A Review of Technical Aspects and Challenges in Opportunistic Rainfall Estimation Using Satellite and Terrestrial Microwave Links: How wireless infrastructure can be used for rainfall monitoring | Zendy

Roberto Nebuloni | Zendy; Filippo Giannetti | Zendy; Fabiola Sapienza | Zendy; Vincenzo Lottici | Zendy; Elisa Adirosi | Zendy; Giacomo Roversi | Zendy; Elia Covi | Zendy; Christian Gianoglio | Zendy; Matteo Colli | Zendy; Carlo De Michele | Zendy

Open Access

A Review of Technical Aspects and Challenges in Opportunistic Rainfall Estimation Using Satellite and Terrestrial Microwave Links: How wireless infrastructure can be used for rainfall monitoring

Author(s) -

Roberto Nebuloni,

Filippo Giannetti,

Fabiola Sapienza,

Vincenzo Lottici,

Elisa Adirosi,

Giacomo Roversi,

Elia Covi,

Christian Gianoglio,

Matteo Colli,

Carlo De Michele

Publication year - 2025

Publication title -

ieee geoscience and remote sensing magazine

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 3.038

H-Index - 31

eISSN - 2168-6831

pISSN - 2473-2397

DOI - 10.1109/mgrs.2025.3573645

Subject(s) - geoscience , power, energy and industry applications , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , signal processing and analysis

With the launch of the National Aeronautics and Space Administration (NASA) Indian Space Research Organisation synthetic aperture radar (NISAR), available open access Earth observation data reach a new milestone with an estimated data rate of ∼70 TB of NISAR science data produced daily. These additional data create many opportunities within the science and user community for new and more in-depth analyses, yet at the same time, most organizations lack the budget, resources, and skills to analyze all this data on-premise. This article introduces concepts on how synthetic aperture radar (SAR) processing software from commercial and open source providers can be coupled with highly scalable cloud processing techniques to digest SAR data at various input levels (from raw to value-added products). Included in this article are examples of the global-scale processing of Sentinel-1 data for InSAR coherence estimation, the input to global biomass modeling, and the routine operational processing of SAR time-series data for national and international near real-time flood mapping. The cloud-scaling Software for Earth big data Processing, Prediction Modeling, and Organization (SEPPO), which is used to meet the processing challenges utilizing Amazon Web Services (AWS) for large volume and operational SAR data processing in complex workflows is introduced.

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