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Seasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco’s Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016–2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change.

Seasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco's Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016-2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change. [ABSTRACT FROM AUTHOR]

In this study, the potential of phenological indicators derived from Sentinel-2A (S2) time series were evaluated to explore the key variables that allow identifying both cropland and crop types. Based on the derived S2 phenological metrics and fitted vegetation indices (VI), 10 feature sets were developed and assessed to discriminate different crop types via Random Forest (RF) classifier. The comparison between VI data-based classifications has shown that NDVI and EVI2 phenological sets could delineate and identify crop types more accurately compared to RENDVI data. Overall, the combined use of fitted VI and phenological features rather than being used separately achieved the best performances. Further, the result of using optimum features was the most accurate among 10 feature sets, with an overall accuracy of 88% and kappa of 0.84. This study constitutes a substantial improvement in crop type identification, which gives a valuable tool to monitor agricultural areas. [ABSTRACT FROM AUTHOR]

Changes in crop yields may have important implications for food security in Morocco. This study intends to develop an explicit model based solely on phenological parameters derived from Moderate-Resolution Imaging Spectroradiometer (MODIS)/NDVI data to monitor wheat grain yield. The developed model allows overcoming missing weather, soil and irrigation supply data without losing the spatial resolution offered by images data. The study period covers a 16-year span between 2000 and 2016, and the considered region is the north-western of Morocco. The model showed a good correlation with ground measurements (R2 = 0.62; p < 0.01). Spatio-temporal variability and trend of wheat yield were examined. The spatial analyses revealed an increase of instability of wheat grain yields across central and southern regions. Such a tool allows managers and policy makers to analyse the agricultural policy impact, to monitor the agronomic potential dynamic, to control the cropping season evolution and to optimize the land use choices. [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]