Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    High-resolution Soil Moisture Prediction from SMOS using Machine Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sudhakara, B.; Maheshwari, A.; Periasamy, M.; Bhattacharjee, S.
    Soil moisture is essential for the land carbon cycle, surface and groundwater circulation, heat transport, energy exchange between these systems and other processes. SMOS's (Soil Moisture and Ocean Salinity) 36-kilometer spatial resolution and 3-day temporal resolution offer valuable insights into soil moisture dynamics. This research paper introduces an innovative approach to enhance our understanding and prediction of SMOS values by applying advanced machine learning models. Our research focuses on developing and implementing advanced downscaling techniques, leveraging advanced machine learning algorithms. The primary objective is to establish a robust framework for estimating soil moisture levels at multiple geographic locations within the study region of Oklahoma, USA. To achieve this, three years of SMOS (Soil Moisture and Ocean Salinity) data was integrated with remotely captured images spanning the full range of the electromagnetic spectrum, from visible to infrared wavelengths. The LSTM model performed significantly better in predicting soil moisture values with 0.041 RMSE (m3/m3) and 0.869 (R2) than the other models. © 2025 IEEE.
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    Predicting High-Resolution Soil Moisture Using MODIS Bands: A Fusion-Regression Approach
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sudhakara, B.; Pais, S.M.; Bhattacharjee, S.
    Soil moisture (SM) is a crucial biophysical parameter that plays a significant role in predicting food security. Monitoring SM is essential as it provides valuable insights into agricultural productivity and the impacts of climate change. Remote sensing platforms retrieve SM data from various satellites, enabling global monitoring. However, the coarse resolution of these datasets limits their ability to capture fine-scale variations in SM. To address this challenge, this study aims to generate a high-resolution SM product using data from the Soil Moisture Active Passive (SMAP) mission. The SMAP data is fused with MODIS spectral bands to create a multi-source dataset, which is then used as input for different regression models to achieve downscaling of SM. Experimental results demonstrate that the Extremely Randomized Trees (ERT) model achieves the minimum error, with RMSE 0.0113 m3/m3, MAE 0.0172 m3/m3, and R2 0.9725. The study focuses on Karnataka, covering the temporal window from 2015 to 2022. © 2025 IEEE.