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Soil Moisture and Ocean Salinity Mission (SMOS): Achievements and Expectations

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 13202

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Special Issue Editors

Dr. Philippe Waldteufel

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Guest Editor

Laboratoire Atmosphères, Observations Spatiales (LATMOS), 11 Boulevard d’Alembert, 78280 Guyancourt, France
Interests: ionospheric physics; radarmeteorology; radiometry; climate

Dr. Yann H. Kerr

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Guest Editor

Centre d’Etudes Spatiales de la Biosphère (CESBIO), 18 Avenue E.Belin, 31401 Toulouse, CEDEX 09, France
Interests: remote sensing and hydrology; microwave and thermal infrared radiometry; soil moisture and VOD; SMOS and SMAP

Dr. Christine Gommenginger

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Guest Editor

National Oceanography Centre (NOC), European Way, Southampton SO14 3ZH, UK
Interests: active and passive microwave remote sensing; satellite oceanography of global, coastal and polar seas; salinity from space; ocean winds, waves and currents; new satellite sensors and missions
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Special Issue Information

Dear Colleagues,

The Soil Moisture and Ocean Salinity (SMOS) mission, an ESA Earth Explorer opportunity mission with contributions from French and Spanish authorities, was launched in November 2009. Twelve years later, the mission is alive and well. As the first spaceborne passive interferometer and L-band operational radiometer, SMOS fills a significant gap by delivering global frequent measurements of soil moisture and ocean salinity. As the time spent in orbit becomes longer, processing algorithms become more refined, and the quality of calibration and retrievals improves, SMOS is supplying unique time series for soil moisture and surface salinity (the longest obtained from space to date); at the same time, the mission continues to offer a continuous flow of new unforeseen results, with unexpected incursions in the domains of cryosphere, sea ice and climate change, particularly when used in combination with data from Aquarius and SMAP. SMOS data are now frequently assimilated in operational numerical weather prediction systems, where they are shown to make a meaningful contribution to improving the forecasting of the global hydrological cycle.

This Special Issue offers an opportunity to take stock of these achievements after 12 years in orbit, and also to look ahead into the future. Contributions related to both soil moisture and ocean salinity topics are welcome.

Dr. Philippe Waldteufel
Dr. Yann H. Kerr
Dr. Christine Gommenginger
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

SMOS mission
radiometry
soil moisture
ocean salinity
weather and climate
water cycle
carbon cycle
cryosphere

Published Papers (6 papers)

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Research

15 pages, 8841 KiB

Open AccessArticle

SMOS ESA RFI Monitoring and Information Tool: Lessons Learned

by Ekhi Uranga, Álvaro Llorente, Judit González, Antonio de la Fuente, Roger Oliva, Yan Soldo and Flávio Jorge

Remote Sens. 2022, 14(21), 5387; https://doi.org/10.3390/rs14215387 - 27 Oct 2022

Cited by 2 | Viewed by 1684

Abstract

The issue of Radio Frequency Interference (RFI) is a widespread problem in most microwave Earth observation missions, and passive instruments are particularly sensitive to RFI. This is the case for SMOS, Soil Moisture and Ocean Salinity, a satellite of the European Space Agency, which operates in the 1400–1427 MHz band, where all emissions are prohibited. Notwithstanding this regulatory framework, SMOS has been affected by RFI all around the world since the beginning of operations in 2010. In the first years of operations, manual detection processes and reporting of RFI to National Regulatory Authorities were in place in order to mitigate the detected sources. After 12 years, a tool called ERMIT (ESA RFI Monitoring and Information Tool) has been developed at ESAC (European Space Astronomy Center). This tool helps the SMOS RFI team in its spectrum monitoring tasks (e.g., RFI monitoring, logging, and reporting) thus allowing it to counteract RFI pollution more efficiently, providing external users with detailed and user-friendly information on the L-band RFI observed by SMOS. The ERMIT tool is now publicly available. This document aims at describing the needs that lead to the development of ERMIT and at presenting the information made available by it. Full article

