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Remote Sensing and Modeling of the Terrestrial Water Cycle

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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 2021) | Viewed by 24575

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Special Issue Editors

Dr. Sayed M. Bateni

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Department of Civil and Environmental Engineering, Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
Interests: satellite data processing; land surface product algorithm; remote sensing classification with machine learning;agrometeorology; agrometeorological disater monitoring with remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Michael Ek

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Guest Editor

University Corporation Atmospheric Research (UCAR)
Interests: land–atmosphere interactions; surface heat fluxes; boundary layer development

Prof. Dr. Hamid Moradkhani

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Guest Editor

Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
Interests: hydrologic modeling and data assimilation; machine learning; food-energy-water nexus; remote sensing; uncertainty and risk analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tongren Xu

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Guest Editor

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: remote sensing; data assimilation; artificial intelligence; hydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hydrologic models and remote sensing are essential tools for studying the changing nature of the terrestrial water cycle and its various components. Advances in the areas of remote sensing and modeling have allowed the integration of these two approaches and the use of multiple sensors and variables simultaneously to better understand the spatial and temporal dynamics of the water cycle and the available water resources at various scales. Some of the major missions that have proven valuable for hydrologic modeling include SMOS, AMSR-E/AMSR2, and SMAP for soil moisture; TRMM and GPM for precipitation; TOPEX and SWOT (planned) for water levels; AIRS/AMSU for atmospheric water vapor and air temperature profiles; and GRACE for water storage changes and MODIS for vegetation and surface temperature. Applications of remotely sensed data from the abovementioned missions have provided unprecedented opportunities to advance the simulation, monitoring, and prediction of the terrestrial water cycle. Data assimilation techniques have been developed to integrate remote sensing observations, in-situ measurements, and hydrologic models to estimate key variables of the hydrologic cycles such as evapotranspiration, precipitation, soil moisture, terrestrial water storage, groundwater streamflow, snow, ice and glaciers, etc.

For this Special Issue, we invite multi-scale, multi-variable, and multi-sensor studies that advance remote sensing techniques and modeling approaches to assess the spatiotemporal variability in water resources and improve our understanding of the terrestrial water cycle. We welcome the submission of manuscripts related to the (1) use of available remote sensing satellite data as well as data from future missions to address hydrologic science questions and expand our knowledge in quantifying the spatial and temporal variations in terrestrial water cycle, (2) application of artificial intelligence approaches in hydrology and remote sensing, and (3) hydrologic data assimilation.

Prof. Sayed M. Bateni
Prof. Michael Ek
Prof. Hamid Moradkhani
Prof. Tongren Xu
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

remote sensing
data assimilation
terrestrial water cycle
artificial intelligence

Published Papers (6 papers)

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Research

20 pages, 4396 KiB

Open AccessArticle

Assessment of TMPA 3B42V7 and PERSIANN-CDR in Driving Hydrological Modeling in a Semi-Humid Watershed in Northeastern China

by Lu Zhang, Zhuohang Xin and Huicheng Zhou

Remote Sens. 2020, 12(19), 3133; https://doi.org/10.3390/rs12193133 - 24 Sep 2020

Cited by 12 | Viewed by 2477

Abstract

Recent developments of satellite precipitation products provide an unprecedented opportunity for better precipitation estimation, and thus broaden hydrological application. However, due to the errors and uncertainties of satellite products, a thorough validation is usually required before putting into the real hydrological application. As such, this study aims to provide a comprehensive evaluation on the performances of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7 and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), as well as their adequacies in simulating hydrological processes in a semi-humid region in the northeastern China. It was found that TMPA 3B42V7 showed a superior performance at the daily and monthly time scales, and had a favorable capture of the rainfall-intensity distribution. Intra-annual comparisons indicated a better representation of TMPA 3B42V7 from January to September, whereas PERSIANN-CDR was more reliable from October to December. The Soil and Water Assessment Tool (SWAT) driven by gauge precipitation data performed excellently with NSE > 0.9, while the performances of TMPA 3B42V7- and PERSIANN-CDR-based models are satisfactory with NSE > 0.5. The performances varied under different flow levels and hydrological years. Water balance analysis indicated a better performance of TMPA 3B42V7 in simulating the hydrological processes, including evapotranspiration, groundwater recharge and total runoff. The runoff compositions (i.e., base flow, subsurface flow, and surface flow) driven by TMPA 3B42V7 were more accordant with the actual hydrological features. This study will not only help recognize the potential satellite precipitation products for local water resources management, but also be a reference for the poor-gauged regions with similar hydrologic and climatic conditions around the world, especially the northeastern China and western Russia. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of the Terrestrial Water Cycle)

