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Relevant bibliographies by topics / INSAT-3D Imager

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Academic literature on the topic 'INSAT-3D Imager'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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Journal articles on the topic "INSAT-3D Imager"

1

Prakash, S., D. Jindal, N. Badal, B. Kartikeyan, and B. Gopala Krishna. "Radiometric Quality Evaluation of INSAT-3D Imager Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 299–305. http://dx.doi.org/10.5194/isprsarchives-xl-8-299-2014.

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INSAT-3D is an advanced meteorological satellite of ISRO which acquires imagery in optical and infra-red (IR) channels for study of weather dynamics in Indian sub-continent region. In this paper, methodology of radiometric quality evaluation for Level-1 products of Imager, one of the payloads onboard INSAT-3D, is described. Firstly, overall visual quality of scene in terms of dynamic range, edge sharpness or modulation transfer function (MTF), presence of striping and other image artefacts is computed. Uniform targets in Desert and Sea region are identified for which detailed radiometric performance evaluation for IR channels is carried out. Mean brightness temperature (BT) of targets is computed and validated with independently generated radiometric references. Further, diurnal/seasonal trends in target BT values and radiometric uncertainty or sensor noise are studied. Results of radiometric quality evaluation over duration of eight months (January to August 2014) and comparison of radiometric consistency pre/post yaw flip of satellite are presented. Radiometric Analysis indicates that INSAT-3D images have high contrast (MTF > 0.2) and low striping effects. A bias of <4K is observed in the brightness temperature values of TIR-1 channel measured during January&ndash;August 2014 indicating consistent radiometric calibration. Diurnal and seasonal analysis shows that Noise equivalent differential temperature (NEdT) for IR channels is consistent and well within specifications.

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Patel, P., H. Bhatt, and A. K. Shukla. "Absolute Vicarious Calibration of recently launched Indian Meteorological Satellite: INSAT-3D imager." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 291–98. http://dx.doi.org/10.5194/isprsarchives-xl-8-291-2014.

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Looking towards the advancements and popularity of remote sensing and an ever increasing need for the development of a variety of new and complex satellite sensors, it has become even more essential to continually upgrade the ability to provide absolute calibration of sensors. This article describes a simple procedure to implement post-launch calibration for VIS and SWIR channels of INSAT-3D imager over land site (Little Rann of Kutch (ROK), Gujarat) on three different days to account for characterization errors or undetermined post-launch changes in spectral response of the sensor. The measurements of field reflectance of study site (of extent ~6 km x 6 km) in the wavelength range 325&ndash;2500 nm, along with atmospheric parameters (Aerosol Optical Depth, Total Columnar Ozone, Water Vapor) and sensor spectral response functions, were input to the 6S radiative transfer model to simulate radiance at top of the atmosphere (TOA) for VIS and SWIR bands. The uncertainty in vicarious calibration coefficients due to measured spatial variability of field reflectance along with due to aerosol types were also computed for the INSAT-3D imager. The effect of surface anisotropy on TOA radiance was studied using a MODIS Bidirectional Reflectance Distribution Function (BRDF) product covering the experimental site. The results show that there is no indication of change in calibration coefficients in INSAT- 3D imager, for VIS and SWIR band over Little ROK. Comparison made between the INSAT-3D imager measured radiance and 6S simulated radiance. Analysis shows that for clear sky days, the INSAT-3D imager overestimates TOA radiance in the VIS band by 5.1 % and in the SWIR band by 11.7 % with respect to 6S simulated radiance. For these bands, in the inverse mode, the 6S corrected surface reflectance was closer to field surface reflectance. It was found that site spatial variability was a critical factor in estimating change in sensor calibration coefficients and influencing uncertainty in TOA radiance for Little ROK.

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Jindal, D., S. Prakash, J. Sanghvi, B. Kartikeyan, and B. Gopala Krishna. "INSAT-3D Quality Analysis System (i3dQAS)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 27, 2014): 257–63. http://dx.doi.org/10.5194/isprsarchives-xl-8-257-2014.

