The evolution of satellite measurement systems continues to improve their research and operational impact and is essential for advancing global observations of the Earth’s atmosphere, clouds, and surface. Measurement system and data product validation is required to fully exploit these data for enabling their intended enhancements in weather prediction, climate monitoring capability, and environmental change detection. Airborne field campaigns can play a vital role in such validation and contribute to assessing and improving satellite sensor measurements and associated data products. The NASA LaRC National Airborne Sounder Testbed – Interferometer (NAST-I) was part of the aircraft payload for the two field experiments conducted to address Suomi NPP (SNPP) validation since the satellite’s launch in late 2011: 1) mid-latitude flights based out of Palmdale, CA during May 2013 (SNPP-1), and 2) flights over Greenland during March 2015 while based out of Keflavik, Iceland (SNPP-2). This presentation focuses on radiance analysis from the SNPP airborne field campaigns with a particular emphasis on NAST-I inter-comparisons with the Cross-track Infrared Sounder (CrIS) for challenging cold scene conditions as observed during SNPP-2.

This paper describes the inter-calibration approach and key results of Kalpana-1geostationary satellite. The normalized calibration technique is attempted in order to re-calibrate the Kalpana-1 Infrared ( 10.8 μm) channel and remove the effect of the temporal non-linearity of sensor response due to degradation of the sensor based on ISCCP (International satellite cloud climatology project) procedure over the Indian Ocean. The study region of the inter-calibration is ± 10° NS latitude of the equator; 64°-84° longitude east. INSAT-3D Infrared ( 10.8 μm) and Mid-infrared ( 3.8 μm) channel data were also used for inter-satellite calibration. The primary data used for Kalpana-1 and INSAT-3D inter-calibration is VHRR (Very High Resolution Radiometer) Infrared brightness temperatures and NOAA-19 AVHRR channel-2 and channel-4 brightness temperatures from May to December 2009 and February 2015. The reprocessing of Kalpana-1 products using NOAA coefficients have also applied to the old Kalpana-1 data. The results show the Kalpana-1 derived products are comparable to NOAA data. The major objective of this work is the generation of a Fundamental Climate Data Record ( FCDR) of calibrated and quality controlled INSAT geostationary sensor data for weather and climate prediction.

Lunar measurements are part of the calibration strategy for the instruments in Earth Observing System and satellites. The purpose of using moon as an absolute radiometric standard for calibration it is used solely as a diffuse reflector whose surface remains unchanged. INSAT-3D is India’s meteorological geostationary satellite an exclusive next-generation mission designed for enhanced meteorological observations having 6 channel imager and 18 channel sounder. INSAT-3D Spacecraft was dedicated to Nation at National Satellite Meteorological Center (NSMC) indigenously designed developed INSAT-3D Meteorological Data Processing System (IMDPS), commissioned at India Meteorological Department (IMD) New Delhi on January 15, 2014. The Moon is being observed from INSAT-3D regularly in the of full-disk operational image of earth with rectangular field of regard in IMDPS New Delhi. INSAT-3D measurements of lunar surface observed in Visible (0.55 - 0.75μm), Short Wave Infrared 1.55 - 1.70μm, Mid Wave Infrared (3.80 - 4.00μm), Water Vapor (WV) 6.50 - 7.10μm, Thermal Infrared (TIR) 1 & 2, 10.3 - 11.3μm & 11.5 - 12.5μm wavelength regions. The visible and infrared wavelengths region provide a new and intriguing methodology of distinguish in sensitivities of Earth observing radiometers.

Satellite ultraspectral infrared sensors provide key data records essential for weather forecasting and climate change science. The Suomi National Polar-orbiting Partnership (Soumi NPP) satellite Environmental Data Records (EDRs) are retrieved from calibrated ultraspectral radiance or Sensor Data Records (SDRs). Understanding the accuracy of retrieved EDRs is critical. The second Suomi NPP Calibration/Validation field campaign was conducted during March 2015 with flights over Greenland. The NASA high-altitude ER-2 aircraft carrying ultraspectral interferometer sounders such as the National Airborne Sounder Testbed-Interferometer (NAST-I) flew under the Suomi NPP satellite that carries the Crosstrack Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS). Herein we inter-compare the EDRs produced from different retrieval algorithms employed on these satellite and aircraft campaign data. The available radiosonde measurements together with the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses are used to assess atmospheric temperature and moisture retrievals from the aircraft and satellite platforms. Preliminary results of this experiment under a winter, Arctic environment are presented.

Aerosols are short-lived with a residual time of about a week in the lower atmosphere and are concentrated around the source of origin. Aerosols are produced by variety of natural processes as well as by anthropogenic activities; it gets distributed in the atmosphere through turbulent mixing as well as transported away from the source of origin and thus results in its large seasonal and spatial variability. In this study, the CIMEL sun-photometer measurements at Kavaratti calibration and validation site are used to characterize the aerosols’ nature at the measurement site. Also, these in-situ measurements are used to validate the satellite sensor derived aerosol optical depth (AOD) parameter. The data analysis shows that the locally generated aerosols are mostly of marine aerosols and other natural aerosols are transported desert dust. The anthropogenic aerosols are transported from mainland and they are found during the pre-monsoon season. Also aerosol measurements for five years (2009 – 2015) are being planned for validating the satellite sensors derived AOD products namely: OceanSat2-OCM2, MODIS-Terra and MODIS-Aqua.

High-resolution observations of downwelling spectral irradiance in the wavelength band of 350-2500 nm carried out using Spectroradiometer at Thiruvananthapuram (8.5°N, 77°E), a coastal station located in the southwest Indian Peninsula adjoining the Arabian Sea, have been used to investigate the instantaneous spectral aerosol direct radiative forcing efficiency (ISADRFE) and its variation with solar zenith angle. This study shows that, during the pre-monsoon season (March-May), magnitude of the ISADRFE maximizes at the wavelength of ~460 nm around noon, with mean values ranging between -270 to -330 Wm^-2μm^-1τ500 ^-1 at 15°<SZA<50°. The wavelength at which the ISADRFE peaks as well as the spectral width of the peak increases with solar zenith angle (SZA), especially at SZA>70°. About 47% of the to the total (broad band) instantaneous shortwave aerosol radiative forcing is contributed by the photosynthetically active radiation at solar zenith angle of 20°, while the corresponding percentage contribution decreases to ~40% at SZA of 75°. At solar zenith angle of 20°, ~8% of IADRFE is contributed by the wavelength bands above 1.5 μm, while at SZA of 75°, the corresponding contribution is ~17%.

Atmospheric aerosols have a large effect on the Earth radiation budget through its direct and indirect effects. A systematic assessment of aerosol effects on Earth’s climate requires global mapping of tropospheric aerosols through satellite remote sensing. However aerosol retrieval over land surface remains a challenging task due to bright background of the land surfaces. Polarized measurements can provide an improved aerosol sensing by providing a means of decoupling the surface and atmospheric contribution. The Indian Space Research Organisation has planned a Multi- Angle Dual-Polarization Instrument (MADPI) onboard a Nano satellite for Earth Monitoring & Observations for Aerosol Monitoring (NEMO-AM). MADPI has three spectral bands in blue, red and near infrared spectral regions with a nominal spatial resolution of 30 m from an altitude of 500 km polar orbit. A study has been taken up with the aim of development of an algorithm for retrieving aerosol optical thickness (AOT) over land surfaces from NEMO-AM polarized measurements. The study has three major components: (1) detailed theoretical modelling exercise for computing the atmospheric and surface polarized contributions, (2) modelling of total satellite-level polarized contribution, and (3) retrieval of aerosol optical thickness (AOT) by comparing the modelled and measured polarized signals. The algorithm has been developed for MADPI/NEMO-AM spectral bands and tested successfully on similar spectral bands of POLDER/PARASOL measurements to retrieve AOT over Indian landmass having diverse atmospheric conditions. POLDER-derived AOT fields were compared with MODIS-AOT products. Results showed a very good match (R²~0.69, RMSE~0.07). Initial results have provided encouraging results, however, comprehensive analysis and testing has to be carried out for establishing the proposed algorithm for retrieving AOT from NEMO-AM measurements.

