The study was conducted at the ICOS Class 1 ecosystem site DE-RuS within the TERENO Eifel Rur observatory in Selhausen, Germany (50°51^′56^′′ N, 6°27^′03^′′ E) (https://ddp.tereno.net/ddp/, last access: 12 October 2026). The 10 ha field is situated at 101–103 m above mean sea level with the soil consisting of Pleistocene loess, Holocene translocated loess, with sand and gravel at deeper levels (Brogi et al., 2020). The western section has shallower sand and gravel deposits, creating spatial variability in crop growth, especially during water-scarce periods. Weather conditions including precipitation and air temperature, during the experiment periods can be found in the appendix (see Table D1 and Fig. D6).

The field experiment site, shown in Fig. 1, was monitored over three growing seasons with different crops each year. In 2021, sugar beet was planted from May to October, starting with 20 % vegetation cover and reaching 98 % by harvest. In 2022, potatoes were grown from May to August, beginning with 5 % cover and closing at 87 %, with ridges between rows. In 2023, winter wheat was planted from April to July, maintaining 94 %–98 % cover, with senescence marked by visible leaf browning. Only the potato crop was irrigated, receiving three 30 mm applications in July. These crop and soil variations provide a basis to evaluate the robustness of very-high-resolution UAV data in ET models like TSEB-PT.

Two DJI Matrice 600 UAV were used, one carrying a LiDAR payload and the other a combined MS and TIR setup (Fig. 2). The LiDAR system included a YellowScan Surveyor with a Velodyne VLP-16 scanner and Applanix APX15 GNSS-IMU, capturing 300 000 pulses s⁻¹ at 903 nm with ∼ 4 cm precision. Flights were conducted at 50 m altitude and 8 m s⁻¹ with 50 % side overlap. A Septentrio Altus NR3 GNSS base station was used for georeferencing. LiDAR data were processed using POSPac (v8.6) and YellowScan's CloudStation.

The TIR sensor, a FLIR Vue Pro R 640 (13 mm), covered the 7.5–13.5 µm range with ±5 °C accuracy and was modified with a heated shutter (ThermalCapture, AirRobot^® GmbH & Co.) for improved inflight calibration. The MS sensor, a MicaSense RedEdge-M (AgEagle Aerial Systems Inc.), captured five spectral bands; red (663–673 nm) and NIR (820–860 nm) bands were used to calculate NDVI. Both sensors were mounted on a 3-axis gimbal and flown at 100 m altitude and 6 m s⁻¹, with approximately 90 % image overlap. TIR calibration used internal corrections and ground temperature reference targets, while MS data were calibrated using a reflectance panel. Data from both platforms were processed in Pix4D. Flight campaign details are in the Appendix (Table D1).

TSEB-PT inputs were grouped into two categories: (1) surface boundary conditions derived from UAV data, including land surface temperature (LST), fractional cover (fc), leaf area index (LAI), crop height (hc), and fraction of LAI that is green (fg); and (2) meteorological forcing variables, including air temperature (Ta), wind speed (u), vapor pressure (ea), and incoming shortwave irradiance (Sdn). UAV-derived variables were processed and calibrated using ground measurements, with detailed methods provided in Sect. 2.3.1–2.3.5. Data processing and raster analysis were conducted in R using the raster and lidR packages, and all datasets were resampled to a common 15 cm ground sampling distance (GSD). The overall workflow is illustrated in Fig. 3.

Model runs were designed to assess the impact of key inputs, including LST (original vs. corrected), LAI (MS-derived GAI vs. LiDAR-derived PAI), and fg (default fg=1 vs. UAV-derived fg= GAI / PAI), on instantaneous fluxes and ET derived from UAV observations. These outputs were compared with eddy covariance (EC) fluxes and ET within the modeled EC footprint. The analysis was conducted sequentially by first evaluating LST configurations, followed by comparisons of LAI inputs (GAI vs. PAI) with fg held constant (fg=1), and finally incorporating dynamic fg (GAI / PAI) with PAI as the structural LAI input.

