Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. While there is growing interest in using satellite-to-earth microwave links (SMLs) for machine learning-based precipitation estimation, direct rainfall estimation from raw signal-to-noise ratio (SNR) data via deep learning remains underexplored. This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. We develop real-time models for rainfall detection and estimation using downlink SNR signals from satellites to user terminals. By leveraging a year-long dataset from multiple locations—including SNR measurements paired with disdrometer and rain-gauge data—we explore and evaluate various ML models. Our final models include ensemble approaches for both rainfall detection and cumulative rainfall estimation. The proposed models provide a reliable solution for estimating precipitation using Earth–satellite microwave links, potentially improving precipitation monitoring. Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a $46\%$ reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions.

1. Increasing resilience to climate change requires innovative approaches and synergy among different actions. To overcome this challenge, the European Union has been stimulating innovative approac...

This study presents a framework for opportunistic rainfall detection and estimation exploiting Signal to Noise Ratio (SNR) data from satellite downlink signals. We develop a heterogeneous ensemble including diverse Machine Learning (ML) architectures for the classification task, namely Conditional Random Field (CRF), Gated Recurrent Unit (GRU), Chronos-Bolt, and TSPulse. Evaluating models characterized by distinct assumptions, scales, and computational complexities provides critical insights into their behavior for the weather and climate remote sensing domain. The resulting ensemble yields a significant improvement in precipitation classification performance. For the regression task, a traditional model leveraging the well-known International Telecommunication Union (ITU) recommendations is compared against a Gradient Boosting Machine (GBM), with the ML-based approach demonstrating a substantial improvement in Key Performance Indicators (KPIs). Finally, we tested the integration of the classification and regression modules via a hard and a soft gating mechanism. While these combinations enhance several point-to-point metrics, they do not improve the estimation of event-total accumulated rainfall. These findings highlight the potential of ML approaches for satellite-based opportunistic environmental monitoring.