In Morocco, monitoring and estimation of wheat yield at the regional and national scales are critical issues for national food security. The recent Sentinel-2 imagery offers potential for managing grain production systems on a field and regional level. The present study was planned based on a time series of six remote sensing indices and Multiple Linear Regression (MLR) methods for real-time estimation of wheat yield using the Google Earth Engine (GEE) platform in a highly heterogeneous and fragmented agricultural region, such as the Tadla Irrigated Perimeter (TIP). First, the spatial distribution of wheat in the TIP region was mapped by performing Random Forest (RF) classification of Sentinel 2 images. Following that, using MLR models, the wheat yield of nine sampled fields was estimated for the different phenological stages of wheat. The yield measured in-situ was the independent variable of the regressions. The dependent variables included the remote sensing indices derived from Sentinel-2. The remote sensing index and the phenological period of the greatest model were investigated to estimate and map the wheat yield in the entire study area. The RF generated the wheat mapping of the study area with an overall accuracy (OA) of 93.82%. Furthermore, the coefficient of determination (R²) of the tested MLR was from 0.53 to 0.89, while the Root Mean Square Error (RMSE) varied from 4.29 to 7.78 q ha⁻¹. The best model was the one that uses the Green Normalized Difference Vegetation Index (GNDVI) in the tillering and maturity stages.

Crop monitoring is critical for sustaining agriculture, preserving natural resources, and dealing with the effects of population growth and climate change. The Sentinel missions, Sentinel-1 and Sentinel-2, provide open imagery at a high spatial and temporal resolution. This research aimed (1) to evaluate the temporal profiles derived from Sentinel-1 and Sentinel-2 time series data in deducing the dates of the phenological stages of wheat from germination to the fully mature plant using the Google Earth Engine (GEE) JavaScript interface and (2) to assess the relationship between phenological stages and optical/ SAR remote sensing indices for developing an accurate phenology estimation model of wheat and extrapolate it to the regional scale. Firstly, the temporal profiles derived from Sentinel-1 and Sentinel-2 remote sensing indices were evaluated in terms of deducing the dates of the phenological stages of wheat. Secondly, the remote sensing indices were used to assess their relationship with phenological stages using the linear regression (LR) technique. Thirdly, the best performing optical and radar remote sensing indices were selected for phenological stage prediction. Fourthly, the spatial distribution of wheat in the TIP region was mapped by performing a Random Forest (RF) classification of the fusion of Sentinel-1 and Sentinel 2 images, with an overall accuracy of 95.02%. These results were used to characterize the growth of wheat on the TIP regional scale using the Temporal Normalized Phenology Index (TNPI) and the predicted models. The obtained results revealed that (1) the temporal profiles of the dense time series of Sentinel-1 and Sentinel-2 indices allowed the dates of the germination, tillering, jointing heading, maturity, and harvesting stages to be determined with the support of the crop calendar. (2) The TNPI_increase and TNPI_decrease revealed that the declining part of the NDVI profile from NDVI_Max, to NDVI_Min2 revealed higher TNPI values (from 0.58 to 1) than the rising part (from 0.08 to 0.58). (3) The most accurate models for predicting phenological stages were generated from the WDVI and VH–VV remote sensing indices, having an R² equal to 0.70 from germination to jointing and an R² equal to 0.84 from heading to maturity.