Following identification of the optimal configuration, instantaneous UAV-based ET estimates were upscaled to daily ET using the reference ET fraction (ETrF) approach and compared with daily EC ET. Reference ET (ET₀) was calculated using the FAO-56 method with EC meteorological data to represent atmospheric evaporative demand and was used to derive a relative ET stress proxy (ET_RAD) from both UAV- and EC-derived ET estimates.

UAV thermal infrared (TIR) observations consistently underestimated ground-surface temperature relative to in-field reference targets during 2021. Uncorrected TIR data had an RMSE of 4.19 °C but maintained strong temporal agreement across dates (R2= 0.97). Ground-target correction reduced the seasonal RMSE to 2.78 °C. Thermal error was most strongly associated with air temperature (r=0.74), followed by wind speed (r=0.55) and relative humidity (r=0.34). These relationships were used in a multiple linear regression model to correct sensor bias in subsequent seasons, producing an average LST increase of 2.2 °C across acquisitions. Correction relationships and validation are presented in Fig. D2, while seasonal differences between original (LST_OG) and corrected (LST_TC) field-average LST are shown in Fig. 5.

Multispectral-derived green area index (GAI) and LiDAR-derived plant area index (PAI) were evaluated against ground-based ceptometer LAI measurements (Fig. 6). PAI showed closer agreement with ground observations across crops, with R2=0.99 and an overall RMSE of 0.27 m² m⁻², compared with R2=0.88 and RMSE =1.02 m² m⁻² for GAI, based on a limited number of calibration samples. Differences between GAI and PAI were greatest during senescence, when GAI declined while LiDAR-derived PAI retained higher canopy structural values.

UAV-derived canopy height (hc) also showed strong agreement with ground measurements (Fig. 7), with an overall RMSE of 0.06 m and R2=0.90 across crops. Winter wheat showed the highest agreement (RMSE =0.01 m; R2=0.99), while potato showed greater error (RMSE = 0.12 m), potentially reflecting ridge planting and heterogeneous canopy closure within sampled areas.

To assess the impact of radiometric calibration on model performance, TSEB-PT estimates derived from original (LST_OG) and target-corrected (LST_TC) UAV thermal imagery were compared with EC fluxes under open and closed energy balance conditions across all three crop seasons (Fig. 8). Thermal correction generally produced slightly lower modeled net radiation (Rn) and latent heat flux (LE) and higher sensible heat flux (H), with the largest responses in sugar beet (2021) and winter wheat (2023) and more moderate changes in potato (2022). Soil heat flux (G) changed comparatively little. Overall, LST_TC improved agreement with EC observations, particularly for LE and under closed energy balance conditions.

RMSE and R2 statistics (Table D2) supported these patterns. Thermal correction generally reduced LE RMSE across crops, with the largest improvements for winter wheat and sugar beet, whereas responses in H were less consistent. Rn showed minimal sensitivity and G generally exhibited smaller and crop-dependent responses. Excluding senescence dates strengthened agreement, particularly for LE. Across configurations, closed EC energy balance comparisons generally produced higher R2 and lower RMSE than open EC comparisons.

Because LE represents evapotranspiration within the energy balance, estimates were converted to ET (mm h⁻¹) for direct evaluation at UAV acquisition times (Fig. 9). Under open EC conditions, LST_TC reduced RMSE from 0.21 to 0.16 mm h⁻¹ for sugar beet, 0.14 to 0.11 mm h⁻¹ for potato, and 0.31 to 0.26 mm h⁻¹ for winter wheat. Thermal correction did not always improve temporal correspondence; for sugar beet, R2 decreased from 0.50 to 0.32 despite the lower RMSE. Excluding senescence dates further strengthened agreement, with the largest improvement in sugar beet (RMSE 0.16 to 0.11 mm h⁻¹; R2 0.32 to 0.79). Closed EC comparisons generally produced higher R2 and slightly lower RMSE across crops.

