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The planning and management of groundwater in the absence of in situ climate data is a delicate task, particularly in arid regions where this resource is crucial for drinking water supplies and irrigation. Here the motivation is to evaluate the role of remote sensing data and Input feature selection method in the Long Short Term Memory (LSTM) neural network for predicting groundwater levels of five wells located in different hydrogeological contexts across the Oum Er-Rbia Basin (OER) in Morocco: irrigated plain, floodplain and low plateau area. As input descriptive variable, four remote sensing variables were used: the Integrated Multi-satellite Retrievals (IMERGE) Global Precipitation Measurement (GPM) precipitation, Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), MODIS land surface temperature (LST), and MODIS evapotranspiration. Three LSTM models were developed, rigorously analyzed and compared. The LSTM-XGB-GS model, was optimized using the GridsearchCV method, and uses a single remote sensing variable identified by the input feature selection method XGBoost. Another optimized LSTM model was also constructed, but uses the four remote sensing variables as input (LSTM-GS). Additionally, a standalone LSTM model was established and also incorporating the four variables as inputs. Scatter plots, violin plots, Taylor diagram and three evaluation indices were used to verify the performance of the three models. The overall result showed that the LSTM-XGB-GS model was the most successful, consistently outperforming both the LSTM-GS model and the standalone LSTM model. Its remarkable accuracy is reflected in high R2 values (0.95 to 0.99 during training, 0.72 to 0.99 during testing) and the lowest RMSE values (0.03 to 0.68 m during training, 0.02 to 0.58 m during testing) and MAE values (0.02 to 0.66 m during training, 0.02 to 0.58 m during testing). The LSTM-XGB-GS model reveals how hydrodynamics, climate, and land-use influence groundwater predictions, emphasizing correlations like irrigated land-temperature link and floodplain-NDVI-evapotranspiration interaction for improved predictions. Finally, this study demonstrates the great support that remote sensing data can provide for groundwater prediction using ANN models in conditions where in situ data are lacking.

Context and background: Wheat is one of the oldest crops in the world and has always been one of the most important staple foods for millions of people around the world, especially in North Africa, where wheat is the most dominant crop. The importance of wheat yield estimation is well known in agricultural management and policy making at regional and national levels. In semi-arid areas such as the case of Morocco, an operational cereal yield estimating system that could assist decision makers in planning annual imports is needed. In some developed countries, several effective tools are now available to monitor crops and optimize farm-level decisions by combining crop simulation models with seasonal forecasts. However, few tools are used to effectively manage crops at the farm level to cope with climate variability and risk. Goal and objectives: The following article presents an overview of current methods used for wheat yield estimation in the world and in Morocco Methodology: Various sections describing traditional methods, simulation models, and remote sensing. Then a section is devoted to the estimation methods used in Morocco and their efficiencies. Results: This article is very useful for researchers working on this subject because it brings together all the methods of estimating wheat yields worldwide and classifies them into categories and then situates Morocco, which is a relevant example of a North African country that is a leader in the use of spatial techniques and in the monitoring of crops, and wheat in particular.

Study region: Atlas Mountains located in Morocco. Study focus: Mountainous regions constitute an area of water production, while water is used in downstream plains. In Central Morocco, the Atlas Mountains represent the most important water supply in the country. The solid part of precipitation forms seasonal snowpack. Snowmelt is important for the water supply for different uses in neighbouring plains. Accurate knowledge of snow water equivalent is key information needed by policy-makers to help design and implement appropriate allocation strategies for water resource management. The objective of this paper is to provide a summary of our research activities on snow hydrology in the Atlas Mountains during the past twenty years. The approach combines in situ measurements, remote sensing, and modeling. New hydrological insights for the region: Following a description of the context of the Moroccan Atlas Mountains and the experimental network, an overview of the main results obtained is presented: the characterization of the spatiotemporal dynamics of snow cover; the impact of the North Atlantic Oscillation on the snow-covered area; the snowmelt contribution to the flows of the Atlas rivers; the contribution of snowmelt to surface and groundwater recharge and the quantification of climate change impacts on snow and associated runoff from the Atlas Mountains. We also present challenges and future research perspectives within this topic.