The Arctic Ocean is a very important region of the globe in which the effect of climate change can be detected over short time periods. We have used the possibility provided by the three infrared sounders TANSO-FTS on the GOSAT platform, IASI-A, and IASI-B on the MetOp platforms to retrieve the sea surface temperature (Tsurf) and the column averaged mixing ratio of several trace gases (CO₂, CH₄, N₂O, O₃) for pairs of nearly coinciding footprints (IFOVs) at small time separations (typically for IASI ~46 min and ~54 min depending on which satellite has first observed the corresponding scene). A strict filtering based on the AVHRR cloud fraction and the radiance analysis within the GOSAT and IASI footprints lead to a large number of quasi-coinciding IFOVs for which a 1D-var inversion (Tsurf and XCO₂ as the main parameters in the state vector, plus scaling factors for the profiles of H₂O and O₃) has been performed. As an example, we used during retrieval the atmospheric window between 940 and 980 cm^-1 (CO₂ laser band) for which the sensitivity to the surface is maximum. The statistics of the comparison between IASI-A and IASI-B retrievals is presented and compared to the corresponding Eumetsat L2 products. The months of July and August for the years 2010 and 2013 have been considered since in these Arctic summer conditions the ice pack coverage is reduced. The differences between these two consecutive years is discussed and a comparison with 2010 (for which only IASI-A was in orbit) is confirming that IASI can indeed be used for climate change studies.

Hyperspectral technologies are being increasingly employed in precision agriculture. By separating the surface and subsurface components of foliar hyperspectral signatures using polarization optics, it is possible to enhance the remote discrimination of different plant species and optimize the assessment of different factors associated with the crops’ health status such as chlorophyll levels and water content. These initiatives, in turn, can lead to higher crop yield and lower environmental impact through a more effective use of freshwater supplies and fertilizers (reducing the risk of nitrogen leaching). It is important to consider, however, that the main varieties of crops, represented by C₃ (e.g., soy) and C₄ (e.g., maize) plants, have markedly distinct morphological characteristics. Accordingly, the influence of these characteristics on their interactions with impinging light may affect the selection of optimal probe wavelengths for specific applications making use of combined hyperspectral and polarization measurements. In this work, we compare the sensitivity of the surface and subsurface reflectance responses of C₃ and C₄ plants to different spectral and geometrical light incidence conditions. In our comparisons, we also consider intra- species variability with respect to specimen characterization data. This investigation is supported by measured biophysical data and predictive light transport simulations. The results of our comparisons indicate that the surface and subsurface reflectance responses of C₃ and C₄ plants depict well-defined patterns of sensitivity to varying illumination conditions. We believe that these patterns should be considered in the design of new high-fidelity crop discrimination and monitoring procedures.

A large gap exists between the potential yield and the yield realized at the agricultural field. Among the factors contributing towards this yield gap are the biotic stresses that affect the crops growth and development. Severity of infestation of the pests and diseases differs between agroclimatic region, individual crops and seasons within a region. Information about the timing of start of infestation of these diseases and pests with their gradual progress in advance could enable plan necessary pesticide schedule for the season, region on the particular crop against the specific menace expected. This could be enabled by development of region, crop and pest-specific prediction models to forewarn these menaces. In India most (70%) of the land-holding size of farmers average 0.39 ha (some even 20 m x 20 m) and only 1% crop growers hold< 10 ha (mean: 17.3 ha). Patchiness of disease and pest incidence could pose problems in its proper assessment and management. Thus, such exercise could be highly time-consuming and labour-intensive for the seventh largest country with difficult terrain, 66% gross cropped area under food crops, lacking in number of skilled manpower and shrinking resources. Remote sensing overcomes such limitations with ability to access all parts of the country and can often achieve a high spatial, temporal and spectral resolution and thus leading to an accurate estimation of area affected. Due to pest and disease stress plants showed different behavior in terms of physiological and morphological changes lead to symptoms such as wilting, curling of leaf, stunned growth, reduction in leaf area due to severe defoliation or chlorosis or necrosis of photosynthetically active parts (Prabhakar et al., 2011; Booteet al., 1983; Aggarwal et al., 2006). Damage evaluation of diseases has been largely done by visual inspections and quantification but visual quantification of plant pest and diseases with accuracy and precision is a tough task. Utilization of remote sensing techniques are based on the assumption that plant pest and disease stresses interfere with physical structure and function of plant and influence the absorption of light energy and therefore changes the reflectance spectrum of plants. Moreover, remote sensing provides better means to objectively quantify crop stress than visual methods and it can be used repeatedly to collect sample measurements non-destructively and non-invasively (Nutteret et al., 1990; Nilson, 1995). Recent advances in the field of spectroscopy and other remote sensing techniques offer much needed technology of hyperspectral remote sensing (Prabhakar et al., 2011). Hyperspectral remote sensing for disease detection helps in monitoring the diseases in plants with the help of different plant spectral properties at the visible, near infrared and shortwave infrared regions ranging from 350 – 2500 nm, which develops specific signatures for a specific stress for a given plant (Yang et al., 2009). It has been effectively used in assessment of disease in agricultural crops like wheat, rice, tomato etc across the world. Cotton (Gissypium hirsutum L.) is one of the major commercial crops grown in India, and supports about 60 million people in the country directly or indirectly through the process of production, processing, marketing and trade (Prabhakar et al., 2011). India ranks first in global acreage, occupying about 33% of world cotton area. With regard to production it is ranked second next to China. In recent years, farmers are facing many challenges because of rising incidents of white flies, jassid, leafhoppers, aphids, mealybugs and stainers. Whiteflies are tiny, sap- sucking insects that may become abundant in vegetable and ornamental plantings, especially during warm weather. They excrete sticky honeydew and cause yellowing or death of leaves. Outbreaks often occur when the natural biological control is disrupted. Management is difficult once populations are high. White flies develop rapidly in warm weather, and populations can build up quickly in situations where natural enemies are ineffective and when weather and host plants favor outbreaks. Large colonies often develop on the undersides of leaves. The most common pest species such as greenhouse white fly (Trialeurodes vaporariorum) and sweet potato white fly (Bemisia tabaci) have a wide host range that includes many weeds and crops. White flies normally lay their tiny oblong eggs on the undersides of leaves. The eggs hatch, and the young white flies gradually increase in size through four nymphal stages called instars. The first nymphal stage (crawler) is barely visible even with a hand lens. The crawlers move around for several hours before settling to begin feeding. Later nymphal stages are immobile, oval, and flattened, with greatly reduced legs and antennae, like small scale insects. The winged adult emerges from the last nymphal stage (sometimes called a pupa, although whiteflies don’t have a true complete metamorphosis). All stages feed by sucking plant juices from leaves and excreting excess liquid as drops of honeydew as they feed. White flies use their piercing, needle like mouthparts to suck sap from phloem, the food-conducting tissues in plant stems and leaves. Large populations can cause leaves to turn yellow, appear dry, or fall off plants. Like aphids, white flies excrete sugary liquid called honeydew, so leaves may be sticky or covered with black sooty mold that grows on honeydew. The honeydew attracts ants, which interfere with the activities of natural enemies that may control white flies and other pests. High white fly infestation was reported at several locations in Punjab during year 2015. The application of non-destructive methods to detect vegetation stress at an early stage of its development is very important for pest management in commercially important crops. Earlier few studies have been done to characterize reflectance spectra of nutrient stress nitrogen deficiency and irrigation management for cotton but no literature is available regarding characterization of spectral reflectance to study white fly infestation. Therefore, the primary objectives of this study are: (i) to study changes in chlorophyll content and water content due to white fly infestation. (ii) characterization of spectral signature from cotton crop infested by white fly, (iii) establishment of most sensitive wavebands to white fly infestation.

Crop acreage estimation is important for advanced planning and taking various policy decisions. The present study was carried out in Indore district using AWiFS sensor satellite data from sowing to maturity period as well as single date LISS-III sensor satellite data of maximum vegetation growth stage of wheat crop. The technique used for single date LISS-III data classification is complete enumeration approach based on supervised classification. While Multi-date AWiFS data classification technique is based on two-stage classification of multi-date dataset by unsupervised Iterative Self Organizing Data Analysis Technique (ISODATA). The acreage estimated using the LISS- III sensor data is 98.41 000’ha while using AWiFS sensor data is 105.70 000’ha. It was found that LISS – III results shows -6.89 percent underestimation as compared to AWiFS estimates. The comparison of both (LISS-III and AWiFS) sensor’s acreage estimates with the actual acreage data (viz. 97.20 000’ha) shows that higher spatial resolution (LISS-III) sensor satellite data have more accuracy than low spatial resolution (AWiFS) sensor.

Banana is one of the major crops of India with increasing export potential. It is important to estimate the production and acreage of the crop. Thus, the present study was carried out to evolve a suitable methodology for estimating banana acreage. Area estimation methodology was devised around the fact that unlike other crops, the time of plantation of banana is different for different farmers as per their local practices or conditions. Thus in order to capture the peak signatures, biowindow of 6 months was considered, its NDVI pattern studied and the optimum two months were considered when banana could be distinguished from other competing crops. The final area of banana for the particular growing cycle was computed by integrating the areas of these two months using LISS III data with spatial resolution of 23m. Estimated banana acreage in the three districts were 11857Ha, 15202ha and 11373Ha for Bharuch, Anand and Vadodara respectively with corresponding accuracy of 91.8%, 90% and 88.16%. Study further compared the use of LISS IV data of 5.8m spatial resolution for estimation of banana using object based as well as per-pixel classification and the results were compared with statistical reports for both the approaches. In the current paper we depict the various methodologies to accurately estimate the banana acreage.

