The UAV-based field measurements were conducted at the Qianyanzhou Experimental Station (26^∘44^′29^′′ N, 115^∘03^′29^′′ E) located in Jiangxi Province, southern China. This station is one of the core flux observation sites of the China Flux Observation Research Network (ChinaFLUX) and is equipped with a comprehensive EC measurement system (Yu et al., 2006). The flux tower has a height of approximately 43 m above ground, with the EC system installed at 31 m above ground level, while the mean forest canopy height is about 18 m.

The long-term observation site, 1 ha in size and centred on the flux tower, is dominated by plantation forests, primarily consisting of Pinus massoniana, Cunninghamia lanceolata, and Pinus elliottii arranged in a strip-mixed pattern with an approximate ratio of 49:48:3, respectively (Huang et al., 2007). The region experiences a typical subtropical monsoon climate, characterized by prevailing southerly winds in summer and northerly winds in winter. The mean annual air temperature is 17.9 °C, and the mean annual precipitation is approximately 1490 mm, with most rainfall occurring between March and June.

To evaluate the performance of the UAV platform for CO₂ vertical flux measurements, flights were conducted from a UAV take-off location approximately 160 m southeast of the flux tower, where the relatively open forest structure allowed safe UAV take-off and landing (Fig. 2a). The UAV-derived fluxes were then directly compared with tower-based measurements. The terrain within approximately 1 km² surrounding the flux tower is characterized by continuous low hills and gentle slopes, with slope angles of approximately 2.8–13.5° and local relative elevation differences of approximately 10–30 m. The approximately 15 m elevation difference between the flux tower and the UAV take-off location is therefore part of the naturally undulating low-hill terrain. Owing to this topographic variability, all UAV flight altitudes reported in this study are referenced to the take-off elevation of the UAV take-off location; consequently, the actual height above the local ground surface varied spatially along the flight path. Within a radius of approximately 350 m northwest of the UAV take-off location, only a limited number of low-rise buildings below the forest canopy used exclusively for scientific research are present, resulting in minimal anthropogenic influence on the measured CO₂ fluxes. Such an experimental condition provides a representative forest ecosystem setting in which UAV-derived fluxes can be evaluated against flux tower observations.

From May 2023 to December 2024, a total of 209 UAV flights were conducted at the site. After quality control, including the removal of records affected by rainfall and other conditions that could compromise instrument performance, 168 flights, corresponding to approximately 70 h of cumulative flight time, were retained for analysis. These included 40 flights in spring, 55 in summer, 53 in autumn, and 20 in winter, with seasons defined according to the Northern Hemisphere meteorological seasons: March–May, June–August, September–November, and December–February, respectively.

Two complementary flight strategies were executed in this study: a box pattern and a profile pattern (Fig. 3a). The box pattern consists of a closed rectangular flight path flown above the forest canopy at multiple altitudes, enabling sampling of horizontal CO₂ transport across a defined control volume. Box flights were conducted at heights of 35, 40, 45, 50, 60, and 85 m above ground level. Using flight-path fitting and screen linear interpolation, the 3-D spatial distributions of the CO₂ mole fraction anomaly, wind vectors, and air density were reconstructed within the box domain. In the present study, we reconstructed the spatially distributed data for visualization purposes only (Fig. 3b), but computed the horizontal fluxes using the original observations directly. Flight-path fitting refers to the construction of a smooth, closed loop from the discrete UAV trajectories at all altitudes, thereby defining the four virtual vertical surfaces of the box. Screen linear interpolation refers to the mapping of CO₂ mole fractions and corrected wind speed measurements observed along the flight path onto a regular 2-D grid on the vertical surfaces for visualization of spatial patterns.

The profile pattern was designed to capture vertical gradients of CO₂ mole fractions, wind speed, and air density from near the surface to the top measurement height. Each box-pattern flight was paired with a vertical profile, and all measurements were completed within approximately 20 min to ensure consistency of the sampled air mass in the control volume. Vertical CO₂ fluxes were computed from the profile data using the gradient method (Sect. 3.2.2), with the analysis height range defined by the lowest and highest box levels (35–85 m). Because the EC system is installed at 31 m above ground level, UAV-derived CO₂ vertical fluxes were calculated using profile data collected at 26 and 36 m, thereby bracketing the EC measurement height for comparison with the tower-based EC measurements.

