The EC setup is mounted at a height of 29 m on a telescopic mast on the border between the selection harvested and non-harvested blocks (61°10.973^′ N 25°15.761^′ E). The system consists of a sonic anemometer Metek uSonic-3 and a CO₂, H₂O gas analyzer LI-COR LI-7200RS. The raw data were post-processed using the in-house software developed by the Finnish Meteorological Institute (FMI), following the standard practices of raw data quality control and corrections (Aubinet et al., 2012) and the quality control of the 30 min average fluxes. The EC data in this study cover the years 2020 and 2021, without long gaps except for 26 July–30 August 2021, when the LI-7200RS filter was clogged. For the particulars on the EC measurements and processing, see Laurila et al. (2021).

Given the position of the EC tower between the two treatments, it was necessary to separate the 30-min flux values based on wind direction. The data were considered to represent the control plot at 20° < WD < 160°, and the harvest plot at 180° < WD < 360°.

Since the EC data were intended to be used solely as an indicator of drought/heat wave effect on ecosystem functions, a robust modeling and gap-filling method was formulated with the purpose of obtaining time series of light-saturated ecosystem photosynthesis rate (Pmax). The EC NEE is gap-filled by the sum of models of ecosystem respiration (Re) and photosynthesis (gross primary productivity, GPP). Nighttime Re was observed to increase exponentially at temperature range 0<Ta<10 °C and become invariant of air temperature at higher Ta, and is therefore modeled using a conditional function, 1Remod=RerefQ10Ta-1010,Ta<10°C7.02,Ta≥10°C where Reref=7.52 µmol CO₂ m⁻² s⁻¹ and Q10=4.4. The apparent Q10 value is slightly lower than 4.9 estimated for Norway spruce stand ecosystem respiration by Wallin et al. (2001). GPP is calculated for daytime (photosynthetically active radiation PAR >50 µmol m⁻² s⁻¹) as the residual of measured EC CO₂ flux and Re, and modeled as a function of PAR in two steps. In the first step, a Michaelis-Menthen function 2GPPmod=PmaxPARk+PAR was fit to all GPP vs. PAR data of the growing season (May–September) in order to calculate the fixed value of the parameter k=340 µmol m⁻² s⁻¹. Then, Eq. (2) was re-fitted to GPP vs. PAR data of each day separately so that to obtain the daily Pmax values while k is kept constant. The variability of daily NEE and maximum photosynthesis rate Pmax are used as proxies of an ecosystem reaction to drought.

The EC footprint was estimated using the model of Kljun et al. (2015) following Alekseychik et al. (2021). The footprint model of Kljun et al. (2015) was solved in Matlab for each 30 min time step on a 2 × 2 m grid. The individual tree contributions to exchange were calculated by interpolating the georeferenced 30 min average EC footprints for the locations of the trees derived from UAV data. However, such an interpolation procedure results in a reduced footprint integral due to the number of trees being smaller than the number of nodes in the original 2 × 2 m footprint grid. Therefore, to preserve the integral footprint value, each individual tree footprint contribution was scaled so that to make the sum of the individual tree contributions equal to the footprint integral value outputted by the Kljun et al. (2015) code. According to this footprint calculation, the Control and Harvest blocks contributed typically 50 %–60 % to the EC fluxes on individual days in the growing season. The rest of the EC flux was contributed by areas beyond study site, which are represented mainly by un-harvested spruce-dominated stands of varied age, except the clearcut in the eastern sector that is partly seen in the UAV maps (Fig. 1). See Appendix B for the extent of footprint and the estimation of individual tree contributions (footprint climatology).

Meteorological parameters were measured at the top of the 29 m EC tower and at ground level within 10 m of the EC tower. At the top of the mast, a radiation sensor CNR4 (Kipp & Zonen B.V., Delft, the Netherlands) was used to record the radiation balance components, from which the net radiation (Rn) was calculated. Upward- and downward-facing PQS radiometers (Kipp & Zonen B.V., Delft, the Netherlands) produced the up-and downwelling photosynthetically active radiation (PAR); air temperature and humidity were measured by HMP155 sensor (Vaisala Oyj, Vantaa Finland). Air temperature and humidity were measured within 10 m from the EC tower by IKES Pt100 (Nokeval Oy, Nokia, Finland) and a HMP155 sensors (Vaisala), respectively, inside a radiation shield at 2 m a.g.l. Peat temperatures at the depths of 5 was measured by IKES Pt100 sensors (Nokeval) and volumetric soil moisture at the depth of 5 cm by ML3 ThetaProbe (Delta-T Devices Ltd., Cambridge, UK) These parameters were collected QML201C (Vaisala) data collection board. Water table depth (WTD) was measured by Odyssey sensors (Data Flow Systems Ltd, New Zealand) in two locations in the Control block and three locations in the Harvest block; block-average WTD was used in the analysis. Precipitation was measured with a weighing rain gauge (Pluvio2, OTT HydroMet GmbH, Kempten, Germany).

