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Applications of Remotely Sensed Data in Hydrology and Climatology (Second Edition)

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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: 30 November 2024 | Viewed by 9589

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Prof. Dr. Yuei-An Liou

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Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli District, Taoyuan City 320317, Taiwan
Interests: remoet sensing; hydrometeorological extremes; vulnerability assessment
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Special Issue Information

Dear Colleagues,

This Special Issue will mainly focus on evaluating individual and integrated studies of hydroclimatic analysis based on satellite observations. The main intention is to present precise and novel information regarding variations in hydrological and climatic characteristics and the improvement of future planning and policy. Currently, remotely sensed data are commonly being used in hydrological and climatological studies at regional or global scales. Satellite observations from passive and active sensors, onboard both geostationary and polar-orbiting satellites, collect information and data in dangerous or inaccessible areas that are very useful for hydrological and climatological studies. Vast numbers of satellite observations are being used in monitoring the terrestrial hydrology for various applications (rainfall, soil moisture, flood extent, surface water level, terrestrial water storage, groundwater, evapotranspiration, discharge, snow and ice, floods, etc.). Similarly, consistent long-term Earth satellite observations and data records are becoming indispensable in providing information for improved detection, attribution, and prediction of global climate and environmental changes in addition to helping decision makers and society to respond and adapt to these changes and variability in a resilient fashion. Finally, remote sensing data can be very useful for improving warning, forecasting, and preparedness, being therefore also useful in hydroclimatic disaster risk management.

Special focus will be given to hybrid methods, modeling, and recent advances in the study of spatiotemporal variations in water and climatic changes using satellite observations. Hence, a wide range of topics are of potential interest to this Special Issue, including but not limited to:

Time series analysis of hydrometeorological parameters using satellite data.
Watershed modeling using remote sensing products or in situ observations.
Application of satellite data on flood, evapotranspiration, snow, soil moisture, groundwater, and soil erosion studies (modeling, improvement, policy, etc.).
Assessment of climate change impacts on extremes, such as flood and drought, using satellite data.
Assessment of climate change impacts on water resources or hydrological cycles using remote sensing products.
Application of statistical and machine learning to satellite-based hydrological and climatological data.
Assessment of climate change impacts on available water resources and agricultural production using satellite observations.
Assessment and improvement of hydroclimatic study at regional or global scales using remote sensing data.

This Special Issue is the second edition of the Special Issue: “Applications of Remotely Sensed Data in Hydrology and Climatology”.

Prof. Dr. Yuei-An Liou
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

climate change
satellite observation
water resources
global water and energy cycles
remote sensing
water reservoir monitoring
cloud, temperature, humidity, precipitation, wind, etc.

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Published Papers (7 papers)

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Research

19 pages, 5705 KiB

Open AccessArticle

Comparative Analysis of Machine Learning Models for Tropical Cyclone Intensity Estimation

by Yuei-An Liou and Truong-Vinh Le

Remote Sens. 2024, 16(17), 3138; https://doi.org/10.3390/rs16173138 - 26 Aug 2024

Viewed by 1521

Abstract

Estimating tropical cyclone (TC) intensity is crucial for disaster reduction and risk management. This study aims to estimate TC intensity using machine learning (ML) models. We utilized eight ML models to predict TC intensity, incorporating factors such as TC location, central pressure, distance to land, landfall in the next six hours, storm speed, storm direction, date, and number from the International Best Track Archive for Climate Stewardship Version 4 (IBTrACS V4). The dataset was divided into four sub-datasets based on the El Niño–Southern Oscillation (ENSO) phases (Neutral, El Niño, and La Niña). Our results highlight that central pressure has the greatest effect on TC intensity estimation, with a maximum root mean square error (RMSE) of 1.289 knots (equivalent to 0.663 m/s). Cubist and Random Forest (RF) models consistently outperformed others, with Cubist showing superior performance in both training and testing datasets. The highest bias was observed in SVM models. Temporal analysis revealed the highest mean error in January and November, and the lowest in February. Errors during the Warm phase of ENSO were notably higher, especially in the South China Sea. Central pressure was identified as the most influential factor for TC intensity estimation, with further exploration of environmental features recommended for model robustness. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology (Second Edition))

