Testing of Automated Photochemical Reflectance Index Sensors as Proxy Measurements of Light Use Efficiency in an Aspen Forest

• Main Authors: Saulo Castro, Arturo Sanchez-Azofeifa
• Format: Article
• Language: English
• Published: MDPI AG 2018-10-01
• Series: Sensors
• Subjects: remote sensing; PRI; LUE model; eddy covariance
• Online Access: http://www.mdpi.com/1424-8220/18/10/3302

Commercially available autonomous photochemical reflectance index (PRI) sensors are a new development in the remote sensing field that offer novel opportunities for a deeper exploration of vegetation physiology dynamics. In this study, we evaluated the reliability of autonomous PRI sensors (SRS-PRI) developed by METER Group Inc. as proxies of light use efficiency (LUE) in an aspen (Populus tremuloides) forest stand. Before comparisons between PRI and LUE measurements were made, the optical SRS-PRI sensor pairs required calibrations to resolve diurnal and seasonal patterns properly. An offline diurnal calibration procedure was shown to account for variable sky conditions and diurnal illumination changes affecting sensor response. Eddy covariance measurements provided seasonal gross primary productivity (GPP) measures as well as apparent canopy quantum yield dynamics (α). LUE was derived from the ratio of GPP to absorbed photosynthetically active radiation (APAR). Corrected PRI values were derived after diurnal and midday cross-calibration of the sensor’s 532 nm and 570 nm fore-optics, and closely related to both LUE (R2 = 0.62, p < 0.05) and α (R2 = 0.72, p < 0.05). A LUE model derived from corrected PRI values showed good correlation to measured GPP (R2 = 0.77, p < 0.05), with an accuracy comparable to results obtained from an α driven LUE model (R2 = 0.79, p < 0.05). The automated PRI sensors proved to be suitable proxies of light use efficiency. The onset of continuous PRI sensors signifies new opportunities for explicitly examining the cause of changing PRI, LUE, and productivity over time and space. As such, this technology represents great value for the flux, remote sensing and modeling community.