In total, 16 spruce trees (see Appendix E) were monitored with sap flow and stem diameter sensors since June 2020 in the harvest and control blocks (8 trees in each) (Peltoniemi, 2023, 2025; Liu et al., 2025).

The Edaphic HPV-06 sensors were installed at a height of 1.3 m on the southern side of the trees (Laurila et al., 2021), outputting the sap flow rate in the units of [L h⁻¹]. The sap flow data cover the years 2020–2021 with few gaps; however, tree No. 5 (Control block) had to be discarded due to poor data quality. Two stem diameter sensors Solartron AX-5 were installed in June 2020 in each tree monitored for sap flow, one above the phloem and one above the xylem.

Spatially resolved data on individual tree parameters, including tree height, canopy area, tree canopy center coordinates, canopy temperature and colour (in RGB, NIR and Red Edge bands) were derived from imagery collected by UAV. The RGB and NIR imagery was used to calculate NDVI, whereas leaf stomatal resistance was estimated from tree canopy temperature. The UAV used to collect the remote sensing data was DJI Matrice 210 V2 with a payload of either DJI a Zenmuse XT2 thermal/RGB camera or a Micasense Altum multispectral camera.

The flights took place on four clear days (Table 1). Special ground control point (GCP) targets were designed to be easily discernible in the RGB, multispectral and thermal images alike. These were constructed as aluminum or polished steel crosses 1 m in width with a smaller cross of black duct tape marking the center. A total of 15 GCPs were used, spread evenly throughout the UAV survey zone of 20 ha.

The mosaicking of RGB, thermal and multispectral images was performed in Agisoft Metashape^®. The UAV multispectral and RGB data were retrieved and processed following the standard methods (Barbedo, 2019; Tmušić et al., 2020), yielding RGB and multispectral orthomosaics, 3D point clouds, and digital elevation models (DEM). The resolution (ground sample distance) of the final orthomosaics is given in Table 1. The processed UAV orthomosaics are shown in Appendix F.

Thermal images were retrieved by UAV in programmed flights on 15 September 2020 and 26 July 2021, ensuring side and frontal image overlap of over 80 %. A thermal emissivity value of 0.98 was used. The thermal images were processed in Agisoft Metashape^®, in which the more detailed RGB point cloud was used in order to achieve better quality in the thermal orthomosaic. The ground sample distance (GSD) of the processed thermal orthomosaic was about 11.4–12.6 cm. The individual tree canopies were identified in the 2020 and 2021 RGB orthomosaics using a custom segmentation algorithm. The values of RGB, NIR and Red Edge bands and canopy temperature were averaged for the areas of the tree canopies for further processing. For details on UAV imagery processing, see Appendix C. The canopy temperature bias related to inter-tree differences in the incident solar radiation at the time of UAV survey was removed using a canopy brightness-based approach (Appendix G).

The Standardized Precipitation-Evapotranspiration index (SPEI) (Vicente-Serrano et al., 2010) was employed as a regional-scale indicator of climatological drought. A long-term time series of SPEI (1980–2021) was calculated for whole territory of Finland as follows. Daily weather data (Finnish Meteorological institute) was used as an input to SpaFHy model (Launiainen et al., 2019) to calculate the effective precipitation at the ground (liquid precipitation and snow melt) and evapotranspiration assuming LAI of 3 m² m⁻² and mesic soil properties (Table 2 in Launiainen et al., 2022). Finally, SPEI on a 1-month timescale was calculated with the climate_indices Python^® package (Adams, 2017) using the effective precipitation and evapotranspiration. The spatial resolution of the resulting SPEI dataset is 10 × 10 km. Monthly SPEI values for May-September of 2020 and 2021, shown in Fig. 2, were retrieved for the Ränskälänkorpi site coordinates.

