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Calibration and Assimilation of Multisensor Satellite Data for Hydrology Estimation

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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 (30 June 2022) | Viewed by 7481

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Prof. Dr. Qiuhong Tang

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Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: water cycle; remote sensing hydrology; land surface modeling; global change; water resources
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Dr. Gang Zhao

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Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
Interests: hydrology; satellite remote sensing; climate change; water resources

Prof. Dr. Yubao Qiu

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1. International Research Center of Big Data for Sustainable Development, Beijing 100094, China
2. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing of snow and ice; mocrowave remote sensing; global change
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Prof. Dr. Jian Peng

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Guest Editor

Helmholtz Centre for Environmental Research—UFZ, Permoserstraße 15, 04318 Leipzig, Germany
Interests: quantitative retrieval of land surface parameters from remote sensing data; radiative transfer in soil–vegetation–atmosphere systems; process-based modeling and data-driven methods; hydroclimatic extremes
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Special Issue Information

Dear Colleagues,

We are undergoing the “golden age” of Earth observations. Over the last several decades, satellites equipped with different types of sensors (e.g., Radar, microwave radiometer and scatterometer, optical imager, laser, gravity) have provided a variety of perspectives for hydrological monitoring. Leveraging these globally available satellite data and numerical modeling, data assimilation has been widely used as an effective tool to facilitate forecast, hindcast, reanalysis, and other applications with spatiotemporally continuous and unbiased simulations. In particular, data assimilation using multisensor satellite data and physically based hydrological or land surface models has emerged to deliver accurate estimates for various hydrological components, including precipitation, soil moisture, evapotranspiration, groundwater, streamflow, snow water equivalent, glacial mass balance, and others. With many new satellites launched (and planned) in the last several years, the unprecedented wealth of new data, together with long-term historical records, opens numerous opportunities for multisensor data assimilation research. Meanwhile, a new challenge for our scientific community has also arisen: How can we maximize the value of satellite data from different sensors and calibrate/assimilate such data for hydrological estimates?

This Special Issue aims to advance the science and technology on multisensor data assimilation for the hydrological cycle. We invite submissions from the perspectives of theoretical, methodological, and applicational advancements. The topics of interest include but are not limited to the data assimilation framework, operational hydrological forecast, hydrological reanalysis, data and model calibration/validation, uncertainty assessments, methodological comparison, data intercomparison, spatiotemporal downscaling, and hybrid machine learning/hydrological modeling.

Prof. Dr. Qiuhong Tang
Dr. Gang Zhao
Dr. Yubao Qiu
Prof. Dr. Jian Peng
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.

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Keywords

satellite remote sensing
data assimilation
multi-sensor
hydrological cycle
numerical modeling

Published Papers (3 papers)

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14 pages, 4216 KiB

Open AccessArticle

Meandering Characteristics of the Yimin River in Hulun Buir Grassland, Inner Mongolia, China

by Yuanyuan Zhou and Qiuhong Tang

Remote Sens. 2022, 14(11), 2696; https://doi.org/10.3390/rs14112696 - 03 Jun 2022

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Abstract

The evolution of meandering rivers continues to attract considerable attention in research and for practical applications, given that it is closely associated with the safety of river systems and riparian zones. There has been much discussion regarding the various channel planform features exhibited by meandering rivers under different river systems and riparian conditions. The Yimin River is a good example and is located southeast of the Hulun Buir Grassland, which is characterised by a fragile ecosystem and little anthropological activity along with active flow during the non-frozen season from May to November each year and relatively low sediment discharge compared with the Yellow River and Mississippi River. Improved analysis of the evolution of the Yimin River from 1975 to 2019 can support increased local species diversity and more effective flood risk and river management. With the combined Google Earth Engine (GEE) platform and the Geographic Information Systems (GIS) technique, remote sensing images, including Landsat images and global surface water data, are used to analyse the channel planform features of the freely meandering river channel in the middle and lower Yimin River. The results show that the percentage of low sinuosity channel bends was higher than that of high-sinuosity bends. Although the bends with an amplitude greater than 0.48 km and sinuosity greater than 2.3 have an evident upstream-skewed trend, the main channel planform features were downstream skewed with 1499 such bends. The river system conditions in the Yimin River, including lower sediment discharge and vegetation cover, are conducive to the development of downstream-skewed bends. The high-sinuosity bends were found to have a relatively larger ratio during 1981–2000, a period with higher mean annual streamflow compared with other time periods. Full article

