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Operational Soil Moisture from ASCAT in Support of Water Resources Management

1
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
3
Faculty of Natural Sciences, Life and Earth Sciences, University Akli Mohand Oulhadj of Bouira, Bouira 10000, Algeria
4
School of Mineral Resources Engineering, Technical University of Crete, 73100 Crete, Greece
5
Department of Soil & Water Resources, Institute of Industrial & Forage Crops, Hellenic Agricultural Organization “Demeter”, 41335 Larisa, Greece
6
Institute of Environment and Sustainable Development and DST-Mahamana Centre for Excellence in Climate Change Research, Banaras Hindu University, Varanasi 221005, India
7
Distributed and Knowledge Management Systems Lab, National Technical University of Athens, 15780 Athens, Greece
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 579; https://doi.org/10.3390/rs11050579
Received: 9 February 2019 / Revised: 26 February 2019 / Accepted: 5 March 2019 / Published: 9 March 2019
(This article belongs to the Special Issue Recent Advances in Satellite Derived Global Land Product Validation)
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Abstract

This study provides the results of an extensive investigation of the Advanced Scaterometter (ASCAT) surface soil moisture global operational product accuracy across three continents (United States of America (USA), Europe, and Australia). ASCAT predictions of surface soil moisture were compared against near concurrent in situ measurements from the FLUXNET observational network. A total of nine experimental sites were used to assess the accuracy of ASCAT Surface Soil Moisture (ASCAT SSM) predictions for two complete years of observations (2010, 2011). Results showed a generally reasonable agreement between the ASCAT product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer. The Root Mean Square Error (RMSE) was below 0.135 m3 m−3 at all of the sites. With a few exceptions, Pearson’s correlation coefficient was above 45%. Grassland, shrublands, and woody savanna land cover types exhibited satisfactory agreement in all the sites analyzed (RMSE ranging from 0.05 to 0.13 m3 m−3). Seasonal performance was tested, but no definite conclusion can be made with statistical significance at this time, as the seasonal results varied from continent to continent and from year to year. However, the satellite and in situ measurements for Needleleaf forests were practically uncorrelated (R = −0.11 and −0.04). ASCAT predictions overestimated the observed values at all of the sites in Australia. A positive bias of approximately 0.05 m3 m−3 was found with respect to the observed values that were in the range 0–0.3 m3 m−3. Better agreement was observed for the grassland sites in most cases (RMSE ranging from 0.09 to 0.10 m3 m−3 and R from 0.46 to 0.90). Our results provide supportive evidence regarding the potential value of the ASCAT global operational product for meso-scale studies and the relevant practical applications. A key contribution of this study is a comprehensive evaluation of ASCAT product soil moisture estimates at different sites around the globe. These sites represent a variety of climatic, environmental, biome, and topographical conditions. View Full-Text
Keywords: surface soil moisture; earth observation; operational products; ASCAT; validation surface soil moisture; earth observation; operational products; ASCAT; validation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Deng, K.A.K.; Lamine, S.; Pavlides, A.; Petropoulos, G.P.; Srivastava, P.K.; Bao, Y.; Hristopulos, D.; Anagnostopoulos, V. Operational Soil Moisture from ASCAT in Support of Water Resources Management. Remote Sens. 2019, 11, 579.

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