Performance of Satellite-Based Evapotranspiration Models in Temperate Pastures of Southern Chile

Abstract

Farmers in the temperate zone of southern Chile have started to irrigate historically rainfed pastures during recent years to reduce dairy productivity losses against increasingly severe summer droughts. The lack of information on pasture water requirements (i.e., evapotranspiration), however, hampers the implementation of efficient irrigation programs. Here, we use in-situ observations to evaluate the skill of four remote sensing Surface Energy Balance (SEB) models and two satellite-based global evapotranspiration products (PML_V2 and GLEAM) to estimate actual evapotranspiration (ETa) of pastures in southern Chile during 2014–2017. Daily ETa measured at an evaluation site over the period ranges between 1.2 mm and 6.2 mm day−1 during the growing season (October–March), with an annual maximum of about 4.8 mm day−1 in January and a minimum 0.6 mm day−1 in June. Only the Simplified SEB (SEBS) model and its operational variant (SSEBop) and the PML_V2 global evapotranspiration product perform well, capturing 63–79% of the variance of in-situ evapotranspiration with an error between 0.75 mm day−1 and 1.1 mm day−1. The readily available PML_V2 product can be used as a convenient way to determine average water footprint of pastures and the two SEBs models can be implemented to monitor irrigation requirements in near-real time from field to regional scales. These results demonstrated a high potential of satellite observations for monitoring evapotranspiration and quantify the water footprint of pastures in southern Chile for a sustainable irrigation practice.

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“…The difference in ET a and in situ values occurs especially during the period without vegetation in arid and semi-arid zones or areas that have complex biophysical conditions, with errors greater than 20%-30% in the estimation, which might reduce the performance of the model by 15%-20%. Indeed, Senay et al (2013) explained that the K c parameter could increase the error to 5%-15% in the pixels adjacent to neighbouring estimate ET a , and SSEBop obtained the best results with a linear adjustment of R 2 = 0.80 and an RMSE of 0.67 mm day À1 (Moletto-Lobos et al, 2020). In both studies, the conclusions regarding the variables that affect the linear correlation are similar.…”

“…In addition, good performance was obtained (RMSE = 0.50 and 0.70 mm day −1 , bias = 0.41 and −0.37 mm day −1 ), and low data dispersion was obtained by checking the applicability of the model for arid and semi‐arid conditions. In southern Chile, four algorithms were applied to estimate ET a , and SSEBop obtained the best results with a linear adjustment of R 2 = 0.80 and an RMSE of 0.67 mm day −1 (Moletto‐Lobos et al, 2020). In both studies, the conclusions regarding the variables that affect the linear correlation are similar.…”

“…The actual evapotranspiration (ET a ) was estimated using the Operational Simplified Surface Energy Balance (SSEBop) model, which has demonstrated its application and versatility on several crops. In Chile, it was previously applied and evaluated by Olivera‐Guerra et al (2017) in vineyards and olive orchards in Copiapó and by Moletto‐Lobos et al (2020) in grasslands in the south of the country. The model was developed by Senay et al (2013) and uses a parameterization based on predefined boundary conditions according to a temperature delta (dT) between the ‘hot or dry’ (LST hot) and ‘cold or wet’ (LST cold) reference points, where the boundary conditions are unique to each pixel.…”

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“…(c) The coupling of the hydrological model and PML‐V2 considers soil storage constraining soil evaporation ( E s ). (d) the PML‐V2 model is feasible in regional estimates (Gan et al., 2021; Zhang et al., 2021), and the PML‐V2 product has been widely shown to perform well across the world (McCormick et al., 2021; McNamara et al., 2021; Moletto‐Lobos et al., 2020; Zhang et al., 2019), indicating a reasonable physics basis. Nevertheless, soil water influences on vegetation dynamics and vegetation change influences on hydrological processes are far more complicated.…”

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“…The PML‐V2 AET and GPP products provide good estimates of the global and regional AET and GPP (Ma et al., 2021; Ma & Zhang, 2022; Moletto‐Lobos et al., 2020) and have been used to study the impact of environmental variables on the AET (McCormick et al., 2021; X. Zhang et al., 2021; Y. Zhang et al., 2021). There are 11 free parameters that maintain the physiological significance of the photosynthetic response and vegetation response to environmental variables in the PML‐V2 model (Y. Zhang et al., 2019).…”