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Dr. Mehrez Zribi
Université de Toulouse, Toulouse, France

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0 Microwave Remote Sensing
0 GNSS-R
0 spatial hydrology
0 Airborne instrumentation for land surfaces

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Journal article
Published: 08 July 2021 in Remote Sensing
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This paper aims to analyze agronomic drought in a highly anthropogenic, semiarid region, the western Mediterranean region. The proposed study is based on Moderate-Resolution Imaging Spectroradiometer (MODIS) and Advanced SCATterometer (ASCAT) satellite data describing the dynamics of vegetation cover and soil water content through the Normalized Difference Vegetation Index (NDVI) and Soil Water Index (SWI). Two drought indices were analyzed: the Vegetation Anomaly Index (VAI) and the Moisture Anomaly Index (MAI). The dynamics of the VAI were analyzed as a function of land cover deduced from the Copernicus land cover map. The effect of land cover and anthropogenic agricultural activities such as irrigation on the estimation of the drought index VAI was analyzed. The VAI dynamics were very similar for the shrub and forest classes. The contribution of vegetation cover (VAI) was combined with the effect of soil water content (MAI) through a new drought index called the global drought index (GDI) to conduct a global analysis of drought conditions. The implementation of this combination on different test areas in the study region is discussed.

ACS Style

Mehrez Zribi; Simon Nativel; Michel Le Page. Analysis of Agronomic Drought in a Highly Anthropogenic Context Based on Satellite Monitoring of Vegetation and Soil Moisture. Remote Sensing 2021, 13, 2698 .

AMA Style

Mehrez Zribi, Simon Nativel, Michel Le Page. Analysis of Agronomic Drought in a Highly Anthropogenic Context Based on Satellite Monitoring of Vegetation and Soil Moisture. Remote Sensing. 2021; 13 (14):2698.

Chicago/Turabian Style

Mehrez Zribi; Simon Nativel; Michel Le Page. 2021. "Analysis of Agronomic Drought in a Highly Anthropogenic Context Based on Satellite Monitoring of Vegetation and Soil Moisture." Remote Sensing 13, no. 14: 2698.

Journal article
Published: 01 July 2021 in Remote Sensing
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In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019).

ACS Style

Hassan Bazzi; Nicolas Baghdadi; Ghaith Amin; Ibrahim Fayad; Mehrez Zribi; Valérie Demarez; Hatem Belhouchette. An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data. Remote Sensing 2021, 13, 2584 .

AMA Style

Hassan Bazzi, Nicolas Baghdadi, Ghaith Amin, Ibrahim Fayad, Mehrez Zribi, Valérie Demarez, Hatem Belhouchette. An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data. Remote Sensing. 2021; 13 (13):2584.

Chicago/Turabian Style

Hassan Bazzi; Nicolas Baghdadi; Ghaith Amin; Ibrahim Fayad; Mehrez Zribi; Valérie Demarez; Hatem Belhouchette. 2021. "An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data." Remote Sensing 13, no. 13: 2584.

Journal article
Published: 28 May 2021 in Remote Sensing
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The Global Ecosystem Dynamics Investigation LiDAR (GEDI) is a new full waveform (FW) based LiDAR system that presents a new opportunity for the observation of forest structures globally. The backscattered GEDI signals, as all FW systems, are distorted by topographic conditions within their footprint, leading to uncertainties on the measured forest variables. In this study, we explore how well several approaches based on waveform metrics and ancillary digital elevation model (DEM) data perform on the estimation of stand dominant heights (Hdom) and wood volume (V) across different sites of Eucalyptus plantations with varying terrain slopes. In total, five models were assessed on their ability to estimate Hdom and four models for V. Results showed that the models using the GEDI metrics, such as the height at different energy quantiles with terrain data from the shuttle radar topography mission’s (SRTM) digital elevation model (DEM) were still dependent on the topographic slope. For Hdom, an RMSE increase of 14% was observed for data acquired over slopes higher than 20% in comparison to slopes between 10 and 20%. For V, a 74% increase in RMSE was reported between GEDI data acquired over slopes between 0–10% and those acquired over slopes higher than 10%. Next, a model relying on the height at different energy quantiles of the entire waveform (HTn) and the height at different energy quartiles of the bare ground waveform (HGn) was assessed. Two sets of the HGn metrics were generated, the first one was obtained using a simulated waveform representing the echo from a bare ground, while the second one relied on the actual ground return from the waveform by means of Gaussian fitting. Results showed that both the simulated and fitted models provide the most accurate estimates of Hdom and V for all slope ranges. The simulation-based model showed an RMSE that ranged between 1.39 and 1.66 m (between 26.76 and 39.26 m3·ha−1 for V) while the fitting-based method showed an RMSE that ranged between 1.26 and 1.34 m (between 26.78 and 36.29 m3·ha−1 for V). Moreover, the dependency of the GEDI metrics on slopes was greatly reduced using the two sets of metrics. As a conclusion, the effect of slopes on the 25-m GEDI footprints is rather low as the estimation on canopy heights from uncorrected waveforms degraded by a maximum of 1 m for slopes between 20 and 45%. Concerning the wood volume estimation, the effect of slopes was more pronounced, and a degradation on the accuracy (increased RMSE) of a maximum of 20 m3·ha−1 was observed for slopes between 20 and 45%.

ACS Style

Ibrahim Fayad; Nicolas Baghdadi; Clayton Alcarde Alvares; Jose Stape; Jean Bailly; Henrique Scolforo; Italo Cegatta; Mehrez Zribi; Guerric Le Maire. Terrain Slope Effect on Forest Height and Wood Volume Estimation from GEDI Data. Remote Sensing 2021, 13, 2136 .

