INSAT-3D Imager Research Articles

Characterisation of dust aerosols and their absorbing efficiency over South Asia using infrared observations from INSAT-3D Imager

ABSTRACT Radiative impacts of dust aerosols exhibit large regional and temporal heterogeneity, despite the global net cooling effect. Hence, accurate assessment of dust aerosol impacts on weather and climate calls for their regional Characterisation and its proper incorporation in climate models. In this study, dust aerosol distribution and its effects on the absorbing efficiency of aerosol system over South Asia are examined using the Infrared Difference Dust Index (IDDI) derived from the Imager onboard ISRO’s INSAT-3D satellite. IDDI estimated from INSAT-3D agrees well (r ranges from 0.4 to 0.7) with the dust aerosol optical depth (DAOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) and Second Modern-Era Retrospective analysis for Research and Applications (MERRA-2). This shows the potential of this parameter for the Characterisation of dust aerosol and its diurnal and seasonal evolutions. Using IDDI from INSAT-3D, this study further examines the changes in aerosol distribution and characteristics over the Indian landmass and surrounding oceanic regions during severe dust storm episodes. Spatial distribution of dust aerosols and prevailing circulation pattern show long range transport of dust particles from their source regions to faraway locations during the dust events. This is further confirmed by the presence of elevated aerosol layers with high particulate depolarization ratio, observed by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). An increase in absorbing efficiency of the aerosol system is observed, due to these transported dust particles, over the South Asian region. Dust absorbing efficiency (DAE) is about 20–25K during the dust storm events. This enhancement in radiation absorption due to the wide spread distribution of dust aerosols over a vast domain would significantly alter the circulation dynamics and weather systems over South Asia, especially during pre-monsoon and monsoon seasons.

Retrieval of total precipitable water from INSAT-3D Imager observations using deep neural network

Total precipitable water (TPW) is a key parameter for monitoring the development of the various weather phenomenon, viz., deep convective systems, thunderstorms, etc. Moreover, various nowcasting applications such as heavy rainfall alerts, cloudbursts, thunderstorms, etc. require high spatio-temporal TPW information, which can be achieved with the satellite-based observations from geostationary platform. Therefore, the present study deals with the estimation of TPW from observations in the water vapour (WV) absorption (6.5–7.0 μm) and thermal infrared (TIR) channels (TIR1 (10.3–11.2 μm); TIR2 (11.5–12.5 μm)) of Imager onboard the third generation Indian National Satellite (INSAT-3D) deployed in a geostationary orbit, using the machine-learning technique. The deep neural network (DNN) technique is utilized here to estimate TPW from INSAT-3D Imager observations over the Indian subcontinent region. Herein, the observations from three channels (WV, TIR1 & TIR2) of INSAT-3D Imager for three years from 2018 to 2020 are collocated with TPW from European Center for Medium range Weather Forecast (ECMWF) reanalysis (ERA5) available at 0.25° uniform spatial resolution to prepare a matchup dataset. The input vector consists of Julian day, geolocation (latitude/longitude), brightness temperatures of WV, TIR1 and TIR2 channels of INSAT-3D Imager, surface emissivity of all three channels and the satellite zenith angles. The ERA5 TPW is the output (predictand) of the DNN. For the training of the DNN, 70 % of the total collocated matchups are selected randomly, and the rest 30 % are used for the testing. The DNN is developed for land and ocean surfaces separately, due to a larger variation in the emissivity over land. The assessment of the developed DNN over both surfaces show the similar statistics. The quality evaluation of the training and testing dataset shows the negligible biases and root-mean-squared error (RMSE) of ∼3.5 mm over the land and ∼4.5 mm over the ocean, respectively, in the predicted TPW when compared with ERA5 TPW. However, the bias (RMSE) in the predicted TPW is observed -0.3 (4.4) mm and 0.4 (4.8) mm, respectively over the land and ocean surfaces with reference to the ERA5 TPW for the independent testing of the developed DNN, which was performed on the observations of INSAT-3D for 2021. Additionally, the comparison with radiosondes’ TPW demonstrates the 6.7 mm RMSE with bias of -0.7 mm for 2021.

Towards Accurate Radiometric Calibration of INSAT-3D and INSAT-3DR IMAGER: Addressing Uncertainty and Error Sources

ABSTRACT 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.