This study aimed to assess the groundwater potential zones (GWPZ) in northern Morocco's Upper Oum Er-Rbia Basin (UOER). In such a semi-arid context, groundwater resources are crucial to sustaining essential human activities, but they are under stress due to increased overuse and climate change. This investigation utilized remote sensing in a GIS framework along with a multi-criteria decision analysis (MCDA) technique using the analytic hierarchy process (AHP) for the first time in this region. Ten thematic layers were created, representing the most significant parameters, which were then weighted and overlaid. The output map shows five levels of potential: very low, low, medium, high, and very high, covering 12%, 19%, 20%, 27%, and 22% of the basin area, respectively. Comparing the assessment results to the borehole yield, the AUC-ROC curve showed a value of 84.5%, which testifies to the excellent performance of the methodology used. Of the 10 criteria used, lithology was shown to be the most significant factor, followed by LULC, slope, and geomorphology. The study results offer an extensive insight into the hydrogeological potential of the UOER basin. These findings are essential for decision-makers and encourage the efficient utilization of groundwater resources, thus supporting broader objectives of sustainable development. [ABSTRACT FROM AUTHOR]

Context and Background Estimating cereal yields in Morocco is still based on the traditional statistical method known as "CCE" crop cutting, which is objective but tedious and requires a lot of logistics, despite the fact that in 2012 a system for predicting cereal yields in Morocco called "CGMS_MA" was introduced to support this method; this system does not allow yields to be estimated at plot level, given the coarse resolution of the integrated satellite images (NDVI_AVHR), which is 1.1km. The use of finer resolution satellite images and more precise techniques is therefore very much in demand. Goal and Objectives The main objective of this research project is to model wheat yield at plot level using phenological parameters derived from SENTINEL 2 'S2' satellite images in semi-arid areas of Morocco. Methodology Two approaches were adopted for this modelling: - An MRL approach based on STEPWISE linear regression using phenological parameters from NDVI-SENTINEL2 satellite images. - An MML/MMA approach based on the use of Artificial intelligence (Maching Learning/Deep Learning) for yield modelling. - The 1st MRL modelling based on STEPWISE linear regression revealed performance indicators R² and RMSE testing a strong correlation between predicted and observed yield (R² = 0.75; RMSE= 7.08q/ha). The estimated wheat yields were validated using the k-fold cross-validation method. The MRL model explained 75% of the spatial variation in yield, with a root mean square error (RMSE) of 3.45 qx/ha. - The 2nd model was designed to improve on the first model by incorporating artificial intelligence techniques. The results The results obtained showed that the use of these techniques gives good results and the performances are higher: The R² correlation coefficient is 0.96 for the MLP deep-Learning algorithm and 0.94 for the Maching Learning algorithms (kNN, RF and CHAID), whereas it is lower for the STEPWISE regression. Finally, these MML/MAA techniques coupled with remotely sensed phenological data from LSP allow good modelling of wheat yields and good crop monitoring; they can form the basis of a high-performance system for estimating wheat yields, especially in the semi-arid zones of Morocco where the crop is highly dependent on climatic variations. [ABSTRACT FROM AUTHOR]

Context and background: Wheat is one of the oldest crops in the world and has always been one of the most important staple foods for millions of people around the world, especially in North Africa, where wheat is the most dominant crop. The importance of wheat yield estimation is well known in agricultural management and policy making at regional and national levels. In semi-arid areas such as the case of Morocco, an operational cereal yield estimating system that could assist decision makers in planning annual imports is needed. In some developed countries, several effective tools are now available to monitor crops and optimize farm-level decisions by combining crop simulation models with seasonal forecasts. However, few tools are used to effectively manage crops at the farm level to cope with climate variability and risk. Goal and objectives: The following article presents an overview of current methods used for wheat yield estimation in the world and in Morocco Methodology: Various sections describing traditional methods, simulation models, and remote sensing. Then a section is devoted to the estimation methods used in Morocco and their efficiencies. Results: This article is very useful for researchers working on this subject because it brings together all the methods of estimating wheat yields worldwide and classifies them into categories and then situates Morocco, which is a relevant example of a North African country that is a leader in the use of spatial techniques and in the monitoring of crops, and wheat in particular. [ABSTRACT FROM AUTHOR]

The present study aimed to propose an operational approach for estimating surface soil moisture from Moderate Resolution Imaging Spectroradiometer (MODIS) data by considering diverse environmental variables such as Normalized Difference Vegetation Index (NDVI), land surface temperature (Ts), evapotranspiration, topographic parameters (elevation and aspect) and soil texture (clay, loam and silt). A soil moisture index (SMI) derived from NDVI-Ts space is combined to all other variables, based on stepwise multiple regression, to develop a new SSMC model. Performance of this model was assessed using field-measured data of SSM. Accuracy was performed by the k-fold cross validation method, it showed a R2 (coefficients of determination) of 0.70, RMSE of 1.58% and unRMSE of 0.5%. In addition, the results of the developed model were compared with another soil moisture model SMM proposed in the irrigated perimeter of Tadla (Morocco), and revealed that the established model provided effectiveness results in the study areas. [ABSTRACT FROM AUTHOR]

