Airborne Thermal Imagery to Detect the Seasonal Evolution of Crop Water Status in Peach, Nectarine and Saturn Peach Orchards

نویسندگان

  • Joaquim Bellvert
  • Jordi Marsal
  • Joan Girona
  • Victoria González-Dugo
  • Elías Fereres
  • Susan L. Ustin
  • Pablo J. Zarco-Tejada
چکیده

In the current scenario of worldwide limited water supplies, conserving water is a major concern in agricultural areas. Characterizing within-orchard spatial heterogeneity in water requirements would assist in improving irrigation water use efficiency and conserve water. The crop water stress index (CWSI) has been successfully used as a crop water status indicator in several fruit tree species. In this study, the CWSI was developed in three Prunus persica L. cultivars at different phenological stages of the 2012 to 2014 growing seasons, using canopy temperature measurements of well-watered trees. The CWSI was then remotely estimated using high-resolution thermal imagery acquired from an airborne platform and related to leaf water potential (ΨL) throughout the season. The feasibility of mapping within-orchard spatial variability of ΨL from thermal imagery was also explored. Results indicated that CWSI can be calculated using a common non-water-stressed baseline (NWSB), upper and lower limits for the entire growing season and for the three studied cultivars. Nevertheless, a phenological effect was detected in the CWSI vs. ΨL relationships. For a specific given CWSI value, ΨL was more negative as the crop developed. This different seasonal response followed the same trend for the three studied cultivars. The approach presented in this study demonstrated that CWSI is a feasible method to assess the spatial variability of tree water status in heterogeneous orchards, and to derive ΨL maps throughout a complete growing season. A sensitivity analysis of varying pixel size showed that a pixel size of 0.8 m or less was needed for precise ΨL mapping of peach and nectarine orchards with a tree crown area between 3.0 to 5.0 m2.

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عنوان ژورنال:
  • Remote Sensing

دوره 8  شماره 

صفحات  -

تاریخ انتشار 2016