A hyperspectral-physiological phenomics system: Measuring diurnal transpiration rates and diurnal reflectance

Shahar Weksler*, Offer Rozenstein, Nadav Haish, Menachem Moshelion, Rony Walach, Eyal Ben-Dor

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


A novel hyperspectral-physiological system that monitors plants dynamic response to abiotic alterations was developed. The system is a sensor-to-plant platform which can determine the optimal time of day during which physiological traits can be successfully identified via spectral means. The directly measured traits include momentary and daily transpiration rates throughout the daytime and daily and periodical plant weight loss and gain. The system monitored and evaluated pepper plants response to varying levels of potassium fertilization. Significant momentary transpiration rates differences were found between the treatments during 07:00-10:00 and 14:00-17:00. The simultaneous frequently measured high-resolution spectral data provided the means to correlate the two measured data sets. Significant correlation coefficients between the spectra and momentary transpiration rates resulted with a selection of three bands (ρ523, ρ697 and ρ818nm) that were used to capture transpiration rate differences using a normalized difference formula during the morning, noon and the afternoon. These differences also indicated that the best results are not always obtained when spectral (remote or proximal) measurements are typically preformed around noon (when solar illumination is the highest). Valuable information can be obtained when the spectral measurements are timed according to the plants' dynamic physiological status throughout the day, which may vary among plant species and should be considered when planning remote sensing data acquisition.

Original languageAmerican English
Article number1493
JournalRemote Sensing
Issue number9
StatePublished - 1 May 2020

Bibliographical note

Publisher Copyright:
© 2020 by the authors.


  • Functional phenotyping
  • Hyperspectral
  • Phenomics
  • Remote sensing
  • Sensor-to-plant
  • Water stress


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