Adra IDRISSI, Abdelaziz Htitiou, Samir Nadem, Abdelghani Boudhar,, Youssef Lebrini, Tarik Benabdelouahab



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 conditions


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.


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



Sentinel-2, Phenological metrics, estimate wheat yield, NDVI, time series

Full Text:

Full Text


An, D., G. Zhao, C. Chang, Z. Wang, P. Li, T. Zhang, and J. J. I. J. o. R. S. Jia (2016). "Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta." 37 (2): 455-470 .

Anagnostou, E. N., V. Maggioni, E. I. Nikolopoulos, T. Meskele, F. Hossain and A. Papadopoulos (2010). "Benchmarking High-Resolution Global Satellite Rainfall Products to Radar and Rain-Gauge Rainfall Estimates." IEEE Transactions on Geoscience and Remote Sensing48 (4): 1667-1683.

Andarzian, B., Bakhshandeh, A. M., Bannayan, M., Emam, Y., Fathi, G., & Alami Saeed, K. (2008). WheatPot: A simple model for spring wheat yield potential using monthly weather data. Biosystems Engineering, 99(4), 487–495.

Bakker, M. M., G. Govers, F. Ewert, M. Rounsevell, and R. Jones (2005). "Variability in regional wheat yields as a function of climate, soil, and economic variables: Assessing the risk of confounding." Agriculture, Ecosystems & Environment110 (3): 195-209.

Balaghi, R., B. Tychon, H. Eerens, and M. Jlibene (2008). "Empirical regression models using NDVI, rainfall, and temperature data for the early prediction of wheat grain yields in Morocco." International Journal of Applied Earth Observation and Geoinformation10 (4): 438-452.

Benabdelouahab, T., Y. Lebrini, A. Boudhar, R. Hadria, A. Htitiou, and H. Lionboui (2019). "Monitoring spatial variability and trends of wheat grain yield over the main cereal regions in Morocco: a remote-based tool for planning and adjusting policies." Geocarto International: 1-20.

Cassel, D. L. (2007). Re-sampling and simulation, the SAS way. Proceedings of the SAS Global Forum 2007 Conference, Cary, NC, SAS Institute Inc.

CIMMYT. (2018). New publications: The importance of wheat in the global food supply to a growing population – CIMMYT. Importance of Wheat in the World.

Doraiswamy, P., J. Hatfield, T. Jackson, B. Akhmedov, J. Prueger, and A. J. R. s. o. e. Stern (2004). "Crop condition and yield simulations using Landsat and MODIS." 92 (4): 548-559 .

B., R. Hadria, S. Erraki, G. Boulet, P. Maisongrande, A. Chehbouni, R. Escadafal, J. Ezzahar, J. C. B. Hoedjes, M. H. Kharrou, S. Khabba, B. Mougenot, A. Olioso, J. C. Rodriguez, and V. Simonneaux (2006). "Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crop coefficients, leaf area index and remotely-sensed vegetation indices." Agricultural Water Management79 (1): 1-27.

Entekhabi, D., R. H. Reichle, R. D. Koster, and W. T. J. J. H. Crow (2010). "Performance metrics for soil moisture retrievals and application requirements." 11 (3): 832-840.

Escolà, A., N. Badia, J. Arnó, and J. A. Martnez-Casasnovas (2017). "Using Sentinel-2 images to implement Precision Agriculture techniques in large arable fields: First results of a case study." Advances in Animal Biosciences8: 377-382.

FAO. (2022). FAO Cereal Supply and Demand Brief | World Food Situation | Food and Agriculture Organization of the United Nations.

Geng, L., M. Ma, X. Wang, W. Yu, S. Jia, and H. J. R. S. Wang (2014). "Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe river basin, China." 6 (3): 2024-2049.

Hadria, R., B. Duchemin, L. Jarlan, G. Dedieu, F. Baup, S. Khabba, A. Olioso, and T. Le Toan (2010). "Potentiality of optical and radar satellite data at high spatio-temporal resolution for the monitoring of irrigated wheat crops in Morocco." International Journal of Applied Earth Observation and Geoinformation12: S32-S37.

Htitiou, A., A. Boudhar, Y. Lebrini, R. Hadria, H. Lionboui, L. Elmansouri, B. Tychon, and T. Benabdelouahab (2019). "The Performance of Random Forest Classification Based on Phenological Metrics Derived from Sentinel-2 and Landsat 8 to Map Crop Cover in an Irrigated Semi-arid Region." Remote Sensing in Earth Systems Sciences2 (4): 208-224.

Iizumi, T., M. Yokozawa, G. Sakurai, M. I. Travasso, V. Romanenkov, P. Oettli, T. Newby, Y. Ishigooka, and J. Furuya (2014). "Historical changes in global yields: major cereal and legume crops from 1982 to 2006." Global Ecology and Biogeography23 (3): 346-357.

Jayawardhana, W. and V. J. P. f. s. Chathurange (2016). "Extraction of agricultural phenological parameters of Sri Lanka using MODIS and NDVI time series data." 6: 235-241.

Kerdsueb, P., P. J. I. J., E. S. Teartisup and Development (2014). "The use of geoinformatics for estimating soil organic matter in the central plain of Thailand." 5 (3): 282.

Lambert, M.-J., P. C. S. Traoré, X. Blaes, P. Baret and P. Defourny (2018). "Estimating smallholder crop production at the village level from Sentinel-2 time series in Mali's cotton belt." Remote Sensing of the Environment216: 647-657.

Lionboui, H., T. Benabdelouahab, A. Htitiou, Y. Lebrini, A. Boudhar, R. Hadria, and F. Elame (2020). "Spatial assessment of losses in wheat production value: A need for an innovative approach to guide risk management policies." Remote Sensing Applications: Society and Environment18: 100300.

Löw, F., U. Michel, S. Dech, C. J. I. j. o. p. Conrad and r. sensing (2013). "Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using support vector machines." 85: 102-119.

McGill, R., J. W. Tukey, and W. A. J. T. A. S. Larsen (1978). "Variations of box plots." 32 (1): 12-16.

Mkhabela, M., P. Bullock, S. Raj, S. Wang, Y. J. A. Yang, and F. Meteorology (2011). "Crop yield forecasting on the Canadian Prairies using MODIS NDVI data." 151 (3): 385-393.

Ouharba, E., Z. El, Z. Triqui, and R. Moussadek (2019). "Impact des Changements Climatiques sur la Céréaliculture au Maroc. Etude de Cas:Rommani (Région de Rabat), Centre du Bassin Versant du Bouregreg. Climate Change Impact on Cereal Culture in Morocco. Impact des Changements Climatiques sur la Céréaliculture à Rommani. "

Quarmby, N., M. Milnes, T. Hindle, and N. J. I. J., R. S. Silleos (1993). "The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction." 14 (2): 199-210.

Rasmussen, M. S. J. I. J. o. R. S. (1992). "Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR." 13 (18): 3431-3442.

Reynolds, P. E., Thevathasan, N. V., Simpson, J. A., Gordon, A. M., Lautenschlager, R. A., Bell, W. F., Gresch, D. A., & Buckley, D. A. (2000). Alternative conifer release treatments affect microclimate and soil nitrogen mineralization. Forest Ecology and Management, 133(1–2), 115–125.

Rouse Jr., J., R. Haas, D. Deering, J. Schell, and J. Harlan (1974). "Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor]. last accessed 2016/11/21.


E-ISSN : 2657-2664