Capability of hyperspectral and thermal data to predict gas exchange and chlorophyll fluorescence parameters in broccoli
The spatial determination of crop water status (CWS) requires the establishment of robust relationships between direct and indirect measurements. The objective of this study was to explore the potentialities of visible-near infrared (VIS-NIR) hyperspectral and thermal (TIR) data to predict gas excha...
| Autores principales: | , , , , , |
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| Formato: | conferenceObject |
| Lenguaje: | Inglés |
| Publicado: |
IEEE
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.11939/8853 https://ieeexplore.ieee.org/document/10424238 |
| Sumario: | The spatial determination of crop water status (CWS) requires the establishment of robust relationships between direct and indirect measurements. The objective of this study was to explore the potentialities of visible-near infrared (VIS-NIR) hyperspectral and thermal (TIR) data to predict gas exchange and chlorophyll fluorescence parameters in a broccoli (‘Brassica oleracea’ cv. ‘Ulysses’) cultivation. For this purpose, six field campaigns were carried during the growing season 2023. The obtained relationships evidence the better accuracies in predicting gas exchange and chlorophyll fluorescence parameters by using the TIR domain in comparison to the use of VIS-NIR hyperspectral data (absolute correlation coefficients of 0.62-0.81 and 0.51-0.67, respectively). The relationships obtained for chlorophyll fluorescence parameters were more accurate than those relationships obtained for gas exchange parameters, independently on the use of TIR or VIS-NIR hyperspectral data. These results suggest that other co-variables should be included in order to improve the obtained relationships (i.e. combination of VIS-NIR and TIR domain, agrometeorological data and soil water content). The identification of the most appropriate methodology for deriving CWS will allow transferring the knowledge acquired in this study to sensors on board proximal/remote platforms (e.g., unmanned aerial vehicles and/or satellites) with the ultimate goal of obtaining spatially distributed CWS estimates. |
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