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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 (1 November 2021) | Viewed by 16764

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Special Issue Editors

Prof. Dr. Karl Schneider

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Guest Editor

Department of Geography, University of Cologne, 50923 Koln, Germany
Interests: hydrogeography; climatology remote sensing development of integrative land surface process models (plant growth, water, nutrients); modeling of energy fluxes at the land and water surface GIS

Prof. Dr. Harrie-Jan Hendricks Franssen

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Guest Editor

Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Interests: data assimilation; inverse modeling; stochastic hydrology; parameter estimation; integrated modeling; land surface hydrology; groundwater hydrology; stream–aquifer interaction

Special Issue Information

Dear Colleagues,

Water fluxes at the land surface are highly variable, both spatially and temporally. The increasing pressure upon our water resources requires that spatial and temporal patterns of hydrological fluxes be recognized in order to mitigate flood and drought effects, to improve water use efficiency (e.g., for agriculture) or to better utilize natural (e.g., soil, groundwater) or human-made water storage. Remote sensing techniques play a key role in accessing hydrological state variables (e.g., soil moisture), water fluxes (e.g., precipitation) or drivers for hydrological fluxes (e.g., LAI), particularly in regions with sparse ground-based measurement networks. A wealth of satellite based systems is available today that provide data and products. These satellite systems are augmented by systems that focus on regional or field scale (e.g., UAVs).

This Special Issue will focus particularly on papers assessing the properties and functions of critical zones governing hydrological fluxes on local to watershed scales. Examples of these critical zones are the soil surface, as it partitions infiltration and surface runoff, the root zone, as it partitions groundwater recharge and transpiration, the plant–soil interface, as it governs evapotranspiration or the riparian zone, as it is essential for ground and surface water interactions. Original research papers and/or review papers that address the use and accuracy of remote sensing data and products to investigate these fluxes, to provide model parameters, to drive models or to improve water management practices are particularly invited.

Prof. Dr. Karl Schneider
Prof. Dr. Harrie-Jan Hendricks Franssen
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.

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

Critical zone hydrology
Evapotranspiration
Soil moisture
Data assimilation

Published Papers (7 papers)

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Research

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33 pages, 29484 KiB

Open AccessArticle

Determination of Weak Terrestrial Water Storage Changes from GRACE in the Interior of the Tibetan Plateau

by Longwei Xiang, Hansheng Wang, Holger Steffen, Baojin Qiao, Wei Feng, Lulu Jia and Peng Gao

Remote Sens. 2022, 14(3), 544; https://doi.org/10.3390/rs14030544 - 24 Jan 2022

Cited by 9 | Viewed by 2745

Abstract

Time series of the Gravity Recovery and Climate Experiment (GRACE) satellite mission have been successfully used to reveal changes in terrestrial water storage (TWS) in many parts of the world. This has been hindered in the interior of the Tibetan Plateau since the derived TWS changes there are very sensitive to the selections of different available GRACE solutions, and filters to remove north-south-oriented (N-S) stripe features in the observations. This has resulted in controversial distributions of the TWS changes in previous studies. In this paper, we produce aggregated hydrology signals (AHS) of TWS changes from 2003 to 2009 in the Tibetan Plateau and test a large set of GRACE solution-filter combinations and mascon models to identify the best combination or mascon model whose filtered results match our AHS. We find that the application of a destriping filter is indispensable to remove correlated errors shown as N-S stripes. Three best-performing destriping filters are identified and, combined with two best-performing solutions, they represent the most reliable solution-filter combinations for determination of weak terrestrial water storage changes in the interior of the Tibetan Plateau from GRACE. In turn, more than 100 other tested solution-filter combinations and mascon solutions lead to very different distributions of the TWS changes inside and outside the plateau that partly disagree largely with the AHS. This is mainly attributed to less effective suppression of N-S stripe noises. Our results also show that the most effective destriping is performed within a maximum degree and order of 60 for GRACE spherical harmonic solutions. The results inside the plateau show one single anomaly in the TWS trend when additional smoothing with a 340-km-radius Gaussian filter is applied. We suggest using our identified best solution-filter combinations for the determination of TWS changes in the Tibetan Plateau and adjacent areas during the whole GRACE operation time span from 2002 to 2017 as well as the succeeding GRACE-FO mission. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Hydrogeography and Climatology)

