Soil moisture retrieval from Sentinel-1 using a first-order radiative transfer model—A case-study over the Po-Valley

Quast, Raphael; Wagner, Wolfgang; Bauer-Marschallinger, Bernhard; Vreugdenhil, Mariëtte

doi:10.1016/j.rse.2023.113651

Cited by 20 publications

(4 citation statements)

References 41 publications

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“…For instance, when one wants to assess the impact of orbit maneuvers, apply sensor fusion, as, e.g., in [17], or create (normalised) backscatter models for sensor calibration and design [47]. It can help also quantifying the sensitivity of certain parameters on orbital variations in a radiative transfer or bio-geophysical model, and thus support applications such as flood mapping [4], wet snow mapping [8], surface soil moisture retrieval [48], and sea ice analysis [49].…”

Section: Discussionmentioning

confidence: 99%

Utilising Sentinel-1’s Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter

Navacchi

Cao²,

Bauer-Marschallinger

et al. 2023

Sensors

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Radiometric Terrain Corrected (RTC) gamma nought backscatter, which was introduced around a decade ago, has evolved into the standard for analysis-ready Synthetic Aperture Radar (SAR) data. While working with RTC backscatter data is particularly advantageous over undulated terrain, it requires substantial computing resources given that the terrain flattening is more computationally demanding than simple orthorectification. The extra computation may become problematic when working with large SAR datasets such as the one provided by the Sentinel-1 mission. In this study, we examine existing Sentinel-1 RTC pre-processing workflows and assess ways to reduce processing and storage overheads by considering the satellite’s high orbital stability. By propagating Sentinel-1’s orbital deviations through the complete pre-processing chain, we show that the local contributing area and the shadow mask can be assumed to be static for each relative orbit. Providing them as a combined external static layer to the pre-processing workflow, and streamlining the transformations between ground and orbit geometry, reduces the overall processing times by half. We conducted our experiments with our in-house developed toolbox named wizsard, which allowed us to analyse various aspects of RTC, specifically run time performance, oversampling, and radiometric quality. Compared to the Sentinel Application Platform (SNAP) this implementation allowed speeding up processing by factors of 10–50. The findings of this study are not just relevant for Sentinel-1 but for all SAR missions with high spatio-temporal coverage and orbital stability.

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Section: Discussionmentioning

confidence: 99%

Utilising Sentinel-1’s Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter

Navacchi

Cao²,

Bauer-Marschallinger

et al. 2023

Sensors

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“…Several approaches were proposed to estimate soil moisture in agricultural environments from Sentinel-1 data [2,4]. While the C-band radar has proven effective in estimating soil moisture, the reliance on Sentinel-1 data from Copernicus arises primarily due to their status as the only freely accessible SAR data available at present.…”

Section: S²mp Soil Moisturementioning

confidence: 99%

“…Soil moisture (SM) plays a major role in several applications, including hydrology, agronomy and environmental risk management [1,2]. Currently, space remote sensing observations provide operational tools for mapping the spatial and temporal variations in SM over wide areas [3][4][5][6][7][8], which is considered essential for land-water management strategies, especially in croplands.…”

Section: Introductionmentioning

confidence: 99%

Retrieving Soil Moisture from Sentinel-1: Limitations over Certain Crops and Sensitivity to the First Soil Thin Layer