Building on the thermal correction analysis, we evaluated the influence of multispectral-derived GAI, LiDAR-derived PAI, and dynamic fraction of LAI that is green (fg= GAI / PAI) on TSEB-PT flux estimates (Fig. 10; Table D3). Differences between GAI and PAI configurations were generally small during early- to mid-season conditions when canopy structure and greenness were closely aligned, with similar agreement with EC observations under both open and closed energy balance comparisons.

Differences became more pronounced during senescence, particularly in winter wheat. Using PAI with fg=1 resulted in greater LE overestimation, whereas GAI improved agreement with EC observations. Using PAI together with dynamic fg further reduced this bias and shifted modeled fluxes closer to the 1:1 relationship (Fig. 10; Table D3). Similar but smaller responses were observed for sugar beet and potato. Across crops, LAI configuration had comparatively small effects on Rn and G, while LE and H showed greater sensitivity. Closed EC comparisons generally produced higher R2 and lower RMSE than open EC comparisons.

ET comparisons further demonstrated these differences (Fig. 11). For winter wheat, under open EC energy balance conditions, PAI with fg=1 produced greater ET overestimation (RMSE ≈ 0.29 mm h⁻¹; R2 ≈ 0.32) than GAI (RMSE ≈ 0.26 mm h⁻¹; R2 ≈ 0.56), while incorporating dynamic fg, with LAI = PAI, reduced RMSE to approximately 0.17 mm h⁻¹ and improved agreement. Across crops, comparisons with closed EC ET generally showed similar or improved performance based on R2 and RMSE. Excluding senescence dates reduced differences among LAI configurations across all crops, with differences between GAI and PAI becoming minimal, indicating similar performance before senescence.

TSEB-PT ET varied in both magnitude and spatial distribution depending on the UAV-derived inputs. To assess their influence on spatial variability, changes in the coefficient of variation (CV) of ET were compared with differences in input CV for LST (TIR_OG vs. TIR_TC), LAI (GAI vs. PAI), and fg (fg=1 vs. GAI / PAI-derived fg) (Fig. D4). LST correction produced substantial changes in ET spatial variability despite the largely uniform temperature offset, while differences between GAI and PAI had a comparatively smaller influence. In contrast, dynamic fg substantially altered ET spatial variability, demonstrating a stronger influence on the spatial representation of ET than LAI source alone.

The spatial effects of LST correction were further examined for potato under early- and late-season canopy conditions (Fig. D5). During early development, when fractional cover was lower, LST correction increased spatial contrasts associated with soil and vegetation contributions to ET. Later in the season, ET reductions following LST correction were greater in areas exhibiting vegetation stress, increasing the contrast between stressed and non-stressed zones. Thus, thermal correction affected not only ET magnitude but also the spatial representation of modeled crop water use.

One of the most practical advantages of TIR based UAV derived ET modeling is its capacity to detect early signals of crop water stress before structural or spectral changes manifest in traditional vegetation indices. In the 2022 potato and 2023 winter wheat seasons, this capability was clearly demonstrated as seen in Figs. 12 and 13.

Comparisons between the 3 and 29 August UAV campaigns in 2022 revealed that, despite minimal changes in both GAI and PAI, the ET maps captured pronounced declines in water use across specific sections of the field. These reductions aligned with known zones of reduced irrigation coverage caused by limitations of the hose reel sprinkler system. Notably, areas along the northwest corner and adjacent to irrigation lanes exhibited significant ET suppression, even though they remained structurally intact in LiDAR and multispectral imagery.

A similar pattern was observed in 2023 with winter wheat, though in this case the stress was most likely attributed to known underlying soil–root interactions that have caused water stress in the past. On 13 June, the ET map revealed broader and more pronounced spatial structures of suppressed transpiration that were not yet visible in the GAI or PAI maps. By 28 June, however, similar spatial patterns emerged in both GAI and PAI, confirming physiological and structural canopy decline.

