Two-source Energy Balance Model Research Articles

Evaluating the precise grapevine water stress detection using unmanned aerial vehicles and evapotranspiration-based metrics

AbstractPrecise irrigation management requires accurate knowledge of crop water demand to adequately optimize water use efficiency, especially relevant in arid and semi-arid regions. While unoccupied aerial vehicles (UAV) have shown great promise to improve the water management for crops such as vineyards, there still remains large uncertainties to accurately quantify vegetation water requirements, especially through physically-based methods. Notably, thermal remote sensing has been shown to be a promising tool to evaluate water stress at different scales, most commonly through the Crop Water Stress Index (CWSI). This work aimed to evaluate the potential of a UAV payload to estimate evapotranspiration (ET) and alternative ET-based crop water stress indices to better monitor and detect irrigation requirements in vineyards. As a case study, three irrigation treatments within a vineyard were implemented to impose weekly crop coefficient (Kc) of 0.2 (extreme deficit irrigation), 0.4 (typical deficit irrigation) and 0.8 (over-irrigated) of reference ET. Both the original Priestley-Taylor initialized two-source energy balance model (TSEB-PT) and the dual temperature TSEB (TSEB-2T), which takes advantage of high-resolution imagery to discriminate canopy and soil temperatures, were implemented to estimate ET. In a first step, both ET models were evaluated at the footprint level using an eddy covariance (EC) tower, with modelled fluxes comparing well against the EC measurements. Secondly, in-situ physiological measurements at vine level, such as stomatal conductance (gst), leaf (Ψleaf) and stem (Ψstem) water potential, were collected simultaneously to UAV overpasses as plant proxies of water stress. Different variants of the CWSI and alternative metrics that take advantage of the partitioned ET from TSEB, such as Crop Transpiration Stress Index (CTSI) and the Crop Stomatal Stress Index (CSSI), were also evaluated to test their statistical relationship against these in-situ physiological indicators using the Spearman correlation coefficient (ρ). Both TSEB-PT and TSEB-2T CWSI related similarly to in-situ measurements (Ψleaf: ρ ~ 0.4; Ψstem: ρ ~ 0.55). On the other hand, stress indicators using canopy fluxes (i.e. CTSI and CSSI) were much more effective when using TSEB-2 T (Ψleaf: ρ = 0.45; Ψstem: ρ = 0.62) compared to TSEB-PT (Ψleaf: ρ = 0.18; Ψstem: ρ = 0.49), revealing important differences in the ET partitioning between model variants. These results demonstrate the utility of physically-based models to estimate ET and partitioned canopy fluxes, which can enhance the detection of vine water stress and quantitatively assess vine water demand to better manage irrigation practices.

Simulation and Assessment of Daily Evapotranspiration in the Heihe River Basin over a Long Time Series Based on TSEB-SM

The high spatial and temporal resolution of recently developed evapotranspiration (ET) products facilitates agricultural water-savings in irrigated areas as well as improved estimates of crop yield, especially in arid and semi-arid regions. However, cloud cover interferes with ET estimates, in particular when using thermal-infrared-based models in temperate and tropical regions. Previous studies have shown that the two-source energy balance (TSEB) model coupled with soil moisture (TSEB-SM) has great potential for estimating surface ET by overcoming this issue. In this study, the TSEB-SM model was first used to generate a spatiotemporally continuous 1 km daily ET dataset across the Heihe River Basin in China from 2000 to 2020, which was then evaluated against four spatially distributed sites (Arou, Huazhaizi, Daman, and Sidaoqiao) and further compared with the two most widely used daily ET datasets (PML-V2 (Penman–Monteith–Leuning) and SEBAL (surface energy balance algorithm for land)). The results showed that the newly developed ET dataset agrees well with ground-based observations and outperforms the PML-V2 and SEBAL products in precisely characterizing the seasonal fluctuations and spatial distribution as well as the spatiotemporal trends of ET. In particular, ET in the Heihe River Basin exhibits clear regional differences. The upstream and midstream grassland and irrigated oasis areas provide much higher annual ET than the downstream desert areas, with a difference of up to 600 mm/year. A three-cornered hat (TCH)-based pixel-by-pixel analysis further demonstrated that the TSEB-SM and PML-V2 products have substantially smaller relative uncertainties as compared to SEBAL ET. In general, the proposed ET datasets are expected to be more beneficial for irrigation scheduling and to provide more efficient water management across the Heihe River Basin.

