This study evaluated the performance of chlorophyll-a (Chl-a) estimates retrieved from the OCM-3 sensor aboard EOS-06 and the OLCI sensors on Sentinel-3A and Sentinel-3B, using in-situ observations collected from the optically complex coastal waters off Kochi, on the southwest coast of India. The number of cloud free satellite-in situ matchups available for OCM-3 during the study period was limited due to frequent cloud cover during satellite overpasses in the study region. To ensure a consistent inter-sensor comparison, an identical set of 36 cloud-free matchup pairs (N = 36) was used for all datasets. Regression analysis between satellite and in-situ data showed a moderate relationship for OCM-3 (R² = 0.66) with the lowest bias (-0.05 mg/m³) and an RMSE of 0.74 mg/m³, indicating stable and unbiased Chl-a estimates. Sentinel-3B OLCI showed a strong regression (R² = 0.89) with a moderate RMSE (0.93 mg/m³) and a positive bias (0.31 mg/m³). Sentinel-3A OLCI also showed a similar performance with a strong regression (R² = 0.82) but had a higher RMSE (1.10 mg/m³) and slight positive bias (0.07 mg/m³), indicating moderate variability in the retrievals. Inter-sensor comparisons showed that OCM-3 agreed reasonably well with both Sentinel-3A and Sentinel-3B in Chl-a retrievals. For OCM-3 and Sentinel-3A, the RMSE, MAE, and bias were 0.47 mg/m³, 0.383 mg/m³, and -0.258 mg/m³, respectively, while for OCM-3 and Sentinel-3B, they were 0.48 mg/m³, 0.403 mg/m³, and -0.252 mg/m³. Overall, the results indicated consistent performance of OCM-3 with minimal variation across the Sentinel-3 sensors. These small differences among sensors were likely due to the use of global retrieval algorithms that were not regionally calibrated for the optical complexity of coastal waters. The findings emphasized the need for region-specific algorithm refinement, supported by in-situ datasets, to improve the accuracy and reliability of satellite-derived Chl-a estimates for monitoring coastal ecosystems in optically complex waters.

The Ocean Colour Monitor (OCM) -3 onboard Earth Observation Satellite (EOS) − 06 was launched by the Indian Space Research Organization (ISRO) in November 2022 as a follow-up to OCM-2 (2009). This study evaluates the chlorophyll-a concentration (Chl -a) retrieved from OCM-3 Global Area Coverage (GAC) data by comparing it with in-situ measurements from the biogeochemical – Argo floats (BGC-Argo) in open ocean waters between August 2024 and March 2025. The results show strong agreement between the two data sets. OCM-3 retrieved Chl-a reveals a mean bias of − 0.05 mg m⁻³, root mean square error (RMSE) of ± 0.15 mg m⁻³ and the unbiased percentage difference is 28.73

Ocean color radiometry at the top of the atmosphere includes water-leaving radiance modulated by atmospheric absorption and scattering from gases and particles. Phytoplankton pigments, total suspended matter and the vertical diffuse attenuation coefficient drive spectral variations in remote sensing reflectance (Rrs), retrievable via dark pixel approximation in OCM-3’s NIR channels using well-known radiative transfer model. OCM-3 data over the Arabian Sea yields remote sensing reflectance (Rrs) retrievals across 412–710 nm (R²=0.64, RMSE = 0.003, bias = 0.0007 sr⁻¹). Chl-a (mg/m3) concentrations perform well in the Arabian Sea (R²=0.40, RMSE = 0.09 mg/m3, bias = 0.15 mg m⁻³) and Bay of Bengal (R²=0.61, RMSE = 0.13 mg/m3, bias = 0.18 mg m⁻³), supporting essential climate variables. These products enable global monitoring of potential fishing zones, harmful algal blooms, coastal systems, and marine ecosystems. The Indian Ocean study area demonstrates the readiness of OCM-3 on board EOS-06 for such operational needs.

