Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): S. Panda; D.M. AmatyaG Sun; A. Bowman
    Date: 2016
    Source: Transactions of the ASABE
    Publication Series: Scientific Journal (JRNL)
    Station: Southern Research Station
    PDF: Download Publication  (3.0 MB)


    Remote sensing has increasingly been used to estimate evapotranspiration (ET) and its supporting parameters in a rapid, accurate, and cost-effective manner. The goal of this study was to develop remote sensing-based models for estimating ET and the biophysical parameters canopy conductance (gc), upper-canopy temperature, and soil moisture for a mature loblolly pine forest ( L.) in the Parker Tract in eastern North Carolina. To validate the remote sensing approach, we acquired long-term on-site eddy flux measurements, including micrometeorological variables and water and energy fluxes. Other measured and derived ET-associated parameters include forest gc, leaf area index, canopy absorbed radiation, canopy temperature, and soil moisture. Multi-temporal cloud-free (≤10% cloud cover) Landsat 7 ETM+ satellite images from 2006-2012 were acquired. Field data for the 2 h (12:00 noon to 2:00 p.m.) means were used in the model, coinciding with the image acquisition time. Individual Landsat bands (1 through 7) and developed image vegetation indices (NDVI, SAVI, and VVI) for the study site were obtained through automated geospatial models and were correlated to measured ET flux and related parameters. An excellent coefficient of determination (R2 = 0.93, n = 42) was obtained for the upper-canopy temperature versus band 6 (thermal infrared) model. However, a low correlation (R2 = 0.36, n = 6) was obtained for gc versus band 5 (mid-infrared) model. The correlation for soil moisture versus band 7 was poor (R2 = 0.05, n = 42), perhaps due to heavy canopy and pine litter ground cover. However, the ET estimation model with multiple image information variables, such as bands 5 and 7, provided a good correlation (R2 = 0.55, n = 35) with less spatial and temporal variation in the datasets, along with no data mining application in model building. Therefore, this study suggests that the remote sensing approach is promising for estimating ET with good accuracy (average model prediction residual error = 25.46 W m-2, 6.18% of the average ET values used in the analysis) for a mature homogenous pine forest. Further work is needed to develop robust remote sensing-based ET models by including spatial variability, sound data mining, high-resolution imagery, and advanced image processing to account for potential modeling uncertainties.

    Publication Notes

    • You may send email to to request a hard copy of this publication.
    • (Please specify exactly which publication you are requesting and your mailing address.)
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.


    Panda, S.;Amatya, D.M.; Sun, G; Bowman, A. 2016.Remote estimation of a managed pine forest evapotranspiration with geospatial technology. Transactions of the ASABE. 59(6): 1695-1705.  11 p.


    Google Scholar


    canopy conductance, canopy temperature, evapotranspiration, model builder, NDVI, python script, SAVI, soil moisture, spatial analysis, VVI

    Related Search

    XML: View XML
Show More
Show Fewer
Jump to Top of Page