Evaluating the impact of different UAV thermal sensors on evapotranspiration estimation

Accurate evapotranspiration (ET) estimation is vital for precise irrigation management. Remote sensing provides a unique method for obtaining spatial and temporal ET information. With technological advancements, several unmanned aerial vehicle (UAV) thermal sensors have been developed. However, the impact of thermal sensors on ET estimation is unclear. This study evaluated the impact of different UAV thermal sensors, including Micasense Altum and Flir Duo Pro-R (FDP-R), on ET estimation using the Two Source Energy Balance (TSEB) model. A field experiment was conducted during the 2021 sorghum growing period, with irrigation treatments consisting of four different regimes: full irrigation (S1), 70 % of S1 (S2), 40 % of S1 (S3), and rainfed (S4). The results revealed no statistically significant differences between the estimated ET values using Micasense Altum and FDP-R thermal sensors. The TSEB model's performance was entirely satisfactory for full irrigation, with RMSE values of 5.63 mm for Micasense Altum and 7.17 mm for FDP-R, in 10 days. However, the accuracy deteriorated with increasing water stress, reaching 29.02 mm for Micasense Altum and 25.12 mm for FDP-R, in 10 days in rainfed plots. The study results highlight the capability of both Micasense Altum and FDP-R thermal sensors to provide comparable ET estimates, particularly under full irrigation conditions. However, the decline in accuracy with increased water stress underlines a potential limitation of the TSEB model when applied to varying irrigation regimes. These insights emphasize the importance of adjustment of TSEB input parameters such as αPT coefficient, resistance terms etc. and sensor technologies, particularly in water-stressed environments, to ensure accurate ET estimation. This study demonstrated the potential of high-resolution UAV thermal sensors for precision irrigation management tasks. Further studies with different thermal sensors are needed to understand this technology's benefits fully. The impact of different climate conditions on ET estimation should also be explored for accurate results.

Estimation of evapotranspiration fluxes over mustard using a Two-Source Energy Balance model

Abstract A Two-Source Energy Balance (TSEB) model computes surface energy fluxes using soil surface temperature and canopy temperature. An experiment was carried out in a research farm near the agrometeorological observatory, at Anand, India to parameterize the TSEB model for the mustard (Brassica Juncea) crop, to estimate surface energy fluxes and validate the TSEB-2T model. The TSEB-2T model was validated using net radiation measurements. Results revealed that modeled net radiation under all sowings. Very late sowing had comparatively high dr 0.61, r 0.78** and low RMSE 58.12 Wm−2, MAE 46.47 Wm−2 and low MBE 1.66. Net radiation over mustard ranged from 269 to 538 Wm−2 with relatively high peaks in the second sowing date. Sensible heat flux was relatively high during early growth and after the seed development phase. Latent heat flux and sensible heat flux had inverse partitioning patterns during the crop cycle of the mustard. Ground heat flux had negligible partitioning from net radiation after the seed initiation stage. During vegetative to pod initiation phases, the net radiation fraction for latent heat was high compared to sensible heat. Peak daily evapotranspiration based on modeled latent heat during the flowering to pod initiation phase was about 3.7 mm day−1.

