Advertisement

Canopy Temperature-Based Water Stress Indices: Potential and Limitations

  • Manoj Kumar Nanda
  • Utpal Giri
  • Nimai Bera
Chapter

Abstract

Water stress in plant is associated with reduced availability of soil moisture under higher ambient temperature and wider vapour pressure deficit for a considerable period of time. Instruments like pressure chambers and porometers are being used to quantify crop water stress under field conditions, but their use is limited because of the numerous time-consuming measurements that must be made. The application of thermal indices involving canopy temperature for monitoring crop water stress and irrigation scheduling has been demonstrated by several researchers in the last five decades since the evolution of portable infrared thermometers in the 1960s. As the temperature of plant canopy is a manifestation of canopy energy balance, a water-stressed canopy is hotter than a well-watered one under the same environmental conditions. Infrared thermometer integrates the thermal radiation from all exposed surfaces in the field of view of the instrument that included the plant surface and exposed soil surfaces into a single measurement and converts it into temperature unit applying the principle of Stefan-Boltzmann law. However, different plant physiological as well as microclimatic factors like solar radiation, turbulence, air temperature and humidity must influence the canopy temperature at the time of observation. Hence, stomatal conductance and transpiration rates cannot be estimated by canopy temperature alone. In other words, canopy temperature alone is not enough to make estimates of plant water status. For this reason many researchers have attempted to normalize the canopy temperature to account for the influence of other variable microclimatic parameters like vapour pressure deficit, air temperature, wind speed, solar radiation, etc.

In the past few decades, a number of thermal indices have been applied to estimate crop water stress under field condition. The difference between canopy temperature and air temperature (canopy-air temperature difference, CATD) was the first and one of the most commonly used thermal indices to quantify crop water stress. The summation of CATD over some critical period in the crop’s life cycle was termed as stress degree day (SDD). Similarly, the difference between canopy temperature of stressed and non-stressed plants has been used as an index called temperature stress day (TSD). The “canopy temperature variability” (CTV) takes into account the spatial variability of canopy temperature in crop field which was found to be higher in stressed plant than that of non-stressed plant. The temperature-time threshold (TTT) method assumes that the stress is not occurring in the crop until the canopy temperature reaches certain threshold value and calculates the amount of time that canopy temperature is greater than temperature threshold to quantify moisture stress. The crop water stress index (CWSI) further normalizes the canopy-air temperature difference with vapour pressure deficit of air. The calculation of CWSI quantifies the moisture stress of a plant as a comparison of its canopy temperature with that of a non-water-stressed plant and a maximum stressed plant with respect to their differences from the ambient air temperature at a given vapour pressure deficit. Conceptually, CWSI of a non-stressed and fully stressed (non-transpiring) plant is 0 and 1, respectively. The water deficit index (WDI) integrated the percent vegetation coverage and canopy temperature to compensate the effect of soil background that interferes in the remote measurement of canopy temperature through infrared thermometry. The “Biologically Identified Optimal Temperature Interactive Console (BIOTIC)” is an irrigation protocol that provides irrigation scheduling based upon measurements of canopy temperatures and the temperature optimum of the crop species of interest. But some critical issues like impact of rapid fluctuation in radiation and wind speed on crop water stress, crop to crop variability in stress tolerance and the requirement of stress at particular phenophases of some crops have not been duly focused. Thus the canopy temperature-based water stress indices have limited application in irrigation scheduling at field scale. However, with advancement of satellite-based optical and thermal remote sensing in recent years, there is a renewed interest in thermal indices for crop stress monitoring.

Keywords

Infrared thermometry Canopy air temperature difference (CATD) Stress degree day (SDD) Canopy temperature variability (CTV) Temperature stress day (TSD) Crop water stress index (CWSI) Water deficit index (WDI) 