To obtain accurate and reliable wind speed measurements, the raw data acquired by the ultrasonic anemometer were processed using a dedicated wind speed calibration algorithm. This algorithm, developed by Yang et al. (2025), was specifically developed for the UAV platform and the mounted instrumentation used in this study, and employs computational fluid dynamics (CFD) simulations to characterize the flow-field distortion by the UAV rotor-induced airflows. Hundreds of simulation scenarios were conducted, encompassing a wide range of flight altitudes, wind directions, and ambient wind speeds, in order to quantify the influence of rotor-induced airflow disturbances on wind measurements. The simulated disturbance fields were subsequently combined with UAV motion and attitude compensation schemes, yielding a comprehensive correction algorithm that simultaneously accounts for UAV translation, rotation, and rotor effects. The algorithm was validated with 3-D wind measurements at multiple levels on an 80 m meteorological tower (Yang et al., 2025). All wind measurements used in this study were corrected using this algorithm, ensuring consistency and comparability with reference-grade wind observations.

CO₂ emissions associated within the control volume defined by the virtual box were quantified using a mass-balance framework that integrates measurements obtained from box-pattern and profile-pattern UAV flights. Throughout this study, the term “net CO₂ emission rate” refers to a signed, control-volume-integrated mass-balance quantity expressed in (kg h⁻¹). Positive values represent net CO₂ release from the control volume and may include CO₂ supplied by vegetation and soil respiration, whereas negative values represent net CO₂ uptake, primarily associated with vegetation photosynthesis. Thus, a negative net emission rate indicates that the forest acts as a net CO₂ sink during the measurement period. This approach assumes that CO₂ mole fraction and wind fields across the control volume boundaries are sampled with sufficient spatial coverage and that atmospheric conditions remain steady or quasi-steady during the measurement period. Under these conditions, the net emission rate can be inferred from the fluxes crossing the boundaries of the control volume.

According to the divergence theorem, the rate of change of mass of a stable compound within a control volume equals the surface integral of the mass flux through its enclosing surfaces. Consequently, the net CO₂ emission rate within the volume equals the net outward flux integrated across all box wall surfaces, where positive flux contributions represent CO₂ mass leaving the box and negative flux contributions represent CO₂ mass entering the box. Based on the integral form of the continuity equation, the emission rate within the control volume enclosed by the box pattern can be expressed as 1EC=EC,H+EC,HT+EC,V+EC,VT-EC,M where EC denotes the net CO₂ emission rate within the control volume, EC,H and EC,V denote the integrals of horizontal and vertical advective fluxes through the box walls, respectively; EC,HT and EC,VT represent the integrated turbulent fluxes in the horizontal and vertical directions, respectively, and EC,M accounts for changes in CO₂ mass associated with air-density variations. Equation (1) forms the basis of the TERRA emission inversion model (Gordon et al., 2015).

To investigate the contributions of individual transport components to the net CO₂ emission, the TERRA framework was updated here, particularly with respect to the treatment of the vertical flux, so that the vertical turbulent flux could be calculated directly from the UAV measurements rather than parametrized through an entrainment assumption.

For clarity, u, v, and w denote the instantaneous east-west, north-south, and vertical wind-speed components, respectively, with positive values indicating eastward, northward, and upward flow. Throughout this section, c denotes the instantaneous CO₂ mass concentration (mg m⁻³), calculated as: c=MRχCρair, where χC is the measured CO₂ mole fraction (ppm), ρair is the concurrently measured air density (kg m⁻³), and MR is the ratio of the molar mass of CO₂ to that of air. With these units, the equation yields c directly in mg m⁻³. For the Reynolds decomposition of the concurrent 10 Hz tower observations, each variable q∈{u,v,w,c} was decomposed over non-overlapping 30 min intervals as: q‾=1k∑i=1kqi, and turbulent fluctuation: qi′=qi-q‾, where k is the number of valid observations and is nominally 18 000 for a complete interval.

These concurrent tower observations were used to assess the relative importance of the mean and turbulent wind components and, consequently, the relative magnitudes of the corresponding transport terms in Eq. (1). Further details of the tower-data processing are provided in Sect. 3.4. For the horizontal component, the mean east-west wind component, u‾, was typically 2–3 m s⁻¹, whereas the 95th percentile of u′, calculated from the instantaneous 10 Hz deviations from the corresponding 30 min means defined above, was less than 0.1 m s⁻¹. Thus, the mean horizontal wind component was more than one order of magnitude larger than the turbulent fluctuation, indicating that EC,H was substantially larger than EC,HT under the observed conditions.