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22 pages, 6074 KiB

Open AccessArticle

On the Radiative Transfer Model for Soil Moisture across Space, Time and Hydro-Climates

by Maheshwari Neelam and Binayak P. Mohanty

Remote Sens. 2020, 12(16), 2645; https://doi.org/10.3390/rs12162645 - 17 Aug 2020

Cited by 2 | Viewed by 3414

Abstract

A framework is proposed for understanding the efficacy of the microwave radiative transfer model (RTM) of soil moisture with different support scales, seasonality (time), hydroclimates, and aggregation (scaling) methods. In this paper, the sensitivity of brightness temperature T_B (H- and V-polarization) to physical variables (soil moisture, soil texture, surface roughness, surface temperature, and vegetation characteristics) is studied. Our results indicate that the sensitivity of brightness temperature (V- or H-polarization) is determined by the upscaling method and heterogeneity observed in the physical variables. Under higher heterogeneity, the T_B sensitivity to vegetation and roughness followed a logarithmic function with an increasing support scale, while an exponential function is followed under lower heterogeneity. Surface temperature always followed an exponential function under all conditions. The sensitivity of T_B at H- or V- polarization to soil and vegetation characteristics varied with the spatial scale (extent and support) and the amount of biomass observed. Thus, choosing an H- or V-polarization algorithm for soil moisture retrieval is a tradeoff between support scales, and land surface heterogeneity. For largely undisturbed natural environments such as SGP’97 and SMEX04, the sensitivity of T_B to variables remains nearly uniform and is not influenced by extent, support scales, or an upscaling method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 and SMAPVEX12, the sensitivity to variables is highly influenced by the distribution of land surface heterogeneity and upscaling methods. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of the Terrestrial Water Cycle)

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29 pages, 5614 KiB

Open AccessFeature PaperArticle

Feasibility of Estimating Turbulent Heat Fluxes via Variational Assimilation of Reference-Level Air Temperature and Specific Humidity Observations

by Elahe Tajfar, Sayed M. Bateni, Essam Heggy and Tongren Xu

Remote Sens. 2020, 12(7), 1065; https://doi.org/10.3390/rs12071065 - 26 Mar 2020

Cited by 1 | Viewed by 2643

Abstract

This study investigated the feasibility of partitioning the available energy between sensible (H) and latent (LE) heat fluxes via variational assimilation of reference-level air temperature and specific humidity. For this purpose, sequences of reference-level air temperature and specific humidity were assimilated into an atmospheric boundary layer model (ABL) within a variational data assimilation (VDA) framework to estimate H and LE. The VDA approach was tested at six sites (namely, Arou, Audubon, Bondville, Brookings, Desert, and Willow Creek) with contrasting climatic and vegetative conditions. The unknowns of the VDA system were the neutral bulk heat transfer coefficient (C_HN) and evaporative fraction (EF). EF estimates were found to agree well with observations in terms of magnitude and day-to-day fluctuations in wet/densely vegetated sites but degraded in dry/sparsely vegetated sites. Similarly, in wet/densely vegetated sites, the variations in the C_HN estimates were found to be consistent with those of the leaf area index (LAI) while this consistency deteriorated in dry/sparely vegetated sites. The root mean square errors (RMSEs) of daily H and LE estimates at the Arou site (wet) were 25.43 (Wm⁻²) and 55.81 (Wm⁻²), which are respectively 57.6% and 45.4% smaller than those of 60.00 (Wm⁻²) and 102.21 (Wm⁻²) at the Desert site (dry). Overall, the results show that the VDA system performs well at wet/densely vegetated sites (e.g., Arou and Willow Creek), but its performance degrades at dry/slightly vegetated sites (e.g., Desert and Audubon). These outcomes show that the sequences of reference-level air temperature and specific humidity have more information on the partitioning of available energy between the sensible and latent heat fluxes in wet/densely vegetated sites than dry/slightly vegetated sites. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of the Terrestrial Water Cycle)

