Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil

Amazirh, Abdelhakim; Merlin, Olivier; Er‐Raki, Salah; Gao, Qi; Rivalland, Vincent; Malbéteau, Yoann; Khabba, Saïd; Escorihuela, María José

doi:10.1016/j.rse.2018.04.013

Cited by 135 publications

(96 citation statements)

References 115 publications

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“…In this study, we use both Sentinel-1A and Sentinel-1B backscatter data, with a resolution of 10 m and a temporal resolution of 12 days, for each satellite. Previous studies have shown that VV (vertical-vertical) polarization data, in comparison to VH (vertical-horizontal) polarization, show high sensitivity to soil moisture [37][38][39][40][41][42]. VH data, in its turn, has a higher sensitivity to volume scattering, which depends strongly on the geometrical alignment and characteristics of the vegetation.…”

Section: Sentinel-1 Datamentioning

confidence: 99%

Irrigation Mapping Using Sentinel-1 Time Series at Field Scale

et al. 2018

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The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical–vertical) and VH (vertical–horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.

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Section: Sentinel-1 Datamentioning

confidence: 99%

Irrigation Mapping Using Sentinel-1 Time Series at Field Scale

et al. 2018

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“…Direct scattering from the canopy and the soil, as well as multiple interactions between vegetation components and the background soil, contributes to the scattering characteristics of the SAR response [26]. Thus, eliminating the vegetation and surface roughness contributions to the SAR backscattering is essential for accurately retrieving SSM over agricultural fields during the growing season [27], which requires the determination of soil surface roughness [28] and canopy properties [28,29]. The parameterization of surface roughness and its spatial variability can pose major challenges for SSM retrieval [30].…”

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confidence: 99%

Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

Xing

et al. 2019

Remote Sensing

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Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R 2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season. of soil moisture data over a large region multiple times is very time-consuming and costly. Satellite remote sensing through optical or microwave sensors provides an effective and cost-efficient means of monitoring and assessing SSM at different scales [7-10]. For example, Sadeghi et al.[8] estimated SSM with a RMSE (root mean square error) less than 0.04 cm 3 /cm −3 with local calibration from Sentinel-2 and Landsat-8 observations for the Walnut Gulch and Little Washita watersheds. Tomer et al. [11] used the RADARSAT-2 images to calculate relative soil moisture. When comparing with the SMOS soil moisture, the results showed good temporal behavior with RMSE of approximately 0.05 m 3 /m 3 and a correlation coefficient of approximately 0.9. For passive microwave, the SMAP-radiometer-based soil moisture data product meets its expected performance of 0.04 m 3 /m 3 volumetric soil moisture (unbiased RMSE) [10]. Optical remote sensing is often hampered by cloud cover, while microwave remote sensing has all-weather day-and-night penetration capabilities [11]. However, the lower spatial resolutions of the passive microwave sensors in orbit limit their applicability to retrieve SSM over individual agricultural fields [12]. Currently, only active sensors, like radars, especially Synthetic Aperture Radars (SARs) (such as C-band Sentinel-1A and 1B, RADARSAT-2, and L-band ALOS-2), can provide observations at high spatial resolutions of about 10 m to 100 m with a relatively coarse temporal resolution (2-4 weeks) [2,12].There is a high correlation between radar backscattering coefficient and SSM since the dielectric constant of soil is directly proportional to the amount of water held in the soil [13]. Therefore, the...

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“…For thermal data, land surface temperature (LST) derived from thermal infrared remote sensing data have been used in a variety of applications such as, among others, climate studies [62,63], the monitoring of crop water consumption and water stress detection [64][65][66][67], vegetation monitoring [68,69], soil moisture estimation [70][71][72]. Canopy temperature has long been recognized as a good indicator for crop water status and as a potential tool for irrigation scheduling.…”

Section: Remote Sensingmentioning

confidence: 99%

“…Especially, the Sentinel-1 (S1) constellation (composed of two satellites S1-A and S1-B) potentially provides SAR data at 20 m resolution every 3 days [74]. Thus, numerous studies have investigated and exploited the sensitivity of the radar signal to SM [70,[75][76][77][78][79][80].…”

Section: Remote Sensingmentioning

confidence: 99%

Integrating Remote Sensing Data into Fuzzy Control System for Variable Rate Irrigation Estimates

Mendes¹,

Araújo²,

Er-Raki³

2019

Irrigation - Water Productivity and Operation, Sustainability and Climate Change

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Variable rate irrigation (VRI) is the capacity to vary the depth of water application in a field spatially. Developing precise management zones is necessary to efficient variable rate irrigation technologies. Intelligent fuzzy inference system based on precision irrigation knowledge, i.e., a system capable of creating prescriptive maps to control the rotation speed of the central pivot. Based on the VRI-prescribed map created by the intelligent system of decision-making, the pivot can increase or decrease its speed, reaching the desired depth of application in a certain irrigation zone. Therefore, this strategy of speed control is more realistic compared to traditional methods. Results indicate that data from the edaphoclimatic variables, when well fitted to the fuzzy logic, can solve uncertainties and non-linearities of an irrigation system and establish a control model for highprecision irrigation. Because remote sensing provides quick measurements and easy access to crop information for large irrigation areas, images will be used as inputs. The developed fuzzy system for pivot control is original and innovative. Furthermore, the artificial intelligent systems can be applied widely in agricultural areas, so the results were favorable to the continuity of studies on precision irrigation and application of the fuzzy logic in precision agriculture.

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Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil

Cited by 135 publications

References 115 publications

Irrigation Mapping Using Sentinel-1 Time Series at Field Scale

Irrigation Mapping Using Sentinel-1 Time Series at Field Scale

Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data

Integrating Remote Sensing Data into Fuzzy Control System for Variable Rate Irrigation Estimates

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