In the heart of the Mediterranean, where water scarcity is a growing concern, a team of researchers led by Pierre Rouault from the UMR 1114 EMMAH at Avignon University has developed a novel approach to optimize irrigation water management. Their work, published in the journal ‘Agricultural Water Management’ (which translates to ‘Water Management in Agriculture’), integrates Sentinel-2 satellite data into the Simulation of Evapo-Transpiration of Applied Water (SIMETAW) model, offering a more precise and dynamic tool for water management in agriculture.
The study focuses on assessing irrigation water requirements and evapotranspiration in orchards at the plot scale. The standard SIMETAW model uses a simplified water balance approach with a single uniform soil layer and fixed crop coefficients (Kc) for each crop type. However, this approach doesn’t account for the spatial variability of crops across a watershed. To address this limitation, Rouault and his team modified the SIMETAW model to incorporate remote sensing data.
“We wanted to capture the spatial variability of crops at the watershed scale,” Rouault explains. “By using Sentinel-2 images, we could estimate the crop coefficient (Kc) for each crop in the basin, which significantly improved the model’s performance.”
The modified SIMETAW model was calibrated using distributed soil moisture measurements collected from 2021 to 2024. The team also used data on water volumes used for irrigation and farmer surveys to assess the model’s performance in simulating irrigation water requirements at both the plot level and the basin scale.
The results were promising. The model simulated soil water content (SWC) across the monitored orchards with good accuracy, providing R² values of 0.7 and 0.9 for simulations in 2022 and 2023, respectively. The simulation of the quantity of irrigation water in farms showed a strong correlation with reported data (R= 0.81). Moreover, the incorporation of remote sensed Kc improved the model’s performance by 17% compared to the standard version.
“This research is a game-changer for water management in agriculture,” says Rouault. “By providing more accurate estimates of irrigation water requirements, we can help farmers and water managers make informed decisions, ultimately leading to more sustainable water use.”
The implications of this research extend beyond the agricultural sector. As water scarcity becomes an increasingly global issue, the need for efficient water management strategies grows. This study demonstrates the potential of integrating remote sensing data into water management models, offering a scalable solution that could be applied in various regions and contexts.
Looking ahead, Rouault and his team plan to further validate and refine their model. They also aim to explore the potential of using other remote sensing data, such as thermal imagery, to improve the model’s accuracy even further.
As the world grapples with the impacts of climate change, innovative solutions like this one will be crucial in ensuring water security and sustainability. By harnessing the power of remote sensing and advanced modeling, we can make significant strides towards a more water-wise future.