Combing both simulated and field-measured data to develop robust hyperspectral indices for tracing canopy transpiration in drought-tolerant plant

  • Jia Jin
  • Quan WangEmail author
  • Jinlin Wang


Transpiration plays a key role in water and energy fluxes at various scales. While in recent remote sensing offers a fast and convenient method for tracing transpiration at multiple scales, the approach is mostly indirect and relies on energy balance. Although several hyperspectral indices have been reported to show potentials for tracing transpiration directly, both at leaf and canopy scales, they remain in pioneer stages and need extensive validations. In this study, we used the Soil, Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model calibrated to arid ecosystems in Central Asia, to generate a simulated dataset for validation. Furthermore, new and robust indices have been developed by combining both simulated and in situ measured datasets. Results suggested that the SR(1525, 2150), ND(1425, 2145), and previously reported index of dSR(660,1040) have significant relationships with both simulated and in situ measured transpiration. Further analyses revealed that the ND(1425,2145) shows consistent performance, even with different methodologies of combining simulation and field-measured datasets. Statistically significant results were obtained in this study, even for a dominant drought-tolerant species in arid land, a place that typically has weak vegetation reflectance under strong background radiation. We foresee the approach being conducted in other regions where vegetation reflectance dominates. This may lead to robust hyperspectral indices being developed for directly tracing transpiration at various scales.


Arid ecosystem Haloxylon ammodendron ND((1425,2145) SCOPE model 



We thank the members of the Quantitative Remote Sensing Group for their help on field works.

Funding information

This study is financially supported by the NSFC project (Grant No. 41371364).


