Automated and continuous estimation of FAPAR from distributed wireless PAR sensor networks

Accurate estimation of the fraction of absorbed photosynthetically active radiation (FAPAR) is crucial for understanding plant productivity and ecosystem dynamics. A number of indirect measurement techniques are used for estimating FAPAR with hand-held instruments, but researchers have identified discrepancies among different techniques when using them to validate satellite land products. Many researchers have also utilised photosynthetically active radiation (PAR) sensors to obtain quantitative measurements of PAR, but these lack robust measurement frameworks and protocols. Only very limited research has started on automated wireless PAR network systems to measure at finer temporal scales as well as to reduce human error and logistical costs. This study evaluates the performance of two flux (2f) and four flux (4f) FAPAR measurement systems and digital hemispherical photography (DHP) across multiple vegetation types (e.g., vineyard, broadleaf deciduous forest, savanna woodland) and different temporal scales (instantaneous and daily integrated). Results reveal strong agreement (R2 > 0.99, RMSE ≤ 0.04) between 2f- and 4f-FAPAR for all three study sites, with minimal overestimation (bias ≤ 0.04) by the 2f systems, suggesting that it can substitute, over similar environments, the more complex and costly 4f setup without substantially compromising accuracy. Daily integrated FAPAR exhibited greater stability and lower uncertainty compared to instantaneous FAPAR, underscoring its importance for long-term ecosystem monitoring. However, instantaneous FAPAR remains essential for satellite product validation due to its alignment with satellite overpass times. Additionally, 2f-FAPAR showed a good relationship with DHP-derived FAPAR. The findings highlight the potential of the 2f wireless PAR network as an automated, cost-effective, and reliable tool for canopy light absorption studies, offering substantial advantages for both ground-based ecosystem monitoring and remote sensing applications.