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9 pages, 3329 KiB

Open AccessCommunication

Application of Sentinel-1B Polarimetric Observations to Soil Moisture Retrieval Using Neural Networks: Case Study for Bare Siberian Chernozem Soil

by Konstantin Muzalevskiy and Anatoly Zeyliger

Remote Sens. 2021, 13(17), 3480; https://doi.org/10.3390/rs13173480 - 02 Sep 2021

Cited by 4 | Viewed by 1725

Abstract

Sentinel-1 is currently the only synthetic-aperture radar, which radar measurements of the earth’s surface to be carried out, regardless of weather conditions, with high resolution up to 5–40 m and high periodicity from several to 12 days. Sentinel-1 creates a technological platform for the development of new globally remote sensing algorithms of soil moisture, not only for hydrological and climatic model applications, but also on a single field scale for individual farms in precision farming systems used. In this paper, the potential of soil moisture remote sensing using polarimetric Sentinel-1B backscattering observations was studied. As a test site, the fallow agricultural field with bare soil near the Minino village (56.0865°N, 92.6772°E), Krasnoyarsk region, the Russian Federation, was chosen. The relationship between the cross-polarized ratio, reflectivity, and the soil surface roughness established Oh used as a basis for developing the algorithm of soil moisture retrieval with neural networks (NNs) computational model. Two NNs is used as a universal regression technique to establish the relationship between scattering anisotropy, entropy and backscattering coefficients measured by the Sentinel-1B on the one hand and reflectivity on the other. Finally, the soil moisture was found from the soil reflectivity in solving the inverse problem using the Mironov dielectric model. During the field campaign from 21 May to 25 August 2020, it was shown that the proposed approach allows us to predict soil moisture values in the layer thickness of 0.00–0.05 m with the root-mean-square error and determination coefficient not worse than 3% and 0.726, respectively. The validity of the proposed approach needs additional verification on a wider dataset using soils of different textures, a wide range of variations in soil surface roughness, and moisture. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Hydrogeography and Climatology)

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22 pages, 5010 KiB

Open AccessArticle

The Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis

by Haojin Zhao, Carsten Montzka, Roland Baatz, Harry Vereecken and Harrie-Jan Hendricks Franssen

Remote Sens. 2021, 13(16), 3068; https://doi.org/10.3390/rs13163068 - 04 Aug 2021

Cited by 4 | Viewed by 2050

Abstract

Land surface models (LSMs) simulate water and energy cycles at the atmosphere–soil interface, however, the physical processes in the subsurface are typically oversimplified and lateral water movement is neglected. Here, a cross-evaluation of land surface model results (with and without lateral flow processes), the National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) mission soil moisture product, and cosmic-ray neutron sensor (CRNS) measurements is carried out over a temperate climate region with cropland and forests over western Germany. Besides a traditional land surface model (the Community Land Model (CLM) version 3.5), a coupled land surface-subsurface model (CLM-ParFlow) is applied. Compared to CLM stand-alone simulations, the coupled CLM-ParFlow model considered both vertical and lateral water movement. In addition to standard validation metrics, a triple collocation (TC) analysis has been performed to help understanding the random error variances of different soil moisture datasets. In this study, it is found that the three soil moisture datasets are consistent. The coupled and uncoupled model simulations were evaluated at CRNS sites and the coupled model simulations showed less bias than the CLM-standalone model (−0.02 cm³ cm⁻³ vs. 0.07 cm³ cm⁻³), similar random errors, but a slightly smaller correlation with the measurements (0.67 vs. 0.71). The TC-analysis showed that CLM-ParFlow reproduced better soil moisture dynamics than CLM stand alone and with a higher signal-to-noise ratio. This suggests that the representation of subsurface physics is of major importance in land surface modeling and that coupled land surface-subsurface modeling is of high interest. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Hydrogeography and Climatology)

