Single-frequency dielectric model of frozen mineral soils for frequencies of the basic satellites

Миронов, В. Л.; Фомин, С. В.; Lukin, Yu. I.; Karavaysky, A. Y.; Molostov, I. P.

doi:10.1109/piers.2017.8262108

Cited by 1 publication

(1 citation statement)

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“…The Park model does not yet include the effect of freezing on the soil relative permittivity, but this also very significant, as documented by several papers (Zhang et al, 2003;Mironov et al, 2017b;Baghdadi et al, 2018a;Park et al, 2011;Mironov et al, 2017a). A typical relationship between soil relative permittivity and temperature is shown in Figure 8.…”

Section: Soil Temperaturementioning

confidence: 99%

The significance of soil properties to the estimation of soil moisture from C-band synthetic aperture radar

Beale

Snapir

Waine

et al. 2019

Preprint

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Abstract. Soil Moisture is a key variable in hydrology, weather and climate modelling. Research has been directed to the estimation of soil moisture over wide areas through a combination of modelling, in-situ measurement and remote sensing to improve the accuracy of hydrological and meteorological forecasting. For monitoring and controlling irrigation and other agricultural purposes, there is also a need to capture local variability. Significant soil moisture differences are observed between and within fields due to land use, soil properties, drainage, tillage, vegetation, solar radiation, air temperature, wind, rain and other factors. Taking the United Kingdom as an example, the average area of agricultural fields is about 12 hectares, requiring a mapping resolution of less than 100 m. Satellite-based remote sensing, including the use of C-band SAR (such as on Sentinel-1), has the potential to satisfy this requirement, but many current data products are aggregated to a spatial resolution of at least 1km and/or provide soil moisture in relative units or indices. Both strategies mitigate the uncertainties introduced by field-scale variability in soil hydrological and vegetation properties. Geospatial datasets of soil properties and land use, crop modelling and other remote sensing techniques may provide an alternative approach to mitigating this variability and allow finer scale products to be produced with acceptable errors. This paper looks at the role of soil properties in the estimation of soil moisture from C-band SAR. We show that information on the soil texture, organic matter content, surface temperature, land use and crop modelling should be important inputs to the success of retrieving soil moisture at the field scale. Previously published data provides guidance in setting soil roughness parameters, based on soil properties, following farming operations such as primary tillage. Beyond soil moisture retrieval, there is exciting potential in SAR remote sensing data to improve the spatial resolution and mapping accuracy of some soil properties.

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