(This article belongs to the Special Issue Soil Moisture and Ocean Salinity Mission (SMOS): Achievements and Expectations)

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20 pages, 13907 KiB

Open AccessArticle

Ocean–Atmosphere Variability in the Northwest Atlantic Ocean during Active Marine Heatwave Years

by Lydia D. Sims, Bulusu Subrahmanyam and Corinne B. Trott

Remote Sens. 2022, 14(12), 2913; https://doi.org/10.3390/rs14122913 - 18 Jun 2022

Cited by 3 | Viewed by 1644

Abstract

The Northwest (NW) Atlantic has experienced extreme ecological impacts from Marine Heatwaves (MHWs) within the past decade. This paper focuses on four MHW active years (2012, 2016, 2017, and 2020) and the relationship between Sea Surface Temperature anomalies (SSTA), Sea Surface Salinity anomalies (SSSA), North Atlantic Oscillation (NAO), Geopotential Height anomalies (ZA), and anomalous Jet Stream positions (JSPA). Multichannel singular spectrum analysis (MSSA) reveals the strongest temporal covariances between SSSA and SSTA, and JSPA and SSTA for all years, particularly for 2020 (SSSA–SSTA: 50%, JSPA–SSTA: 51%) indicating that this active MHW year was more atmospherically driven, followed by 2012, which had the second highest temporal covariances (SSSA–SSTA: 47%, JSPA–SSTA: 50%) between these parameters. Spatial correlations for SSSA and SSTA between NAO during MHW active years disrupt the long–term (2010–2020) positive relationship in the NW Atlantic. SSSA and JSPA, and SSSA and SSTA were strongly correlated across the NW Atlantic; 2012 SSSA–JSPA correlations were strong and positive between 56–62°W, and 2016, 2017, and 2020 SSSA–JSPA correlations were mostly strong and negative, with strong positive correlations present near the coastline (70–66°W) or off the NW Atlantic shelf (52–48°W). SSSA–SSTA showed the opposite correlations of similar spatial distributions of SSSA–JSPA for all MHW active years. This indicates strong relationships between JSPA, SSSA, and SSTA during MHWs. Understanding the temporal and spatial interplay between these parameters will aid in better monitoring and prediction of MHWs. Full article

(This article belongs to the Special Issue Soil Moisture and Ocean Salinity Mission (SMOS): Achievements and Expectations)

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21 pages, 6422 KiB

Open AccessArticle

Satellite and In Situ Sampling Mismatches: Consequences for the Estimation of Satellite Sea Surface Salinity Uncertainties

by Clovis Thouvenin-Masson, Jacqueline Boutin, Jean-Luc Vergely, Gilles Reverdin, Adrien C. H. Martin, Sébastien Guimbard, Nicolas Reul, Roberto Sabia, Rafael Catany and Odile Hembise Fanton-d’Andon

Remote Sens. 2022, 14(8), 1878; https://doi.org/10.3390/rs14081878 - 13 Apr 2022

Cited by 4 | Viewed by 1786

Abstract

Validation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters’ depth, which are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016–2018 period, the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using SSS from a high-resolution model reanalysis, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity is observed in regions with large estimated sampling mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly taken into account. We find that (1) the observed differences between Argo and CCI data in dynamical regions (river plumes, fronts) are mainly due to the sampling mismatch; (2) overall, the uncertainties are well estimated in CCI version 3, much improved compared to CCI version 2. There are a few dynamical regions where discrepancies remain and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated. Full article

(This article belongs to the Special Issue Soil Moisture and Ocean Salinity Mission (SMOS): Achievements and Expectations)