AMA Style

Ibrahim Fayad, Nicolas Baghdadi, Clayton Alcarde Alvares, Jose Stape, Jean Bailly, Henrique Scolforo, Italo Cegatta, Mehrez Zribi, Guerric Le Maire. Terrain Slope Effect on Forest Height and Wood Volume Estimation from GEDI Data. Remote Sensing. 2021; 13 (11):2136.

Chicago/Turabian Style

Ibrahim Fayad; Nicolas Baghdadi; Clayton Alcarde Alvares; Jose Stape; Jean Bailly; Henrique Scolforo; Italo Cegatta; Mehrez Zribi; Guerric Le Maire. 2021. "Terrain Slope Effect on Forest Height and Wood Volume Estimation from GEDI Data." Remote Sensing 13, no. 11: 2136.

Journal article
Published: 27 May 2021 in Remote Sensing
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Surface soil moisture (SSM) is a key variable for many environmental studies, including hydrology and agriculture. Synthetic aperture radar (SAR) data in the C-band are widely used nowadays to estimate SSM since the Sentinel-1 provides free-of-charge C-band SAR images at high spatial resolution with high revisit time, whereas the use of L-band is limited due to the low data availability. In this context, the main objective of this paper is to develop an operational approach for SSM estimation that mainly uses data in the C-band (Sentinel-1) with L-bands (ALOS/PALSAR) as additional data to improve SSM estimation accuracy. The approach is based on the use of the artificial neural networks (NNs) technique to firstly derive the soil roughness (Hrms) from the L-band (HH polarization) to then consider the L-band-derived Hrms and C-band SAR data (VV and VH polarizations) in the input vectors of NNs for SSM estimation. Thus, the Hrms estimated from the L-band at a given date is assumed to be constant for a given times series of C-band images. The NNs were trained and validated using synthetic and real databases. The results showed that the use of the L-band-derived Hrms in the input vector of NN in addition to C-band SAR data improved SSM estimation by decreasing the error (bias and RMSE), mainly for SSM values lower than 15 vol.% and regardless of Hrms values. Based on the synthetic database, the NNs that neglect the Hrms underestimate and overestimate the SSM (bias ranges between −8.0 and 4.0 vol.%) for Hrms values lower and higher than 1.5 cm, respectively. For Hrms 2.0 cm and SSM between 8 to 22 vol.%, the accuracy on the SSM estimation improved and the overestimation decreased by 2.2 vol.% (from 4.5 to 2.3 vol.%). From the real database, the use of Hrms estimated from the L-band brought a significant improvement of the SSM estimation accuracy. For in situ SSM less than 15 vol.%, the RMSE decreased by 1.5 and 2.2 vol.% and the bias by 1.2 and 2.6 vol.%, for Hrms values lower and higher than 1.5 cm, respectively.

ACS Style

Mohamad Hamze; Nicolas Baghdadi; Marcel El Hajj; Mehrez Zribi; Hassan Bazzi; Bruno Cheviron; Ghaleb Faour. Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data. Remote Sensing 2021, 13, 2102 .

AMA Style

Mohamad Hamze, Nicolas Baghdadi, Marcel El Hajj, Mehrez Zribi, Hassan Bazzi, Bruno Cheviron, Ghaleb Faour. Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data. Remote Sensing. 2021; 13 (11):2102.

Chicago/Turabian Style

Mohamad Hamze; Nicolas Baghdadi; Marcel El Hajj; Mehrez Zribi; Hassan Bazzi; Bruno Cheviron; Ghaleb Faour. 2021. "Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data." Remote Sensing 13, no. 11: 2102.

Journal article
Published: 04 April 2021 in Remote Sensing
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This paper discusses the potential of L-band Advanced Land Observing Satellite-2 (ALOS-2) and C-band Sentinel-1 radar data for retrieving soil parameters over cereal fields. For this purpose, multi-incidence, multi-polarization and dual-frequency satellite data were acquired simultaneously with in situ measurements collected over a semiarid area, the Merguellil Plain (central Tunisia). The L- and C-band signal sensitivity to soil roughness, moisture and vegetation was investigated. High correlation coefficients were observed between the radar signals and soil roughness values for all processed multi-configurations of ALOS-2 and Sentinel-1 data. The sensitivity of SAR (Synthetic Aperture Radar) data to soil moisture was investigated for three classes of the normalized difference vegetation index (NDVI) (low vegetation cover, medium cover and dense cover), illustrating a decreasing sensitivity with increasing NDVI values. The highest sensitivity to soil moisture under the dense cover class is observed in L-band data. For various vegetation properties (leaf area index (LAI), height of vegetation cover (H) and vegetation water content (VWC)), a strong correlation is observed with the ALOS-2 radar signals (in HH(Horizontal-Horizontal) and HV(Horizontal-Vertical) polarizations). Different empirical models that link radar signals (in the L- and C-bands) to soil moisture and roughness parameters, as well as the semi-empirical Dubois modified model (Dubois-B) and the modified integral equation model (IEM-B), over bare soils are proposed for all polarizations. The results reveal that IEM-B performed a better accuracy comparing to Dubois-B. This analysis is also proposed for covered surfaces using different options provided by the water cloud model (WCM) (with and without the soil–vegetation interaction scattering term) coupled with the best accuracy bare soil backscattering models: IEM-B for co-polarization and empirical models for the entire dataset. Based on the validated backscattering models, different options of coupled models are tested for soil moisture inversion. The integration of a soil–vegetation interaction component in the WCM illustrates a considerable contribution to soil moisture precision in the HV polarization mode in the L-band frequency and a neglected effect on C-band data inversion.

ACS Style

Emna Ayari; Zeineb Kassouk; Zohra Lili-Chabaane; Nicolas Baghdadi; Safa Bousbih; Mehrez Zribi. Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors. Remote Sensing 2021, 13, 1393 .

AMA Style

Emna Ayari, Zeineb Kassouk, Zohra Lili-Chabaane, Nicolas Baghdadi, Safa Bousbih, Mehrez Zribi. Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors. Remote Sensing. 2021; 13 (7):1393.

