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Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest

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  • French, Andrew N.
  • Hunsaker, Douglas J.
  • Sanchez, Charles A.
  • Saber, Mazin
  • Gonzalez, Juan Roberto
  • Anderson, Ray

Abstract

A three-year study was conducted to assess the ability of satellite-based vegetation index (VI) images to track evapotranspiration over wheat. While the ability of using VIs, notably with the Normalized Difference Vegetation Index (NDVI), to track vegetation growth has been well established, the operational capability to accurately estimate the crop coefficient (Kc) and crop evapotranspiration (ETc) at farm-scale from spaceborne platforms has not been widely studied. The study evaluated wheat ET over 7 sites between 2016 and 2019 in Yuma and Maricopa, Arizona, USA estimated by using Sentinel 2 and Venus satellites to map NDVI time-series for entire wheat cropping seasons, December to June. The basal crop coefficient (Kcb) was modeled by the NDVI time-series and the daily FAO56 reference ETo was obtained by near-by weather network stations. Eddy covariance (EC) stations in each field observed ETc during the same seasonal periods, and applied irrigation amounts were logged. The experiment found that remote sensing of NDVI and modeled Kcb accurately estimated Kc and crop ET during mid-season through senescence in most cases. However, NDVI-based estimation performed less well during early season (<60 days after planting), when observed ETc was highly variable due to frequent rain and irrigation at low crop cover. Mid-season Kc values observed for the seven wheat fields were from 0.92 to 1.14, and end of season Kc values ranged from about 0.20 to 0.40, in close agreement to values reported elsewhere. Seasonal VI-based transpiration and ETc values ranged from 467 to 618 mm, closely agreeing with seasonal EC data, which ranged 499–684 mm. Using the Venus sensor, the study in Maricopa in 2019 revealed that when augmented by a background soil water balance model, water stressed wheat can be detected mid-season with NDVI. This capability is specifically due to the sensor’s ability to provide well-calibrated images every 2 days. Findings from this study will help farmers, irrigators, and water managers use and understand the capabilities of visible near infrared remote sensing to track ETc from space.

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  • French, Andrew N. & Hunsaker, Douglas J. & Sanchez, Charles A. & Saber, Mazin & Gonzalez, Juan Roberto & Anderson, Ray, 2020. "Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest," Agricultural Water Management, Elsevier, vol. 239(C).
  • Handle: RePEc:eee:agiwat:v:239:y:2020:i:c:s037837742030233x
    DOI: 10.1016/j.agwat.2020.106266
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    1. Er-Raki, S. & Chehbouni, A. & Boulet, G. & Williams, D.G., 2010. "Using the dual approach of FAO-56 for partitioning ET into soil and plant components for olive orchards in a semi-arid region," Agricultural Water Management, Elsevier, vol. 97(11), pages 1769-1778, November.
    2. Duchemin, B. & Hadria, R. & Erraki, S. & Boulet, G. & Maisongrande, P. & Chehbouni, A. & Escadafal, R. & Ezzahar, J. & Hoedjes, J.C.B. & Kharrou, M.H. & Khabba, S. & Mougenot, B. & Olioso, A. & Rodrig, 2006. "Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices," Agricultural Water Management, Elsevier, vol. 79(1), pages 1-27, January.
    3. Jayanthi, Harikishan & Neale, Christopher M.U. & Wright, James L., 2007. "Development and validation of canopy reflectance-based crop coefficient for potato," Agricultural Water Management, Elsevier, vol. 88(1-3), pages 235-246, March.
    4. Gonzalez-Dugo, M.P. & Mateos, L., 2008. "Spectral vegetation indices for benchmarking water productivity of irrigated cotton and sugarbeet crops," Agricultural Water Management, Elsevier, vol. 95(1), pages 48-58, January.
    5. Taylor, Richard D. & Koo, Won W., 2015. "2015 Outlook of the U.S. and World Wheat Industries, 2015-2024," Agribusiness & Applied Economics Report 201310, North Dakota State University, Department of Agribusiness and Applied Economics.
    6. Drerup, Philipp & Brueck, Holger & Scherer, Heinrich W., 2017. "Evapotranspiration of winter wheat estimated with the FAO 56 approach and NDVI measurements in a temperate humid climate of NW Europe," Agricultural Water Management, Elsevier, vol. 192(C), pages 180-188.
    7. Bausch, Walter C., 1995. "Remote sensing of crop coefficients for improving the irrigation scheduling of corn," Agricultural Water Management, Elsevier, vol. 27(1), pages 55-68, April.
    8. Campos, Isidro & Neale, Christopher M.U. & Calera, Alfonso & Balbontín, Claudio & González-Piqueras, Jose, 2010. "Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.)," Agricultural Water Management, Elsevier, vol. 98(1), pages 45-54, December.
    9. Pôças, I. & Calera, A. & Campos, I. & Cunha, M., 2020. "Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches," Agricultural Water Management, Elsevier, vol. 233(C).
    10. Er-Raki, S. & Chehbouni, A. & Guemouria, N. & Duchemin, B. & Ezzahar, J. & Hadria, R., 2007. "Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region," Agricultural Water Management, Elsevier, vol. 87(1), pages 41-54, January.
    11. Rozenstein, Offer & Haymann, Nitai & Kaplan, Gregoriy & Tanny, Josef, 2018. "Estimating cotton water consumption using a time series of Sentinel-2 imagery," Agricultural Water Management, Elsevier, vol. 207(C), pages 44-52.
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