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Seasonal Crop Water Balance Using Harmonized Landsat-8 and Sentinel-2 Time Series Data

Viviana Gavilán / Mario Lillo-Saavedra / Eduardo Holzapfel / Diego Rivera / Angel García-Pedrero

Efficient water management in agriculture requires a precise estimate of evapotranspiration ( $E T$ ). Although local measurements can be used to estimate surface energy balance components, these values cannot be extrapolated to large areas due to the heterogeneity and complexity of agriculture environment. This extrapolation can be done using satellite images that provide information in visible and thermal infrared region of the electromagnetic spectrum; however, most current satellite sensors do not provide this end, but they do include a set of spectral bands that allow the radiometric behavior of vegetation that is highly correlated with the $E T$ . In this context, our working hypothesis states that it is possible to generate a strategy of integration and harmonization of the Normalized Difference Vegetation Index ( $N D V I$ ) obtained from Landsat-8 ( $L 8$ ) and Sentinel-2 ( $S 2$ ) sensors in order to obtain an $N D V I$ time series used to estimate $E T$ through fit equations specific to each crop type during an agricultural season (December 2017−March 2018). Based on the obtained results it was concluded that it is possible to estimate $E T$ using an $N D V I$ time series by integrating data from both sensors $L 8$ and $S 2$ , which allowed to carry out an updated seasonal water balance over study site, improving the irrigation water management both at plot and water distribution system scale.

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Seasonal Crop Water Balance Using Harmonized Landsat-8 and Sentinel-2 Time Series Data

Viviana Gavilán / Mario Lillo-Saavedra / Eduardo Holzapfel / Diego Rivera / Angel García-Pedrero

Efficient water management in agriculture requires a precise estimate of evapotranspiration ( $E T$ ). Although local measurements can be used to estimate surface energy balance components, these values cannot be extrapolated to large areas due to the heterogeneity and complexity of agriculture environment. This extrapolation can be done using satellite images that provide information in visible and thermal infrared region of the electromagnetic spectrum; however, most current satellite sensors do not provide this end, but they do include a set of spectral bands that allow the radiometric behavior of vegetation that is highly correlated with the $E T$ . In this context, our working hypothesis states that it is possible to generate a strategy of integration and harmonization of the Normalized Difference Vegetation Index ( $N D V I$ ) obtained from Landsat-8 ( $L 8$ ) and Sentinel-2 ( $S 2$ ) sensors in order to obtain an $N D V I$ time series used to estimate $E T$ through fit equations specific to each crop type during an agricultural season (December 2017−March 2018). Based on the obtained results it was concluded that it is possible to estimate $E T$ using an $N D V I$ time series by integrating data from both sensors $L 8$ and $S 2$ , which allowed to carry out an updated seasonal water balance over study site, improving the irrigation water management both at plot and water distribution system scale.

Seasonal Crop Water Balance Using Harmonized Landsat-8 and Sentinel-2 Time Series Data

Viviana Gavilán / Mario Lillo-Saavedra / Eduardo Holzapfel / Diego Rivera / Angel García-Pedrero

Efficient water management in agriculture requires a precise estimate of evapotranspiration ( $E T$ ). Although local measurements can be used to estimate surface energy balance components, these values cannot be extrapolated to large areas due to the heterogeneity and complexity of agriculture environment. This extrapolation can be done using satellite images that provide information in visible and thermal infrared region of the electromagnetic spectrum; however, most current satellite sensors do not provide this end, but they do include a set of spectral bands that allow the radiometric behavior of vegetation that is highly correlated with the $E T$ . In this context, our working hypothesis states that it is possible to generate a strategy of integration and harmonization of the Normalized Difference Vegetation Index ( $N D V I$ ) obtained from Landsat-8 ( $L 8$ ) and Sentinel-2 ( $S 2$ ) sensors in order to obtain an $N D V I$ time series used to estimate $E T$ through fit equations specific to each crop type during an agricultural season (December 2017−March 2018). Based on the obtained results it was concluded that it is possible to estimate $E T$ using an $N D V I$ time series by integrating data from both sensors $L 8$ and $S 2$ , which allowed to carry out an updated seasonal water balance over study site, improving the irrigation water management both at plot and water distribution system scale.

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Title:

Seasonal Crop Water Balance Using Harmonized Landsat-8 and Sentinel-2 Time Series Data

Contributors:

Viviana Gavilán (author) / Mario Lillo-Saavedra (author) / Eduardo Holzapfel (author) / Diego Rivera (author) / Angel García-Pedrero (author)

Published in:

Water, Vol 11, Iss 11, p 2236 (2019)

Publication date:

2019

ISSN:

DOI:

https://doi.org/10.3390/w11112236

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

Unknown

Keywords:

agricultural water management , evapotranspiration , harmonization remote sensing data , Hydraulic engineering , TC1-978 , Water supply for domestic and industrial purposes , TD201-500

Metadata by DOAJ is licensed under CC BY-SA 1.0

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