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Article

Prediction of Open Woodland Transpiration Incorporating Sun-Induced Chlorophyll Fluorescence and Vegetation Structure

1
CSIRO, Environment, Waite Campus, Adelaide, SA 5064, Australia
2
Centre for Applied Water Science, University of Canberra, Canberra, ACT 2601, Australia
3
Remote Sensing Research Centre, School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
4
College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730020, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(1), 143; https://doi.org/10.3390/rs16010143
Submission received: 9 November 2023 / Revised: 14 December 2023 / Accepted: 25 December 2023 / Published: 28 December 2023
(This article belongs to the Special Issue Monitoring Ecohydrology with Remote Sensing)

Abstract

Transpiration (T) represents plant water use, while sun-induced chlorophyll fluorescence (SIF) emitted during photosynthesis, relates well to gross primary production. SIF can be influenced by vegetation structure, while uncertainties remain on how this might impact the relationship between SIF and T, especially for open and sparse woodlands. In this study, a method was developed to map T in riverine floodplain open woodland environments using satellite data coupled with a radiative transfer model (RTM). Specifically, we used FluorFLiES, a three-dimensional SIF RTM, to simulate the full spectrum of SIF for three open woodland sites with varying fractional vegetation cover. Five specific SIF bands were selected to quantify their correlation with field measured T derived from sap flow sensors. The coefficient of determination of the simulated far-red SIF and field measured T at a monthly scale was 0.93. However, when comparing red SIF from leaf scale to canopy scale to predict T, performance declined by 24%. In addition, varying soil reflectance and understory leaf area index had little effect on the correlation between SIF and T. The method developed can be applied regionally to predict tree water use using remotely sensed SIF datasets in areas of low data availability or accessibility.
Keywords: evapotranspiration; SIF; river basin management; carbon cycle; water cycle; 3-D model; Murray–Darling Basin evapotranspiration; SIF; river basin management; carbon cycle; water cycle; 3-D model; Murray–Darling Basin

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MDPI and ACS Style

Gao, S.; Woodgate, W.; Ma, X.; Doody, T.M. Prediction of Open Woodland Transpiration Incorporating Sun-Induced Chlorophyll Fluorescence and Vegetation Structure. Remote Sens. 2024, 16, 143. https://doi.org/10.3390/rs16010143

AMA Style

Gao S, Woodgate W, Ma X, Doody TM. Prediction of Open Woodland Transpiration Incorporating Sun-Induced Chlorophyll Fluorescence and Vegetation Structure. Remote Sensing. 2024; 16(1):143. https://doi.org/10.3390/rs16010143

Chicago/Turabian Style

Gao, Sicong, William Woodgate, Xuanlong Ma, and Tanya M. Doody. 2024. "Prediction of Open Woodland Transpiration Incorporating Sun-Induced Chlorophyll Fluorescence and Vegetation Structure" Remote Sensing 16, no. 1: 143. https://doi.org/10.3390/rs16010143

APA Style

Gao, S., Woodgate, W., Ma, X., & Doody, T. M. (2024). Prediction of Open Woodland Transpiration Incorporating Sun-Induced Chlorophyll Fluorescence and Vegetation Structure. Remote Sensing, 16(1), 143. https://doi.org/10.3390/rs16010143

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