The availability of concurrently high spatiotemporal resolution remote sensing data is highly desirable as they represent a key element for effective monitoring in various environmental applications. However, due to the tradeoff between the spatial resolution and acquisition frequency of current satellites, such data are still lacking. Many studies have been undertaken trying to overcome these problems; however, a couple of long-standing limitations remain, including accommodating abrupt temporal changes, dealing with complex and heterogeneous landscapes, and integrating other satellite datasets as well. Accordingly, this paper proposes a deep learning spatiotemporal data fusion approach based on Very Deep Super-Resolution (VDSR) to fuse the NDVI retrievals from Sentinel-2 and Landsat 8 images. The performances of VDSR are analyzed in comparison with those of two other classical methods, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method. The results obtained indicate that VDSR outperforms other data fusion algorithms as it generated the least blurred images and the most accurate predictions of synthetic NDVI values, particularly in areas with heterogeneous landscapes and abrupt land-cover changes. The proposed algorithm has broad prospects to improve near-real-time agricultural monitoring purposes and derivation of crop status conditions in the field-scale. [ABSTRACT FROM AUTHOR]

Remote sensing offers spatially explicit and temporally continuous observational data of various land surface parameters such as vegetation index, land surface temperature, soil moisture, leaf area index, and evapotranspiration, which can be widely leveraged for various applications at different scales and contexts. One of the main applications is agricultural monitoring, where a smart system based on precision agriculture requires a set of satellite images with a high resolution, both in time and space to capture the phenological stages and fine spatial details, especially in landscapes with various spatial heterogeneity and temporal variation. These requirements sometimes cannot be provided by a single sensor due to the trade-off required between spatial and temporal resolutions and/or the influence of cloud cover. The data availability of new generation multispectral sensors of Landsat-8 (L8) and Sentinel-2 (S2) satellites offers unprecedented options for such applications. Given this, the current study aims to display how the synergistic use of these optical sensors can efficiently support such an application. Herein, this study proposes a deep learning spatiotemporal data fusion method to fill the need for predicting a dense time series of vegetation index with fine spatial resolution. The results show that the developed method creates more accurate fused NDVI time-series data that were able to derive phenological stages and characteristics in single-crop fields, while keeps more spatial details in such a heterogeneous landscape. [ABSTRACT FROM AUTHOR]

Surface soil moisture content (SSMC) monitoring constitutes an important parameter to estimate crop water requirements, especially in arid and semi-arid areas. Remote sensing became a useful tool for estimating SSMC. Two approaches were applied for monitoring the SSMC during the 2013/14 cropping season in the irrigated perimeter of Tadla (Morocco) using multispectral bands of Landsat-8 OLI images. The first approach examined the potential of visible and short-wave infrared drought index (VSDI), normalized multi-band drought index (NMDI) and short-wave infrared water stress index (SIWSI), to retrieve SSMC. The second approach attempted to develop a new SSMC model based on evaluation of the correlations between multispectral bands and measured SSMC using a stepwise multiple regression analysis. Results showed that the established model is highly correlated with the measured SSMC at all crop growth stages with R2 of 0.87, 0.85 and 0.89, for bare soil, partially covered and entirely covered by vegetation, respectively. [ABSTRACT FROM AUTHOR]

In arid and semi-arid areas, rainfall is often characterized by a strong spatial and temporal variability. These environmental factors, combined with the sparsity of the measurement networks in developing countries, constitute real constraints for water resources management. In recent years, several spatial rainfall measurement sources have become available, such as TRMM data (Tropical Rainfall Measurement Mission). In this study, the TRMM 3B42 Version 7 product was evaluated using rain gauges measurements from 19 stations in the Oum-Er-Bia (OER) basin located in the center of Morocco. The relevance of the TRMM product was tested by direct comparison with observations at different time scales (daily, monthly, and annual) between 1998 and 2010. Results show that the satellite product provides poor estimations of rainfall at the daily time scale giving an average Pearson correlation coefficient (r) of 0.2 and average Root Mean Square Error (RMSE) of 10 mm. However, the accuracy of TRMM rainfall is improved when temporally averaged to monthly time scale (r of 0.8 and RMSE of 28 mm) or annual time scale (r of 0.71 and RMSE of 157 mm). Moreover, improved correlation with observed data was obtained for data spatially averaged at the watershed scale. Therefore, at the monthly and annual time scales, TRMM data can be a useful source of rainfall data for water resources monitoring and management in ungauged basins in semi-arid regions. [ABSTRACT FROM AUTHOR]

