Developments and achievements in atmospheric sciences and space meteorology in India

Space research in India began in the early sixties, with the establishment of the Thumba Equatorial Rocket Launching Station. Indigenously developed rocket payloads were carried in foreign rockets and flown for studying various atmospheric parameters, which are unique to the tropics. In the seventies, Indian-made rockets became available. Since then, there has been rapid progress in the technical advancement, which helped the atmospheric scientists in taking up more challenging and contemporary problems, related to mesospheric winds, ionospheric irregularities, stratospheric ozone, role of aerosols in atmospheric radiative transfer, etc. India entered into the satellite era in 1975, with the development of the Aryabhatta satellite, the first Indian experimental satellite, followed by Bhaskara-1 in 1979, which carried a microwave radio meter for retrieval of atmospheric water vapour and cloud liquid water contents. Since then, there have been several satellites, such as the INSAT series for meteorology and communication, Indian Remote Sensing (IRS) satellite series, and Stretched Rohini Satellite System (SROSS) for in situ observation of the ionosphere, which are all built in India and launched from Indian soil. High quality data being obtained from these satellite missions are helping scientists in taking up problems that are of regional and global scales and in studying the changes that are taking place in the earth atmosphere system, in a more holistic way. This paper attempts to provide an overview of the scientific developments and highlights some results

The evolution of the earth observation system in India

The Indian Earth Observations Programme has been applications- driven and national development has been its prime motivation. From the experimental satellite Bhaskara-I launched in 1979 to the recent Cartosat-2B launched in July 2010, India's Earth Observations capability has increased manifold. The Enhancement in observation capabilities are not only in spatial, spectral, temporal and radiometric resolutions, but also in their coverage and value-added products. The sensors built over this period provide observations over land, atmosphere and oceans in visible, infrared, thermal and microwave regions of the electro magnetic spectrum. Earth Observation data has been extensively used in inventories, monitoring and conservation plans of various natural resources of the country for societal benefits. An institutional mechanism for the absorption of technology at different levels of governance in the country has been built through the concept of the National Natural Resources Management System. The Establishment of various centres/institutions in different states, central agencies as well as academic and research institutions has helped capacity building in the area of remote sensing technology and applications programmes. The paper reviews the evolution of the Earth Observation System in the country in the last three decades and briefly discusses future directions

Remote sensing applications: an overview

Remote Sensing (RS) refers to the science of identification of earth surface features and estimation of their geo-biophysical properties using electromagnetic radiation as a medium of interaction. Spectral, spatial, temporal and polarization signatures are major characteristics of the sensor/target, which facilitate target discrimination. Earth surface data as seen by the sensors in different wavelengths (reflected, scattered and/or emitted) is radiometrically and geometrically corrected before extraction of spectral information. RS data, with its ability for a synoptic view, repetitive coverage with calibrated sensors to detect changes, observations at different resolutions, provides a better alternative for natural resources management as compared to traditional methods. Indian Earth Observation (EO) programme has been applications-driven and national development has been its prime motivation. From Bhaskara to Cartosat, India's EO capability has increased manifold. Improvements are not only in spatial, spectral, temporal and radiometric resolutions, but also in their coverage and value-added products. Some of the major operational application themes, in which India has extensively used remote sensing data are agriculture, forestry, water resources, land use, urban sprawl, geology, environment, coastal zone, marine resources, snow and glacier, disaster monitoring and mitigation, infrastructure development, etc. The paper reviews RS techniques and applications carried out using both optical and microwave sensors. It also analyses the gap areas and discusses the future perspectives

Developing Ocean Color Algorithm using Moderate Resolution Imaging Spectroradiometer (MODIS) Sensor for Shallow Coastal Water Bodies