Hyperspectral remote sensing can aid in discriminating crop residue owing to the ability of narrow bands to capture the unique absorption feature of soil and residue. The present study was carried out to find out the suitable narrow spectral bands and hyper-spectral indices for discriminating wheat residue (stubble and burnt). Ground spectra of wheat residue and the adjoining soil were collected using the ASD fieldspec™ spectroradiometer. The best spectral range was derived using the Stepwise Discriminating Analysis (SDA). ‘F’ statistics from one-way ANOVA was used to find out the best index for discriminating wheat residue from soil. EO1-Hyperion data over Anand-Borsad region of Gujarat state in India was acquired free of cost from USGS earth explorer website (http://eo1.usgs.gov/) to apply the field based result over the Hyperion scene. Spectral Angle Mapper (SAM) classification scheme was used to generate the wheat residue cover over the Hyperion scene. Among the hyperspectral indices evaluated for this study the Cellulose Absorption Index (CAI) was found to be the best and hence CAI was used to classify the Hyperion scene for discriminating crop residue in field and also the burnt wheat residue. Results indicated that the wave bands at 10 nm width in the SWIR spectral region specifically from 1500-1700nm and 1900 to 2300nm are most suitable for wheat residue discrimination. The SAM classification technique is suitable for classifying the wheat residues with an overall accuracy of around 80 % whereas classification based on CAI could be used successfully to identify both wheat stubble and the burnt residues. This study concluded that wheat residue can be mapped for a large area with an accuracy of 80% using the space borne hyperspectral data with.

Assimilation of a new observation dataset in an NWP system may affect the quality of an existing observation data set against the model background (short forecast), which in-turn influence the use of an existing observation in the NWP system. Effect of the use of one data set on the use of another data set can be quantified as positive, negative or neutral. Impact of the addition of new dataset is defined as positive if the number of assimilated observations of an existing type of observation increases, and bias and standard deviation decreases compared to the control (without the new dataset) experiment. Recently a new dataset, Megha Tropiques SAPHIR radiances, which provides atmospheric humidity information, is added in the Unified Model 4D-VAR assimilation system. In this paper we discuss the impact of SAPHIR on the assimilation of hyper-spectral radiances like AIRS, IASI and CrIS. Though SAPHIR is a Microwave instrument, its impact can be clearly seen in the use of hyper-spectral radiances in the 4D-VAR data assimilation systems in addition to other Microwave and InfraRed observation. SAPHIR assimilation decreased the standard deviation of the spectral channels of wave number from 650 -1600 cm^-1 in all the three hyperspectral radiances. Similar impact on the hyperspectral radiances can be seen due to the assimilation of other Microwave radiances like from AMSR2 and SSMIS Imager.

The availability of high resolution temperature and water vapor data is important for the study of mesoscale scale weather phenomena. As, Atmospheric Infrared Sounder (AIRS), Cross-Track Infrared Sounder (CrIS) and European Infrared Atmospheric Sounding Interferometer (IASI) provide high resolution atmospheric profiles by measuring radiations in many thousands of different channels. The AIRS, on the EOS-Aqua polar-orbiting satellite, was the first of a new generation of meteorological advanced sounders able to provide hyper- spectral data for operational and research use. The CrIS is a Fourier Transform Michelson interferometer instrument launched on board the Suomi National Polar- Orbiting Partnership (Suomi NPP) satellite on 28 October 2011. CrIS is a major step forward in the U.S. operational infrared (IR) sounding capability previously provided by the High-resolution Infrared Spectrometer (HIRS). The IASI is the most advanced instrument carried on the MetOp satellite on 19 October 2006. As a result, demonstration of the benefit of hyper-spectral data on Numerical Weather Prediction (NWP) has been a high priority. This work focuses on the assessment of the potential values of satellite hyper-spectral radiance data in the NGFS (National Centre for Medium Range Weather Forecasting-Global Forecast System). An Observing System Experiments (OSEs) has been conducted to examine the impact of hyper-spectral radiances and detail results are presented.

The hyperspectral radiances from Atmospheric InfraRed Sounder (AIRS), on board NASA-AQUA satellite, have been processed through the Observation Processing System (OPS) and assimilated in the Variational Assimilation (VAR) System of NCMRWF Unified Model (NCUM). Numerical experiments are conducted in order to study the impact of the AIRS radiance in the NCUM analysis and forecast system. NCMRWF receives AIRS radiance from EUMETCAST through MOSDAC. AIRS is a grating spectrometer having 2378 channels covering the thermal infrared spectrum between 3 and 15 μm. Out of 2378 channels, 324 channels are selected for assimilation according to the peaking of weighting function and meteorological importance. According to the surface type and day-night conditions, some of the channels are not assimilated in the VAR. Observation Simulation Experiments (OSEs) are conducted for a period of 15 days to see the impact of AIRS radiances in NCUM. Statistical parameters like bias and RMSE are calculated to see the real impact of AIRS radiances in the assimilation system. Assimilation of AIRS in the NCUM system reduced the bias and RMSE in the radiances from instruments onboard other satellites. The impact of AIRS is clearly seen in the hyperspectral radiances like IASI and CrIS and also in infrared (HIRS) and microwave (AMSU, ATMS, etc.) sensors.

Accuracy of global NWP depends more on the contribution of satellite data than the surface based observations. This is achieved through the better usage of satellite data within the data assimilation system. Efforts are going on at NCMRWF to add more and more satellite data in the assimilation system both from Indian and international satellites in geostationary and polar orbits. Impact of the new dataset is assessed through Observation System Experiments (OSEs), through which the impact of the data is evaluated comparing the forecast output with that of a control run. This paper discusses one such OSEs with Infrared Atmospheric Sounder Interferometer (IASI) onboard MetOp-A and B. IASI is the main payload instrument for the purpose of supporting NWP. IASI provides information on the vertical structure of the atmospheric temperature and humidity with an accuracy of 1K and a vertical resolution of 1 km, which is necessary to improve NWP. IASI measures the radiance emitted from the Earth in 8641 channels, covering the spectral interval 645-2760 cm^-1. The high volume data resulting from IASI presents many challenges, particularly in the area of assimilation. Out of these 8641 channels, 314 channels are selected depending on the relevance of information in each channel to assimilate in the NCMRWF 4D-VAR assimilation system. Studies show that the use of IASI data in NWP accounts for 40% of the impact of all satellite observations in the NWP forecasts, especially microwave and hyperspectral infrared sounding techniques are found to give the largest impacts

This study demonstrates the advantage of the assimilation of Cross-track Infrared Sounder (CrIS) radiances of the Suomi-NPP satellite observation using 4D-Var assimilation system with global NCMRWF Unified Model (NCUM). The observation pre-processing system, quality control and thinning strategy for CrIS observations in addition to the impact of this observation in the analysis also discussed. Observation bias statistics are computed against the NCUM model fields from a short-range forecast (background) for quality control. The impact on forecasts is evaluated using “Observing System Simulation Experiments (OSSE's)”. The combined effect of hyperspectral and microwave radicalizes. The results show that CrIS data reduces the total number of observations and increases the RMS values for hyperspectral radiances.

Forest biomass acts as a backbone in regulating the climate by storing carbon within itself. Thus the assessment of forest biomass is crucial in understanding the dynamics of the environment. Traditionally the destructive methods were adopted for the assessment of biomass which were further advanced to the non-destructive methods. The allometric equations developed by destructive methods were further used in non-destructive methods for the assessment, but they were mostly applied for woody/commercial timber species. However now days Remote Sensing data are primarily used for the biomass geospatial pattern assessment. The Optical Remote Sensing data (Landsat8, LISS III, etc.) are being used very successfully for the estimation of above ground biomass (AGB). However optical data is not suitable for all atmospheric/environmental conditions, because it can’t penetrate through clouds and haze. Thus Radar data is one of the alternate possible ways to acquire data in all-weather conditions irrespective of weather and light. The paper examines the potential of ALOS PALSAR L-band dual polarisation data for the estimation of AGB in the Corbett Tiger Reserve (CTR) covering an area of 889 km2. The main focus of this study is to explore the accuracy of Polarimetric Scattering Model (Extended Water Cloud Model (EWCM) with respect to Backscatter model in the assessment of AGB. The parameters of the EWCM were estimated using the decomposition components (Raney Decomposition) and the plot level information. The above ground biomass in the CTR ranges from 9.6 t/ha to 322.6 t/ha.

