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Effect of observation scale on remote sensing based estimates of evapotranspiration in a semi-arid row cropped orchard environment

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Abstract

Understanding in detail the spatial distribution of evapotranspiration (ET) in row cropped fruit production areas with diverse water requirements is vital for monitoring water use and efficient irrigation scheduling. Spatially distributed ET for these environments can be estimated using remote sensing (RS). However, the computation of RS based ET under such conditions is complicated because of the complex parameterizations that are required to derive ET for the mixed pixels comprising of bare soil and well-watered plants typical of row cropped areas. Also, the parameterization of these processes is not scale invariant, owing to change in the percentage of vegetation cover in the mixed pixels across remote sensing observation scales. In this study, our main objectives were (1) to isolate and evaluate the effect of varying spatial scales (comparable to canopy sizes and larger) of the remote sensing data on ET estimates; and (2) provide an operational method for estimating remote sensing based ET for row cropped conditions. ET was computed using an empirical technique (S-SEBI: Simplified-Surface Energy Balance Index Algorithm) for almond and pistachio orchards from remote sensing imagery collected at a scale comparable to the canopy sizes of the trees (5.8 and 7.2 m) and a scale that was much larger than the canopy size (120 m) using the MASTER and Landsat sensors, respectively. In order to account for the effect of mixed pixels, a Normalized Difference Vegetation Index based correction factor was applied to the derived ET values and the results averaged for different fields were validated with Penman–Monteith based ET estimates. It was found that the corrected mean ET estimates at 120 m were in agreement with the Penman–Monteith based ET estimates (RMSEaverage = 0.12 mm/h), whereas they were underestimated at the finer resolutions. Our results indicated that a remote sensing pixel resolution comparable to the row spacing and smaller and comparable to the canopy size overestimated the land surface temperature and consequently, underestimated ET when using operational models that do not account for vegetation and soil temperature separately. The results of the application of the NDVI correction factor indicates that good spatial estimates of crop ET can be made for crops growing in orchards using simple ET models that require minimal data and freely available Landsat imagery. These findings are very encouraging for the regular monitoring of crop health and effective management of irrigation water in highly water stressed agricultural environments.

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References

  • Allen, R. G., & Pereira, L. S. (2009). Estimating crop coefficients from fraction of ground cover and height. Irrigation Science, 28(1), 17–34. doi:10.1007/s00271-009-0182-z.

    Article  Google Scholar 

  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome 300: 6541. http://www.fao.org/docrep/X0490E/X0490E00.htm. Accessed 5 Dec 2016.

  • Allen, R. G., Pruitt, W. O., Wright, J. L., Howell, T. A., Ventura, F., Snyder, R., et al. (2006). A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO56 Penman-Monteith method. Agricultural Water Management, 81(1), 1–22. doi:10.1016/j.agwat.2005.03.007.

    Article  Google Scholar 

  • Bastiaanssen, W., 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, 198–212. doi:10.1016/S0022-1694(98)00253-4.

    Article  Google Scholar 

  • Brutsaert, W. (1975). On a derivable formula for long-wave radiation from clear skies. Water Resources Research, 11(5), 742–744. doi:10.1029/WR011i005p00742.

    Article  Google Scholar 

  • Brutsaert, W., & Chen, D. (1996). Diurnal variation of surface fluxes during thorough drying (or severe drought) of natural prairie. Water Resources Research, 32(7), 2013–2019. doi:10.1029/96WR00995.

    Article  Google Scholar 

  • Caselles, V., & Sobrino, J. È. A. (1989). Determination of frosts in orange groves from NOAA-9 AVHRR data. Remote Sensing of Environment, 29(2), 135–146. doi:10.1016/0034-4257(89)90022-9.

    Article  Google Scholar 

  • Chang, N. B., & Hong, Y. (2012). Multiscale hydrologic remote sensing: Perspectives and applications. Boca Raton: CRC Press Inc.

    Book  Google Scholar 

  • Chehbouni, A., Escadafal, R., Duchemin, B., Boulet, G., Simonneaux, V., Dedieu, G., et al. (2008). An integrated modelling and remote sensing approach for hydrological study in arid and semi-arid regions: The SUDMED Programme. International Journal of Remote Sensing, 29(17–18), 5161–5181. doi:10.1080/01431160802036417.

    Article  Google Scholar 

  • Cheng, T., Riaño, D., Koltunov, A., Whiting, M. L., Ustin, S. L., & Rodriguez, J. (2013). Detection of diurnal variation in orchard canopy water content using MODIS/ASTER airborne simulator (MASTER) data. Remote Sensing of Environment, 132, 1–12. doi:10.1016/j.rse.2012.12.024.

    Article  Google Scholar 

  • Chirouze, J., Boulet, G., Jarlan, L., Fieuzal, R., Rodriguez, J. C., Ezzahar, J., et al. (2014). Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate. Hydrology and Earth System Sciences, 18, 1165–1188. doi:10.5194/hess-18-1165-2014.

    Article  Google Scholar 

  • Crago, R. (1996). Conservation and variability of the evaporative fraction during the daytime. Journal of Hydrology, 180(1–4), 173–194. doi:10.1016/0022-1694(95)02903-6.

    Article  Google Scholar 

  • Daughtry, C. S. T., Kustas, W. P., Moran, M. S., Pinter, P. J., Jr., Jackson, R. D., Brown, P. W., et al. (1990). Spectral estimates of net radiation and soil heat flux. Remote Sensing of Environment, 32(2–3), 111–124. doi:10.1016/0034-4257(90)90012-B.

    Article  Google Scholar 

  • 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. Irrigation Science, 26(3), 223–237. doi:10.1007/s00271-007-0088-6.