For the vertical component, the opposite relationship was observed. The magnitude of the mean vertical wind component, w‾, was typically less than 0.02 m s⁻¹, whereas the 95th percentile of w′ reached approximately 0.3 m s⁻¹. Thus, the vertical turbulent fluctuation was more than one order of magnitude larger than the mean vertical wind component, indicating that EC,VT was substantially larger than EC,V. On this basis, the vertical advective term was neglected in Eq. (1).

The density-related term (EC,M) can be expressed as 2EC,M=∭cdρairdtdxdydz(2) where t is time, and x, y, and z denote the east-west, north-south, and vertical spatial coordinates, respectively. Thus, x and y describe the horizontal directions, whereas z represents the vertical direction.

Because the vertical extent (≈50 m), horizontal extent (≈150 m), and duration of the UAV flights are limited, air-density variation over time dρairdt within the control volume can be assumed to be negligible, and EC,M was therefore neglected. Accurate estimation of the remaining flux terms (EC,H and EC,VT) enables computation of the net CO₂ emission rate. The reliability of the box-pattern approach for resolving CO₂ emission is further supported by Han et al. (2024), who conducted a series of controlled UAV-based CO₂ validation experiments over an industrial coking facility.

The uncertainty of the UAV-derived CO₂ emission estimates was evaluated by considering both measurement-related uncertainties and methodological uncertainties associated with the UAV-based 3-D flux quantification framework. The net CO₂ emission, EC, was calculated from the horizontal transport contribution, EC,H, and the vertical turbulent contribution, EC,VT. Therefore, uncertainty was assessed for the input variables and calculation procedures involved in both terms.

The measurement-related uncertainty was divided into two components: CO₂ analyzer uncertainty (δCO2) and wind measurement uncertainty (δV). Assuming that the two uncertainty components are independent and represent standard-deviation-type uncertainties, they were combined in quadrature to obtain the measurement-propagated uncertainty: 18δmeas2=δCO22+δV2 Here, δCO2 represents the uncertainty associated with the measured CO₂ mole fraction, including analyzer precision, calibration uncertainty, and short-term measurement noise. Wind-related uncertainty was included in δV, which includes uncertainty arising from the ultrasonic anemometer measurements, and the wind-correction procedure used to correct for these effects.

The EC system installed at the flux tower consists of a 3-D ultrasonic anemometer (CSAT3, Campbell Scientific, USA), an open-path CO2/H2O analyzer (LI-7500, LI-COR Biosciences, USA), and a datalogger (CR3000, Campbell Scientific, USA). Raw data are acquired at a sampling frequency of 10 Hz. In the present study, concurrent EC data from the flux tower were processed after correcting the time lag between wind and gas measurements following Sahlée et al. (2007), and standard quality-control procedures were applied to the turbulence data (Sect. S1 in the Supplement).

Fluxes were computed over 30 min averaging intervals, and coordinate rotation, virtual temperature correction, and frequency-response correction were applied to ensure high-quality vertical flux estimates. For the EC calculations, the measured wind components were first transformed into a streamline coordinate system through coordinate rotation. In this rotated coordinate system, us denotes the streamwise wind component aligned with the mean horizontal wind direction. Following standard EC formulations (Webb et al., 1980; Baldocchi, 2003), the friction velocity (u*) and scalar fluxes (Fv) were computed as 20u*=(-us′w′‾)1/221Fv=w′c′‾ where the primes denote the turbulent fluctuations defined in Sect. 3.2, which were calculated relative to the corresponding non-overlapping 30 min means.

To evaluate the consistency between the two CO₂ analyzers, a ground-based co-located intercomparison experiment was conducted using the open-path CO2/H2O gas analyzer (LI-7500) employed in the EC system and the closed-path analyzer (MIRA) used on the UAV. During the experiment, the two analyzers were placed side by side at the same ground location. Their sampling inlets were positioned at the same height and as close to each other as practicable to ensure that both instruments sampled the same air mass simultaneously. The experiment was conducted from 21:00 to 22:00 LT on 12 August 2024 (Fig. 4), with the LI-7500 operating at 10 Hz and the MIRA analyzer at 1 Hz. A fixed time lag of 5 s, associated with air flow through the sampling line, was applied to synchronize the MIRA measurements with the LI-7500 data.

