Abstract

The radiometric uncertainty of Synthetic Aperture Radar (SAR) observations and weather-related surface conditions caused by frozen conditions, snow and intercepted rain affect the backscatter ( σ 0 ) observations and limit the accuracy of soil moisture retrievals. This study estimates Sentinel-1’s radiometric uncertainty, identifies the effects of weather-related surface conditions on σ 0 and investigates their impact on soil moisture retrievals for various conditions regarding soil moisture, surface roughness and incidence angle. Masking rules for the surface conditions that disturb σ 0 were developed based on meteorological measurements and timeseries of Sentinel-1 observations collected over five forests, five meadows and five cultivated fields in the eastern part of the Netherlands. The Sentinel-1 σ 0 observations appear to be affected by frozen conditions below an air temperature of 1 ∘ C , snow during Sentinel-1’s morning overpasses on meadows and cultivated fields and interception after more than 1.8 m m of rain in the 12 h preceding a Sentinel-1 overpass, whereas dew was not found to be of influence. After the application of these masking rules, the radiometric uncertainty was estimated by the standard deviation of the seasonal anomalies timeseries of the Sentinel-1 forest σ 0 observations. By spatially averaging the σ 0 observations, the Sentinel-1 radiometric uncertainty improves from 0.85 dB for a surface area of 0.25 h a to 0.30 dB for 10 h a for the VV polarization and from 0.89 dB to 0.36 dB for the VH polarization, following approximately an inverse square root dependency on the surface area over which the σ 0 observations are averaged. Deviations in σ 0 were combined with the σ 0 sensitivity to soil moisture as simulated with the Integral Equation Method (IEM) surface scattering model, which demonstrated that both the disturbing effects by the weather-related surface conditions (if not masked) and radiometric uncertainty have a significant impact on the soil moisture retrievals from Sentinel-1. The soil moisture retrieval uncertainty due to radiometric uncertainty ranges from 0.01 m 3 m − 3 up to 0.17 m 3 m − 3 for wet soils and small surface areas. The impacts on soil moisture retrievals are found to be weakly dependent on the surface roughness and the incidence angle, and strongly dependent on the surface area (or the σ 0 disturbance caused by a weather-related surface condition for a specific land cover type) and the soil moisture itself.

Highlights

  • Earth observations made by Synthetic Aperture Radar (SAR) instruments can be used to estimate soil moisture at field scale (e.g., [1,2,3,4,5])

  • The standard deviation is larger when no moving average is applied (Figure 3a,b), so the sS1 would be overestimated when the σ0 seasonal dynamics are not removed. This is explained by the variations in the timeseries due to seasonal dynamics and instabilities in the Sentinel-1 σ0 observations that are still included, which is reflected in the autocorrelation being significantly above 0.0 (Figure 3c,d)

  • The smaller window for meadows and cultivated fields can be explained by the shorter growth cycles of vegetation and harvesting and the shorter timescales of soil moisture dynamics, which strongly affect the σ0 from these fields

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Summary

Introduction

Earth observations made by Synthetic Aperture Radar (SAR) instruments can be used to estimate soil moisture at field scale (e.g., [1,2,3,4,5]). Forest σ0 can be assumed to be unaffected by variations in soil moisture and surface roughness, which can cause large and abrupt changes in the σ0 over other land covers, and forests should behave as fairly time-invariant targets [10,11,12]. This property of the forest σ0 was exploited to estimate the radiometric stability of SAR observations by calculating the standard deviation of the σ0 timeseries [10,11,12]

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