To evaluate the operational applicability of UAV-derived ET, instantaneous estimates were upscaled to daily values using the reference ET fraction (ETrF) approach and compared with daily EC ET under open and closed energy balance conditions and with FAO-56 reference ET (ET_o) (Allen et al., 1998; Appendix Sect. C3). Upscaling followed Cammalleri et al. (2014) using the best-performing TSEB-PT configuration (LST = LST_TC, LAI = PAI, fg= GAI / PAI, and LiDAR-derived hc). A relative ET stress proxy (ET_RAD = ET / ET_o ) was calculated to characterize crop water use relative to atmospheric evaporative demand.

Figure 14 presents daily EC ET, ET_o, and upscaled UAV ET aggregated over the field, EC flux footprint, and potential water-limitation zones (PWLZ; Figs. 12–13). UAV-derived daily ET generally followed the temporal patterns of EC ET and ET_o across crops, while PWLZ estimates frequently differed from field and footprint averages. Correspondingly, ET_RAD declined below 0.7 and 0.5 during several periods, particularly within PWLZs, indicating progressively greater reductions in ET relative to atmospheric demand.

Daily UAV-derived ET performance against EC observations is summarized in Fig. 15. Agreement varied among crops and EC energy balance treatments. Sugar beet showed moderate agreement (RMSE = 0.96–1.30 mm d⁻¹; R2=0.57–0.61), while potato showed the strongest agreement (RMSE = 1.53–1.61 mm d⁻¹; R2 ≈ 0.80). Winter wheat showed the weakest agreement (RMSE = 1.90–2.30 mm d⁻¹; R2=0.27–0.30).

Bias was also crop dependent, ranging from -0.18 to -0.76 mm d⁻¹ for sugar beet, +0.18 to -0.51 mm d⁻¹ for potato, and +1.25 to +1.85 mm d⁻¹ for winter wheat. Differences between open and closed EC comparisons demonstrate the influence of energy balance treatment on model evaluation and the uncertainty associated with EC-derived ET as a reference.

TSEB-PT's strong sensitivity to land surface temperature (LST) is well established (e.g., Hoffmann et al., 2016), but this study extends those findings using very-high-resolution UAV based thermal imagery under uniform crop cover. Uncorrected LST exhibited a significant underestimation, consistent with prior UAV studies (e.g., Kelly et al., 2019; Han et al., 2020; Wan et al., 2024; Tunca et al., 2023; Liu et al., 2024). This bias led to systematic ET overestimation. While R2 declined slightly after correction, the improved thermal realism enhanced physical consistency in ET estimates, particularly important for cumulative ET tracking and water balance assessments.

Thermal bias was most severe under high air temperature and wind speed, precisely when crops face peak transpiration demand and are most vulnerable to stress. This trend aligns with Sagan et al. (2019), who reported increased UAV TIR underestimation under warmer conditions. In our results, stressed areas showed amplified ET deviations with LST correction, indicating greater sensitivity to thermal accuracy. Underestimated LST muted ET suppression signals, reducing contrast between stressed and non-stressed zones and potentially misguiding irrigation responses. In addition, corrected LST sharpened spatial gradients in ET by enhancing energy balance contrast, particularly under dominant water or energy limitation conditions. This raised the standard deviation of ET values across the field and improved spatial differentiation of stress signals.

Despite onboard radiometric correction and a heated shutter for non-uniformity calibration, ground based reference targets remained essential for accurate LST and ET retrieval. When such targets are unavailable, alternate approaches like the Dual-Temperature Difference (DTD) variant of TSEB-PT has shown to improve estimations (Hoffmann et al., 2016). Although in our case, DTD yielded only moderate improvements compared to ground-calibrated correction when used for the 2021 campaign (results not shown).

Finally, LST accuracy could be further improved through spatially dynamic emissivity correction. While constant emissivity values are appropriate under full canopy closure during peak growth, they become less valid under partial canopy conditions (e.g., early growth stages in sugar beet and potato) and during senescence, particularly in cereal crops such as winter wheat where strong contrasts between green, functioning vegetation and brown, senescent material introduce greater emissivity variability. Recent work has shown that emissivity can be effectively parameterized using NDVI to distinguish between soil, vegetation, and mixed pixels in high-resolution UAV datasets (e.g., NDVI segmentation or scaling approaches) (Heinemann et al., 2020). Applying such methods could improve LST retrievals in heterogeneous and senescing canopies, thereby enhancing spatial ET estimation and stress detection.

