Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Multi-Platform Hydrometeorological Monitoring and Analysis Using Remote Sensing

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 4204

Share This Special Issue

Special Issue Editors

Dr. Marios Anagnostou

E-Mail Website
Guest Editor

National Observatory of Athens, Institute of Enviromental Research and Sustainable Development, Lofos Kofou, 15236 Athens, Greece
Interests: X-band weather radar; dual-polarization; precipitation and microphysical estimation; precipitation retrieval; flash flood; nowcasting
Special Issues, Collections and Topics in MDPI journals

Dr. John Kalogiros

E-Mail Website
Guest Editor

Institute of Environmental Research and Sustainable Development, National Observatory of Athens, I. Metaxa and V. Pavlou, P. Penteli, 15236 Athens, Greece
Interests: remote sensing; weather radar; precipitation; flood forecasting; atmospheric turbulence; air–sea interaction
Special Issues, Collections and Topics in MDPI journals

Dr. Jianzhong Lu

E-Mail Website
Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: remote sensing of the environment; water color remote sensing; hydrological remote sensing; hydrology modelling and data assimilation; climate change and environment response; disaster monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multi-platform of remote sensing technology and weather modeling system, with the significant development of data acquiring and models, provided a sophisticated tool to elucidate hydrometerology dynamics over the past few decades, from which weather- and hydrology-related parameter monitoring and prediction may benefit. Therefore, we are urgently aiming to develop multi-platform hydrometeorological monitoring and analysis using remote sensing in order to provide scientific information for agriculture cultivation and hydrometeorologic disaster forecast.

In this Special Issue, we invite submissions that incorporate studies on remote sensing monitoring and multi-platform hydrometeorology analysis to solve newly emerged meteorological and hydrologic problems, as well as to develop applications of modern monitoring and modelling technologies. This Special Issue will cover research on methods of weather parameter retrieval, the remote sensing of hydrology and hydrometeorologic analysis using multi-platform data, etc. As well as new findings, studies on hydrometeorologic disaster that require novel approaches, the development of new tools, or improvements in existing models are welcome.

Dr. Marios Anagnostou
Dr. John Kalogiros
Dr. Jianzhong Lu
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

remote sensing of hydrology
weather radar
multi-platform analysis
soil moisture
precipitation
evapotranspiration
meteorology
weather reanalysis
flood and drought

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

24 pages, 14189 KiB

Open AccessArticle

Spatiotemporal Evolution Features of the 2022 Compound Hot and Drought Event over the Yangtze River Basin

by Lilu Cui, Linhao Zhong, Jiacheng Meng, Jiachun An, Cheng Zhang and Yu Li

Remote Sens. 2024, 16(8), 1367; https://doi.org/10.3390/rs16081367 - 12 Apr 2024

Viewed by 341

Abstract

A rare compound hot and drought (CHD) event occurred in the Yangtze River Basin (YRB) in the summer of 2022, which brought serious social crisis and ecological disaster. The analysis of the causes, spatiotemporal characteristics and impacts of this event is of great significance and value for future drought warning and mitigation. We used the Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) data, meteorological data, hydrological data and satellite remote sensing data to discuss the spatiotemporal evolution, formation mechanism and the influence of the CHD event. The results show that the drought severity caused by the CHD event was the most severe during 2003 and 2022. The CHD event lasted a total of five months (from July to November), and there were variations in the damage in different sub-basins. The Wu River Basin (WRB) is the region where the CHD event lasted the longest, at six months (from July to December), while it also lasted four or five months in all the other basins. Among them, the WRB, Dongting Lake Rivers Basin (DLRB) and Mainstream of the YRB (MSY) are the three most affected basins, whose hot and drought severity values are 7.750 and −8.520 (WRB), 7.105 and −9.915 (DLRB) and 6.232 and −9.143 (MSY), respectively. High temperature and low precipitation are the direct causes of the CHD event, and the underlying causes behind this event are the triple La Niña and negative Indian Ocean Dipole event. The two extreme climate events made the Western Pacific Subtropical High (WPSH) unusually strong, and then the WPSH covered a more northerly and westerly region than in previous years and remained entrenched for a long period of time over the YRB and its adjacent regions. Moreover, this CHD event had a devastating impact on local agricultural production and seriously disrupted daily life and production. Our results have implications for the study of extreme disaster events. Full article

(This article belongs to the Special Issue Multi-Platform Hydrometeorological Monitoring and Analysis Using Remote Sensing)