This study analyses the spatial and temporal variability of chlorophyll-a in Chesapeake Bay; assesses the performance of Ocean Color 3M (OC3M) algorithm; and develops a novel algorithm to estimate chlorophyll-a for coastal shallow water. The OC3M algorithm yields an accurate estimate of chlorophyll-a concentration for deep ocean water (RMSE=0.016), but it failed to perform well in the coastal water system (RMSE=23.17) of Chesapeake Bay. A novel algorithm was developed which utilizes green and red bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The novel algorithm derived the chlorophyll-a concentration more accurately in Chesapeake Bay (RMSE=4.20) than the OC3M algorithm. The study indicated that the algorithm that uses red bands could improve the satellite estimation of chlorophyll-a in the coastal water system by reducing the noise associated with bottom reflectance and colored dissolved organic matter (CDOM

Quantitative Mapping of Cyanobacterial Blooms Using Oceansat-1 OCM Satellite Data

Cyanobacteria represent a major harmful algal group in fresh to brackish water environments. Lac des Allemands, a freshwater lake of 49 km2 southwest of New Orleans, Louisiana, provides a natural laboratory for remote characterization of cyanobacteria blooms because of their seasonal occurrence. This dissertation makes a contribution to research methodology pertaining to atmospheric correction of satellite data and development of remote sensing algorithms to quantify cyanobacterial pigments. The Ocean Color Monitor (OCM) sensor provides radiance measurements similar to Sea-viewing Wide Field-of-View Sensor (SeaWiFS) but with higher spatial resolution. However, OCM does not have a standard atmospheric correction procedure and the comprehensive suite of atmospheric correction procedures for ocean (or lake) is not available in the literature in one place. Atmospheric correction of satellite data over inland lakes, estuaries and coastal waters is also challenging due to difficulties in the estimation of aerosol scattering accurately over these optically complex water bodies. Thus an atmospheric correction procedure was developed to obtain more accurate spectral remote sensing reflectance (Rrs) over Lac des Allemands from OCM data based on NASA’s extensive work for SeaWiFS. Since OCM was not well calibrated, a new vicarious calibration procedure was also developed to adjust OCM radiance values to SeaWiFS radiance as SeaWiFS is well calibrated over its entire life. Empirical inversion algorithms were developed to convert the OCM Rrs at bands centered at 510.6 and 556.4 nm to concentrations of phycocyanin (PC), the primary cyanobacterial pigment. For the algorithms to be uniformly valid over all areas (or all bio-optical regimes) of the lake, a holistic approach was developed to minimize the influence of other optically active constituents on the PC algorithms. Similarly, empirical algorithms to estimate chlorophyll a (Chl a) concentrations were developed using OCM bands centered at 556.4 and 669 nm. The best PC algorithm (R2=0.7450, p\u3c0.0001, n=72) yielded a root mean square error (RMSE) of 36.92 µg/L with a relative RMSE of 10.27%, and a mean absolute error (MAE) of 21.79 µg/L with a relative MAE of 6.06% (PC from 2.75 to 363.50 µg/L, n=48). The best algorithm for Chl a (R2=0.7510, p\u3c0.0001) produced an RMSE of 31.19 µg/L, with relative RMSE = 15.70% and a MAE of 16.56 µg/L, with relative MAE = 8.33% (Chl a from 9.46 to 212.76 µg/L, n=48). The results demonstrate the preliminary success of using the 360 x 236 m resolution OCM data to map cyanobacterial blooms in a small lake. While more field data are required to further validate the long-term performance of the algorithms, at present the algorithms may be implemented to process OCM data in an automated setup to provide timely information on the lake’s bloom conditions. Similarly, retrospective processing may provide a long-term time series of bloom characteristics to document potential trends. The applicability of the algorithms can be extended to other lakes after necessary testing

Optical classification

Optical oceanography or Marine optics is the study of light propagation in the ocean surface through absorption or scattering processes. Marine bio-optics is the term used when the absorption and scattering by particles and dissolved substances are of biological origin. Ocean color is defined as the spectral variation of the water leaving radiance that can be related to the optical constituents present in the medium (Jerlov, 1976; Morel, 1974). Visible Spectral radiometry or Ocean colour remote sensing is the study on spectral signals of optically active materials using satellite observations. When sunlight reaches the upper water column or the photic zone of the ocean surface, the light propagation is determined by the optical properties of seawater