IMSRA rainfall estimation validation and variability over Indian region during GAJA cyclone

Rainfall during a cyclonic event is very high compared to the monsoon seasons, which bring heavy damage to the lives of humans and cattle and other severe bruises. To prevent such conditions, accurate prediction of rainfall is necessary. 
 Before prediction, there should be adequate knowledge about how rainfall is distributed spatially, how it varies from time to time, etc., This study attempts to infer the rainfall distribution during GAJA cyclone over the Indian region with the INSAT-3D IMAGER data. The IMRSRA and IMC (IMSRA-Modified) datasets are used for rainfall retrieval during landfall of GAJA cyclone, which is of 0.10 X 0.10 spatial resolution and temporal resolution of 30min. An IMSRA-Merged data product is derived from the IMC dataset. The obtained result is validated by the IMD rain gauge data, global data products such as IMERG and GSMaP. The performance of IMSRA-Merged is good compared to all other datasets that show reliable rainfall and a good correlation (>0.5).

High-resolution outgoing long wave radiation data (2014–2020) of INSAT-3D Imager and its comparison with Clouds and Earth’s Radiant Energy System (CERES) data

As a proxy of convection INSAT-3D satellite-derived product Outgoing Long Wave Radiation (OLR) data is available in both high temporal and spatial ranges over 40°N–40°S & 35°E–135°E. Daily gridded data set of 7 years of data (2014–2020) has been generated at 10 km × 10 km resolution and the same is compared with Clouds and Earth’s Radiant Energy System (CERES) instrument data taken from CERES as reference. Almost all the INSAT-3D data set generated is Global Space-based Inter-Calibration System (GSICS) corrected. The spatiotemporal consistency of the data set was statistically analyzed and found to be reasonably good agreement having a bias of ∼±5–6 W/m2 over above said domain.This inter-comparison is essential to get confidence in the data sets and release it further in the public domain for any scientific study. Again, this data set will be very useful in diagnosing the variations of convection at different scales (daily, weekly, monthly, annual, seasonal, intra-seasonal, etc.) & an important repository of Daily Climate Data Records (DCDR) for future studies. The specified domain of the present study is affected throughout the year with variable (weak, moderate, intense, or severe) spatiotemporal Inter-Tropical Convergence Zone (ITCZ) convection streams due to different types of weather activities (winter, pre-monsoon, monsoon, and post-monsoon) throughout the year. To visualize the importance of this high-resolution OLR data set a case study of Cyclone Amphan and Vayu is presented. The extremely severe intense convection (OLR departure −112 W/m2 with INSAT-3D new data set whereas −104 W/m2 in CERES data) was observed in both the data sets on 18th May-2020 at 13.7–16°N & 86.2–86.8°E during the super cyclonic stage of Amphan. A similar type of variation in the OLR has been noticed for Vayu Cyclone (OLR departure −94 W/m2 with INSAT-3D new data set whereas −86 W/m2 in CERES data). This information is very useful in impact-based forecasting and further future actions for disaster managers/decision-makers. The localized convective features during cyclone activity over the Indian Ocean region both in the Arabian Sea and the Bay of Bengal are well captured with this new data set and the difference in OLR of INSAT -3D and CERES -9 W/m2 and -12 W/m2 respectively.

INSAT-3D SST and its diurnal variability assessment using in-situ and MODIS observations

Sea Surface Temperature is an Essential Climate Variable used in oceanographic and meteorological application studies such as air-sea interaction, ocean mixing, boundary layer processes, and ocean state forecast, which require high temporal resolution SST. Indian geostationary satellite INSAT-3D imager has been designed to provide such information at 30 min intervals, 4 km spatial resolution along with several other parameters over the North Indian Ocean. In this study, the 30-minute interval INSAT-3D SST and its diurnal variability are validated (inter-compared) with collocated in-situ buoy measurements and compared with contemporary MODerate-resolution Imaging Spectroradiometer SST observations. A reasonable agreement exists between the INSAT-3D and in situ data with a correlation coefficient of 0.70 and Root Mean Square Error (RMSE) of 1.28 °C. This relationship is better for the daytime observations and during the pre and post-monsoon seasons, while the relationship is relatively weaker for the nighttime and monsoon seasons with remarked imprints from convective processes in the upper oceans. For the Arabian Sea and Bay of Bengal regions, the INSAT-3D SST shows better correlation and negative bias in comparison with in situ observations, while it is weaker with positive bias for the Equatorial Indian Ocean. Inter-comparison with MODIS Aqua/Terra day and night passes SST also shows a similar relationship, however with a much-improved correlation coefficient (∼0.95) and highly consistent in spatial and temporal variability with a cool bias of 0.14 °C. INSAT-3D SST shows a significantly high average diurnal temperature difference of 2.24 °C, whereas buoys show 0.54 °C. These high RMSE and diurnal variability results suggest that INSAT-3D SST retrieval can be improved to study the many oceanographic processes such as eddies, fronts, and estimation of air-sea fluxes. Therefore, this study helps to improve the retrieval algorithm of INSAT-3D SST, by developing the region-specific regression coefficients along with data merging and assimilation techniques.