Solar Induced chlorophyll Fluorescence (SIF) is a direct measure of photosynthesis rate as it is emitted by the photosynthetic system. This paper reports a method to model SIF over India by assimilating spatial inputs (LAI, Chlorophyll content etc.) into FluorMOD leaf and canopy model. Modelled SIF was then compared to Orbiting Carbon Observatory (OCO-2) SIF observations from September 2014 to August 2015. Modelled SIF at 757 nm averaged over the country varied between 0.18 to 0.33 W m^-2 sr^-1 μm^-1 whereas SIF at 771 nm varied between 0.10 to 0.19 W m^-2 sr^-1 μm^-1. OCO-2 observed SIF at 757 nm averaged over the country ranged from 0.18 to 0.42 Wm^-2sr^-1μm^-1. Fairly good agreement (r=0.77, p<0.01 at 757nm; r=0.71, p<0.05 at 771 nm) was observed between modelled and observed SIF except for summer months of April and May. This paper presents a new approach to upscale a leaf and canopy level model to estimate SIF over entire country.

Present Study is being taken to retrieve Leaf Area Indexn(LAI) in Himalayan forest system using vegetation indices developed from Hyperion EO-1 hyperspectral data. Hemispherical photograph were captured in the month of March and April, 2012 at 40 locations, covering moist tropical Sal forest, subtropical Bauhinia and pine forest and temperate Oak forest and analysed using an open source GLA software. LAI in the study region was ranging in between 0.076 m²/m² to 6.00 m²/m². These LAI values were used to develop spectral models with the FLAASH corrected Hyperion measurements.Normalized difference vegetation index (NDVI) was used taking spectral reflectance values of all the possible combinations of 170 atmospherically corrected channels. The R² was ranging from lowest 0.0 to highest 0.837 for the band combinations of spectral region 640 nm and 670 nm. The spectral model obtained was, spectral reflectance (y) = 0.02x LAI(x) - 0.0407.

Multispectral remote sensing (MRS) sensors have proved their potential in acquiring and retrieving information of Land Use Land (LULC) Cover features in the past few decades. These MRS sensor generally acquire data within limited broad spectral bands i.e. ranging from 3 to 10 number of bands. The limited number of bands and broad spectral bandwidth in MRS sensors becomes a limitation in detailed LULC studies as it is not capable of distinguishing spectrally similar LULC features. On the counterpart, fascinating detailed information available in hyperspectral (HRS) data is spectrally over determined and able to distinguish spectrally similar material of the earth surface. But presently the availability of HRS sensors is limited. This is because of the requirement of sensitive detectors and large storage capability, which makes the acquisition and processing cumbersome and exorbitant. So, there arises a need to utilize the available MRS data for detailed LULC studies. Spectral reconstruction approach is one of the technique used for simulating hyperspectral data from available multispectral data. In the present study, spectral reconstruction approach is utilized for the simulation of hyperspectral data using EO-1 ALI multispectral data. The technique is implemented using python programming language which is open source in nature and possess support for advanced imaging processing libraries and utilities. Over all 70 bands have been simulated and validated using visual interpretation, statistical and classification approach.

The rapid advancements in technology have facilitated easy availability of multisensor and multiresolution remote sensing data. Multisensor, multiresolution data contain complementary information and fusion of such data may result in application dependent significant information which may otherwise remain trapped within. The present work aims at improving classification by fusing features of coarse resolution hyperspectral (1 m) LWIR and fine resolution (20 cm) RGB data. The classification map comprises of eight classes. The class names are Road, Trees, Red Roof, Grey Roof, Concrete Roof, Vegetation, bare Soil and Unclassified. The processing methodology for hyperspectral LWIR data comprises of dimensionality reduction, resampling of data by interpolation technique for registering the two images at same spatial resolution, extraction of the spatial features to improve classification accuracy. In the case of fine resolution RGB data, the vegetation index is computed for classifying the vegetation class and the morphological building index is calculated for buildings. In order to extract the textural features, occurrence and co-occurence statistics is considered and the features will be extracted from all the three bands of RGB data. After extracting the features, Support Vector Machine (SVMs) has been used for training and classification. To increase the classification accuracy, post processing steps like removal of any spurious noise such as salt and pepper noise is done which is followed by filtering process by majority voting within the objects for better object classification.

High-spatial resolution Remote Sensing (RS) data provides detailed information which ensures high-definition visual image analysis of earth surface features. These data sets also support improved information extraction capabilities at a fine scale. In order to improve the spatial resolution of coarser resolution RS data, the Super Resolution Reconstruction (SRR) technique has become widely acknowledged which focused on multi-angular image sequences. In this study multi-angle CHRIS/PROBA data of Kutch area is used for SR image reconstruction to enhance the spatial resolution from 18 m to 6m in the hope to obtain a better land cover classification. Various SR approaches like Projection onto Convex Sets (POCS), Robust, Iterative Back Projection (IBP), Non-Uniform Interpolation and Structure-Adaptive Normalized Convolution (SANC) chosen for this study. Subjective assessment through visual interpretation shows substantial improvement in land cover details. Quantitative measures including peak signal to noise ratio and structural similarity are used for the evaluation of the image quality. It was observed that SANC SR technique using Vandewalle algorithm for the low resolution image registration outperformed the other techniques. After that SVM based classifier is used for the classification of SRR and data resampled to 6m spatial resolution using bi-cubic interpolation. A comparative analysis is carried out between classified data of bicubic interpolated and SR derived images of CHRIS/PROBA and SR derived classified data have shown a significant improvement of 10-12% in the overall accuracy. The results demonstrated that SR methods is able to improve spatial detail of multi-angle images as well as the classification accuracy.

Remote sensing, in recent days, has seen the emergence of hyperspectral sensors as the helping aspect to numerous applications. Hyperspectral remote sensing often contains data with narrow spectral bandwidth (10nm) that enables the feature identification and distinction of spectral similar features. Hyperion L1R data consists of 242 bands and out of which some bands contain noise (a pattern reorganization that hinders information). After removal of these sensor errors like bad bands and dropped line error, atmospheric correction using Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) is carried out to get true ground reflectance. It was observed in the spectral reflectance curve of the obtained product is still affected with several noise and in order to remove this noise some corrections are required before atmospheric correction. In this study various noise reduction techniques like Minimum Noise Fraction (MNF), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are implemented to observe their effect on sensor error removed Hyperion data before carrying out atmospheric correction. At first noise reduction techniques (MNF, PCA and ICA) are applied separately to the Hyperion sensor error corrected product and then inverse of (MNF, PCA and ICA) are carried out respectively. It was observed that applying atmospheric correction after applying MNF & inverse MNF was giving better spectral profiles of the features (i.e. with less noise). From the above spectral profiles we can conclude that the data which is atmospherically corrected after applying MNF & inverse MNF is giving better results i.e. the spectral profiles of vegetation, water, urban and coal field is smooth and less noisy in comparison with the spectral profiles obtained by directly applying atmospheric correction and the spectral profiles obtained by applying PCA & inverse PCA and ICA &inverse ICA. After applying PCA & inverse PCA and then atmospheric correction the spectral profiles of the objects are smooth but not as good as those spectral profile obtained by applying MNF & inverse MNF. It was also observed that the nature of reflectance of the feature at the Short Wave Infrared (SWIR) region has abruptly increased after applying ICA & inverse ICA and atmospheric correction. So the atmospherically corrected data obtained after applying ICA & inverse ICA cannot be used for further processing. The atmospherically corrected data obtained after applying MNF & inverse MNF can be used for further hyperspectral data processing like feature identification and classification as it gives the true spectral nature of the features.

In this study, we present an unsupervised change detection method using multi-spectral and multi-temporal remotely sensed imageries. This method is a pre-classification approach based on a spectral rule-based per-pixel classifier (SRC) developed by Baraldi et al. (2006). SRC is purely based on spectral-domain prior knowledge, such that no training or supervision process is needed. To explore its capability to detect change, we applied it in the Zhoushan Islands, Zhejiang, China. First, images were classified by SRC, and change detection was performed by two separate methods. One was the comparison of the merged categories obtained by reclassifying the pre-classification types of SRC. The other was comparing bi-temporal pre-classification types directly. The classification accuracy of the merged categories based on SRC was compared to the Maximum Likelihood Classifier and Support Vector Machine. The accuracy of the change detection was assessed and compared to results processed by the common post-classification comparison and change vector analysis methods. Results show that the change detection by directly comparing pre-classification types of SRC had the highest accuracy (overall accuracy was 90%, kappa coefficient was 0.81) among these methods and that the method of comparing merged categories was the worst (overall accuracy was 73%, kappa coefficient was only 0.46).