► Show Figures

Figure 1

22 pages, 10041 KiB

Open AccessArticle

Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture

by Liming Zhu, Guizhi Tian, Huifeng Wu, Maohua Ding, A-Xing Zhu and Tianwu Ma

Remote Sens. 2024, 16(7), 1225; https://doi.org/10.3390/rs16071225 - 30 Mar 2024

Viewed by 626

Abstract

Soil moisture (SM) is a crucial environmental variable, and it plays an important role in energy and water cycles. SM data retrieval based on microwave satellite remote sensing has garnered significant attention due to its spatial continuity, wide observational coverage, and relatively low cost. Validating the accuracy of satellite remote sensing SM products is a critical step in enhancing data credibility, which plays a vital role in ensuring the effective application of satellite remote sensing data across various fields. Firstly, this study focused on Henan Province and evaluated the accuracy of the SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture (SPL3SMP_E) product along with its application in agriculture. The evaluation was based on in situ SM data from 55 stations in Henan Province. The assessment metrics used in this study include mean difference (MD), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the Pearson correlation coefficient (R). The time span of this study is from 2017 to 2020. The evaluation results indicated that the SPL3SMP_E soil moisture product performs well, as reflected by an ubRMSE value of 0.045 (m³/m³), which was relatively close to the product’s design accuracy of 0.04 (m³/m³). Moreover, the accuracy of the product was unaffected by temporal factors, but the product exhibited strong spatial aggregation, which was closely related to land use types. Then, this study explored the response of the SPL3SMP_E product to irrigation signals. The precipitation and irrigation data from Henan Province were employed to investigate the response of the SPL3SMP_E soil moisture product to irrigation. Our findings revealed that the SPL3SMP_E soil moisture product was capable of capturing over 70% of irrigation events in the study area, indicating its high sensitivity to irrigation signals in this region. In this study, the SPL3SMP_E product was also employed for monitoring agricultural drought in Henan Province. The findings revealed that the collaborative use of the SPL3SMP_E soil moisture product and machine learning algorithms proves highly effective in monitoring significant drought events. Furthermore, the integration of multiple indices demonstrated a notable enhancement in the accuracy of drought monitoring. Such an evaluation holds significant implications for the effective application of satellite remote sensing SM data in agriculture and other domains. Full article

(This article belongs to the Special Issue Multi-Platform Hydrometeorological Monitoring and Analysis Using Remote Sensing)

► Show Figures

Figure 1

25 pages, 10754 KiB

Open AccessArticle

Joint Retrieval of Multiple Species of Ice Hydrometeor Parameters from Millimeter and Submillimeter Wave Brightness Temperature Based on Convolutional Neural Networks

by Ke Chen, Jiasheng Wu and Yingying Chen

Remote Sens. 2024, 16(6), 1096; https://doi.org/10.3390/rs16061096 - 20 Mar 2024

Viewed by 519

Abstract

Submillimeter wave radiometers are promising remote sensing tools for sounding ice cloud parameters. The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS−SG) is the first operational submillimeter wave radiometer used for ice cloud remote sensing. Ice clouds simultaneously contain three species of ice hydrometeors—ice, snow, and graupel—the physical distributions and submillimeter wave radiation characteristics of which differ. Therefore, jointly retrieving the mass parameters of the three ice hydrometeors from submillimeter brightness temperatures is very challenging. In this paper, we propose a multiple species of ice hydrometeor parameters retrieval algorithm based on convolutional neural networks (CNNs) that can jointly retrieve the total content and vertical profiles of ice, snow, and graupel particles from submillimeter brightness temperatures. The training dataset is generated by a numerical weather prediction (NWP) model and a submillimeter wave radiative transfer (RT) model. In this study, an end to end ICI simulation experiment involving forward modeling of the brightness temperature and retrieval of ice cloud parameters was conducted to verify the effectiveness of the proposed CNN retrieval algorithm. Compared with the classical Unet, the average relative errors of the improved RCNN–ResUnet are reduced by 11%, 25%, and 18% in GWP, IWP, and SWP retrieval, respectively. Compared with Bayesian Monte Carlo integration algorithm, the average relative error of the total content retrieved by RCNN–ResUnet is reduced by 71%. Compared with BP neural network algorithm, the average relative error of the vertical profiles retrieved by RCNN–ResUnet is reduced by 69%. In addition, this algorithm was applied to actual Advanced Technology Microwave Sounder (ATMS) 183 GHz observed brightness temperatures to retrieve graupel particle parameters with a relative error in the total content of less than 25% and a relative error in the profile of less than 35%. The results show that the proposed CNN algorithm can be applied to future space borne submillimeter wave radiometers to jointly retrieve mass parameters of ice, snow, and graupel. Full article

(This article belongs to the Special Issue Multi-Platform Hydrometeorological Monitoring and Analysis Using Remote Sensing)