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18 pages, 15576 KiB

Open AccessArticle

Quantifying the Responses of Evapotranspiration and Its Components to Vegetation Restoration and Climate Change on the Loess Plateau of China

by Linjing Qiu, Yiping Wu, Zhaoyang Shi, Yuting Chen and Fubo Zhao

Remote Sens. 2021, 13(12), 2358; https://doi.org/10.3390/rs13122358 - 16 Jun 2021

Cited by 14 | Viewed by 2360

Abstract

Quantitatively identifying the influences of vegetation restoration (VR) on water resources is crucial to ecological planning. Although vegetation coverage has improved on the Loess Plateau (LP) of China since the implementation of VR policy, the way vegetation dynamics influences regional evapotranspiration (ET) remains controversial. In this study, we first investigate long-term spatiotemporal trends of total ET (TET) components, including ground evaporation (GE) and canopy ET (CET, sum of canopy interception and canopy transpiration) based on the GLEAM-ET dataset. The ET changes are attributed to VR on the LP from 2000 to 2015 and these results are quantitatively evaluated here using the Community Land Model (CLM). Finally, the relative contributions of VR and climate change to ET are identified by combining climate scenarios and VR scenarios. The results show that the positive effect of VR on CET is offset by the negative effect of VR on GE, which results in a weak variation in TET at an annual scale and an increased TET is only shown in summer. Regardless of the representative concentration pathway (RCP4.5 or RCP8.5), differences resulted from the responses of TET to different vegetation conditions ranging from −3.7 to −1.2 mm, while climate change from RCP4.5 to RCP8.5 caused an increase in TET ranging from 0.1 to 65.3 mm. These findings imply that climate change might play a dominant role in ET variability on the LP, and this work emphasizes the importance of comprehensively considering the interactions among climate factors to assess the relative contributions of VR and climate change to ET. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Hydrogeography and Climatology)

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18 pages, 4655 KiB

Open AccessArticle

Spatial Difference of Terrestrial Water Storage Change and Lake Water Storage Change in the Inner Tibetan Plateau

by Baojin Qiao, Bingkang Nie, Changmao Liang, Longwei Xiang and Liping Zhu

Remote Sens. 2021, 13(10), 1984; https://doi.org/10.3390/rs13101984 - 19 May 2021

Cited by 13 | Viewed by 2322

Abstract

Water resources are rich on the Tibetan Plateau, with large amounts of glaciers, lakes, and permafrost. Terrestrial water storage (TWS) on the Tibetan Plateau has experienced a significant change in recent decades. However, there is a lack of research about the spatial difference between TWSC and lake water storage change (LWSC), which is helpful to understand the response of water storage to climate change. In this study, we estimate the change in TWS, lake water storage (LWS), soil moisture, and permafrost, respectively, according to satellite and model data during 2005−2013 in the inner Tibetan Plateau and glacial meltwater from previous literature. The results indicate a sizeable spatial difference between TWSC and LWSC. LWSC was mainly concentrated in the northeastern part (18.71 ± 1.35 Gt, 37.7% of the total) and southeastern part (22.68 ± 1.63 Gt, 45.6% of the total), but the increased TWS was mainly in the northeastern region (region B, 18.96 ± 1.26 Gt, 57%). Based on mass balance, LWSC was the primary cause of TWSC for the entire inner Tibetan Plateau. However, the TWS of the southeastern part increased by 3.97 ± 2.5 Gt, but LWS had increased by 22.68 ± 1.63 Gt, and groundwater had lost 16.91 ± 7.26 Gt. The increased TWS in the northeastern region was equivalent to the increased LWS, and groundwater had increased by 4.47 ± 4.87 Gt. Still, LWS only increased by 2.89 ± 0.21 Gt in the central part, and the increase in groundwater was the primary cause of TWSC. These results suggest that the primary cause of increased TWS shows a sizeable spatial difference. According to the water balance, an increase in precipitation was the primary cause of lake expansion for the entire inner Tibetan Plateau, which contributed 73% (36.28 Gt) to lake expansion (49.69 ± 3.58 Gt), and both glacial meltwater and permafrost degradation was 13.5%. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Hydrogeography and Climatology)

