Special Issue "Estimating Meteorological Variables by Remote Sensing Data"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Carmen Recondo
Website
Guest Editor
Remote Sensing Applications (RSApps) Research Group, Area of Cartographic, Geodesic and Photogrammetric Engineering, Department of Mining Exploitation and Prospecting & Institute of Natural Resources and Territorial Planning (INDUROT); University of Oviedo. Campus de Mieres, C/ Gonzalo Gutiérrez Quirós s/n, 33600 Mieres, Asturias, Spain.
Interests: estimation and cartography of meteorological variables (Ta, e0, RH) from remote sensing (RS) data (LST, W); estimation of the albedo and evapotranspiration from RS data; estimation of the state of vegetation and soils from RS data; spectral indexes for vegetation and soils; environmental risk models
Prof. Dr. Federico Porcù
Website
Guest Editor
Department of Physics and Astronomy, Viale Berti Pichat, 6/2, 40126 Bologna, Italy
Interests: remote sensing; clouds; aerosol; precipitation; agrometeorology; natural hazards
Special Issues and Collections in MDPI journals
Dr. Juanjo Peón
Website
Guest Editor
University Institute of Space Sciences and Technologies of Asturias (ICTEA), University of Oviedo, Independencia 13, 33004 Oviedo, Asturias, Spain
Interests: meteorological variables; land surface temperature; air temperature; soil properties; topsoil organic carbon; hyperspectral imaging; VNIR spectroscopy; spectral indices

Special Issue Information

Dear Colleagues,

Meteorological variables are key parameters in most environmental studies. Traditionally, these data have been obtained at ground-level meteorological stations, but although these in situ data are invaluable, continuous, and precise, they are also local and spatially sparse. Remote sensing allows obtaining these variables at a regular spatial scale together to a high/medium temporal scale. This means that it is crucial to do studies and maps at regional and global scales which will help us to understand the changes produced in the Earth and how they relate to each other. Remote-sensing techniques have been demonstrated to have a high potential for estimating meteorological variables such as surface air temperature, water vapour pressure, humidity, solar surface radiation, and precipitation, and also derived variables such as albedo and evapotranspiration. However, new methods and algorithms and more calibration/validation works and ideas about new optical, thermal, and radar sensors are necessary to improve the estimation of these variables by remote sensing, making remote-sensing techniques really operational.

We are pleased to announce the Special Issue "Estimating Meteorological Variables by Remote Sensing Data" of the journal Remote Sensing. We would like to invite you to submit manuscripts about your recent research focusing on, but not limited to, the following topics:

  • Novel methods and algorithms to estimate the different meteorological variables;
  • Calibration and validation studies in different areas around the world;
  • Comparison and evaluation of different methods/algorithms;
  • Meteorological variables maps at regional, national, and global scales based on remote-sensing data;
  • Methods for merging in situ data with remote-sensing data;
  • Ideas and suggestions about new sensors to improve the estimation of these variables.

Review articles covering one or more of these topics are also welcome.

Dr. Carmen Recondo
Prof. Federico Porcù
Dr. Juanjo Peón
Guest Editor

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 papers will be 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 2200 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

  • Surface air temperature
  • Surface water vapor pressure
  • Humidity
  • Precipitation
  • Solar surface radiation
  • Albedo
  • Evapotranspiration

Published Papers (2 papers)

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Research

Open AccessArticle
Prediction of Leaf Wetness Duration Using Geostationary Satellite Observations and Machine Learning Algorithms
Remote Sens. 2020, 12(18), 3076; https://doi.org/10.3390/rs12183076 - 19 Sep 2020
Abstract
Leaf wetness duration (LWD) and plant diseases are strongly associated with each other. Therefore, LWD is a critical ecological variable for plant disease risk assessment. However, LWD is rarely used in the analysis of plant disease epidemiology and risk assessment because it is [...] Read more.
Leaf wetness duration (LWD) and plant diseases are strongly associated with each other. Therefore, LWD is a critical ecological variable for plant disease risk assessment. However, LWD is rarely used in the analysis of plant disease epidemiology and risk assessment because it is a non-standard meteorological variable. The application of satellite observations may facilitate the prediction of LWD as they may represent important related parameters and are particularly useful for meteorologically ungauged locations. In this study, the applicability of geostationary satellite observations for LWD prediction was investigated. GEO-KOMPSAT-2A satellite observations were used as inputs and six machine learning (ML) algorithms were employed to arrive at hourly LW predictions. The performances of these models were compared with that of a physical model through systematic evaluation. Results indicated that the LWD could be predicted using satellite observations and ML. A random forest model exhibited larger accuracy (0.82) than that of the physical model (0.79) in leaf wetness prediction. The performance of the proposed approach was comparable to that of the physical model in predicting LWD. Overall, the artificial intelligence (AI) models exhibited good performances in predicting LWD in South Korea. Full article
(This article belongs to the Special Issue Estimating Meteorological Variables by Remote Sensing Data)
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Open AccessFeature PaperArticle
C-Band Dual-Doppler Retrievals in Complex Terrain: Improving the Knowledge of Severe Storm Dynamics in Catalonia
Remote Sens. 2020, 12(18), 2930; https://doi.org/10.3390/rs12182930 - 10 Sep 2020
Abstract
Convective activity in Catalonia (northeastern Spain) mainly occurs during summer and autumn, with severe weather occurring 33 days per year on average. In some cases, the storms have unexpected propagation characteristics, likely due to a combination of the complex topography and the thunderstorms’ [...] Read more.
Convective activity in Catalonia (northeastern Spain) mainly occurs during summer and autumn, with severe weather occurring 33 days per year on average. In some cases, the storms have unexpected propagation characteristics, likely due to a combination of the complex topography and the thunderstorms’ propagation mechanisms. Partly due to the local nature of the events, numerical weather prediction models are not able to accurately nowcast the complex mesoscale mechanisms (i.e., local influence of topography). This directly impacts the retrieved position and motion of the storms, and consequently, the likely associated storm severity. Although a successful warning system based on lightning and radar observations has been developed, there remains a lack of knowledge of storm dynamics that could lead to forecast improvements. The present study explores the capabilities of the radar network at the Meteorological Service of Catalonia to retrieve dual-Doppler wind fields to study the dynamics of Catalan thunderstorms. A severe thunderstorm that splits and a tornado-producing supercell that is channeled through a valley are used to demonstrate the capabilities of an advanced open source technique that retrieves dynamical variables from C-band operational radars in complex terrain. For the first time in the Iberian Peninsula, complete 3D storm-relative winds are obtained, providing information about the internal dynamics of the storms. This aids in the analyses of the interaction between different storm cells within a system and/or the interaction of the cells with the local topography. Full article
(This article belongs to the Special Issue Estimating Meteorological Variables by Remote Sensing Data)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

