Seasonal adaptation of the thermal‐based two‐source energy balance model for estimating evapotranspiration in a semiarid tree‐grass ecosystem

© 2020 by the authors.The thermal-based two-source energy balance (TSEB) model has accurately simulated energy fluxes in a wide range of landscapes with both remote and proximal sensing data. However, tree-grass ecosystems (TGE) have notably complex heterogeneous vegetation mixtures and dynamic phenological characteristics presenting clear challenges to earth observation and modeling methods. Particularly, the TSEB modeling structure assumes a single vegetation source, making it difficult to represent the multiple vegetation layers present in TGEs (i.e., trees and grasses) which have different phenological and structural characteristics. This study evaluates the implementation of TSEB in a TGE located in central Spain and proposes a new strategy to consider the spatial and temporal complexities observed. This was based on sensitivity analyses (SA) conducted on both primary remote sensing inputs (local SA) and model parameters (global SA). The model was subsequently modified considering phenological dynamics in semi-arid TGEs and assuming a dominant vegetation structure and cover (i.e., either grassland or broadleaved trees) for different seasons (TSEB-2S). The adaptation was compared against the default model and evaluated against eddy covariance (EC) flux measurements and lysimeters over the experimental site. TSEB-2S vastly improved over the default TSEB performance decreasing the mean bias and root-mean-square-deviation (RMSD) of latent heat (LE) from 40 and 82 W m−2 to −4 and 59 W m−2, respectively during 2015. TSEB-2S was further validated for two other EC towers and for different years (2015, 2016 and 2017) obtaining similar error statistics with RMSD of LE ranging between 57 and 63 W m−2. The results presented here demonstrate a relatively simple strategy to improve water and energy flux monitoring over a complex and vulnerable landscape, which are often poorly represented through remote sensing models.The research received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995. It was also funded by Ministerio de Economía y Competitividad through FLUXPEC CGL2012-34383 and SynerTGE CGL2015-G9095-R (MINECO/FEDER, UE) projects. The research infrastructure at the measurement site in Majadas de Tiétar was partly funded through the Alexander von Humboldt Foundation, ELEMENTAL (CGL 2017-83538-C3-3-R, MINECO-FEDER) and IMAGINA (PROMETEU 2019; Generalitat Valenciana).Peer reviewe

UAS-based high resolution mapping of evapotranspiration in a Mediterranean tree-grass ecosystem

Este artículo está sujeto a una licencia CC BY 4.0Understanding the impact of land use and land cover change on surface energy and water budgets is increasingly important in the context of climate change research. Eddy covariance (EC) methods are the gold standard for high temporal resolution measurements of water and energy fluxes, but cannot resolve spatial heterogeneity and are limited in scope to the tower footprint (few hundred meter range). Satellite remote sensing methods have excellent coverage, but lack spatial and temporal resolution. Long-range unmanned aerial systems (UAS) can complement these other methods with high spatial resolution over larger areas. Here we use UAS thermography and multispectral data as inputs to two variants of the Two Source Energy Balance Model to accurately map surface energy and water fluxes over a nutrient manipulation experiment in a managed semi-natural oak savanna from peak growing season to senescence. We use energy flux measurements from 6 EC stations to evaluate the performance of our method and achieve good accuracy (RMSD ≈ 60 W m− 2 for latent heat flux). We use the best performing latent heat estimates to produce very high-resolution evapotranspiration (ET) maps, and investigate the drivers of ET change over the transition to the senescence period. We find that nitrogen and nitrogen plus phosphorus treatments lead to significant increases in ET (P < 0.001) for both trees (4 and 6%, respectively) and grass (12 and 9%, respectively) compared to the control. These results highlight that the high sensitivity and spatial and temporal resolution of a UAS system allows the precise estimation of relative water and energy fluxes over heterogeneous vegetation cover.This research was supported by the DAAD/BMBF program Make Our Planet Great Again – German Research Initiative Project MONSOON (grant number 57429870).Peer reviewe

How nitrogen and phosphorus availability change water use efficiency in a Mediterranean savanna ecosystem

