Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model

Senay, Gabriel B.; Friedrichs, M.; Morton, Charles; Parrish, Gabriel E. L.; Schauer, Matthew; Khand, Kul; Kagone, Stefanie; Boiko, Olena; Huntington, Justin L.

doi:10.1016/j.rse.2022.113011

Cited by 45 publications

(27 citation statements)

References 45 publications

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“…The CMRSET algorithm has been recently updated (Guerschman et al, 2022) (i.e., CMRSET v2), and monthly Landsat‐resolution estimates of AET have been produced in Google Earth Engine (GEE) for the entire Australian continent (McVicar et al, 2022) making similar analysis easily achievable anywhere in Australia. For the conterminous United States, Senay et al (2022) used the SSEBop model to estimate Landsat‐resolution estimates of AET in GEE, and elsewhere, the required inputs to CMRSET, SSEBop and other remote sensing‐based AET algorithms are all accessible from GEE globally.…”

Section: Discussionmentioning

confidence: 99%

Regional‐scale partitioning of transmission losses and groundwater recharge using satellite estimates of actual evapotranspiration in an arid environment

2022

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Rivers in arid regions often rely on flow generated from wetter regions upstream, leading to high transmission losses of downstream flows. These transmission losses support a range of ecosystems, but partitioning the volume of the transmission losses across the floodplain, riparian zone and in‐channel is difficult. This study presents a methodology relying primarily on multi‐decade satellite remotely sensed actual evapotranspiration estimates to partition these losses. The method was applied to the ~40,000 km2 floodplain of Cooper Creek in the central Australian arid zone, where first, the alluvial landscape was classified based on actual evapotranspiration rates, and second, both regional‐ (i.e., for the entire floodplain) and local‐scale (i.e., for each waterhole) water balances were calculated to partition these losses. Regional‐scale results estimated that 82% of transmission losses occurred on the floodplain, 13% in the riparian zone and 5% from open water in the river channel and waterholes. These results showed that a refinement of the conceptual model of recharge from the waterholes is necessary as vast areas of the riparian zone are likely to be accessing a shallow freshwater lens rather than a discrete freshwater lens below the permanent waterholes. This method can be used in other data‐poor arid river systems as it uses globally accessible data sources.

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Section: Discussionmentioning

confidence: 99%

Regional‐scale partitioning of transmission losses and groundwater recharge using satellite estimates of actual evapotranspiration in an arid environment

2022

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“…hence, it suffers from biases, especially over heterogeneous surfaces (Rasp et al, 2018;Laipelt et al, 2020). However, despite moderate accuracy and biases at regional scales, ground-based assimilation and reanalysis data have become important sources of meteorological inputs for ET estimates (Mu et al, 2011;Zhang et al, 2019;Allam et al, 2021;Senay et al, 2022). Laipelt et al (2020) and Kayser et al (2022) observed that the use of ground measurements or global reanalysis data as meteorological inputs had modest effects only on the accuracy of SEBAL to estimate ET.…”

Section: Sources Of Error and Further Research For Steepmentioning

confidence: 99%

“…The computational capacity and the effectiveness of GEE for running SEB models should be commended. Although other studies have demonstrated GEE's strength (Laipelt et al, 2021;Jaafar et al, 2022;Senay et al, 2022), this platform has some limitations when it comes to the number of iterations, e.g. a convergence threshold cannot be set to stop the within-loop iterations of H calculations; instead a fixed number of iterations needs to be defined.…”

Section: Sources Of Error and Further Research For Steepmentioning

confidence: 99%

STEEP: a remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

Bezerra¹,

Cunha²,

Falente³

et al. 2023

Preprint

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Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications of the aerodynamic resistances. The use of these SEB models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET is mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called STEEP. Our model uses the Plant Area Index to represent the woody structure of the plants in calculating the moment roughness length. In the aerodynamic resistance for heat transfer, the parameter kB-1 was included, correcting it with RS soil moisture. Besides, the remaining λET in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the STEEP algorithm on the Google Earth Engine platform, using worldwide free data. Four sites with eddy covariance data located in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region, were used to evaluate the STEEP model. Our results show that STEEP based on the specific characteristics of the SDTF increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which in general, ET for these SDTF is overestimated by traditional SEB models, as happened in our research with the SEBAL. The STEEP model had similar or superior behaviour and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF.

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“…hence, it suffers from biases, especially over heterogeneous surfaces (Rasp et al, 2018). However, despite moderate accuracy and biases at regional scales, ground-based assimilation and reanalysis data have become important sources of meteorological inputs for ET estimates (Mu et al, 2011;Zhang et al, 2019;Allam et al, 2021;Senay et al, 2022). Laipelt et al (2020) and Kayser et al (2022) showed that global reanalysis data when used as meteorological inputs had modest effects only on the accuracy of SEBAL for estimating ET.…”

Section: Sources Of Error and Further Research For Steepmentioning

confidence: 99%

Section: Sources Of Error and Further Research For Steepmentioning

confidence: 99%

STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

Bezerra

Cunha

Valente

et al. 2023

Agricultural and Forest Meteorology

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Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model

Cited by 45 publications

References 45 publications

Regional‐scale partitioning of transmission losses and groundwater recharge using satellite estimates of actual evapotranspiration in an arid environment

Regional‐scale partitioning of transmission losses and groundwater recharge using satellite estimates of actual evapotranspiration in an arid environment

STEEP: a remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests

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