Most of the multispectral sensors acquire data in several broad wavelength bands and are capable of extracting different Land Cover features while hyperspectral sensors contain ample spectral data in narrow bandwidth (10- 20nm). The spectrally rich data enable the extraction of useful quantitative information from earth surface features. Endmembers are the pure spectral components extracted from the remote sensing datasets. Most approaches for Endmember extraction (EME) are manual and have been designed from a spectroscopic viewpoint, thus neglecting the spatial arrangement of the pixels. Therefore, EME techniques which can consider both spectral and spatial aspects are required to find more accurate Endmembers for Subpixel classification. Multispectral (EO-1 ALI and Landsat 8 OLI) and Hyperspectral (EO-1 Hyperion) datasets of Udaipur region, Rajasthan is used in this study. All the above mentioned datasets are preprocessed and converted to surface reflectance using Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH). Further Automated Endmember extraction and Subpixel classification is carried out using Multiple Endmember Spectral Mixture Analysis (MESMA). Endmembers are selected from spectral libraries to be given as input to MESMA. To optimize these spectral libraries three techniques are deployed i.e. Count based Endmember selection (CoB), Endmember Average RMSE (EAR) and Minimum Average Spectral Angle (MASA) for endmember selection. Further identified endmembers are used for classifying multispectral and hyperspectral data using MESMA and SAM. It was observed from the obtained classified results that diverse features, spread over a pixel, which are spectrally same are well classified by MESMA whereas SAM was unable to do so.

Remote Sensing possess of new technological trend that empowered with advanced hyperspectral sensors which combine imaging and spectroscopy in a single system. Interpretation of remotely sensed imagery involve basics of spectroscopy for identifying and mapping minerals, as different minerals have unique reflectance and absorption pattern across different wavelengths, which act as their identifying signatures. In this paper Hyperion data have been used for mapping of minerals in Aravalli fold belt of the South-Eastern Rajasthan. Rock samples collected from the study area were used to generate spectra using Spectroradiometer in the laboratory conditions. The spectra generated were validated with USGS spectral library. The imageries have undergone standard image processing techniques, such as atmospheric correction through Fast line of sight atmospheric absorption for hypercubes (FLAASH) model, Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and N-d visualization for endmember selection. Further, Spectral Angle Mapper technique (SAM) was used for mapping minerals that belong to carbonate, clay and silicate groups.

This study aimed at identifying and mapping hydrothermally altered minerals in parts of Delhi fold belt near Jaipur city, India, using EO-1 Hyperion data. The rock type found in this area is Quartzite of Ajabgarh Series of Delhi Super group. Space based Hyperion hyperspectral data results have been analyzed and compared with different laboratory analysis techniques like X-ray diffraction and spectroradiometer on the selected rock samples. Though, the SNR of the Hyperion image was low (18:1) but different hydrothermally altered and clay minerals like muscovite, kaolinite, kaolinitesmectite and montmorillonite have been identified. The data was in good confirmation with the laboratory and spectroscopic analysis. Surface mineralogy of this area was mapped with the help of Hyperion image and was found in good confirmation with the field observations. On the basis of the mineralogy of the area, it can be concluded that this zone is mainly subjected to the intermediate argillic alteration. During the field survey a fault has also been identified in the study area.

This paper focus on R&D being carried out at CSIR-NAL on Enhanced Synthetic Vision System (ESVS) for Indian regional transport aircraft to enhance all weather operational capabilities with safety and pilot Situation Awareness (SA) improvements. Flight simulator has been developed to study ESVS related technologies and to develop ESVS operational concepts for all weather approach and landing and to provide quantitative and qualitative information that could be used to develop criteria for all-weather approach and landing at regional airports in India. Enhanced Vision System (EVS) hardware prototype with long wave Infrared sensor and low light CMOS camera is used to carry out few field trials on ground vehicle at airport runway at different visibility conditions. Data acquisition and playback system has been developed to capture EVS sensor data (image) in time synch with test vehicle inertial navigation data during EVS field experiments and to playback the experimental data on ESVS flight simulator for ESVS research and concept studies. Efforts are on to conduct EVS flight experiments on CSIR-NAL research aircraft HANSA in Degraded Visual Environment (DVE).

Surface maturity estimation of the lunar regolith revealed selenological process behind the formation of lunar surface, which might be provided vital information regarding the geological evolution of earth, because lunar surface is being considered as 8-9 times older than as that of the earth. Spectral reflectances data from Moon mineralogy mapper (M3), the hyperspectral sensor of chandrayan-1 coupled with the standard weight percentages of FeO from lunar returned samples of Apollo and Luna landing sites, through data interpolation techniques to generate the weight percentage FeO map of the target lunar locations. With the interpolated data mineral maps were prepared and the results are analyzed.

Urban expansion, water bodies and climate change are inextricably linked with each other. The macro and micro level climate changes are leading to extreme precipitation events which have severe consequences on flooding in urban areas. Flood simulations shall be helpful in demarcation of flooded areas and effective flood planning and preparedness. The temporal availability of satellite rainfall data at varying spatial scale of 0.10 to 0.50 is helpful in near real time flood simulations. The present research aims at analysing stream flow and runoff to monitor flood condition using satellite rainfall data in a hydrologic model. The satellite rainfall data used in the research was NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), which is available at 30 minutes temporal resolution. Landsat data was used for mapping the water bodies in the study area. Land use land cover (LULC) data was prepared using Landsat 8 data with maximum likelihood technique that was provided as an input to the HEC-HMS hydrological model. The research was applied to one of the urbanized cities of India, viz. Dehradun, which is the capital of Uttarakhand State. The research helped in identifying the flood vulnerability at the basin level on the basis of the runoff and various socio economic parameters using multi criteria analysis.

Present study employs reef-up approach to map coral reef zones along the Sentinel Island of Andaman using high spectral resolution offered by hyper spectral imagery by Hyperion mission of NASA. This data consisting of 242 spectral bands, provide a unique ability to identify Coral substrate based on their spectral properties. We applied atmospheric correction with the help of Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) module of ENVI software. This atmospherically corrected was used to extract Coral Reef Zones (CRZ) based on specific threshold limits after subtracting data of 782.95nm band from 579.45nm band of Hyperion imagery. Both of these bands were chosen due to their property of exhibiting maximum spectral contrast that determines threshold limits to distinguish a coral area from its non-coral counterpart. These CRZs were compared with the coral reef zones base map developed using LISS-III data by INCOIS, Hyderabad and SAC, Ahmadabad under CZS project. We observed that extracted CRZ area was 85.25 m² and 110.1 m² using LISS-III and Hyperion Data respectively. Despite the overestimation of CRZ by Hyperion data as compared to LISS-III, the spatial distribution of CRZ showed reasonable similarity in both.

The Elorza crater is located in the Ophir Planum region of Mars with a 40-km diameter, centered near 550.25’ W, 80.72’ S. Since the Elorza crater has clays rich deposits it was one of the important crater to understand the past aqueous alteration processes of planet Mars.

The Earth’s airglow layer, when observed in the limb view mode, appears to be a double layer. LiVHySI onboard YOUTHSAT (inclination 98.730, apogee 817 km, launched by Indian Space Research Organization in April, 2011) is an Earth’s limb viewing camera measuring airglow emissions in the spectral window of 550-900 nm. Total altitude coverage is about 500 km with command selectable lowest altitude. During few of the orbits we have observed the double layer structure and obtained absolute spectral intensity and altitude profile for 630 nm airglow emission. Our night time observations of upper atmosphere above dip equator carried out on 3rd May, 2011 show a prominent 630 nm double layer structure. The upper airglow layer consists of the 630 nm atomic oxygen O(1D) emission line and lower layer consists of OH(9-3) meinel band emission at 630 nm. The volume emission rate as a function of altitude is simulated for our observational epoch and the modeled limb intensity distribution is compared with the observations. The observations are in good agreement with the simulated intensity distribution.

