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Correction: Zhu, S., et al. A New Digital Lake Bathymetry Model Using the Step-Wise Water Recession Method to Generate 3D Lake Bathymetric Maps Based on DEMs. Water 2019, 11, 1151
Open AccessArticle

Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation

1
Research Group of Hydrological and Environmental Modelling (GIHMA), Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València, 46022 Valencia, Spain
2
Department of Crop Production Ecology, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
3
Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research—UFZ, 04318 Leipzig, Germany
4
School of Geographical and Earth Sciences, University of Glasgow, Glasgow G12 8QQ, UK
*
Author to whom correspondence should be addressed.
Water 2019, 11(12), 2613; https://doi.org/10.3390/w11122613
Received: 31 October 2019 / Revised: 19 November 2019 / Accepted: 3 December 2019 / Published: 11 December 2019
The aim of this study was to implement an eco-hydrological distributed model using only remotely sensed information (soil moisture and leaf area index) during the calibration phase. Four soil moisture-based metrics were assessed, and the best alternative was chosen, which was a metric based on the similarity between the principal components that explained at least 95% of the soil moisture variation and the Nash-Sutcliffe Efficiency (NSE) index between simulated and observed surface soil moisture. The selected alternative was compared with a streamflow-based calibration approach. The results showed that the streamflow-based calibration approach, even presenting satisfactory results in the calibration period (NSE = 0.91), performed poorly in the validation period (NSE = 0.47) and Leaf Area Index (LAI) and soil moisture were neither sensitive to the spatio-temporal pattern nor to the spatial correlation in both calibration and validation periods. Hence, the selected soil moisture-based approach showed an acceptable performance in terms of discharges, presenting a negligible decrease in the validation period (ΔNSE = 0.1) and greater sensitivity to the spatio-temporal variables’ spatial representation. View Full-Text
Keywords: eco-hydrological modelling; remotely sensed soil moisture; objective-function; spatial correlation eco-hydrological modelling; remotely sensed soil moisture; objective-function; spatial correlation
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MDPI and ACS Style

Echeverría, C.; Ruiz-Pérez, G.; Puertes, C.; Samaniego, L.; Barrett, B.; Francés, F. Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation. Water 2019, 11, 2613.

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