As shown in Fig. 4, the CO₂ mole fractions measured by the two instruments exhibit highly consistent temporal variability, although small differences in absolute values and short-term fluctuations are present. Both datasets show a pronounced increase in CO₂ mole fraction during the initial phase of the experiment, followed by enhanced variability with multiple peaks, and sustained elevated mole fractions during the later period. The overall temporal coherence between the two datasets is strong, indicating that both instruments capture the same underlying CO₂ fluctuation dynamics. The MIRA measurements, with an average value of approximately 451.7 ppm, tended to report slightly lower CO₂ mole fractions than the LI-7500 measurements, which had an average value of approximately 451.8 ppm. This attenuation is primarily attributed to high-frequency losses associated with the closed-path sampling system, including viscous damping and signal smoothing during sample transport through the 1/8 in. outer-diameter polytetrafluoroethylene (PTFE) sampling tube. Because the sampling tube used in this study was relatively short (approximately 0.5 m) and the sample residence time inside the tube was 0.5 s, its influence on CO₂ measurements is expected to be limited. Such attenuation is a well-documented characteristic of closed-path systems and does not compromise the ability of the analyzer to resolve mean CO₂ mole fraction levels and mesoscale variability (Burba et al., 2012; Metzger et al., 2016). In summary, despite minor attenuations at high frequencies, the closed-path MIRA analyzer demonstrates strong agreement with the EC-based LI-7500 measurements, supporting its suitability for CO₂ measurements from the UAV-based platform.

Further, the scatterplot of the CO₂ measurement results from both instruments, shown in Fig. 5, reveals a strong linear relationship. The data are closely distributed around a narrow band of the fitted linear regression line, with a Pearson correlation coefficient of r=0.986. Because both analyzers are subject to measurement uncertainties, reduced major axis (RMA) regression was applied. The RMA regression yielded a slope of 0.966 and an intercept of 1.64 ppm. The close clustering of the data points around the fitted line indicates low random error and high precision between the two instruments. This regression result indicates that the UAV observations were able to capture temporal variability in CO₂ in the real atmosphere, such as minute-scale increases, decreases, and multi-peak structures. In addition, the calculations of the horizontal line flux divergence and the vertical flux both require subtraction of the background CO₂ mole fraction and estimation of the CO₂ gradient, respectively. Therefore, the systematic bias between the two instruments is expected to have a negligible impact on the flux calculations.

To illustrate the application of the developed box-profile framework at the individual-flight level, this subsection examines a representative early morning flight conducted on 8 August 2024 within the 100 × 100 × 50 m³ control volume. Complete paired box-pattern and profile-pattern measurements were available for this flight, allowing the horizontal advective and vertical turbulent contributions to be combined to determine the local net CO₂ emission rate. This analysis is intended as a process-level illustration rather than a statistical summary of the complete dataset; the spatial-scale analysis involving multiple observations is presented in Sect. 4.4.

Figure 9 shows the 3-D CO₂ mole fraction anomaly field (ΔCO₂) along the box walls, calculated relative to a constant background defined as the mean of the lowest 5 % of the time-series CO₂ observations. The reconstructed ΔCO₂ distribution exhibits pronounced spatial heterogeneity, with localized regions of elevated CO₂ mole fraction embedded within relatively low-background regions. In particular, a distinct hotspot reaching ∼40 ppm is observed near the upper-right sector of the box, while elevated ΔCO₂ values also form elongated bands along portions of the lateral walls. This non-uniform pattern indicates that the near-canopy CO₂ field was far from horizontally homogeneous during the sampling window, consistent with the expectation of enhanced stratification and limited mixing in early morning conditions.

The wind vector shown in Fig. 9 indicates a prevailing southwesterly flow. The spatial alignment of the ΔCO₂ enhancements with the box boundaries suggests that lateral advection and flux divergence played a central role in shaping the observed CO₂ mole fraction gradients: CO₂-enriched air masses are transported into the control volume on the upwind side and redistributed along the walls, producing strong cross-boundary gradients that dominate the mass balance. Consistent with this interpretation, the box-mass balance calculation yields a positive net CO₂ emission rate of Ec=4.48±0.67 kg h⁻¹ for the 100 × 100 × 50 m³ control volume. Decomposition of the individual mass-balance terms indicates that the horizontal advective contribution (EC,H=4.22±0.63 kg h⁻¹) accounts for nearly all of the net emission rate, whereas the vertical turbulent contribution is small (EC,VT=0.26±0.04 kg h⁻¹). The near-zero vertical term is also consistent with the concurrent EC observations similarly indicating a negligible turbulent vertical CO₂ flux. According to the sign convention defined in Sect. 3.2, the positive Ec indicates net outward CO₂ transport from the finite UAV sampling box, primarily resulting from horizontal flux divergence during this flight. This result should not be interpreted as the net CO₂ exchange between the entire forest ecosystem and the overall atmosphere, because the UAV box only encloses a limited area above the canopy. Instead, the box-based flux calculation is intended to quantify local CO₂ mass balance and to diagnose the contribution of horizontal advection, which can affect the interpretation and spatial representativeness of vertical CO₂ flux measurements.

