Remote sensing of soil moisture content, over bare field at 1.4 GHz frequency

Wang, J. R.; Choudhury, Bhaskar J.

doi:10.1029/jc086ic06p05277

Cited by 416 publications

(171 citation statements)

References 10 publications

(8 reference statements)

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“…The present version uses the Dobson model (Dobson et al, 1985) of the soil microwave dielectric properties. The soil roughness is accounted for using the Wang and Choudhury (1981) model, based on two parameters: h and Q. The Q parameter is set to zero, and the h parameter is related to SSM as proposed by Saleh et al (2007) for a grassland site in southwestern France, and applied by Pardé et al (2011b) to the CAROLS data:…”

Section: Ssm and Vod Retrievalsmentioning

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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Section: Ssm and Vod Retrievalsmentioning

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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“…Soil surface roughness was accounted for in the modelling of the radiometer data by using the semi-empirical approach of Wang and Choudhury [26], which can be described as follows:…”

Section: Radiative Transfer Modelmentioning

confidence: 99%

Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

et al. 2018

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Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their hydrological characteristics using, for instance, space-borne L-band brightness temperature observations. However, there are still open issues with respect to soil moisture retrieval techniques over organic soils. In view of this, organic soil blocks with their vegetation cover were collected from a heathland in the Skjern River catchment in western Denmark and then transported to a remote sensing field laboratory in Germany where their structure was reconstituted. The controlled conditions at this field laboratory made it possible to perform tower-based L-band radiometer measurements of the soils over a period of two months. Brightness temperature data were inverted using a radiative transfer (RT) model for estimating the time variations in the soil dielectric permittivity and the vegetation optical depth. In addition, the effective vegetation scattering albedo parameter of the RT model was retrieved based on a two-step inversion approach. The remote estimations of the dielectric permittivity were compared to in situ measurements. The results indicated that the radiometer-derived dielectric permittivities were significantly correlated with the in situ measurements, but their values were systematically lower compared to the in situ ones. This could be explained by the difference between the operating frequency of the L-band radiometer (1.4 GHz) and that of the in situ sensors (70 MHz). The effective vegetation scattering albedo parameter was found to be polarization dependent. While the scattering effect within the vegetation could be neglected at horizontal polarization, it was found to be important at vertical polarization. The vegetation optical depth estimated values over time oscillated between 0.10 and 0.19 with a mean value of 0.13. This study provides further insights into the characterization of the L-band brightness temperature signatures of organic soil surface layers and, in particular, into the parametrization of the RT model for these specific soils. Therefore, the results of this study are expected to improve the performance of space-borne remote sensing soil moisture products over areas dominated by organic soils.

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“…In addition, the roughness parameters, such as the polarization mixing ratio ðQÞ and the roughness coefficient ðhÞ, are the key variables for accurate estimations of the brightness temperature. The Fresnel power reflectivity from a rough surface can be expressed as (Wang and Choudhury 1981) …”

Section: Soil Moisture and Surface Temperaturementioning

confidence: 99%

A New Satellite-Based Data Assimilation Algorithm to Determine Spatial and Temporal Variations of Soil Moisture and Temperature Profiles

Pathmathevan¹,

Koike²,

2003

Journal of the Meteorological Society of Japan

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This paper focuses on the development and application of a new One-Dimensional Variational (1DVAR) data assimilation algorithm for estimating the spatial and temporal variations of soil moisture and temperature profiles, by grid-based analysis using remote sensing and in situ observations. This algorithm employs a heuristic optimization approach, simulated annealing (SA), which is capable of minimizing the Variational cost function without using adjoint models. The present assimilation scheme assimilates passive microwave remote sensing observations of brightness temperature into the land surface scheme (LSS), Simple Biosphere Model2 (SiB2). The LSS is used as a model operator, and a Radiative Transfer Model (RTM) is used as an observational operator. The modeling system has been applied, and validated, using data from the GAME-Tibet (Global Energy and Water cycle Experiment (GEWEX) Asian Monsoon Experiment in Tibet) mesoscale field experiment. Compared to SiB2, our assimilation scheme solves the major initialization problem, and estimates the soil temperature and soil moisture at the surface layer and in the root zone with significant improvements.

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Remote sensing of soil moisture content, over bare field at 1.4 GHz frequency

Cited by 416 publications

References 10 publications

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

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

Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

A New Satellite-Based Data Assimilation Algorithm to Determine Spatial and Temporal Variations of Soil Moisture and Temperature Profiles

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