Evaluation of transpiration in different almond production systems with two-source energy balance models from UAV thermal and multispectral imagery

AbstractA growing number of intensive irrigated production systems of the almond crop have been established in recent years. However, there is little information regarding the crop water requirements. Remote sensing-based models such as the two-source energy balance (TSEB) have proven to be reliable ways to accurately estimate actual crop evapotranspiration. However, few efforts have been made to validate the transpiration with sap flow measurements in woody row crops with different production systems and water status. In this study, the TSEB Priestley-Taylor (TSEB-PT) and contextual approach (TSEB-2T) models were assessed to estimate canopy transpiration. In addition, the effect of applying a basic clumping index for heterogeneous randomly placed clumped canopies and a rectangular hedgerow clumping index on the TSEB transpiration estimation was assessed. The TSEB inputs were obtained from high resolution multispectral and thermal imagery using an unmanned aerial vehicle. The leaf area index (LAI), stem water potential (Ψstem) and fractional intercepted photosynthetically active radiation (fIPAR) were also measured. Significant differences were observed in transpiration between production systems and irrigation treatments. The combined use of the TSEB-2T with the C&N-R transmittance model gave the best transpiration estimations for all production systems and irrigation treatments. The use of in situ PAR transmittance in the TSEB-2T model significantly improved the root mean squared error. Thus, the better agreement observed with the TSEB when using the C&N-R model and in situ PAR transmittance highlights the importance of improving radiative transfer models for shortwave canopy transmittance, especially in woody row crops.

Application of a two source energy balance model coupled with satellite based soil moisture and thermal infrared data

Satellite retrievals of thermal infrared data from the Earth’s surface when combined with the surface energy balance equation are widely used to monitor land surface evapotranspiration (ET). Unfortunately thermal infrared based models using this approach have a systematic positive bias with ET during soil water stress conditions. The Two-source Energy Balance model using information on Soil Moisture as another key constraint (TSEB-SM) improves model performance and utility. Here, high resolution satellite soil moisture data from SMAP (Soil Moisture Active Passive) were used to provide soil water boundary conditions for the TSEB-SM model. This SMAP driven TSEB-SM approach was applied in the Heihe river basin in China across a wide range of soil moisture conditions using MODIS thermal infrared land surface temperature (LST) data. The ET estimates from TSEB-SM were validated using flux tower measurements over the main land cover types in the study area and compared with the results from the original TSEB model. The comparison showed that the TSEB-SM approach reduced the RMSE error in latent heat flux estimates by 5 % to 20 % at satellite overpass time, and TSEB-SM outperformed the original TSEB model in daily ET estimates (RMSE values of 1.0 mm/d versus 1.5 mm/d, respectively) across all validation sites. The magnitude of the TSEB-SM improvements were larger early and late in the growing season and in arid areas, consistent with soil moisture improving estimates of ET particularly under times of higher water stress. The use of SMAP satellite soil moisture data with the TSEB-SM model increased the reliability of ET estimation and provides an improved capacity for continuos monitoring of drought effects on the global water cycle.

The effect of pixel heterogeneity on surface heat and water vapor flux estimated by the remote sensing-based model coupled with deep learning