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INSAT-3D, an advanced meteorological satellite of ISRO carrying Imager and Sounder payloads is designed for acquiring data required for detailed climatological applications. At established data centres, Raw (L-0) data is processed to generate systematically corrected (Level-1) and geo-physical (Level-2) products which are further disseminated for use in climate related studies. Data quality evaluation (DQE) of basic data products of INSAT-3D is being operationally carried out at MOSDAC, Ahmedabad to monitor in-orbit sensor health, quantify data quality and provide feedback to various missions teams for corrective actions. DQE methodology and quality parameters have been defined keeping in view repeatability of data acquisition and quality requirements of meteorological data. In this paper, we have described INSAT-3D Quality Analysis System (i3dQAS) designed to facilitate effective data quality monitoring through web based portal. Overall context of i3dQAS in a typical data centre set-up, major objectives and design aspects to highlight the architectural and functional view of system are described. Utility of i3dQAS for mission monitoring is described through analysis scenarios for monitoring payload health and geometric accuracy of Imager.

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Venkat Ratnam, Madineni, Alladi Hemanth Kumar, and Achuthan Jayaraman. "Validation of INSAT-3D sounder data with in situ measurements and other similar satellite observations over India." Atmospheric Measurement Techniques 9, no. 12 (2016): 5735–45. http://dx.doi.org/10.5194/amt-9-5735-2016.

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Abstract. To date, several satellites measurements are available which can provide profiles of temperature and water vapour with reasonable accuracies. However, the temporal resolution has remained poor, particularly over the tropics, as most of them are polar orbiting. At this juncture, the launch of INSAT-3D (Indian National Satellite System) by the Indian Space Research Organization (ISRO) on 26 July 2013 carrying a multi-spectral imager covering visible to long-wave infrared made it possible to obtain profiles of temperature and water vapour over India with higher temporal and vertical resolutions and altitude coverage, besides other parameters. The initial validation of INSAT-3D data is made with the high temporal (3 h) resolution radiosonde observations launched over Gadanki (13.5° N, 79.2° E) during a special campaign and routine evening soundings obtained at 12:00 UTC (17:30 LT). We also compared INSAT-3D data with the radiosonde observations obtained from 34 India Meteorological Department stations. Comparisons were also made over India with data from other satellites like AIRS, MLS and SAPHIR and from ERA-Interim and NCEP reanalysis data sets. INSAT-3D is able to show better coverage over India with high spatial and temporal resolutions as expected. Good correlation in temperature between INSAT-3D and in situ measurements is noticed except in the upper tropospheric and lower stratospheric regions (positive bias of 2–3 K). There is a mean dry bias of 20–30 % in the water vapour mixing ratio. Similar biases are noticed when compared to other satellites and reanalysis data sets. INSAT-3D shows a large positive bias in temperature above 25° N in the lower troposphere. Thus, caution is advised when using these data for tropospheric studies. Finally it is concluded that temperature data from INSAT-3D are of high quality and can be directly assimilated for better forecasts over India.

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Gangwar, Rishi Kumar, and Pradeep Kumar Thapliyal. "Variational Based Estimation of Sea Surface Temperature from Split-Window Observations of INSAT-3D/3DR Imager." Remote Sensing 12, no. 19 (2020): 3142. http://dx.doi.org/10.3390/rs12193142.

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Infrared (IR) radiometers from geostationary (GEO) satellites have an advantage over low-earth orbiting (LEO) satellites as they provide continuous observations to monitor the diurnal variations in the sea surface temperature (SST), typically better than 30-minute interval. However, GEO satellite observations suffer from significant diurnal and seasonal biases arising due to varying sun-earth-satellite geometry, leading to biases in SST estimates from conventional non-linear regression-based algorithms (NLSST). The midnight calibration issue occurring in GEO sensors poses a different challenge altogether. To mitigate these issues, we propose SST estimation from split-window IR observations of INSAT-3D and 3DR Imagers using One-Dimensional Variational (1DVAR) scheme. Prior to SST estimation, the bias correction in Imager observations is carried out using cumulative density function (CDF) matching. Then NLSST and 1DVAR algorithms were applied on six months of INSAT-3D/3DR observations to retrieve the SST. For the assessment of the developed algorithms, the retrieved SST was validated against in-situ SST measurements available from in-situ SST Quality Monitor (iQuam) for the study period. The quantitative assessment confirms the superiority of the 1DVAR technique over the NLSST algorithm. However, both the schemes under-estimate the SST as compared to in-situ SST, which may be primarily due to the differences in the retrieved skin SST versus bulk in-situ SST. The 1DVAR scheme gives similar accuracy of SST for both INSAT-3D and 3DR with a bias of −0.36 K and standard deviation (Std) of 0.63 K. However, the NLSST algorithm provides slightly less accurate SST with bias (Std) of −0.18 K (0.87 K) for INSAT-3DR and −0.27 K (0.95 K) for INSAT-3D. Both the NLSST and 1DVAR algorithms are capable of producing the accurate thermal gradients from the retrieved SST as compared to the gradients calculated from daily Multiscale Ultrahigh Resolution (MUR) level-4 analysis SST acquired from Group for High-Resolution Sea Surface Temperature (GHRSST). Based on these spatial gradients, thermal fronts can be generated that are very useful for predicting potential fishery zones (PFZ), which is available from GEO satellites, INSAT-3D/3DR, in near real-time at 15-minute intervals. Results from the proposed 1DVAR and NLSST algorithms suggest a marked improvement in the SST estimates with reduced diurnal/seasonal biases as compared to the operational NLSST algorithm.

