Monitoring Relative Surface Soil Moisture Changes Across the Thames Basin using Sentinel-1

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Calibrated data of backscatter (σ (θ,t)) must be normalised to remove the dependance on Local Incidence Angle (θ), via the following: where β is a normalisation factor, and Θ is a reference angle (40 for this study).
For this study, four different normalisation combinations were used: a single and multiple regression model at both annual and monthly temporal scales at multiple spatial scales (1km, 500m, 250m, and 100m) [5], however data shown here is from the multiple regression model at the monthly temporal scale at 100m, as detailed below: where β (t) is the monthly multiple regression normalisation factor, (t) is the non-normalised sensitivty, (t) is the mean backscatter, and a, b, and c, are constants for month t.
After angle normalisation, the rSSM timeseries can be calculated using the TWCDA, [1]: where S(Θ) is the normalised Sensitivity between the wet backscatter threshold (σ (Θ)) and the dry backscatter threshold (σ (Θ)) (Fig. 4).The relative soil moisture data have a 14-orbit moving average applied, to smooth out the noise from single rainfall events, in order to better discuss the long term changes.
Overall, there is agreement between in-situ data and satellite data (R :0.54, RMSE 16.7%), which adds weight to the precipitation animation comparison.
Over the summer months, interesting discrepancy is present, as the rSSM signal registers an increase over the summer, whilst the COSMOS-UK VWCI data often decreases.
This is due to an increase in vegetation growth at the surface, adding an additional contribution to the backscatter signal observed.
Most obvious over the summer of 2018, where no rain fell between end of May and July.
Source of the River Thames is in the west (elevation 350 mASL, in Kemble, Gloucestershire), with the fluvial endpoint being at Teddington Lock in Greater London, some 230 km downstream [3].
Upsteam area to the west is predominately rural, comprising of a mix of agricultural land and woodland over rolling hills on chalk and limestone geology, with flatter areas being on clays.
Towards the centre and the east of the catchment, the land becomes increasingly urbanised, as the River Thames flows through Oxford, Reading, and into London.
Climate of the River Thames Catchment is categorised as Temperate Oceanic (Cfb) by the Koppen climate classification, and received an average of 747 mm (24.9 in) of precipitation annually, over the 1981 -2010 time period.
Typical temporal pattern is present (wetting over the autumn, drying out over the spring), which matches the general seasonal precipitation cycle for the Area of Interest Similar to Fig. 6, a wetting in the summer months can be seen -more prominantly in the Arable and Horticulture signal, but present in both.
This is due to vegetation growth over the summer, as there is an increased contribution of backscatter signal from overlying vegetation.
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You need to publish it again if you want to be displayed.ABSTRACT Soil moisture is a critical component in many meteorological, hydrological, and agricultural applications, and understanding its spatial and temporal dynamics is vital for the understanding of these processes.Satellite-based remote sensing offers the ability to synoptically capture this spatiotemporal information over large areas, compared to more site-based in-situ field measurements.In this study, we use Sentinel-1 SAR imagery of the River Thames catchment, United Kingdom, over the period 2015 -2020.A backscatter normalisation process is applied to account for the use of multiple satellite viewing geometries.A change-detection algorithm utilising backscatter power is then applied to the timeseries, to estimate relative surface soil moisture (rSSM) across the study area.To determine information across the large river watershed, smaller subcatchments, and intra-field scales, the rSSM time series is replicated at multiple spatial scales (1 km, 500m, 250m, and 100m).Although positive biases are present during the growing season of arable farmland, comparison with rainfall data and in-situ soil moisture probes shows there is good agreement with the temporal cycle of soil moisture.These data are being used to evaluate natural flood management by land use and management across a wide area to better understand relationships between surface wetness and water storage in relation to land cover and underlying geology for the Landwise project (Landwise-NFM.org).
Fig 5  shows an animation of the 2-hourly precipitation rate[7] over the Area of Interest on the 11th September 2018., between 1600Z and 1800Z, plotted upon the estimated 100m rSSM values, observed at 1800Z.Spatial pattern of the 2-hourly precipitation corresponds well to the spatial pattern of that of the rSSM values, as the regions of wet soil within the rSSM signal spatially match the areas receiving precipitation within the last two hours.