Abstract
The Sentinel-2 and Sentinel-3 satellite constellation contains most of the spatial, temporal and spectral characteristics required for accurate, field-scale actual evapotranspiration (ET) estimation. The one remaining major challenge is the spatial scale mismatch between the thermal-infrared observations acquired by the Sentinel-3 satellites at around 1 km resolution and the multispectral shortwave observations acquired by the Sentinel-2 satellite at around 20 m resolution. In this study we evaluate a number of approaches for bridging this gap by improving the spatial resolution of the thermal images. The resulting data is then used as input into three ET models, working under different assumptions: TSEB, METRIC and ESVEP. Latent, sensible and ground heat fluxes as well as net radiation produced by the models at 20 m resolution are validated against observations coming from 11 flux towers located in various land covers and climatological conditions. The results show that using the sharpened high-resolution thermal data as input for the TSEB model is a sound approach with relative root mean square error of instantaneous latent heat flux of around 30% in agricultural areas. The proposed methodology is a promising solution to the lack of thermal data with high spatio-temporal resolution required for field-scale ET modelling and can fill this data gap until next generation of thermal satellites are launched.
Highlights
The fluxes of water and energy at the surface of the Earth are critical to quantify for many applications in the fields of climatology, meteorology, hydrology and agronomy
We removed all the cases in which the S3 image was contaminated by clouds in the vicinity of the flux towers or in which the Sea and Land Surface Temperature Radiometer (SLSTR) view zenith angle was larger than 45 degrees
G showed similar behaviour as well, but in this case GMETRIC is computed differently as it is a function of surface Rn [31,32] as opposed to two-source energy balance (TSEB) and ESVEP where, as two-source models, G is computed from Rn,S [36,44]
Summary
The fluxes of water (e.g., evapotranspiration—ET) and energy (e.g., of latent and sensible heat) at the surface of the Earth are critical to quantify for many applications in the fields of climatology, meteorology, hydrology and agronomy. Water and energy fluxes show large spatio-temporal variability since they are highly dependent on the meteorological conditions, and on different characteristics and properties of the land surface, such as soil moisture/water availability, land cover type and amount of vegetation biomass and its health. Remote sensing data can provide spatially-distributed information about relevant land surface states and properties used to model the relevant fluxes and this technology addresses a key limitation of conventional point scale observations when estimating fluxes at watershed and regional scales. While there are a variety of existing remote sensing ET methods and data options available [2,3], none is fully satisfying the user needs for reliable, operational and easy accessible estimates and tools able to derive ET at agricultural-parcel scale. The limitations have so far primarily been centred on the lack of suitable satellite-based input data sources
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