► Show Figures

Graphical abstract

27 pages, 11457 KiB

Open AccessArticle

Assessing the Applicability of Three Precipitation Products, IMERG, GSMaP, and ERA5, in China over the Last Two Decades

by Hongwu Zhou, Shan Ning, Da Li, Xishan Pan, Qiao Li, Min Zhao and Xiao Tang

Remote Sens. 2023, 15(17), 4154; https://doi.org/10.3390/rs15174154 - 24 Aug 2023

Cited by 1 | Viewed by 954

Abstract

The accuracy of gridded precipitation products is uncertain in different temporal and spatial dimensions. Analyzing the applicability of precipitation products is a prerequisite before applying them to hydrometeorological and other related research. In this study, we selected three gridded precipitation products, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and the fifth generation of atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5), including their data from 2001 to 2020. Using the data from 699 ground observation stations, we evaluated the applicability of these three precipitation products in China. Based on five statistical and five classification indicators, we first assessed the applicability of the three precipitation products on daily, monthly, and annual time scales, respectively, and then evaluated their applicability in different spatial dimensions, including basins, agriculture, and geomorphology. The results showed that: (1) IMERG data had the best accuracy on annual and monthly time scales, with both correlation coefficient (CC) values greater than 0.95 and Kling–Gupta efficiency (KGE) values greater than 0.90. On a daily time scale, the accuracy of all three precipitation products differed when statistical or categorical indicators were considered alone. However, the applicability of IMERG data was best among the three precipitation products when both types of indicators were considered. (2) The accuracy of the three precipitation products gradually decreased along the southeast–northwest direction. The applicability of ERA5 data was better in northern regions than in other regions in China, especially in arid and semi-arid regions in northern China. The applicability of IMERG data was better in southern regions with more precipitation and in high-altitude regions than in other regions in China. (3) The applicability of the three precipitation products in plain areas was generally better than in mountain areas. Among them, ERA5 data were more accurate in plain areas, while IMERG data were more accurate in mountain areas. This study can provide a reference for the selection of data sources of gridded precipitation products in different time scales and spatial dimensions in China. Full article

(This article belongs to the Special Issue Multi-Platform Hydrometeorological Monitoring and Analysis Using Remote Sensing)

► Show Figures

Graphical abstract

28 pages, 9652 KiB

Open AccessArticle

The Spread of Multiple Droughts in Different Seasons and Its Dynamic Changes

by Shuang Zhu, Wenying Huang, Xiangang Luo, Jun Guo and Zhe Yuan

Remote Sens. 2023, 15(15), 3848; https://doi.org/10.3390/rs15153848 - 02 Aug 2023

Cited by 1 | Viewed by 1103

Abstract

Investigating the propagation and influencing mechanism that transitions a meteorological drought to a hydrological drought in a changing environment is crucial for understanding the formation process and mechanism of hydrological drought. Furthermore, it is essential to establish an effective hydrological drought warning system based on meteorological drought. To assess the dynamic changes in the spread of meteorological drought to hydrological drought during various seasons, this study employs the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), and Normalized Vegetation Index (NDVI) to represent meteorological, hydrological, and vegetation droughts, respectively, in the Ganjiang River Basin (GRB) from 2002 to 2020. Considering that meteorological drought can be caused not only by insufficient precipitation but also by excessive evaporation, an additional index, namely the Evaporative Demand Drought Index (EDDI), is constructed to quantify meteorological drought resulting from evaporation factors. The article analyzes the characteristics of the spatiotemporal evolution of meteorological, hydrological, and vegetation drought. The Spearman rank correlation coefficient is employed to calculate the propagation time of different seasons from meteorological drought to hydrological/vegetation drought and from hydrological drought to vegetation drought. Furthermore, we examine the propagation relationship among meteorological, hydrological, and vegetation drought in the time-frequency domain through cross-wavelet analysis and explore the key factors and physical mechanisms that influence the propagation of drought in various seasons. The result shows: The propagation time from meteorological to hydrological drought (SPI-SRI) is shortest in spring, extended during summer and autumn, and longest in winter. The meteorological drought arising from excessive evapotranspiration in autumn has the most substantial impact on hydrological drought. Vegetation drought and meteorological/hydrological drought exhibit significant intermittent resonance periods in 0~6 months and significant stable resonance periods in 7~15 months. Full article

(This article belongs to the Special Issue Multi-Platform Hydrometeorological Monitoring and Analysis Using Remote Sensing)

► Show Figures

Journal Menu

Journal Browser

Multi-Platform Hydrometeorological Monitoring and Analysis Using Remote Sensing

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI