Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data

Ma, Can; Li, Xiaoqiong; McCabe, Matthew F.

doi:10.3390/rs12142303

Cited by 64 publications

(46 citation statements)

References 101 publications

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“…With recent developments in synthetic aperture radar (SAR) retrieval of soil moisture, observations have been available at 1 km scale from C-band SAR (Paloscia et al, 2013) or active-passive microwave data merging (Das et al, 2019). Some studies have also derived soil moisture data at 100 m or finer scale (Escorihuela et al, 2018;Lei et al, 2020;Vergopolan et al, 2020;Ma et al, 2020). Though remote sensing soil moisture data may not currently be available on a daily basis at high resolution, improvement in model simulation is still expected as is suggested by Fig.…”

Section: Potential For High-resolution Yield Estimationmentioning

confidence: 99%

Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model

Chibarabada

Ziliani

et al. 2021

Agricultural Water Management

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Parameter calibration is normally required prior to crop model simulation, which can be a time-consuming and data-intensive task. Meanwhile, the growth stages of different hybrids/cultivars of the same crop often show some similarities, which implies that phenological parameters calibrated for one hybrid/cultivar may be useful for the simulation of another. In this study, a data assimilation framework is proposed to reduce the requirement for parameter calibration for maize simulation using AquaCrop. The phenological parameters were uniformly scaled from previous research performed in a different location for a different maize hybrid, and other parameters were taken from default settings in the model documentation. To constrain simulation uncertainties, soil moisture and canopy cover observations were assimilated both separately and jointly in order to update model states. The methodology was tested across a rain-fed field in Nebraska for 6 growing seasons. The results suggested that the under-calibrated model with uniformly scaled phenological parameters captured the temporal dynamics of crop growth, but may lead to large estimation bias. Data assimilation effectively improved model performance, and the joint assimilation outperformed single-variable assimilation. When soil moisture and canopy cover were jointly assimilated, the overall yield estimates (RMSE = 1.24 t/ha, nRMSE = 11.48%, R 2 = 0.695) were improved over the no-assimilation case (RMSE = 2.01 t/ha, nRMSE = 18.61%, R 2 = 0.338). Sensitivity analyses suggested that the improvement was still evident with temporally sparse soil moisture observations and a small ensemble size. Further testing using observations within 90 days after planting demonstrated that the method was able to predict yield around 3 months before harvest (RMSE = 1.7 t/ha, nRMSE = 15.74%). This study indicated that maize yield can be estimated and predicted accurately by monitoring the soil moisture and canopy status, which has potential for regional applications using remote sensing data.

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Section: Potential For High-resolution Yield Estimationmentioning

confidence: 99%

Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model

Chibarabada

Ziliani

et al. 2021

Agricultural Water Management

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“…Spaceborne remote sensing (RS) provides spatially explicit information as satellites sense the same ground trace in regular time intervals, allowing for continuous monitoring (Babaeian et al, 2019). Estimates of θ are retrieved from different sensors measuring optical and thermal spectra (e.g., Rahimzadeh-Bajgiran et al, 2013;Zhang and Zhou, 2016), by passive and active microwave sensors (e.g., Schmugge and Jackson, 1997;Das and Paul, 2015), or the synergistic use of different sensor types such as using radar and optical data from Sentinel-2, Landsat, and MODIS (e.g., Attarzadeh et al, 2018;Ayehu et al, 2020;Foucras et al, 2020;Han et al, 2020;Ma et al, 2020). Synthetic Aperture Radars (SAR) are among the most effective and flexible active microwave sensor systems (e.g., Wang and Qu, 2009;Santi et al, 2016) due to their ability to penetrate the near-surface soil layer up to a depth of 5 cm (i.e., for C-band), which in turn enables to observe θ by directly relating the microwave scattering and emission to the water content of the focused object (e.g., Paloscia et al, 2013;Santi et al, 2016;Mohanty et al, 2017;Babaeian et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

et al. 2021

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Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNovSAR) or the combination of 43 SAR and 10 terrain parameters (θNovSAR+Terrain). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNovSAR+Terrain with 0.025 and 0.020 m3 m−3, and 89% compared to θNovSAR with 0.028 and 0.022 m3 m−3, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNovSAR+Terrain is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.

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“…Efforts have been taken to use the C-band synthetic aperture radar (SAR) systems of the RADARSAT Constellation Mission (RCM) and the Sentinel-1 SAR constellations for soil moisture retrievals. But at L-and C-bands, surface roughness from vegetation can have an orderof-magnitude-stronger effect on the Fresnel reflection coefficients than soil moisture [5,6].…”

Section: Introductionmentioning

confidence: 99%

Stokes Reflectometer for Soil Moisture,Subterranean Voids, and Subsurface Ice

2021

RSL

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The Stokes reflectometer concept presented here takes a novel approach to microwave remote sensing of soils. It relies on a constellation of low-power, low-mass, continuous wave, ultrahighfrequency transmitters and receivers. The systems are arranged in a forward scattering configuration over a range of angles of incidence and scattering set to cross the Brewster angle to retrieve soil properties. Proof of concept is provided by a series of COMSOL Multiphysics finite element runs to model Stokes parameters (SP) of forward-scattered RF signals through layers of varying compositions, dielectric constants, and surface roughness, assuming no sources of contamination during transmission. Significant variations for all four SP were demonstrated. At frequencies around 430 MHz, contamination during transmission is due to Faraday rotation (FR) in the ionosphere. A hybrid-polarimetric configuration minimized ionospheric contamination and provided for simplified expressions to retrieve FR angles and scattering coefficients from the received polarimetric power. With these, SP can be correctly calculated to soil moisture retrievals.

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Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data

Cited by 64 publications

References 101 publications

Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model

Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

Stokes Reflectometer for Soil Moisture,Subterranean Voids, and Subsurface Ice

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