Special Issue "Contributions of Remote Sensing to Hydrologic Flux Quantification"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (15 August 2021).

Special Issue Editor

Prof. Dr. Kenneth J. Tobin
E-Mail Website
Guest Editor
Texas A&M International University
Interests: remote sensing; precipitation; soil moisture; global climate change

Special Issue Information

Dear Colleagues,

Since the launch of Landsat 1, back in 1972, space-based remote sensing has transformed how we visualize the Earth’s surface. Space agencies across the globe have invested billions on numerous missions designed to better conceptualize earth processes over the last five decades. Given the importance of water resources, many of these missions have a strong hydrology science focus, such as TRMM and GPM for precipitation, SMOS and SMAP for soil moisture, MODIS for evapotranspiration, the planned SWOT mission for surface runoff, and GRACE for changes in total terrestrial water, to name only a few. In this Special Issue, we encourage the submission of novel studies that use space-based remote sensing data to quantify any hydrologic flux from the local to global scale. Approaches can include but are not necessarily limited to direct comparison of remote sensing data with ground-based observations, hydrological modeling efforts that incorporate remotely sensed data, and/or the fusion of remotely sensed observations with land surface model output based on data assimilation or other methodologies. Studies that examine long-term trends in hydrologic fluxes that can be potentially linked to global climate change during the satellite era are particularly welcome.

Prof. Dr. Kenneth J. Tobin
Guest Editor

Manuscript Submission Information

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Keywords

  • Remote sensing
  • Precipitation
  • Soil moisture
  • Evapotranspiration
  • Terrestrial water
  • Global climate change
  • Satellite era

Published Papers (4 papers)