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Mishra, M. K., G. Rastogi, and P. Chauhan. "Operational Retrieval of aerosol optical depth over Indian subcontinent and Indian Ocean using INSAT-3D/Imager product validation." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 277–82. http://dx.doi.org/10.5194/isprsarchives-xl-8-277-2014.

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Aerosol optical depth (AOD) over Indian subcontinent and Indian Ocean region is derived operationally for the first time from the geostationary earth orbit (GEO) satellite INSAT-3D Imager data at 0.65 μm wavelength. Single visible channel algorithm based on clear sky composites gives larger retrieval error in AOD than other multiple channel algorithms due to errors in estimating surface reflectance and atmospheric property. However, since MIR channel signal is insensitive to the presence of most aerosols, therefore in present study, AOD retrieval algorithm employs both visible (centred at 0.65 μm) and mid-infrared (MIR) band (centred at 3.9 μm) measurements, and allows us to monitor transport of aerosols at higher temporal resolution. Comparisons made between INSAT-3D derived AOD (τ<sub>I</sub>) and MODIS derived AOD (τ<sub>M</sub>) co-located in space (at 1&deg; resolution) and time during January, February and March (JFM) 2014 encompasses 1165, 1052 and 900 pixels, respectively. Good agreement found between τ<sub>I</sub> and τ<sub>M</sub> during JFM 2014 with linear correlation coefficients (R) of 0.87, 0.81 and 0.76, respectively. The extensive validation made during JFM 2014 encompasses 215 co-located AOD in space and time derived by INSAT 3D (τ<sub>I</sub>) and 10 sun-photometers (τ<sub>A</sub>) that includes 9 AERONET (Aerosol Robotic Network) and 1 handheld sun-photometer site. INSAT-3D derived AOD i.e. τ<sub>I</sub>, is found within the retrieval errors of τ<sub>I</sub> = ±0.07 ±0.15τ<sub>A</sub> with linear correlation coefficient (R) of 0.90 and root mean square error equal (RMSE) to 0.06. Present work shows that INSAT-3D aerosol products can be used quantitatively in many applications with caution for possible residual clouds, snow/ice, and water contamination.

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Dey, I., M. V. Shukla, P. K. Thapliyal, and C. M. Kishtawal. "Evaluation of operational INSAT-3D UTH product, using Radiosonde, Meteosat-7 and NCEP Analysis." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 27, 2014): 247–52. http://dx.doi.org/10.5194/isprsarchives-xl-8-247-2014.

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Recently available satellite observations from the water vapor channel (6.5&ndash;7.1 μm) of the Imager on-board India's geostationary satellite, INSAT-3D have been used to estimate Upper Tropospheric Humidity (UTH). In this study, operationally retrieved UTH product has been compared and validated for the period of Jan&ndash;Jun, 2014, using in-situ and satellite measurements. In-situ measurements of UTH have been indirectly derived using humidity profiles obtained from a network of radiosonde stations from NOAA/ESRL database. Meteosat-7 UTH products have been used as satellite measurements. The validation of INSAT-3D UTH against UTH derived from radiosonde profiles shows reasonable agreement, with linear correlation coefficients ranging from 0.78 to 0.87 and the slope of the regression line ranging from 0.52 to 0.77. The UTH tends to overestimate observed humidity by ~4 % with RMS difference of ~12 %. Comparison of INSAT-3D UTH product with Meteosat-7 UTH product suggests a good match with RMS difference of 7.61% and a mean bias of &minus;0.43 %, linear correlation coefficients varying from 0.88 to 0.93 and slope of the regression line varying from 0.64 to 1.08. The UTH products from INSAT-3D and Meteosat-7 have also been inter-compared by validating the two against the UTH derived from a set of collocated radiosonde observations. INSAT-3D UTH shows a RMSD of 10.65 % and bias of 0.78 % which matches very well with Meteosat-7 UTH with a RMSD of 10.31 % and bias of &minus;0.53 %.

