Crop mapping and yield estimation of wheat in the Bekaa plain of Lebanon. (Cartographie et prédiction de la productivité du blé cultivé dans la Plaine de la Bekaa, Liban)

With global production exceeding 750 million tons in 2017, wheat is considered a staple food for the world's population. Wheat mapping and monitoring could then be a very effective tool for achieving the Sustainable Development Goals (SDG2-Zero Hunger). In Lebanon, wheat receives technical and financial support, yet many errors occur in estimating the wheat acreage due to absence of reliable agricultural census and lack of wheat mapping using satellite images. In addition, identifying the best rotation type and agricultural practices leads to identify the most efficient wheat-based cropping system in terms of productivity (protein production and net profit), efficiency (water and nitrogen use), as well as the economic risk on the farmer. Thus, The aim of the current study, which is conducted in the Bekaa plain of Lebanon, is to utilize remote sensing technology and crop modelling for supporting policy makers and end-users in making strategic decisions regarding one of the most food security-driving crop in the Mediterranean (i.e. winter wheat). The first part of the thesis evaluates the potential of optical data for early winter wheat mapping by allowing the transfer of knowledge from one year to another (2016 and 2017 in this study). For its high spatial and temporal resolutions, Sentinel-2 data are employed. Results show that when the developed approach was applied on Sentinel-2 time series of 2017 in using 2016 ground truth data, the overall accuracy reaches 87.0%, whereas, when implemented using 2017 ground truth data, the overall accuracy is 82.6% on 2016 data. The outputs are executed up to six weeks before harvest, as well as distinguishing winter wheat from similar cereals (barley and triticale). The second part of the thesis examines the ability of the SAR (Synthetic Aperture Radar) C-band data of the new radar satellite (Sentinel-1) regarding its ability to monitor winter wheat crop by identifying the economically important phenological phases that cannot be detected relying solely on NDVI derived from optical satellite Sentinel-2. Results show that VV polarization at incidence angle of 32°-34° is best for predicting heading, VH polarization at incidence angle of 43°-45° for predicting soft dough, and the ratio VV/VH at incidence angle of 32°-34° for predicting germination and harvesting.The third part of the thesis is dedicated to test, in contrasted biophysical and management conditions, the hypothesis that promoting wheat-fava bean rotation leads to a significantly better productivity and resources use efficiency, as well as, reducing economic risk than the promoted intensive wheat-wheat and wheat-potato rotations. The cropping simulation model “CropSyst” is used after being calibrated and validated by using experimental data for different wheat-based rotations combining different soil, climate and management options. The results show that there is no particular optimal scenario that can simultaneously ensure high productivity, reduce economic risk, and achieve high wheat- water- and nitrogen-use efficiency. However, the wheat-fava bean rotation cultivated with no wheat fertilization appears to be a better substitute to the wheat-wheat rotation in terms of protein production in both (low and high) Water Holding Capacity (WHC) soils (0.93 t/ha versus 0.8 t/ha in low WHC and 1.34 t/ha versus 1.17 t/ha in high WHC). This cropping system could achieve a higher net profit, showing high resource-use efficiency and good economic sustainability. Moreover, a very high profit could only be attained with the wheat-potato rotation (8640 US$/ha and 12170 US$/ha), yet with low input-efficiency and high economic risk.