Chicago/Turabian Style

Emna Ayari; Zeineb Kassouk; Zohra Lili-Chabaane; Nicolas Baghdadi; Safa Bousbih; Mehrez Zribi. 2021. "Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors." Remote Sensing 13, no. 7: 1393.

Preprint content
Published: 04 March 2021
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This paper aims to analyze the agronomic drought in a highly anthropogenic  semi-arid region, North Africa. In the context of the Mediterranean climate, characterized by frequent droughts, North Africa is particularly affected. Indeed, in addition to this climatic aspect, it is one of the areas most affected by water scarcity in the world. Thus, understanding and describing agronomic drought is essential. The proposed study is based on remote sensing data from TERRA-MODIS and ASCAT satellite, describing the dynamics of vegetation cover and soil water content through NDVI and SWI indices. Two indices are analyzed, the Vegetation Anomaly Index (VAI) and the Moisture Anomaly Index (MAI). The dynamics of the VAI is analyzed for different types of regions (agircultural, forest areas). The contribution of vegetation cover is combined with the effect of soil water content through a new drought index combining the VAI and MAI. A discussion of this combination is proposed on different study areas in the study region. It illustrates the complementarity of these two informations in analysis of agronomic drought.

ACS Style

Mehrez Zribi; Simon Nativel; Michel Le Page. Analysis of agronomic drought over North Africa using remote sensing satellite data. 2021, 1 .

AMA Style

Mehrez Zribi, Simon Nativel, Michel Le Page. Analysis of agronomic drought over North Africa using remote sensing satellite data. . 2021; ():1.

Chicago/Turabian Style

Mehrez Zribi; Simon Nativel; Michel Le Page. 2021. "Analysis of agronomic drought over North Africa using remote sensing satellite data." , no. : 1.

Preprint content
Published: 03 March 2021
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Soil moisture is a key component for water resources management especially for irrigation needs estimation. We analyze in the present study, the potential of L-band data, acquired by (Advanced Land Observing Satellite-2) ALOS-2, to retrieve soil moisture over bare soils and cereal fields located in semi-arid area in the Kairouan plain.

In this context, we evaluate radar signal sensitivity to roughness, soil moisture and vegetation biophysical parameters. Based on multi-incidence radar data (28°, 32.5° and 36°), high correlations characterize relationships between backscattering coefficients in dual-polarization (HH and HV) and root mean square of heights (Hrms) and Zs, parameters, Sensitivity of radar data to soil moisture was discussed for three classes of NDVI (less than 0.25 for bare soils and dispersed vegetation, between 0.25 and 0.5 for medium vegetation and greater than 0.5 for dense cereals). With vegetation development, where NDVI values are higher than 0.25, SAR signal remains sensitive to soil moisture in HH pol. This sensitivity to moisture disappears, in HV pol for dense vegetation. For covered fields, L-band signal is very sensitive to Vegetation Water Content (VWC), with R² values ranging between 0.76 and 0.61 in HH and HV polarization respectively.

Simulating signal behavior is carried out through various models over bare soils and covered cereal fields. Over bare soils, proposed empirical expressions, modified versions of Integral Equation Model (IEM-B) and Dubois models (Dubois-B) are evaluated, generally for HH and HV polarizations. Best consistency is observed between real data and IEM-B backscattering simulations in HH polarization. More discrepancies between real and modelled data are observed in HV polarization.

Furthermore, to simulate L-band signal behavior over covered fields, the inversion of Water Cloud Model (WCM) coupled to different bare soil models is realized through direct equations and Look-up tables. Two options of WCM, are tested (with and without soil-vegetation interaction scattering term). For the first option, results highlight the good performance of IEM-B coupled to WCM in HH polarization with RMSE value between estimated and in situ moisture measurements equal to 4.87 vol.%. By adding soil – cereal interaction term in the second option of WCM, results reveal a stable accuracy in HH polarization and an important improvement of soil moisture estimations in HV polarization, with RMSE values are ranging between 6 and 7 vol.%.

ACS Style

Emna Ayari; Zeineb Kassouk; Zohra Lili Chabaane; Safa Bousbih; Mehrez Zribi. Soil moisture estimation over cereals fields using l-band alos2 data (merguellil case – KAIROUAN). 2021, 1 .

AMA Style

Emna Ayari, Zeineb Kassouk, Zohra Lili Chabaane, Safa Bousbih, Mehrez Zribi. Soil moisture estimation over cereals fields using l-band alos2 data (merguellil case – KAIROUAN). . 2021; ():1.

Chicago/Turabian Style

Emna Ayari; Zeineb Kassouk; Zohra Lili Chabaane; Safa Bousbih; Mehrez Zribi. 2021. "Soil moisture estimation over cereals fields using l-band alos2 data (merguellil case – KAIROUAN)." , no. : 1.

Journal article
Published: 11 February 2021 in Hydrology and Earth System Sciences
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In the context of major changes (climate, demography, economy, etc.), the southern Mediterranean area faces serious challenges with intrinsically low, irregular, and continuously decreasing water resources. In some regions, the proper growth both in terms of cropping density and surface area of irrigated areas is so significant that it needs to be included in future scenarios. A method for estimating the future evolution of irrigation water requirements is proposed and tested in the Tensift watershed, Morocco. Monthly synthetic crop coefficients (Kc) of the different irrigated areas were obtained from a time series of remote sensing observations. An empirical model using the synthetic Kc and rainfall was developed and fitted to the actual data for each of the different irrigated areas within the study area. The model consists of a system of equations that takes into account the monthly trend of Kc, the impact of yearly rainfall, and the saturation of Kc due to the presence of tree crops. The impact of precipitation change is included in the Kc estimate and the water budget. The anthropogenic impact is included in the equations for Kc. The impact of temperature change is only included in the reference evapotranspiration, with no impact on the Kc cycle. The model appears to be reliable with an average r2 of 0.69 for the observation period (2000–2016). However, different subsampling tests of the number of calibration years showed that the performance is degraded when the size of the training dataset is reduced. When subsampling the training dataset to one-third of the 16 available years, r2 was reduced to 0.45. This score has been interpreted as the level of reliability that could be expected for two time periods after the full training years (thus near to 2050). The model has been used to reinterpret a local water management plan and to incorporate two downscaled climate change scenarios (RCP4.5 and RCP8.5). The examination of irrigation water requirements until 2050 revealed that the difference between the two climate scenarios was very small (< 2 %), while the two agricultural scenarios were strongly contrasted both spatially and in terms of their impact on water resources. The approach is generic and can be refined by incorporating irrigation efficiencies.

