Articles
Water stress detection in olive at the farm level using high-resolution multispectral airborne imagery: assessment against canopy temperature
Article number
1395_4
Pages
23 – 30
Language
English
Abstract
Thermal imagery and derived temperature-based indicators have been proven successful remote sensing methods to monitor water stress in crops.
The canopy-air temperature difference (Tc-Ta) has been widely used to monitor water status, as it is directly related to the reduction of transpiration under water stress conditions.
However, thermal imaging sensors provide lower spatial resolutions than multispectral cameras, and accurate canopy temperature retrievals require sensor stability.
Plant biochemical constituents such as the xanthophyll content (Cx) are sensitive to water stress.
Cx can be tracked by the photochemical reflectance index (PRI) and other PRI-based indices, but these are highly affected by the structure and other pigments.
In this study, we used thermal and multispectral imagery from a piloted aircraft over an olive orchard covering more than 5,000 ha to obtain tree-level canopy temperature to map water stress.
The whole farm comprised 11 olive cultivars and 12 years of plantation.
The airborne multispectral imagery collected with ten spectral bands between 444 and 842 nm (10-57 nm bandwidth) was used to derive tree-level Cx through machine learning by accounting for the crown structure (leaf area index, LAI) and chlorophyll content (Ca+b) to assess the sensitivity of water stress observed by canopy temperature.
Results showed that model-estimated Cx tracked water stress more accurately than PRI indices.
Grouping by cultivar, the Cx relationships yielded against the thermal water stress indicators varied between R2=0.45 (n=176) for Picual and R2=0.95 (n=16) for Arbosana. These findings progress the development of new tools for precision irrigation using reflectance imagery sensitive to photosynthetic pigments influenced by water stress.
The canopy-air temperature difference (Tc-Ta) has been widely used to monitor water status, as it is directly related to the reduction of transpiration under water stress conditions.
However, thermal imaging sensors provide lower spatial resolutions than multispectral cameras, and accurate canopy temperature retrievals require sensor stability.
Plant biochemical constituents such as the xanthophyll content (Cx) are sensitive to water stress.
Cx can be tracked by the photochemical reflectance index (PRI) and other PRI-based indices, but these are highly affected by the structure and other pigments.
In this study, we used thermal and multispectral imagery from a piloted aircraft over an olive orchard covering more than 5,000 ha to obtain tree-level canopy temperature to map water stress.
The whole farm comprised 11 olive cultivars and 12 years of plantation.
The airborne multispectral imagery collected with ten spectral bands between 444 and 842 nm (10-57 nm bandwidth) was used to derive tree-level Cx through machine learning by accounting for the crown structure (leaf area index, LAI) and chlorophyll content (Ca+b) to assess the sensitivity of water stress observed by canopy temperature.
Results showed that model-estimated Cx tracked water stress more accurately than PRI indices.
Grouping by cultivar, the Cx relationships yielded against the thermal water stress indicators varied between R2=0.45 (n=176) for Picual and R2=0.95 (n=16) for Arbosana. These findings progress the development of new tools for precision irrigation using reflectance imagery sensitive to photosynthetic pigments influenced by water stress.
Authors
T. Poblete, V. Gonzalez-Dugo, A. Hornero, P.J. Zarco-Tejada
Keywords
abiotic stress, airborne thermal and multispectral, large-scale monitoring, xanthophylls (Cx)
Groups involved
- Division Temperate Tree Nuts
- Division Vine and Berry Fruits
- Division Temperate Tree Fruits
- Division Precision Horticulture and Engineering
- Division Plant-Environment Interactions in Field Systems
- Division Tropical and Subtropical Fruit and Nuts
- Working Group Precision Management of Orchards and Vineyards
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