  1. Allen, R. G., Tasumi, M., & Trezza, R. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of Irrigation and Drainage Engineering, 133(4), 380–394.CrossRefGoogle Scholar
  2. Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212–213, 198–212.CrossRefGoogle Scholar
  3. Chen, D., Huang, J., & Jackson, T. J. (2005). Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands. Remote Sensing of Environment, 98(2–3), 225–236.CrossRefGoogle Scholar
  4. Chen, Y., Xu, C., Chen, Y., Liu, Y., & Li, W. (2013). Progress, challenges and prospects of eco-hydrological studies in the Tarim River basin of Xinjiang, China. [journal article]. Environmental Management, 51(1), 138–153.CrossRefGoogle Scholar
  5. Cornell, J., & Berger, R. (1987). Factors that influence the value of the coefficient of determination in simple linear and nonlinear regression models. Phytopathology, 77(1), 63–70.CrossRefGoogle Scholar
  6. Courault, D., Seguin, B., & Olioso, A. (2005). Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches. [journal article]. Irrigation and Drainage Systems, 19(3), 223–249.CrossRefGoogle Scholar
  7. Daughtry, C. S. T., Biehl, L. L., & Ranson, K. J. (1989). A new technique to measure the spectral properties of conifer needles. Remote Sensing of Environment, 27(1), 81–91.CrossRefGoogle Scholar
  8. Dzikiti, S., Verreynne, J. S., Stuckens, J., Strever, A., Verstraeten, W. W., Swennen, R., & Coppin, P. (2010). Determining the water status of Satsuma mandarin trees [Citrus Unshiu Marcovitch] using spectral indices and by combining hyperspectral and physiological data. Agricultural and Forest Meteorology, 150(3), 369–379.CrossRefGoogle Scholar
  9. Eitel, J. U. H., Gessler, P. E., Smith, A. M. S., & Robberecht, R. (2006). Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. Forest Ecology and Management, 229(1–3), 170–182.CrossRefGoogle Scholar
  10. El Baki, A. M. A. A. (2013). Estimation of evapotranspiration from airborne hyperspectral scanner data using the SCOPE model. Enschede: University of Twente.Google Scholar
  11. El-Hendawy, S., Al-Suhaibani, N., Hassan, W., Tahir, M., & Schmidhalter, U. (2017). Hyperspectral reflectance sensing to assess the growth and photosynthetic properties of wheat cultivars exposed to different irrigation rates in an irrigated arid region. PLoS One, 12(8), e0183262.CrossRefGoogle Scholar
  12. Feret, J.-B., François, C., Asner, G. P., Gitelson, A. A., Martin, R. E., Bidel, L. P. R., Ustin, S. L., le Maire, G., & Jacquemoud, S. (2008). PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, 112(6), 3030–3043.CrossRefGoogle Scholar
  13. Féret, J.-B., François, C., Gitelson, A., Asner, G. P., Barry, K. M., Panigada, C., Richardson, A. D., & Jacquemoud, S. (2011). Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sensing of Environment, 115(10), 2742–2750.CrossRefGoogle Scholar
  14. Glenn, E. P., Nagler, P. L., & Huete, A. R. (2010). Vegetation index methods for estimating evapotranspiration by remote sensing. [journal article]. Surveys in Geophysics, 31(6), 531–555.CrossRefGoogle Scholar
  15. Gong, C., Wang, J., Hu, C., Wang, J., Ning, P., & Bai, J. (2015). Interactive response of photosynthetic characteristics in Haloxylon ammodendron and Hedysarum scoparium exposed to soil water and air vapor pressure deficits. Journal of Environmental Sciences, 34, 184–196.CrossRefGoogle Scholar
  16. Gowda, P. H., Chavez, J. L., Colaizzi, P. D., Evett, S. R., Howell, T. A., & Tolk, J. A. (2008). ET mapping for agricultural water management: Present status and challenges. [journal article]. Irrigation Science, 26(3), 223–237.CrossRefGoogle Scholar
  17. Granier, A. (1985). A new method of sap flow measurement in tree stems. Annales Des Sciences Forestieres, 42(2), 193–200.CrossRefGoogle Scholar
  18. Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3), 337–352.CrossRefGoogle Scholar
  19. Huang, G., & Li, Y. (2015). Phenological transition dictates the seasonal dynamics of ecosystem carbon exchange in a desert steppe. Journal of Vegetation Science, 26(2), 337–347.CrossRefGoogle Scholar
  20. Huang, G., & Li, Y. (2017). Photodegradation effects are related to precipitation amount, precipitation frequency and litter traits in a desert ecosystem. Soil Biology and Biochemistry, 115(Supplement C), 383–392.CrossRefGoogle Scholar
  21. Huché-Thélier, L., Crespel, L., Gourrierec, J. L., Morel, P., Sakr, S., & Leduc, N. (2016). Light signaling and plant responses to blue and UV radiations—Perspectives for applications in horticulture. Environmental and Experimental Botany, 121, 22–38.CrossRefGoogle Scholar
  22. Imanishi, J., Sugimoto, K., & Morimoto, Y. (2004). Detecting drought status and LAI of two Quercus species canopies using derivative spectra. Computers and Electronics in Agriculture, 43(2), 109–129.CrossRefGoogle Scholar