Nutrient availability, especially of nitrogen (N) and phosphorus (P), is of major importance for every organism and at a larger scale for ecosystem functioning and productivity. Changes in nutrient availability and potential stoichiometric imbalance due to anthropogenic nitrogen deposition might lead to nutrient deficiency or alter ecosystem functioning in various ways. In this study, we present 6 years (2014–2020) of flux-, plant-, and remote sensing data from a large-scale nutrient manipulation experiment conducted in a Mediterranean savanna-type ecosystem with an emphasis on the effects of N and P treatments on ecosystem-scale water-use efficiency (WUE) and related mechanisms. Two plots were fertilized with N (NT, 16.9 Ha) and N + P (NPT, 21.5 Ha), and a third unfertilized plot served as a control (CT). Fertilization had a strong impact on leaf nutrient stoichiometry only within the herbaceous layer with increased leaf N in both fertilized treatments and increased leaf P in NPT. Following fertilization, WUE in NT and NPT increased during the peak of growing season. While gross primary productivity similarly increased in NT and NPT, transpiration and surface conductance increased more in NT than in NPT. The results show that the NPT plot with higher nutrient availability, but more balanced N:P leaf stoichiometry had the highest WUE. On average, higher N availability resulted in a 40% increased leaf area index (LAI) in both fertilized treatments in the spring. Increased LAI reduced aerodynamic conductance and thus evaporation at both fertilized plots in the spring. Despite reduced evaporation, annual evapotranspiration increased by 10% (48.6 ± 28.3 kg H2O m−2), in the NT plot, while NPT remained similar to CT (−1%, −6.7 ± 12.2 kgH2O m−2). Potential causes for increased transpiration at NT could be increased root biomass and thus higher water uptake or rhizosphere priming to increase P-mobilization through microbes. The annual net ecosystem exchange shifted from a carbon source in CT (75.0 ± 20.6 gC m−2) to carbon-neutral in both fertilized treatments [−7.0 ± 18.5 gC m−2 (NT) 0.4 ± 22.6 gC m−2 (NPT)]. Our results show, that the N:P stoichiometric imbalance, resulting from N addition (without P), increases the WUE less than the addition of N + P, due to the strong increase in transpiration at NT, which indicates the importance of a balanced N and P content for WUE

Spatial estimation of actual evapotranspiration over irrigated turfgrass using sUAS thermal and multispectral imagery and TSEB model

Green urban areas are increasingly affected by water scarcity and climate change. The combination of warmer temperatures and increasing drought poses substantial challenges for water management of urban landscapes in the western U.S. A key component for water management, actual evapotranspiration (ETa) for landscape trees and turfgrass in arid regions is poorly documented as most rigorous evapotranspiration (ET) studies have focused on natural or agricultural areas. ET is a complex and non-linear process, and especially difficult to measure and estimate in urban landscapes due to the large spatial variability in land cover/land use and relatively small areas occupied by turfgrass in urban areas. Therefore, to understand water consumption processes in these landscapes, efforts using standard measurement techniques, such as the eddy covariance (EC) method as well as ET remote sensing-based modeling are necessary. While previous studies have evaluated the performance of the remote sensing-based two-source energy balance (TSEB) in natural and agricultural landscapes, the validation of this model in urban turfgrass remains unknown. In this study, EC flux measurements and hourly flux footprint models were used to validate the energy fluxes from the TSEB model in green urban areas at golf course near Roy, Utah, USA. High-spatial resolution multispectral and thermal imagery data at 5.4 cm were acquired from small Unmanned Aircraft Systems (sUAS) to model hourly ETa. A protocol to measure and estimate leaf area index (LAI) in turfgrass was developed using an empirical relationship between spectral vegetation indices (SVI) and observed LAI, which was used as an input variable within the TSEB model. In addition, factors such as sUAS flight time, shadows, and thermal band calibration were assessed for the creation of TSEB model inputs. The TSEB model was executed for five datasets collected in 2021 and 2022, and its performance was compared against EC measurements. For ETa to be useful for irrigation scheduling, an extrapolation technique based on incident solar radiation was used to compute daily ETa from the hourly remotely-sensed UAS ET. A daily flux footprint and measured ETa were used to validate the daily extrapolation technique. Results showed that the average of corrected daily ETa values in summer ranged from about 4.6 mm to 5.9 mm in 2021 and 2022. The Near Infrared (NIR) and Red Edge-based SVI derived from sUAS imagery were strongly related to LAI in turfgrass, with the highest coefficient of determination (R2) (0.76–0.84) and the lowest root mean square error (RMSE) (0.5–0.6). The TSEB’s latent and sensible heat flux retrievals were accurate with an RMSE 50 W m−2 and 35 W m−2 respectively compared to EC closed energy balance. The expected RMSE of the upscaled TSEB daily ETa estimates across the turfgrass is below 0.6 mm  day−1, thus yielding an error of 10% of the daily total. This study highlights the ability of the TSEB model using sUAS imagery to estimate the spatial variation of daily ETa for an urban turfgrass surface, which is useful for landscape irrigation management under drought conditions.Peer reviewe