Quality assessment of Outgoing Longwave Radiation (OLR) derived from INSAT-3D Imager: Impact of GSICS correction

Quality assessment of Outgoing Longwave Radiation (OLR) derived from INSAT-3D Imager: Impact of GSICS correction

Correction to: Algorithm for the Estimation of Continental Scale Land-Surface Broadband Albedo from INSAT-3D Imager Data

Correction to: Algorithm for the Estimation of Continental Scale Land-Surface Broadband Albedo from INSAT-3D Imager Data

Retrieval of Total Precipitable Water from Thermal Infrared Observations of Insat-3D Imager over the Ocean

Retrieval of Total Precipitable Water from Thermal Infrared Observations of Insat-3D Imager over the Ocean

Assimilation of INSAT-3D imager water vapour clear sky brightness temperature in the NCMRWF’s assimilation and forecast system

This paper describes the direct assimilation of water vapour (WV) clear sky brightness temperatures (CSBTs) from the INSAT-3D imager in the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM) assimilation and forecast system. INSAT-3D imager WV CSBTs show a systematic bias of 2–3 K compared to the data simulated from the model first guess fields in the pre-assimilation study. The bias in the INSAT-3D imager WV CSBTs is removed using a statistical bias correction prior to assimilation. The impact of INSAT-3D imager WV channel CSBTs is investigated through different approaches: (i) single observation experiments and (ii) global assimilation experiments using the hybrid-four-dimensional variational technique. Single observation experiments of channels of the same frequency from different instruments like the INSAT-3D imager and sounder, and the Meteosat visible and infrared imager (MVIRI) onboard Meteosat-7, show the INSAT-3D imager and MVIRI WV channels have a similar impact on the analysis increment. Global assimilation clearly shows the positive impact of the INSAT-3D imager WV CSBTs on the humidity and upper tropospheric wind fields, whereas the impact on the temperature field, particularly over the tropics, is neutral. Validation of model forecasted parameters with the in situ radio sonde observations also showed the positive impact of assimilation on the humidity and wind fields. INSAT-3D imager WV CSBTs have been assimilated operationally in NCUM since August 2018.

Use of the Moon to promote on-board calibration of INSAT-3D imager visible channel

INSAT-3D is a multipurpose geosynchronous spacecraft with main meteorological payloads imager and sounder for enhanced meteorological observations. The accuracy of these derived products is vital for day-to-day weather forecast and assimilation in numerical weather product models. One of the most important channels in the INSAT-3D satellite is the visible (0.55–0.75 µm, VIS) sensor, because in this channel cloud properties are prominently analyzed and it correlates well with more active weather and can be used to assist in forecasting. The Moon has been widely used for calibration of earth-observation missions across international community due to its temporal stability absence of any intervening medium such as atmosphere and its spectral resemblance to ground targets. As a preliminary step, attempt has been made to calibrate the VIS channel of INSAT-3D imager using Moon as a calibration source. For the requirements, a GIRO (RObotic Lunar Observatory, ROLO) model has been installed in National Satellite Meteorological Centre, INSAT-3D Meteorological Data Processing System, India Meteorological Department and New Delhi for output irradiance of Moon. Further, this irradiance is compared with INSAT-3D-measured Moon irradiance during July 2015 to June 2016. The preliminarily result shows INSAT-3D is overestimating against the GIRO model output irradiance. However, the pattern found consistency during the period, and no degradation can be estimated with this short span of time and found useful to generate the coefficients.

Physical retrieval of sea-surface temperature from INSAT-3D imager observations

This paper presents results of physical retrieval of sea-surface temperature (SST) from the INSAT-3D imager observations. Radiance measurements from two thermal infrared imager channels are used for retrieving SST. Prior to their use in the retrieval process, the INSAT-3D radiances are corrected for the biases using a newly developed algorithm. The physical retrieval of SST is performed using an optimal estimation method. The optimisation is performed under two scenarios. In the first scenario, the SST retrieval problem is assumed to be linear while in the second one, the assumption of linearity is relaxed. Further, retrievals are performed by considering the reduced, as well as, the full state vector. Overall, physical retrieval performs much better than the regression-based approach. Moreover, in the physical retrieval, the best performance is obtained in the second scenario with the use of the full state vector. Validation of these retrievals with the iQuam SST resulted in a standard deviation (bias) of ∼0.65 (0.19) K while those from the regression-based method are found to have standard deviation (bias) of ∼0.68 (–0.17) K.