  1. Title: Calibration and validation studies in different areas around the world

Brief Description: This research will be a validation study of the Global Precipitation Measurement (GPM) Mission’s global precipitation product (IMERG, or the Integrated Multi-Satellite Retrievals for GPM) over Great Britain and Ireland.  The IMERG product is produced using remote sensing techniques, by combining precipitation estimates from the GPM space-borne constellation of microwave radiometers and infrared sensors, with calibration to ground-based gauges.  This product will be validated by comparison with precipitation estimates from the Met Office ground radar network.  In particular, this study will focus on comparing surface rainfall amounts, occurrences, and the amplitude & phase of the diurnal cycle from the two products.

Author list: Daniel Watters and Alessandro Battaglia;

  1. Title: Aviation meteorology and satellite radar interferometry in Eurarctic airfields

Author list: Aleksey Sharov

  1. Title: C-band dual-Doppler retrievals in complex terrain: improving the knowledge of severe storm dynamics in Catalonia

Author List: Anna del Moral (1), Tammy M. Weckwerth (2), Tomeu Rigo (3), Michael M. Bell (4), and Maria Carmen Llasat (1)

Abstract: Convective activity in Catalonia (northeastern Spain) mainly occurs during summer and autumn and many of the thunderstorms tend to produce severe weather. In some of the cases, the storms have unexpected propagation characteristics, likely due to a combination of the complex topography and the thunderstorms’ auto-propagation mechanisms.  Due to the local nature of the events, Numerical Weather Prediction models are not able to accurately nowcast the motion of storms, their severity or the mesoscale triggering mechanisms. Although we have developed a successful warning system based on lightning jump and radar centroid-tracker, there remains a lack of knowledge of storm dynamics that could lead to forecast improvements. For this reason, the present study explores the capabilities of the radar network at the Meteorological Service of Catalonia to retrieve dual-Doppler wind fields to study dynamics of Catalan thunderstorms. A splitting severe thunderstorm and a channeling tornado are used to demonstrate the capabilities of an advanced open source technique on successfully retrieving dynamical variables from C-band operational radars in complex terrain. For the first time in the Iberian Peninsula, the complete 3D storm-relative winds are obtained, providing information about different interactions of convective cores with themselves and the surrounding environment.

Keywords: dual-Doppler retrievals; C-band radar; severe weather; storm dynamics; topography; nowcasting

4. Title: Leaf wetness duration estimation via satellite image and machine learning algorithms

Author list: Ju-Young Shin1, Bu-Yo Kim1*, Kyu Rang Kim1, Junsang Park1, Joo-Wan Cha1, and Jong-Chul Ha1

Affiliation: 1Applied Meteorology Research Division, National Institute of Meteorological Sciences, Seogwipo 63568, Korea

Abstract: Leaf wetness duration (LWD) is one of the key meteorological parameters for modeling plant diseases in agriculture. Because LWD is not a standard observational element in World Meteorological Organization, this variable is rarely measured in weather stations. Due to the limited number of stations, the LWD observations are difficult to use in agricultural areas. The LWD is linked to the near surface energy balance that can be represented by the Penman-Monteith equation. This energy balance status can be estimated from the images produced by satellites. Use of the satellite images can be an alternative to estimate LWD in ungauged areas and investigate its spatial distribution. Therefore, the current study aims to propose a methodology of LWD estimation using satellite image. The energy balance model requires a large number of input variables such as air temperature, wind speed, net radiation, and dew point temperature. Some of them are unable to be obtained from the images by satellites, for instance, wind speed. The functional relationship between LWD and satellite products is found by use of machine learning algorithms instead of an energy balance equation. To estimate LWD, the time interval of observed data has to be equal to or shorter than hour. Images by the geostationary satellite should be employed. Thus, GEO-KOMPSAT-2A is selected for the satellite. The results of this study will improve our understanding on LWD, particularly to spatial distribution of LWD. Furthermore, this study contributes to enhance our capacity to estimate and predict plant disease epidemics on large agriculture areas.

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