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Satapathy, J., P. K. Thapliyal, M. V. Shukla, and C. M. Kishtawal. "Synergistic use of Imager Window observations for Cloud Clearing of Sounder Observation for INSAT-3D." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 315–20. http://dx.doi.org/10.5194/isprsarchives-xl-8-315-2014.

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The retrieval of atmospheric temperature and water vapor profiles from infrared Sounder are severely limited by the presence of cloud. Therefore, retrieval from infrared sounding observations is performed only over clear-sky atmospheric conditions. The probability of finding a clear-sky pixel at spatial resolution of 10 km is found to be very small globally. This study presents a quantitative analysis of the clear-sky probability that is carried out for different months over the Indian region for INSAT-3D Sounder. The probability of a clear-sky is found to be ~7 % for the field of view of 10 km corresponding to the INSAT-3D Sounder. This statistical analysis is established using MODIS cloud mask having 95 % confidence level at 1 km resolution spread in the region between 50E&ndash;110E and 30S&ndash;30N. This necessitates cloud clearing to remove the effect of partial clouds in the Sounder FOV to provide a clear-sky equivalent sounding retrieval. <br><br> Various methods were explored to derive the cloud-cleared radiances using supplementary information such as high resolution infrared or microwave observations. This study presents an effort to use the existing traditional method to derive optimal cloudcleared radiances for INSAT-3D Sounder, by estimating the fractional cloud cover using collocated high resolution INSAT-3D Imager window channel observation. The final Sounder cloud-cleared radiances have been validated with the operational AIRS L2 cloud-cleared radiance products. <br><br> Nevertheless, the statistical analysis of clear-sky probability over Indian region also provides a significant insight towards the dependency of spatial resolution and the considerable field-of-regard (FOR) in obtaining the clear-sky area in the satellite observations. This, in a way, necessitates the cloud-clearing for coarser resolution sensors and at the same time, states the benefits of using very high resolution sensors. It has been observed that FOV of 1km and by choosing a reasonably good FOR can eliminate the cloudy-sky hindrances by increasing the probability of clear-sky from 5 % to 50 %.

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Kumawat, Nandkishor, K. N. Babu, Mehul R. Pandya, Saurabh Tripathi, and V. Sathiyamoorthy. "Towards Accurate Radiometric Calibration of INSAT-3D and INSAT-3DR IMAGER: Addressing Uncertainty and Error Sources." International Journal of Remote Sensing 44, no. 20 (2023): 6298–328. http://dx.doi.org/10.1080/01431161.2023.2265541.

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Ojha, Satya P., and Randhir Singh. "Physical retrieval of sea-surface temperature from INSAT-3D imager observations." Tellus A: Dynamic Meteorology and Oceanography 71, no. 1 (2019): 1554421. http://dx.doi.org/10.1080/16000870.2019.1657767.

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Book chapters on the topic "INSAT-3D Imager"

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Shastry, K. Aditya, B. S. Aneesh, M. P. Chinmay, C. Gowtham Patel, and G. N. Shashank. "Enhancing Cyclone Preparedness: Deep Learning Methods with INSAT-3D Satellite Imagery." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81369-6_16.

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Pal, Soumyajit, Uma Das, and Oishila Bandyopadhyay. "Detecting Tropical Cyclones in INSAT-3D Satellite Images Using CNN-Based Model." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31407-0_27.

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Kumar, Divvela Vishnu Sai, Deepak Arora, and Shivam Tiwari. "Enhanced Cyclone Intensity Estimation Through CNN Analysis of INSAT-3D IR Imagery." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8836-1_30.

