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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: closed (25 July 2024) | Viewed by 18450

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Special Issue Editors

Dr. Haobo Li

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Guest Editor

School of Science, RMIT University, Melbourne, VIC 3001, Australia
Interests: GNSS meteorology; GNSS atmospheric monitoring; data assimilation; numerical weather prediction; climate analysis and climate risks assessment
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Prof. Dr. Suelynn Choy

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Guest Editor

School of Science, RMIT University, Melbourne, VIC 3001, Australia
Interests: precise satellite positioning and navigation; geodesy; disaster management; atmospheric remote sensing
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Dr. Yuriy Kuleshov

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Guest Editor

Australian Bureau of Meteorology, 700 Collins Street, Docklands, Melbourne, VIC 3008, Australia
Interests: satellite remote sensing; climate change; hazardous natural phenomena; tropical cyclones; precipitation enhancement
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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

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Related Special Issue

Advanced Satellite Earth Observing Technologies for Weather and Climate Resilience in Remote Sensing (1 article)

Published Papers (11 papers)

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Research

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18 pages, 6070 KiB

Open AccessArticle

Diurnal Cycles of Cloud Properties and Precipitation Patterns over the Northeastern Tibetan Plateau During Summer

by Bangjun Cao, Xianyu Yang, Yaqiong Lu, Jun Wen and Shixin Wang

Remote Sens. 2024, 16(21), 4059; https://doi.org/10.3390/rs16214059 - 31 Oct 2024

Abstract

In the context of rising temperatures and increasing humidity in Northwest China, substantial gaps remain in understanding the mechanisms of land–atmosphere cloud–precipitation coupling across the northeastern Tibetan Plateau (TP), Loess Plateau (LP), and Huangshui Valley (HV). This study addresses these gaps by investigating cloud properties and precipitation patterns utilizing the Fengyun-4 Satellite Quantitative Precipitation Estimation Product (FY4A-QPE) and ERA5 datasets. We specifically focus on Lanzhou, a pivotal city within the LP, and Xining, which epitomizes the HV. Our findings reveal that diurnal variations in precipitation are significantly less pronounced in the eastern regions compared to northeastern TP. This discrepancy is attributed to marked diurnal fluctuations in convective available potential energy (CAPE) and wind shear between 200 and 500 hPa. While both cities share similar wind shear patterns and moisture transport directions, Xining benefits from enhanced snowmelt and effective water retention in surrounding mountains, resulting in higher precipitation levels. Conversely, Lanzhou suffers from moisture deficits, with dry, hot winds exacerbating the situation. Notably, precipitation in Xining is strongly correlated with CAPE, influenced by diurnal variability, and intensified by valley and lake–land breezes, which drive afternoon convection. In contrast, Lanzhou’s precipitation exhibits a weak relationship with CAPE, as even elevated values fail to generate significant cloud formation due to insufficient moisture. The ongoing trends of warming and humidification may lead to improved precipitation patterns, especially in the HV, with potential ecological benefits. However, concentrated rainfall during summer afternoons and midnights raises concerns regarding extreme weather events, highlighting the susceptibility of the HV to geological hazards. This research underscores the need to further explore the uncertainties inherent in precipitation dynamics in these regions. Full article

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

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21 pages, 8286 KiB

Open AccessArticle

An Ambient Adaptive Global Navigation Satellite System Total Electron Content Predictive Model for Short-Term Rapid Geomagnetic Storm Events

by Renato Filjar, Ivan Heđi, Jasna Prpić-Oršić and Teodor Iliev

Remote Sens. 2024, 16(16), 3051; https://doi.org/10.3390/rs16163051 - 19 Aug 2024

Viewed by 705

Abstract

Satellite navigation is an essential component of the national infrastructure. Space weather and ionospheric conditions are the prime sources of GNSS (global navigation satellite system) positioning, navigation, and timing (PNT) service disruptions and degradations. Protection, toughening, and augmentation (PTA) of GNSS PNT services require novel approaches in ionospheric effects mitigation. Standard global ionospheric correction models fail in the mitigation of high-dynamics and local ionospheric disturbances. Here, we demonstrate that in the case of the short-term fast-developing geomagnetic storm, a machine learning-based environment-aware GNSS ionospheric correction model for sub-equatorial regions may provide a substantial improvement over the existing global Klobuchar model, considered a benchmark. The proposed machine learning-based model utilises just the geomagnetic field density component observations as a predictor to estimate TEC/GNSS ionospheric delay as the prediction model target. Further research is needed to refine the methodology of machine learning model development selection and validation and to establish an architecture-agnostic framework for GNSS PTA development. Full article