  23. Jin, J., & Wang, Q. (2016). Hyperspectral indices based on first derivative spectra closely trace canopy transpiration in a desert plant. Ecological Informatics, 35, 1–8.CrossRefGoogle Scholar
  24. Jin, J., Wang, Q., Wang, J., & Otieno, D. (2019). Tracing water and energy fluxes and reflectance in an arid ecosystem using the integrated model SCOPE. Journal of Environmental Management, 231, 1082–1090.CrossRefGoogle Scholar
  25. Katul, G. G., Oren, R., Manzoni, S., Higgins, C., & Parlange, M. B. (2012). Evapotranspiration: A process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system. Reviews of Geophysics, 50(3), RG3002.CrossRefGoogle Scholar
  26. Kustas, W. P., & Norman, J. M. (1999). Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agricultural and Forest Meteorology, 94(1), 13–29.CrossRefGoogle Scholar
  27. Kuusk, A. (2001). A two-layer canopy reflectance model. Journal of Quantitative Spectroscopy and Radiative Transfer, 71(1), 1–9.CrossRefGoogle Scholar
  28. le Maire, G., François, C., & Dufrêne, E. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89(1), 1–28.CrossRefGoogle Scholar
  29. le Maire, G., François, C., Soudani, K., Berveiller, D., Pontailler, J.-Y., Bréda, N., et al. (2008). Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sensing of Environment, 112(10), 3846–3864.CrossRefGoogle Scholar
  30. Li, Z., & Li, R. (1981). Anatomical observation of assimilating branches of nine xerophytes in Gansu. Acta Botanica Sinica, 23(3), 181–185.Google Scholar
  31. Li, P., & Wang, Q. (2012). Retrieval of chlorophyll for assimilating branches of a typical desert plant through inversed radiative transfer models. International Journal of Remote Sensing, 34(7), 2402–2416.CrossRefGoogle Scholar
  32. Li, P., & Wang, Q. (2013). Developing and validating novel hyperspectral indices for leaf area index estimation: Effect of canopy vertical heterogeneity. Ecological Indicators, 32, 123–130.CrossRefGoogle Scholar
  33. Li, S.-G., Asanuma, J., Kotani, A., Davaa, G., & Oyunbaatar, D. (2007). Evapotranspiration from a Mongolian steppe under grazing and its environmental constraints. Journal of Hydrology, 333(1), 133–143.CrossRefGoogle Scholar
  34. Li, L., Luo, G., Chen, X., Li, Y., Xu, G., Xu, H., & Bai, J. (2011). Modelling evapotranspiration in a Central Asian desert ecosystem. Ecological Modelling, 222(20–22), 3680–3691.CrossRefGoogle Scholar
  35. Marino, G., Pallozzi, E., Cocozza, C., Tognetti, R., Giovannelli, A., Cantini, C., & Centritto, M. (2014). Assessing gas exchange, sap flow and water relations using tree canopy spectral reflectance indices in irrigated and rainfed Olea europaea L. Environmental and Experimental Botany, 99, 43–52.CrossRefGoogle Scholar
  36. Marshall, M., Thenkabail, P., Biggs, T., & Post, K. (2016). Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation). Agricultural and Forest Meteorology, 218–219, 122–134.CrossRefGoogle Scholar
  37. McDowell, N. G., White, S., & Pockman, W. T. (2008). Transpiration and stomatal conductance across a steep climate gradient in the southern Rocky Mountains. Ecohydrology, 1(3), 193–204.CrossRefGoogle Scholar
  38. Monteith, J. L. (1965). Evaporation and environment. Paper presented at the Symposia of the society for experimental biology,Google Scholar
  39. Naithani, K. J., Ewers, B. E., & Pendall, E. (2012). Sap flux-scaled transpiration and stomatal conductance response to soil and atmospheric drought in a semi-arid sagebrush ecosystem. Journal of Hydrology, 464–465, 176–185.CrossRefGoogle Scholar
  40. Norman, J. M., Kustas, W. P., & Humes, K. S. (1995). Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology, 77(3), 263–293.CrossRefGoogle Scholar
  41. Pavan, G., Jacquemoud, S., De Rosny, G., Rambaut, J., Frangi, J., Bidel, L., et al. (2004). Ramis: A new portable field radiometer to estimate leaf biochemical content. In 7th International Conference on Precision Agriculture and Other Precision Resources Management (pp. 1366–1379).Google Scholar
  42. Penman, H. L. (1948). Natural evaporation from open water, bare soil and grass. Paper presented at the Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences,Google Scholar
  43. Philip, J. R. (1966). Plant water relations: Some physical aspects. Annual Review of Plant Physiology, 17(1), 245–268.CrossRefGoogle Scholar
  44. Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software, 79, 214–232.CrossRefGoogle Scholar
  45. Priestley, C. H. B., & Taylor, R. J. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100(2), 81–92.CrossRefGoogle Scholar