Accurate quantification of sensible (H) and latent (LE) heat fluxes are desperately crucial for agricultural irrigation strategy, drought monitoring, water resource management, and climate change. Regional-scale H and LE were derived generally from satellite remote sensing data using relevant modeling methods. However, one of the challenges of modeling methods is to deal with sub-pixel heterogeneity, because the model usually assumes homogeneous conditions within a pixel. In this study, a hybrid model, which coupled deep neural network (DNN) and the remote sensing-based two-source energy balance (TSEB) model, was developed to estimate H and LE at multiple spatial resolutions to further quantify the influence of pixel size on the modeled fluxes. First, the DNN-based land surface temperature (LST) estimation model was developed and validated. LST at multiple spatial resolutions (i.e., 1, 5, 10, 30, 100, and 1000 m) were generated by the DNN method using multi-source satellite data including the GaoFen-2 (GF-2, a Chinese high-resolution optical remote-sensing satellite), Sentinel-2, and Landsat-8 images, respectively. Then, the hybrid DNN-TSEB model was used to estimate H and LE at multi-pixel sizes. Finally, modeled H and LE at 1 ∼ 1000 m were validated by eddy covariance (EC) and optical-microwave scintillometer (OMS) flux observation. Results revealed that the DNN-based LST model was a potential approach to generate LST at multiple pixel sizes. The hybrid DNN-TSEB model can produce more accurate H and LE when different land cover types were discriminated over heterogeneous farmland (e.g., the average bias for H and LE at a spatial resolution of 1 m were −9 and −6 W/m−2, respectively). Besides, the deviation of modeled fluxes from the observations increased as the spatial resolution became coarser, particularly the pixel size was greater than 30 m (average bias for H and LE were −34 and 30 W m−2, respectively). However, the magnitude of bias of fluxes estimates remained unchanged when the pixel sizes of satellite data were between 100 and 1000 m. The more important finding was that the hybrid model was a promising approach, which can obtain accurate estimates of H and LE at a high spatial resolution. In addition, the performance of the model based on GF-2 data was better than that based on Sentinel-2 and Landsat-8 data. The results suggest that pixel heterogeneity should be considered when estimating H and LE over heterogeneous landscapes with various surface types.

Evaluating European ECOSTRESS Hub Evapotranspiration Products Across a Range of Soil‐Atmospheric Aridity and Biomes Over Europe

AbstractThe ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio‐temporal resolution (∼70 m, 1–5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio‐temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability and atmospheric aridity. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature (LST) retrievals, we generated ECOSTRESS ET products over Europe and Africa using three models with different structures and parameterization schemes, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non‐parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites in Europe over six different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ∼70 W m−2). However, the relatively complex TSEB model produced a higher RMSE of ∼90 W m−2. Comparison between STIC ET estimates and the operational ECOSTRESS ET product from NASA PT‐JPL model showed a larger RMSE (around 50 W m−2 higher) for the PT‐JPL ET estimates. Substantial overestimation (>80 W m−2) in PT‐JPL ET estimates was evident over shrublands and savannas, presumably due to weak constraint of LST in the model. Overall, the EEH supports ET retrieval for the future high‐resolution thermal missions.

Estimating ecosystem evaporation and transpiration using a soil moisture coupled two-source energy balance model across FLUXNET sites

The two-source energy balance model coupled with soil moisture (TSEB-SM) was evaluated against observations from a global set of 57 eddy covariance (EC) sites, part of the FLUXNET2015 dataset. In addition, modeled soil evaporation (E) and transpiration (T) were compared with the values obtained from the Transpiration Estimation Algorithm (TEA) and underlying water use efficiency (uWUE) approaches. The TSEB-SM model framework using near-surface soil moisture improved the agreement to EC-observed sensible and latent heat fluxes, reducing mean absolute percentage error (MAPE) by about 30% and root mean square error (RMSE) by about 44 W/m2 across all sites. The results show that the advantage of the TSEB-SM model, with respect to the original TSEB, becomes more evident as the ratio of actual to potential evapotranspiration (AET/PET) decreases. The E and T produced by TSEB-SM has better correlation with the results of uWUE partitioning than TSEB, especially under low soil water content condition. Likewise, TSEB-SM is superior to TSEB in simulating T when compared with sap flow measurements derived from the SAPFLUXNET database. These results imply that the development and application of TSEB-SM has made significant advances in modeling surface water fluxes, even though uncertainties remain. The approach used in TSEB-SM, driving the model with an extensive remotely sensed parameter set, gives valuable information on water use and provides an alternative to Global Climate Models where complex interactions of ecosystems are parametrized. Thus, TSEB-SM provides a unique insight into the flow of energy and the role of surface fluxes in the global water cycle.

