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Correction: Singh, A., et al. Remote Sensing of Storage Fluctuations of Poorly Gauged Reservoirs and State Space Model (SSM)-Based Estimation. Remote Sens. 2015, 7, 17113–17134
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Remote Sens. 2016, 8(11), 959; doi:10.3390/rs8110959

Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data

1
Centre d’Etudes Spatiales de la Biosphère Université de Toulouse, Centre National d’Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Dévelopement (IRD), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France
2
European Centre for Medium-Range Weather Forecasts (ECMWF), Shinfield Park, RG2 9AX Reading, UK
3
Faculty of Earth and Life Sciences, VU University Amsterdam (VUA), 1081 HV Amsterdam, The Netherlands
4
Transmissivity B.V., Huygensstraat 34, 2201 AZ Noordwijk, The Netherlands
5
Interactions Sol Plante Atmosphére (ISPA), Unité Mixte de Recherche 1391, Institut National de la Recherche Agronomique (INRA), CS 20032, 33882 Villenave d’ornon cedex, France
6
European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editors: Prashant K. Srivastava, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 19 July 2016 / Revised: 4 November 2016 / Accepted: 9 November 2016 / Published: 18 November 2016
View Full-Text   |   Download PDF [16495 KB, uploaded 18 November 2016]   |  

Abstract

A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer—Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures ( T b ) to the SMOS L3 SM dataset on the concurrent mission period of 1.5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship. The sensitivity of AMSR-E T b ’s to soil temperature ( T s o i l ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E T b ’s to retrieve T s o i l is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and T s o i l information was chosen to obtain SM in the 2003–2011 period. The new dataset shows a low bias (<0.02 m3/m3) and low standard deviation of the difference (<0.04 m3/m3) with respect to SMOS L3 SM over most of the globe’s surface. The new dataset was evaluated together with other AMSR-E SM datasets and the Climate Change Initiative (CCI) SM dataset against the MERRA-Land and ERA-Land models for the 2003–2011 period. All datasets show a significant bias with respect to models for boreal regions and high correlations over regions other than the tropical and boreal forest. All of the global SM datasets including AMSR-E NN were also evaluated against a large number of in situ measurements over four continents. Over Australia, all datasets show a strong level of agreement with in situ measurements. Models perform better over Europe and mountainous regions in North America. Remote sensing datasets (in particular NN and the Land Parameter Retrieval Model (LPRM)) perform as well as models for other North American sites and perform better than models over the Sahel region. View Full-Text
Keywords: soil moisture; passive radiometry; neural networks; SMOS (Soil Moisture and Ocean Salinity) mission soil moisture; passive radiometry; neural networks; SMOS (Soil Moisture and Ocean Salinity) mission
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MDPI and ACS Style

Rodríguez-Fernández, N.J.; Kerr, Y.H.; van der Schalie, R.; Al-Yaari, A.; Wigneron, J.-P.; de Jeu, R.; Richaume, P.; Dutra, E.; Mialon, A.; Drusch, M. Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data. Remote Sens. 2016, 8, 959.

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