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23 pages, 10000 KiB

Open AccessArticle

Assessment of PERSIANN Satellite Products over the Tulijá River Basin, Mexico

by Lorenza Ceferino-Hernández, Francisco Magaña-Hernández, Enrique Campos-Campos, Gabriela Adina Morosanu, Carlos E. Torres-Aguilar, René Sebastián Mora-Ortiz and Sergio A. Díaz

Remote Sens. 2024, 16(14), 2596; https://doi.org/10.3390/rs16142596 - 16 Jul 2024

Viewed by 857

Abstract

Precipitation is a fundamental component of the Earth’s hydrological cycle. Therefore, monitoring precipitation is paramount, as accurate information is needed to prevent natural hydrological disasters, such as floods and droughts. However, measuring precipitation using rain gauges is complicated due to their sparse spatial distribution. Satellite precipitation products (SPPs) are an alternative source of rainfall data. This study aimed to evaluate the performance of PERSIANN-CCS and PDIR-Now SPPs over the Tulijá River Basin (Chiapas, Mexico) using scatter plots, categorical statistics, descriptive statistics, and decomposing total bias. Additionally, bias correction was performed using the quantile mapping (QM) method. QM is a technique used to improve the fit of SPPs with respect to rainfall observations through a transfer function, aiming to reduce systematic errors in SPPs. The results indicate that the PDIR-Now product tends to overestimate rainfall to a large extent, thus showing better performance in detecting rain events. Meanwhile, PERSIANN-CCS underestimates precipitation to a lesser extent. The findings of this study demonstrate that correcting the bias of SPPs improves estimations of rainfall records, thereby reducing the percentage bias and root mean square error. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology (Second Edition))

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25 pages, 19921 KiB

Open AccessArticle

Evaluation of Daily and Hourly Performance of Multi-Source Satellite Precipitation Products in China’s Nine Water Resource Regions

by Hongji Gu, Dingtao Shen, Shuting Xiao, Chunxiao Zhang, Fengpeng Bai and Fei Yu

Remote Sens. 2024, 16(9), 1516; https://doi.org/10.3390/rs16091516 - 25 Apr 2024

Cited by 1 | Viewed by 1007

Abstract

Satellite precipitation products (SPPs) are of great significance for water resource management and utilization in China; however, they suffer from considerable uncertainty. While numerous researchers have evaluated the accuracy of various SPPs, further investigation is needed to assess their performance across China’s nine major water resource regions. This study used the latest precipitation dataset of the China Meteorological Administration’s Land Surface Data Assimilation System (CLDAS-V2.0) as the benchmark and evaluated the performance of six SPPs—GSMaP, PERSIANN, CMORPH, CHIRPS, GPM IMERG, and TRMM—using six indices: correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI), at both daily and hourly scales across China’s nine water resource regions. The conclusions of this study are as follows: (1) The performance of the six SPPs was generally weaker in the west than in the east, with the Continental Basin (CB) exhibiting the poorest performance, followed by the Southwest Basin (SB). (2) At the hourly scale, the performance of the six SPPs was weaker compared to the daily scale, particularly in the high-altitude CB and the high-latitude Songhua and Liaohe River Basin (SLRB), where observing light precipitation and snowfall presents significant challenges. (3) GSMaP, CMORPH, and GPM IMERG demonstrated superior overall performance compared to CHIRPS, PERISANN, and TRMM. (4) CMORPH was found to be better suited for application in drought-prone areas, showcasing optimal performance in the CB and SB. GSMaP excelled in humid regions, displaying the best overall performance in the remaining seven basins. GPM IMERG serves as a complementary precipitation data source for the first two. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology (Second Edition))