Spatio-temporal changes of evapotranspiration over the Zoige Wetland Basin in China using TSEB and GF satellites

ABSTRACT The Zoige wetland located in the eastern margin of the Tibet Plateau, is very important for the runoff regulation and biodiversity in the Yellow River basin. Due to the large non-uniformity of the alpine wetland surface, the use of higher spatial resolution satellite remote sensing data is essential to match or validate the results of ground observation. The land evapotranspiration in this survey was estimated by using the GF-4 satellite data from 2016 to 2020 into the Two Source Energy Balance (TSEB) model, and the estimated evapotranspiration is compared to the ground observation. The results show that: (1) the regression analysis of the actual observed evapotranspiration and the evapotranspiration estimated by the TSEB model shows that the actual evapotranspiration and the evapotranspiration calculated by the TSEB model have a good positive linear correlation (R2 > 0.8) at the Maqu observation site and the Huahu wetland observation site. Therefore, the accuracy of the evapotranspiration calculated by the TSEB model is high. (2) The spatio-temporal distribution of evapotranspiration and relevant land surface variables is generally characterized by high in the southeast and low in the northwest over the Zoige wetland basin. (3) The mean daily evapotranspiration for different GF-4 observation over the Zoige area is as follows: 1.8 mm on 8 April 2018, 3.6 mm on 20 July 2016, 1.4 mm on 29 November 2020 and 0.9 mm on 15 December 2017, and the magnitudes of evapotranspiration of the different surface are as follows: waterbody > grassland > bare land. (4) The histogram of the frequency distribution of evapotranspiration show that 3.2–4.0 mm on 20 July 2016, 0–0.8 mm on 15 December 2017, 0.8–1.6 mm on 8 April 2018 and 1.6–2.4 mm on 29 November 2020 are the maximum evapotranspiration range over the Zoige wetlands area.

Accurate estimates of land surface energy fluxes and irrigation requirements from UAV-based thermal and multispectral sensors

The two-source energy balance model estimates canopy transpiration (Tr) and soil evaporation (E) traditionally from satellite partitions of remotely sensed land surface temperature (LST) and the Priestley-Taylor equation (TSEB-PT) at seasonal time with limited accuracy. The high spatial–temporal resolution spectral data collected by unmanned aerial vehicles (UAVs) provide valuable opportunity to estimate Tr and E precisely, improve the understanding of the seasonal and the diurnal cycle of evapotranspiration (ET), and timely detect agricultural drought. The UAV data vary in spatial resolution and the uncertainty imposed on the TSEB-PT outcome has thus far not being considered. To address these challenges and prospects, a new energy flux modelling framework based on TSEB-PT for high spatial resolution thermal and multispectral UAV data is proposed in this paper. Diurnal variations of LST in agricultural fields were recorded with a thermal infrared camera installed on an UAV during drought in 2018 and 2019. Observing potato as a test crop, LST, plant biophysical parameters derived from corresponding UAV multispectral data, and meteorological forcing variables were employed as input variables to TSEB-PT. All analyses were conducted at different pixelation of the UAV data to quantify the effect of spatial resolution on the performance. The 1 m spatial resolution produced the highest correlation between Tr modelled by TSEB-PT and measured by sap flow sensors (R2 = 0.80), which was comparable to the 0.06, 0.1, 0.5 and 2 m pixel sizes (R2 = 0.76–0.78) and markedly higher than the lower resolutions of 2 to 24 m (R2 = 0.30–0.72). Modelled Tr was highly and significantly correlated with measured leaf water potential (R2 = 0.81) and stomatal conductance (R2 = 0.74). The computed irrigation requirements (IRs) reflected the field irrigation treatments, ET and conventional irrigation practices in the area with high accuracy. It was also found that using a net primary production model with explicit representation of temperature influences made it possible to distinguish effects of drought vis-a-vis heat stress on crop productivity and water use efficiency. The results showed excellent model performance for retrieving Tr and ET dynamics under drought stress and proved that the proposed remote sensing based TSEB-PT framework at UAV scale is a promising tool for the investigation of plant drought stress and water demand; this is particularly relevant for local and regional irrigations scheduling.