Both GAI and PAI served as effective LAI inputs for TSEB-PT during peak growth, when structural (PAI) and pigment-based (GAI) indicators were closely aligned, yielding similar ET estimates. This is consistent with a prior one-source energy balance study showing that LiDAR-derived LAI can perform comparably to multispectral-derived LAI for ET estimation (Wei et al., 2023). However, few studies have examined how model performance shifts under stress or senescence, when canopy structure and function can diverge.

When fg was held at its default value of 1, PAI remained high due to persistent canopy structure and was associated with greater ET overestimation during these periods. In contrast, GAI declined with senescence and produced more conservative ET estimates that better aligned with EC observations, particularly in winter wheat, where structural density persisted as canopy greenness and transpiration declined. Although PAI and ground-based ceptometer LAI showed better agreement in absolute magnitude, they did not reflect the decline in green canopy area to the same extent as GAI. The improved performance of GAI relative to PAI may therefore indicate that its decline indirectly represented part of the reduction in transpiring green vegetation that was otherwise not accounted for when fg remained at its default value, whereas PAI better retained information on persistent canopy structure.

For sugar beet and potato, spatially varying NDVI-derived fc accounted for partial canopy cover and soil exposure, providing complementary information on the horizontal distribution of vegetation. Together with LAI, fc contributes to canopy clumping and the directional vegetation fraction used to decompose radiometric temperature into soil and canopy component temperatures. These component temperatures influence soil–canopy sensible heat partitioning and, consequently, the energy balance from which G is estimated (Eq. A4 in Appendix A). The crop-specific differences in ground heat flux (Table D2) therefore suggest that the relative performance of GAI and PAI may depend partly on canopy architecture. PAI may have improved G estimates in winter wheat by better representing canopy density and structural shading in the tall, closed canopy, whereas GAI performed better in the lower, more open sugar beet and potato canopies with greater vegetation–soil contrast, suggesting that the relative benefits of multispectral and LiDAR inputs may depend on canopy architecture. However, these interpretations are inferred from the model behavior observed in this study, and targeted sensitivity testing is needed to establish the underlying mechanisms.

Distinguishing passive canopy cooling from actual transpiration is critical in dense canopies like winter wheat, where structure remains even as physiological function declines. Unlike eddy covariance systems, which directly measure vapor flux, TSEB-PT relies on LST as a proxy. Without accounting for canopy function, models may incorrectly attribute radiative cooling to transpiration. In this study, combining LiDAR based structural LAI (PAI) and MS derived pigment-sensitive LAI (GAI) enabled the calculation of a spatially dynamic fg, allowing canopy transpiration within the Priestley–Taylor formulation to be scaled according to physiological status, improving ET agreement with EC observations, particularly during early stress and senescence.

The dynamic fg also improved model performance before pronounced visible senescence, indicating sensitivity to periods when transpiration was suppressed due to stress. Although fg is an available TSEB-PT input, it is frequently omitted and retained at its default value of 1 in applications. Comparable explicit structural–functional scaling is absent from single-source models such as SEBAL and METRIC. More broadly, these approaches were developed primarily for conventional remote-sensing applications, where detailed characterization of canopy structure and function is more limited than with current UAV observations. UAV platforms provide an opportunity to better exploit this distinction by combining high-resolution three-dimensional structural information from LiDAR with spectral information related to canopy greenness.

Previous UAV-based TSEB-PT studies that did not dynamically parameterize fg also reported ET overestimation under conditions of declining canopy function. Gómez-Candón et al. (2021) reported ET overestimation in wheat (RMSE = 0.24 mm h⁻¹), particularly later in the season during senescence. Similarly, Tunca (2023) reported overestimation in stressed sorghum and suggested adjustment of the Priestley–Taylor coefficient (α) as a potential solution. The present results indicate that dynamic fg provides a spatially explicit means of scaling canopy transpiration within the Priestley–Taylor formulation, rather than relyingon a uniform adjustment of αPT. Bozorgi et al. (2024) likewise identified assumptions concerning the fg as a potential source of TSEB bias, further supporting the importance of representing changes in the fraction of LAI that is green.