The drought conditions occurring locally at the research site were assessed separately as atmospheric and soil drought, as it was expected that these conditions might show independent effects on the plants. The soil moisture threshold marking the initiation of drought stress in central and northern European conifer forest varies widely in previous literature as much as the definition of the drought stress itself: 0.15 (Galiano et al., 2017), 0.2 (Arend et al., 2021), 0.45 (Kowalska et al., 2020), 0.10–0.13 (Střelcová et al., 2013), and 0.09 m³ m⁻³ (Clausnitzer et al., 2011). The variation in the reported critical SWC values is once again due to the contrasting definitions of drought stress and the particular site conditions and experimental setup; it is also important to note that the above values were reported for mineral soil stands. In the present study, soil drought was defined as surface soil water content (θ5cm) below 0.2 m³ m⁻³, as the values marking the onset of the driest period in July 2021. This value is slightly above typical wilting point of surface peat in drained forest peatlands (Menberu et al., 2021). The air drought was defined on a daily basis as daily mean atmospheric vapor pressure deficit (VPD) exceeding 1 kPa, which similarly marks high VPD stress for Boreal ecosystems (McGloin et al., 2019; Jia et al., 2016; Lasslop et al., 2010). The following analyses consider the presence of air and soil drought defined above, separately or in combination.

Several quantities indicative of ecosystem reaction to drought were calculated from meteorological and EC data. By virtue of using the eddy-covariance fluxes and turbulence parameters, the below proxies are representative of stand-scale drought response (one should be mindful of the EC footprint shown in Appendix B). The Bowen ratio (BR, [–]) was calculated as the median ratio of sensible (H) to latent heat (LE) flux of the daytime hours (09:00–17:00 GMT+3). 3BR=HLE-1 High values of Bowen ratio indicate a relative deficit of evapotranspiration, thus pointing at drought stress in vegetation.

The aerodynamic resistance to water vapour transport ra [s m⁻¹] was calculated after Verma (1989), 4ra=Uu∗2+κB-1κu∗ where U is the mean wind speed above the canopy, u∗ the friction velocity, κB-1 the logarithm of the ratio between momentum and heat roughness length (Owen and Thomson, 1963), assumed to equal 2, and κ the von Kármán's constant (=0.4).

Surface resistance to water vapour transport calculated from eddy-covariance and atmospheric data (rs,EC [s m⁻¹]) was defined after Thom et al. (1975) as: 5rs,EC=raζBRγ-1+ρCpVPDγRn1+BR where ρ the air density [kg m⁻³], Rn the net radiation [W m⁻²], ζ the slope of the saturation vapour pressure curve [kPa K⁻¹], and C_p the specific heat of air at constant pressure [J kg⁻¹ K⁻¹]. Surface conductance g_s, EC [mm s⁻¹] was calculated as a reciprocal of rs,EC. Canopy water stress index (CWSI, [–]) was computed after Jackson et al. (1981), 6CWSI=γ1+rs,ECra-1-γζ+γ1+rs,ECra-1, where γ is the psychrometric constant [kPa K⁻¹].

Daily ecosystem water-use efficiency WUE [gC kg_H2O⁻¹] was defined using the daily integrals of GPP [g C m⁻² d⁻¹] and ET [mm d⁻¹]: 7WUE=GPPET-1, while the ecosystem light use efficiency (LUE, [–]) was obtained as the daily integrals of GPP and PAR: 8LUE=GPPPAR-1. Penman-Monteith potential evapotranspiration was calculated using the standard expression (Monteith, 1965): 9PETPM=Rnζ+ρCpVPDra-1ζ+γ1+rsra-1Λ,(9) Where Rn is the net radiation and Λ the latent heat of vaporization.

Tree-level stomatal conductance gs,sap [L h⁻¹ m⁻² kPa⁻¹] was derived by normalizing the measured sap-flow of a tree (s) [L h⁻¹] by sapwood area (Asap) [m²] and VPD for the current 15 min averaging period [kPa] (Oren et al., 1999; Köstner et al., 1992): 10gs,sap=s(Asap∗VPD)-1 Along with the stem diameter change (Sect. 2.2.3), this quantity, for individual trees, or averaged for the control and harvest blocks, provides the most direct insight into tree functioning. However, it represents only a few trees that have the sensors, and is thus very local. In order to enable comparison with EC-based proxies, the recorded sap flow was upscaled to the plot and EC footprint scales based on the relationship between instantaneous sap flow rate and tree height (Appendix E). Tree height was chosen as the predictor of sap flow as this structural property is most readily derivable from a UAV digital elevation model (DEM).