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Kumar, Amit, Anil Kumar Singh, Nitesh Awasthi, and Virendra Singh. "Natural Hazard." In Interdisciplinary Approaches to Information Systems and Software Engineering. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7784-3.ch006.

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Tropical cyclones are also known as typhoons or hurricanes. Also, special emphasis is given on the various aspects associated with cyclogenesis, like the six essential parameters required for cyclogenesis as given by W. M. Grey, and Dvorak technique is discussed in this chapter. INSAT-3D is an indigenous advanced dedicated meteorological satellite in geostationary orbit, which was launched on 26th July 2013. INSAT-3D was declared operational by IMD on 15th January 2014. INSAT-3D has four payloads, namely. IMAGER, SOUNDER, data relay transponder (DRT), and satellite aided search, aid and rescue (SAR). Three rainfall estimates are being generated from INSAT-3D, namely, hydro-estimator (HE), INSAT multispectral rainfall algorithm (IMSRA), and QPE (quantitative precipitation estimate). It has been found in this study that IMSRA performs better during initial stages of cyclogenesis (i.e., during T1.0, T1.5, and T2.0); during mature stages T2.5 to T3.0, HE performs better. During weakening stages IMSRA gives better results.

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Conference papers on the topic "INSAT-3D Imager"

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Vasanthi, D., A. Arul Jothi, Sivasakthi Thanigainathan, and H. Keerthivasan. "Cyclone Intensity Estimation Using INSAT-3D IR Imagery." In 2024 IEEE 5th India Council International Subsections Conference (INDISCON). IEEE, 2024. http://dx.doi.org/10.1109/indiscon62179.2024.10744256.

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Swetha, K., Neelima Gogineni, Gundeti Srinivas, E. N. Vijaya Kumari, Manohar Madgi, and T.Benarji. "Cyclone Intensity Estimation based on Deep Learning using INSAT-3D IR Image." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714758.

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Parihar, Shailesh, A. K. Sharma, A. K. Mitra, et al. "Calibration strategy of INSAT-3D meteorological satellite imager using the moon at IMD, New Delhi." In SPIE Asia-Pacific Remote Sensing, edited by Allen M. Larar, Prakash Chauhan, Makoto Suzuki, and Jianyu Wang. SPIE, 2016. http://dx.doi.org/10.1117/12.2224543.

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Indira Rani, S., Ruth Taylor, John P. George, and E. N. Rajagopal. "Use of INSAT-3D sounder and imager radiances in the 4D-VAR data assimilation system and its implications in the analyses and forecasts." In SPIE Asia-Pacific Remote Sensing, edited by Eastwood Im, Raj Kumar, and Song Yang. SPIE, 2016. http://dx.doi.org/10.1117/12.2223496.

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Vayadande, Kuldeep, Tejas Adsare, Tejas Dharmik, Neeraj Agrawal, Aishwarya Patil, and Sakshi Zod. "Cyclone Intensity Estimation on INSAT 3D IR Imagery Using Deep Learning." In 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA). IEEE, 2023. http://dx.doi.org/10.1109/icidca56705.2023.10099964.

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N, Uma, Jithin Vinod, K. M. Komal, and Joshua James. "Deep Learning Based Cyclone Intensity Estimation using INSAT-3D IR Imagery." In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2024. http://dx.doi.org/10.1109/iatmsi60426.2024.10502881.

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Sena S, Divya Vasantha, Sanjay B, Julius Fusic S, and Hariprasad M. "Cyclone Intensity Estimation Based on Deep - Learning Using Insat 3D IR Imagery." In 2023 International Conference on Energy, Materials and Communication Engineering (ICEMCE). IEEE, 2023. http://dx.doi.org/10.1109/icemce57940.2023.10434169.

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A, Archana, Martina Jose Mary M, Shyamala Devi R, and Yogeshkannah K. "Cyclone Intensity Estimation Leveraging Graph Convolution Recurrent Networks and INSAT 3D Imagery." In 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2024. http://dx.doi.org/10.1109/sceecs61402.2024.10481869.

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A, Pugazhenthi, and Lakshmi Sutha Kumar. "Cloud Extraction from INSAT-3D Satellite Image by K-Means and Fuzzy C-Means Clustering Algorithms." In 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2020. http://dx.doi.org/10.1109/icscan49426.2020.9262330.

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