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Estimating Meteorological Variables by Remote Sensing Data

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 27719

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Special Issue Editors

Dr. Carmen Recondo

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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

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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, Collections and Topics in MDPI journals

Dr. Juanjo Peón

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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 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

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

Published Papers (9 papers)

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Research

20 pages, 5901 KiB

Open AccessArticle

Remote Sensing Products Validated by Flux Tower Data in Amazon Rain Forest

by Victor Hugo da Motta Paca, Gonzalo E. Espinoza-Dávalos, Rodrigo da Silva, Raphael Tapajós and Avner Brasileiro dos Santos Gaspar

Remote Sens. 2022, 14(5), 1259; https://doi.org/10.3390/rs14051259 - 04 Mar 2022

Cited by 5 | Viewed by 2958

Abstract

This work compares methods of climate measurements, such as those used to measure evapotranspiration, precipitation, net radiation, and temperature. The satellite products used were compared and evaluated against flux tower data. Evapotranspiration was validated against the SSEBop monthly and GLEAM daily and monthly products, respectively, and the results were RMSE = 24.144 mm/month, NRMSE = 0.223, r² = 0.163, slope = 0.411; RMSE = 1.781 mm/day, NRMSE = 0.599, r² = 0.000, slope = 0.006; RMSE = 36.17 mm/month, NRMSE = 0.401, r² = 0.002, and slope = 0.026. Precipitation was compared with the CHIRPS data, K67 was not part of the CHIRPS station correction. The results for both the daily and monthly comparisons were RMSE = 18.777 mm/day, NRMSE = 1.027, r² = 0.086, slope = 0.238 and RMSE = 130.713 mm/month, NRMSE = 0.706, r² = 0.402, and slope = 0.818. The net radiation validated monthly with CERES was RMSE = 75.357 W/m², NRMSE = 0.383, r² = 0.422, and slope = 0.867. The temperature results, as compared to MOD11C3, were RMSE = 2.829 °C, NRMSE = 0.116, r² = 0.153, and slope = 0.580. Comparisons between the remote sensing products and validation against the ground data were performed on a monthly basis. GLEAM and CHIRPS daily were the data sets with considerable discrepancy. Full article

(This article belongs to the Special Issue Estimating Meteorological Variables by Remote Sensing Data)

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14 pages, 5967 KiB

Open AccessArticle

Transforming Access to and Use of Climate Information Products Derived from Remote Sensing and In Situ Observations

by Gloriose Nsengiyumva, Tufa Dinku, Remi Cousin, Igor Khomyakov, Audrey Vadillo, Rija Faniriantsoa and Amanda Grossi

Remote Sens. 2021, 13(22), 4721; https://doi.org/10.3390/rs13224721 - 22 Nov 2021

Cited by 8 | Viewed by 2771

Abstract

Making climate-sensitive economic sectors resilient to climate trends and shocks, through adaptation to climate change and managing uncertainties associated with climate extremes, will require effective use of climate information to help practitioners make climate-informed decisions. The provision of weather and climate information will depend on the availability of climate data and its presentation in formats that are useful for decision making at different levels. However, in many places around the world, including most African countries, the collection of climate data has been seriously inadequate, and even when available, poorly accessible. On the other hand, the availability of climate data by itself may not lead to the uptake and use of such data. These data must be presented in user-friendly formats addressing specific climate information needs in order to be used for decision-making by governments, as well as the public and private sectors. The generated information should also be easily accessible. The Enhancing National Climate Services (ENACTS) initiative, led by Columbia University’s International Research Institute for Climate and Society (IRI), has been making efforts to overcome these challenges by supporting countries to improve the available climate data, as well as access to and use of climate information products at relevant spatial and temporal scales. Challenges to the availability of climate data are alleviated by combining data from the national weather observation network with remote sensing and other global proxies to generate spatially and temporally complete climate datasets. Access to climate information products is enhanced by developing an online mapping service that provides a user-friendly interface for analyzing and visualizing climate information products such as maps and graphs. Full article

(This article belongs to the Special Issue Estimating Meteorological Variables by Remote Sensing Data)

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17 pages, 38036 KiB

Open AccessArticle

Evaluation and Improvement of FY-4A AGRI Quantitative Precipitation Estimation for Summer Precipitation over Complex Topography of Western China

by Jing Ren, Guirong Xu, Wengang Zhang, Liang Leng, Yanjiao Xiao, Rong Wan and Junchao Wang

