Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an artificial neural network approach

Abstract

Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ETi) to daily evapotranspiration (ETd) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation (RS) to be the most robust reference variable explaining the ratio between ETd and ETi. This study aims to contribute in ETi upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation (RSd ∕ RSi) as a factor for converting ETi to ETd. This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict RSd from RSi followed by using the RSd ∕ RSi ratio to convert ETi to ETd across different terrestrial ecosystems. Using RSi and RSd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing RSd and further used it to upscale ETi to ETd. The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geographic locations. RS-based upscaled ETd produced a significant linear relation (R2 = 0.65 to 0.69), low bias (−0.31 to −0.56 MJ m−2 d−1; approx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m−2 d−1; approx. 10 %) with the observed ETd, although a systematic overestimation of ETd was also noted under persistent cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN training reduced the systematic overestimation tendency in predominantly overcast days. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANN-driven RS-based ETi upscaling method and was found to produce lowest RMSE under cloudy conditions. Sensitivity analysis revealed variable sensitivity of the method to biome selection and high ETd prediction errors in forest ecosystems are primarily associated with greater rainfall and cloudiness. The overall methodology appears to be promising and has substantial potential for upscaling ETi to ETd for field and regional-scale evapotranspiration mapping studies using polar orbiting satellites. (Résumé d'auteur

Accuracy of temporal upscaling instantaneous evapotranspiration insimulating daily values in remote sensing applications

“…The upscaling methods exhibited the highest simulation accuracy from 11:00-14:00. This is consistent with previous results (Delogu et al, 2012;Wandera et al, 2017).…”

“…Delogu et al (2012) used four European flux sites to evaluate the performance of the EF(Rn-G) and EF(PET) methods at different times from 10:00 to 14:00, finding that the simulation accuracy at 11:00-13:00 was slightly higher than outside of this time range. Based on the 126 FLUXNET sites from 1999 to 2006, Wandera et al (2017) evaluated the EF(Rs) method at different times between 10:30 and 14:00, and found that there was only slight variance in the accuracy of daily LE simulations during this period. However, the performance of upscaling methods during other daytime periods has seldom been investigated.…”

“…The hypothesis is that during clear-sky conditions, shortwave incoming radiation is strongly correlated with extra-terrestrial radiation, although it deviates in cloudy conditions. The daily τ is calculated as follows (Baigorria et al, 2004;Wandera et al, 2017): where R sd and R ed are the observed daily incoming shortwave radiation and calculated top-of-atmosphere solar irradiance ( in MJ• m -2 d -1 ; converted from W m -2 ), during the daytime, respectively. https://doi.org/10.5194/hess-2021-73 Preprint.…”

“…Their results showed that the ETrF method had the best performance among the four methods, while the EF(Rs) was the second best. In general, previous research has largely evaluated upscaling methods on a regional scale, while Wandera et al (2017) only used EF methods for global evaluation.…”

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