INSAT-3D and MODIS retrieved sea surface temperature validation and assessment over waters surrounding the Indian subcontinent

ABSTRACTThe INSAT-3D imager (4 km) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on-board Aqua and Terra space-platforms level-2 (1 km) sea surface temperature (SSTskin) product accuracy has been analysed over waters surrounding the Indian subcontinent by indirect comparison method using collocated bulk in-situ measurements (SSTdepth) for 3 years (October 2013–October 2016). Statistical results show that root mean square error of all the three satellites is in range of around 0.60–0.70°C. Retrieval error is found to be slightly more in case of validation against iQuam data set. INSAT-3D is showing more underestimation with bias ranging from about −0.16°C to −0.20°C than MODIS sensor having bias in range of about 0.06°C to −0.12°C. All the three missions are slightly underestimating over open-ocean with bias ranging in 0–0.17°C. INSAT-3D is significantly underestimating in-situ observations over the Arabian Sea (approximate bias = 0.27°C). Seasonal validation analysis reveals relatively high retrieval error during monsoon season than pre-monsoon and post-monsoon seasons. MODIS sensor is showing significant underestimation during monsoon with bias ranging from approximately −0.29°C to −0.58°C. Overall, all the three missions are performing similarly well over the study area.

Detection of Fog Using Temporally Consistent Algorithm With INSAT-3D Imager Data Over India

A fog detection algorithm using INSAT-3D imager data has been developed and tested. The algorithm emphasizes continuous fog detection as the current fog detection algorithm which is used for the operational fog product generation over India is having temporal discontinuity during dawn and dusk. Moreover, currently, the INSAT-3D fog product over India is being generated with different algorithms for day and night. In this paper, a single algorithm using brightness temperature difference of 3.9 and 10.8 μm wavelength for both day and night has been used. The thermal infrared, water vapor, and visible channel spectral information (for daytime) has been used as additional criteria to reduce false alarm. The thresholds used are determined dynamically using an image-based method. The spatial homogeneity test, which is a special characteristic of fog, has also been carried out to minimize false detection. Validation was carried out against visibility data over five airports in the Indo-Gangetic (IG) plains of India. It shows that probability of detection and false detection to be 66% and 10%, respectively, for 2013–2014 and 68% and 10% for 2015–2016. The algorithm is able to capture the entire life cycle of fog from formation to dissipation, consistently for both the season with different weather conditions.

Reflectance-based vicarious calibration of INSAT-3D using high-reflectance ground target

This study is carried out as the post-launch calibration for visible (IMG-VIS) and shortwave (IMG-SWIR) bands of INSAT-3D imager and visible (SND-VIS) band of Indian National Satellite System (INSAT)-3D sounder over land site (Little Rann of Kutch (ROK), Gujarat) on five clear-sky days. This calibration activity is performed to account for the characterisation errors or undetermined post-launch changes in sensor spectral response. We had measured the surface reflectance and atmospheric variables at the site synchronising with the viewing and solar geometry of the INSAT-3D scan. Top of atmosphere (TOA) spectral radiances were computed using 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum) radiative transfer (RT) code with the surface reflectance and atmospheric variables as well as spectral response function (SRF) of each channel. The uncertainties involved due to spatial variability of site and variation in aerosol type in calibration coefficients were also computed. MODIS Bidirectional Reflectance Distribution Function (BRDF) product is used to account the effect of surface anisotropy on TOA spectral radiance. Comparison between 2014 and 2015 vicarious calibration results, indicate that the INSAT-3D measured radiance are stable within 0.38%, 0.18% and 0.13% for IMG-VIS, IMG-SWIR and SND-VIS, respectively. 6SV simulated atmospherically corrected reflectances were found to match much better with the observed surface reflectance in the inverse mode for all three bands. Comparing these results with the previous year’s analysis, there is no indication of major change in calibration coefficients for all three bands of INSAT-3D.