SW-MW Imaging Infrared Spectrometer, the Hyperspectral optical imaging instrument is envisaged to map geomorphology and mineralogy of lunar surface. The instrument is designed to image the electro-magnetic energy emanating from moon’s surface with high spectral and spatial resolution for the mission duration from an altitude of 100 km. It is designed to cover 0.8 to 5 μm in 250 spectral bands with GSD 80m and swath 20km. Primarily, there are three basic optical segments in the spectrometer. They are fore optics, dispersing element and focusing elements. The payload is designed around a custom developed multi-blaze convex grating optimized for system throughput. The considerations for optimization are lunar radiation, instrument background, optical throughput, and detector sensitivity. HgCdTe (cooled using a rotary stirling cooler) based detector array (500x256 elements, 30μm) is being custom developed for the spectrometer. Stray light background flux is minimized using a multi-band filter cooled to cryogenic temperature. Mechanical system realization is being performed considering requirements such as structural, opto-mechanical, thermal, and alignment. The entire EOM is planned to be maintained at ~240K to reduce and control instrument background. Al based mirror, grating, and EOM housing is being developed to maintain structural requirements along with opto- mechanical and thermal. Multi-tier radiative isolation and multi-stage radiative cooling approach is selected for maintaining the EOM temperature. EOM along with precision electronics packages are planned to be placed on the outer and inner side of Anti-sun side (ASS) deck. Power and Cooler drive electronics packages are planned to be placed on bottom side of ASS panel. Cooler drive electronics is being custom developed to maintain the detector temperature within 100mK during the imaging phase. Low noise detector electronics development is critical for maintaining the NETD requirements at different target temperatures. Subsequent segments of the paper bring out system design aspects and trade-off analyses.

The increased desire for visible light/infrared dual band imaging systems with compact structure, high resolution coverage and without significantly complicates the optical design is increasing. Such systems are particularly desirable for aerial imaging applications where increased levels of target reconnaissance, precision target location and designation are required in cost-effective and light weight systems. Therefore, in this paper, a high resolution Electro-optical/infrared (EO/IR) system to simultaneously operate in the visible band and short wave infrared band (SWIR) band using ZEMAX software are presented. It has a common aperture using Ritchey-Chrétien (R-C) system and the spectrum is separated using a dichroic beam splitter. The results of the optical design indicated that our proposed approach meets the technical performance requirements for high image performance and compact size. This system can be applied in aerial remote sensing applications.

Trace gas monitoring system inside the crew cabin of manned space shuttle requires high precision sensor instrumentation with quick response, smaller mass, compact and less power consumption. Trace gases like Carbon dioxide and carbon monoxide are the primary gases whose concentration required to be measure precisely and continuously inside the crew cabin to mitigate hazardous accidental events to occur. Conventional chemical reaction based or mass spectrometer based instruments for monitoring these gases are either bulky or relatively slower in response. Compared to that, Non Dispersive Infrared sensor (NDIR) technique is accurate, quick in response and compact. This paper covers design and realization of indigenously developed proto model Carbon Dioxide sensor based on NDIR technique. Developed sensor has resolution of better than 1ppm with measurement range from 0 to 5% Vol.

TIRCIS (Thermal Infra-Red Compact Imaging Spectrometer), uses a Fabry-Perot interferometer, an uncooled microbolometer array, and push-broom scanning to acquire hyperspectral image data. Radiometric calibration is provided by blackbody targets while spectral calibration is achieved using monochromatic light sources. The instrument has a mass of <10 kg and dimensions of 53 cm × 25 cm × 22 cm. The optical design yields a 120 m ground sample size given an orbit of 500 km. Over the wavelength interval of 7.5 to 14 microns up to 90 spectral samples are possible. Our performance model indicates signal-to-noise ratios of 400-800:1.

Spectroscopic analysis is carried out for lithological discrimination in a study area in the Pali and Ajmer districts of Rajasthan, western India using laboratory-based, field-based and space-borne data. We first explored the feasibility of the Landsat-8 (Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS)) and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) imagery for lithological mapping. Laboratory spectra of the samples of rocks exposed in the area were collected using FieldSpec3 spectroradiometer in the VNIR-SWIR region and resampled to the LANDSAT 8 and ASTER spectral bands. The spectral angle mapper (SAM) algorithm was used to map the lithologies using the resampled laboratory spectra as references. The resulting map was validated based on field geological mapping. Fourier Transform Infra-Red (FTIR) spectroscopy of selected rock samples was carried out to correlate spectral absorption features in the TIR-IR regions with the vibrational energy.

An accurate spatial mapping and characterization of land cover features in cryospheric regions is an essential procedure for many geoscientific studies. A novel semi-automated method was devised by coupling spectral index ratios (SIRs) and geographic object-based image analysis (OBIA) to extract cryospheric geospatial information from very high resolution WorldView 2 (WV-2) satellite imagery. The present study addresses development of multiple rule sets for OBIA-based classification of WV-2 imagery to accurately extract land cover features in the Larsemann Hills, east Antarctica. Multilevel segmentation process was applied to WV-2 image to generate different sizes of geographic image objects corresponding to various land cover features with respect to scale parameter. Several SIRs were applied to geographic objects at different segmentation levels to classify land mass, man-made features, snow/ice, and water bodies. We focus on water body class to identify water areas at the image level, considering their uneven appearance on landmass and ice. The results illustrated that synergetic usage of SIRs and OBIA can provide accurate means to identify land cover classes with an overall classification accuracy of ≈97%. In conclusion, our results suggest that OBIA is a powerful tool for carrying out automatic and semiautomatic analysis for most cryospheric remote-sensing applications, and the synergetic coupling with pixel-based SIRs is found to be a superior method for mining geospatial information.

Current research study emphasizes the importance of advanced digital image processing methods in order to delineate between various LULC features. In the case of the Antarctica, the present LC (snow/ice, landmass, water, vegetation etc.) and the present LU (research stations of various nations) needs to be mapped accurately for the hassle free routine activities. Geo-location has become the most important part of geosciences studies. In this paper we have tried to locate three most important features (snow/ice, landmass, and water) and also have extracted the extent of the same using the multisource classification (image fusion/pansharpening) and pattern recognition (supervised/unsupervised methods, index ratio methods). Innovation in developing spectral index ratios has led us to come up with an unique ratio named Normalized Difference Landmass Index (NDLI) which performed better (Avg. Bias: 51.99m) than other ratios such as Normalized Difference Snow/Ice Index (NDSII) (Avg. Bias: -1572.11m) and Normalized Difference Water Index (NDWI) (Avg. Bias: 1886.60m). The practiced trial and error methodology quantifies the productivity of not only the classification methods over one other but also that of the fusion methods. In present study, classifiers used (Mahalanobis and Winner Takes All) performed better (Avg. Bias: 122.16 m) than spectral index ratios (Avg. Bias: 620.16 m). The study also revealed that newly introduced bands in WorldView-2, band 1 (Coastal Blue), 4 (Yellow), 6 (Red-edge) and 8 (Near Infrared-2) along with traditional bands have the capacity to mine the polar geospatial information with utmost accuracy and efficiency.

High resolution satellite data provide high spatial, spectral and contextual information. Spatial and contextual information of image objects are in demand to extract the information from high resolution satellite data. The supraglacial environment includes several features that are present on the surface of the glacier. The extraction of features from supraglacial environment is quite challenging using pixel-based image analysis. To overcome this, objectoriented approach is implemented. This paper aims at the extraction of geo-information from the supraglacial environment from high resolution satellite image by object-oriented image analysis using the fuzzy logic approach. The object-oriented image analysis involves the multiresolution segmentation for the creation of objects followed by the classification of objects using the fuzzy logic approach. The multiresolution segmentation is executed on the pixel level initially which merges pixels for the creation of objects thus minimizing their heterogeneity. This is followed by the development of rule sets for the classification of various features such as blue ice, debris, snow from the supraglacial environment in WorldView-2 data. The area of extracted feature is compared with the reference data and misclassified area of each feature using various bands is determined. The present object oriented classification achieved an overall accuracy of ≈ 92% for classifying supraglacial features. Finally, it is suggested that Red band is quite effective in the extraction of blue ice and snow, while NIR1 band is effective in debris extraction.

Collecting and updating forest inventory data play an important part in the forest management. The data can be obtained directly by using exact enough but low efficient ground based methods as well as from the remote sensing measurements. We present applications of airborne hyperspectral remote sensing for the retrieval of such important inventory parameters as the forest species and age composition. The hyperspectral images of the test region were obtained from the airplane equipped by the produced in Russia light-weight airborne video-spectrometer of visible and near infrared spectral range and high resolution photo-camera on the same gyro-stabilized platform. The quality of the thematic processing depends on many factors such as the atmospheric conditions, characteristics of measuring instruments, corrections and preprocessing methods, etc. An important role plays the construction of the classifier together with methods of the reduction of the feature space. The performance of different spectral classification methods is analyzed for the problem of hyperspectral remote sensing of soil and vegetation. For the reduction of the feature space we used the earlier proposed stable feature selection method. The results of the classification of hyperspectral airborne images by using the Multiclass Support Vector Machine method with Gaussian kernel and the parametric Bayesian classifier based on the Gaussian mixture model and their comparative analysis are demonstrated.