ACS Style

Michel Le Page; Younes Fakir; Lionel Jarlan; Aaron Boone; Brahim Berjamy; Saïd Khabba; Mehrez Zribi. Projection of irrigation water demand based on the simulation of synthetic crop coefficients and climate change. Hydrology and Earth System Sciences 2021, 25, 637 -651.

AMA Style

Michel Le Page, Younes Fakir, Lionel Jarlan, Aaron Boone, Brahim Berjamy, Saïd Khabba, Mehrez Zribi. Projection of irrigation water demand based on the simulation of synthetic crop coefficients and climate change. Hydrology and Earth System Sciences. 2021; 25 (2):637-651.

Chicago/Turabian Style

Michel Le Page; Younes Fakir; Lionel Jarlan; Aaron Boone; Brahim Berjamy; Saïd Khabba; Mehrez Zribi. 2021. "Projection of irrigation water demand based on the simulation of synthetic crop coefficients and climate change." Hydrology and Earth System Sciences 25, no. 2: 637-651.

Journal article
Published: 11 December 2020 in Remote Sensing
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Better management of water consumption and irrigation schedule in irrigated agriculture is essential in order to save water resources, especially at regional scales and under changing climatic conditions. In the context of water management, the aim of this study is to monitor irrigation activities by detecting the irrigation episodes at plot scale using the Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) time series over intensively irrigated grassland plots located in the Crau plain of southeast France. The method consisted of assessing the newly developed irrigation detection model (IDM) at plot scale over the irrigated grassland plots. First, four S1-SAR time series acquired from four different S1-SAR acquisitions (different S1 orbits), each at six-day revisit time, were obtained over the study site. Next, the IDM was applied at each available SAR image from each S1-SAR series to obtain an irrigation indicator at each SAR image (no, low, medium, or high irrigation possibility). Then, the irrigation indicators obtained at each image from each S1-SAR time series (four series) were added and combined by threshold value criteria to determine the existence or absence of an irrigation event. Finally, the performance of the IDM for irrigation detection was assessed by comparing the in situ recorded irrigation events at each plot and the detected irrigation events. The results show that using only the VV polarization, 82.4% of the in situ registered irrigation events are correctly detected with an F_score value reaching 73.8%. Less accuracy is obtained using only the VH polarization, where 79.9% of the in situ irrigation events are correctly detected with an F_score of 72.2%. The combined use of the VV and VH polarization showed that 74.1% of the irrigation events are detected with a higher F_score value of 76.4%. The analysis of the undetected irrigation events revealed that, in the presence of very well-developed vegetation cover (normalized difference of vegetation index (NDVI) ≥ 0.8); higher uncertainty in irrigation detection is observed, where 80% of the undetected events correspond to an NDVI value greater than 0.8. The results also showed that small-sized plots encounter more false irrigation detections than large-sized plots certainly because the pixel spacing of S1 data (10 m × 10 m) is not adapted to small size plots. The obtained results prove the efficiency of the S1 C-band data and the IDM for detecting irrigation events at the plot scale, which would help in improving the irrigation water management at large scales especially with availability and global coverage of the S1 product.

ACS Style

Hassan Bazzi; Nicolas Baghdadi; Ibrahim Fayad; François Charron; Mehrez Zribi; Hatem Belhouchette. Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data. Remote Sensing 2020, 12, 4058 .

AMA Style

Hassan Bazzi, Nicolas Baghdadi, Ibrahim Fayad, François Charron, Mehrez Zribi, Hatem Belhouchette. Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data. Remote Sensing. 2020; 12 (24):4058.

Chicago/Turabian Style

Hassan Bazzi; Nicolas Baghdadi; Ibrahim Fayad; François Charron; Mehrez Zribi; Hatem Belhouchette. 2020. "Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data." Remote Sensing 12, no. 24: 4058.

Journal article
Published: 05 November 2020 in Water
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This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) as a training dataset. The paper also addresses the transferability of the trained ANN across climatic and soil texture conditions. Data from the International Soil Moisture Network (ISMN) were collected for several networks with variable soil texture and climate classes. Several scaling, feature extraction, and training approaches were tested. An artificial neural network employing rolling averages (ANNRAV) of SSM over 10, 30, and 90 days was developed. The results show that applying a standard scaling (SSCA) to the ANN input features improves the correlation, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) for 52%, 91%, and 87%, respectively, of the tested stations, compared to MinMax scaling (MMSCA). Different training sets are suggested, namely, training on data from all networks, data from one network, or data of all networks excluding one. Based on these trainings, new transferability (TranI) and contribution (ContI) indices are defined. The results show that one network cannot provide the best prediction accuracy if used alone to train the ANN. They also show that the removal of the less contributing networks enhances performance. For example, elimination of the densest network (SCAN) from the training enhances the mean correlation by 20.5% and the mean NSE by 42.5%. This motivates the implementation of a data filtering technique based on the ANN’s performance. A median, max, and min correlation of 0.77, 0.96, and 0.65, respectively, are obtained by the model after data filtering. The performances are also analyzed with respect to the covered climatic regions and soil texture, providing insights into the robustness and limitations of the approach, namely, the need for complementary information in highly evaporative regions. In fact, the ANN using only SSM to predict RZSM has low performance when decoupling between the surface and root zones is observed. The application of ANN to obtain spatialized RZSM will require integrating remote sensing-based surface soil moisture in the future.

