Uncertainty of effective roughness parameters calibrated on bare agricultural land using Sentinel-1 SAR

Uncertainty of roughness parameters has effect on soil moisture retrievals with backscatter models from Synthetic Aperture Radar observations. The uncertainty of soil moisture retrievals is important information for the usability of these estimates. In this paper we introduce a methodology to estimate the uncertainty of effective roughness parameters in the Integral Equation Method surface backscatter model, using a Bayesian Markov Chain Monte Carlo approach. Using Sentinel-1 imagery we demonstrate the methodology for a selected field, showing the posterior uncertainty distributions of the roughness parameters, and the effect on the backscatter model simulations and soil moisture inversions. The estimated total uncertainty of the soil moisture retrievals with the optimum parameter set is 0.043 m3/m3, which is slightly higher than the root mean square error of 0.040 m3/m3 of the retrievals compared to in situ soil moisture measurements

Bodemvocht uit satellietdata:wat kan de Nederlandse waterbeheerder ermee?

Het onderzoeksproject ‘Optimizing Water Availability with Sentinel-1 Satellites’ heeft als doel te onderzoeken hoe satellietdata gebruikt kan worden in het Nederlandse waterbeheer. Het onderzoek laat zien dat de satelliet Sentinel-1 buiten het groeiseizoen om al een vrij goed beeld geeft van het bodemvochtgehalte. Hiermee kan bijvoorbeeld de berijdbaarheid van landbouwpercelen in kaart gebracht kan worden. Ook is met Deltares en HKV een data-assimilatietool ontwikkeld die ingezet kan worden om simulaties met het Landelijk Hydrologisch Model te verbeteren

Supplementary data of 'Sentinel-1 soil moisture content retrieval over meadows using a physically based scattering model' (updated)

The data and supplementary figures of the study 'Soil moisture content retrieval over meadows from Sentinel-1 and Sentinel-2 data using physically based scattering models?, of which the scientific manuscript is under review. The dataset includes for 21 study fields: - Tables of LAI estimates derived from Sentinel-2 satellite imagery. Sentinel-2 LAI maps were generated by VITO and obtained from the VITO Product Distribution Portal. We processed the maps to field-scale LAI estimates. This contains modified Copernicus Sentinel data [2015-2019]. - Tables of soil moisture content retrievals from Sentinel-1 satellite observations, obtained without vegetation correction and with a vegetation correction. This contains modified Copernicus Sentinel data [2015-2018]. - Time series figures of the soil moisture content retrievals and references. - Table with coordinates of the study fields. Replacement note: this dataset is a replacement of the dataset of 2021-11-17 (see description details). In the dataset of 2021-11-17, CDF matching was applied to the Sentinel-2 LAI estimates before the retrieval of soil moisture content from Sentinel-1. This replacement version contains the soil moisture content retrievals from Sentinel-1 obtained without applying CDF matching to the Sentinel-2 LAI estimates

Impacts of Radiometric Uncertainty and Weather-Related Surface Conditions on Soil Moisture Retrievals with Sentinel-1

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 ( σ0 ) 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 σ0 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 σ0 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 σ0 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 σ0 observations. By spatially averaging the σ0 observations, the Sentinel-1 radiometric uncertainty improves from 0.85 dB for a surface area of 0.25 ha to 0.30 dB for 10 ha 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 σ0 observations are averaged. Deviations in σ0 were combined with the σ0 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 m3 m−3 up to 0.17 m3 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 σ0 disturbance caused by a weather-related surface condition for a specific land cover type) and the soil moisture itself

Sentinel-1 soil moisture content and its uncertainty over sparsely vegetated fields

Soil moisture content (SMC) retrievals from synthetic aperture radar (SAR) observations do not exactly match with in situ references due to imperfect retrieval algorithms, and uncertainties in the model parameters, SAR observations and in situ references. Information on the uncertainty of SMC retrievals would contribute to their applicability. This paper presents a methodology for deriving the SMC retrieval uncertainty and decomposing this in its constituents. A Bayesian calibration framework was used for deriving the total uncertainty and the model parameter uncertainty. The methodology was demonstrated with the integral equation method (IEM) surface scattering model, which was employed for reproducing Sentinel-1 backscatter (σ0) observations and the retrieval of SMC over four sparsely vegetated fields in the Netherlands. For two meadows the calibrated surface roughness parameter distributions are remarkably similar between the ascending and the descending Sentinel-1 orbits as well as between the two meadows, and yield consistent SMC retrievals for the calibration and validation periods (RMSDs of 0.076 m3 m-3 to 0.11 m3 m-3). These results are promising for operational retrieval of SMC over meadows. In contrast, the surface roughness parameter distributions of two fallow maize fields differ significantly and the surface roughness conditions changing over time result in less consistent SMC retrievals (calibration RMSDs of 0.096 m3 m-3 and 0.13 m3 m-3 versus validation RMSDs of 0.26 m3 m-3). The SMC retrieval uncertainty derived with the Bayesian calibration successfully reproduces the uncertainty estimated empirically using in situ references. The main uncertainty originates from the in situ references and the Sentinel-1 observations, whereas the contribution from the surface roughness parameters is relatively small. The presented research yields further insights into the surface roughness of agricultural fields and SMC retrieval uncertainties, and these insights can be used to guide SAR-based SMC product developments

Twelve years of profile soil moisture and temperature measurements in Twente, the Netherlands

Spread across Twente and its neighbouring regions in the east of the Netherlands, a network of 20 profile soil moisture and temperature (5, 10, 20, 40, and 80ĝ€¯cm depths) monitoring stations was established in 2009. Field campaigns have been conducted covering the growing seasons of 2009, 2015, 2016, and 2017, during which soil sampling rings and handheld probes were used to measure the top 5ĝ€¯cm volumetric soil moisture content (VSM) of 28 fields near 12 monitoring stations. In this paper, we describe the design of the monitoring network and the field campaigns, adopted instrumentation, experimental setup, field sampling strategies, and the development of sensor calibration functions. Maintenance and quality control procedures and issues specific to the Twente network are discussed. Moreover, we provide an overview of open third-party datasets (i.e. land cover/use, soil information, elevation, groundwater, and meteorological observations) that can support the use and analysis of the Twente soil moisture and temperature datasets beyond the scope of this contribution. An indication for the spatial representativeness of the permanent monitoring stations is provided through comparisons of the 5ĝ€¯cm station measurements with the top 5ĝ€¯cm field-Averaged VSM derived from the field campaign measurements. The results reveal in general reasonable agreements and root mean squared errors that are dominated by underestimations of the field-Averaged VSM, which is particularly apparent for the grass fields and is strong after heavy rain. Further, we discuss the prospects the datasets offer to investigate (i) the reliability of soil moisture references that serve the development and validation of soil moisture products, and (ii) the water and energy exchanges across the groundwater-vadose-zone-Atmosphere continuum within a lowland environment in a changing climate. The datasets discussed are publicly available at 10.17026/dans-znj-wyg5 (Van der Velde et al., 2022)