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18 pages, 5084 KiB

Open AccessArticle

Analyzing Changes in Frozen Soil in the Source Region of the Yellow River Using the MODIS Land Surface Temperature Products

by Huiyu Cao, Bing Gao, Tingting Gong and Bo Wang

Remote Sens. 2021, 13(2), 180; https://doi.org/10.3390/rs13020180 - 07 Jan 2021

Cited by 22 | Viewed by 2585

Abstract

The degradation of the frozen soil in the Qinghai–Tibetan Plateau (QTP) caused by climate warming has attracted extensive worldwide attention due to its significant effects on the ecosystem and hydrological processes. In this study, we propose an effective approach to estimate the spatial distribution and changes in the frozen soil using the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature products as inputs. A comparison with in-situ observations suggests that this method can accurately estimate the mean daily land surface temperature, the spatial distribution of the permafrost, and the maximum thickness of the seasonally-frozen ground in the source region of the Yellow River, located in the northeastern area of the QTP. The results of The Temperature at the Top of the Permafrost model indicates that the area of permafrost in the source region of the Yellow River decreased by 4.82% in the period from 2003 to 2019, with an increase in the areal mean air temperature of 0.35 °C/10 years. A high spatial heterogeneity in the frozen soil changes was revealed. The basin-averaged active layer thickness of the permafrost increased at a rate of 5.46 cm/10 years, and the basin-averaged maximum thickness of the seasonally-frozen ground decreased at a rate of 3.66 cm/10 years. The uncertainties in calculating the mean daily land surface temperature and the soil’s thermal conductivity were likely to influence the accuracy of the estimation of the spatial distribution of the permafrost and the maximum thickness of the seasonally-frozen ground, which highlight the importance of the better integration of field observations and multi-source remote sensing data in order to improve the modelling of frozen soil in the future. Overall, the approach proposed in this study may contribute to the improvement of the application of the MODIS land surface temperature data in the study of frozen soil changes in large catchments with limited in-situ observations in the QTP. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Hydrogeography and Climatology)

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12 pages, 5976 KiB

Open AccessTechnical Note

Improve the Accuracy of Water Storage Estimation—A Case Study from Two Lakes in the Hohxil Region of North Tibetan Plateau

by Baojin Qiao, Jianting Ju, Liping Zhu, Hao Chen, Jinlei Kai and Qiangqiang Kou

Remote Sens. 2021, 13(2), 293; https://doi.org/10.3390/rs13020293 - 15 Jan 2021

Cited by 6 | Viewed by 1886

Abstract

Lake water storage is essential information for lake research. Previous studies usually used bathymetric data to acquire underwater topography by interpolation method, and to therefore estimate water storage. However, due to the large area of Tibetan Plateau (TP) lakes, the method of bathymetry was challenging to cover the whole region of one lake, and the accuracy of the underwater topography, in which no bathymetric data covered, was low, which resulted in a comparatively large error of lake water storage estimation and its change. In this study, we used Shuttle Radar Topography Mission (SRTM) and in situ bathymetric data to establish the underwater topography of Hohxil Lake (HL) and Lexiewudan Lake (LL) in the Hohxil Region of North TP and estimate and analyzed the changes of lake level and water storage. The results showed HL and LL’s water storage was 5.12 km³ and 5.31 km³ in 2019, respectively, and their level increased by 0.5 m/y and 0.57 m/y during 2003−2018, respectively. They were consistent with those (0.5 m/y and 0.5 m/y) from altimetry data, and they were much more accurate than those results (0.077 m/y and 0.156 m/y) from bathymetric data. These findings indicated that this method could improve the accuracy of lake water storage and change estimation. We estimated water storage of two lakes by combining with multitemporal Landsat images, which had doubled since 1976. Our results suggested that the increasing precipitation may dominate the lake expansion by comparing with the change of temperature and precipitation and the increasing glacial meltwater contributed approximately 4.8% and 10.7% to lake expansion of HL and LL during 2000–2019 based on the glacier mass balance data, respectively. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Hydrogeography and Climatology)

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