Performance of the improved two-source energy balance model for estimating evapotranspiration over the heterogeneous surface

Performance of the improved two-source energy balance model for estimating evapotranspiration over the heterogeneous surface

ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards

Evapotranspiration (ET) is a crucial part of commercial grapevine production in California, and the partitioning of this quantity allows the separate assessment of soil and vine water and energy fluxes. This partitioning has an important role in agriculture since it is related to grapevine stress, yield quality, irrigation efficiency, and growth. Satellite remote sensing-based methods provide an opportunity for ET partitioning at a subfield scale. However, medium-resolution satellite imagery from platforms such as Landsat is often insufficient for precision agricultural management at the plant scale. Small, unmanned aerial systems (sUAS) such as the AggieAir platform from Utah State University enable ET estimation and its partitioning over vineyards via the two-source energy balance (TSEB) model. This study explores the assessment of ET and ET partitioning (i.e., soil water evaporation and plant transpiration), considering three different resistance models using ground-based information and aerial high-resolution imagery from the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). We developed a new method for temperature partitioning that incorporated a quantile technique separation (QTS) and high-resolution sUAS information. This new method, coupled with the TSEB model (called TSEB-2TQ), improved sensible heat flux (H) estimation, regarding the bias, with around 61% and 35% compared with the H from the TSEB-PT and TSEB-2T, respectively. Comparisons among ET partitioning estimates from three different methods (Modified Relaxed Eddy Accumulation—MREA; Flux Variance Similarity—FVS; and Conditional Eddy Covariance—CEC) based on EC flux tower data show that the transpiration estimates obtained from the FVS method are statistically different from the estimates from the MREA and the CEC methods, but the transpiration from the MREA and CEC methods are statistically the same. By using the transpiration from the CEC method to compare with the transpiration modeled by different TSEB models, the TSEB-2TQ shows better agreement with the transpiration obtained via the CEC method. Additionally, the transpiration estimation from TSEB-2TQ coupled with different resistance models resulted in insignificant differences. This comparison is one of the first for evaluating ET partitioning estimation from sUAS imagery based on eddy covariance-based partitioning methods.

Applications of a Thermal-Based Two-Source Energy Balance Model Coupling the Sun-Induced Chlorophyll Fluorescence Data

Quantifying and monitoring land surface evapotranspiration (ET) is an essential task for understanding the earth’s water, energy, and carbon cycles. ET, specifically plant transpiration ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> ), is closely linked to the photosynthesis, which is coupled through stomatal function. However, the mechanistic links between sun-induced chlorophyll fluorescence (SIF) information indicating canopy photosynthetic activity and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> are complex and difficult to derive empirically. An empirical SIF- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> relationship at ecosystem scale was developed and coupled to the two-source energy balance model (TSEB-SIF) to estimate the ET and its components, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> and soil evaporation, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> . By comparing model predictions with observations from an irrigated cropland site located in a semiarid region, the TSEB-SIF model shows a slightly better performance to the TSEB model in estimating ET, especially under water deficit conditions. Moreover, the TSEB-SIF model more reliably partitioned the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> from ET, while the TSEB model tended to overestimate the contribution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> to ET.

Evaluation of Partitioned Evaporation and Transpiration Estimates within the DisALEXI Modeling Framework over Irrigated Crops in California