As shown in Fig. D4, fg influenced spatial ET variability more strongly than LAI type alone, demonstrating the sensitivity of modeled ET to fg and the importance of accurately representing this variable. This sensitivity was greater for sugar beet and potato than for winter wheat. The high LAI of winter wheat may have contributed to its reduced response, as exponential radiation attenuation in dense canopies results in diminishing changes in radiation interception with increasing LAI (Campbell and Norman, 1998; Kustas and Norman, 1999); however, the mechanisms underlying the observed crop-specific sensitivity to fg require further investigation.

UAV LiDAR methods can now provide purely structural, high-resolution information that complements multispectral estimates of canopy function, helping to reduce pigment-related biases inherent in reflectance-only approaches (Chirouze et al., 2014). Unlike maximum NDVI methods, which may not fully capture structural changes during senescence, combining LiDAR-derived structure with MS-based greenness allows fg to better represent the green fraction relative to total canopy structure. This integrated approach can improve ET partitioning and may be beneficial for broader application in UAV-based TSEB-PT and other ET modeling workflows.

To support applications in irrigation management and water balance assessment, instantaneous evapotranspiration (ET) estimates derived from UAV-based TSEB-PT were upscaled to daily totals. TSEB provides flux estimates corresponding to the time of thermal acquisition, requiring temporal upscaling for daily ET estimation. In this study, daily ET was derived using the ETrF approach (Sect. 3.6; Eq. C2) based on the best-performing instantaneous configuration (LST = LST_TC, LAI = PAI, fg= GAI / PAI). This approach enabled direct comparison with EC-derived daily ET and with reported daily ET estimates from previous studies.

The resulting daily ET estimates showed good agreement with EC observations across crops, although winter wheat remained the most challenging. Relatively few studies report daily field-scale TSEB performance for the crops examined here, and the available studies are predominantly satellite-based. For winter wheat, reported daily ET RMSE values are approximately 1.5–2.0 mm d⁻¹ (El Hazdour et al., 2025). Other satellite-based energy-balance approaches have reported similar errors; for example, Zoratipour et al. (2023) obtained daily winter wheat ET RMSEs of 2.15 and 1.53 mm d⁻¹ for SEBAL and SEBS, respectively. These comparisons place the present UAV-based TSEB-PT results within the range reported for other remote-sensing ET approaches, while UAV observations provide greater spatial detail and acquisition flexibility. This is consistent with Nassar et al. (2020), who showed that increasing model grid size had a comparatively smaller effect on field-scale mean fluxes than on within-field variability, while Mokhtari et al. (2021) demonstrated the value of higher-resolution UAV data for resolving fine-scale spatial variability in ET. However, direct comparisons between studies should be interpreted cautiously because of differences in spatial and temporal resolution, crop conditions, and validation and EC-processing methods.

Crop-specific behavior further illustrates differences in model performance. Winter wheat showed a tendency toward ET overestimation, consistent with previous studies reporting positive bias under dense canopy conditions (El Hazdour et al., 2025), where structural persistence may lead to overestimation of transpiration when physiological activity declines. In contrast, studies on sugar beet (Acharya and Sharma, 2021) and potato (Ebert et al., 2022) have reported slight underestimation of ET, which may be associated with higher soil exposure during early growth stages. Greater soil exposure increases the contribution of the soil component to radiometric temperature and influences soil–canopy energy partitioning within TSEB (Norman et al., 1995; Kustas and Norman, 1999). Consistent with this interpretation, the largest underestimations in this study occurred during early growth stages of sugar beet and potato, when soil fractions were highest (Fig. 14).