Remote sensing at high resolutions achievable by UAV combines the benefits of single-tree representativeness and stand-scale coverage. A novel method for tree physiological stress estimation based on UAV thermal imaging was developed by adapting an approach designed originally for the use in greenhouses and laboratory experiments (Jones, 1992; Leinonen et al., 2006; Guilioni et al., 2008) to open forest canopy conditions. In the following, the subscript can denotes the vegetation index values averaged for the projected area of an individual tree crown. In the case of isolateral leaves and using both wet and dry leaf references, the surface temperature-based stomatal resistance can be defined as (Guilioni et al., 2008): 11rsTcan=ra,w+2Δγ-1rH,RTcan-TwTd-Tcan, where Tcan is the tree crown temperature, Tw the wet reference temperature, Td the dry reference temperature and ra,w the boundary layer resistance to water vapour transfer [s m⁻¹], estimated as (Verma 1989): 12ra,w=0.92ra.

In Eq. (11), rH,R is the parallel resistance to heat (rH) and radiative transfer (rR): 13rH,R=rHrRrH+rR14rR=ρCp8ϵσTa3,15rH=ra2.

The individual tree surface conductance gsTcan is then calculated as an inverse of the resistance rsTcan.

The dry and wet references were constructed as two piles of spruce branches with the areas of 1 m² and up to ca. 30 cm high, lying on a frame raised 70 cm above ground on four wooden poles. Mounting the branch piles on a raised frame was intended to mimic the forest canopy by enabling better aeration of the branch piles from the bottom and thus creating a closer imitation of aerodynamic resistance to water vapor transport in a real canopy. The wet and dry references were captured by the UAV thermal camera from a ca. 40 m altitude a.g.l. just before the start of the planned flight. The references were in direct sunlight. Immediately before being captured, one of the branch piles was sprayed with 15 L of water from a watering can, which is therefore equivalent to 15 mm of rainfall. The dry and wet reference temperatures Td and Tw were obtained as the mean radiometric temperatures of the two piles. Appendix D details the arrangement of the dry and wet references and the water pool (providing the reference temperature) for the correction of the mean surface temperature in the UAV thermal orthomosaic.

The meteorological drought that occurred in southern Finland in June-July 2021 was characterized by low soil moisture levels and persistently high VPD, and affected ecosystem CO₂ exchange and tree sap flow in the drained peatland forest. The EC CO₂ fluxes, which are primarily representative of the Harvest block, indicate that the net CO₂ sink is diminished under air drought (Sect. 3.2), which is manifested as the drop of photosynthetic efficiency and the actual level of photosynthesis. Pmax, WUE and LUE indicate constrained photosynthetic capacity during the air drought periods in both 2020 and 2021 (Fig. 4). The change in vegetation functioning between the two years is readily seen as a twice lower asymptotic limit of the mean light response curve of the non-harvested block in 2021 (Fig. 6). A similar twofold drop of photosynthesis rate that was observed in Harvest is largely due to drought, with a likely compensation from selection harvesting as it increases the radiation receipt by tree crowns. Drought-induced photosynthetic efficiency loss in Norway spruce is known to be sharp, and more pronounced than that of Scots pine (e.g. Zlobin et al., 2019). Matkala et al. (2021) reported a drop in maximum photosynthesis and reference respiration in hot and dry years based on EC measurements in a Norway spruce-dominated stand. Although stronger droughts may reduce photosynthesis and the availability of carbohydrates by causing a drop in stomatal conductance and metabolic activity (Dreyer et al., 1997; Escós et al., 2000), stomatal closure may also prevent any negative effects from mild and moderate droughts, ensuring fast recovery and continued growth (Gessler et al., 2020RefCheck: please correct “Gessler et al., 2020”. No exact match was found in the reference list.). This was accompanied by declining reference respiration (Fig. 4c, d), which likewise indicates a suppression of heterotrophic respiration under drought, as observed in previous studies (e.g. Matkala et al., 2021; Lindroth et al., 2020; Krasnova et al., 2022). The lowered Pmax and Re_ref led to a large decline in the mean levels of GPP and Re in June–July 2021 relative to the corresponding period of 2020 (Fig. 5).