  46. Richter, K., Atzberger, C., Hank, T. B., & Mauser, W. (2012). Derivation of biophysical variables from Earth observation data: Validation and statistical measures. Journal of Applied Remote Sensing, 6(1), 063557.CrossRefGoogle Scholar
  47. Rodríguez-Pérez, J. R., Riaño, D., Carlisle, E., Ustin, S., & Smart, D. R. (2007). Evaluation of hyperspectral reflectance indexes to detect grapevine water status in vineyards. American Journal of Enology and Viticulture, 58(3), 302–317.Google Scholar
  48. Saltelli, A., Tarantola, S., & Chan, K. P. S. (1999). A quantitative model-independent method for global sensitivity analysis of model output. Technometrics, 41(1), 39–56.CrossRefGoogle Scholar
  49. Schaeffer, S. M., Williams, D. G., & Goodrich, D. C. (2000). Transpiration of cottonwood/willow forest estimated from sap flux. Agricultural and Forest Meteorology, 105(1–3), 257–270.CrossRefGoogle Scholar
  50. Sonobe, R., & Wang, Q. (2017). Towards a universal hyperspectral index to assess chlorophyll content in deciduous forests. Remote Sensing, 9(3), 191.CrossRefGoogle Scholar
  51. Su, Z. (2002). The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences, 6(1), 85–100.CrossRefGoogle Scholar
  52. Sun, P., Wahbi, S., Tsonev, T., Haworth, M., Liu, S., & Centritto, M. (2014). On the use of leaf spectral indices to assess water status and photosynthetic limitations in Olea europaea L. during water-stress and recovery. PLoS One, 9(8), e105165.CrossRefGoogle Scholar
  53. Thenkabail, P. S. (2015). Remote sensing of water resources, disasters, and urban studies. Boca Raton: CRC Press.CrossRefGoogle Scholar
  54. Thenkabail, P. S., Lyon, J. G., & Huete, A. (2012). Hyperspectral remote sensing of vegetation. CRC Press.Google Scholar
  55. Timmermans, W. J., Kustas, W. P., Anderson, M. C., & French, A. N. (2007). An intercomparison of the surface energy balance algorithm for land (SEBAL) and the two-source energy balance (TSEB) modeling schemes. Remote Sensing of Environment, 108(4), 369–384.CrossRefGoogle Scholar
  56. van der Tol, C. (2015). SCOPE Version 1.61 User Manual.Google Scholar
  57. van der Tol, C., Verhoef, W., Timmermans, J., Verhoef, A., & Su, Z. (2009). An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance. Biogeosciences, 6(12), 3109–3129.CrossRefGoogle Scholar
  58. Verstraeten, W. W., Veroustraete, F., & Feyen, J. (2008). Assessment of evapotranspiration and soil moisture content across different scales of observation. Sensors, 8(1), 70–117.CrossRefGoogle Scholar
  59. Wang, K., & Dickinson, R. E. (2012). A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews of Geophysics, 50(2), RG2005.CrossRefGoogle Scholar
  60. Wang, Q., & Jin, J. (2015). Leaf transpiration of drought tolerant plant can be captured by hyperspectral reflectance using PLSR analysis. [research articles]. iForest - Biogeosciences and Forestry, 9, 30–37.CrossRefGoogle Scholar
  61. Wang, Q., & Li, P. (2012a). Hyperspectral indices for estimating leaf biochemical properties in temperate deciduous forests: Comparison of simulated and measured reflectance data sets. Ecological Indicators, 14(1), 56–65.CrossRefGoogle Scholar
  62. Wang, Q., & Li, P. (2012b). Identification of robust hyperspectral indices on forest leaf water content using PROSPECT simulated dataset and field reflectance measurements. Hydrological Processes, 26(8), 1230–1241.CrossRefGoogle Scholar
  63. Wang, Q., & Li, P. (2013). Canopy vertical heterogeneity plays a critical role in reflectance simulation. Agricultural and Forest Meteorology, 169, 111–121.CrossRefGoogle Scholar
  64. Wang, S., Chen, X., Zhou, K., & Wang, Z. (2014). A preliminary study on the transpiration rate based on high spectral index method for Tamarix ramosissima in the southern periphery of the Gurbantunggut Desert. Journal of Desert Research, 34(4), 1023–1030.Google Scholar
  65. Yao, W., Han, M., & Xu, S. (2010). Estimating the regional evapotranspiration in Zhalong wetland with the two-source energy balance (TSEB) model and Landsat7/ETM+ images. Ecological Informatics, 5(5), 348–358.CrossRefGoogle Scholar
  66. Zheng, C., & Wang, Q. (2014). Water-use response to climate factors at whole tree and branch scale for a dominant desert species in central Asia: Haloxylon ammodendron. Ecohydrology, 7(1), 56–63.CrossRefGoogle Scholar
  67. Zheng, C., & Wang, Q. (2015). Seasonal and annual variation in transpiration of a dominant desert species, Haloxylon ammodendron, in Central Asia up-scaled from sap flow measurement. Ecohydrology, 8(5), 948–960.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Xinjiang Institute of Ecology and Geography, CASUrumqiChina
  2. 2.Faculty of AgricultureShizuoka UniversityShizuokaJapan

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