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23 pages, 6325 KiB

Open AccessArticle

Interannual Variability of the Congo River Plume-Induced Sea Surface Salinity

by Meike Sena Martins and Detlef Stammer

Remote Sens. 2022, 14(4), 1013; https://doi.org/10.3390/rs14041013 - 19 Feb 2022

Cited by 8 | Viewed by 2124

Abstract

Based on satellite surface salinity (SSS) observations from the SMOS, Aquarius and SMAP missions, we investigate the interannual SSS variability during the period from 2010 to 2020 in the Gulf of Guinea, impacted by the Congo River run-off. Combined with in situ data, the available 11 years of satellite salinity data suggest that the plume of Congo run-off primarily spreads into western directions, leading to reduced SSS. A fraction of it also shows a coastal southward extent subject to interannual variability influenced by coastal trapped waves. The low-salinity water is associated with high values of net primary production, confirming the riverine origin of the nutrient rich plume. No correlation can be found between the plume patterns and the different upwelling strengths in the subsequent upwelling months, nor could a correlation be found with the occurrence of the Benguela Niños. Linking the occurrence of a barrier layer to the occurrence of low-salinity plumes remains difficult, mainly because of the sparseness of in situ data. However, the influence of the low-salinity layer is evident in its stronger stratification and an increased available potential energy. Full article

(This article belongs to the Special Issue Soil Moisture and Ocean Salinity Mission (SMOS): Achievements and Expectations)

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17 pages, 5884 KiB

Open AccessArticle

From SMOS Soil Moisture to 3-hour Precipitation Estimates at 0.1° Resolution in Africa

by Thierry Pellarin, Alexandre Zoppis, Carlos Román-Cascón, Yann H. Kerr, Nemesio Rodriguez-Fernandez, Geremy Panthou, Nathalie Philippon and Jean-Martial Cohard

Remote Sens. 2022, 14(3), 746; https://doi.org/10.3390/rs14030746 - 5 Feb 2022

Cited by 2 | Viewed by 2032

Abstract

Several recent studies have shown that knowledge of the spatiotemporal dynamics of soil moisture intrinsically contains information on precipitation. In this study, we show how SMOS measurements can be used to generate a near-real-time precipitation product with a spatial resolution of 0.1° and a temporal resolution of 3 h. The principle consists of assimilating the SMOS data into a model that simulates the evolution of soil moisture, which is forced by a satellite precipitation product. The assimilation of SMOS soil moisture leads to an adjustment of the satellite precipitation rates. Using data from more than 200 rain gauges set up in Africa between 2010 and 2021, we show that the PrISM algorithm (for Precipitation Inferred from Soil Moisture) almost systematically improves the initial precipitation product. One of the original features of this study is that we used the IMERG-Early satellite precipitation product, which has a finer spatial resolution (0.1°) than SMOS (~0.25°). Despite this, the methodology reduces both the RMSE and bias of IMERG-Early. The RMSE is reduced from 8.0 to 6.3 mm/day, and the absolute bias is reduced from 0.81 to 0.63 mm/day on average over the 200 rain gauges. PrISM performs even slightly better on average than IMERG-Final in terms of RMSE (6.8 mm/day for IMERG-Final) but better scores are obtained by IMERG-Final in terms of absolute bias (0.35 mm/day), which utilizes a network of field measurements to correct the biases of the IMERG-Early product with a 2.5-month delay. Therefore, the use of SMOS soil moisture measurements for Africa can be an advantageous alternative to the use of gauge measurements for debiasing rainfall satellite products in real time. Full article

(This article belongs to the Special Issue Soil Moisture and Ocean Salinity Mission (SMOS): Achievements and Expectations)

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37 pages, 11793 KiB

Open AccessArticle

The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data

by Sébastien Guimbard, Nicolas Reul, Roberto Sabia, Sylvain Herlédan, Ziad El Khoury Hanna, Jean-Francois Piollé, Frédéric Paul, Tong Lee, Julian J. Schanze, Frederick M. Bingham, David Le Vine, Nadya Vinogradova-Shiffer, Susanne Mecklenburg, Klaus Scipal and Henri Laur

Remote Sens. 2021, 13(22), 4600; https://doi.org/10.3390/rs13224600 - 16 Nov 2021

Cited by 6 | Viewed by 2478

Abstract

The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scienctific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns. Full article

(This article belongs to the Special Issue Soil Moisture and Ocean Salinity Mission (SMOS): Achievements and Expectations)

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