Remote Sens. 2021, 13(21), 4366; https://doi.org/10.3390/rs13214366 - 29 Oct 2021

Cited by 11 | Viewed by 1929

Abstract

Satellite quantitative precipitation estimation (QPE) can make up for the insufficiency of ground observations for monitoring precipitation. Using an Advanced Geosynchronous Radiation Imager (AGRI) on the FengYun-4A (FY-4A) satellite and rain gauges (RGs) for observations in the summer of 2020. The existing QPE of the FY-4A was evaluated and found to present poor accuracy over the complex topography of Western China. Therefore, to improve the existing QPE, first, cloud classification thresholds for the FY-4A were established with the dynamic clustering method to identify convective clouds. These thresholds consist of the brightness temperatures (TBs) of FY-4A water vapor and infrared channels, and their TB difference. Then, quantitative cloud growth rate correction factors were introduced to improve the QPE of the convective-stratiform technique. This was achieved using TB hourly variation rates of long-wave infrared channel 12, which is able to characterize the evolution of clouds. Finally, the dynamic time integration method was designed to solve the inconsistent time matching between the FY-4A and RGs. Consequently, the QPE accuracy of the FY-4A was improved. Compared with the existing QPE of the FY-4A, the correlation coefficient between the improved QPE of the FY-4A and the RG hourly precipitation increased from 0.208 to 0.492, with the mean relative error and root mean squared error decreasing from −47.4% and 13.78 mm to 8.3% and 10.04 mm, respectively. However, the correlation coefficient is not sufficiently high; thus, the algorithm needs to be further studied and improved. Full article

(This article belongs to the Special Issue Estimating Meteorological Variables by Remote Sensing Data)

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17 pages, 8739 KiB

Open AccessArticle

A Simple and Efficient Method for Correction of Basin-Scale Evapotranspiration on the Tibetan Plateau

by Yuqing Feng, Xingxing Kuang, Sihai Liang, Suning Liu, Yingying Yao, Yueqing Xie and Chunmiao Zheng

Remote Sens. 2021, 13(19), 3958; https://doi.org/10.3390/rs13193958 - 02 Oct 2021

Cited by 6 | Viewed by 2265

Abstract

Evapotranspiration (ET) is one of the important components of the global hydrologic cycle, energy exchange, and carbon cycle. However, basin scale actual ET (hereafter ET_a) is difficult to estimate accurately. We present an evaluation of four actual ET products (hereafter ET_p) in seven sub-basins in the Tibetan Plateau. The actual ET calculated by the water balance method (hereafter ET_ref) was used as the reference for correction of the different ET_p. The ET_ref and ET_p show obvious seasonal cycles, but the ET_p overestimated or underestimated the ET of the sub-basins in the Tibetan Plateau. A simple and effective method was proposed to correct the basin-scale ET_p. The method was referred to as ratio bias correction, and it can effectively remove nearly all biases of the ET_p. The proposed method is simpler and more effective in correcting the four ET_p compared with the gamma distribution bias correction method. The reliability of the ET_p is significantly increased after the ratio bias correction. The ratio bias correction method was used to correct the ET_p in the seven sub-basins in the Tibetan Plateau, and regional ET was significantly improved. The results may help improve estimation of the ET of the Tibetan Plateau and thereby contribute to a better understanding of the hydrologic cycle of the plateau. Full article

(This article belongs to the Special Issue Estimating Meteorological Variables by Remote Sensing Data)

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20 pages, 40264 KiB

Open AccessArticle

Evaluation of the MODIS (C6) Daily Albedo Products for Livingston Island, Antarctic

by Alejandro Corbea-Pérez, Javier F. Calleja, Carmen Recondo and Susana Fernández

Remote Sens. 2021, 13(12), 2357; https://doi.org/10.3390/rs13122357 - 16 Jun 2021

Cited by 10 | Viewed by 2413

Abstract

Although extensive research of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data is available on the Greenland Ice Sheet, there is a lack of studies evaluating MODIS albedo products over Antarctica. In this paper, MOD10A1, MYD10A1, and MCD43 (C6) daily albedo products were compared with the in situ albedo data on Livingston Island, South Shetland Islands (SSI), Antarctica, from 2006 to 2015, for both all-sky and clear-sky conditions, and for the entire study period and only the southern summer months. This is the first evaluation in which MYD10A1 and MCD43 are also included, which can be used to improve the accuracy of the snow BRDF/albedo modeling. The best correlation was obtained with MOD10A1 in clear-sky conditions (r = 0.7 and RMSE = 0.042). With MCD43, only data from the backup algorithm could be used, so the correlations obtained were lower (r = 0.6). However, it was found that there was no significant difference between the values obtained for all-sky and for clear-sky data. In addition, the MODIS products were found to describe the in situ data trend, with increasing albedo values in the range between 0.04 decade⁻¹ and 0.16 decade⁻¹. We conclude that MODIS daily albedo products can be applied to study the albedo in the study area. Full article