Bazzi,

Baghdadi,

Nino

et al. 2023

Water

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This paper presents a comparison between the Sentinel-1 (S1)/Sentinel-2 (S2)-derived soil moisture products at plot scale (S2MP) and in situ soil moisture measurements at a 10 cm depth for several winter and summer crops. Specifically, the paper discusses the consistency between the in situ soil moisture measurements, usually performed at a 10 cm soil depth, and the variable S1 C-band penetration depth in soil due to soil humidity conditions, vegetation development and S1 acquisition configuration. The aim is to provide end users with the strength and limitations of S1-derived soil moisture, mainly the S2MP soil moisture product, for their further applications. Both the estimated and measured soil moisture (SM) were evaluated over three testing fields in a Mediterranean climatic context, with crop cycles including wheat, tomato, cover crops and soybeans. The main results showed that the comparison between the S2MP-estimated SM based on S1 backscattering (at ~5 cm depth) with a 10 cm in situ SM is not always relevant during the crop cycle. In dry conditions, the S1 SM significantly underestimated the 10 cm SM measurements with an underestimation that could reach around 20 vol.% in some extremely dry conditions. This high underestimation was mainly due to the difference between the topsoil SM captured by the S1 sensor and the 10 cm in depth SM. Moderately wet conditions due to rainfall or irrigation showed less of a difference between the S1-estimated SM and the 10 cm in situ SM and varying between −10 and −5 vol.% due to the homogeneity of the SM at different soil depths. For extremely wet conditions, the S1 SM started to underestimate the SM values with an underestimation that can reach an order of −10 vol.%. A comparison of the S1-estimated SM as a function of the vegetation development showed that, for the studied crop types, the S1 SM estimates are only valid for low and moderate vegetation cover with a Normalized Difference Vegetation Index (NDVI) of less than 0.7. For dense vegetation cover (NDVI > 0.7), overestimations of the SM (average bias of about 4 vol.%) are mainly observed for developed tomato and soybean crops due to fruits’ emergence, whereas an extreme underestimation (average bias reaching −15.5 vol.%) is found for developed wheat cover due to the vertical structure of the wheat kernels. The results also suggest that the optimal SM estimations by S1 could be mainly obtained at low radar incidence angles (incidence angle less than 35°).

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“…Later on, Dari et al (2023) implemented the methodology to develop high-resolution and regional-scale irrigation data sets for three major basins: the Ebro basin (in Spain), the Po river valley (in Italy), and the Murray-Darling basin (in Australia). In this case, the SM-based inversion approach was forced with Sentinel-1 soil moisture data obtained through the RT1 (firstorder Radiative Transfer) model (Quast et al, 2019(Quast et al, , 2023 for the European sites and with CYGNSS (Cyclone Global Navigation Satellite System) soil moisture (Freeman et al, 2020) for the Australian pilot area. A similar framework was adopted by Zhang et al (2022) for producing global irrigation estimates relying on coarse resolution soil moisture data from multiple sensors.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Hydrological Impacts of Irrigation on a Mediterranean Agricultural Context Through Explicit Satellite‐Derived Irrigation Estimates

Dari,

Quintana‐Seguí,

Barella‐Ortiz

et al. 2024

Water Resources Research

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Water budget over anthropized basins is profoundly altered by human interventions, among which irrigation prevails. This study investigates how such water use affects hydrological flux and state variables by comparing two SURFEX/ISBA (SURFace EXternalisée/Interaction Sol Biosphère Atmosphère) Land Surface Model simulations performed over a portion of the Ebro basin (Spain) that includes a heavily irrigated area. The first simulation is forced by atmospheric input only, while the second one is fed with atmospheric forcing plus remote sensing‐derived irrigation amounts added to the liquid precipitation. Four main hydrological output variables are compared under the two configurations: surface soil moisture (SM), total evaporative flux (i.e., the sum of components from bare soil and vegetation) (E), drainage, and runoff. Results indicate that SM and E are the variables mostly impacted during the main watering season. Considering mean daily rates per each month, a maximum increase of +30% and of +220% is found over the irrigation districts in July for SM and E, respectively. The reliability of the results is supported by comparisons with satellite evapotranspiration data from MODIS (MODerate‐resolution Imaging Spectroradiometer). The inclusion of irrigation reduces the RMSE between monthly anomalies of modeled E and of MODIS reference data by 41% during the main watering season. Further validation through wavelet coherence analysis is also proposed. This study sheds light on the potential of explicit satellite‐derived irrigation water use data that, when integrated into modeling platforms, allow us to track and quantify anthropogenic impacts on water resources.

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Soil moisture retrieval from Sentinel-1 using a first-order radiative transfer model—A case-study over the Po-Valley

Cited by 20 publications

References 41 publications

Utilising Sentinel-1’s Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter

Utilising Sentinel-1’s Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter

Retrieving Soil Moisture from Sentinel-1: Limitations over Certain Crops and Sensitivity to the First Soil Thin Layer

Quantifying the Hydrological Impacts of Irrigation on a Mediterranean Agricultural Context Through Explicit Satellite‐Derived Irrigation Estimates

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