Algorithms for target detection in hyperspectral data successfully detect full pixel targets; but, they fail to simultaneously detect part of target which may be lying partially in surrounding pixels. This requires development of algorithms which can simultaneously detect the sub pixel targets in surrounding pixels so that shape of the target can be recovered for identification. Super resolution mapping is one such method for target identification and enhancement. Aim of this paper is to perform a comparative assessment of various existing super resolution mapping techniques and to present a super resolution mapping technique which can preferably work on non – random allocation of sub-pixels and non recursive optimization.

Hyperspectral sensors offer narrow spectral bandwidth facilitating better discrimination of various ground materials. However, high spectral resolutions of these sensors result in larger data volumes, and thus pose computation challenges. The increased computational complexity limit the use of hyperspectral data, where applications demands moderate accuracies but economy of processing and execution time. Also the high dimensionality of the feature space adversely affect classification accuracies when the number of training samples is limited – a consequence of Hughes’ effect. A reduction in the number of dimensions lead to the Hughes effect, thus improving classification accuracies. Dimensionality reduction can be accomplished by: (i) feature selection, that is, selection of sub-optimal subset of the original set of features and (ii) feature extraction, that is, projection of the original feature space into a lower dimensional subspace that preserves most of Information. In this contribution, we propose a novel method of feature section by identifying and selecting the optimal bands based on spectral decorrelation using a local curve fitting technique. The technique is implemented on the Hyperion data of a study area from Western India. The results shows that the proposed algorithm is efficient and effective in preserving the useful original information for better classification with reduced data size and dimension.

Satellite images and raster data with valuable information are expensive and important. The security of these images is of concern in todays networked environment where data can be distributed and copied easily. Moreover, these data-sets are quite complex in nature and not many robust watermarking techniques have been proposed to deal with these issues. This paper is an attempt to develop a robust watermarking algorithm for Raster data-sets of Satellite Images. Though robustness is necessary to resist external attacks on satellite data, the presence of fragile watermarks in data should also be considered to detect tampering and even locate the area of attack. The proposed algorithm, Texture based LSB and DWT, is tested against attacks such as smoothing and contrast enhancement. The results and analysis are discussed for the same.

With the development of remote sensing technologies, it has become possible to obtain an overview of landscape elements which helps in studying the changes on earth’s surface due to climate, geological, geomorphological and human activities. Remote sensing measures the electromagnetic radiations from the earth’s surface and match the spectral similarity between the observed signature and the known standard signatures of the various targets. However, problem lies when image classification techniques assume pixels to be pure. In hyperspectral imagery, images have high spectral resolution but poor spatial resolution. Therefore, the spectra obtained is often contaminated due to the presence of mixed pixels and causes misclassification. To utilise this high spectral information, spatial resolution has to be enhanced. Many factors make the spatial resolution one of the most expensive and hardest to improve in imaging systems. To solve this problem, post-processing of hyperspectral images is done to retrieve more information from the already acquired images. The algorithm to enhance spatial resolution of the images by dividing them into sub-pixels is known as super-resolution and several researches have been done in this domain.In this paper, we propose a new method for super-resolution based on ant colony optimization and review the popular methods of sub-pixel mapping of hyperspectral images along with their comparative analysis.

Experimentations with applications of machine learning algorithms such as random forest (RF), support vector machines (SVM) and fuzzy inference system (FIS) to lithological classification of multispectral datasets are described. The input dataset such as LANDSAT-8 and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) in conjunction with Shuttle Radar Topography Mission (SRTM) digital elevation are used. The training data included image pixels with known lithoclasses as well as the laboratory spectra of field samples of the major lithoclasses. The study area is a part of Ajmer and Pali Districts, Western Rajasthan, India. The main lithoclasses exposed in the area are amphibolite, granite, calc-silicates, mica-schist, pegmatite and carbonates. In a parallel implementation, spectral parameters derived from the continuum-removed laboratory spectra of the field samples (e.g., band depth) were used in spectral matching algorithms to generate geological maps from the LANDSAT-8 and ASTER data. The classification results indicate that, as compared to the SVM, the RF algorithm provides higher accuracy for the minority class, while for the rest of the classes the two algorithms are comparable. The RF algorithm effectively deals with outliers and also ranks the input spectral bands based on their importance in classification. The FIS approach provides an efficient expert-driven system for lithological classification. It based on matching the image spectral features with the absorption features of the laboratory spectra of the field samples, and returns comparable results for some lithoclasses. The study also establishes spectral parameters of amphibolite, granite, calc-silicates, mica-schist, pegmatite and carbonates that can be used in generating geological maps from multispectral data using spectral matching algorithms.

Satellite based multispectral imagery contains various quantitative information related to surface and atmosphere. To extract the accurate information about surface, we need to correct atmospheric influence which is introduced by the atmosphere. Atmospheric correction of multispectral satellite imagery is an important prerequisite to derive geophysical parameters from satellite data. In this study surface reflectance is retrieved using the Scheme for Atmospheric Correction of Resourcesat-2 (SACRS2). The SACRS2 is physics based atmospheric correction scheme developed at Space Applications Centre (SAC), ISRO based on radiative transfer model 6SV (The Second Simulation of the Satellite Signal in the Solar Spectrum vector version). SACRS2 method is easily applicable for atmospheric correction of multispectral data. A detail analysis has been carried out to retrieve surface reflectance from Resourcesat-2 AWiFS, LISS-3 and LISS-4 data using SACRS2 method. The retrieved surface reflectance from SACRS2 for AWiFS, LISS-3 and LISS-4 have been compared with in-situ measurements. The comparison showed a good match of reflectance derived by SACRS2 scheme with the in-situ measurements.

Remote sensing images are representations of ground objects as interpreted from space sensors. Image are captured using reflected electromagnetic signal, on the basis of signal intensity identification of objects are achieved. Thus signal values also depends upon sensor capabilities and object characteristics. Sensor which capture remotely sensed images are analyzed on the basis of ground object spectral values. Using images spectral values, ground object characteristic identification are also achieved. In real scenario noise are comprised along with signal which leads to distracts all objects identifications. It also varies across all over the spectral values and on various feature classes. Signal to noise ratio (SNR) describes the quality of a measurement. Higher signal to noise ratio in the image, spectral values of image helps in identification of ground object of presumable quality. SNR computation is prerequisite process before carrying identification analysis on objects. The SNR represents a useful statistics that are computed and compared across different ground features. Hyperion hyperspectral image data set is used to carry study of Signal to Noise ratio. SNR computation is important process, but it is less studied by researches and scientist communities. SNR are computed using three algorithms Homogenous Area, Nearly Homogenous Area and Geostatistical on various feature classes and compared to evaluate its performance on different features. Geostatistical Algorithms is considering large number of spatial pixels, which are heterogeneous never the less results are varies less in comparison to other used algorithms Homogenous Area and Nearly Homogenous area. Feature Barren land have high SNR while comparing with other feature classes using all three used algorithms. Barren land have high signal reflectance and less absorption by atmosphere. The signal to noise ratio is established to be varying across function of both spectral values and ground features.

Hyperspectral imaging has become a standard for most applications that require precision based analysis. This is due to the fine spectral resolution that hyperspectral data offers. Target detection based on hyperspectral imaging is one of the significant applications required for numerous defence, surveillance as well as many civilian studies. This involves detailed analysis of all bands of hyperspectral data for presence of the desired target. However, there exist many parameters which may have bearing on the performance of detection algorithm. These include sensor related parameters like noise, calibration etc., spatial parameters like size, shape and location etc. and scene parameters like illumination variation, target composition, colour, background etc. This paper demonstrates the implications of three scene parameters namely illumination, background and colour on detection of many known targets using the hyperspectral data. The hyperspectral data acquired over Rochester Institute of Technology (RIT) for experimental purposes has been used. Three popular detection algorithms namely, ACE, MF, SAM have been implemented for target detection and the impact of selected parameters is assessed.

This paper presents a self-learning tool, which contains a number of virtual experiments for processing and analysis of Optical/Infrared and Synthetic Aperture Radar (SAR) images. The tool is named Virtual Satellite Image Processing and Analysis Lab (v-SIPLAB) Experiments that are included in Learning Tool are related to: Optical/Infrared - Image and Edge enhancement, smoothing, PCT, vegetation indices, Mathematical Morphology, Accuracy Assessment, Supervised/Unsupervised classification etc.; Basic SAR - Parameter extraction and range spectrum estimation, Range compression, Doppler centroid estimation, Azimuth reference function generation and compression, Multilooking, image enhancement, texture analysis, edge and detection. etc.; SAR Interferometry - BaseLine Calculation, Extraction of single look SAR images, Registration, Resampling, and Interferogram generation; SAR Polarimetry - Conversion of AirSAR or Radarsat data to S2/C3/T3 matrix, Speckle Filtering, Power/Intensity image generation, Decomposition of S2/C3/T3, Classification of S2/C3/T3 using Wishart Classifier [3]. A professional quality polarimetric SAR software can be found at [8], a part of whose functionality can be found in our system. The learning tool also contains other modules, besides executable software experiments, such as aim, theory, procedure, interpretation, quizzes, link to additional reading material and user feedback. Students can have understanding of Optical and SAR remotely sensed images through discussion of basic principles and supported by structured procedure for running and interpreting the experiments. Quizzes for self-assessment and a provision for online feedback are also being provided to make this Learning tool self-contained. One can download results after performing experiments.