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

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24 pages, 4092 KiB

Open AccessArticle

The Sensitivity of Polar Mesospheric Clouds to Mesospheric Temperature and Water Vapor

by Jae N. Lee, Dong L. Wu, Brentha Thurairajah, Yuta Hozumi and Takuo Tsuda

Remote Sens. 2024, 16(9), 1563; https://doi.org/10.3390/rs16091563 - 28 Apr 2024

Viewed by 867

Abstract

Polar mesospheric cloud (PMC) data obtained from the Aeronomy of Ice in the Mesosphere (AIM)/Cloud Imaging and Particle Size (CIPS) experiment and Himawari-8/Advanced Himawari Imager (AHI) observations are analyzed for multi-year climatology and interannual variations. Linkages between PMCs, mesospheric temperature, and water vapor (H₂O) are further investigated with data from the Microwave Limb Sounder (MLS). Our analysis shows that PMC onset date and occurrence rate are strongly dependent on the atmospheric environment, i.e., the underlying seasonal behavior of temperature and water vapor. Upper-mesospheric dehydration by PMCs is evident in the MLS water vapor observations. The spatial patterns of the depleted water vapor correspond to the PMC occurrence region over the Arctic and Antarctic during the days after the summer solstice. The year-to-year variabilities in PMC occurrence rates and onset dates are highly correlated with mesospheric temperature and H₂O. They show quasi-quadrennial oscillation (QQO) with 4–5-year periods, particularly in the southern hemisphere (SH). The combined influence of mesospheric cooling and the mesospheric H₂O increase provides favorable conditions for PMC formation. The global increase in mesospheric H₂O during the last decade may explain the increased PMC occurrence in the northern hemisphere (NH). Although mesospheric temperature and H₂O exhibit a strong 11-year variation, little solar cycle signatures are found in the PMC occurrence during 2007–2021. Full article

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

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18 pages, 1750 KiB

Open AccessArticle

Evaluating the Polarimetric Radio Occultation Technique Using NEXRAD Weather Radars

by Antía Paz, Ramon Padullés and Estel Cardellach

Remote Sens. 2024, 16(7), 1118; https://doi.org/10.3390/rs16071118 - 22 Mar 2024

Viewed by 904

Abstract

Currently, it remains a challenge to effectively monitor areas experiencing intense precipitation and the associated atmospheric conditions on a global scale. This challenge arises due to the limitations on both active and passive remote sensing methods. Apart from the lack of observations in remote areas, the quality of some observations deteriorates when heavy precipitation is present, making it difficult to obtain highly accurate measurements of the thermodynamic parameters driving these weather events. However, there is a promising solution in the form of the Global Navigation Satellite System (GNSS) Polarimetric Radio Occultation (PRO) technique. This approach provides a way to assess the large-scale bulk-hydrometeor characteristics of regions with heavy precipitation and the meteorological conditions associated with them. PRO offers vertical profiles of atmospheric variables, including temperature, pressure, water vapor pressure, and information about hydrometeors, all in a single fine-vertical resolution observation. To continue validating the PRO technique, we make use of polarimetric weather data from Next Generation Weather Radars (NEXRAD), focusing on comparing specific differential phase shift (

K_{d p}

) structures to PRO observable differential phase shift (

Δ Φ

). We have seen that PAZ and NEXRAD exhibit a good agreement on the vertical structure of the observable

Δ Φ

and that their combination could be useful for enhancing our understanding of the microphysics underlying heavy precipitation events. 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, 5320 KiB

Open AccessArticle

Comparison and Synthesis of Precipitation Data from CloudSat CPR and GPM KaPR

by Yanzhi Liang, Leilei Kou, Anfan Huang, Haiyang Gao, Zhengjian Lin, Yanqing Xie and Liguo Zhang

