Crop water status estimation using thermography: multi-year model development using ground-based thermal images

Crop water status estimation using thermography: multi-year model development using ground-based... Thermal crop sensing technologies have potential as tools for monitoring and mapping crop water status. To create maps of water status from thermal images, a reliable relationship between direct water status measures like leaf water potential (LWP) and thermal water status measures like temperature and crop water stress index (CWSI) should be established for different crops and for different growth stages. The objective of this study was to define the relationships for cotton between LWP and CWSI derived from high-resolution ground-based thermal images and more specifically to examine whether robust relationships exist between the two measures for different varieties, through a cotton growing season, across seasons and under different geographical areas (different climate and soils). A dataset from three cotton growing seasons and from different geographical areas was built to explore the relationship between CWSI and LWP in cotton. CWSI was calculated based on ground-based thermal images and measured dry (T air  + 5 °C) and wet references (Artificial wet reference surface—AWRS). A linear CWSI–LWP relationship was found with high coefficient of determination (R2 = 0.7). This relationship changed over the cotton growth stages and different CWSI–LWP relationships were established to the flowering, boll-filling and defoliation stages. The boll-filling relationship was found to be insensitive to a range of meteorological conditions. The flowering and the boll-filling models were initially validated using diagonal (oblique) thermal images from dates that were not used for calibration. For CWSI calculation, the average temperature of the lowest decile was used for the wet reference instead of the AWRS. The comparison between predicted and observed values of the validation sets yielded RMSE of 0.18 and 0.15 for the flowering and boll-filling stages, respectively. The successful use of the lowest decile as the wet reference enables a future application of the CWSI–LWP relationship to map LWP at a commercial field scale. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Crop water status estimation using thermography: multi-year model development using ground-based thermal images

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Publisher
Springer US
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-014-9378-1
Publisher site
See Article on Publisher Site

References

  • An insight to the performance of crop water stress index for olive trees
    Agam, N; Cohen, Y; Berni, JAJ; Alchanatis, V; Kool, D; Dag, A; Yermiyahu, U; Ben-Gal, A
  • Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging
    Alchanatis, V; Cohen, Y; Cohen, S; Moller, M; Sprinstin, M; Meron, M; Tsipris, J; Saranga, Y; Sela, E
  • Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery
    Berni, JAJ; Zarco-Tejada, PJ; Sepulcre-Canto, G; Fereres, E; Villalobos, F
  • A remote irrigation monitoring and control system for continuous move systems. Part A: Description and development
    Chavez, JL; Pierce, FJ; Elliott, TV; Evans, RG

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