Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Bhattacharjee, S.Subject: search.filters.subject.ODIAC

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    Prediction of High-Resolution Atmospheric CO2 Concentration from OCO-2 using Machine Learning
    (Association for Computing Machinery, 2023) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, A.K.
    Carbon Dioxide (CO2) is a greenhouse gas (GHG) emitted by human anthropogenic activities. The satellite measurement of atmospheric column-averaged CO2 concentration (XCO2) provides an excellent opportunity to understand the global carbon cycle for a large comprehensive temporal range. Orbiting Carbon Observatory-2 (OCO-2) satellite provides highly accurate data with a spatial resolution of approximately 3 km2. However, OCO-2 measures one location on the Earth's surface almost fortnightly. Also, the clouds and aerosols cause missing data. In this work, the OCO-2 measurements, along with Open-Source Data Inventory for Anthropogenic CO2 (ODIAC) emission estimate, are considered. A spatial upscaling, followed by different machine learning methods are used to predict high-resolution, continuous mapping of XCO2. The prediction models are evaluated using the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) for Germany, considering a temporal range of November 2018 to December 2019. The least error is attained by monthly model, achieving a MAE and RMSE of 0.707 ppm and 1.187 ppm, respectively, using the extremely randomized trees (ERT) method. The predictions are externally validated using Total Carbon Column Observing Network (TCCON) ground-based measurements as well. © 2023 ACM.
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    Downscaled XCO2 Estimation Using Data Fusion and AI-Based Spatio-Temporal Models
    (Institute of Electrical and Electronics Engineers Inc., 2024) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.; Chen, J.
    One of the well-known greenhouse gases (GHGs) produced by anthropogenic human activity is carbon dioxide (CO2). Understanding the carbon cycle and how negatively it affects the ecosystem requires analysis of the rise in CO2 concentration. This work aims to map CO2 concentration for the entire surface, making it useful for regional carbon cycle analysis. Here, column-averaged CO2 dry mole fraction, called XCO2, measured by the orbiting carbon observatory-2 (OCO-2) satellite, is used. Because of spectral interference by the clouds and aerosols, there are many missing footprints in the Level-2 swath of OCO-2, making it disruptive to understand any assessment related to the carbon cycle. The objective of this work is to predict 1 km2 XCO2 using data resampling and machine learning models. This work achieves a minimum mean absolute error (MAE) and root mean square error (RMSE) of 0.3990 and 0.8090 ppm, using the monthly models. © 2004-2012 IEEE.
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    High-Spatial-Resolution Estimation of XCO2 Using a Stacked Ensemble Model
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.; Balamurugan, V.; Chen, J.
    Highlights: What are the main findings? The study develops a customized stacked ensemble model that generalizes (Formula presented.) predictions across multiple country, such as Germany, France, and Japan. It produces gap-filled high-resolution monthly, seasonal, and yearly maps, highlighting vegetation dynamics and seasonal cycles. What is the implication of the main finding? The customized stacked ensemble model provides reliable cross-country (Formula presented.) predictions at 1 (Formula presented.) resolution, validated against TCCON and CAMS, supporting large-scale environmental monitoring. Seasonal and yearly analyses show vegetation dynamics and photosynthetic activity significantly influence (Formula presented.), enhancing the model’s adaptability for agriculture, different climate assessments, and future global mapping. One of the leading causes of climate change and global warming is the rise in carbon dioxide ((Formula presented.)) levels. For a precise assessment of (Formula presented.) ’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing (Formula presented.) sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2) (Formula presented.) retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled (Formula presented.) concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1 (Formula presented.)) (Formula presented.). When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and (Formula presented.) of 0.90. © 2025 by the authors.