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Special Issue "Advanced Satellite Remote Sensing Techniques for Meteorological, Climate and Hydroscience Studies"

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

Deadline for manuscript submissions: 29 February 2024 | Viewed by 4082

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Special Issue Editors

Dr. Haobo Li

E-Mail Website
Guest Editor

State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
Interests: GNSS meteorology; GNSS atmospheric remote sensing; data assimilation; numerical weather prediction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Suelynn Choy

E-Mail Website
Guest Editor

School of Science, RMIT University, Melbourne, Australia
Interests: global navigation satellite systems; precise positioning; navigation; satellite-based augmentation systems; earth observation; atmospheric sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Yuriy Kuleshov

E-Mail Website
Guest Editor

Australian Bureau of Meteorology, 700 Collins Street, Docklands, Melbourne, VIC 3008, Australia
Interests: climatology of severe weather phenomena (tropical cyclones, thunderstorms and lightning); climate monitoring and prediction; satellite remote sensing for climate monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Mayra I. Oyola-Merced

E-Mail Website
Guest Editor

Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: space-borne observation; atmospheric modeling; clouds and severe weather; satellite data assimilation; climate monitoring; air quality

Dr. Xiaoming Wang

E-Mail Website
Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: precise positioning; atmospheric remote sensing; GNSS meteorology; climate monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the context of global warming, frequent occurrences of natural disasters caused by severe weather events and climatic hazards have resulted in substantial damage and losses to properties and livelihoods. This highlights a pressing need to understand the intrinsic nature of these phenomena and refine the methods for their effective detection and early warning. The evolution of atmospheric water vapor and other critical components contained in the hydrological cycle is proven to have significant implications for determining the intensity, time and extent of potential severe weather events and climatic phenomena. Consequently, continuous, timely and accurate monitoring of these constituents is crucial for weather forecasting and climate change analysis.

Satellite remote sensing technology, e.g., weather satellite-based sensing techniques and the Global Navigation Satellite Systems (GNSS) atmospheric sounding technique, has undergone unprecedented development in recent years. New space-based data streams, e.g., the International GNSS Service (IGS) data and products, are opening up new opportunities for monitoring weather events at a multi-spatiotemporal scale, which also serve as the backbone of the Earth system models. This Special Issue is aimed at increasing the utilization and uptake of satellite remote sensing data, as well as to provide promising methods for the monitoring of severe weather events and essential climate variables, thereby contributing to the United Nations Sustainable Development Goals. To take advantage of the cutting-edge satellite remote sensing technology, especially advanced GNSS atmospheric sounding techniques, this Special Issue mainly focuses on, but is not limited to:

Effective mining/analysis of multi-type satellite data and their derivatives;
Advanced multi-GNSS data processing, atmospheric sounding and modeling;
Synthetic application from the use of satellite remote sensing data and products;
Data assimilation technique in operational earth system models;
Advanced machine learning-based approaches for climate monitoring, weather prediction and hydrological investigation;
Furthermore, miscellaneous interdisciplinary researches, advanced methods and new applications towards the fields of meteorology, climatology and hydrology are also welcomed.

Dr. Haobo Li
Prof. Dr. Suelynn Choy
Dr. Yuriy Kuleshov
Dr. Mayra I. Oyola-Merced
Dr. Xiaoming Wang
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

GNSS atmospheric sounding
International GNSS service data and products
severe weather forecasting
meteorology
climate monitoring
hydroscience
numerical weather prediction model
GNSS tropospheric tomography
miscellaneous advanced methods and applications

Published Papers (4 papers)

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Research

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22 pages, 8868 KiB

Open AccessArticle

Comparative Assessment of Spire and COSMIC-2 Radio Occultation Data Quality

and

Remote Sens. 2023, 15(21), 5082; https://doi.org/10.3390/rs15215082 - 24 Oct 2023

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Abstract

In this study, we investigate the performances of a commercial Global Navigation Satellite System (GNSS) Radio Occultation (RO) mission and a new-generation RO constellation, i.e., Spire and Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2), respectively. In the statistical comparison between Spire and COSMIC-2, the results indicate that although the average signal-to-noise ratio (SNR) of Spire is far weaker than that of COSMIC-2, the penetration of Spire is comparable to, and occasionally even better than, that of COSMIC-2. In our analysis, we find that the penetration depth is contingent upon various factors including SNR, GNSS, RO modes, topography, and latitude. With the reanalysis of the European Centre for Medium-Range Weather Forecasts and Radiosonde as the reference data, the identical error characteristics of Spire and COSMIC-2 reveal that overall, the accuracy of Spire’s neutral-atmosphere data products was found to be comparable to that of COSMIC-2. Full article

(This article belongs to the Special Issue Advanced Satellite Remote Sensing Techniques for Meteorological, Climate and Hydroscience Studies)

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31 pages, 11667 KiB

Open AccessArticle

A Multi-Model Ensemble Pattern Method to Estimate the Refractive Index Structure Parameter Profile and Integrated Astronomical Parameters in the Atmosphere

and

Remote Sens. 2023, 15(6), 1584; https://doi.org/10.3390/rs15061584 - 14 Mar 2023

Viewed by 1042

Abstract

In this study, we devised a constraint method, called multi-model ensemble pattern (MEP), to estimate the refractive index structure parameter (

C_{n}^{2}

) profiles based on observational data and multiple existing models. We verified this approach against radiosonde data from field [...] Read more.

