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Remote Sensing
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Precipitation Estimations Based on Satellite Observations
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Precipitation Estimations Based on Satellite Observations

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1173

Special Issue Editors

Dr. Yuanwei Wang

E-Mail Website
Guest Editor
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: environment; precipitation; hydrological modeling; watershed hydrology; climate change; remote sensing
Prof. Dr. Francisco J. Tapiador

E-Mail Website
Guest Editor
Earth and Space Sciences (ESS) Research Group, Faculty of Environmental Sciences and Biochemistry, University of Castilla-La Mancha (UCLM), Avda. Carlos III s/n, E-45071 Toledo, Spain
Interests: precipitation; remote sensing; tropical cyclones; climate change; social sciences; microphysics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precipitation is a key element in Earth's climate system. Traditional precipitation measurement methods, such as rain gauges, have limitations in spatial coverage, especially in remote areas. Satellite-based precipitation observations offer a solution with their wide-ranging coverage and frequent revisits. However, accurately estimating precipitation from satellite data remains a challenge due to complex cloud physics and signal-related issues.

This Special Issue aims to advance the field of precipitation estimations using satellite observations. We invite submissions on new algorithms and models for more accurate estimates. Papers focusing on validating satellite-based precipitation products against ground-based data and improving sensor calibration are also welcome. Additionally, we encourage studies on regional and global precipitation patterns detected by satellites, as well as their applications in weather forecasting, water resources management, and agriculture. By bringing together such research, we hope to enhance our understanding and utilization of satellite-based precipitation estimations.

Dr. Yuanwei Wang
Prof. Dr. Francisco J. Tapiador
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • precipitation estimation
  • satellite observations
  • remote sensing
  • algorithm development
  • validation and calibration
  • climate applications

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Published Papers (1 paper)

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Research

25 pages, 16504 KB  
Article
High-Resolution, Low-Latency Multi-Satellite Precipitation Merging by Correcting with Weather Radar Network Data
by Seungwoo Baek, Soorok Ryu, Choeng-Lyong Lee, Francisco J. Tapiador and Gyuwon Lee
Remote Sens. 2025, 17(10), 1702; https://doi.org/10.3390/rs17101702 - 13 May 2025
Viewed by 872
Abstract
Satellite-based precipitation products (SPPs) have become a crucial source of quantitative global precipitation data. Geostationary Orbit (GEO) satellites provide high spatiotemporal resolution but tend to have lower accuracy, while Low Earth Orbit (LEO) satellites provide more precise precipitation estimates but suffer from lower [...] Read more.
Satellite-based precipitation products (SPPs) have become a crucial source of quantitative global precipitation data. Geostationary Orbit (GEO) satellites provide high spatiotemporal resolution but tend to have lower accuracy, while Low Earth Orbit (LEO) satellites provide more precise precipitation estimates but suffer from lower temporal resolution due to their limited observation frequency. This study proposes an efficient algorithm for integrating and enhancing precipitation estimates from multiple satellite observations. The target domain includes the Full Disk (FD) and the extended East Asia (EA) regions, both of which are observable by GEO satellites, such as Himawari-8, serving as the GEO platform in this study. The algorithm involves four steps: pre-data preparation, LEO morphing, adjustment, and final merging. It produces Early and Late composite products with 10-min temporal and up to 2 km spatial resolution and significantly reduces latency compared to IMERG. Specifically, the Early and Late products can be generated with approximate latencies of 90 min and 270 min, respectively—much faster than Integrated Multi-satellite Retrievals for GPM (IMERG)’s Early (4-h) and Late (14-h) products. A key feature of the proposed method is the use of accuracy-based weighting derived from radar-based validation, enabling dynamic merging that reflects the reliability of each satellite observation. Statistical validation using Global Telecommunication System (GTS) precipitation data confirmed the positive impact of the proposed bias correction and merging method. In particular, the Late product achieved accuracy comparable to or higher than that of IMERG Early and IMERG Late, despite its significantly shorter latency. However, its accuracy was still lower than that of IMERG Final, which benefits from additional gauge-based correction but is released with a delay of several months. Full article
(This article belongs to the Special Issue Precipitation Estimations Based on Satellite Observations)
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