Timely and accurate information about land cover is an important and extensively used application of remote sensing data. After successful launch of Landsat 8 is providing a new data source for monitoring land cover, which has the potential to improve the earth surface features characterization. Mapping of Leaf area Index (LAI) in larger area may be impossible when we rely on field measurements. Remote sensing data have been continuing efforts to develop different methods to estimate LAI. In this present study, an attempt has been made to discriminate various land cover features and empirical equation is used for retrieve biophysical parameter (LAI) for satellite NDVI data. Support vector machine classification was performed for Muzaffarnagar district using LANDSAT 8 operational land imager data to separate out major land cover classes (water, fallow, built up, sugarcane, orchard, dense vegetation and other crops). Ground truth data was collected using JUNO GPS which was used in developing the spectral signatures for each classes. The LAI-NDVI existing empirical equation is used to prepare LAI map. It is found that the LAI values in village foloda region maximum LAI pixels in the range 3.10 and above and minimum in the range 1.0 to 1.20. It is also concluded that the LAI values between 1.70 and 3.10 is having most of the sugarcane crop pixels at maximum vegetative growth stage. It shows that the sugarcane crop condition in the study area was very good.

Spectral analysis of ionospheric disturbances of seismic origin will aid for the detection and prediction of the unavoidable natural disasters like Earthquakes. These disturbances for an earthquake occurred in Kawalu, West Java Indonesia with a magnitude of 4.3 on Richter scale was analyzed. The earthquake has occurred on 12th December 2013 at 7:02 hours universal time coordinate i.e at 12:32 hours local time coordinate. Maximum entropy method was applied on the ionopsheric disturbances seen on the earthquake day. The enhancement in the energy of the ionosphere has a high value at the beginning. It had a slow initial decrement and then a rapid fall down is observed. The method may profoundly represent the effect impending earthquake.

The cooling water discharged from the coastal plants flow into the sea continuously, whose temperature is higher than original sea surface temperature (SST). The fact will have non-negligible influence on the marine environment in and around where the plants site. Hence, it’s significant to monitor the temporal and spatial variation of the warm-water discharge for the assessment of the effect of the plant on its surrounding marine environment. The paper describes an approach for the dynamic monitoring of the warm-water discharge of coastal plants based on the airborne high-resolution thermal infrared remote sensing technology. Firstly, the geometric correction was carried out for the thermal infrared remote sensing images acquired on the aircraft. Secondly, the atmospheric correction method was used to retrieve the sea surface temperature of the images. Thirdly, the temperature-rising districts caused by the warm-water discharge were extracted. Lastly, the temporal and spatial variations of the warm-water discharge were analyzed through the geographic information system (GIS) technology. The approach was applied to Qinshan nuclear power plant (NPP), in Zhejiang Province, China. In considering with the tide states, the diffusion, distribution and temperature-rising values of the warm-water discharged from the plant were calculated and analyzed, which are useful to the marine environment assessment.

Majority of pixels, in the nature, are non-isothermal in three dimensions, especially for the pixels in meter-scale, tens- meter-scale or hundreds-meter-scale which are paid extensive attention by the researchers in geoscience field. The three-dimensional non-isothermal phenomenon even exists in some pixels in centimeter-scale. For the geosciencific researches, it is significant to determine the component temperatures of a pixel precisely. The airborne WSIS (Wide Spectrum Imaging Spectrometer) data with VNIR (visible-near infrared), SWIR (short-wave infrared) and TIR (thermal infrared) bands were used in the study. First, the components of all the pixels in the image were determined by the linear mixing method. Second, each component emissivity of each pixel was calculated based on an emissivity priori knowledge base. Last, a temperature and emissivity separation algorithm was used to inverse the mean temperature of each pixel, regarded as initial value, the Planck function was linearized to construct a multi-band equation set, and the component temperatures of every pixel were inversed by the Bayesian retrieval technique. The results suggest that the inversion precision of the pixel component temperatures is improved effectively by the Bayesian retrieval technique with the assistance of the VNIR and SWIR hyperspectral remote sensing data.

The high-resolution thermal infrared image, by which the information of a scene can be described in details, is extensively used in many fields including computer vision process, medicine, and remote sensing, etc. This paper introduces a super-resolution reconstruction algorithm in combination of phase related motion estimating algorithm and iterative back-projecting algorithm. Continuous frames of the thermal infrared image aerially shot are extracted, the subpixel displacement of each frame of image relative to the reference image is estimated with the phase related motion estimating algorithm, and then the subpixel displacement data acquired is combined with the iterative back-projecting algorithm to actualize the super-resolution reconstruction of thermal infrared image aerially shot. The thermal infrared images were aerially shot above Zhoushan. The experimental result has proven the image spatial resolution can be effectively improved by this algorithm.

In India, in terms of area under cultivation, citrus is the third most cultivated fruit crop after Banana and Mango. Among citrus group, lime is one of the most important horticultural crops in India as the demand for its consumption is very high. Hence, preparing citrus crop inventories using remote sensing techniques would help in maintaining a record of its area and production statistics. This study shows how accurately citrus orchard can be classified using both IRS Resourcesat-2 LISS-III and LISS-IV data and depicts the optimum bio-widow for procuring satellite data to achieve high classification accuracy required for maintaining inventory of crop. Findings of the study show classification accuracy increased from 55% (using LISS-III) to 77% (using LISS-IV). Also, according to classified outputs and NDVI values obtained, April and May months were identified as optimum bio-window for citrus crop identification.

Space-borne hyperspectral imager is capable of providing large amount of image data with high spatial and spectral resolution. However, with the increase of the resolution, the volume and weight of conventional dispersive (prism/grating) imaging spectrometers also increase significantly. Meanwhile, the demand for the system sensitivity becomes even higher. In this paper, the application of Integrated Stepwise Filter (ISF) in infrared hyperspectral imaging technology was analyzed, including optical efficiency, instrumental background radiation suppression, spectral time delay integration (TDI) and other performances. Several methods which can enhance the system sensitivity were also provided. A compact short-wave infrared (SWIR) hyperspectral imager prototype covering the spectral range of 2.0μm-2.5μm with 6 TDI stages was designed and implemented. The results of the imaging experiment demonstrated that ISF has great application prospects in the field of high sensitivity space-borne hyperspectral imagery.

Accurate radiometric calibration for thermal infrared hyperspectral data is the precondition for further quantitative applications. A thermal infrared hyperspectral field calibration method which is based on non-uniformity repair had been proposed in this paper. Firstly correct global non-uniformity phenomenon caused by detector response difference, with moment matching algorithm; Secondly correct local non-uniformity phenomenon caused by the environment changes, with department moment matching algorithm; thirdly design an aviation experimental, use outfield target to calculate every band’s calibration coefficient. The thermal infrared hyperspectral field experiments show that this method can effectively eliminate the phenomenon of non-uniformity of thermal infrared hyperspectral images, and get high accuracy radiometric calibration results.

To provide accurate data the regular on-board absolute radiometric calibration of a satellite hyperspectral instrument is required. Together with the internal calibration the external calibration using comparison of radiance measurements above special ground test sites and calculated radiances is performed. The top of the atmosphere radiances are calculated using a radiative transfer model basing on atmospheric and surface characteristics measured at the test sites. The paper presents preliminary results of the comparative theoretical analysis of the errors of a satellite hyperspectral instrument radiometric calibration using test sites located at 200 m.a.s.l. and 2000 m.a.s.l. with the atmospheric composition and surface reflectance measurements. The analysis is performed for an instrument with the spectral resolution of 1-8 nm which is typical for special regime of payload GSA of Russian satellite Resurs-P. The errors related with the atmospheric composition and albedo measurement errors and scenarios of the aerosol vertical distribution were theoretically examined. The error is less than 4% in all the cases at all the wavelengths between 400 nm and 1000 nm with the exception of the absorption bands of water vapor. In the absorption bands of water vapor about 720 nm and 820 nm the errors reach 5% at the mountain site and 10% at the downcountry site. In the absorption band of 950 nm the errors reach 15% in mountains and 35% in downcountry.

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