References

  1. Agam N, Cohen Y, Berni JAJ, Alchanatis V, Kool D, Dag A, Yermiyahu U, Ben-Gal A (2013) An insight to the performance of crop water stress index for olive trees. Agric Water Manag 118:79–86CrossRefGoogle Scholar
  2. Alderfasi AA, Nielsen DC (2001) Use of crop water stress index for monitoring water status and scheduling irrigation in wheat. Agric Water Manag 47:69–75CrossRefGoogle Scholar
  3. Alves I, Pereira LS (2000) Non-water-stressed baselines for irrigation scheduling with infrared thermometers: a new approach. Irrig Sci 19:101–106CrossRefGoogle Scholar
  4. Aston AR, Van Bavel CHM (1972) Soil surface water depletion and leaf temperature. Agron J 64(3):368–373CrossRefGoogle Scholar
  5. Blad BL, Rosenberg NJ (1976) Measurement of crop temperature by leaf thermocouple, infrared thermometry, and remotely sensed thermal imagery. Agron J 68:635–641CrossRefGoogle Scholar
  6. Clawson KL, Blad BL (1981) Infrared thermometry for scheduling irrigation of corn. Agron J 74:313–316Google Scholar
  7. Clawson KL, Blad BL (1982) Infrared thermometry for scheduling irrigation of corn. Agron J 74:313–316CrossRefGoogle Scholar
  8. Clawson KL, Jackson RD, Pinter PJ Jr (1989) Evaluating plant water stress with canopy temperature differences. Agron J 81:858–863CrossRefGoogle Scholar
  9. Colak YB, Yazar A, Colak I, Akca H, Duraktekin G (2015) Evaluation of Crop Water Stress Index (CWSI) for eggplant under varying irrigation regimes using surface and subsurface drip systems. Agric Agric Sci Procedia 4:372–382Google Scholar
  10. Çolak YB, Yazarb A (2017) Evaluation of crop water stress index on Royal table grape variety under partial root drying and conventional deficit irrigation regimes in the Mediterranean Region. Sci Hortic 224:384–394Google Scholar
  11. Correia MJ, Coelho D, David MM (2001) Response to seasonal drought in three cultivars of Ceratonia siliqua: leaf growth and water relations. Tree Physiol 21:645–653CrossRefGoogle Scholar
  12. DeJonge KC, Taghvaeian S, Trout TJ, Comas LH (2015) Comparison of canopy temperature-based water stress indices for maize. Agric Water Manag 156:51–62CrossRefGoogle Scholar
  13. Diaz RA, Matthias AD, Hanks RJ (1983) Evapotranspiration and yield estimation of spring wheat from canopy temperature. Agron J 75:805–810CrossRefGoogle Scholar
  14. Ehrler WL, Idso SB, Jackson RD, Reginato RJ (1978) Wheat canopy temperature: relation to plant water potential. Agron J 70:251–256CrossRefGoogle Scholar
  15. Erhler WL (1973) Cotton leaf temperatures as related to soil water depletion and meteorological factors. Agron J 65(3):404–409CrossRefGoogle Scholar
  16. Gardner BR, Blad BL, Garrity DP, Watts DG (1981a) Relationships between crop temperature, grain yield, evapotranspiration and phenological development in two hybrids of moisture stressed sorghum. Irrig Sci 2:213–224CrossRefGoogle Scholar
  17. Gardner BR, Blad BL, Watts DG (1981b) Plant and air temperatures in differentially irrigated corn. Agric Meteorol 25:207–217CrossRefGoogle Scholar
  18. Gates DM (1980) Biophysical ecology. Springer, New YorkCrossRefGoogle Scholar
  19. González-Dugo MP, Moran MS, Mateos L, Bryant R (2006) Canopy temperature variability as an indicator of crop water stress severity. Irrig Sci 24(4):233–240CrossRefGoogle Scholar
  20. Hatfield JL (1983) The utilization of thermal infrared radiation measurements from grain sorghum as a method of assessing their irrigation requirements. Irrig Sci 3:259–268CrossRefGoogle Scholar
  21. Helyes L, Pek Z, McMichael B (2006) Relationship between the stress degree day index and biomass production and the effect and timing of irrigation in snap bean (Phaseolus vulgaris var. Nanus) stands: results of a long term experiments. Acta Bot Hungar 48(3–4):311–321CrossRefGoogle Scholar
  22. Idso SB (1982) Non-water-stressed baselines: a key to measuring and interpreting plant water stress. Agric Meteorol 27:59–70CrossRefGoogle Scholar
  23. Idso SB, Jackson RD, Reginato RJ (1977) Remote sensing of crop yields. Science 196:19–25CrossRefGoogle Scholar
  24. Idso SB, Jackson RD, Reginato J (1978) Remote sensing for agricultural water management and crop yield prediction. Agric Water Manag 1:299–310CrossRefGoogle Scholar
  25. Idso SB, Jackson RD, Pinter PJ Jr, Reginato RJ, Hatfield JL (1981) Normalizing the stress degree day for environmental variability. Agric Meteorol 24:45–55CrossRefGoogle Scholar
  26. Jackson RD (1982) Canopy temperature and crop water stress. Adv Irrig 1:43–85CrossRefGoogle Scholar
  27. Jackson RD, Reginto RJ, Idso SB (1977) Wheat canopy temperature: a practical tool for evaluating water requirements. Water Resour Res 13:51–656CrossRefGoogle Scholar
  28. Jackson RD, Pinter Jr PJ, Reginato RJ, Idso SB (1980) Hand-held radiometry. Agricultural Reviews Manuals ARM-W-19, United States Department of Agriculture, Science and Education Administration, Western Region, OaklandGoogle Scholar