Future improvements in daily ET estimation could involve both alternative upscaling formulations and improved temporal sampling. Evaporative-fraction, radiation-based, and other scaling approaches can exhibit different performance depending on phenology, environmental conditions, and acquisition time (Nassar et al., 2021). Recent developments include improved evaporative-fraction-based temporal scaling (Wei et al., 2025) and machine-learning approaches for instantaneous-to-daily flux upscaling (Tunca, 2026). UAV platforms also provide flexibility in acquisition timing and the potential for repeated observations within a day. Together, these developments suggest that improved daily ET estimation may require a combination of appropriate temporal-scaling methods, optimized acquisition timing, and, where feasible, multi-temporal observations that better characterize the diurnal ET cycle.

Additional UAV sensor capabilities could further improve TSEB parameterization. Multi-layer LiDAR gap-fraction profiles could improve characterization of vertical canopy structure (Bates et al., 2026b), while LiDAR intensity has shown relationships with crop LAI (Hütt et al., 2022) and biomass (Montzka et al., 2023), potentially extending LiDAR from a purely structural measurement toward a dual-purpose source of structural and intensity-based canopy information. Ground-classified LiDAR observations could also provide information on soil-surface roughness, potentially helping to guide the selection or adjustment of soil roughness and resistance parameters, which have been shown to influence TSEB performance (Li et al., 2019). Additional information on canopy and surface moisture could be provided by emerging UAV radar systems, including synthetic aperture radar (SAR) and ground-penetrating radar (GPR) (Wu and Lambot, 2022), while integration of surface soil moisture has been shown to improve TSEB transpiration estimates (Song et al., 2023). Hyperspectral UAV observations offer another complementary source of physiological information, potentially enabling retrieval of SIF and PRI, with SIF providing information linked to photosynthetic activity and transpiration and PRI providing sensitivity to changes in photosynthetic light-use efficiency and stress responses (Jonard et al., 2020; Zheng et al., 2024). These indicators could help refine fg or support alternative approaches to better represent canopy physiological status in TSEB-PT. Together, these sensors offer opportunities to advance UAV-based ET modeling by integrating complementary information on three-dimensional canopy structure, moisture status, and canopy function.

Another important consideration is the influence of shadowing on remote sensing inputs. Studies have shown that shadow pixels can introduce bias in LST and ET estimation, as shaded surfaces exhibit lower temperatures that may lead to artificially elevated modeled ET under some conditions (Mokhtari et al., 2021). Additional work has demonstrated that shadowing can significantly affect vegetation indices and the retrieval of fc and LAI, thereby providing an additional pathway through which illumination conditions can influence TSEB flux partitioning (Lu et al., 2022; Gao et al., 2023). These effects may be particularly important in discontinuous canopies with greater soil exposure (Duran-Gomez et al., 2026). However, shaded pixels form part of the composite radiometric signal, and their removal may not necessarily improve flux estimation; recent work instead supports approaches that retain or compensate for shadow effects (Aboutalebi et al., 2019; Duran-Gomez et al., 2026). LiDAR-derived structural inputs provide an advantage in this context because they are substantially less dependent on illumination conditions than passive sensor observations. Although shadow effects were likely reduced in this study by near-solar-noon acquisitions and predominantly developed canopies, future UAV-TSEB studies should further evaluate shadow-compensation strategies for both multispectral and thermal inputs.

UAV acquisition flexibility also provides opportunities to improve temporal representation. Flights can be targeted near optimal acquisition periods or repeated throughout the day to characterize diurnal ET variability and changing illumination conditions, including the movement and influence of shadows. This flexibility may also complement satellite observations by supporting temporal interpolation between satellite overpasses (El Hazdour et al., 2025), as well as high-resolution validation and spatial sharpening of satellite-derived products (Jaafar et al., 2022). Combining the spatial detail and acquisition flexibility of UAV observations with the broader spatial and temporal coverage of satellite systems may therefore improve both within-field characterization and temporal continuity of ET estimates.