ACS Style

Roïya Souissi; Ahmad Al Bitar; Mehrez Zribi. Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe. Water 2020, 12, 3109 .

AMA Style

Roïya Souissi, Ahmad Al Bitar, Mehrez Zribi. Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe. Water. 2020; 12 (11):3109.

Chicago/Turabian Style

Roïya Souissi; Ahmad Al Bitar; Mehrez Zribi. 2020. "Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe." Water 12, no. 11: 3109.

Journal article
Published: 22 October 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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A new approach based on the change detection technique is proposed for the estimation of surface soil moisture (SSM) from a time series of radar measurements. A new index of reflectivity (IR) is defined that uses radar signals and Fresnel coefficients. This index is equal to 0 in the case of the smallest value of the Fresnel coefficient, corresponding to the driest conditions and the weakest radar signal, and is equal to 1 for the highest value of the Fresnel coefficient, corresponding to the wettest soil conditions and the strongest radar signal. The Integrated Equation Model (IEM) is used to simulate the behavior of radar signals as a function of soil moisture and roughness. This approach validates the greater usefulness of the IR compared with that of the commonly used index of SSM (ISSM), which assumes that the SSM varies linearly as a function of radar signal strength. The IR-based approach was tested using Sentinel-1 radar data recorded over three regions: Banizombou (Niger), Merguellil (Tunisia), and Occitania (France). The IR approach was found to perform better for the estimation of SSM than the ISSM approach based on comparisons with ground measurements over bare soils.

ACS Style

Mehrez Zribi; Myriam Foucras; Nicolas Baghdadi; Jerome Demarty; Sekhar Muddu. A New Reflectivity Index for the Retrieval of Surface Soil Moisture From Radar Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 818 -826.

AMA Style

Mehrez Zribi, Myriam Foucras, Nicolas Baghdadi, Jerome Demarty, Sekhar Muddu. A New Reflectivity Index for the Retrieval of Surface Soil Moisture From Radar Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):818-826.

Chicago/Turabian Style

Mehrez Zribi; Myriam Foucras; Nicolas Baghdadi; Jerome Demarty; Sekhar Muddu. 2020. "A New Reflectivity Index for the Retrieval of Surface Soil Moisture From Radar Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 818-826.

Journal article
Published: 24 August 2020 in Remote Sensing of Environment
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Radar data at C-band has shown great potential for the monitoring of soil and canopy hydric conditions of wheat crops. In this study, the C-band Sentinel-1 time series including the backscattering coefficients σ0 at VV and VH polarization, the polarization ratio (PR) and the interferometric coherence ρ are first analyzed with the support of experimental data gathered on three plots of irrigated winter wheat located in the Haouz plain in the center of Morocco covering five growing seasons. The results showed that ρ and PR are tightly related to the canopy development. ρ is also sensitive to soil preparation. By contrast, σ0 was found to be widely linked to changes in surface soil moisture (SSM) during the first growth stages when Leaf Area Index remains moderate (<1.5 m2/m2). In addition, drastic changes in the crop geometry associated to heading had a strong impact on the C-band σ0, in particular for VH polarization. The coupled water cloud and Oh models (WCM) were then calibrated and validated on the study sites. The comparison between the predicted and observed σ0 yielded a root mean square error (RMSE) values ranging from 1.50 dB to 2.02 dB for VV and between 1.74 dB to 2.52 dB for VH with significant differences occurring in the second part of the season after heading. Finally, new approaches based on the inversion of the WCM for SSM retrieval over wheat fields were proposed using Sentinel-1 radar data only. To this objective, the dry above-ground biomass (AGB) and the vegetation water content (VWC) were retrieved from the interferometric coherence and the PR. The relationships were then used as the vegetation descriptor in the WCM. The best retrieval results were obtained using the relationship between ρVV and the AGB (R and RMSE of 0.82, 0.05 m3/m3 respectively and no bias). The new retrieval approaches were then applied to a large database covering a rainfed field in Morocco and 18 plots of rainfed and irrigated wheat of the Kairouan plain (Tunisia) and compared to other classical techniques of SSM retrieval including simple linear relationships between SSM and σ0. The method based on the WCM and the ρVV-AGB relationships also provided with slightly better results than the others on the validation database (r = 0.75, RMSE = 0.06 m3/m3 and bias = 0.01 m3/m3 over the 18 plots of Tunisia) but the simple linear relationships performed also reasonably well (r = 0.62, RMSE = 0.07, bias = −0.01 in Tunisia for instance). This study opens perspectives for high resolution soil moisture mapping from Sentinel-1 data over south Mediterranean wheat crops and in fine, for irrigation scheduling and retrieval through the assimilation of these new products in an evapotranspiration model.

ACS Style

Nadia Ouaadi; Lionel Jarlan; Jamal Ezzahar; Mehrez Zribi; Saïd Khabba; Elhoussaine Bouras; Safa Bousbih; Pierre-Louis Frison. Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas. Remote Sensing of Environment 2020, 251, 112050 .

AMA Style

Nadia Ouaadi, Lionel Jarlan, Jamal Ezzahar, Mehrez Zribi, Saïd Khabba, Elhoussaine Bouras, Safa Bousbih, Pierre-Louis Frison. Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas. Remote Sensing of Environment. 2020; 251 ():112050.

Chicago/Turabian Style

Nadia Ouaadi; Lionel Jarlan; Jamal Ezzahar; Mehrez Zribi; Saïd Khabba; Elhoussaine Bouras; Safa Bousbih; Pierre-Louis Frison. 2020. "Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas." Remote Sensing of Environment 251, no. : 112050.