In this study, we devised a constraint method, called multi-model ensemble pattern (MEP), to estimate the refractive index structure parameter (

C_{n}^{2}

) profiles based on observational data and multiple existing models. We verified this approach against radiosonde data from field campaigns in China’s eastern and northern coastal areas. Multi-dimensional statistical evaluations for the

C_{n}^{2}

profiles and integrated astronomical parameters have proved MEP’s relatively reliable performance in estimating optical turbulence in the atmosphere. The correlation coefficients of MEP and measurement overall

C_{n}^{2}

in two areas are up to 0.65 and 0.76. A much higher correlation can be found for a single radiosonde profile. Meanwhile, the difference evaluation of integrated astronomical parameters also shows its relatively robust performance compared to a single model. The prowess of this reliable approach allows us to carry out regional investigation on optical turbulence features with routine meteorological data soon. Full article

(This article belongs to the Special Issue Advanced Satellite Remote Sensing Techniques for Meteorological, Climate and Hydroscience Studies)

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20 pages, 22235 KiB

Open AccessArticle

Impacts of Shape Assumptions on Z–R Relationship and Satellite Remote Sensing Clouds Based on Model Simulations and GPM Observations

and

Remote Sens. 2023, 15(6), 1556; https://doi.org/10.3390/rs15061556 - 12 Mar 2023

Viewed by 1191

Abstract

In this study, the spherical particle model and ten nonspherical particle models describing the scattering properties of snow are evaluated for potential use in precipitation estimation from spaceborne dual-frequency precipitation radar. The single scattering properties of nonspherical snow particles are computed using discrete dipole approximation (DDA), while those of spherical particles are determined using Mie theory. The precipitation profiles from WRF output are then input to a forward radiative transfer model to simulate the radar reflectivity at Ka-band and Ku-band. The results are validated with Global Precipitation Mission Dual-Frequency Precipitation Radar measurements. Greater consistency between the simulated and observed reflectivity is obtained when using the sector- and dendrite-shape assumptions. For the case in this study, when using the spherical-shape assumption, radar underestimates the error of the cloud’s top by about 300 m and underestimates the error of the cloud’s area by about 15%. As snowflake shapes change with temperature, we use the range between −40 °C and −5 °C to define three temperature layers. The relationships between reflectivity (Z) and precipitation rate (R) are fitted separately for the three layers, resulting in

Z = 134.59 \cdot R^{1.184}

(sector) and

Z = 127.35 \cdot R^{1.221}

(dendrite) below −40 °C. Full article

(This article belongs to the Special Issue Advanced Satellite Remote Sensing Techniques for Meteorological, Climate and Hydroscience Studies)

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Review

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20 pages, 3393 KiB

Open AccessReview

Review Analysis of Irrigation and Application of Remote Sensing in the Lower Mekong River Basin

and

Remote Sens. 2023, 15(15), 3856; https://doi.org/10.3390/rs15153856 - 03 Aug 2023

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Abstract

Irrigated agriculture is indispensable to the Lower Mekong River Basin (LMB), which ensures food security and provides livelihoods for tens of millions of people. Irrigation, agricultural production, hydropower and aquatic ecosystem health are intertwined in LMB, so it is necessary to adopt a holistic approach to analyze irrigation problems. Here, we discuss the challenges and opportunities of LMB irrigation. Bibliometric analysis is carried out to determine the characteristics and patterns of watershed irrigation literature, such as the importance of authors, affiliated institutions, and their distribution in China. Based on bibliometric analysis, research topics are determined for thematic review. Firstly, we investigated the factors that directly affect the demand and supply of irrigation water and associated crop yield impacts. Secondly, we analyzed the influence of water availability, land use and climate change on agricultural irrigation. Thirdly, we analyzed the adverse effects of improper irrigation management on the environment, such as flow pattern change, ecosystem deterioration and land subsidence caused by groundwater overexploitation. Fourthly, the time–space mismatch between water supply and demand has brought serious challenges to the comprehensive water resources management in cross-border river basins. In each specific application area, we sorted out the technologies in which remote sensing technology is used. We hope that this review will contribute to in-depth research and decision analysis of remote sensing technology in agricultural irrigation. Full article

(This article belongs to the Special Issue Advanced Satellite Remote Sensing Techniques for Meteorological, Climate and Hydroscience Studies)

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Special Issue "Advanced Satellite Remote Sensing Techniques for Meteorological, Climate and Hydroscience Studies"

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