Accurate characterization of evapotranspiration (ET) is imperative in water-limited cropping systems such as California vineyards and almond orchards. Satellite-based ET modeling techniques, including the atmosphere–land exchange inverse model (ALEXI) and associated flux disaggregation technique (DisALEXI), have proven reliable in determining field scale ET. However, validation efforts typically focus on ET and omit an evaluation of partitioned evaporation (E) and transpiration (T). ALEXI/DisALEXI is based on the two-source energy balance (TSEB) model, making it uniquely qualified to derive E and T individually. The current study evaluated E and T estimates derived using two formulations of DisALEXI; one based on Priestley-Taylor (DisALEXI-PT) and the other on Penman-Monteith (DisALEXI-PM). The modeled values were validated against partitioned fluxes derived from the conditional eddy covariance (CEC) approach using EC flux towers in three wine grape vineyards and three almond orchards for the year 2021. Modeled estimates were derived using Landsat 8 Collection 2 thermal infrared and surface reflectance imagery as well as Harmonized Landsat and Sentinel-2 surface reflectance datasets as input into DisALEXI. The results indicated that the modeled total ET fluxes were similar between the two methods, but the partitioned values diverged, with DisALEXI-PT overestimating E and slightly underestimating T when compared to CEC estimates. Conversely, DisALEXI-PM agreed better with CEC-derived E and overestimated T estimates under non-advective conditions. Compared to one another, DisALEXI-PM estimated canopy temperatures ~5 °C cooler and soil temperatures ~5 °C warmer than DisALEXI-PT, causing differences in E and T of −2.6 mm day−1 and +2.6 mm day−1, respectively. The evaluation of the iterative process required for DisALEXI indicates DisALEXI-PM ET values converge on ALEXI ET with proportionate adjustments to E and T, while DisALEXI-PT convergence is driven by adjustments to E. The analysis presented here can potentially drive improvements in the modeling framework to provide specific soil and canopy consumptive water use information in unique canopy structures, allowing for improved irrigation and water use efficiencies in these water-limited systems.

Understanding Drainage Dynamics and Irrigation Management in a Semi-Arid Mediterranean Basin

Irrigation is one of the main users of water worldwide and its overuse may affect the natural regimes of water systems. To avoid this, drainage and irrigation management needs to be improved. This study aims to determine the amount of water lost to drainage in a semi-arid Mediterranean irrigated area. Water use, rainfall and drainage were monitored for 12 months (2019–2020) in a 425 ha sub-basin in the Algerri-Balaguer irrigation district (8000 ha, NE Spain). In addition, irrigation requirements were estimated using the single-crop FAO-56 method and a two-source energy balance model (TSEB) was used to estimate actual evapotranspiration in the sub-basin. Water lost to drainage in the sub-basin was estimated as 18% of the total water that entered the perimeter as irrigation and rainfall, which are almost five time higher than theoretical requirements of leaching. Out of the total drainage water, 57% was estimated to be irrigation water and 43% rainwater. The average amount of irrigation water used was 614 mm and irrigation efficiency in the sub-basin was estimated at 80.2% and averaged actual evapotranspiration at 1144 mm. The available margin of improvement is between 19.3% of the present irrigation drainage ratio and the 3.8% estimated with the leaching requirement model.

Two source energy balance maize evapotranspiration estimates using close-canopy mobile infrared sensors and upscaling methods under variable water stress conditions

Mobile infrared thermometers (IRTs) mounted on moving platforms provide one-time-of-day radiometric measurements (Tr), which can be used to calculate instantaneous actual evapotranspiration (ETa) using the two-source energy balance (TSEB) model. However, irrigation scheduling decisions utilize daily ETa estimates, hence the need for time scaling. This study evaluated different upscaling methods to calculate daily maize ETa using one-time-of day Tr under varying water stress conditions. Mobile IRTs were mounted on a high clearance mobile sensing platform and collected Tr in remote locations under full, deficit and rainfed conditions. Seven scaling methods via two pathways were employed to obtain daily ETa. First pathway was scaling one-time-of-day Tr (SC) whereas the second pathway involved use of six upscaling methods of instantaneous ETa including: original and modified evaporative factor ((EF)o, (EF)m) as well as crop coefficient ((Kc)o, (Kc)m), direct canopy resistance (Direct- rc), and solar radiation ratio (Rn/Rs); and all were compared to a neutron-based soil water balance (SWB) determined ETa. From the results, SC outperformed other methods in comparison to SWB ETa across all the selected treatments with smaller discrepancies and lower RMSE (0.9–1.7 mm d−1 vs. 0.7–4.3 mm d−1 for other methods). Furthermore, methods including SC, (EF)o, (EF)m, and Rn/Rs had their daily average ETa values in close agreement to SWB ETa with mean ETa differences ranging between 0.2 and 1.6 mm d−1. Overall, SC method performed better in fully irrigated maize (r2 = 0.52, RMSE = 0.9 mm d−1) than in deficit irrigated maize ( r2 = 0.48, RMSE = 1.4 mm d−1) but worst in rainfed maize (r2 = 0.16, RMSE = 1.7 mm d−1). This implies that SC is more suited for irrigated rather than rainfed settings. Importantly, the choice of any method depends on data requirements, irrigation water management strategy, and ETa estimation accuracy.

