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Measurement of Hydrologic Variables with Remote Sensing

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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: closed (20 June 2022) | Viewed by 14352

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Special Issue Editors

Prof. Dr. Dongkyun Kim

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Guest Editor

Department of Civil Engineering, Hongik University, Seoul 04066, Korea
Interests: stochastic modeling of environmental variables; watershed modeling; remote sensing of hydrologic variables

Prof. Dr. Li-Pen Wang

E-Mail Website
Guest Editor

Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
Interests: multi-sensor data combination based upon the theories of Bayesian statistics and fractals; stochastic spatial-temporal rainfall modelling based upon point process theory; advanced image processing techniques and GPU programming

Special Issue Information

Dear Colleagues,

It has been regarded as an extremely challenging task to obtain a comprehensive understanding of hydrologic phenomena because of their large spatial extent and high spatiotemporal variability. Remote sensing techniques have significantly advanced the available solutions to this chronic issue of hydrologic research. For example, weather radars provide real-time observation of precipitation over spatial coverage encompassing several hundred kilometers at the resolution of several hundred meters and minutes, and satellite remote sensing techniques allow us to observe water and energy fluxes between the land surface and atmosphere at a global scale, such as land surface temperature, soil moisture, evapotranspiration, snow water equivalent, and vegetation/land cover. In addition, unmanned aerial vehicles (UAV) are deployed to measure the variability of hydrologic variables at a spatial resolution of several centimeters. Taking this into account, this Special Issue aims to publish original research articles concerning the observation of hydrologic variables using the state-of-the-art remote sensing techniques. The following specific topics will be especially welcomed:

Remote sensing of hydrologic variables using satellites;
Application of weather radars to observe precipitation;
Noble approach of observing hydrologic variables using UAVs.

Prof. Dr. Dongkyun Kim
Prof. Dr. Li-Pen Wang
Guest Editors

Manuscript Submission Information

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Keywords

Satellite
Radar
UAV
Rainfall
Precipitation
Soil moisture
Evapotranspiration
Solar radiation

Published Papers (5 papers)

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Research

15 pages, 4657 KiB

Open AccessArticle

Flood Inflow Estimation in an Ungauged Simple Serial Cascade of Reservoir System Using Sentinel-2 Multi-Spectral Imageries: A Case Study of Imjin River, South Korea

by Jin Gyeom Kim, Boosik Kang and Sungmo Kim

Remote Sens. 2022, 14(15), 3699; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153699 - 02 Aug 2022

Cited by 5 | Viewed by 1352

Abstract

The Imjin River is a representative transboundary river in the Korean Peninsula, originating from North Korea and flowing into South Korea. Upstream of Imjin River, on the North Korean side, is the Hwanggang Dam, with a 350 million m³ of storage capacity, which is in operation for power generation and water supply. The sharing of the operation information of Hwanggang Dam has been limited due to political and military tension. South Korea has constructed the Gunnam Flood Control Reservoir downstream of the Imjin River to prevent potential flood damage due to the urgent and unilateral release from the Hwanggang Dam. However, it is difficult to manage the flood of the Imjin River basin under the situation of limited shared real-time information on the Hwanggang Dam operation. In this study, a hydrological analysis system was established to estimate the inflow and release of the Hwanggang Dam by building a lumped hydrological model and an Auto ROM (Reservoir Operation Method)-based reservoir operation algorithm. To estimate the inflow of the Gunnam Flood Control Reservoir, the water level of the Hwanggang Dam was calculated using the Sentinel-2 multi-spectral images, and the hydrological analysis system was calibrated. The evaluation index of the water level of the ungauged Hwanggang Dam derived from January 2017 to August 2020 using the hydrological analysis system was shown to have a coefficient of determination of 0.76 and an RMSE of 3.97 m. In the case of the flood event in August 2020, the coefficient of determination of the flood inflow in the Gunnam Flood Control Zone was calculated to be 0.86. Full article

(This article belongs to the Special Issue Measurement of Hydrologic Variables with Remote Sensing)

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23 pages, 11478 KiB

Open AccessArticle

Application of a Novel Hybrid Method for Flood Susceptibility Mapping with Satellite Images: A Case Study of Seoul, Korea

by Roya Narimani, Changhyun Jun, Saqib Shahzad, Jeill Oh and Kyoohong Park

Remote Sens. 2021, 13(14), 2786; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142786 - 15 Jul 2021

Cited by 14 | Viewed by 3201

Abstract

This paper proposes a novel hybrid method for flood susceptibility mapping using a geographic information system (ArcGIS) and satellite images based on the analytical hierarchy process (AHP). Here, the following nine multisource environmental controlling factors influencing flood susceptibility were considered for relative weight estimation in AHP: elevation, land use, slope, topographic wetness index, curvature, river distance, flow accumulation, drainage density, and rainfall. The weight for each factor was determined from AHP and analyzed to investigate critical regions that are more vulnerable to floods using the overlay weighted sum technique to integrate the nine layers. As a case study, the ArcGIS-based framework was applied in Seoul to obtain a flood susceptibility map, which was categorized into six regions (very high risk, high risk, medium risk, low risk, very low risk, and out of risk). Finally, the flood map was verified using real flood maps from the previous five years to test the model’s effectiveness. The flood map indicated that 40% of the area shows high flood risk and thus requires urgent attention, which was confirmed by the validation results. Planners and regulatory bodies can use flood maps to control and mitigate flood incidents along rivers. Even though the methodology used in this study is simple, it has a high level of accuracy and can be applied for flood mapping in most regions where the required datasets are available. This is the first study to apply high-resolution basic maps (12.5 m) to extract the nine controlling factors using only satellite images and ArcGIS to produce a suitable flood map in Seoul for better management in the near future. Full article

