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

• Published in: Water (Basel) Vol. 11; no. 11; p. 2236
• Main Authors: Gavilán, Viviana, Lillo-Saavedra, Mario, Holzapfel, Eduardo, Rivera, Diego, García-Pedrero, Angel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2019
• Subjects: Agricultural production; agricultural water management; Agriculture; Crops; Earth resources technology satellites; Electric waves; Electromagnetic radiation; Electromagnetic waves; Electromagnetism; Energy balance; Evapotranspiration; Force and energy; harmonization remote sensing data; Heterogeneity; Irrigation; Irrigation water; Landsat; Normalized difference vegetative index; Remote sensing; Satellite imagery; Satellites; Seasons; Sensors; Spectral bands; Surface energy; Surface properties; Time series; Vegetation; Water; Water balance; Water balance (Hydrology); Water distribution; Water distribution systems; Water engineering; Water management; Chile
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w11112236

Efficient water management in agriculture requires a precise estimate of evapotranspiration (ET). 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 ET. 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 (NDVI) obtained from Landsat-8 (L8) and Sentinel-2 (S2) sensors in order to obtain an NDVI time series used to estimate ET 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 ET using an NDVI time series by integrating data from both sensors L8 and S2, 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.