An optimized surface aerodynamic temperature approach to estimate maize sensible heat flux and evapotranspiration

Costa-Filho, Edson; Chávez, José L.; Zhang, Huihui; Andales, Allan A.

doi:10.1016/j.agrformet.2021.108683

Cited by 13 publications

(19 citation statements)

References 70 publications

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“…The G:Rn ratio can be predicted with reasonable accuracy through the use of empirical relationships with soil, vegetation, and temperature characteristics (Bastiaanssen, 1995;Murray and Verhoef, 2007;Allen et al, 2011;Danelichen et al, 2014). Challenges in estimating λET as a residual of the energy balance are mostly associated with the uncertainties in H (Gokmen et al, 2012;Paul et al, 2014;Mohan et al, 2020a, b;Costa-Filho et al, 2021). The bulk heat transfer calculation that is used to compute H involves variables related to the temperature gradient and to the aerodynamic resistance for heat transfer (rah).…”

Section: Introductionmentioning

confidence: 99%

“…The bulk heat transfer calculation that is used to compute H involves variables related to the temperature gradient and to the aerodynamic resistance for heat transfer (rah). If any of these variables are poorly estimated, the performance of SEB models will be reduced (Verhoef et al, 1997a, b;Su et al, 2001;Gokmen et al, 2012;Costa-Filho et al, 2021;Liu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Another problem with RS-based SEB models is that these methods are imprecise when applied to non-agricultural environments, such as forests, deserts, sparse savannahs or rangelands and riparian systems, because of the heterogeneous nature of the vegetation, terrain, soils, and water availability in these environments. This causes the flux estimates obtained with the SEB method, and the underlying aerodynamic parameters, to be highly variable (Allen et al, 2011;Gokmen et al, 2012;Barraza et al, 2017;Chen and Liu, 2020;Costa-Filho et al, 2021). This is especially true in Seasonally Dry Tropical Forests (SDTF) regions, where there is a large spatiotemporal variation in vegetation density, in vegetation structural parameters such as canopy height, crown shape and branching, and water availability.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Bezerra¹,

Cunha²,

Falente³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The G:Rn ratio can be predicted with reasonable accuracy through the use of empirical relationships with soil, vegetation, and temperature characteristics (Bastiaanssen, 1995;Murray and Verhoef, 2007;Allen et al, 2011;Danelichen et al, 2014). Challenges in estimating λET as a residual of the energy balance are mostly associated with the uncertainties in H (Gokmen et al, 2012;Paul et al, 2014;Mohan et al, 2020a, Mohan et al, 2020bCosta-Filho et al, 2021). The bulk heat transfer calculation that is used to compute H involves variables related to the temperature gradient and to the aerodynamic resistance for heat transfer (rah).…”

Section: Introductionmentioning

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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“…TheCosta-Filho et al (2021) T o resulting modelling errors (MBE±RMSE) were -0.14±0.50 °C for the optimized models (Equations 5-8). Overall, results seemed to indicate that the optimized T o model improved the estimation of maize H fluxes (error -6±19 W m -2…”

mentioning

confidence: 98%

Remote Sensing and Aerodynamic Temperature-Based Energy Balance Models to Estimate Crop Evapotranspiration Rates

Chávez¹

2023

JAS

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Different methods exist to measure or estimate actual crop evapotranspiration (ETa). However, some methods require a large number of data input or strict field conditions. Remote sensing based ETa algorithms based on extreme thermal pixels (hot and cold) have limitations when required extreme pixels are not present in the acquired thermal infra-red imagery. In addition, satellite overpass frequency and spatial pixel resolution may be a limitation for some agricultural fields and micro-climates. Surface energy balance methods that use surface radiometric temperatures often fail to perform well under drought, limited irrigation, salt affected soils, or under sparse vegetation conditions. One option is to measure or estimate the crop/surface sensible heat flux through the aerodynamic temperature approach, then calculate the available energy and solve the energy balance for latent heat flux. Thus, this study presents different published algorithms that characterize the crop or field surface aerodynamic temperature and then applies them to different conditions for evaluation. Determining spatial ETa continuously has the potential to improve the irrigation water management decision making. The aerodynamic temperature approach was initially developed with good results as a function of surface radiometric temperature, air temperature, crop leaf area index, and wind speed or surface aerodynamic resistance. However, the inclusion of the crop fractional percent cover and of a new resistance term (turbulent-mixing row resistance) greatly improved the estimation of the sensible heat and latent heat fluxes, when evaluated with heat flux data derived from eddy covariance energy balance towers. Results also indicate that the aerodynamic method has transferability potential to different regions, crops, and irrigation methods than the conditions encountered in the method development.

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An optimized surface aerodynamic temperature approach to estimate maize sensible heat flux and evapotranspiration

Cited by 13 publications

References 70 publications

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

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

Remote Sensing and Aerodynamic Temperature-Based Energy Balance Models to Estimate Crop Evapotranspiration Rates

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