophyll algorithms for the Arabian Sea. The bio-optical data sets represent the typical range of biooptical conditions expected in this region and are composed of 47 stations encompassing chlorophyll concentration, between 0.072 and 5.90 mg m^-3, with 43 observations in case I water and 4 observations in case II water. Six empirical chlorophyll algorithms [i.e. Aiken-C, POLDER-C, OCTS-C, Morel-3, Ocean Chlorophyll-2 (OC2) and Ocean Chlorophyll-4 (OC4)] were selected for analysis on the Arabian Sea data set. Numerous statistical and graphical criterions were used to evaluate the performance of these algorithms. Among these six chlorophyll algorithms two chlorophyll algorithms (i.e. OC2 and OC4) performed well in the case I waters of the Arabian Sea. The OC2 algorithm, a modified cubic polynomial function which uses ratio of R_rs490 nm and R_rs555 nm (where, R_rs is remote sensing reflectance), performed well with r²=0.85; rms =0.15. The OC4 algorithm, a four-band (443, 490, 510, 555 nm), maximum band ratio formulation was found best on the basis of statistical analysis results with r²=0.85 and rms=0.14. Both OC2 and OC4 algorithms failed to estimate chlorophyll inTrichodesmium dominated waters. The OC2 algorithm was preferred over OC4 algorithm for routine processing of the OCM data to generate chlorophyll-a images, as it uses a band ratio of 490/555 nm and atmospheric correction is more accurate in 490 nm compared to 443 nm band, which is used by OC4 algorithm.     

Satellite and In Situ Observations of a Phytoplankton Bloom from Coastal Bay of Bengal: Role in pCO<Subscript>2</Subscript> Modulation

A phytoplankton bloom was monitored in coastal waters of Bay of Bengal and its influence in water column properties was investigated. Significant draw down of CO₂ was noted within the vicinity of the bloom associated with high chlorophyll biomass. Microscopic analysis revealed diatoms as the dominant population. Skeletonema costatum a diatom, reached cell density of 36,898 cells l^?1 within the bloom. The lowest surface pCO₂ observed was 287 µatm at the southern end of the transect covarying with surface chlorophyll of 1.090 µg l^?1. At the northern end the surface pCO₂ went as low as 313 µatm. The pCO₂ levels below the mixed layer increased twice of that of surface value (~600 µatm). The chlorophyll values observed by Ocean Colour Monitor-2 were modestly related with the in situ measurements. The primary productivity derived from growth rate, assimilation number and maximum surface chlorophyll was 160.6 mg C m^?2 day^?1 leading to a modest sequestration ~of 0.08 Gg of carbon per day by the surface waters. Our observations reflects the potential role of diatom blooms on coastal carbon dynamics therefore should be carefully monitored in realm of anthropogenic changes.