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Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index

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  • Kaplan, Gregoriy
  • Fine, Lior
  • Lukyanov, Victor
  • Malachy, Nitzan
  • Tanny, Josef
  • Rozenstein, Offer

Abstract

In cotton, an optimal balance between vegetative and reproductive growth will lead to high yields and water-use efficiency. Remote sensing estimations of vegetation variables such as crop coefficient (Kc), Leaf Area Index (LAI), and crop height during plant development can improve irrigation management. Optical and Synthetic Aperture Radar (SAR) satellite imagery can be a useful data source since they provide synoptic cover at fixed time intervals. Furthermore, they can better capture the spatial variability in the field compared to point measurements. Since clouds limit optical observations at times, the combination with SAR can provide information during cloudy periods. This study utilized optical imagery acquired by Sentinel-2 and SAR imagery acquired by Sentinel-1 over cotton fields in Israel. The Sentinel-2-based vegetation indices that are best suited for cotton monitoring were identified, and the most robust Sentinel-2 models for Kc, LAI, and height estimation achieved R2 = 0.879, RMSE= 0.0645 (MERIS Terrestrial Chlorophyll Index, MTCI); R2 = 0.9535, RMSE= 0.8 (MTCI); and R2 = 0.8883, RMSE= 10 cm (Enhanced Vegetation Index, EVI), respectively. Additionally, a model based on the output of the SNAP Biophysical Processor LAI estimation algorithm was superior to the empirical LAI models of the best-performing vegetation indices (R2 =0.9717, RMSE=0.6). The most robust Sentinel-1 models were obtained by applying an innovative local incidence angle normalization method with R2 = 0.7913, RMSE= 0.0925; R2 = 0.6699, RMSE= 2.3; R2 = 0.6586, RMSE= 18 cm for the Kc, LAI, and height estimation, respectively. This work paves the way for future studies to design decision support systems for better irrigation management in cotton, even at the sub-plot level, by monitoring the heterogeneous development of the crop from space and adapting the irrigation accordingly to reach the target development at different growth stages during the season.

Suggested Citation

  • Kaplan, Gregoriy & Fine, Lior & Lukyanov, Victor & Malachy, Nitzan & Tanny, Josef & Rozenstein, Offer, 2023. "Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index," Agricultural Water Management, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:agiwat:v:276:y:2023:i:c:s0378377422006035
    DOI: 10.1016/j.agwat.2022.108056
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    References listed on IDEAS

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    1. González-Dugo, M.P. & Escuin, S. & Cano, F. & Cifuentes, V. & Padilla, F.L.M. & Tirado, J.L. & Oyonarte, N. & Fernández, P. & Mateos, L., 2013. "Monitoring evapotranspiration of irrigated crops using crop coefficients derived from time series of satellite images. II. Application on basin scale," Agricultural Water Management, Elsevier, vol. 125(C), pages 92-104.
    2. Gregoriy Kaplan & Lior Fine & Victor Lukyanov & V. S. Manivasagam & Josef Tanny & Offer Rozenstein, 2021. "Normalizing the Local Incidence Angle in Sentinel-1 Imagery to Improve Leaf Area Index, Vegetation Height, and Crop Coefficient Estimations," Land, MDPI, vol. 10(7), pages 1-23, June.
    3. Gregoriy Kaplan & Offer Rozenstein, 2021. "Spaceborne Estimation of Leaf Area Index in Cotton, Tomato, and Wheat Using Sentinel-2," Land, MDPI, vol. 10(5), pages 1-13, May.
    4. Rozenstein, Offer & Haymann, Nitai & Kaplan, Gregoriy & Tanny, Josef, 2019. "Validation of the cotton crop coefficient estimation model based on Sentinel-2 imagery and eddy covariance measurements," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    5. Mateos, L. & González-Dugo, M.P. & Testi, L. & Villalobos, F.J., 2013. "Monitoring evapotranspiration of irrigated crops using crop coefficients derived from time series of satellite images. I. Method validation," Agricultural Water Management, Elsevier, vol. 125(C), pages 81-91.
    6. Pereira, L.S. & Paredes, P. & Hunsaker, D.J. & López-Urrea, R. & Mohammadi Shad, Z., 2021. "Standard single and basal crop coefficients for field crops. Updates and advances to the FAO56 crop water requirements method," Agricultural Water Management, Elsevier, vol. 243(C).
    7. 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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    Cited by:

    1. Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).
    2. Xianglong Fan & Xin Lv & Pan Gao & Lifu Zhang & Ze Zhang & Qiang Zhang & Yiru Ma & Xiang Yi & Caixia Yin & Lulu Ma, 2022. "Establishment of a Monitoring Model for the Cotton Leaf Area Index Based on the Canopy Reflectance Spectrum," Land, MDPI, vol. 12(1), pages 1-19, December.

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