Remote Sens. 2024, 16(5), 745; https://doi.org/10.3390/rs16050745 - 21 Feb 2024

Cited by 1 | Viewed by 982

Abstract

Employing different bands of radar to detect precipitation information in identical regions enables the acquisition of a more comprehensive precipitation cloud structure, thereby refining the continuity and completeness of precipitation measurements. This study first compared the coincident data from CloudSat W-band cloud profiling radar (CPR) and Global Precipitation Measurement Mission (GPM) Ka-band precipitation radar (KaPR) from 2014 to 2017, and then a synthesis of the radar reflectivity from CPR and KaPR was attempted to obtain a complete cloud and precipitation structure. The findings of the reflectivity comparisons indicated that the echo-top height identified by CPR is on average 3.6 to 4.2 km higher than that from KaPR, due to the higher sensitivity. Because of strong attenuation of CPR by liquid-phase particles, the reflectivity below the height of the melting layer usually shows an opposite gradient to KaPR with decreasing altitude. The difference in the near-surface rain rates of CPR and KaPR was found to be related to reflectivity gradients in the vertical direction, and the larger the reflectivity gradients, the greater the differences in near-surface rain rates. For better representing the complete vertical structure of precipitation clouds and improving the consistency of the reflectivity and precipitation rate, the radar reflectivity was weighted, synthesized from CPR and KaPR based on the gradient difference of the reflectivity from the two radars. We presented the synthesis results for a stratiform cloud and a deep convective case, and Spearman’s rank correlation coefficient (r_s) between the GPM combined radiometer precipitation rate and the radar reflectivity was utilized to analyze the performance of the synthesis. The consistency between synthesized reflectivity and precipitation rate in the non-liquid phase was improved compared with KaPR, and the r_s of the ice and mixed phases was increased by about 12% and 10%, respectively. 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, 6460 KiB

Open AccessArticle

Multiscale Spatiotemporal Variations of GNSS-Derived Precipitable Water Vapor over Yunnan

by Minghua Wang, Zhuochen Lv, Weiwei Wu, Du Li, Rui Zhang and Chengzhi Sun

Remote Sens. 2024, 16(2), 412; https://doi.org/10.3390/rs16020412 - 20 Jan 2024

Cited by 1 | Viewed by 1191

Abstract

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the Global Navigation Satellite System (GNSS) data collected from 12 stations situated in Yunnan, which are part of the Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) data from 2011 to 2022. The retrieved PWV data at Station KMIN were evaluated by the nearby radiosonde data, and the results show that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm, respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the stations’ heights and latitudes. Differences between average PWV in the wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. The amplitudes of the PWV annual cycles are 9.75–20.94 mm. The spatial variation of these amplitudes is similar to that of the average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed. Full article

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

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22 pages, 8868 KiB

Open AccessArticle

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

by Cong Qiu, Xiaoming Wang, Kai Zhou, Jinglei Zhang, Yufei Chen, Haobo Li, Dingyi Liu and Hong Yuan

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

Cited by 5 | Viewed by 4051

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

by Hanjiu Zhang, Liming Zhu, Gang Sun, Kun Zhang, Ying Liu, Xuebin Ma, Haojia Zhang, Qing Liu, Shengcheng Cui, Tao Luo, Xuebin Li and Ningquan Weng

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

Cited by 4 | Viewed by 1846

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

by Liting Mai, Shuping Yang, Yu Wang and Rui Li

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

Cited by 2 | Viewed by 2084

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

by Guanghui Wang, Sadia Bibi, Tingju Zhu, Fuqiang Tian and Marcelo A. Olivares

Remote Sens. 2023, 15(15), 3856; https://doi.org/10.3390/rs15153856 - 3 Aug 2023

Cited by 2 | Viewed by 2666

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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Other

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16 pages, 12435 KiB

Open AccessTechnical Note

Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia

by Raied Saad Alharbi, Vu Dao, Claudia Jimenez Arellano and Phu Nguyen

Remote Sens. 2024, 16(4), 703; https://doi.org/10.3390/rs16040703 - 17 Feb 2024

Cited by 3 | Viewed by 1669

Abstract

In the past decade, Saudi Arabia has witnessed a surge in flash floods, resulting in significant losses of lives and property. This raises a need for accurate near-real-time precipitation estimates. Satellite products offer precipitation data with high spatial and temporal resolutions. Among these, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Dynamic Infrared Rain Rate near-real-time (PDIR-Now) stands out as a novel, global, and long-term resource. In this study, a rigorous comparative analysis was conducted from 2017 to 2022, contrasting PDIR-Now with rain gauge data. This analysis employs six metrics to assess the accuracy of PDIR-Now across various daily rainfall rates and four yearly extreme precipitation indices. The findings reveal that PDIR-Now slightly underestimates light precipitation but significantly underestimates heavy precipitation. Challenges arise in regions characterized by orographic rainfall patterns in the southwestern area of Saudi Arabia, emphasizing the importance of spatial resolution and topographical considerations. While PDIR-Now successfully captures annual maximum 1-day and 5-day precipitation measurements across rain gauge locations, it exhibits limitations in the length of wet and dry spells. This research highlights the potential of PDIR-Now as a valuable tool for precipitation estimation, offering valuable insights for hydrological, climatological, and water resource management studies. Full article

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

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