Accurate estimation of chlorophyll-a (Chl-a) is essential for monitoring phytoplankton biomass and marine ecosystem health. This study evaluates the performance of the Ocean Colour Monitor-3 (OCM-3) sensor in retrieving Chl-a concentrations in coastal and offshore waters of the southwest Bay of Bengal, using in-situ data collected between March—2023 and April—2024. Validation included regression analysis, Bland–Altman plots, and statistical error metrics. The performance of OCM-3’s OC4 algorithm was compared with global algorithms OC5 and OC6. In coastal waters, in-situ Chl-a ranged from 0.19 to 3.34 μg/l, while OCM-3 estimates ranged from 0.50 to 2.88 mg/m3. Offshore values ranged from 0.43 to 1.12 μg/l (in-situ) and 0.41–0.70 mg/m3 (OCM-3). OCM-3 showed good correlation with in-situ measurements (R2 = 0.62 coastal, 0.63 offshore), and lower RMSE (0.50 μg/l coastal, 0.18 mg/m3 offshore). OC5 and OC6 exhibited higher errors and under/overestimation trends, particularly in offshore waters. OCM-3 demonstrated reliable performance for Chl-a retrieval, but coastal uncertainties highlight the need for region-specific algorithm tuning. Continued validation with expanded in-situ datasets is recommended to enhance accuracy for ecological monitoring in the Bay of Bengal.

This study examines the long-term changes in phytoplankton size classes (PSCs) in the Arabian Sea (AS) using the remote sensing reflectance (Rrs) data collected over 12 years (2010-2021) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The Rrs spectra were inverted to chlorophyll-a (Chl-a) concentrations using a non-linear optimisation method, which were then used to estimate the PSC using a region specific three-component model. The analysis is carried out for all four seasons, i.e., winter (December-February), pre-monsoon (March-May), monsoon (June-September) and post-monsoon (October-November). A machine learning random forest (RF) model is employed to predict the seasonal and long-term variability in PSCs and to quantify the influence of environmental drivers. The seasonal climatology of three size classes - micro (larger), nano (medium-sized), and pico (smaller) - reveals that micro-phytoplankton predominantly occupy the northern AS during winter and pre-monsoon seasons, contributing over 50% to the total Chl-a. During the monsoon season, a significant rise in micro-phytoplankton contribution (60-80%) is noted off the coasts of Somalia, Oman and Kerala due to strong upwelling. In contrast, nano-phytoplankton contributions are minimal during the pre-monsoon season but remain fairly consistent in other seasons, and pico-phytoplankton dominates the oligotrophic waters of the central and southern AS during pre- and post-monsoon. The analysis of PSCs from 2010 to 2021 shows a strong decreasing trend in micro-phytoplankton concentration (-0.13 ± 0.19 mg m-3 year-1), accompanied by a steady increase in pico-phytoplankton (0.0009 ± 0.0005 mg m-3 year-1) and nano-phytoplankton (0.001 ± 0.0009 mg m-3 year-1). To elucidate these long-term trends, RF model was instrumental in identifying key environmental drivers, with sea surface temperature (SST) emerging as the most influential factor affecting pico- and micro-phytoplankton. The feature importance scores for SST are highest during winter and pre-monsoon for both pico-phytoplankton and micro-phytoplankton, underscoring the sensitivity of these classes to temperature changes. RF model also highlights the role of mixed layer depth (MLD) and wind speed (WS) in driving the seasonal shifts in PSCs, particularly during the monsoon and post-monsoon periods. These findings suggest that the rise in SST, coupled with changes in vertical mixing and stratification, drives the shift towards smaller cells, mainly pico-phytoplankton in the AS. This shift towards smaller cells indicates a possible decline in marine food chain efficiency, reduced carbon export rates and declining primary productivity-a real concern for food security in the region.

Following the legacy of the OCM series, OCM-3 onboard EOS-06 was launched on November 26, 2022 to cater the global needs with better accuracy. It has 13 bands in VNIR (400–1010 nm range) with 1500 km swath for monitoring the ocean. Basically, it operates in two modes, one is Local Area Coverage mode, i.e. 360 m resolution and the second one is Global Area Coverage for global ocean in low resolution mode (1.1 km) at regular cycles. Remote sensing reflectance derived from space using standard atmospheric correction, works very well in open ocean while the same approach fails in the optically complex waters, so to overcome this problem, 870 nm was paired with 1010 nm of OCM-3 instead of 780–870 nm to recover the coastal radiometry basically at blue channels. They suffer a lot because of overestimation of atmospheric characterization at NIR channels and then extrapolated to blue channel during radiative transfer modeling. In addition, absorbing aerosols and coloured dissolved organic matter are equally responsible in the coastal region as well. The role of additional bands of ocean colour monitor (OCM-3) was discussed in this manuscript.