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27 pages, 8061 KiB

Open AccessArticle

Applicability of Precipitation Products in the Endorheic Basin of the Yellow River under Multi-Scale in Time and Modality

by Weiru Zhu and Kang Liang

Remote Sens. 2024, 16(5), 872; https://doi.org/10.3390/rs16050872 - 29 Feb 2024

Viewed by 921

Abstract

Continuous and accurate precipitation data are critical to water resource management and eco-logical protection in water-scarce and ecologically fragile endorheic or inland basins. However, in typical data-scarce endorheic basins such as the endorheic basin of the Yellow River Basin (EBYRB) in China, multi-source precipitation products provide an opportunity to accurately capture the spatial distribution of precipitation, but the applicability evaluation of multi-source precipitation products under multi-time scales and multi-modes is currently lacking. In this context, our study evaluates the regional applicability of seven diverse gridded precipitation products (APHRODITE, GPCC, PERSIANN-CDR, CHIRPS, ERA5, JRA55, and MSWEP) within the EBYRB considering multiple temporal scales and two modes (annual/monthly/seasonal/daily precipitation in the mean state and monthly/daily precipitation in the extreme state). Furthermore, we explore the selection of suitable precipitation products for the needs of different hydrological application scenarios. Our research results indicate that each product has its strengths and weaknesses at different time scales and modes of coupling. GPCC excels in capturing annual, seasonal, and monthly average precipitation as well as monthly and daily extreme precipitation, essentially meeting the requirements for inter-annual or intra-annual water resource management in the EBYRB. CHIRPS and PERSIANN-CDR have higher accuracy in extreme precipitation assessment and can provide near real-time data, which can be applied as dynamic input precipitation variables in extreme precipitation warnings. APHRODITE and MSWEP exhibit superior performance in daily average precipitation that can provide data for meteorological or hydrological studies at the daily scale in the EBYRB. At the same time, our research also exposes typical problems with several precipitation products, such as MSWEP’s abnormal assessment of summer precipitation in certain years and ERA5 and JRA55’s overall overestimation of precipitation assessment in the study area. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology (Second Edition))

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19 pages, 10172 KiB

Open AccessArticle

Reconstructing Snow Cover under Clouds and Cloud Shadows by Combining Sentinel-2 and Landsat 8 Images in a Mountainous Region

by Yanli Zhang, Changqing Ye, Ruirui Yang and Kegong Li

Remote Sens. 2024, 16(1), 188; https://doi.org/10.3390/rs16010188 - 2 Jan 2024

Cited by 1 | Viewed by 1758

Abstract

Snow cover is a sensitive indicator of global climate change, and optical images are an important means for monitoring its spatiotemporal changes. Due to the high reflectivity, rapid change, and intense spatial heterogeneity of mountainous snow cover, Sentinel-2 (S2) and Landsat 8 (L8) satellite imagery with both high spatial resolution and spectral resolution have become major data sources. However, optical sensors are more susceptible to cloud cover, and the two satellite images have significant spectral differences, making it challenging to obtain snow cover beneath clouds and cloud shadows (CCSs). Based on our previously published approach for snow reconstruction on S2 images using the Google Earth Engine (GEE), this study introduces two main innovations to reconstruct snow cover: (1) combining S2 and L8 images and choosing different CCS detection methods, and (2) improving the cloud shadow detection algorithm by considering land cover types, thus further improving the mountainous-snow-monitoring ability. The Babao River Basin of the Qilian Mountains in China is chosen as the study area; 399 scenes of S2 and 35 scenes of L8 are selected to analyze the spatiotemporal variations of snow cover from September 2019 to August 2022 in GEE. The results indicate that the snow reconstruction accuracies of both images are relatively high, and the overall accuracies for S2 and L8 are 80.74% and 88.81%, respectively. According to the time-series analysis of three hydrological years, it is found that there is a marked difference in the spatial distribution of snow cover in different hydrological years within the basin, with fluctuations observed overall. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology (Second Edition))

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24 pages, 11557 KiB

Open AccessArticle

Multiscale Evaluation of Gridded Precipitation Datasets across Varied Elevation Zones in Central Asia’s Hilly Region

by Manuchekhr Gulakhmadov, Xi Chen, Aminjon Gulakhmadov, Muhammad Umar Nadeem, Nekruz Gulahmadov and Tie Liu

Remote Sens. 2023, 15(20), 4990; https://doi.org/10.3390/rs15204990 - 17 Oct 2023

