{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T06:04:06Z","timestamp":1775541846551,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,18]],"date-time":"2016-11-18T00:00:00Z","timestamp":1479427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer\u2014Earth 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    \u2019s 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    \u2019s 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\u20132011 period. The new dataset shows a low bias (&lt;0.02 m3\/m3) and low standard deviation of the difference (&lt;0.04 m3\/m3) with respect to SMOS L3 SM over most of the globe\u2019s 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\u20132011 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.<\/jats:p>","DOI":"10.3390\/rs8110959","type":"journal-article","created":{"date-parts":[[2016,11,21]],"date-time":"2016-11-21T11:16:05Z","timestamp":1479726965000},"page":"959","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3796-149X","authenticated-orcid":false,"given":"Nemesio","family":"Rodr\u00edguez-Fern\u00e1ndez","sequence":"first","affiliation":[{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re Universit\u00e9 de Toulouse, Centre National d\u2019Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le D\u00e9velopement (IRD), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France"},{"name":"European Centre for Medium-Range Weather Forecasts (ECMWF), Shinfield Park, RG2 9AX Reading, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6352-1717","authenticated-orcid":false,"given":"Yann","family":"Kerr","sequence":"additional","affiliation":[{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re Universit\u00e9 de Toulouse, Centre National d\u2019Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le D\u00e9velopement (IRD), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robin","family":"Van der Schalie","sequence":"additional","affiliation":[{"name":"Faculty of Earth and Life Sciences, VU University Amsterdam (VUA), 1081 HV Amsterdam, The Netherlands"},{"name":"Transmissivity B.V., Huygensstraat 34, 2201 AZ Noordwijk, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7530-6088","authenticated-orcid":false,"given":"Amen","family":"Al-Yaari","sequence":"additional","affiliation":[{"name":"Interactions Sol Plante Atmosph\u00e9re (ISPA), Unit\u00e9 Mixte de Recherche 1391, Institut National de la Recherche Agronomique (INRA), CS 20032, 33882 Villenave d\u2019ornon cedex, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Pierre","family":"Wigneron","sequence":"additional","affiliation":[{"name":"Interactions Sol Plante Atmosph\u00e9re (ISPA), Unit\u00e9 Mixte de Recherche 1391, Institut National de la Recherche Agronomique (INRA), CS 20032, 33882 Villenave d\u2019ornon cedex, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"De Jeu","sequence":"additional","affiliation":[{"name":"Faculty of Earth and Life Sciences, VU University Amsterdam (VUA), 1081 HV Amsterdam, The Netherlands"},{"name":"Transmissivity B.V., Huygensstraat 34, 2201 AZ Noordwijk, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philippe","family":"Richaume","sequence":"additional","affiliation":[{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re Universit\u00e9 de Toulouse, Centre National d\u2019Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le D\u00e9velopement (IRD), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0643-2643","authenticated-orcid":false,"given":"Emanuel","family":"Dutra","sequence":"additional","affiliation":[{"name":"European Centre for Medium-Range Weather Forecasts (ECMWF), Shinfield Park, RG2 9AX Reading, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arnaud","family":"Mialon","sequence":"additional","affiliation":[{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re Universit\u00e9 de Toulouse, Centre National d\u2019Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le D\u00e9velopement (IRD), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias","family":"Drusch","sequence":"additional","affiliation":[{"name":"European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1175\/BAMS-D-11-00254.1","article-title":"The ESA climate change initiative: Satellite data records for essential climate variables","volume":"94","author":"Hollmann","year":"2013","journal-title":"Bull. Am. Meteorol. 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