In this paper, the performance of the sun glint masking algorithm implemented on India’s EOS-06 OCM-3 operational ocean colour products has been evaluated by testing on different glint-contaminated OCM-3 scenes from various parts of the global ocean. The sun-glint-contaminated scenes were taken based on the visible appearance of glint on grayscale radiance images. Sun glint masking was then performed by applying a threshold on normalised glint reflectance, which is estimated from the Cox and Munk model. It was observed that the percentage of pixels in an image lost to sun glint varied from less than 10

Satellite-derived ocean colour products, such as chlorophyll-a (chl-a) and coloured dissolved organic matter (CDOM) concentration, are essential for monitoring coastal and marine ecosystems. This study presents a comprehensive validation and inter-sensor comparison of chl-a concentrations derived from the Ocean Colour Monitor-3 (OCM-3) aboard ISRO's EOS-06 satellite and Ocean Colour Instrument (OCI) aboard NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite. In-situ measurements collected from the Bay of Bengal during the pre-monsoon season were used to validate and assess the accuracy of satellite-derived chl-a data. The correlation between spectrophotometric and High Performance Liquid Chromatography (HPLC) measurements of chl-a was moderate (R2 = 0.57) in the study area, with HPLC-based validation showing better agreement with satellite data compared to spectrophotometric measurements. HPLC is recommended for validating remote sensing chl-a products due to its reliability, especially at lower concentrations (< 0.1 mg m−3). The comparison of both sensors with HPLC data revealed a higher correlation for OCM-3 (R2 = 0.67) compared to PACE (R2 = 0.15), although PACE-OCI exhibited a lower RMSE (0.05 mg m−3) than OCM-3 (0.30 mg m−3). The inter-sensor comparison (R2 = 0.53, log bias = 0.2) highlighted that OCM-3 tended to overestimate chl-a concentrations in the region, while PACE-OCI values were closer to in-situ measurements. These findings provide key insights into the sensor performance of both OCM-3 and PACE-OCI, especially in oligotrophic waters. The regions of low chl-a are often used for calibrating any ocean colour sensor and, therefore, the performance in such areas needs critical assessment.

India's extensive coastline and rich oceanic resources present a unique opportunity to enhance its economy sustainably through the Blue Economy and meet the goals of the United Nations Ocean Decade (2021–2030). Advanced geospatial technologies, remote sensing, Artificial Intelligence/Machine Learning (AI/ML), and analysis-ready data, play pivotal roles in this endeavour and open up niche business development opportunities. This paper explores the integration of state-of-the-art remote sensing sensors and Geographic Information Systems (GIS) tools in advancing India's Blue Economy, highlighting their applications in various maritime activities. The advent of sophisticated satellite sensors has significantly broadened our understanding of oceanic phenomena. Satellite missions, like ISRO’s EOS-06/Oceansat-3, Radar Imaging Satellite EOS-04/RISAT, NASA’s Surface Water and Ocean Topography (SWOT) and PACE missions, along with the European Space Agency's Sentinel series, both optical and microwave systems, are at the forefront of ocean monitoring. For instance, SWOT's capabilities in measuring sea surface height provide invaluable insights into ocean dynamics, which is essential for climate research and water management. Oceansat-3 contributes to our understanding of ocean colour and sea surface wind fields, facilitating the monitoring of marine biological productivity and surface winds vital for weather forecasting and disaster management. These satellites provide critical data that support a range of applications, from safe ship navigation and rip current warnings for beach tourism to the identification of sustainable fishing zones. Additionally, observations from space-borne sensors are instrumental in tracking oil spills, locating marine debris, monitoring coastal zones, and analysing climate change impacts. GIS plays a crucial role in enhancing the utility of the data derived from these sensors by automating the analysis and retrieval of information across large volumes of satellite and model data. This chapter discusses the impact of advanced remote sensing technologies and geospatial and AI/ML tools to develop comprehensive solutions for marine and coastal management. By leveraging these technological advancements, India can not only ensure the sustainable development of its maritime resources but also strengthen its resilience against environmental challenges. This chapter underlines the necessity of embedding advanced remote sensing and GIS capabilities into India's strategic planning for its Blue Economy. Embracing these technologies will enable more informed decision-making, promoting sustainability and enhancing economic value derived from marine ecosystems.