A remote sensing-based three-source energy balance model to improve global estimations of evapotranspiration in semi-arid tree-grass ecosystems

It is well documented that energy balance and other remote sensing-based evapotranspiration (ET) models face greater uncertainty over water-limited tree-grass ecosystems (TGEs), representing nearly 1/6th of the global land surface. Their dual vegetation strata, the grass-dominated understory and tree-dominated overstory, make for distinct structural, physiological and phenological characteristics, which challenge models compared to more homogeneous and energy-limited ecosystems. Along with this, the contribution of grasses and trees to total transpiration (T), along with their different climatic drivers, is still largely unknown nor quantified in TGEs. This study proposes a thermal-based three-source energy balance (3SEB) model, accommodating an additional vegetation source within the well-known two-source energy balance (TSEB) model. The model was implemented at both tower and continental scales using eddy-covariance (EC) TGE sites, with variable tree canopy cover and rainfall (P) regimes and Meteosat Second Generation (MSG) images. 3SEB robustly simulated latent heat (LE) and related energy fluxes in all sites (Tower: LE RMSD ~60 W/m2; MSG: LE RMSD ~90 W/m2), improving over both TSEB and seasonally changing TSEB (TSEB-2S) models. In addition, 3SEB inherently partitions water fluxes between the tree, grass and soil sources. The modelled T correlated well with EC T estimates (r > .76), derived from a machine learning ET partitioning method. The T/ET was found positively related to both P and leaf area index, especially compared to the decomposed grass understory T/ET. However, trees and grasses had contrasting relations with respect to monthly P. These results demonstrate the importance in decomposing total ET into the different vegetation sources, as they have distinct climatic drivers, and hence, different relations to seasonal water availability. These promising results improved ET and energy flux estimations over complex TGEs, which may contribute to enhance global drought monitoring and understanding, and their responses to climate change feedbacks.The research received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie TRuStEE project (grant agreement No 721995). It was also funded by Ministerio de Economía y Competitividad through SynerTGE CGL2015-G9095-R funded by MCIN/ AEI /10.13039/501100011033/ FEDER ‘a way of making Europe’. The study also benefitted from the DIVERSPEC-TGA project, funded by the Ministerio de Ciencia e Innovación of Spain MCIN/ AEI /10.13039/501100011033. The infrastructure at ES-LM1 was partly funded through the Alexander von Humboldt Foundation, ELEMENTAL (CGL 2017-83538-C3-3-R, MINECO-FEDER) and IMAGINA (PROMETEU 2019; Generalitat Valenciana). Funding for the US-Ton AmeriFlux site was provided by the U.S. Department of Energy's Office of Science. This research was also supported by the NASA Ecostress project. We thank Siyan Ma for contributing to the collection and processing of US-Ton’s in situ data. USDA is an equal opportunity provider and employer.Peer reviewe

Application of a remote-sensing three-source energy balance model to improve evapotranspiration partitioning in vineyards