    Article  Google Scholar 

  • Henderson-Sellers, B. (1984). A new formula for latent heat of vaporization of water as a function of temperature. Quarterly Journal of the Royal Meteorological Society, 110(466), 1186–1190. doi:10.1002/qj.49711046626.

    Article  Google Scholar 

  • Irish, R. R. Landsat 7 science data users handbook. National Aeronautics and Space Administration, Report (2000): 430-15

  • Kustas, W., Li, F., Jackson, T. J., Prueger, J. H., MacPherson, J. I., & Wolde, M. (2004). Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa. Remote Sensing of Environment, 92(4), 535–547. doi:10.1016/j.rse.2004.02.020.

    Article  Google Scholar 

  • Kustas, W., Perry, E., Doraiswamy, P. C., & Moran, M. S. (1994). Using satellite remote sensing to extrapolate ET estimates in time and space over a semiarid rangeland basin. Remote Sensing of Environment, 49(3), 275–286. doi:10.1016/0034-4257(94)90022-1.

    Article  Google Scholar 

  • Mauser, W., & Schadlich, S. (1998). Modelling the spatial distribution of ET on different scales using remote sensing data. Journal of Hydrology, 212, 250–267. doi:10.1016/S0022-1694(98)00228-5.

    Article  Google Scholar 

  • McCabe, M. F., Balick, L. K., Theiler, J., Gillespie, A. R., & Mushkin, A. (2008). Linear mixing in thermal infrared temperature retrieval. International Journal of Remote Sensing, 29(17–18), 5047–5061. doi:10.1080/01431160802036474.

    Article  Google Scholar 

  • McCabe, M. F., & Wood, E. F. (2006). Scale influences on the remote estimation of ET using multiple satellite sensors. Remote Sensing of Environment, 105(4), 271–285. doi:10.1016/j.rse.2006.07.006.

    Article  Google Scholar 

  • Moran, M. S., Humes, K. S., & Pinter, P. J., Jr. (1997). The scaling characteristics of remotely-sensed variables for sparsely-vegetated heterogeneous landscapes. Journal of Hydrology, 190(3), 337–362. doi:10.1016/S0022-1694(96)03133-2.

    Article  Google Scholar 

  • Price, J. C. (1990). Using spatial context in satellite data to infer regional scale evapotranspiratoin. IEEE Transactions on Geoscience and Remote Sensing, 28(5), 940–948. doi:10.1109/36.58983.

    Article  Google Scholar 

  • Pruitt, W.O. & Doorenbos, J. (1977). Proceeding of the International Round Table Conference on “Evapotranspiration”. Budapest, Hungary.

  • Roerink, G., Su, Z., & Mementi, M. (2000). S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25(2), 147–157. doi:10.1016/S1464-1909(99)00128-8.

    Article  Google Scholar 

  • Roy, S., Ophori, D. & Kefauver, S. (2013). Estimation of actual evapotranspiration using surface energy balance algorithms for land model: A case study in San Joaquin Valley, California. Journal of Environmental Hydrology, 21, Paper 14.

  • Shuttleworth, W. J., Gurney, R. J., Hsu, A. Y., & Ormsby, J. P. (1989). FIFE: The variation in energy partition at surface flux sites. IAHS Publication, 186, 67–74.

    Google Scholar 

  • Sobrino, J. A., Gómez, M., Jiménez-Muñoz, J. C., & Olioso, A. (2007). Application of a simple algorithm to estimate daily evapotranspiration from NOAA–AVHRR images for the Iberian Peninsula. Remote Sensing of Environment, 110(2), 139–148. doi:10.1016/j.rse.2007.02.017.

    Article  Google Scholar 

  • Stagakis, S., González-Dugo, V., Cid, P., Guillén-Climent, M. L., & Zarco-Tejada, P. J. (2012). Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 47–61. doi:10.1016/j.isprsjprs.2012.05.003.

    Article  Google Scholar 

  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. doi:10.1016/0034-4257(79)90013-0.

    Article  Google Scholar 

  • Valor, E., & Caselles, V. (1996). Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Remote Sensing of Environment, 57(3), 167–184. doi:10.1016/0034-4257(96)00039-9.

    Article  Google Scholar 

  • Verstraeten, W., Veroustraete, F., & Feyen, J. (2005). Estimating evapotranspiration of European forests from NOAA-imagery at satellite overpass time: Towards an operational processing chain for integrated optical and thermal sensor data products. Remote Sensing of Environment, 96(2), 256–276. doi:10.1016/j.rse.2005.03.004.

    Article  Google Scholar 

Download references

Acknowledgement

The effort of Nick Clinton in atmospherically correcting the MASTER data and Cassie Knierim in providing calibrated MASTER land surface temperatures (2009) is acknowledged as is the effort of the entire agricultural group from the Student Airborne Program held in 2009 and 2010 and Blake Sanden, for collecting ground truth data. The data for the MASTER sensor was obtained as part of the Student Airborne Research Program, organized by NSERC in collaboration with NASA. The authors would also like to thank the organizers and mentors of the SARP program – Rick Shetter and Dr. Susan L. Ustin. Finally, we acknowledge the funding support of NASA Earth and Space Science Fellowship (NNX13AN64H), NASA THPs (NNX08AF55G and NNX09AK73G) and NSF (DMS-09-34837) grants.

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Correspondence to Nandita Gaur.

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Gaur, N., Mohanty, B.P. & Kefauver, S.C. Effect of observation scale on remote sensing based estimates of evapotranspiration in a semi-arid row cropped orchard environment. Precision Agric 18, 762–778 (2017). https://doi.org/10.1007/s11119-016-9486-1

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