Journal article
Published: 21 August 2020 in Remote Sensing
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The Global Ecosystem Dynamics Investigation (GEDI) Light Detection And Ranging (LiDAR) altimetry mission was recently launched to the International Space Station with a capability of providing billions of high-quality measurements of vertical structures globally. This study assesses the accuracy of the GEDI LiDAR altimetry estimation of lake water levels. The difference between GEDI’s elevation estimates to in-situ hydrological gauge water levels was determined for eight natural lakes in Switzerland. The elevation accuracy of GEDI was assessed as a function of each lake, acquisition date, and the laser used for acquisition (beam). The GEDI elevation estimates exhibit an overall good agreement with in-situ water levels with a mean elevation bias of 0.61 cm and a standard deviation (std) of 22.3 cm and could be lowered to 8.5 cm when accounting for instrumental and environmental factors. Over the eight studied lakes, the bias between GEDI elevations and in-situ data ranged from -13.8 cm to +9.8 cm with a standard deviation of the mean difference ranging from 14.5 to 31.6 cm. Results also show that the acquisition date affects the precision of the GEDI elevation estimates. GEDI data acquired in the mornings or late at night had lower bias in comparison to acquisitions during daytime or over weekends. Even though GEDI is equipped with three identical laser units, a systematic bias was found based on the laser units used in the acquisitions. Considering the eight studied lakes, the beams with the highest elevation differences compared to in-situ data were beams 1 and 6 (standard deviations of -10.2 and +18.1 cm, respectively). In contrast, the beams with the smallest mean elevation difference to in-situ data were beams 5 and 7 (-1.7 and -2.5 cm, respectively). The remaining beams (2, 3, 4, and 8) showed a mean difference between -7.4 and +4.4 cm. The standard deviation of the mean difference, however, was similar across all beams and ranged from 17.2 and 22.9 cm. This study highlights the importance of GEDI data for estimating water levels in lakes with good accuracy and has potentials in advancing our understanding of the hydrological significance of lakes especially in data scarce regions of the world.

ACS Style

Ibrahim Fayad; Nicolas Baghdadi; Jean Stéphane Bailly; Frédéric Frappart; Mehrez Zribi. Analysis of GEDI Elevation Data Accuracy for Inland Waterbodies Altimetry. Remote Sensing 2020, 12, 2714 .

AMA Style

Ibrahim Fayad, Nicolas Baghdadi, Jean Stéphane Bailly, Frédéric Frappart, Mehrez Zribi. Analysis of GEDI Elevation Data Accuracy for Inland Waterbodies Altimetry. Remote Sensing. 2020; 12 (17):2714.

Chicago/Turabian Style

Ibrahim Fayad; Nicolas Baghdadi; Jean Stéphane Bailly; Frédéric Frappart; Mehrez Zribi. 2020. "Analysis of GEDI Elevation Data Accuracy for Inland Waterbodies Altimetry." Remote Sensing 12, no. 17: 2714.

Preprint content
Published: 02 July 2020
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In a context of major changes (climate, demography, economy, etc.), the Southern Mediterranean area faces serious challenges with intrinsically low, irregular and continuously decreasing water resources. In some regions, the proper dynamic of irrigated areas is very important so that it is needed to include it in future scenarios. A method for estimating the future evolution of irrigation water requirements is proposed and tested in the Tensift watershed, Morocco. Monthly synthetic crop coefficients (Kc) of the different irrigated areas were obtained from a time series of remote sensing observations. An empirical model using the synthetic Kc and rainfall was developed and fitted to the actual data. The model appears to be reliable with an average r2 of 0.69 for the observation period (2000–2016). The sub sampling tests suggested that a loss of performance (r2 = 0.45) is to be expected for two time periods after the observations (2050). This flexible system of equations has been used to reinterpret a local water management plan and to incorporate two downscaled climate change scenarios (RCP4.5 and RCP8.5). The examination of irrigation water requirements until 2050 revealed that the difference between the two climate scenarios was very small (

ACS Style

Michel Le Page; Younes Fakir; Lionel Jarlan; Aaron Boone; Brahim Berjamy; Saïd Khabba; Mehrez Zribi. Projection of irrigation water demand based on the simulation of synthetic crop coefficients and climate change. 2020, 2020, 1 -24.

AMA Style

Michel Le Page, Younes Fakir, Lionel Jarlan, Aaron Boone, Brahim Berjamy, Saïd Khabba, Mehrez Zribi. Projection of irrigation water demand based on the simulation of synthetic crop coefficients and climate change. . 2020; 2020 ():1-24.

Chicago/Turabian Style

Michel Le Page; Younes Fakir; Lionel Jarlan; Aaron Boone; Brahim Berjamy; Saïd Khabba; Mehrez Zribi. 2020. "Projection of irrigation water demand based on the simulation of synthetic crop coefficients and climate change." 2020, no. : 1-24.

Journal article
Published: 19 June 2020 in Remote Sensing
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Short-term freeze/thaw cycles, which mostly occur in the northern hemisphere across the majority of land surfaces, are reported to cause severe economic losses over broad areas of Europe and North America. Therefore, in order to assess the extent of frost damage in the agricultural sector, the objective of this study is to build an operational approach capable of detecting frozen plots at the plot scale in a near real-time scenario using Sentinel-1 (S1) data. C-band synthetic aperture radar (SAR) data show high potential for the detection of freeze/thaw surface states due to the significant alterations to the dielectric properties of the soil, which are distinctly observable in the backscattered signal. In this study, we propose an approach that relies on change detection in the high-resolution Sentinel-1 C-band SAR backscattered coefficients, to determine surface states at the plot scale as either frozen or unfrozen. A threshold analysis is first performed in order to determine the best thresholds for three distinct land cover classes, and for each polarization mode (VH, and VV). S-1 SAR data are then used to detect a plot’s surface state as either unfrozen, mild-to-moderately frozen or severely frozen. A temperature-based filter has also been applied at the end of the detection chain to eliminate false detections in the freezing detection algorithm due mainly to rainfall, irrigation, tillage, or signal noise. Our approach has been tested over two study sites in France, and the output results, using either VH or VV, compared qualitatively well with both in situ air temperature data and soil temperature data provided by ERA5-Land. Overall, our algorithm was able to detect all freezing episodes over the analyzed S-1 SAR time series, and with no false detections. Moreover, given the high-resolution aspect of S-1 SAR data, our algorithm is capable of mapping the local variation of freezing episodes at plot scale. This is in contrast with previous products that only offer coarser results across larger areas (low spatial resolution).