  29. Jackson RD, Idso SB, Reginato RJ, Pinter PJ Jr (1981) Canopy temperature as a crop water stress indicator. Water Resour Res 17:1133–1138CrossRefGoogle Scholar
  30. Keener ME, Kircher PL (1983) The use of canopy temperature as an indicator of drought stress in humid regions. Agric Meteorol 28:339–349CrossRefGoogle Scholar
  31. Kelly HL (1989) Remote measurement of turf water stress and turf biomass. Dissertation, The University of ArizonaGoogle Scholar
  32. Kimes DS, Idso SB, Pinter PJ Jr, Reginato RJ, Jackson RD (1980) View angle effects in the Radiometric measurement of plant canopy temperatures. Remote Sens Environ 10:273–284CrossRefGoogle Scholar
  33. Kirkham MB (2005) Principles of soil and plant water relations. Elsevier Academic Press, AmsterdamGoogle Scholar
  34. Kramer PJ (1983) Water relations of plants. Academic Press, New York, pp 404–406Google Scholar
  35. Legg BJ, Long LF (1975) Turbulent diffusion within a wheat canopy: II. Results and interpretation. Q I R Meteorol Soc 101:611–628CrossRefGoogle Scholar
  36. Li L, Nielsen DC, Yu Q, Ma L, Ahuja LR (2010) Evaluating the crop water stress index and its correlation with latent heat and CO2 fluxes over winter wheat and maize in the North China plain. Agric Water Manag 97:1146–1155CrossRefGoogle Scholar
  37. Linacre ET (1967) Further notes on a feature of leaf and air temperature. Arch Meteorol Geophys Bioklimatol Ser B 15:422–426CrossRefGoogle Scholar
  38. Mahan JR, Burke JJ, Wanjura DF, Upchurch DR (2005) Determination of temperature and time thresholds for BIOTIC irrigation of peanut on the Southern High Plains of Texas. Irrig Sci 23:45–152CrossRefGoogle Scholar
  39. Monteith JL, Szeicz G (1962) Radiative temperature in the heat balance of natural surfaces. Q J R Meteorol Soc 88:496–507CrossRefGoogle Scholar
  40. Monteith JL (1973) Principles of environmental physics. Edward Arnold, LondonGoogle Scholar
  41. Moran MS, Clarke TR, Inoue Y, Vidal L (1994) Estimating crop water deficit using relation between surface-air temperature and spectral vegetation index. Remote Sens Environ 49:246–263CrossRefGoogle Scholar
  42. Nielsen DC, Gardner BR (1987) Scheduling irrigations for corn with crop water stress index (CWSI). Appl Agric Res 2(5):295–300Google Scholar
  43. Petropoulos G, Carlson T, Wooster M, Islam S (2009) A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture. Prog Phys Geogr 33:224–250CrossRefGoogle Scholar
  44. Reginato RJ (1983) Field quantification of crop water stress. Trans ASAE 26(3):0772–0775CrossRefGoogle Scholar
  45. Reginato RJ, Idso SB, Jackson RD (1978) Estimating forage crop production: a technique adaptable to remote sensing. Remote Sens Environ 7:77–80CrossRefGoogle Scholar
  46. Taghvaeian S, Chávez JL, Hansen NC (2012) Infrared thermometry to estimate crop water stress index and water use of irrigated maize in northeastern Colorado. Remote Sens Environ 4:3619–3637CrossRefGoogle Scholar
  47. Tanner CB (1963) Plant temperatures. Agron J 55:210CrossRefGoogle Scholar
  48. Testi L, Goldhamer DA, Iniesta F, Salinas M (2008) Crop water stress index is a sensitive water stress indicator in pistachio trees. Irrig Sci 26:395–405CrossRefGoogle Scholar
  49. Thom AS, Oliver HR (1977) On Penman’s equation for estimating regional evaporation. Q J R Meteorol Soc 103(436):345–357CrossRefGoogle Scholar
  50. Turner NC (1991) Measurement and influence of environmental factors on stomatal conductance in the field. Agric For Meteorol 54:137–154CrossRefGoogle Scholar
  51. Upchurch DR, Wanjura DF, Burke JJ, Mahan JR (1996) Biologically-identified optimal temperature interactive console (BIOTIC) for managing irrigation. US Patent 5(539):637Google Scholar
  52. Van Bavel CHM, Ehrler WL (1968) Water loss from a sorghum field and stomatal control. Agron J 60:84–86Google Scholar
  53. Walker GK, Hatfield JL (1979) Test of the stress-degree-day concept using multiple planting dates of red kidney beans. Agron J 71:967–971CrossRefGoogle Scholar
  54. Wang D, Gartung J (2010) Infrared canopy temperature of early-ripening peach trees under post harvest deficit irrigation. Agric Water Manag 97(11):1787–1794CrossRefGoogle Scholar
  55. Wanjura DF, Upchurch DR, Mahan JR (1995) Control of irrigation scheduling using temperature-time thresholds. Trans ASAE 38:403–409CrossRefGoogle Scholar
  56. Yuan G, Luo Y, Sun X, Tang D (2004) Evaluation of a crop water stress index for detecting water stress in winter wheat in the North China Plain. Agric Water Manag 64:29–40CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Manoj Kumar Nanda
    • 1
  • Utpal Giri
    • 2
  • Nimai Bera
    • 3
  1. 1.Bidhan Chandra Krishi ViswavidyalayaMohanpur, NadiaIndia
  2. 2.College of Agriculture, TripuraLembucherraIndia
  3. 3.Regional Research Centre, ICAR-Central Institute of Freshwater AquacultureKalyani, NadiaIndia

Personalised recommendations