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

Abstract

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.

Retrieval of Surface Soil Moisture From Sentinel-1 Time Series for Reclamation of Wetland Sites

“…Soil moisture is affected by the soil texture (determines water holding capacity), topography (affects runoff and infiltration), land cover (influences evapotranspiration), and climate (precipitation, wind, humidity, and solar illumination) [12]. The paper of Beale et al [13] looks at the role of soil properties in the estimation of soil moisture from C-band SAR. It was show that information on the soil texture, organic matter content, surface temperature, land use and crop modelling are important inputs to the success of retrieving soil moisture at the field scale.…”

A Deep Learning Data Fusion Model Using Sentinel-1/2, SoilGrids, SMAP, and GLDAS for Soil Moisture Retrieval

“…We do not use pedotransfer functions explicitly since our models can learn the required mapping. Soil information is essential because it is related to maximum water holding capacity (porosity), infiltration and to some extent, evaporation rates, which are critical controls on water retention and storage (Beale et al, 2019;Pan et al, 2012). Soil information obtained from soil grids has been used as a proxy for the disaggregation of soil moisture in prior work (Leenaars et al, 2018;Montzka et al, 2018).…”

Characterization of Land Cover Seasonality in Sentinel-1 Time Series Data

“…ascending acquisitions are acquired for this region at 16:59 UTC and descending acquisitions at 05:25 UTC. Some investigations about the effects of dew on the backscatter in Lband have been performed in (Riedel et al, 2002) and (Beale et al, 2019). For C-Band, (Gillepsie et al, 1990) observed an increase of the backscatter for wheat and mentioned that the different ways in which dew is distributed on the plants could help with crop type identification by radar.…”