(This article belongs to the Special Issue Calibration and Assimilation of Multisensor Satellite Data for Hydrology Estimation)

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19 pages, 5360 KiB

Open AccessArticle

Integration of Satellite Precipitation Data and Deep Learning for Improving Flash Flood Simulation in a Poor-Gauged Mountainous Catchment

by Xuan Tang, Zhaorui Yin, Guanghua Qin, Li Guo and Hongxia Li

Remote Sens. 2021, 13(24), 5083; https://doi.org/10.3390/rs13245083 - 14 Dec 2021

Cited by 9 | Viewed by 2790

Abstract

Satellite remote sensing precipitation is useful for many hydrological and meteorological applications such as rainfall-runoff forecasting. However, most studies have focused on the use of satellite precipitation on daily, monthly, or larger time scales. This study focused on flash flood simulation using satellite precipitation products (IMERG) on an hourly scale in a poorly gauged mountainous catchment in southwestern China. Deep learning (long short-term memory, LSTM) was used, merging satellite precipitation and gauge observations, and the merged precipitation data were used as inputs for flood simulation based on the HEC-HMS model, compared with the gauged precipitation data and original IMERG data. The results showed that the application of original IMERG data used directly in the HEC-HMS hydrological model had much lower accuracy than that of gauged data and merged data. The simulation using the merged precipitation in HEC-HMS exhibited much better performances than gauged data. The mean NSE improved from 0.84 to 0.87 for calibration and 0.80 to 0.84 for verification, while the lower NSE improved from 0.81 to 0.84 for calibration and 0.73 to 0.86 for verification, which showed that accuracy and robustness were both significantly improved. Results of this study indicate the advances of remote sensing precipitation with deep learning for flash flood forecasting in mountainous regions. It is likely that more significant improvements can be made in flash flood forecasting by employing multi-source remote sensing products and deep learning merging methods considering the impact of complex terrain. Full article

(This article belongs to the Special Issue Calibration and Assimilation of Multisensor Satellite Data for Hydrology Estimation)

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24 pages, 14768 KiB

Open AccessArticle

The Variability of Summer Atmospheric Water Cycle over the Tibetan Plateau and Its Response to the Indo-Pacific Warm Pool

by Deli Meng, Wanjiao Song, Qing Dong, Zi Yin and Wenbo Zhao

Remote Sens. 2021, 13(22), 4676; https://doi.org/10.3390/rs13224676 - 19 Nov 2021

Viewed by 1691

Abstract

The Tibetan Plateau (TP), atmosphere, and Indo-Pacific warm pool (IPWP) together constitute a regional land–atmosphere–ocean water vapor transport system. This study uses remote sensing data, reanalysis data, and observational data to explore the spatiotemporal variations of the summer atmospheric water cycle over the TP and its possible response to the air-sea interaction in the IPWP during the period 1958–2019. The results reveal that the atmospheric water cycle process over the TP presented an interannual and interdecadal strengthening trend. The climatic precipitation recycle ratio (PRR) over the TP was 18%, and the stronger the evapotranspiration, the higher the PRR. On the interdecadal scale, the change in evapotranspiration has a significant negative correlation with the Pacific Decadal Oscillation (PDO) index. The variability of the water vapor transport (WVT) over the TP was controlled by the dynamic and thermal conditions inside the plateau and the external air-sea interaction processes of the IPWP. When the summer monsoon over the TP was strong, there was an anomalous cyclonic WVT, which increased the water vapor budget (WVB) over the TP. The central and eastern tropical Pacific, the maritime continent and the western Indian Ocean together constituted the triple Sea Surface Temperature (SST) anomaly, which enhanced the convective activity over the IPWP and induced a significant easterly wind anomaly in the middle and lower troposphere, and then generated pronounced easterly WVT anomalies from the tropical Pacific to the maritime continent and the Bay of Bengal. Affected by the air-sea changes in the IPWP, the combined effects of the upstream strengthening and the downstream weakening in the water vapor transport process, directly and indirectly, increased the water vapor transport and budget of TP. Full article

(This article belongs to the Special Issue Calibration and Assimilation of Multisensor Satellite Data for Hydrology Estimation)

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