Assessing evapotranspiration in a lettuce crop with a two-source energy balance model

Assessing evapotranspiration in a lettuce crop with a two-source energy balance model

Application of the two-source energy balance model with microwave-derived soil moisture in a semi-arid agricultural region

Evapotranspiration (ET) is a critical component of the water, carbon and energy cycles of the land surface. Remote sensing-based models provide the possibility of mapping ET from field to regional and even global scales. A key boundary condition of the Two-Source Energy Balance (TSEB) model for computing ET is land surface temperature (LST). The proposed modification to TSEB is to use a combination of near surface soil moisture (SM) derived by microwave and LST as dual boundary conditions along with a more physically-based transpiration formulation for computing ET. Both TSEB and TSEB-SM were applied to an irrigated agricultural area with surrounding semi-arid sparse natural vegetation. In general, TSEB-SM model performance when compared to measured fluxes was similar to the TSEB model. However, the TSEB-SM model computed more reliable estimates of LE under dry soil surface/low vegetation cover conditions, primarily in the arid and semiarid natural ecosystems surrounding the irrigated agricultural area. These comparisons indicate that TSEB-SM may provide more reliable flux estimation and ET partitioning over sparsely vegetated areas compared to the traditional formulations used in TSEB. With reliable microwave remote sensing of soil moisture, TSEB-SM has potential for ET monitoring in both natural and agricultural landscapes.

Remote sensing based evapotranspiration modeling for sugarcane in Brazil using a hybrid approach

The increasing pressure on water resources in agricultural areas requires the implementation of innovative tools and solutions to improve irrigation water management. Against that background, this research presents the application of a remote sensing-based methodology for estimating actual evapotranspiration (ETa) based on two-source energy balance model (TSEB) and remote sensing-water balance (RSWB) coupling for sugarcane crop in Brazil using the hybrid model Spatial EvapoTranspiration Modeling Interface (SETMI). Estimated results through SETMI and field data using the eddy covariance system (EC) considering two growing seasons were used to validate the energy balance components and ETa. In addition, the basal crop coefficient as a function of the spectral reflectance (Kcbrf) was developed through the soil-adjusted vegetation index (SAVI) and observed ET. Modeled energy balance components showed a strong correlation to the ground data from EC, with ET presenting R2 equal to 0.94 and a Pearson correlation coefficient (ρ) equal to 0.88. Regarding Kcbrf, the Kcb-SAVI relationship for sugarcane presented a high correlation with an R2 value of 0.85 and an "ρ" equal to 0.92. On average, considering the whole season, Kcb was equal to 0.75 and 0.73 for the 4th ratoon and 5th ratoon, respectively. Overall, the average Kc throughout the period was 0.73 and 0.70 for the 4th and 5th ratoons respectively, and the maximum Kc of about 1.23 for both growing seasons. On average, accumulated ETa presented 1025 mm resulting in ETa rates of 2.9 mm per day considering the two seasons. Crop water productivity (WP) obtained values similar between the seasons, averaging 12.6, 21.7, and 12.3 kg m−3 for WPp+i, WPi and WPET, respectively. The SETMI hybrid model produced suitable estimated daily ETa values over the two growing seasons through remote sensing based on the Kcb-SAVI relationship and good performance of TSEB model during the evaluated growing periods confirming the applicability of the model under tropical conditions in Brazil focusing on improving irrigation management in sugarcane crop.