The radiometric performance of Ocean Colour Monitor onboard EOS-06 (OceanSat-3) using case-1 water calibration and validation site at Kavaratti has been carried out during its initial period (1st December 2022–30th May 2023). The operational atmospheric correction algorithm is used to simulate the sensor radiance using in-situ measurements of water leaving radiance and aerosol. The percentage of relative difference is higher ( 6

The Indian Space Research Organisation (ISRO) launched Oceansat-3 to gather crucial ocean surface data for climate and marine resource monitoring. This study aims to validate Oceansat-3’s operational products (remote sensing reflectance, Rrs, and Chlorophyll-a concentration, Chl-a) in the southern Bay of Bengal through in-situ measurements. In-situ data were collected using boat and ship cruise. Statistical methods, including Mean Bias (MB), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), coefficient of determination (R2), frequency distribution, and Bland Altman Plot, were employed to quantify the assessment between satellite and in-situ data. Results indicate consistency of Oceansat-3 products in representing oceanographic features, even though observed changes in OCM3 spectral remote sensing reflectance accuracy during the measurement period, the Bland-Altman plot results reveal that the mean bias approaches zero, indicating negligible systematic errors between in-situ and satellite-derived Rrs, thus reflecting commendable concordance between the two measurements. Further, the MAE values spanned from 0.001 to 0.003 sr−1. RMSE values varied, with Rrs 710 showing the least error (0.0007 sr−1) and Rrs 443 the most significant error (0.0035 sr−1). The estimated bias (0.99 mg/m3) and mean absolute error (1.59 mg/m3) for Chl-a in the Bay of Bengal region align with findings from other studies.

This manuscript describes reflectance-based vicarious calibration exercise of the Airborne Visible InfraRed Imaging Spectrometer Next Generation (AVIRIS-NG) sensor during its second phase of observation campaigns over the Indian subcontinent. Vicarious calibration methods are one of the significant approaches that have been practiced successfully for the absolute radiometric calibration of various spaceborne and airborne sensors to ascertain its desired performance in terms of data quality and its accuracy. Calibration campaigns were performed for AVIRIS-NG sensor during its phase-2 campaign overpass at the Desalpar (25th March 2018) and Amarapur (27th March 2018) calibration sites of Gujarat, India. Results from ground-based measurements of atmospheric conditions and surface reflectance including descriptions of the test sites are summarized in this work. Based on the in-situ data and images from the AVIRISNG sensor, the at-sensor apparent radiances are simulated using the 6SV radiative transfer model. The comparison shows good agreement between AVIRIS-NG measured radiance and 6SV simulated apparent radiance at the two calibration sites using desert aerosol model. The gains (spectrally) derived agree to within - 2 % each other. Furthermore, an analysis is performed to determine or identify systematic and random errors, and the overall uncertainty is evaluated for reflectance-based method (total uncertainty of less than 4 % spectrally). (c) 2023 Published by Elsevier B.V. on behalf of COSPAR.

Radar imaging SATellite (RISAT-1A), also known as Earth observation satellite-04 (EOS-04), is a follow-on mission of India's first indigenously developed spaceborne C-band synthetic aperture radar (SAR) on-board RISAT-1 satellite. This article provides a description of the post-launch calibration and data quality evaluation of EOS-04 launched on 14 February 2022. Calibration devices (corner reflectors) of different shapes and sizes were deployed at Ahmedabad and Amrapur calibration sites in Gujarat, India from 2 to 24 April 2022, and their response in the EOS-04 data was used to assess the radiometric and polarimetric calibration. The results of the analysis showed satisfactory radiometric and polarimetric data quality. Geolocation accuracy was assessed using the ground-surveyed position of the corner reflectors and was found to be in accordance with the specified values of less than 50 m.

