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Michel Le Page
CESBIO (CNES/CNRS/INRAE/IRD/UPS), 18 av. Edouard Belin, bpi 2801, CEDEX 09, 31401 Toulouse, France

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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: 07 July 2021 in Remote Sensing
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Agricultural water use represents more than 70% of the world’s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R > 0.98, RMSE < 32 mm and bias < 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = −18.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.

ACS Style

Nadia Ouaadi; Lionel Jarlan; Saïd Khabba; Jamal Ezzahar; Michel Le Page; Olivier Merlin. Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region. Remote Sensing 2021, 13, 2667 .

AMA Style

Nadia Ouaadi, Lionel Jarlan, Saïd Khabba, Jamal Ezzahar, Michel Le Page, Olivier Merlin. Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region. Remote Sensing. 2021; 13 (14):2667.

Chicago/Turabian Style

Nadia Ouaadi; Lionel Jarlan; Saïd Khabba; Jamal Ezzahar; Michel Le Page; Olivier Merlin. 2021. "Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region." Remote Sensing 13, no. 14: 2667.

Journal article
Published: 16 March 2021 in Remote Sensing
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This study aims to evaluate a remote sensing-based approach to allow estimation of the temporal and spatial distribution of crop evapotranspiration (ET) and irrigation water requirements over irrigated areas in semi-arid regions. The method is based on the daily step FAO-56 Soil Water Balance model combined with a time series of basal crop coefficients and the fractional vegetation cover derived from high-resolution satellite Normalized Difference Vegetation Index (NDVI) imagery. The model was first calibrated and validated at plot scale using ET measured by eddy-covariance systems over wheat fields and olive orchards representing the main crops grown in the study area of the Haouz plain (central Morocco). The results showed that the model provided good estimates of ET for wheat and olive trees with a root mean square error (RMSE) of about 0.56 and 0.54 mm/day respectively. The model was then used to compare remotely sensed estimates of irrigation requirements (RS-IWR) and irrigation water supplied (WS) at plot scale over an irrigation district in the Haouz plain through three growing seasons. The comparison indicated a large spatio-temporal variability in irrigation water demands and supplies; the median values of WS and RS-IWR were 130 (175), 117 (175) and 118 (112) mm respectively in the 2002–2003, 2005–2006 and 2008–2009 seasons. This could be attributed to inadequate irrigation supply and/or to farmers’ socio-economic considerations and management practices. The findings demonstrate the potential for irrigation managers to use remote sensing-based models to monitor irrigation water usage for efficient and sustainable use of water resources.

ACS Style

Mohamed Kharrou; Vincent Simonneaux; Salah Er-Raki; Michel Le Page; Saïd Khabba; Abdelghani Chehbouni. Assessing Irrigation Water Use with Remote Sensing-Based Soil Water Balance at an Irrigation Scheme Level in a Semi-Arid Region of Morocco. Remote Sensing 2021, 13, 1133 .

AMA Style

Mohamed Kharrou, Vincent Simonneaux, Salah Er-Raki, Michel Le Page, Saïd Khabba, Abdelghani Chehbouni. Assessing Irrigation Water Use with Remote Sensing-Based Soil Water Balance at an Irrigation Scheme Level in a Semi-Arid Region of Morocco. Remote Sensing. 2021; 13 (6):1133.

Chicago/Turabian Style

Mohamed Kharrou; Vincent Simonneaux; Salah Er-Raki; Michel Le Page; Saïd Khabba; Abdelghani Chehbouni. 2021. "Assessing Irrigation Water Use with Remote Sensing-Based Soil Water Balance at an Irrigation Scheme Level in a Semi-Arid Region of Morocco." Remote Sensing 13, no. 6: 1133.

