IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v276y2023ics0378377422006035.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422006035
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2022.108056?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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. Mahmoud, Shereif H. & Gan, Thian Yew, 2019. "Irrigation water management in arid regions of Middle East: Assessing spatio-temporal variation of actual evapotranspiration through remote sensing techniques and meteorological data," Agricultural Water Management, Elsevier, vol. 212(C), pages 35-47.
    4. Teixeira, Antônio & Leivas, Janice & Struiving, Tiago & Reis, João & Simão, Fúlvio, 2021. "Energy balance and irrigation performance assessments in lemon orchards by applying the SAFER algorithm to Landsat 8 images," Agricultural Water Management, Elsevier, vol. 247(C).
    5. Pereira, L.S. & Paredes, P. & López-Urrea, R. & Hunsaker, D.J. & Mota, M. & Mohammadi Shad, Z., 2021. "Standard single and basal crop coefficients for vegetable crops, an update of FAO56 crop water requirements approach," Agricultural Water Management, Elsevier, vol. 243(C).
    6. Mokhtari, Ali & Noory, Hamideh & Vazifedoust, Majid & Bahrami, Mahdi, 2018. "Estimating net irrigation requirement of winter wheat using model- and satellite-based single and basal crop coefficients," Agricultural Water Management, Elsevier, vol. 208(C), pages 95-106.
    7. Jovanovic, N. & Pereira, L.S. & Paredes, P. & Pôças, I. & Cantore, V. & Todorovic, M., 2020. "A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods," Agricultural Water Management, Elsevier, vol. 239(C).
    8. Wang, Tianxin & Melton, Forrest S. & Pôças, Isabel & Johnson, Lee F. & Thao, Touyee & Post, Kirk & Cassel-Sharma, Florence, 2021. "Evaluation of crop coefficient and evapotranspiration data for sugar beets from landsat surface reflectances using micrometeorological measurements and weighing lysimetry," Agricultural Water Management, Elsevier, vol. 244(C).
    9. Mokhtari, Ali & Noory, Hamideh & Vazifedoust, Majid & Palouj, Mojtaba & Bakhtiari, Atousa & Barikani, Elham & Zabihi Afrooz, Ramezan Ali & Fereydooni, Fatemeh & Sadeghi Naeni, Ali & Pourshakouri, Farr, 2019. "Evaluation of single crop coefficient curves derived from Landsat satellite images for major crops in Iran," Agricultural Water Management, Elsevier, vol. 218(C), pages 234-249.
    10. Campos, Isidro & Balbontín, Claudio & González-Piqueras, Jose & González-Dugo, Maria P. & Neale, Christopher M.U. & Calera, Alfonso, 2016. "Combining a water balance model with evapotranspiration measurements to estimate total available soil water in irrigated and rainfed vineyards," Agricultural Water Management, Elsevier, vol. 165(C), pages 141-152.
    11. Salgado, Ramiro & Mateos, Luciano, 2021. "Evaluation of different methods of estimating ET for the performance assessment of irrigation schemes," Agricultural Water Management, Elsevier, vol. 243(C).
    12. 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.
    13. El Hajj, Marcel M. & Johansen, Kasper & Almashharawi, Samer K. & McCabe, Matthew F., 2023. "Water uptake rates over olive orchards using Sentinel-1 synthetic aperture radar data," Agricultural Water Management, Elsevier, vol. 288(C).
    14. Yousaf, Wasif & Awan, Wakas Karim & Kamran, Muhammad & Ahmad, Sajid Rashid & Bodla, Habib Ullah & Riaz, Mohammad & Umar, Muhammad & Chohan, Khurram, 2021. "A paradigm of GIS and remote sensing for crop water deficit assessment in near real time to improve irrigation distribution plan," Agricultural Water Management, Elsevier, vol. 243(C).
    15. 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).
    16. Consoli, S. & Vanella, D., 2014. "Mapping crop evapotranspiration by integrating vegetation indices into a soil water balance model," Agricultural Water Management, Elsevier, vol. 143(C), pages 71-81.
    17. Carpintero, E. & Mateos, L. & Andreu, A. & González-Dugo, M.P., 2020. "Effect of the differences in spectral response of Mediterranean tree canopies on the estimation of evapotranspiration using vegetation index-based crop coefficients," Agricultural Water Management, Elsevier, vol. 238(C).
    18. Campos, Isidro & Neale, Christopher M.U. & Suyker, Andrew E. & Arkebauer, Timothy J. & Gonçalves, Ivo Z., 2017. "Reflectance-based crop coefficients REDUX: For operational evapotranspiration estimates in the age of high producing hybrid varieties," Agricultural Water Management, Elsevier, vol. 187(C), pages 140-153.
    19. Garrido-Rubio, Jesús & González-Piqueras, Jose & Campos, Isidro & Osann, Anna & González-Gómez, Laura & Calera, Alfonso, 2020. "Remote sensing–based soil water balance for irrigation water accounting at plot and water user association management scale," Agricultural Water Management, Elsevier, vol. 238(C).
    20. Zhang, Yu & Han, Wenting & Zhang, Huihui & Niu, Xiaotao & Shao, Guomin, 2023. "Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach," Agricultural Water Management, Elsevier, vol. 275(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:276:y:2023:i:c:s0378377422006035. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.