Cited by 1 | Viewed by 1158

Abstract

The lack of observed data makes research on the cryosphere and ecology extremely difficult, especially in Central Asia’s hilly regions. Before their direct hydroclimatic uses, the performance study of gridded precipitation datasets (GPDS) is of utmost importance. This study assessed the multiscale ground evaluation of three reanalysis datasets (ERA5, MEERA2, and APHRO) and five satellite datasets (PERSIANN-PDIR, CHIRPS, GPM-SM2Rain, SM2Rain-ASCAT, and SM2Rain-CCI). Several temporal scales (daily, monthly, seasonal (winter, spring, summer, autumn), and annual) of all the GPDS were analyzed across the complete spatial domain and point-to-pixel scale from January 2000 to December 2013. The validation of GPDS was evaluated using evaluation indices (Root Mean Square Error, correlation coefficient, bias, and relative bias) and categorical indices (False Alarm Ratio, Probability of Detection, success ratio, and Critical Success Index). The performance of all GPDS was also analyzed based on different elevation zones (≤1500, ≤2500, >2500 m). According to the results, the daily estimations of the spatiotemporal tracking abilities of CHIRPS, APHRO, and GPM-SM2Rain are superior to those of the other datasets. All GPDS performed better on a monthly scale than they performed on a daily scale when the ranges were adequate (CC > 0.7 and r-BIAS (10)). Apart from the winter season, the CHIRPS beat all the other GPDS in standings of POD on a daily and seasonal scale. In the summer, all GPDS showed underestimations, but GPM showed the biggest underestimation (−70). Additionally, the CHIRPS indicated the best overall performance across all seasons. As shown by the probability density function (PDF %), all GPDS demonstrated more adequate performance in catching the light precipitation (>2 mm/day) events. APHRO and SM2Rain-CCI typically function moderately at low elevations, whereas all GPDS showed underestimation across the highest elevation >2500 m. As an outcome, we strongly suggest employing the CHIRPS precipitation product’s daily, and monthly estimates for hydroclimatic applications over the hilly region of Tajikistan. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology (Second Edition))

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26 pages, 8028 KiB

Open AccessArticle

Significant Disparity in Spatiotemporal Changes of Terrestrial Evapotranspiration across Reanalysis Datasets in China from 1982 to 2020

by Jiaxin Bai, Guocan Wu and Yuna Mao

Remote Sens. 2023, 15(18), 4522; https://doi.org/10.3390/rs15184522 - 14 Sep 2023

Cited by 1 | Viewed by 1150

Abstract

Due to limited observational data, there remains considerable uncertainty in the estimation and spatiotemporal variations of land surface evapotranspiration (ET). Reanalysis products, with their advantages of high spatiotemporal resolution, global coverage, and long-term data availability, have emerged as powerful tools for studying ET. Nevertheless, the accuracy of reanalysis ET products varies among different products and the reasons for these accuracy differences have not been thoroughly investigated. This study evaluates the ability of different reanalysis ET products to reproduce the spatiotemporal patterns and long-term trends of ET in China, using remote sensing and water-balance-derived ET as reference. We investigate the possible reasons for their disparity by analyzing the three major climatic factors influencing ET (precipitation, solar radiation, and temperature). The findings reveal that compared to the water balance ET, the Global Land Evaporation Amsterdam Model (GLEAM) product is capable of reproducing the mean, interannual variability, and trends of ET, making it suitable for validating reanalysis ET products. In comparison to GLEAM ET, all reanalysis ET products exhibit consistent climatology and spatial distribution but show a clear overestimation, with multi-year averages being overestimated by 16–40%. There are significant differences among the reanalysis products in terms of interannual variability, long-term trends, and attribution. Within the common period of 2003–2015, GLEAM and water balance ET products demonstrate consistent increasing trends. The second-generation Modern-Era Retrospective analysis for Research and Applications (MERRA2) and the offline (land-only) replay of MERRA (MERRA-Land) could produce similar increasing trends because of the consistent precipitation trends with observed precipitation. The European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) and ERA5-Land cannot capture the consistent increasing trends as they obtain decreasing precipitation. These findings have significant implications for the development of reanalysis products. Full article

(This article belongs to the Special Issue Applications of Remotely Sensed Data in Hydrology and Climatology (Second Edition))

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