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: 07 October 2020 in Agronomy
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In this study, a simple model, based on a light-use-efficiency model, was developed in order to estimate growth and yield of the irrigated winter wheat under semi-arid conditions. The originality of the proposed method consists in (1) the modifying of the expression of the conversion coefficient (εconv) by integrating an appropriate stress threshold (ksconv) for triggering irrigation, (2) the substitution of the product of the two maximum coefficients of interception (εimax) and conversion (εconv_max) by a single parameter εmax, (3) the modeling of εmax as a function of the Cumulative Growing Degree Days (CGDD) since sowing date, and (4) the dynamic expression of the harvest index (HI) as a function of the CGDD and the final harvest index (HI0) depending on the maximum value of the Normalized Difference Vegetation Index (NDVI). The calibration and validation of the proposed model were performed based on the observations of wheat dry matter (DM) and grain yield (GY) which were collected on the R3 irrigated district of the Haouz plain (center of Morocco), during three agricultural seasons. Further, the outputs of the simple model were also evaluated against the AquaCrop model estimates. The model calibration allowed the parameterization of εmax in four periods according to the wheat phenological stages. By contrast, a linear evolution was sufficient to represent the relationship between HI and CGDD. For the model validation, the obtained results showed a good agreement between the estimated and observed values with a Root Mean Square Error (RMSE) of about 1.07 and 0.57 t/ha for DM and GY, respectively. These correspond to a relative RMSE of about 19% for DM and 20% for GY. Likewise, although of its simplicity, the accuracy of the proposed model seems to be comparable to that of the AquaCrop model. For GY, R2, and RMSE values were respectively 0.71 and 0.44 t/ha for the developed approach and 0.88 and 0.37 t/ha for AquaCrop. Thus, the proposed simple light-use-efficiency model can be considered as a useful tool to correctly reproduce DM and GY values.

ACS Style

Saïd Khabba; Salah Er-Raki; Jihad Toumi; Jamal Ezzahar; Bouchra Ait Hssaine; Michel Le Page; Abdelghani Chehbouni. A Simple Light-Use-Efficiency Model to Estimate Wheat Yield in the Semi-Arid Areas. Agronomy 2020, 10, 1524 .

AMA Style

Saïd Khabba, Salah Er-Raki, Jihad Toumi, Jamal Ezzahar, Bouchra Ait Hssaine, Michel Le Page, Abdelghani Chehbouni. A Simple Light-Use-Efficiency Model to Estimate Wheat Yield in the Semi-Arid Areas. Agronomy. 2020; 10 (10):1524.

Chicago/Turabian Style

Saïd Khabba; Salah Er-Raki; Jihad Toumi; Jamal Ezzahar; Bouchra Ait Hssaine; Michel Le Page; Abdelghani Chehbouni. 2020. "A Simple Light-Use-Efficiency Model to Estimate Wheat Yield in the Semi-Arid Areas." Agronomy 10, no. 10: 1524.

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: 06 February 2019 in Scientific Reports
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In a context of high stress on water resources and agricultural production at the global level, together with climate change marked by an increase in the frequency of these events, drought is considered to be a strong threat both socially and economically. The Mediterranean region is a hot spot of climate change; it is also characterized by a scarcity of water resources that places intense pressure on agricultural productivity. This article analyzes the potential for using multiple remote sensing tools in the quantification and predictability of drought in Northwest Africa. Three satellite products are considered: the Normalized Difference Vegetation Index (NDVI), Soil Moisture Index (SWI), and Land Surface Temperature (LST). A discussion of the variability of these products and their inter-correlation is presented, illustrating a generally high consistency between them. Statistical anomaly indices are then computed and a drought severity mapping is presented. The results illustrate in particular a high percentage of dry conditions in the region studied during the last ten years (2007–2017). Finally, we propose the use of the analog statistical approach to identify similar evolutions of the three variables in the past. Although this technique is not a forecast, it provides a strong indication of the plausible future trajectory of a given hydrological season.

ACS Style

Michel Le Page; Mehrez Zribi. Analysis and Predictability of Drought In Northwest Africa Using Optical and Microwave Satellite Remote Sensing Products. Scientific Reports 2019, 9, 1 -13.

AMA Style

Michel Le Page, Mehrez Zribi. Analysis and Predictability of Drought In Northwest Africa Using Optical and Microwave Satellite Remote Sensing Products. Scientific Reports. 2019; 9 (1):1-13.

Chicago/Turabian Style

Michel Le Page; Mehrez Zribi. 2019. "Analysis and Predictability of Drought In Northwest Africa Using Optical and Microwave Satellite Remote Sensing Products." Scientific Reports 9, no. 1: 1-13.