ACS Style

Ibrahim Fayad; Nicolas Baghdadi; Hassan Bazzi; Mehrez Zribi. Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data. Remote Sensing 2020, 12, 1976 .

AMA Style

Ibrahim Fayad, Nicolas Baghdadi, Hassan Bazzi, Mehrez Zribi. Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data. Remote Sensing. 2020; 12 (12):1976.

Chicago/Turabian Style

Ibrahim Fayad; Nicolas Baghdadi; Hassan Bazzi; Mehrez Zribi. 2020. "Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data." Remote Sensing 12, no. 12: 1976.

Journal article
Published: 19 May 2020 in Remote Sensing
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Although the real timing and flow rates used for crop irrigation are controlled at the scale of individual plots by the irrigator, they are not generally known by the farm upper management. This information is nevertheless essential, not only to compute the water balance of irrigated plots and to schedule irrigation, but also for the management of water resources at regional scales. The aim of the present study was to detect irrigation timing using time series of surface soil moisture (SSM) derived from Sentinel-1 radar observations. The method consisted of assessing the direction of change of surface soil moisture (SSM) between observations and a water balance model, and to use thresholds to be calibrated. The performance of the approach was assessed on the F-score quantifying the accuracy of the irrigation event detections and ranging from 0 (none of the irrigation timing is correct) to 100 (perfect irrigation detection). The study focused on five irrigated and one rainfed plot of maize in South-West France, where the approach was tested using in situ measurements and surface soil moisture (SSM) maps derived from Sentinel-1 radar data. The use of in situ data showed that (1) irrigation timing was detected with a good accuracy (F-score in the range (80–83) for all plots) and (2) the optimal revisit time between two SSM observations was 2–4 days. The higher uncertainties of microwave SSM products, especially when the crop is well developed (normalized difference of vegetation index (NDVI) > 0.7), degraded the score (F-score = 69), but various possibilities of improvement were discussed. This paper opens perspectives for the irrigation detection at the plot scale over large areas and thus for the improvement of irrigation water management.

ACS Style

Michel Le Page; Lionel Jarlan; Marcel M. El Hajj; Mehrez Zribi; Nicolas Baghdadi; Aaron Boone. Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products. Remote Sensing 2020, 12, 1621 .

AMA Style

Michel Le Page, Lionel Jarlan, Marcel M. El Hajj, Mehrez Zribi, Nicolas Baghdadi, Aaron Boone. Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products. Remote Sensing. 2020; 12 (10):1621.

Chicago/Turabian Style

Michel Le Page; Lionel Jarlan; Marcel M. El Hajj; Mehrez Zribi; Nicolas Baghdadi; Aaron Boone. 2020. "Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products." Remote Sensing 12, no. 10: 1621.

Journal article
Published: 04 May 2020 in Remote Sensing
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In the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decision tree-based method relying on the change detection in the S1 backscattering coefficients at plot scale. First, the behavior of the S1 backscattering coefficients following irrigation events has been analyzed at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France), and Catalonia (northeast Spain). To eliminate the uncertainty between rainfall and irrigation, the S1 synthetic aperture radar (SAR) signal and the soil moisture estimations at grid scale (10 km × 10 km) have been used. Then, a tree-like approach has been constructed to detect irrigation events at each S1 date considering additional filters to reduce ambiguities due to vegetation development linked to the growth cycle of different crops types as well as the soil surface roughness. To enhance the detection of irrigation events, a filter using the normalized differential vegetation index (NDVI) obtained from Sentinel-2 optical images has been proposed. Over the three study sites, the proposed method was applied on all possible S1 acquisitions in ascending and descending modes. The results show that 84.8% of the irrigation events occurring over agricultural plots in Montpellier have been correctly detected using the proposed method. Over the Catalonian site, the use of the ascending and descending SAR acquisition modes shows that 90.2% of the non-irrigated plots encountered no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. Results over Catalonia also show that the proposed method allows the discrimination between irrigated and non-irrigated plots with an overall accuracy of 85.9%. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season where two and more irrigation events have been detected for 90% of the irrigated plots. The novelty of the proposed method resides in building an effective unsupervised tool for near real-time detection of irrigation events at plot scale independent of the studied geographical context.

ACS Style

Hassan Bazzi; Nicolas Baghdadi; Ibrahim Fayad; Mehrez Zribi; Hatem Belhouchette; Valérie Demarez. Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data. Remote Sensing 2020, 12, 1456 .

AMA Style

Hassan Bazzi, Nicolas Baghdadi, Ibrahim Fayad, Mehrez Zribi, Hatem Belhouchette, Valérie Demarez. Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data. Remote Sensing. 2020; 12 (9):1456.

Chicago/Turabian Style

Hassan Bazzi; Nicolas Baghdadi; Ibrahim Fayad; Mehrez Zribi; Hatem Belhouchette; Valérie Demarez. 2020. "Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data." Remote Sensing 12, no. 9: 1456.

