Planning and Scheduling of Payloads of AstroSat During Initial and Normal Phase Observations

On 28th September 2015, India launched its first astronomical space observatory AstroSat, successfully. AstroSat carried five astronomy payloads, namely, (i) Cadmium Zinc Telluride Imager (CZTI), (ii) Large Area X-ray Proportional Counter (LAXPC), (iii) Soft X-ray Telescope (SXT), (iv) Ultra Violet Imaging Telescope (UVIT) and (v) Scanning Sky Monitor (SSM) and therefore, has the capability to observe celestial objects in multi-wavelength. Four of the payloads are co-aligned along the positive roll axis of the spacecraft and the remaining one is placed along the positive yaw axis direction. All the payloads are sensitive to bright objects and specifically, require avoiding bright Sun within a safe zone of their bore axes in orbit. Further, there are other operational constraints both from spacecraft side and payloads side which are to be strictly enforced during operations. Even on-orbit spacecraft manoeuvres are constrained to about two of the axes in order to avoid bright Sun within this safe zone and a special constrained manoeuvre is exercised during manoeuvres. The planning and scheduling of the payloads during the Performance Verification (PV) phase was carried out in semi-autonomous/manual mode and a complete automation is exercised for normal phase/Guaranteed Time Observation (GuTO) operations. The process is found to be labour intensive and several operational software tools, encompassing spacecraft sub-systems, on-orbit, domain and environmental constraints, were built-in and interacted with the scheduling tool for appropriate decision-making and science scheduling. The procedural details of the complex scheduling of a multi-wavelength astronomy space observatory and their working in PV phase and in normal/GuTO phases are presented in this paper.     

Chlorophyll — A Concentration Along West Coast of India Using IRS-P3 MOS-B Data

Remote sensing of ocean colour yields information on the constituents of sea water, such as the concentration of phytoplankton pigments, suspended sediments and yellow substances. It is well understood that the study of ocean colour is significantly related with the primary production and zonation of potential fishing sites in coastal and oceanic waters. The major pigment constituent is predominated by chlorophyll-a (ocean colour pigment of phytoplankton). The chlorophyll mapping on regular basis plays a major role in assessing water quality and classifying different water types. IRS P-3 MOS-B satellite data for three consecutive passes of path 94, during the period of January-February 1997 have been used to derive chlorophyll-a concentration. The present study emphasizes on the chlorophyll mapping using IRS-P3 MOS-B data for the coastal and offshore water of Maharashtra coast, India.     

Temporal Assessment of Growing Stock, Biomass and Carbon Stock of Indian Forests

The dynamics of terrestrial ecosystems depends on interactions between carbon, nutrient and hydrological cycles. Terrestrial ecosystems retain carbon in live biomass (aboveground and belowground), decomposing organic matter, and soil. Carbon is exchanged naturally between these systems and the atmosphere through photosynthesis, respiration, decomposition, and combustion. Human activities change carbon stock in these pools and exchanges between them and the atmosphere through land-use, land-use change, and forestry.In the present study we estimated the wood (stem) biomass, growing stock (GS) and carbon stock of Indian forests for 1984 and 1994. The forest area, wood biomass, GS, and carbon stock were 63.86 Mha, 4327.99 Mm³, 2398.19 Mt and 1085.06 Mt respectively in 1984 and with the reduction in forest area, 63.34 Mha, in 1994, wood biomass (2395.12 Mt) and carbon stock (1083.69 Mt) also reduced subsequently. The Conifers, of temperate region, stocked maximum carbon in their woods, 28.88 to 65.21 t C ha⁻¹, followed by Mangrove forests, 28.24 t C ha⁻¹, Dipterocarp forests, 28.00 t C ha⁻¹, and Shorea robusta forests, 24.07 t C ha⁻¹. Boswellia serrata, with 0.22 Mha forest area, stocked only 3.91 t C ha⁻¹. To have an idea of rate of carbon loss the negative changes (loss of forest area) in forest area occurred during 1984–1994 (10yrs) and 1991–1994 (4yrs) were also estimated. In India, land-use changes and fuelwood requirements are the main cause of negative change. Total 24.75 Mt C was lost during 1984–1994 and 21.35 Mt C during 1991–94 at a rate of 2.48 Mt C yr⁻¹ and 5.35 Mt C yr⁻¹ respectively. While in other parts of India negative change is due to multiple reasons like fuelwood, extraction of non-wood forest products (NWFPs), illicit felling etc., but in the northeastern region of the country shifting cultivation is the only reason for deforestation. Decrease in forest area due to shifting cultivation accounts for 23.0% of the total deforestation in India, with an annual loss of 0.93 Mt C yr⁻¹.     

Use of SARAL/AltiKa Observations for Modeling River Flow

In the absence of many gauging stations in the major and mighty river systems, there is a need for satellite-based observations to estimate temporal variations in the river water storage and associated water management. In this study, SARAL/AltiKa application for setting up hydraulic model (HEC-RAS) and river flow simulations over Tapi River India has been discussed. Waveform data of 40 Hz from Ka band altimeter has been used for water levels retrieval in the Tapi river. SARAL/AltiKa retrieved water levels were converted to discharge in the upstream location (track-926) using the rating curve available for the nearby gauging site and using linear spatial interpolation technique. Steady state simulations were done for various flow conditions in the upstream. Validation of river flow model was done in the downstream location (track-367) by comparing simulated and altimeter retrieved water levels (RMSE 0.67 m). Validated model was used to develop rating curve between water levels and simulated discharge for the downstream location which enables to monitor discharge variations from satellite platform in the absence of in situ observations. It has been demonstrated that SARAL/AltiKa data has potential for river flow monitoring and modeling which will feed for flood disaster forecasting, management and planning.     

