EGU2020-19331
https://doi.org/10.5194/egusphere-egu2020-19331
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Evaluation of Sentinel-2 SMI and Sentinel-3 SLSTR data for estimating evapotranspiration in an irrigated olive orchard in Southern Iberian Peninsula.

Sergio David Aguirre García1,2, Sergio Aranda-Barranco1,2, Hector Nieto3, Penélope Serrano-Ortiz1,2, Enrique P. Sánchez-Cañete2,4, and Juan L. Guerrero-Rascado2,4
Sergio David Aguirre García et al.
  • 1University of Granada, Ecology Department, 18010 Granada, Spain (sergiodag@ugr.es)
  • 2Andalusian Institute Earth System Research IISTA–CEAMA, 18006 Granada, Spain
  • 3Complutum Tecnologías de la Información Geográfica S.L. (COMPLUTIG), Colegios 2, 28801 Alcalá de Henares
  • 4University of Granada, Applied Physics Department, 18010 Granada, Spain

Olive trees are one of the most important crops in the Mediterranean basin (10.5 Mha), accounting for 97.5% of the world’s olive cultivation area with relevant social and economic benefits and ecological consequences. Concretely, it takes up 2.7 Mha in Spain, of which more than 1.6 are in Andalusia. Olive cultivation demands climate-smart management to facilitate crop adaptation to climate scenario and predictable development. A more efficient water use and management optimization is an especially important issue and, therefore, quantifying and modeling evapotranspiration (ET) is essential.
However, given the lack of a satellite thermal mission with both high spatial resolution and frequent revisit time, we have evaluated in this work a data fusion methodology (Gao et al., 2012) that combines Sentinel-2 and Sentinel-3 images with the two-source energy balance model (Norman et al.,1995) proposed by Guzinski & Nieto et al. (2019). Estimates of actual ET were produced at 20 m resolution from January 2016 to December 2019 in an irrigated olive grove in Southern Iberian Peninsula. Preliminary results have been validated (every 5-10 days depending on Sentinel images availability and cloud cover) by ground-based in situ data using Eddy Covariance (EC) technique, showing mean absolute errors between estimated values and those obtained by EC: 156 Wm-2 (net radiation), 76 Wm-2 (soil heat flux), 36 Wm-2 (sensible heat flux), 210 Wm-2 (latent heat flux).

Gao, F., Kustas, W. P., & Anderson, M. C. (2012). A data mining approach for sharpening thermal satellite imagery over land. Remote Sensing, 4(11), 3287–3319. https://doi.org/10.3390/rs4113287

Guzinski, R., & Nieto, H. (2019). Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high resolution evapotranspiration estimations. Remote Sensing of Environment, 221, 157–172. https://doi.org/10.1016/j.rse.2018.11.019

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(3–4), 263–293. https://doi.org/10.1016/01681923(95)02265Y

How to cite: Aguirre García, S. D., Aranda-Barranco, S., Nieto, H., Serrano-Ortiz, P., Sánchez-Cañete, E. P., and Guerrero-Rascado, J. L.: Evaluation of Sentinel-2 SMI and Sentinel-3 SLSTR data for estimating evapotranspiration in an irrigated olive orchard in Southern Iberian Peninsula., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19331, https://doi.org/10.5194/egusphere-egu2020-19331, 2020

Comments on the presentation

AC: Author Comment | CC: Community Comment | Report abuse

Presentation version 1 – uploaded on 03 May 2020
  • CC1: Comment on EGU2020-19331, Hamideh Nouri, 06 May 2020

    Interesting research. Could you please explain how you decided to use thermal data not optical?

    • AC1: Reply to CC1, Sergio David Aguirre García, 06 May 2020

      Thanks for your comment Hamideh Nouri.

      The TSEB model is based on the relationship between net radiation, soil heat flux and sensible and latent heat fluxes those depend on their temperatures, and the emissive portion of the terrestrial spectrum, where the heat from the study surface is detected is located in the range of thermal infrared. In addition, thermal data are necessary to detect water stress in the short term, something that we are not capable to get through multispectral data.

      On the one hand, we used the S8 and S9 bands (centered at 10.854 and 12.0225 micrometers respectively of thermal infrared spectrum) of SLSTR sensor onboard Sentinel-3 to get heat fluxes, and on the other hand we use the MSI (MultiSpectral Instrument, in optical, near and shortwave infrared spectrum) onboard Sentinel-2 to characterize the vegetation state and to sharpen SLSTR imagery at MSI resolution (20 m), because of lack of satellite thermal mission with high spatial resolution.

  • AC2: Comment on EGU2020-19331, Sergio David Aguirre García, 07 May 2020

    As the chat has gone very fast for me (thinking in English), I would like to answer some questions that I have left, so:

    - Have you used the SNAP plug-in to implement the method? Is it working?

    SNAP senet plugin works but we have used a more efficient implementation of the algorithm, so we have barely used SNAP only for a few Sentinel preprocessing tasks.

    - Do S3 data come from Copernicus scientific hub?

    Yes.

    - Have you validated Sentinel-3 LST?

    Not at this specifit site but S3 LST team states that the product agrees well with in situ measurements.

    - Which specific Sentinel 2 bands did you use?

    All he bands
    Some complementary information:

    • CC2: Reply to AC2, Hamideh Nouri, 07 May 2020

      Thanks for all the info Sergio. All the best.