Editorial
Published: 30 March 2020 in Remote Sensing
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Soil moisture is a key parameter when it comes to understanding the processes related to the water cycle on continental surfaces (infiltration, evapotranspiration, runoff, etc

ACS Style

Mehrez Zribi; Clément Albergel; Nicolas Baghdadi. Editorial for the Special Issue “Soil Moisture Retrieval using Radar Remote Sensing Sensors”. Remote Sensing 2020, 12, 1100 .

AMA Style

Mehrez Zribi, Clément Albergel, Nicolas Baghdadi. Editorial for the Special Issue “Soil Moisture Retrieval using Radar Remote Sensing Sensors”. Remote Sensing. 2020; 12 (7):1100.

Chicago/Turabian Style

Mehrez Zribi; Clément Albergel; Nicolas Baghdadi. 2020. "Editorial for the Special Issue “Soil Moisture Retrieval using Radar Remote Sensing Sensors”." Remote Sensing 12, no. 7: 1100.

Preprint content
Published: 23 March 2020
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An accurate knowledge of irrigation timing and rate is essential to compute the water balance of irrigated plots. However, at the plot scale irrigation is a data essentially known by the irrigator. These data do not go up to higher management scales, thus limiting both the management of water resources on a regional scale and the development of irrigation decision support tools at the farm scale. The study focuses on 6 experimental plots in the south-west of France. The new method consists in assessing surface soil moisture (SSM) change between observations and a water balance model. The approach was tested using both in situ measurements and surface soil moisture (SSM) maps derived from Sentinel-1 radar data. The score is obtained by assessing if the irrigation event is detected within +/- three days. The use of in situ SSM showed that: (1) the best revisit time between two SSM observations is 3 days; short gaps is subject to uncertainties while longer gap miss possible SSM variations; (2) in general, higher rates (>20mm) of irrigation are well identified while it is very difficult to identify irrigation event when it is raining or when irrigation rates are small (<10mm). When using the SSM microwave product, the performances are degraded but are still acceptable given the discontinuity of irrigation events: 34% of absolute error and a bias of 5% for the whole season. Although high vegetation cover degrades the SSM absolute estimates, the dynamic appeared to be in accordance with in-situ measurements.

ACS Style

Michel Le Page; Lionel Jarlan; Aaron Boone; Mohammad El Hajj; Nicolas Baghdadi; Mehrez Zribi. Detection of irrigation events on maize plots using sentinel-1 soil moisture products. 2020, 1 .

AMA Style

Michel Le Page, Lionel Jarlan, Aaron Boone, Mohammad El Hajj, Nicolas Baghdadi, Mehrez Zribi. Detection of irrigation events on maize plots using sentinel-1 soil moisture products. . 2020; ():1.

Chicago/Turabian Style

Michel Le Page; Lionel Jarlan; Aaron Boone; Mohammad El Hajj; Nicolas Baghdadi; Mehrez Zribi. 2020. "Detection of irrigation events on maize plots using sentinel-1 soil moisture products." , no. : 1.

Preprint content
Published: 23 March 2020
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Soil texture is a key parameter in agricultural processes and an important measure for agricultural prediction, water cycle, filtering of pollutants and carbon storage. Besides, its estimation is essential for agronomists, hydrologists, geologists and environmentalists and for modeling in these application areas. Several studies have been based on understanding and modeling the biological, physical and chemical processes in the soil. Regarding the texture of the soil, few researches propose soil texture spatialization, and are generally based on ground measurements. Among other things, field observations or laboratory analyzes are very expensive and are not very representative. Indeed, the soil texture presents a strong heterogeneity even at the scale of a field. It is then necessary to use precise and spatialized information on soils.

These methods are generally based on remote sensing data and particularly optical data to restore soil component. However, these techniques are strongly affected by atmospheric conditions. This constraint is not valid for Radar sensors (Radio Detection And Ranging). Radar data are mainly sensitive to soil moisture and soil roughness, and has also been evaluated for its ability to perform texture measurements.

The aim of this study is evaluate the potential of these techniques based on optical and radar data for soil texture estimation. By its composition, its structure, its texture and its porosity, soil moisture is strongly influenced by the soil nature. With the arrival of Sentinel-1 (S-1) and Sentinel-2 (S-2) ESA spatial missions, data are acquired with high spatial and temporal resolution between July and early December 2017, on a semi-arid area in central Tunisia. This study is therefore conducted using S-2 SWIR (Short-Wave Infrared) bands (B11 and B12, most sensitive to clay) and soil moisture products derived from radar data. And algorithms based on the support vector machine (SVM) and random forest (RF) methods are proposed for the classification and mapping of clay content.

In order to evaluate the approach and determine the adequate data (between optical and radar data) allowing to precisely characterize the clay content, a cross-validation was used. The SWIR bands lead to less satisfactory outcomes compared to soil moisture. With an overall accuracy of approximately 65%, soil moisture achieved the best performance for estimating soil texture. The results also showed that RF and SVM are robust classifiers for texture estimation despite the small number of training data. However, RF displays greater accuracy and speed of simulation compared to SVM.

ACS Style

Safa Bousbih; Mehrez Zribi; Zohra Lili-Chabaane; Nicolas Baghdadi; Azza Gorrab; Nadhira Ben Aissa. Evaluation of the potential of Sentinel-1 and Sentinel-1 data for clay content mapping. 2020, 1 .

AMA Style

Safa Bousbih, Mehrez Zribi, Zohra Lili-Chabaane, Nicolas Baghdadi, Azza Gorrab, Nadhira Ben Aissa. Evaluation of the potential of Sentinel-1 and Sentinel-1 data for clay content mapping. . 2020; ():1.

Chicago/Turabian Style

Safa Bousbih; Mehrez Zribi; Zohra Lili-Chabaane; Nicolas Baghdadi; Azza Gorrab; Nadhira Ben Aissa. 2020. "Evaluation of the potential of Sentinel-1 and Sentinel-1 data for clay content mapping." , no. : 1.