High-resolution satellite imagery to assess orchard characteristics impacting water use

Rouault, Pierre; Courault, Dominique; Flamain, Fabrice; Pouget, Guillaume; Doussan, Claude; Lopez-Lozano, Raul; McCabe, Matthew; Debolini, Marta

doi:10.1016/j.agwat.2024.108763

Cited by 4 publications

(1 citation statement)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These models can also be based on some remote sensing information to be applied at larger scales(Olioso et al 1999;Duchemin et al 2006;Courault et al 2010Courault et al , 2021 or to identify plot characteristics(Abubakar et al 2022). However, few studies focus on large areas dominated by perennial crops due to the di culty of identifying characteristics of this type of heterogeneous land cover, although some techniques are being evaluated(El Hajj et al 2018;Rouault et al 2024). The few studies that consider this type of crop are usually based on eld surveys or work in close contact with farmers, coupled with modeling(Kpadonou et al 2015;Naulleau 2021…”

mentioning

confidence: 99%

Unravelling the heterogeneity of farms irrigation practices on Mediterranean perennial agricultural systems for the optimization of water resource management

Pierre,

Dominique,

Fabrice

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

In the Mediterranean region, the quantity of water utilized for agricultural purposes ranges from 50 to 70%. Among the most water-demanding agricultural sectors are arboriculture and perennial crops. Orchards are particularly reliant on irrigation, a dependency that has been further intensified by climate change and the resulting reduction in water resources. This study aims to classify farms at the watershed scale according to their irrigation water consumption, and starting from this classification we aim to propose a method for estimating water consumption for irrigation at large scale and for heterogeneous land covers. The classification employed a variety of statistical methods to ensure robust results, including machine learning and regression approaches. Each method was applied independently, and the most common class allocation was retained. The study was conducted in the Ouvèze-Ventoux basin in south-eastern France, using data from various sources at both field and watershed scales. The data obtained from 21 farms provided accurate information on irrigation water usage, which was validated by data from the watershed's water manager. The benchmark analysis identified farms with high irrigation rates with 90% accuracy. Within these heavily irrigated orchards, a second benchmark identified heavily irrigated plots with 68% precision. Maps estimating water consumption were created at the watershed and municipal scales. The estimated total irrigation water use closely matched the actual consumption, with only a 14% deviation. This methodology offers an accessible estimation of water consumption at the watershed scale, without the need to rely on crop models. Moreover, the methodology accurately identifies areas with high irrigation demand based on actual irrigation practices.

show abstract