Faculty Publications
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Item 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.Item Forecasting of Fine-Grained SIF of OCO-2 Using Multi-source Data and AI-Based Techniques(Springer, 2025) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.The emission of unused energy absorbed from the sunlight by plants and other photosynthetic organisms is known as solar-induced fluorescence (SIF). SIF is a direct proxy for the photosynthetic activity of the plants used to monitor drought, crop yield estimation, ecological processes, and carbon cycles. Comprehending the SIF dynamics beforehand helps gain an understanding of vegetation dynamics, carbon cycle, and crop phenology. This study explores the potential of using Orbiting Carbon Observatory-2 (OCO-2) SIF data for forecasting SIF at regional scales. The research utilizes machine learning models and data fusion to forecast the SIF data, by establishing relationships between observed SIF from past timestamps and the Enhanced Vegetation Index. The lasso regression achieves minimal error of RMSE 0.0355 Wm-2nm-1sr-1 and MAPE of 16.9093% for forecasting monthly SIF data. In contrast, the light gradient boosting machine model (LG) performs well for a larger non-linear dataset, i.e., seasonal models achieving a RMSE of 0.0389 Wm-2nm-1sr-1 and MAPE of 17.4895%, respectively. Karnataka and Maharastra, the two Indian states, are considered as the study areas for this work for a temporal window of 2017–2019. Fine-grained, uniformly distributed SIF forecasting provides valuable insights for understanding vegetation responses to environmental changes, optimizing agricultural practices, and developing climate change mitigation strategies. © Indian Society of Remote Sensing 2025.Item High-Resolution Soil Moisture Estimation: A Case Study in Coastal India(Springer, 2025) Sudhakara, B.; Bhattacharjee, S.The high-resolution soil moisture (SM) is significant for agricultural production, hydrological modelling, weather prediction, and climate studies at the regional scale. Current satellite and derived SM products span a wide range of coarse spatial resolutions. This work predicts high-resolution and precise SM estimates for the USA and the western coastal regions of India by utilizing remote sensing data. The study implements and compares five models for SM prediction in the study regions, including machine learning and deep learning models. The extra trees regressor model outperformed all other models in the USA and India datasets. It achieved the best performance metrics, with an MAE of 1.080 mm, RMSE of 1.715 mm, and R2 of 0.958 for the USA, and an MAE of 0.847 mm, RMSE of 1.554 mm, and R2 of 0.955 for India. The study validates the predicted data using 20 stations from the International Soil Moisture Network in the USA. It aims to support decision-making for agricultural practices, hydrology, and climate adaptation by providing valuable insights into the area’s spatio-temporal dynamics of SM. © Indian Society of Remote Sensing 2025.