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Remote Sensing
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Hydrometeorological Prediction and Mapping
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Hydrometeorological Prediction and Mapping

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 9252

Special Issue Editors

Prof. Dr. Donglian Sun

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Guest Editor
Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA
Interests: remote sensing natural hazards; surface and atmosphere information
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paul Houser

E-Mail Website
Guest Editor
Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yongwei Sheng

E-Mail Website
Guest Editor
Department of Geography, University of California, Los Angeles (UCLA), P.O. Box 951524, 1255 Bunche Hall, Los Angeles, CA 90095, USA
Interests: hydrology; lake dynamics; water resources; vegetation monitoring; glacier changes; remote sensing; geographic information systems (GIS); Tibetan Plateau; Arctic; Central Asia
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With global warming and the acceleration of the global water cycle, hydrometeorological extreme events like flood and drought have become more and more frequent, and induce risks to human settlements, especially in an era of rapid population growth. Predicting and monitoring the occurrence, intensity, and evolution of these hydrometeorological events have therefore become important for disaster responses, mitigation, and management to save lives and reduce economic losses. We hope this session will contribute to hydrometeorological prediction from modeling and mapping from remote sensing observations, such as flood and drought, and related variables, including precipitation, land surface temperature, evapoatranpiration (ET), stream flow/runoff, soil moisture, snow/ice cover, etc., to foster hydrometeorological forecasting, monitoring, and impact assessment to strengthen preparedness and responses and reduce hydrometeorological disaster losses. We solicit contributions from modeling and remote sensing, hazard response, and preparedness fields that study hydrometeorological hazards across spatial scales.

Dr. Donglian Sun
Dr. Paul Houser
Prof. Yongwei Sheng
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

  • Hydrometeorological prediction from modeling
  • Hydrometeorological mapping from remote sensing observations

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Published Papers (2 papers)

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Research

23 pages, 10573 KiB  
Article
Fusion of Five Satellite-Derived Products Using Extremely Randomized Trees to Estimate Terrestrial Latent Heat Flux over Europe
by Ke Shang, Yunjun Yao, Yufu Li, Junming Yang, Kun Jia, Xiaotong Zhang, Xiaowei Chen, Xiangyi Bei and Xiaozheng Guo
Remote Sens. 2020, 12(4), 687; https://doi.org/10.3390/rs12040687 - 19 Feb 2020
Cited by 29 | Viewed by 3657
Abstract
An accurate estimation of spatially and temporally continuous latent heat flux (LE) is essential in the assessment of surface water and energy balance. Various satellite-derived LE products have been generated to enhance the simulation of terrestrial LE, yet each individual LE product shows [...] Read more.
An accurate estimation of spatially and temporally continuous latent heat flux (LE) is essential in the assessment of surface water and energy balance. Various satellite-derived LE products have been generated to enhance the simulation of terrestrial LE, yet each individual LE product shows large discrepancies and uncertainties. Our study used Extremely Randomized Trees (ETR) to fuse five satellite-derived terrestrial LE products to reduce uncertainties from the individual products and improve terrestrial LE estimations over Europe. The validation results demonstrated that the estimation using the ETR fusion method increased the R2 of five individual LE products (ranging from 0.53 to 0.61) to 0.97 and decreased the RMSE (ranging from 26.37 to 33.17 W/m2) to 5.85 W/m2. Compared with three other machine learning fusion models, Gradient Boosting Regression Tree (GBRT), Random Forest (RF), and Gaussian Process Regression (GPR), ETR exhibited the best performance in terms of both training and validation accuracy. We also applied the ETR fusion method to implement the mapping of average annual terrestrial LE over Europe at a resolution of 0.05 ◦ in the period from 2002 to 2005. When compared with global LE products such as the Global Land Surface Satellite (GLASS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the fusion LE using ETR exhibited a relatively small gap, which confirmed that it is reasonable and reliable for the estimation of the terrestrial LE over Europe. Full article
(This article belongs to the Special Issue Hydrometeorological Prediction and Mapping)
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20 pages, 3343 KiB  
Article
Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E/AMSR-2 and MODIS/GOES Observations
by Donglian Sun, Yu Li, Xiwu Zhan, Paul Houser, Chaowei Yang, Long Chiu and Ruixin Yang
Remote Sens. 2019, 11(14), 1704; https://doi.org/10.3390/rs11141704 - 18 Jul 2019
Cited by 32 | Viewed by 5061
Abstract
Land surface temperature (LST) is an important input to the Atmosphere–Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution [...] Read more.
Land surface temperature (LST) is an important input to the Atmosphere–Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) products, but clouds affect them. While passive microwave (e.g., AMSR-E and AMSR-2) sensors can penetrate non-rainy clouds and observe the Earth’s surface, but usually with a coarse spatial resolution, how to utilize multiple instruments’ advantages is an important methodology in remote sensing. In this study, we developed a new five-channel algorithm to derive LST from the microwave AMSR-E and AMSR-2 measurements and calibrate to the MODIS and GOES LST products. A machine learning method is implemented to further improve its performance. The MODIS and GOES LST products still show better performance than the AMSR-E and AMSR-2 LSTs when evaluated against the ground observations. Therefore, microwave LSTs are only used to fill the gaps due to clouds in the MODIS and GOES LST products. A gap filling method is further applied to fill the remaining gaps in the merged LSTs and downscale to the same spatial resolution as the MODIS and GOES products. With the daily integrated LST at the same spatial resolution as the MODIS and GOES products and available under nearly all sky conditions, the drought index, like the ESI, can be updated on daily basis. The initial implementation results demonstrate that the daily drought map can catch the fast changes of drought conditions and capture the signals of flash drought, and make flash drought monitoring become possible. It is expected that a drought map that is available on daily basis will benefit future drought monitoring. Full article
(This article belongs to the Special Issue Hydrometeorological Prediction and Mapping)
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