These observations tie in with the previous studies showing the negative effects of air drought on Boreal forest GPP and Re. Mirabel et al. (2023) concluded that high VPD limits the growth of typical Boreal conifer species, particularly in combination with high temperature, while GPP was also found to be limited by VPD stress (Zhong et al., 2023). Granier et al. (2007) found both GPP and Re to be substantially reduced at very low soil water content in a range of forests of Boreal and temperate European forests during the 2003 drought; Lindroth et al. (2020) report the same regarding the 2018 drought, and note that the site-mean GPP deficit correlated with the deficit in gs in a range of Boreal sites. Such negative impacts may be caused by VPD in absence of soil drought (Schönbeck et al., 2022).

The NEE shows a peak at VPD of about 1 kPa, and a decline at stronger air drought (Fig. 5). A substantial fraction of this deficit may be contributed by ground vegetation that is less resilient to drought stress (Martínez-García et al., 2024). At the same time, the general tendency of both GPP and Re to increase with increasing VPD was preserved in the drought conditions (Fig. 5), likely for the reason of persistently high PAR, air and soil T at the times of air drought.

Soil and air droughts may either coincide, or occur separately (Fig. 2b). Most of the 2021 drought was manifested as VPD stress, with low soil water content occurring only in the second half of July (Fig. 3). Periods of air drought occurred in May and August 2020 and May 2021, when SPEI showed no meteorological drought, and soil water was kept up by regular precipitation. Whether soil drought imposed any additional stress on the Norway spruce trees in this study is unclear. The EC data showed enhanced photosynthesis on the combined soil and air drought days in the Harvest block in 2021, which had a sufficient number of drought days on the record.

While the air drought thus suppressed the photosynthetic efficiency in 2021, as was reliably shown by the CO₂ exchange in Control, the mounting soil dryness and prolonged VPD stress do not seem to have had a similar effect in June–July 2021. Both Pmax and GPP increased throughout the drought of 2021, as well as during the hotter period of 2020 (Figs. 4–6).

It is unlikely that the weather of pre-harvest year 2020 augmented the drought response of 2021. Mid-late summer drought was shown to influence the growth in the following year (Laurent et al., 2003) via effects on bud formation (Salminen and Jalkanen, 2005), carbon allocation (Waring and Franklin, 1979), carbohydrate reserves for bud expansion and initial growth in the next growing season (Bouriaud and Popa, 2009). However, the weather in July–September 2020 was mild in terms of air temperature and soil water content.

It is known that thinning intensity affects the drought sensitivity of a stand (Kohler et al., 2010; Misson et al., 2003). The previous studies generally agree on increased drought resistance and recovery as a result of partial harvest (Elkin et al., 2015; Manrique-Alba et al., 2020; Giuggiola et al., 2013; Sohn et al., 2016). The current study found the majority of Control block trees to increase their transpiration during air drought (increasing evaporative demand), whereas an opposite dynamic was in place at Harvest (Fig. 7). This relative sap flow deficit at Control may be seen as a dip between 12-16 PM, when the VPD is at the maximum (Fig. 8). The shape of mean SF vs. VPD curve in summer-2021 was similar between the trees in both blocks, in contrast to the report of highly heterogeneous sap flow patterns during the extreme 2003 drought (Gartner et al., 2009). Additionally, the possible positive effect of thinning follows from the analysis of light response curves, as the post-selection harvesting (CCF) light response curve is higher at air and soil drought than at no drought condition, while the opposite holds in regard to pre-harvest conditions (Fig. 6c, d).

Thinning increases individual tree transpiration but decreases transpiration at stand level (e.g. Laurent et al., 2003), and in this study, the selection-harvested block showed a twice lower transpiration than the Control block, based on upscaled tree sap flow data of the 2021 growing season (Fig. 9). Therefore, this study supports the previous observations that a thinned stand exhibits a drop in total transpiration, even if the individual trees are able to transpire more than they did before the thinning. Elsewhere, a removal of 61 % of basal area was found to lead to mean tree-level sap flow increase of 27 %, but a 34 % reduction in the stand-level transpiration (Zavadilová et al., 2023). At the same time, of economic relevance is the observation of a higher stem diameter growth in Harvest than Control, which was not apparently affected by the weather differences between 2020 and 2021 (Appendix H).