Overall, the 3-D ΔCO₂ structure shown in Fig. 9 highlights that, at this moment, CO₂ exchange above the forest canopy was controlled predominantly by horizontal transport under heterogeneous CO₂ mole fraction fields. This highlights the importance of resolving spatial gradients and lateral boundary fluxes in order to diagnose horizontal advection and local CO₂ redistribution within the UAV sampling box. These processes can affect the interpretation and spatial representativeness of vertical flux measurements from a single-point flux tower, but they are not directly observable from the tower measurement alone.

To verify whether net emissions (EC) calculated using the mass-balance approach differ across spatial scales, emission computations were made for three box-shaped control volumes constructed from the UAV flight tracks, with horizontal dimensions of 50 × 50 m², 100 × 100 m², and 150 × 150 m², respectively, and a common vertical extent of 50 m. The vertical contribution term EC,VT and the horizontal transport contribution term EC,H within each control volume were separately computed as per the detailed outlines above. In this analysis, EC,H was calculated separately for each box size using the corresponding box-pattern boundary observations, whereas EC,VT was derived from the paired vertical profile measurements collected during the same UAV flight. The net emissions were normalized by the areas of the boxes to give area-normalized emission intensity eC=EC/A, where A denotes the bottom area of the box. A total of 168 UAV flights were retained after quality control for the overall study. However, the number of observations within a specific season and time-of-day period was substantially smaller. Because the purpose of this analysis was to assess spatial-scale dependence, rather than seasonal or diurnal variability, only measurements obtained within the same season and time-of-day period were compared, thereby minimizing differences in ecosystem activity and atmospheric conditions. In addition, only cases with valid observations for all three control volume sizes (50 × 50 × 50, 100 × 100 × 50, and 150 × 150 × 50 m³) were retained to ensure an internally consistent comparison across spatial scales. After applying these criteria, seven observation days from summer noon and seven observation days from autumn morning were selected for the spatial-heterogeneity analysis. An observation day did not necessarily correspond to a single UAV flight; multiple UAV measurements could be conducted within the selected time window on the same day. These two periods also showed relatively good agreement between UAV-derived vertical fluxes and EC observations in Sect. 4.2.

For the seven selected summer noon observation days, UAV measurements were conducted between 13:00 and 14:00 LT (UTC+08:00) under predominantly southeasterly winds. The EC flux measurements corresponding to these UAV observation periods varied by less than 8 % among the seven selected days. Similarly, for the seven selected autumn morning observation days, UAV measurements were conducted between 06:00 and 07:00 LT (UTC+08:00) under predominantly southwesterly winds, and the corresponding EC flux measurements varied by less than 5 % among the seven selected days. These relatively small variations indicate that the selected observations within each season-time-period subset were obtained under broadly comparable ecosystem-exchange conditions. The selected datasets were intended to provide internally comparable subsets for evaluating spatial-scale dependence rather than to represent the full seasonal climatology.

If the CO₂ source-sink distribution in this region were spatially uniform, eC would be expected to be similar among the three control volume sizes, i.e., eC (150) ≈eC(100) ≈eC (50). For the seven selected summer noon observation days, the eC were -0.25, -0.30, and -0.24 mg m⁻² s⁻¹ for the 50 × 50 × 50, 100 × 100 × 50, and 150 × 150 × 50 m³ control volumes, respectively (Fig. 10). These consistently negative values indicate net CO₂ uptake during the summer noon period, while the relatively small differences among the three spatial scales suggest weak scale dependence under these conditions. Furthermore, one-way analysis of variance (ANOVA) shows that the mean differences in eC among the three scales were not statistically significant, with an ANOVA F-statistic of 0.349, indicating low between-group variance relative to within-group variance, and a p-value of 0.710, indicating that the observed differences could be readily explained by random variation rather than a true scale effect. Overall, under summer noon conditions, the area-normalized net exchange intensity is insensitive to the horizontal scales examined, and within-group variability dominates (Fig. 10).

