ESA's Soil Moisture and Ocean Salinity Mission: Mission Performance and Operations

Mecklenburg, Susanne; Drusch, Matthias; Kerr, Yann H.; Font, Jordi; Martín-Neira, Manuel; Delwart, Steven; Buenadicha, Guillermo; Reul, Nicolás; Daganzo-Eusebio, Elena; Oliva, Roger; Crapolicchio, Raffaele

doi:10.1109/tgrs.2012.2187666

Cited by 203 publications

(114 citation statements)

References 48 publications

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“…SMOS measurements are partly influenced by Radio Frequency Interference (RFI), which comes from radar, TV, and radio transmission (Mecklenburg et al, 2012). The detection of RFI sources and the mitigation of RFI influence are critical steps for the further retrieval of geophysical parameters.…”

Section: Radio Frequency Interferencementioning

confidence: 99%

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SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Kaleschke²,

et al. 2014

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Abstract. Following the launch of ESA's Soil Moisture andOcean Salinity (SMOS) mission, it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In the first demonstration study, sea ice thickness up to 50 cm has been derived using a semi-empirical algorithm with constant tie-points. Here, we introduce a novel iterative retrieval algorithm that is based on a thermodynamic sea ice model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within the SMOS spatial resolution are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS-based sea ice thickness data set from 2010 on. The data set is compared to and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study (Kaleschke et al., 2012).

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Section: Radio Frequency Interferencementioning

confidence: 99%

“…The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA) was launched in November 2009, and for the first time, globally measures Earth's radiation at a frequency of 1.4 GHz in the L-band (Mecklenburg et al, 2012). The spatial resolution varies from about 35 km to more than 50 km.…”

Section: Introductionmentioning

confidence: 99%

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Kaleschke²,

et al. 2014

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“…One of these constraints could be the assimilation of some aspects of storage in the model. One such possibility would be to examine the usefulness of the soil moisture and ocean salinity (SMOS) satellite (Mecklenburg et al, 2012;Jackson et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Parameter-state ensemble data assimilation using Approximate Bayesian Computing for short-term hydrological prediction

Davison

Fortin

Pietroniro

et al. 2017

Preprint

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Abstract. The main sources of uncertainty in hydrological modelling can be summarized as structural errors, parameter errors, and data errors. Operational modellers are generally more concerned with predictive ability than model errors, and Data Assimilation (DA) methods are commonly employed to merge models with observations to improve predictive ability. This ) watershed just north of Lake Superior in Ontario, Canada using the Canadian semi-distributed hydrologic land-surface scheme MESH. The study examines how well the approach works given various levels of certainty in the data; beginning with certainty in the streamflow and precipitation, followed by uncertainty in the streamflow and certainty in the precipitation, and finally uncertainty in both the streamflow and precipitation. The approach is found to work in this case when streamflow and precipitation is fairly certain, while being more challenging to implement in 10 a forecasting scenario where future streamflow and precipitation is much less certain. The main challenge is determined to be related to parametric uncertainty and ideas for overcoming this challenge are discussed.

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“…How to obtain reliable soil moisture products has received increasing interest in recent years and investigators have discussed this issue from ground measurement, remote sensing observation and model simulation aspects (Dorigo et al, 2011;Fares et al, 2013;Romano, 2014;Seneviratne et al, 2010). In the past few decades, several research groups have successfully retrieved soil moisture products from multiple remote sensing sensors (Draper et al, 2009;Entekhabi et al, 2010;Mecklenburg et al, 2012). However, these retrieval methods perform poorly for estimating soil moisture values in cold regions such as the Tibetan Plateau (TP) area (Chen et al, 2013b;Su et al, 2011;Zeng et al, 2015), and only the value of a few centimeters depth can be derived, which is limited for many applications.…”

Section: Introductionmentioning

confidence: 99%

Improving soil organic carbon parameterization of land surface model for cold regions in the Northeastern Tibetan Plateau, China

Sun

Chen

et al. 2016

Ecological Modelling

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a b s t r a c tMost current land surface models (LSMs) show poor performance in soil water and temperature simulations for cold regions because of the inappropriate model structures and ill-parameterizations. This study aims to explore how to parameterize soil organic carbon (SOC) in term of soil moisture simulations using the Community Land Model version 4.0 (CLM4.0) model and the Dynamic Land Model version 1.0 (DLM1.0) model. Firstly, we discuss the sensitivities of modeled soil moisture to SOC based on observations. The SOC parameterizations in both CLM4.0 and DLM1.0 were proved poor against the measured SOC through sensitivity analysis for the Heihe River watershed area (cold but with high SOC). A SOC parameterization was developed and tested based on multiple year observations. Our findings are twofold: (i) the soil organic parameterizations in CLM4.0 and DLM1.0 are inappropriate for simulating soil moisture in cold regions with high SOC, and the modified SOC parameterization significantly improves the soil moisture simulations; (ii) the impacts of SOC on soil moisture vary significantly with seasons, which is largest for JJA (June, July and August) followed by SON (September, October and November), MAM (March, April and May) and DJF (December, January and February). This study highlights that the impacts of SOC should be fully considered for LSMs soil moisture simulations, especially for cold areas with high SOC.

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ESA's Soil Moisture and Ocean Salinity Mission: Mission Performance and Operations

Cited by 203 publications

References 48 publications

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Parameter-state ensemble data assimilation using Approximate Bayesian Computing for short-term hydrological prediction

Improving soil organic carbon parameterization of land surface model for cold regions in the Northeastern Tibetan Plateau, China

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