(This article belongs to the Special Issue Estimating Meteorological Variables by Remote Sensing Data)

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17 pages, 5370 KiB

Open AccessArticle

Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

by Wan-Ru Huang, Pin-Yi Liu, Ya-Hui Chang and Cheng-An Lee

Remote Sens. 2021, 13(4), 622; https://doi.org/10.3390/rs13040622 - 09 Feb 2021

Cited by 9 | Viewed by 1964

Abstract

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan. Full article

(This article belongs to the Special Issue Estimating Meteorological Variables by Remote Sensing Data)

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23 pages, 6410 KiB

Open AccessArticle

Precipitation Type Classification of Micro Rain Radar Data Using an Improved Doppler Spectral Processing Methodology

by Albert Garcia-Benadi, Joan Bech, Sergi Gonzalez, Mireia Udina, Bernat Codina and Jean-François Georgis

Remote Sens. 2020, 12(24), 4113; https://doi.org/10.3390/rs12244113 - 16 Dec 2020

Cited by 20 | Viewed by 4503

Abstract

This paper describes a methodology for processing spectral raw data from Micro Rain Radar (MRR), a K-band vertically pointing Doppler radar designed to observe precipitation profiles. The objective is to provide a set of radar integral parameters and derived variables, including a precipitation type classification. The methodology first includes an improved noise level determination, peak signal detection and Doppler dealiasing, allowing us to consider the upward movements of precipitation particles. A second step computes for each of the height bin radar moments, such as equivalent reflectivity (Z_e), average Doppler vertical speed (W), spectral width (σ), the skewness and kurtosis. A third step performs a precipitation type classification for each bin height, considering snow, drizzle, rain, hail, and mixed (rain and snow or graupel). For liquid precipitation types, additional variables are computed, such as liquid water content (LWC), rain rate (RR), or gamma distribution parameters, such as the liquid water content normalized intercept (N_w) or the mean mass-weighted raindrop diameter (D_m) to classify stratiform or convective rainfall regimes. The methodology is applied to data recorded at the Eastern Pyrenees mountains (NE Spain), first with a detailed case study where results are compared with different instruments and, finally, with a 32-day analysis where the hydrometeor classification is compared with co-located Parsivel disdrometer precipitation-type present weather observations. The hydrometeor classification is evaluated with contingency table scores, including Probability of Detection (POD), False Alarm Rate (FAR), and Odds Ratio Skill Score (ORSS). The results indicate a very good capacity of Method3 to distinguish rainfall and snow (PODs equal or greater than 0.97), satisfactory results for mixed and drizzle (PODs of 0.79 and 0.69) and acceptable for a reduced number of hail cases (0.55), with relatively low rate of false alarms and good skill compared to random chance in all cases (FAR < 0.30, ORSS > 0.70). The methodology is available as a Python language program called RaProM at the public github repository. Full article

(This article belongs to the Special Issue Estimating Meteorological Variables by Remote Sensing Data)

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20 pages, 3605 KiB

Open AccessArticle

Prediction of Leaf Wetness Duration Using Geostationary Satellite Observations and Machine Learning Algorithms

by Ju-Young Shin, Bu-Yo Kim, Junsang Park, Kyu Rang Kim and Joo Wan Cha

Remote Sens. 2020, 12(18), 3076; https://doi.org/10.3390/rs12183076 - 19 Sep 2020

Cited by 7 | Viewed by 3140

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 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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26 pages, 35908 KiB

Open AccessFeature PaperArticle

C-Band Dual-Doppler Retrievals in Complex Terrain: Improving the Knowledge of Severe Storm Dynamics in Catalonia

by Anna del Moral, Tammy M. Weckwerth, Tomeu Rigo, Michael M. Bell and María Carmen Llasat

Remote Sens. 2020, 12(18), 2930; https://doi.org/10.3390/rs12182930 - 10 Sep 2020

Cited by 6 | Viewed by 3933

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’ 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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