Atmospheric correction using 1240 and 2130 nm combination of MODIS SWIR channels

It is essential to improve understanding of coastal ocean since the majority of the world's primary production occurs on continental shelves and the coastal ocean is most utilized and impacted by humans. The first step in ocean-colour data processing is the removal of atmospheric contribution from the sensor-detected radiance to enable detection of optically active oceanic constituents e.g. chlorophyll-a, suspended sediment etc. Black ocean assumption at the near infrared (NIR) wavelengths as applied to perform atmospheric correction fails for coastal turbid waters due to the presence of highly scattering sediments which cause sufficient water-leaving radiance in NIR wavelengths and lead to over-estimation of aerosol radiance for λ <700nm resulting in negative water leaving radiance for λ <500nm. The assumption of zero water-leaving radiance at the NIR wavelengths was replaced by the assumption of zero water-leaving radiance at the short wave infrared (SWIR) wavelengths over the coastal turbid waters and atmospheric correction was performed using these SWIR wavelengths. Physically realistic and positive water leaving radiances throughout the spectrum and especially for shorter wavelengths (412nm, 443nm, 490nm) were obtained over coastal turbid waters using this concept

Use of satellite observations for operational oceanography: recent achievements and future prospects

The paper gives an overview of the development of satellite oceanography over the past five years focusing on the most relevant issues for operational oceanography. Satellites provide key essential variables to constrain ocean models and/or serve downstream applications. New and improved satellite data sets have been developed and have directly improved the quality of operational products. The status of the satellite constellation for the last five years was, however, not optimal. Review of future missions shows clear progress and new research and development missions with a potentially large impact for operational oceanography should be demonstrated. Improvement of data assimilation techniques and developing synergetic use of high resolution satellite observations are important future priorities

Book of Abstracts & Lead Articles The Second International Symposium Remote Sensing for Ecosystem Analysis and Fisheries

SAFARI (Societal Applications in Fisheries and Aquaculture using Remotely-Sensed Imagery) is an initiative which provides a forum for coordination, at the international level, of activities in global fisheries research and management. The forum is open to all interested parties, including policy makers, research scientists, government managers, and those involved in the fishing industries. SAFARI organizes international workshops and symposia as a platform to discuss the latest research in Earth observation and fisheries management, information sessions aimed at the fisheries industry, government officials and resource managers, representation at policy meetings, and producing publications relevant to the activities. SAFARI gains worldwide attention through collaboration with other international networks, such as ChloroGIN (Chlorophyll Global Integrated Network), IOCCG (International Ocean-Colour Coordinating Group), POGO (Partnership for Observation of the Global Oceans) and the oceans and society: Blue Planet Initiative of the intergovernmental organization, the Group on Earth Observations (GEO)

Observations on physico-chemical variability of seawater along Tamil Nadu coast, India onboard CRV Sagar Purvi

320-328Parameters that define the environmental variables, such as, phytoplankton biomass, chl-a, and nutrient concentration have been studied and analyzed during July 2017. During this study, vertical distribution of physico-chemical and water quality parameters has also been analyzed. Decrease in water temperature and dissolved oxygen from the surface to deep water up to the thermocline and oxycline were observed, which would be in relation to oxygen minimum zone. At the depth of 100 m, the concentration of chl-a is found high as compared to the surface water. The estimation of deep chlorophyll maximum has been chosen as a major investigation in this study. Moderate and high chl-a concentration (0.5-2.8 mg m-3) regardless of less NO3 flux (0.01-0.6 µM) have been recorded through in-situ and satellite observations. The concentration of SiO4 (5-35 µM) is likely enhanced in the vertical and surface water productivity. Principal component analysis and multiple linear regressions were carried out in order to determine the difference of the variables between the surface and deep water