Finally, further improvements may arise from testing alternative resistance formulations and soil heat flux parameterizations. Modified resistance schemes have improved TSEB performance under particular canopy conditions (Li et al., 2019; Lun et al., 2026), while simplified G parameterizations remain an important source of uncertainty (Hoffmann et al., 2016). In this study, G was sensitive to LAI representation, with GAI performing better in the more open sugar beet and potato canopies and PAI in dense winter wheat, further motivating targeted testing of how UAV-derived structural and spectral canopy information can best parameterize these model inputs. Model configuration may also be adapted to canopy conditions. Preliminary application of TSEB-2T to the 2021 sugar beet campaign (data not shown) indicated improved performance during early development under low vegetation cover, whereas TSEB-PT was more robust under dense canopy conditions. Future work could therefore examine how model configuration, parameterization, and UAV-derived canopy inputs can be adapted according to canopy architecture and phenological stage.

Moving toward practical farm management applications, physically based models such as TSEB-PT offer advantages by explicitly representing land–atmosphere energy exchange without requiring the extensive training datasets typically needed for data-driven approaches such as machine learning. In this study, TSEB-PT provided ET estimates across three crop seasons with relatively limited ground inputs, consisting primarily of a small number of control plots for LAI and canopy height calibration and early-season thermal reference targets. This demonstrates the potential of combining physically based modeling with UAV observations, with complementary sensor information improving characterization of canopy structure, function, and surface temperature.

The high spatial resolution of UAV ET mapping enables detection of sub-field variability that may not be resolved by conventional scouting or coarser satellite observations. In this study, TIR-derived ET revealed crop water stress patterns before corresponding changes became evident in multispectral GAI or LiDAR-derived PAI and hc. In potato, ET maps revealed zones of reduced ET following irrigation events, consistent with under-irrigation (Fig. 12), while persistently low ET in winter wheat corresponded with shallow gravel layers and areas exhibiting potential water stress and earlier senescence (Fig. 13). Such spatial information can help identify zones of contrasting crop water use and support targeted irrigation management.

A daily relative ET stress proxy (ETRAD= ET / ET₀) was used to characterize crop water use relative to atmospheric demand, consistent with previous approaches (Giardina et al., 2023). Comparisons among field averages, EC footprints, and delineated stress zones (Fig. 14) demonstrate how spatial aggregation can mask localized stress signals, particularly in potato during 3–29 August. However, low ET_RAD does not necessarily indicate water limitation, as reduced ET may also reflect other physiological constraints on transpiration. Complementary information, such as in situ or remotely sensed soil moisture, could therefore help attribute ET reductions to water deficit and improve interpretation of ET-based stress signals.

Previous studies similarly support energy-balance approaches for crop water-stress assessment. TSEB-derived transpiration and stomatal conductance have provided robust stress indicators compared with temperature-based indices alone (Nieto et al., 2022), while combining ET-related information with CWSI has improved discrimination of irrigation treatments and water deficits in UAV applications (Sánchez-Virosta et al., 2025). Operationally, UAV-derived ET could be combined with rainfall and irrigation records to estimate spatial crop water deficits and support irrigation scheduling. Additional integration with gross primary productivity (GPP) and water-use efficiency (WUE) could further connect crop water use with productivity (Elfarkh et al., 2023). High-resolution ET information has consequently been identified as valuable for agricultural water management (Wanniarachchi and Sarukkalige, 2022), with UAV-based approaches offering opportunities for spatially targeted interventions (Ankela et al., 2026).

Despite these advantages, operational implementation requires consideration of spatial scale. The 15 cm GSD outputs generated here exceed the resolution typically required for irrigation decisions, which are generally implemented at irrigation-sector or management-zone scales (von Hebel et al., 2021). Such zones can be defined by soil, yield, crop, and ET variability and typically operate at meter- to tens-of-meter scales or as a limited number of homogeneous field units (Serrano et al., 2020; Flint et al., 2023). The principal value of very-high-resolution UAV ET may therefore lie not in managing individual pixels, but in identifying meaningful within-field heterogeneity that can subsequently be aggregated into actionable management zones. This provides a multi-scale framework in which fine-resolution observations support stress detection and characterization, while aggregated information supports irrigation planning and decision-making.