MODELING WHEAT YIELD BY USING PHENOLOGYCAL METRICS DERIVED FROM SENTINEL2 IN ARID AND SEMI-ARID REGIONS- A case study in MOROCCO-

ABSTRACT  Context and background Wheat is one of the oldest cultivated plants in the world and has always been one of the most important staples for millions of people around the world and especially in North Africa, where wheat is the most used crop for typical food industry. Thus, an operational crop production system is needed to help decision makers make early estimates of potential food availability Yield estimation using remote sensing data has been widely studied, but such information is generally scarce in arid and semi-arid regions such as North Africa, where interannual variations in climatic factors, and spatial variability in particular, are major risks to food security.Goal and Objectives: The aim of this study is to develop a model to estimate wheat yield based on phenological metrics derived from SENTINEL-2 NDVI images in order to generalize a spatial model to estimate wheat yields in Morocco's semi-arid conditionsMethodology:The 10 m NDVI time series was integrated into TIMESAT software to extract wheat phenology-related metrics during the 2018-2019 agricultural season, the period in which ground truth data was collected.  Through the multiple stepwise regression method, all phenological metrics were used to predict wheat yield. Moreover, the accuracy and stability of produced models were evaluated using a K-fold cross-validation (K-fold CV) method.Results:The results of the obtained models indicated a good linear correlation between predicted yield and field observations (R2 = 0.75 and RMSE of 7.08q/ha). The obtained method could be a good tool for decision makers to orient their actions under different climatic conditions

The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco

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

La boite à outils de l’analyse des politiques publiques : une variété de modèles

Logic dictates that the development of public policy should be based on studies, analyses, consultations, an assessment of the various options and, finally, a synthesis of the available information. The more soundly based this policy development process is, the greater the chances of adopting the right decision. Within this framework, one of the major concerns that hampers decision-makers is the choice of a mode of analysis that enables them to study public policy effectively; before, during and after its actual implementation. The main aim of this article is to present a toolbox for public policy analysis, containing models designed by researchers over the years. The analytical models discussed in this paper are those most widely used in the policy field, namely the process or stages model, and the rational model and its derivatives, namely complete rationality, bounded rationality and incrementalism.   Keywords: policy models, policy analysis, stages model, rational model, incrementalism JEL Classification : J18, J58, F68, H40 Paper type: Theoretical ResearchLa logique veut que l’élaboration d’une politique publique s’appuie sur des études, des analyses, des consultations, une évaluation des diverses options et, enfin, une synthèse des informations disponibles. Plus ce processus d’élaboration des politiques est fait sur des bases solides, plus les chances d’adopter la bonne décision sont fortes.  Dans ce cadre, l’une des préoccupations majeures qui gênent les décideurs, est le choix d’un mode d’analyse qui leur permet d’étudier efficacement une politique publique ; avant, durant et après sa mise en œuvre effective. Le présent article a comme principal objectif, de présenter une boite à outil de l’analyse des politiques publiques, contenant les modèles conçus par les chercheurs au fil des années. Les modèles d’analyse traités dans notre travail sont les modèles les plus utilisés dans le domaine politique à savoir : le modèle des étapes (process or stages model, et le modèle rationnel (rational model) ainsi que ses dérivés, à savoir la rationalité complète, la rationalité limitée et l’incrémentalisme.   Mots-clés : Modèles des politiques publiques, l’analyse des politiques publiques, modèle des étapes, modèle rationnel, l’incrémentalisme Classification JEL: J18, J58, F68, H40 Type de l’article: Recherche théoriqu

Deep Learning-Based Spatiotemporal Fusion Approach for Producing High-Resolution NDVI Time-Series Datasets

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

When climate variability partly compensates for groundwater depletion: An analysis of the GRACE signal in Morocco

International audienceSince April 2002, the Gravity Recovery and Climate Experiment (GRACE) mission have opened new pathways for hydrologists to monitor the changes in terrestrial total water storage (TWS). Here, the Center for Space Research (CSR), Goddard Space Flight Center (GSFC), Jet Propulsion Laboratory (JPL), and the average (AVG) GRACE mascon solutions were used to examine the changes in TWS and groundwater storages (GWS) in Morocco, with an emphasis on natural replenishment events and their link to snow cover area (SCA) and rainfall variability. New hydrological insights for the region: The results showed that GRACE TWS from AVG (TWS AVG) and GSFC (TWS GSFC) can fairly describe the temporal patterns of the groundwater level (GWL). Moreover, during 2002-2020, the TWS underwent a strong depletion relatively masked by natural recharge events. This was revealed as we identified two intermittent depletion episodes with statistically significant rates (− 1.03 ± 0.11 to − 0.31 ± 0.1 cm yr − 1) higher than those obtained for the long-term trend lines (− 0.28 ± 0.11 to − 0.15 ± 0.07 cm yr − 1). The TWS appeared to be strongly linked with the SCA metrics and rainfall indices with 1-3 months of lag. Our findings suggest that the rainfall distribution can be more insightful about changes in groundwater levels compared to the rainfall monthly totals

Snow Lapse Rate Changes in the Atlas Mountain in Morocco Based on MODIS Time Series during the Period 2000–2016

The spatio-temporal distribution of snow cover metrics in a mountainous area is mainly related to the climatic conditions as well as to the prevailing morphological conditions. The present study aimed to investigate the altitudinal sensitivity of snow cover metrics using the MODIS Terra snow cover product (MOD10A1 v5). Annual snow metrics, including start of snow season (SOSS), end of snow season (EOSS), and snow cover duration (SCD) were extracted from snow-covered area (SCA) maps, which had been pre-processed using a cloud removal algorithm; the maps were of the Atlas Mountains, taken from the period of 2001–2016. In addition, a linear regression was applied to derive an annual altitudinal gradient for each snow metric in relation to various spatial scales in order to analyze the interdependency between snow and topography, and especially to assess the potential temporal trend of the snow gradient. Results indicated that elevation was the principal regulator of snow presence where snow was mostly accumulated above 2500 m. The annual altitudinal gradients for EOSS and SCD showed a marked negative trend beginning in 2007. However, the SOSS altitudinal gradient was marked by a positive trend. The mean SCD gradient for the entire Atlas Mountains decreased from 6 days/100 m to 3 days/100 m. This is a new and important finding since it may indicate the impact of climate change on the dynamics of snow metrics and provides guidance for water managers to better manage the snowmelt water with different terrain features