In ocean colour remote sensing, radiance at the sensor level can be modeled using molecular scattering and particle scattering based on existing mathematical models and gaseous absorption in the atmosphere. The modulation of light field by optical constituents within the seawater waters results in the spectral variation of water leaving radiances that can be related to phytoplankton pigment concentration, total suspended matter, vertical diffuse attenuation coefficients etc. Atmospheric correction works very well over open ocean using NIR channels of ocean colour sensors to retrieve geophysical products with reasonable accuracy while it fails over sediment laden and/or optically complex waters. To resolve this issue, a combination of SWIR channels or NIR-SWIR channels are configured in some ocean colour sensors such as Sentinel- OLCI, EOS- 06 OCM etc. Ocean Colour Monitor (OCM)-3 on board EOS -06 was launched on Nov 26, 2022. It has 13 bands in VNIR (400-1010 nm range) with 1500 km swath for ocean colour monitoring. Arabian Sea near Gujarat coast is chosen as our study site to showcase the geophysical products derived using OCM-3 onboard EOS-06.

ocean colour radiometry at top of the atmosphere manifests the contribution of water leaving radiance with the response of absorption and scattering due to gaseous molecules and particles present in the atmosphere. Modulation of light with phytoplankton pigment concentration, total suspended matter, its vertical attenuation coefficients results in the spectral variation of remote sensing reflectances. Retrieval of remote sensing reflectance with an assumption of dark pixel approximation at NIR channels in OCM-3 works very well using well-known radiative transfer model while the same approach misleads the retrievals over optically turbid waters. To address this matter, the SWIR band with NIR is paired to overcome the retrieval of optical radiometry basically in blue channels as well. Ocean Colour Monitor (OCM) on board EOS − 06 was launched on Nov 26, 2022 with 13 bands in VNIR (400–1010 nm range) with ~ 1500 km swath for ocean colour monitoring. It is operated in two modes such as a) Local Area Coverage (LAC) mode, to cater mainly for user’s real-time requirement of high resolution data (366 m). b) Global Area Coverage (GAC) mode, to cover the global ocean at regular cycles in low resolution mode (1.1 km). It is ready to cater the global needs like potential fishing zones, harmful algal blooms, coastal monitoring system and all related to marine ecosystems as well. Arabian Sea near Gujarat coast is chosen as our study site to showcase the geophysical products derived using OCM-3 onboard EOS-06.

Ocean colour spectral observations play a significant contribution in mapping the earth marine resources through measurements with its inverted geo-physical/biophysical parameters. The retrieval of parameters from the basic sensor measurements highly depends on atmospheric scattering and absorption of light energy by its constituents. Hence the quantitative applications using these datasets are directly affected by the uncertainty in radiative transfer modeling towards atmospheric scattering and absorption and associated sensor degradation with time. Here authors presented an automation of radiometric calibration approach for ocean colour monitor of Oceansat-II (Jan 2017–Dec 2017) dataset through top of the atmosphere radiance simulation using a non-linear optimization technique. This algorithm also provides an alternative approach of calibrating the sensor vicariously through reduced dependency of systematic congruent in-situ measurements. Since Kavaratti in Lakshadweep, India is already a well-known site for calibrating the ocean colour sensors. The OCM cloud free images over this calibration site are utilized to perform its radiometric assessment for the year 2017 using radiative transfer model coupled with bio-optical model where the synchronous, relevant model inputs are simulated. The significant variations in the radiometric calibration coefficients were realized across the spectral bands 412 to 865 nm i.e. 5.5

This study examines the spatio-temporal variability of phytoplankton functional types (PFT) in the Northern Indian Ocean (2014-2023) using satellite data from the Copernicus Marine Service. Parameters include chlorophyll-a, phytoplankton size classes (pico, nano, and microplankton), and specific PFTs like Prochlorococcus, haptophytes, diatoms, and dinoflagellates. In the Arabian Sea, nanoplankton showed a shift from negative to positive anomalies post-2018, influenced by El Nino. Prochlorococcus and prokaryotes exhibited positive anomalies during post-monsoon seasons post-2016 due to rising sea temperatures. In the Bay of Bengal, chlorophyll-a anomalies shifted positive during the 2018 north-east monsoon, driven by Cyclone Ockhi. Spatial analysis revealed higher chlorophyll-a in the Western Bay. Microplankton trends were consistent across regions, while nanoplankton showed a positive decade-long increase, with pronounced peaks in the Western Bay. Overall, the study underscores the sensitivity of phytoplankton communities to climate variability, particularly ENSO events and cyclones, highlighting the role of remote sensing in tracking marine ecosystem changes.