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22 pages, 9443 KiB

Open AccessArticle

A Bayesian Three-Cornered Hat (BTCH) Method: Improving the Terrestrial Evapotranspiration Estimation

by Xinlei He, Tongren Xu, Youlong Xia, Sayed M. Bateni, Zhixia Guo, Shaomin Liu, Kebiao Mao, Yuan Zhang, Huaize Feng and Jingxue Zhao

Remote Sens. 2020, 12(5), 878; https://doi.org/10.3390/rs12050878 - 09 Mar 2020

Cited by 28 | Viewed by 5431

Abstract

In this study, a Bayesian-based three-cornered hat (BTCH) method is developed to improve the estimation of terrestrial evapotranspiration (ET) by integrating multisource ET products without using any a priori knowledge. Ten long-term (30 years) gridded ET datasets from statistical or empirical, remotely-sensed, and land surface models over contiguous United States (CONUS) are integrated by the BTCH and ensemble mean (EM) methods. ET observations from eddy covariance towers (ET_EC) at AmeriFlux sites and ET values from the water balance method (ET_WB) are used to evaluate the BTCH- and EM-integrated ET estimates. Results indicate that BTCH performs better than EM and all the individual parent products. Moreover, the trend of BTCH-integrated ET estimates, and their influential factors (e.g., air temperature, normalized differential vegetation index, and precipitation) from 1982 to 2011 are analyzed by the Mann–Kendall method. Finally, the 30-year (1982 to 2011) total water storage anomaly (TWSA) in the Mississippi River Basin (MRB) is retrieved based on the BTCH-integrated ET estimates. The TWSA retrievals in this study agree well with those from the Gravity Recovery and Climate Experiment (GRACE). Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of the Terrestrial Water Cycle)

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18 pages, 7446 KiB

Open AccessArticle

Responses of Water Use Efficiency to Drought in Southwest China

by Jingxue Zhao, Tongren Xu, Jingfeng Xiao, Shaomin Liu, Kebiao Mao, Lisheng Song, Yunjun Yao, Xinlei He and Huaize Feng

Remote Sens. 2020, 12(1), 199; https://doi.org/10.3390/rs12010199 - 06 Jan 2020

Cited by 46 | Viewed by 5378

Abstract

Water use efficiency (WUE) measures the tradeoff between carbon uptake and water consumption in terrestrial ecosystems. It remains unclear how the responses of WUE to drought vary with drought severity. We assessed the spatio-temporal variations of ecosystem WUE and its responses to drought for terrestrial ecosystems in Southwest China over the period 2000–2017. The annual WUE values varied with vegetation type in the region: Forests (3.25 gC kg⁻¹H₂O) > shrublands (2.00 gC kg⁻¹H₂O) > croplands (1.76 gC kg⁻¹H₂O) > grasslands (1.04 gC kg⁻¹H₂O). During the period 2000–2017, frequent droughts occurred in Southwest China, and overall, drought had an enhancement effect on WUE. However, the effects of drought on WUE varied with vegetation type and drought severity. Croplands were the most sensitive to drought, and slight water deficiency led to the decline of cropland WUE. Over grasslands, mild drought increased its WUE while moderate and severe drought reduced its WUE. For forests and shrublands, mild and moderate drought increased their WUE, and only severe drought reduce their WUE, indicating that these ecosystems had stronger resistance to drought. Assessing the patterns and trends of ecosystem WUE and its responses to drought are essential for understanding plant water use strategy and informing ecosystem water management. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of the Terrestrial Water Cycle)

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17 pages, 3245 KiB

Open AccessArticle

Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

by Chelsea Dandridge, Venkat Lakshmi, John Bolten and Raghavan Srinivasan

Remote Sens. 2019, 11(22), 2709; https://doi.org/10.3390/rs11222709 - 19 Nov 2019

Cited by 29 | Viewed by 4257

Abstract

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (June–September) as compared to the dry season (November–January). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA. Full article

(This article belongs to the Special Issue Remote Sensing and Modeling of the Terrestrial Water Cycle)

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