Atmospheric Motion Vectors from INSAT-3D: Initial quality assessment and its impact on track forecast of cyclonic storm NANAUK

The advanced Indian meteorological geostationary satellite INSAT-3D was launched on 26 July 2013 with an improved imager and an infrared sounder and is placed at 82°E over the Indian Ocean region. With the advancement in retrieval techniques of different atmospheric parameters and with improved imager data have enhanced the scope for better understanding of the different tropical atmospheric processes over this region. The retrieval techniques and accuracy of one such parameter, Atmospheric Motion Vectors (AMV) has improved significantly with the availability of improved spatial resolution data along with more options of spectral channels in the INSAT-3D imager. The present work is mainly focused on providing brief descriptions of INSAT-3D data and AMV derivation processes using these data. It also discussed the initial quality assessment of INSAT-3D AMVs for a period of six months starting from 01 February 2014 to 31 July 2014 with other independent observations: i) Meteosat-7 AMVs available over this region, ii) in-situ radiosonde wind measurements, iii) cloud tracked winds from Multi-angle Imaging Spectro-Radiometer (MISR) and iv) numerical model analysis. It is observed from this study that the qualities of newly derived INSAT-3D AMVs are comparable with existing two versions of Meteosat-7 AMVs over this region. To demonstrate its initial application, INSAT-3D AMVs are assimilated in the Weather Research and Forecasting (WRF) model and it is found that the assimilation of newly derived AMVs has helped in reduction of track forecast errors of the recent cyclonic storm NANAUK over the Arabian Sea. Though, the present study is limited to its application to one case study, however, it will provide some guidance to the operational agencies for implementation of this new AMV dataset for future applications in the Numerical Weather Prediction (NWP) over the south Asia region.

Detection of Asian dust storms from geostationary satellite observations of the INSAT-3D imager

Asian dust storm outbreaks significantly influence air quality, weather, and climate. Therefore, it is desirable to have qualitative and quantitative information on the time, location, and coverage of these outbreaks at high spatial and temporal resolution. The imager on board the Indian metrological geostationary satellite INSAT-3D observes Asia at a temporal resolution of 30 min and a spatial resolution of 1, 4, 8, and 4 km in the visible, middle infrared (MIR), water vapour (WV), and thermal infrared (TIR) bands, respectively. In this article, an algorithm is described for detecting desert dust storms from INSAT-3D imager data. The algorithm described here is a combination of various pre-existing methods such as infrared split-window, MIR and TIR brightness temperature difference, and visible to MIR reflectance ratio, which are based on the fact that dust exhibits features of spectral dependence and contrast over the visible, MIR, and TIR spectrum that are different from clouds, surface, and clear-sky atmosphere. Using the Atmospheric Infrared Sounder (Aqua/AIRS) dust score as proxy, INSAT-3D dust storm products were tested under different scenarios such as dust storms and dust transport in Asia. TIR observations from the geostationary platform of INSAT-3D allows computation of the infrared difference dust index (IDDI), which gives a quantitative measure of dust loading relative to clear atmosphere. Moreover, due to the high temporal resolution (30 min) of INSAT-3D observations, INSAT-3D-derived dust products allow more precise monitoring of dust transportation as compared with dust products derived from polar satellite observations.

Detection of Day Time Fog Over India Using INSAT-3D Data

A simple and new fog detection algorithm using any geostationary satellite data that is having at least one visible and one thermal channel is developed. The visible channel observation is used to detect fog, whereas the thermal infrared channel data are used to eliminate the medium/high clouds and snow area. This method is based on temporal differencing technique. Thresholds used in this technique are derived dynamically for each data. It also includes the spatial homogeneity test, which is a unique characteristic of fog. The algorithm is tested with half hourly data of INSAT-3D imager from 0400 to 0930 UTC of 2013–2014 winter months. The fog maps were validated with visibility data available at hourly interval for five different locations over the Indo-Gangetic plains of India for January 2014. The main advantage of this algorithm is its capability to detect day time fog without using the $3.9\hbox{-}\upmu {\text {m}}$ channel, which is having both emissive and reflective components during day time. This technique is found to have potential for operational use.

Absolute Vicarious Calibration of recently launched Indian Meteorological Satellite: INSAT-3D imager

Abstract. 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 325–2500 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.

Operational Retrieval of aerosol optical depth over Indian subcontinent and Indian Ocean using INSAT-3D/Imager product validation

Abstract. 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 (τI) and MODIS derived AOD (τM) co-located in space (at 1° resolution) and time during January, February and March (JFM) 2014 encompasses 1165, 1052 and 900 pixels, respectively. Good agreement found between τI and τM 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 (τI) and 10 sun-photometers (τA) that includes 9 AERONET (Aerosol Robotic Network) and 1 handheld sun-photometer site. INSAT-3D derived AOD i.e. τI, is found within the retrieval errors of τI = ±0.07 ±0.15τA 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.