This study describes On-Orbit absolute radiometric calibration for the Ocean Colour Monitor2 (OCM2) onboard Oceansat-2 satellite through a vicarious calibration experiment performed at the Great Rann of Kutch calibration site in Gujarat, India, in February 2022. To achieve accurate and consistent calibration for the OCM2 channels, a reflectance-based calibration method was used which relies on synchronous in-situ measurements of surface reflectance and atmospheric parameters at the time of satellite overpass. In this exercise, the 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) radiative transfer (RT) model was used to simulate the Top Of Atmosphere (TOA) spectral radiance for the OCM2 channels. The on-orbit radiometric performance/changes were derived by comparing the 6 SV simulated TOA radiance with those of the OCM2 Level 1B (L1B) data product. The results indicate that the average gain for band 1 to band 8 was found in the range from 0.88 to 1.23 and the relative error from 1.58% to 18.79% for the OCM2 sensor. However, the Root-Mean-Square-Error (RMSE) between the OCM2 measured and the 6 SV simulated TOA radiance data range from 0.17 (mu W cm(-2) sr(-1) nm(-1)) at 443 nm to 2.04 (mu W cm(-2) sr(-1) nm(-1)) at 620 nm. Furthermore, we analysed in detail the various uncertainties in this approach emanating from surface reflectance, atmospheric conditions (ozone, water vapour and aerosol optical depth), aerosol-type assumption in the RT model, BRDF, and inherent accuracy of the 6 SV RT model. The overall uncertainty was within 6% estimated using the reflectance-based calibration method.

SAtellite-based Marine Process Understanding, Development, Research and Applications (SAMUDRA) for blue economy, a technology development program of the Space Applications Centre, is an umbrella program covering research and applications geared toward physical and biological oceanography making use of current and future satellite observations for developing the nation’s blue economy. The main motivation behind this project was to develop satellite and numerical model-based information and value-added products and to demonstrate the implementation of developed applications for operational requirements. The program also aimed at improving existing methodologies for various applications by utilizing space-based inputs. Several field campaigns with the use of NavIC-enabled instruments and NABHMITRA were conducted for measuring biophysical parameters and validation of developed applications in the coastal regions. One of the key aspects of this project was development of web-based customized tools/dissemination system for providing the information to the end users. Some of the key/notable achievements of SAMUDRA were development of a portal OceanEye (tailor-made web-portal for Shipping Corporation of India), storm-surge/inundation system, oil-spill trajectory modeling, level-next potential fishing zone algorithm and rip current alert system.

The proper radiometric calibration of the INSAT-3D and -3DR IMAGER is of utmost importance for effective weather forecasting and monitoring. Accurate and reliable images are crucial for predicting weather patterns, tracking storms, and assessing the impact of natural disasters. This research paper presents an evaluation of the radiometric calibration of the INSAT-3D and -3DR IMAGER using a reflectance-based method referenced to in-situ ground site measurements. Calibration campaigns were conducted at the Great Rann of Kutch (GROK) in Gujarat, India during February 2022 to ensure synchronous measurements of surface reflectance and atmospheric variables. In this study, the radiative transfer model called 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) was employed to simulate the Top-Of-Atmospheric (TOA) radiance. The calibration coefficients for the Visible (VIS) and Short Wave Infrared (SWIR) bands of the IMAGER were determined by comparing the TOA radiance obtained from the radiative transfer model with the Level 1B product of satellite data. The results convincingly demonstrate that the reflectance-based calibration method yields highly accurate and reliable radiometric calibration for both the VIS and SWIR bands of the INSAT-3D and -3DR IMAGER, with an uncertainty significantly smaller than the expected sensor degradation. Specifically, it was found that the VIS band of INSAT-3D underestimates the radiance value by 9.05%, while the VIS band of INSAT-3DR underestimates it by 4.88%. The study also discusses various sources of uncertainty and emphasizes the need for comprehensive uncertainty quantification to ensure the radiometric accuracy of the IMAGER data. Overall, the calibration results indicate that the absolute radiometric accuracy for the VIS and SWIR bands of the IMAGER on-board INSAT-3D and INSAT-3DR satellites is better than 4%. This research contributes to enhancing the reliability and usability of the INSAT-3D and -3DR IMAGER data for weather forecasting and monitoring applications.