Validation of SMOS L1C and L2 Products and Important Parameters of the Retrieval Algorithm in the Skjern River Catchment, Western Denmark

Bircher, Simone; Skou, N.; Kerr, Yann H.

doi:10.1109/tgrs.2012.2215041

Cited by 44 publications

(35 citation statements)

References 66 publications

(69 reference statements)

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“…A subsequent study (Bircher et al, 2012b) showed that in case of soil moisture subgroups of the network stations based on different criteria (e.g. soil type, land cover and composite classes) did not achieve as good statistical results as the entire network average when compared with SMOS data.…”

Section: Discussionmentioning

confidence: 97%

“…Thus, it is also likely that the deviances between SMOS and in situ soil moisture could be lowered by using Mironov in the SMOS retrieval algorithm. Furthermore, in a subsequent study (Bircher et al, 2012b), SMOS data was filtered based on quality parameters contained in the L2 product which showed very good correspondence with an RFI detection scheme based on L1A data (Anterrieu , 2011). Comparing the filtered SMOS data with the network average improved the correlation significantly, nearly meeting up with the one for the ECMWF model.…”

Section: Smos L2 Comparisonmentioning

confidence: 92%

“…As the SMOS dielectric constant is computed from retrieved soil moisture by means of the Dobson model, this implies that at both comparison levels the uncertainty is consistent and remains on either the network or the SMOS data side. Additionally, Bircher et al (2012b) present a study where the network stations were grouped after (a) soil types, (b) land cover classes, and (c) composite class numbers. The respective averages were compared to corresponding SMOS L2 soil moisture data.…”

Section: Smos L2 Comparisonmentioning

confidence: 99%

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A soil moisture and temperature network for SMOS validation in Western Denmark

Bircher

Skou

Jensen

et al. 2012

Hydrol. Earth Syst. Sci.

125

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Abstract. The Soil Moisture and Ocean Salinity Mission (SMOS) acquires surface soil moisture data of global coverage every three days. Product validation for a range of climate and environmental conditions across continents is a crucial step. For this purpose, a soil moisture and soil temperature sensor network was established in the Skjern River Catchment, Denmark. The objectives of this article are to describe a method to implement a network suited for SMOS validation, and to present sample data collected by the network to verify the approach. The design phase included (1) selection of a single SMOS pixel (44 × 44 km), which is representative of the land surface conditions of the catchment and with minimal impact from open water (2) arrangement of three network clusters along the precipitation gradient, and (3) distribution of the stations according to respective fractions of classes representing the prevailing environmental conditions. Overall, measured moisture and temperature patterns could be related to the respective land cover and soil conditions. Texture-dependency of the 0-5 cm soil moisture measurements was demonstrated. Regional differences in 0-5 cm soil moisture, temperature and precipitation between the north-east and south-west were found to be small. A first comparison between the 0-5 cm network averages and the SMOS soil moisture (level 2) product is in range with worldwide validation results, showing comparable trends for SMOS retrieved soil moisture (R 2 of 0.49) as well as initial soil moisture and temperature from ECMWF used in the retrieval algorithm (R 2 of 0.67 and 0.97, respectively). While retrieved/initial SMOS soil moisture indicate significant under-/overestimation of the network data (biases of −0.092/0.057 m 3 m −3 ), the initial temperature is in good agreement (bias of −0.2 • C). Based on these findings, the network performs according to expectations and proves to be well-suited for its purpose. The discrepancies between network and SMOS soil moisture will be subject of subsequent studies.

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Section: Discussionmentioning

confidence: 97%

Section: Smos L2 Comparisonmentioning

confidence: 92%

Section: Smos L2 Comparisonmentioning

confidence: 99%

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A soil moisture and temperature network for SMOS validation in Western Denmark

Bircher

Skou

Jensen

et al. 2012

Hydrol. Earth Syst. Sci.

125

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“…A large number of validation studies of remotely sensed SSM products have been carried out (Albergel et al, 2011;Sánchez et al, 2012;Bircher et al, 2013). These studies are usually performed using airborne and/or ground-observed data over a well-equipped site.…”

mentioning

confidence: 99%

“…Several studies have been performed to validate brightness temperatures too (Rüdiger et al, 2011;Montzka et al, 2013). In Bircher et al (2013) TBs are also validated with network and airborne data over a SMOS pixel in the Skjern River catchment (Denmark). LSMs coupled to radiative transfer models (RTMs) can contribute to the analysis and validation of passive microwave (MW) data.…”

mentioning

confidence: 99%

Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

Barella-Ortiz

Polcher

Rosnay

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. L-band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture (SSM) by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm which yields SSM estimates. The work exposed compares brightness temperatures measured by the SMOS mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The two modelled sets were estimated using a radiative transfer model and state variables from two land-surface models: (i) OR-CHIDEE and (ii) H-TESSEL. The radiative transfer model used is the CMEM.Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations at the moment. Further hypotheses are proposed and will be explored in a forthcoming paper. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies.

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Assimilation of SMOS‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in Western Denmark

Ridler

Madsen

Stisen

et al. 2014

Water Resources Research

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Real surface soil moisture retrieved from the Soil Moisture and Ocean Salinity (SMOS) satellite is downscaled and assimilated in a fully integrated hydrological and soil-vegetation-atmosphere transfer (MIKE SHE SW-ET) model using a bias aware ensemble transform Kalman filter (Bias-ETKF). Satellite-derived soil moisture assimilation in a catchment scale model is typically restricted by two challenges: (1) passive microwave is too coarse for direct assimilation and (2) the data tend to be biased. The solution proposed in this study is to disaggregate the SMOS bias using a higher resolution land cover classification map that was derived from Landsat thermal images. Using known correlations between SMOS bias and vegetation type, the assimilation filter is adapted to calculate biases online, using an initial bias estimate. Real SMOS-derived soil moisture is assimilated in a precalibrated catchment model in Denmark. The objective is to determine if any additional gains can be achieved by SMOS surface soil moisture assimilation beyond the optimized model. A series of assimilation experiments were designed to (1) determine how effectively soil moisture corrections propagate downward in the soil column, (2) compare the efficacy of in situ versus SMOS assimilation, and (3) determine how soil moisture assimilation affects fluxes and discharge in the catchment. We find that assimilation of SMOS improved R 2 soil moisture correlations in the upper 5 cm compared to a network of 30 in situ sensors for most land cover classes. Assimilation also brought modest gains in R 2 at 25 cm depth but slightly degraded the correlation at 50 cm depth. Assimilation overcorrected discharge peaks.

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Validation of SMOS L1C and L2 Products and Important Parameters of the Retrieval Algorithm in the Skjern River Catchment, Western Denmark

Cited by 44 publications

References 66 publications

A soil moisture and temperature network for SMOS validation in Western Denmark

A soil moisture and temperature network for SMOS validation in Western Denmark

Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

Assimilation of SMOS‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in Western Denmark

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