(This article belongs to the Special Issue Measurement of Hydrologic Variables with Remote Sensing)

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22 pages, 4522 KiB

Open AccessArticle

A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

by Xin Yan, Hua Chen, Bingru Tian, Sheng Sheng, Jinxing Wang and Jong-Suk Kim

Remote Sens. 2021, 13(11), 2040; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112040 - 21 May 2021

Cited by 36 | Viewed by 3440

Abstract

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area. Full article

(This article belongs to the Special Issue Measurement of Hydrologic Variables with Remote Sensing)

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19 pages, 37891 KiB

Open AccessArticle

Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based Photogrammetry

by Sangku Lee, Jeongha Park, Eunsoo Choi and Dongkyun Kim

Remote Sens. 2021, 13(4), 828; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040828 - 23 Feb 2021

Cited by 6 | Viewed by 2917

Abstract

Factors influencing the accuracy of UAV-photogrammetry-based snow depth distribution maps were investigated. First, UAV-based surveys were performed on the 0.04 km² snow-covered study site in South Korea for 37 times over the period of 13 days under 16 prescribed conditions composed of various photographing times, flight altitudes, and photograph overlap ratios. Then, multi-temporal Digital Surface Models (DSMs) of the study area covered with shallow snow were obtained using digital photogrammetric techniques. Next, the multi-temporal snow depth distribution maps were created by subtracting the snow-free DSM from the multi-temporal DSMs of the study area. Then, snow depth in these UAV-Photogrammetry-based snow maps were compared to the in situ measurements at 21 locations. The accuracy of each of the multi-temporal snow maps were quantified in terms of bias (median of residuals,

Q_{Δ D}

) and precision (the Normalized Median Absolute Deviation, NMAD). Lastly, various factors influencing these performance metrics were investigated. The results are as follows: (1) the

Q_{Δ D}

and NMAD of the eight surveys performed at the optimal condition (50 m flight altitude and 80% overlap ratio) ranged from −2.30 cm to 5.90 cm and from 1.78 cm to 4.89 cm, respectively. The best survey case had −2.30 cm of

Q_{Δ D}

and 1.78 cm of NMAD; (2) Lower UAV flight altitude and greater photograph overlap lower the NMAD and

Q_{Δ D}

; (3) Greater number of Ground Control Points (GCPs) lowers the NMAD and

Q_{Δ D}

; (4) Spatial configuration and accuracy of GCP coordinates influenced the accuracy of the snow depth distribution map; (5) Greater number of tie-points leads to higher accuracy; (6) Smooth fresh snow cover did not provide many tie-points, either resulting in a significant error or making the entire photogrammetry process impossible. Full article

(This article belongs to the Special Issue Measurement of Hydrologic Variables with Remote Sensing)

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21 pages, 19673 KiB

Open AccessArticle

Reconstruction of Snow Depth Data at Moderate Spatial Resolution (1 km) from Remotely Sensed Snow Data and Multiple Optimized Environmental Factors: A Case Study over the Qinghai-Tibetan Plateau

by Pengtao Wei, Tingbin Zhang, Xiaobing Zhou, Guihua Yi, Jingji Li, Na Wang and Bo Wen

Remote Sens. 2021, 13(4), 657; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040657 - 11 Feb 2021

Cited by 12 | Viewed by 2366

Abstract

Snow depth distribution in the Qinghai-Tibetan plateau is important for atmospheric circulation and surface water resources. In-situ observations at meteorological stations and remote observation by passive microwave remote sensing technique are two main approaches for monitoring snow depth at regional or global levels. However, the meteorological stations are often scarce and unevenly distributed in mountainous regions because of inaccessibility, so are the in-situ snow depth measurements. Passive microwave remote sensing data can alleviate the unevenness issue, but accuracy and spatial (e.g., 25 km) and temporal resolutions are low; spatial heterogeneity in snow depth is thus hard to capture. On the other hand, optical sensors such as moderate resolution imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites can monitor snow at moderate spatial resolution (1 km) and high temporal resolution (daily) but only snow area extent, not snow depth. Fusing passive microwave snow depth data with optical snow area extent data provides an unprecedented opportunity for generating snow depth data at moderate spatial resolution and high temporal resolution. In this article, a linear multivariate snow depth reconstruction (LMSDR) model was developed by fusing multisource snow depth data, optical snow area extent data, and environmental factors (e.g., spatial distribution, terrain features, and snow cover characteristics), to reconstruct daily snow depth data at moderate resolution (1 km) for 16 consecutive hydrological years, taking Qinghai-Tibetan Plateau (QTP) as a case study. We found that snow cover day (SCD) and environmental factors such as longitude, latitude, slope, surface roughness, and surface fluctuation have a significant impact on the variations of snow depth over the QTP. Relatively high accuracy (root mean square error (RMSE) = 2.26 cm) was observed in the reconstructed snow depth when compared with in-situ data. Compared with the passive microwave remote sensing snow depth product, constructing a nonlinear snow depletion curve product with an empirical formula and fusion snow depth product, the LMSDR model (RMSE = 2.28 cm, R² = 0.63) demonstrated a significant improvement in accuracy of snow depth reconstruction. The overall spatial accuracy of the reconstructed snow depth was 92%. Compared with in-situ observations, the LMSDR product performed well regarding different snow depth intervals, land use, elevation intervals, slope intervals, and SCD and performed best, especially when the snow depth was less than 3 cm. At the same time, a long-time snow depth series reconstructed based on the LMSDR model reflected interannual variations of snow depth well over the QTP. Full article

(This article belongs to the Special Issue Measurement of Hydrologic Variables with Remote Sensing)

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