Abstract
The coupling of remote sensing technology and crop growth models represents a promising approach to support crop yield prediction and irrigation management. In this study, five vegetation indices were derived from the Copernicus-Sentinel 2 satellite to investigate their performance monitoring winter wheat growth in a Mediterranean environment in Lebanon’s Bekaa Valley. Among those indices, the fraction of canopy cover was integrated into the AquaCrop model to simulate biomass and yield of wheat grown under rainfed conditions and fully irrigated regimes. The experiment was conducted during three consecutive growing seasons (from 2017 to 2019), characterized by different precipitation patterns. The AquaCrop model was calibrated and validated for different water regimes, and its performance was tested when coupled with remote sensing canopy cover. The results showed a good fit between measured canopy cover and Leaf Area Index (LAI) data and those derived from Sentinel 2 images. The R2 coefficient was 0.79 for canopy cover and 0.77 for LAI. Moreover, the regressions were fitted to relate biomass with Sentinel 2 vegetation indices. In descending order of R2, the indices were ranked: Fractional Vegetation Cover (FVC), LAI, the fraction of Absorbed Photosynthetically Active Radiation (fAPAR), the Normalized Difference Vegetation Index (NDVI), and the Enhanced Vegetation Index (EVI). Notably, FVC and LAI were highly correlated with biomass. The results of the AquaCrop calibration showed that the modeling efficiency values, NSE, were 0.99 for well-watered treatments and 0.95 for rainfed conditions, confirming the goodness of fit between measured and simulated values. The validation results confirmed that the simulated yield varied from 2.59 to 5.36 t ha−1, while the measured yield varied from 3.08 to 5.63 t ha−1 for full irrigation and rainfed treatments. After integrating the canopy cover into AquaCrop, the % of deviation of simulated and measured variables was reduced. The Root Mean Square Error (RMSE) for yield ranged between 0.08 and 0.69 t ha−1 before coupling and between 0.04 and 0.42 t ha−1 after integration. This result confirmed that the presented integration framework represents a promising method to improve the prediction of wheat crop growth in Mediterranean areas. Further studies are needed before being applied on a larger scale.
Highlights
Current agri-food systems rely on relatively few staple crops; among them, cereal production is crucial for the Mediterranean region
The objectives of this study were (i) to investigate the performance of five vegetation indices derived from satellite data in respect to their correlation to field measurements; (ii) to calibrate and validate AquaCrop for assessing the response of winter wheat to different water management strategies; (iii) to examine the suitability of coupling the earth observation data with field observations and AquaCrop model for monitoring of winter wheat crop growth
The collected field data and measurements served for: (i) comparison of some variables estimated from the earth observations based on ESASentinel-2 images and on-field ground measurements; (ii) calibration of the AquaCrop model under different water regimes; (iii) validation of the AquaCrop model; (iv) insertion of remote sensing variable (FVC) into AquaCrop
Summary
Current agri-food systems rely on relatively few staple crops; among them, cereal production is crucial for the Mediterranean region. Impacts of climate change, water scarcity, growing population demand, and economic oscillations pose significant challenges for agriculture and cereal production, in semi-arid regions [1,2]. Wheat is the most widely produced [3], constituting the stable crop for about half of the world’s population [4], with a global annual production of about 730 million tons [5,6]. The Bekaa valley represents the food basket of the country, accounting for 58% of wheat production [7]. The increasing water deficiency in the valley is the main factor threatening farmers to manage better water use for food production [8,9]. A joint application of different technologies as crop growth models and remote sensing data may contribute to the achievements of this scope [10]
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