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Research

Article
A Sentinel-2 Image-Based Irrigation Advisory Service: Cases for Tea Plantations
Water 2021, 13(9), 1305; https://doi.org/10.3390/w13091305 - 07 May 2021
Cited by 1 | Viewed by 563
Abstract
In this study, we aim to develop an inexpensive site-specific irrigation advisory service for resolving disadvantages related to using immobile soil moisture sensors and to the differences in irrigation needs of different tea plantations affected by variabilities in cultivars, plant ages, soil heterogeneity, [...] Read more.
In this study, we aim to develop an inexpensive site-specific irrigation advisory service for resolving disadvantages related to using immobile soil moisture sensors and to the differences in irrigation needs of different tea plantations affected by variabilities in cultivars, plant ages, soil heterogeneity, and management practices. In the paper, we present methodologies to retrieve two biophysical variables, surface soil water content and canopy water content of tea trees from Sentinel-2 (S2) (European Space Agency, Paris, France) images and consider their association with crop water availability status to be used for making decisions to send an alert level. Precipitation records are used as auxiliary information to assist in determining or modifying the alert level. Once the site-specific alert level for each target plantation is determined, it is sent to the corresponding farmer through text messaging. All the processes that make up the service, from downloading an S2 image from the web to alert level text messaging, are automated and can be completed before 7:30 a.m. the next day after an S2 image was taken. Therefore, the service is operated cyclically, and corresponds to the five-day revisit period of S2, but one day behind the S2 image acquisition date. However, it should be noted that the amount of irrigation water required for each site-specific plantation has not yet been estimated because of the complexities involved. Instead, a single irrigation rate (300 t ha−1) per irrigation event is recommended. The service is now available to over 20 tea plantations in the Mingjian Township, the largest tea producing region in Taiwan, free of charge since September 2020. This operational application is expected to save expenditures on buying irrigation water and induce deeper root systems by decreasing the frequency of insufficient irrigation commonly employed by local farmers. Full article
(This article belongs to the Special Issue Contributions of Remote Sensing to Hydrologic Flux Quantification)
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Article
Evaluation of Satellite Precipitation Products in Simulating Streamflow in a Humid Tropical Catchment of India Using a Semi-Distributed Hydrological Model
Water 2020, 12(9), 2400; https://doi.org/10.3390/w12092400 - 26 Aug 2020
Cited by 3 | Viewed by 1403
Abstract
Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a [...] Read more.
Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. In this study, an attempt was made to evaluate the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset in the Gurupura river catchment of India. Distinct testing scenarios for simulating streamflow were made to check the suitability of these satellite precipitation data. The TRMM was able to better estimate rainfall than CHIRPS after performing categorical and continuous statistical results with respect to IMD rainfall data. While comparing the performance of model simulations, the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) were in the range 0.63 to 0.86, 0.62 to 0.86, and −14.98 to 0.87, respectively. Further, an attempt was made to examine the spatial distribution of key hydrological signature, i.e., flow duration curve (FDC) in the 30–95 percentile range of non-exceedance probability. It was observed that TRMM underestimated the flow for agricultural water availability corresponding to 30 percent, even though it showed a good performance compared to the other satellite rainfall-driven model outputs. Full article
(This article belongs to the Special Issue Contributions of Remote Sensing to Hydrologic Flux Quantification)
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Article
Using UAV Visible Images to Estimate the Soil Moisture of Steppe
Water 2020, 12(9), 2334; https://doi.org/10.3390/w12092334 - 19 Aug 2020
Cited by 4 | Viewed by 1044
Abstract
Although unmanned aerial vehicles (UAVs) have been utilized in many aspects of steppe management, they have not been commonly used to monitor the soil moisture of steppes. To explore the technology of detecting soil moisture by UAV in a typical steppe, we conducted [...] Read more.
Although unmanned aerial vehicles (UAVs) have been utilized in many aspects of steppe management, they have not been commonly used to monitor the soil moisture of steppes. To explore the technology of detecting soil moisture by UAV in a typical steppe, we conducted a watered test in the Loess Plateau of China, quantitatively revealing the relationship between the surface soil moisture and the visible images captured using an UAV. The results showed that the surface soil moisture was significantly correlated with the brightness of UAV visible images, and the surface soil moisture could be estimated based on the brightness of the visible images of the UAV combined with vegetation coverage. This study addresses the problem of soil moisture measurement in flat regions of arid and semi-arid steppes at the mesoscale, and contributes to the popularization of the use of UAVs in steppe ecological research. Full article
(This article belongs to the Special Issue Contributions of Remote Sensing to Hydrologic Flux Quantification)
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Article
Improving SWAT Model Calibration Using Soil MERGE (SMERGE)
Water 2020, 12(7), 2039; https://doi.org/10.3390/w12072039 - 18 Jul 2020
Cited by 2 | Viewed by 758
Abstract
This study examined eight Great Plains moderate-sized (832 to 4892 km2) watersheds. The Soil and Water Assessment Tool (SWAT) autocalibration routine SUFI-2 was executed using twenty-three model parameters, from 1995 to 2015 in each basin, to identify highly sensitive parameters (HSP). [...] Read more.
This study examined eight Great Plains moderate-sized (832 to 4892 km2) watersheds. The Soil and Water Assessment Tool (SWAT) autocalibration routine SUFI-2 was executed using twenty-three model parameters, from 1995 to 2015 in each basin, to identify highly sensitive parameters (HSP). The model was then run on a year-by-year basis, generating optimal parameter values for each year (1995 to 2015). HSP were correlated against annual precipitation (Parameter-elevation Regressions on Independent Slopes Model—PRISM) and root zone soil moisture (Soil MERGE—SMERGE 2.0) anomaly data. HSP with robust correlation (r > 0.5) were used to calibrate the model on an annual basis (2016 to 2018). Results were compared against a baseline simulation, in which optimal parameters were obtained by running the model for the entire period (1992 to 2015). This approach improved performance for annual simulations generated from 2016 to 2018. SMERGE 2.0 produced more robust results compared with the PRISM product. The main virtue of this approach is that it constrains parameter space, minimizesing equifinality and promotesing modeling based on more physically realistic parameter values. Full article
(This article belongs to the Special Issue Contributions of Remote Sensing to Hydrologic Flux Quantification)
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