Recent efforts have been concentrated in the development of models to understand and predict the impact of environmental changes on hydrological cycle and water resources in arid and semi-arid regions. In this context, remote sensing data have been widely used to initialize, to force, or to control the simulations of these models. However, for several reasons, including the difficulty in establishing relationships between observational and model variables, the potential offered by satellite data has not been fully used. As a matter of fact, a few hydrological studies that use remote sensing data emanating from different sources (sensors, platforms) have been performed. In this context, the SUDMED programme has been designed in 2002 to address the issue of improving our understanding about the hydrological functioning of the Tensift basin, which is a semi-arid basin situated in central Morocco. The first goal is model development and/or refinement, for investigating the hydrological responses to future scenario about climate change and human pressure. The second aim is the effective use of remote sensing observations in conjunction with process models, to provide operational prognostics for improving water-resource management. The objective of this paper is to present the SUDMED programme, its objectives, and its thrust areas, and to provide an overview of the results obtained in the first phase of the programme (2002-2006). Finally, the lessons learned, future objectives, and unsolved issues are presented. [ABSTRACT FROM AUTHOR]

Abstract: The High-Atlas mountainous region in Morocco is a true water tower for the neighbouring arid plains, where the water resources are intensively and increasingly subjected to exploitation for agriculture and tourism. In order to manage this resource sustainably, it is necessary to describe accurately all the processes that contribute to the hydrological cycle of the area, and, in particular, to know the respective contributions of liquid and solid precipitations to runoff. In this context, a seven-year time series of SPOT-VEGETATION images is used for mapping snow-covered areas. The spatial and temporal variations of the snow cover are analyzed for the entire High-Atlas region as well as by altitudinal zones. The spatial distribution of snow-covered areas appears logically controlled by elevation, and its temporal fluctuations can be clearly used to identify dry and wet seasons. In addition, a possible control of snowfalls by the Northern Atlantic climate variability, and, in particular, the North Atlantic Oscillation, is highlighted. Finally, this study shows how satellite remote sensing can be useful for the long-term observation of the intra- and inter-annual variability of snowpacks in rather inaccessible regions where the network of meteorological stations is deficient. [Copyright &y& Elsevier]

Science is the seed of a decent life, with which we sow hope in the present and which we irrigate with the perfecting of good deeds. It is even crucial in the Mediterranean southern frontiers where the cultural erosion dissolves the structure of a society abandoned by the arms and brains of its youth. Soil-water-vegetation crisis should not be underestimated; coupled with socioeconomic congestion it would lead to an irremediable crash. Here, we show that the first and most difficult step to face soil degradation is to cultivate the right idea and develop it into a well-established community culture. We found in northern Morocco that 94.5% of farmers have no qualification and 82.6% of them act in a way that worsens soil degradation even if they are aware of the severity of the problem. This confused perception of ideas originates inappropriate labour behaviours non-aligned with public actions. Our results show that the impact of this is a high potential regional erosion rate of 27.7 t/ha/year which is equivalent to a massive potential gross amount of soil loss of 44.3 Mt/year. We show that this leads to an overall vegetation decrease related mainly to the anthropogenic pressure then to climate and lithology. We anticipate that the solution must be comprehensive, participatory, strategic and innovative, led by education and scientific research (Citizen Science) and involving all actors equally. In its broad context, the only path to achieve the coordination and alignment of actions would be through a gradual change of perception and involvement based on a time-consuming culture of assimilation and acceptance rather than a culture of rapid reform. Unlabelled Image • Potential soil erosion, phenological dynamics and farmers' perception were studied. • High soil loss rate of 27.7 t/ha/y equal to a potential gross amount of 44.3 Mt/y. • Phenological assessment show negative trend and vegetation production decrease. • Most farmers lack knowledge on erosion control measures and soil conservation. • Citizen Science and unified perception to align Public-Farmers protective actions [ABSTRACT FROM AUTHOR]