Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation

Authors: KERR, Y. - Collaborator; AL-YARRI, A. - French National Institute For Agricultural Research; RODRIGUEZ-FERNANDEZ, N. - Collaborator; PARRENS, M. - Collaborator; MOLERO, B. - Collaborator; LEROUX, D. - Collaborator; BIRCHER, S. - Collaborator; MAHMOODI, A. - Collaborator; MIALON, A. - Collaborator; RICHAUME, P. - Collaborator; DELWART, S. - European Space Agency; ALBITAR, A. - Collaborator; PELLARIN, T. - Collaborator; BINDLISH, R. - Science Systems, Inc; Jackson, Thomas; RUDIGER, C. - Monash University; WALDTEUFEL, P. - Collaborator; MECKLENBURG, S. - European Space Agency; WIGNERON, J. - French National Institute For Agricultural Research

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2016
Publication Date: 7/30/2016
Publication URL: http://handle.nal.usda.gov/10113/5648518
Citation: Kerr, Y., Al-Yarri, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B., Leroux, D., Bircher, S., Mahmoodi, A., Mialon, A., Richaume, P., Delwart, S., Albitar, A., Pellarin, T., Bindlish, R., Jackson, T.J., Rudiger, C., Waldteufel, P., Mecklenburg, S., Wigneron, J. 2016. Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment. 180:40-63. doi:10.1016/j.rse.2016.01.042.

Interpretive Summary: The performance of the Soil Moisture Ocean Salinity satellite (SMOS) over its first six years of operation was assessed. This included product evolution over time, different types of products, and inter-comparison with other soil moisture products. Different datasets were used that ranged from well-equipped and maintained sites (the dense networks), to sparse networks, model outputs and soil moisture products from other sensors. Results demonstrated that the newer version of the soil moisture algorithm is superior to its predecessor. The results of the analysis of the other SMOS products produced from the neural network algorithm is very encouraging and may lead to a very efficient approach to provide near real time soil moisture product and could be one way to achieve a seamless transition between soil moisture sensors from the past and the future to make a long term soil moisture data record. Finally, when compared to other model or satellite products it was found that SMOS is more consistent globally and often gives the best results. The overall assessment supports the accuracy and reliability of SMOS soil moisture products for use in hydrologic and agricultural modelling and monitoring.

Technical Abstract: The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. The commissioning phase ended in May 2010. Subsequently, the satellite has been in operation for over 5 years while the retrieval algorithms from Level 1 to Level 2 underwent significant evolutions as knowledge improved. Moreover, other approaches for retrieval at Level 2 over land were investigated while Level 3 and 4 were initiated. In this paper these improvements were assessed by inter-comparisons of the current Level 2 (V620) against the previous version (V551) and new products (using neural networks referred to as SMOS-NN) and Level 3 (referred to as SMOS-L3). In addition a global evaluation of different SMOS soil moisture (SM) products (SMOS-L2, SMOS-L3, and SMOS-NN) was performed comparing products with those of model simulations and other satellites. Finally, all products were evaluated against in situ measurements of soil moisture (SM). To achieve such a goal a set of metrics to evaluate different satellite products are suggested. The study demonstrated that the V620 shows a significant improvement (including those at level1 improving level2)) with respect to the earlier version V551. Results also show that neural network based approaches can yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rainforests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved while RFI sources in several areas of the world are significantly reduced. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.