Effect of Interplanetary Turbulences Causing High/low Amplitude Anisotropic Wave Trains in Cr Intensity

The high/low-amplitude anisotropic wave train events (HAE/LAE) in CR intensity have been investigated during the period 1981–1990, using the neutron monitoring data for different latitudes. In all, 21 HAE and 15 LAE cases have been studied. It has been observed that the phase of the diurnal anisotropy remains in the same co-rotational direction for the majority of the HAE/LAE cases. However, it has also been observed that the phase of the diurnal anisotropy changes to later hours in some of the HAE cases, whereas it changes to early hours in some of the LAE cases. Further, the amplitude of the semi-diurnal anisotropy for HAE/LAE cases remains statistically the same; however, the phase of the semi-diurnal anisotropy for HAE has been found to shift to later hours for all events. Furthermore, the HAE occur dominantly during the declining phase of solar activity, whereas LAE occur dominantly during the minimum solar activity period. The geomagnetic activity index-A_p has been observed to remain low during the period of each HAE/LAE. The possible phenomenon to cause the enhanced/low-amplitude daily variation has been proposed to appear on the back side of the Sun.     

Structure Inclination Angles in the Convective Atmospheric Surface Layer

Two-point correlations of the fluctuating streamwise velocity are examined in the atmospheric surface layer over the salt flats of Utah’s western desert, and corresponding structure inclination angles are obtained for neutral, stable and unstable conditions. The neutral surface-layer results supplement evidence for the invariance of the inclination angle given in Marusic and Heuer (Phys Rev Lett 99:114504, 2007). In an extension of those results it is found that the inclination angle changes drastically under different stability conditions in the surface layer, varying systematically with the Monin–Obukhov stability parameter in the unstable regime. The variation is parametrized and subsequently can be used to improve existing near-wall models in the large-eddy simulation of the atmospheric surface layer.     

Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters - Initial Model Results

Ocean-colour remote sensing in optically shallow waters is influenced by contribution from the water column depth as well as by the substrate type. Therefore, it is required to include the contribution from the water column and substrate bottom type for bathymetry estimation. In this report we demonstrate the use of Artificial Neural Network (ANN) based approach to spectrally distinguish various benthic bottom types and estimate depth of substrate bottom simultaneously in optically shallow waters. We have used in-water radiative transfer simulation modeling to generate simulated top-of-the-water column reflectance the four major benthic bottom types viz. sea grass, coral sand, green algae and red algae using Hydrolight simulation model. The simulated remote sensing reflectance, for the four benthic bottom types having benthic bottom depth up to 30 m were generated for moderately clear waters. A multi-layer perceptron (MLP) type neural network was trained using the simulated data. ANN based approach was used for classification of the benthic bottom type and simultaneous inversion of bathymetry. Simulated data was inverted to yield benthic bottom type classification with an accuracy of ~98% for the four benthic substrate types and the substrate depth were estimated with an error of 0% for sea grass, 1% for coral sand and 1–3% for green and red algae up to 25 m, whereas for substrate bottom deeper than 25 m depth the classification errors increased by 2–5% for three substrate bottom types except sea grass bottom type. The initial results are promising which needs validation using the in-situ measured remote sensing reflectance spectra for implementing further on satellite data.     

Vulnerability and adaptation to climate variability and water stress in Uttarakhand State, India

This paper presents a participatory approach to investigate vulnerability and adaptive capacity to climate variability and water stress in the Lakhwar watershed in Uttarakhand State, India. Highly water stressed microwatersheds were identified by modelling surface runoff, soil moisture development, lateral runoff, and groundwater recharge. The modelling results were shared with communities in two villages, and timeline exercises were carried out to allow them to trace past developments that have impacted their lives and livelihoods, and stimulate discussion about future changes and possible adaptation interventions.     

Applications of generalized additive model (GAM) to satellite-derived variables and fishery data for prediction of fishery resources distributions in the Arabian Sea

This study aims to illustrate how remotely sensed oceanic variables and fishing operations data can be used to predict suitable habitat of fishery resources in Geographic Information System. We used sea surface height anomaly (SSHa), sea surface temperature (SST), chlorophyll concentration (CC), photosynthetically active radiation (PAR) and fishing depth as predictor variables. Fishery data of Indian squid (Loligo spp.) and catfish (Tachysurus spp.) for study period (1998–2004) were segregated randomly to create training and validation. Catch was normalized into Catch per unit Effort (kg h^?1). Generalized additive modelling was performed on training data and then tested on validation data. Suitable ranges of SST, CC, SSHa and PAR for different species distributions were derived and integrated to predict their spatial distributions. Results indicated good match between predicted and actual catch. Monthly probability maps of predicted habitat areas coincide with high catch of the particular month for the study period.     

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.