In addition, during summer at noon, the median values of EC,H/EC,VT in Table 5 remain very small (from -0.025 to -0.006), indicating that the net CO₂ budget is dominated by the vertical turbulent-exchange term. This conclusion is further supported by the absolute magnitudes of the two components. For the 50 × 50 × 50, 100 × 100 × 50, and 150 × 150 × 50 m³ control volumes, the median EC,H values were 0.0687, 0.0817, and 0.273 kg h⁻¹, respectively, whereas the corresponding median EC,VT values were -2.77, -13.6, and -12.5 kg h⁻¹, respectively. Under summer noon conditions, stronger turbulence and more efficient vertical mixing likely suppress the relative influence of horizontal CO₂ gradients, so that horizontal transport contributes only a minor fraction to the virtual control volume budget. Although the negative values of EC,H/EC,VT indicate that the horizontal and vertical components often have opposite signs, the magnitude of EC,H remains much smaller than that of EC,VT.

Another case study of spatial heterogeneity is based on the autumn morning dataset (Fig. 11). The mean values in Fig. 11 indicate that the emission intensities (eC) derived for different box sizes (0.02, 0.21, and 0.11 mg m⁻² s⁻¹, respectively) differ to some extent. The differences among box sizes are substantially larger than the uncertainty of the method used in this study (30 %), representing differences in eC among the different box sizes. One-way ANOVA also showed that the differences among the three scales were significant (F=4.482, p=0.025). This provides direct evidence that the net CO₂ emission intensity derived for the three virtual control volumes above the canopy is spatially heterogeneous.

Compared with the summer noon conditions, the role of horizontal transport becomes much more pronounced during autumn mornings. The median values of EC,H/EC,VT increase to 3.18, 0.944, and 1.32 for the 50 × 50 × 50, 100 × 100 × 50, and 150 × 150 × 50 m³ control volumes, respectively. Importantly, these elevated ratios reflect not only the presence of horizontal CO₂ gradients, but also the fact that the vertical turbulent-exchange component is comparatively weak during this period. Specifically, for the 50 × 50 × 50, 100 × 100 × 50, and 150 × 150 × 50 m³ control volumes, the median EC,H values were 0.105, 3.52, and 6.41 kg h⁻¹, respectively, whereas the corresponding median EC,VT values were 0.033, 3.73, and 4.85 kg h⁻¹, respectively. This indicates that, under weak-turbulence morning conditions, horizontal CO₂ transport can become comparable to, or even exceed, the vertical turbulent component. Such behavior is consistent with enhanced spatial heterogeneity in CO₂ exchange across the different control volumes. As the control volume scale increases, small-scale horizontal gradient structures are progressively averaged out, reducing the relative contribution of EC,H and leading to the observed scale dependence of EC. The component-wise contribution analysis (Table 5) indicates that the relative contribution of the horizontal transport term is markedly enhanced. The median metric, median EC,H/EC,VT, is 3.18 for 50 × 50 × 50 m³, 0.944 for 100 × 100 × 50 m³, and 1.32 for 150 × 150 × 50 m³. These results suggest that there is significant spatial heterogeneity in the CO₂ emissions/uptake in the range covered by the different boxes leading to horizontal CO₂ mole fraction gradients, and as a result near-surface horizontal CO₂ gradients under the measured horizontal winds are non-negligible within small control volumes.

This scale sensitivity further highlights the true advantage of UAV observations for characterizing spatial heterogeneity. UAV measurements can provide multipoint, 3-D CO₂ distributions and associated wind speed gradient information within a short time window, enabling the simultaneous estimation of EC,H (horizontal transport) and EC,VT (vertical transport) within a consistent data framework. Moreover, by varying the box sizes, the same dataset can be re-aggregated into different spatial scales to facilitate a multi-scale comparison. In contrast, EC flux towers essentially measure point-based vertical turbulent fluxes, whose representativeness is determined by a footprint that varies with wind direction and atmospheric stability. As a result, flux tower observations yield a footprint-weighted effective flux, making it difficult to construct a spatial control volume and impossible to explicitly resolve the lateral transport term EC,H. Therefore, box-size dependence driven by spatial heterogeneity is more readily captured by UAV observations, whereas flux towers are limited in both observation geometry and the types of physical quantities they can diagnose.