Articles
Evaluating the relationship between sun-induced chlorophyll fluorescence and transpiration in sparse Australian floodplain woodlands using sap flow measurements
Article number
1419_19
Pages
153 – 160
Language
English
Abstract
Floodplain vegetation is experiencing more severe drought and extreme weather events due to climate change.
Ensuring vegetation has a sufficient water supply is of considerable concern to sustain ecosystem vitality, condition and function.
Transpiration serves as an effective factor to monitor tree water status, offering insights into vegetation function under various environmental pressures, including water and heat stress.
Sun-induced chlorophyll fluorescence (SIF), emitted during photosynthesis, plays a pivotal role in indicating carbon assimilation and water uptake.
It could also be used as a surrogate to transpiration measurements to understand tree plant water relations.
Previous studies have demonstrated a strong correlation between SIF and transpiration in densely forested regions, however, the sparse structure of floodplain woodlands introduces uncertainties into this relationship by increasing noise in the SIF measurement.
To evaluate this relationship further, three open woodland sites with varying fractional vegetation cover (sparse, moderate-sparse and normal) were chosen.
Ten sap flow loggers were installed on six trees to measure transpiration within a 50-m2 plot for each site.
A three-dimensional radiative transfer model, FluorFLiES, was applied to simulate full spectrum SIF by incorporating LiDAR data to accurately model woodland vegetation structure.
Five specific SIF bands, ranging from red to near-infrared, were selected to quantify their correlation with field measured transpiration.
The coefficient of determination of the simulated far-red SIF and field measured transpiration was 0.85 and 0.93 at hourly and monthly scales, respectively.
Across all SIF bands, the correlation between SIF and transpiration improved with increasing fractional vegetation cover.
In summary, this study can be applied regionally to predict transpiration using remotely sensed SIF data sets in the absence of in-situ data or in areas of low data availability or accessibility.
Ensuring vegetation has a sufficient water supply is of considerable concern to sustain ecosystem vitality, condition and function.
Transpiration serves as an effective factor to monitor tree water status, offering insights into vegetation function under various environmental pressures, including water and heat stress.
Sun-induced chlorophyll fluorescence (SIF), emitted during photosynthesis, plays a pivotal role in indicating carbon assimilation and water uptake.
It could also be used as a surrogate to transpiration measurements to understand tree plant water relations.
Previous studies have demonstrated a strong correlation between SIF and transpiration in densely forested regions, however, the sparse structure of floodplain woodlands introduces uncertainties into this relationship by increasing noise in the SIF measurement.
To evaluate this relationship further, three open woodland sites with varying fractional vegetation cover (sparse, moderate-sparse and normal) were chosen.
Ten sap flow loggers were installed on six trees to measure transpiration within a 50-m2 plot for each site.
A three-dimensional radiative transfer model, FluorFLiES, was applied to simulate full spectrum SIF by incorporating LiDAR data to accurately model woodland vegetation structure.
Five specific SIF bands, ranging from red to near-infrared, were selected to quantify their correlation with field measured transpiration.
The coefficient of determination of the simulated far-red SIF and field measured transpiration was 0.85 and 0.93 at hourly and monthly scales, respectively.
Across all SIF bands, the correlation between SIF and transpiration improved with increasing fractional vegetation cover.
In summary, this study can be applied regionally to predict transpiration using remotely sensed SIF data sets in the absence of in-situ data or in areas of low data availability or accessibility.
Publication
Authors
S. Gao, T.M. Doody
Keywords
SIF, radiative transfer model, water management
Online Articles (22)