Dr. Ritesh Gautam Profile

Dr. Ritesh Gautam

Assistant Professor at Indian Institute of Technology Bombay

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Publications (3)

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Proceedings Article | 6 May 2016 Paper

A merged aerosol dataset based on MODIS and MISR Aerosol Optical Depth products

Manoj Singh, Ritesh Gautam, Parvatham Venkatachalam

Proceedings Volume 9876, 987627 (2016) https://doi.org/10.1117/12.2223485

KEYWORDS: MODIS, Aerosols, Satellites, Data fusion, Remote sensing, Data modeling, Process modeling, Atmospheric modeling

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Aerosol Optical Depth (AOD) products available from MODIS and MISR observations are widely used for aerosol characterization, and global/environmental change studies. These products are based on different retrieval-algorithms, resolutions, sampling, and cloud-screening schemes, which have led to global/regional biases. Thus a merged product is desirable which bridges this gap by utilizing strengths from each of the sensors. In view of this, we have developed a “merged” AOD product based on MODIS and MISR AOD datasets, using Bayesian principles which takes error distributions from ground-based AOD measurements (from AERONET). Our methodology and resulting dataset are especially relevant in the scenario of combining multi-sensor retrievals for satellite-based climate data records; particularly for long-term studies involving AOD. Specifically for MISR AOD product, we also developed a methodology to produce a gap-filled dataset, using geostatistical methods (e.g. Kriging), taking advantage of available MODIS data. Merged and spatially-complete AOD datasets are inter-compared with other satellite products and with AERONET data at three stations- Kanpur, Jaipur and Gandhi College, in the Indo-Gangetic Plains. The RMSE of merged AOD (0.08-0.09) is lower than MISR (0.11-0.20) and MODIS (0.15-0.27). It is found that merged AOD has higher correlation with AERONET data (r within 0.92-0.95), compared to MISR (0.74-0.86) and MODIS (0.69-0.84) data. In terms of Expected Error, the accuracy of valid merged AOD is found to be superior as percent of merged AOD within error envelope are larger (71-92%), compared to MISR (43-61%) and MODIS (50-70%).

Proceedings Article | 5 May 2016 Paper

Light absorbing impurity deposition over the Himalayan-Karakoram-Hindu Kush-Tibetan cryosphere: a review and satellite-based characterization

Rithwik Kodamana, Ritesh Gautam

Proceedings Volume 9877, 98771M (2016) https://doi.org/10.1117/12.2227996

KEYWORDS: Satellites, Reflectivity, Aerosols, Remote sensing, Absorption, Landsat, MODIS, Earth observing sensors, Minerals, Visible radiation, Solar radiation

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Light absorbing impurities such as black carbon and dust reduce the reflectance of snow/ice surface. The impurities absorb the incoming solar radiation thereby accelerating snow aging and melting. This further accelerates the processes of snow albedo reduction and melting. A recently-conducted ice core study in Mera Peak shows that annual dust mass fluxes (10.4+/-2.8 g m^-2 yr^-1) are a few orders of magnitude higher than black carbon (7.9+/-2.8 g m^-2 yr^-1). A similar study conducted in the Tibetan Plateau showed a decrease in the amount of mineral dust deposition since 1940s indicating that the increased glacier melt can be attributed to increased black carbon emission than dust. The concentrations of black carbon and dust peak during the pre-monsoon season. Spectral reflectance curves derived from satellite imagery for the Himalayan Tibetan Plateau showed domination of dust-induced solar absorption during the pre-monsoon season. Spatial distribution of reflectance also depends on the transport pathway of impurities, with the south western Hindu Kush and Himalaya experiencing greater dust influx, deposition and snow albedo reduction than northern regions of Karakoram. In this study, we characterize the light absorbing impurities deposited in Himalayan regions using multi spectral data from MODIS and LANDSAT. On comparing the spectral reflectance curves derived from MODIS rand LANDSAT for the overlapping periods and areas and by observing the VIS-NIR gradient of spectral reflectance, determination of the type of light absorbing impurity, mainly mineral dust, and its relation to snow properties are derived.

Proceedings Article | 3 May 2016 Paper

An automated fog monitoring system for the Indo-Gangetic Plains based on satellite measurements

Dinesh Patil, Reema Chourey, Sarwar Rizvi, Manoj Singh, Ritesh Gautam

Proceedings Volume 9882, 988226 (2016) https://doi.org/10.1117/12.2228006

KEYWORDS: MODIS, Environmental sensing, Visibility through fog, Visibility, Fiber optic gyroscopes, Satellites, MODIS, Clouds, Aerosols, Detection and tracking algorithms, Image storage

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Fog is a meteorological phenomenon that causes reduction in regional visibility and affects air quality, thus leading to various societal and economic implications, especially disrupting air and rail transportation. The persistent and widespread winter fog impacts the entire the Indo-Gangetic Plains (IGP), as frequently observed in satellite imagery. The IGP is a densely populated region in south Asia, inhabiting about 1/6th of the world’s population, with a strong upward pollution trend. In this study, we have used multi-spectral radiances and aerosol/cloud retrievals from Terra/Aqua MODIS data for developing an automated web-based fog monitoring system over the IGP. Using our previous and existing methodologies, and ongoing algorithm development for the detection of fog and retrieval of associated microphysical properties (e.g. fog droplet effective radius), we characterize the widespread fog detection during both daytime and nighttime. Specifically, for the night time fog detection, the algorithm employs a satellite-based bi-spectral brightness temperature difference technique between two spectral channels: MODIS band-22 (3.9μm) and band-31 (10.75μm). Further, we are extending our algorithm development to geostationary satellites, for providing continuous monitoring of the spatial-temporal variation of fog. We anticipate that the ongoing and future development of a fog monitoring system would be of assistance to air, rail and vehicular transportation management, as well as for dissemination of fog information to government agencies and general public. The outputs of fog detection algorithm and related aerosol/cloud parameters are operationally disseminated via http://fogsouthasia.com/.

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