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A Penman-Monteith evapotranspiration model with bulk surface conductance derived from remotely sensed spatial contextual information.

Authors :
Shekar N C, Sanjay
Nandagiri, Lakshman
Source :
International Journal of Remote Sensing. Feb2020, Vol. 41 Issue 4, p1486-1511. 26p. 4 Charts, 5 Graphs.
Publication Year :
2020

Abstract

A novel approach involving the use of the contextual information in a scatter plot of Moderate Resolution Imaging Spectrometer (MODIS) derived Land Surface Temperature versus Fraction of Vegetation (LST vs. Fv) has been proposed in this study to obtain pixel-wise values of bulk surface conductance (Gs) for use in the Penman-Monteith (PM) model for latent heat flux (λET) estimation. Using a general expression for Gs derived by assuming a two-source total λET (canopy transpiration plus soil evaporation) approach proposed by previous researchers, minimum and maximum values of Gs for a given region can be inferred from a trapezoidal scatter plot of pixel-wise values of LST and corresponding Fv. Using these as limiting values, Gs values for each pixel can be derived through interpolation and subsequently used with the PM model to estimate λET for each pixel. The proposed methodology was implemented in 5 km × 5 km areas surrounding each of four flux towers located in tropical south-east Asia. Using climate data from the tower and derived Gs values the PM model was used to obtain pixel-wise instantaneous λET values on six selected dates/times at each tower. Excellent comparisons were obtained between tower measured λET and those estimated by the proposed approach for all four flux tower locations (R2 = 0.85–0.96; RMSE = 18.27–33.79 W m–2). Since the LST- Fv trapezoidal method is simple, calibration-free and easy to implement, the proposed methodology has the potential to provide accurate estimates of regional evapotranspiration with minimal data inputs. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01431161
Volume :
41
Issue :
4
Database :
Academic Search Index
Journal :
International Journal of Remote Sensing
Publication Type :
Academic Journal
Accession number :
139682733
Full Text :
https://doi.org/10.1080/01431161.2019.1668074