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Improved accuracy of evapotranspiration (ET) estimation, including its partitioning between transpiration (T) and surface evaporation (E), is key to monitor agricultural water use in vineyards, especially to enhance water use efficiency in semi-arid regions such as California, USA. Remote-sensing methods have shown great utility in retrieving ET from surface energy balance models based on thermal infrared data. Notably, the two-source energy balance (TSEB) has been widely and robustly applied in numerous landscapes, including vineyards. However, vineyards add an additional complexity where the landscape is essentially made up of two distinct zones: the grapevine and the interrow, which is often seasonally covered by an herbaceous cover crop. Therefore, it becomes more complex to disentangle the various contributions of the different vegetation elements to total ET, especially through TSEB, which assumes a single vegetation source over a soil layer. As such, a remote-sensing-based three-source energy balance (3SEB) model, which essentially adds a vegetation source to TSEB, was applied in an experimental vineyard located in California’s Central Valley to investigate whether it improves the depiction of the grapevine-interrow system. The model was applied in four different blocks in 2019 and 2020, where each block had an eddy-covariance (EC) tower collecting continuous flux, radiometric, and meteorological measurements. 3SEB’s latent and sensible heat flux retrievals were accurate with an overall RMSD ~ 50 W/m2 compared to EC measurements. 3SEB improved upon TSEB simulations, with the largest differences being concentrated in the spring season, when there is greater mixing between grapevine foliage and the cover crop. Additionally, 3SEB’s modeled ET partitioning (T/ET) compared well against an EC T/ET retrieval method, being only slightly underestimated. Overall, these promising results indicate 3SEB can be of great utility to vineyard irrigation management, especially to improve T/ET estimations and to quantify the contribution of the cover crop to ET. Improved knowledge of T/ET can enhance grapevine water stress detection to support irrigation and water resource management.Funding and logistical support for the GRAPEX project were provided by E. and J. Gallo Winery and from the NASA Applied Sciences-Water Resources Program (Grant No. NNH17AE39I). This research was also supported in part by the U.S. Department of Agriculture, Agricultural Research Service.Peer reviewe

Analysis of the functional diversity of the herbaceous stratum in a ‘dehesa’ ecosystem using in situ hyperspectral proximal sensing

[ES] El presente trabajo aborda la estimación de la diversidad funcional (FD) del estrato herbáceo en un ecosistema de dehesa mediante datos hiperespectrales obtenidos con un espectro-radiómetro portátil ASD FieldSpec® 3 (Analytical Spectral Devices Inc., Boulder, CO, EE. UU.). Los datos ópticos se correlacionaron con variables biofísicas (área foliar específica (SLA), biomasa aérea (AGB), índice de área foliar (LAI) y contenido de nitrógeno (N%)) e índices de diversidad tradicionales como Shannon y Evenness y otros relacionados con la diversidad funcional como el índice multidimensional de dispersión funcional (FDis) usando como variables predictoras: a) índices de vegetación (VIs) y métodos de regresión simple, y b) bandas espectrales y Partial Least Squares Regression (PLSR). Las correlaciones, especialmente de las variables biofísicas relacionadas con FDis, mejoraron sustancialmente al utilizar información hiperespectral (R2 >0.6, rRMSE <0.1) lo que confirma el interés de la dimensión espectral para estimar la diversidad funcional de un ecosistema complejo como es el caso de los pastizales semi-áridos.[EN] The aim of this paper is the estimation of functional diversity (FD) of the herbaceous stratum in a ‘dehesa’ ecosystem using hyperspectral data obtained with an ASD FieldSpec® 3 (Analytical Spectral Devices Inc., Boulder, CO, EE. UU.) spectroradiometer. Optical data were correlated with biophysical variables (specific leaf area (SLA), above-ground biomass (AGB), leaf area index (LAI) and nitrogen content (N%)) and traditional diversity indices (Shannon and Evenness) and functional diversity indices (FDis) using as predictor variables: a) vegetation indices (VIs) and simple regression methods, and b) spectral bands and Partial Least Squares Regression (PLSR). Correlations, especially with biophysical variables, improved substantially when using hyperspectral information (R2 >0.6, rRMSE <0.1), which confirms the interest of the spectral dimension to estimate the functional diversity of a complex ecosystem such as semi-arid grasslands.Peer reviewe