Two-year global simulation of L-band brightness temperatures over land

Pellarin, Thierry; Wigneron, Jean‐Pierre; Calvet, Jean‐Christophe; Berger, Michael; Douville, Hervé; Ferrazzoli, P.; Kerr, Yann H.; López-Baeza, Ernesto; Pulliainen, Jouni; Simmonds, L. P.; Waldteufel, Philippe

doi:10.1109/tgrs.2003.815417

Cited by 114 publications

(70 citation statements)

References 18 publications

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“…Sky brightness temperature T sky entering the aperture of the sky-looking antenna is simulated using the model described in [32,33] …”

Section: Active Cold Source (Acs) Calibration Using Sky Measurementsmentioning

confidence: 99%

“…Furthermore, synchronous theoretical time series T j sky were simulated using [32] (using Equation (6) with Equation (7)) based on the voltage sample-means recorded for the first = 1, … , + sky measurements. Furthermore, synchronous theoretical time series were simulated using [32] (Box 4 in the flow-chart). Naturally, the measurement-based time-series Figure 7.…”

Section: Effective Transmission Losses and Acs Temperaturesmentioning

confidence: 99%

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Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis

2017

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Abstract:The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer raw data is introduced. This procedure includes a thorough explanation of the calibration and filtering including a refined radio frequency interference (RFI) mitigation approach. This new mitigation approach not only performs better than conventional "normality" tests (kurtosis and skewness) but also allows for the quantification of measurement uncertainty introduced by non-thermal noise contributions. The brightness temperatures of natural snow covered areas and areas with a reflector beneath the snow are simulated for varying amounts of snow liquid water content distributed across the snow profile. Both measured and simulated brightness temperatures emanating from natural snow covered areas and areas with a reflector beneath the snow reveal noticeable sensitivity with respect to snow liquid water. This indicates the possibility of estimating snow liquid water using L-band radiometry. It is also shown that distinct daily increases in brightness temperatures measured over the areas with the reflector placed on the ground indicate the onset of the snow melting season, also known as "early-spring snow".

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“…Sky brightness temperature T sky entering the aperture of the sky-looking antenna is simulated using the model described in [32,33] …”

Section: Active Cold Source (Acs) Calibration Using Sky Measurementsmentioning

confidence: 99%

Section: Effective Transmission Losses and Acs Temperaturesmentioning

confidence: 99%

Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis

2017

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“…(3a) was derived by Pellarin et al (2003b) from field data over a variety of crops, with a VWC to LAI ratio of 0.5 kg m −2 . For the SMOSREX grassland, de Rosnay et al (2006) and Saleh et al (2006b) found very good correlations between the green vegetation VWC and the green LAI (r 2 close to 0.9), with VWC to LAI ratio values ranging between 0.3 and 0.4 LAI kg m −2 .…”

Section: L-band B Values Through Time and Vegetation Coversmentioning

confidence: 99%

Spatial and temporal variability of biophysical variables in southwestern France from airborne L-band radiometry

Zakharova¹,

Calvet²,

Lafont³

et al. 2012

Hydrol. Earth Syst. Sci.

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“…Wigneron et al (1995Wigneron et al ( , 2003 have shown the possibility to retrieve biophysical variables, including SSM, from bipolarized and multiangular microwave T b . The core component of the SMOS soil moisture retrieval algorithm is the L-band Microwave Emission of the Biosphere (L-MEB) model which simulates the microwave emission at L-band from the soil-vegetation layer (Pellarin et al, 2003b;Wigneron et al, 2007;Kerr, 2010;Panciera et al, 2009). The main difficulty in the estimation of soil moisture using microwave radiometry arises from the presence of a dense overlying vegetation: the vegetation layer attenuates the soil emission and adds its own emission to the soil emission.…”

Section: Introductionmentioning

confidence: 99%

Comparing soil moisture retrievals from SMOS and ASCAT over France

Parrens

Zakharova

Lafont

et al. 2012

Hydrol. Earth Syst. Sci.

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Abstract. The first products derived over France in 2010 from the L-band brightness temperatures (T b ) measured by the SMOS (Soil Moisture and Ocean Salinity) satellite, launched in November 2009, were compared with the surface soil moisture (SSM) estimates produced by the C-band Advanced Scatterometer, ASCAT, launched in 2006 on board METOP-A. SMOS and ASCAT SSM products were compared with the simulations of the ISBA-A-gs model and with in situ measurements from the SMOSMANIA network, including 21 stations located in southern France. ASCAT tended to correlate better than SMOS with ISBA-A-gs. The significant anomaly correlation coefficients between in situ observations and the SMOS (ASCAT) product ranged from 0.23 to 0.48 (0.35 to 0.96). However, in wet conditions, similar results between the two satellite products were found. An attempt was made to derive SSM from regressed empirical logarithmic equations using a combination of SMOS T b at different incidence angles and different polarizations, and the Leaf Area Index (LAI) modeled by ISBA-A-gs. The analysis of the intercept coefficient of the regression showed an impact of topography. A similar analysis applied to ASCAT and SMOS SSM values showed a more limited impact of topography on the intercept coefficient of the SMOS SSM product, while fewer residual geographic patterns were found for the ASCAT SSM.

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Two-year global simulation of L-band brightness temperatures over land

Cited by 114 publications

References 18 publications

Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis

Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis

Spatial and temporal variability of biophysical variables in southwestern France from airborne L-band radiometry

Comparing soil moisture retrievals from SMOS and ASCAT over France

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