The ratio between the upscaled SF and EC ET stayed constant during the drought of 2021, and its mean value of 0.7 was substantially higher than approx. 0.5 predicted for a similar stand with LAI =2 m² m⁻² by a process-based model (Fig. 12 in Leppä et al., 2020) (Fig. 9). Such a dominance of tree transpiration over forest floor ET is common in mature forests, particularly in drought conditions (Launiainen et al., 2019; Martínez-García et al., 2024).

The decline in transpiration during drought is further reflected in Bowen ratio changes; the mean daytime June–July Harvest BR was 0.33 in 2020 and 1.49 in 2021, while the Control BR was respectively 0.25 in 2020 and 0.95 in 2021. Harvest thus shows consistently higher mean BR values, but that may be explained by the higher contribution of the ground to the block-average BR values in Harvest, than by higher stress level in that block. McGloin et al. (2019) identified similar drought-induced increases in the Bowen ratio of two Czech Norway spruce stands, from 1.52 to 2.50–2.70. Launiainen (2010) identified the typical no-drought BR value for a Scots pine stand as being somewhat below 1 in the summer months, with the values of 3–4 and above indicating extreme drought. In the current study, CWSI gave qualitatively similar information to BR and temperature-based stomatal conductance.

Therefore, based on the above, we may confirm the first hypothesis related to Control block trees being more impacted by the drought than the trees in the selection-thinned Harvest block, as the inter-block differences in tree transpiration, photosynthesis and stem growth are all indicative of a better physiological state of the trees in the Harvest block. This evidence may be seen as a warning of lowered Norway spruce health and growth in peat soils under the projected drier and hotter Boreal climate.

Assessing the spatial diversity in tree transpiration is essential for early detection of drought stress. This can be done by UAV-based remote sensing and by sap flow measurements. Therefore, the search for the trees that are the first to exhibit drought reaction was one of the aims of the present study. Our findings suggest that there were indeed a minority of trees which are not resilient to drought. In Harvest, these trees revealed themselves by increased canopy temperature and lowered NDVI, while in Control the stress was detectable based on constrained sap flow.

The relative transpiration deficit in the individual trees in the Control block was well detected by sap flow sensors (Sect. 3.3, Figs. 7, 8). The afternoon sap flow deficit in the monitored Control block trees, in relation to those in the Harvest block, is a clear sign of water stress in the afternoon when the VPD is at its diurnal peak (Fig. 8). This is further illustrated on an individual tree basis by saturating sap flow behaviour at high VPD under soil drought conditions.

However, UAV remote sensing indicated a considerable increase in the spatial heterogeneity of individual tree canopy temperature and NDVI, which may also be interpreted as increase in stress relative to the reference (non-drought) period (Sect. 3.4, Fig. 10). However, the inter-tree differences in canopy temperature and NDVI at peak drought are higher in the Harvest than Control (Figs. 10, 11), which conflicts with the direct evidence of higher drought stress in the Control block obtained from sap flow data. In particular, the trees in the first row the ditches in the Harvest block have markedly lower NDVI_can and generally warmer canopies than those further away from the ditches, but are not measured by sap flow sensors; they therefore are possibly stressed, but we cannot verify this for lack of direct monitoring data of these specific trees. The variation in the degree of mutual shadowing by the trees is an unlikely reason for this spatial trend, as the Harvest has a uniformly low tree density and the drone flights were conducted at the time of the highest solar elevation. A similar spatial picture can be seen along some major ditches in the Control block (Fig. 10). Hence, it is possible that the trees monitored with sap flow sensors in the Harvest block do not cover the range of drought stress response that occurs within the Harvest block, and possibly likewise within the Control block. A further aspect that may have influenced the assessment of stressed trees in the pre-drought and drought NDVI_can maps is the fact the pre-drought NDVI data was obtained before noon, while the drought data were sampled in the afternoon, when maximum drought stress is shown by sap flow sensors.

Notwithstanding the uncertainties, more on which will be said below, UAV remote sensing has an undeniable strength in that it allows assessing the statistics of individual tree parameters within a stand. In the present study, the increase in the inter-tree disparity in NDVI_can (Fig. 10h), canopy temperature and the derived T-based stomatal resistance under drought, (Fig. 11b) may be considered to be an indication of stress in trees, as the least resilient trees show more extreme values of vegetation indices compared with the main tree population.

UAV mapping is a very useful tool enabling the study of spatial distributions of canopy and ground colour, reflectance, temperature and structure. However, the UAV data also have substantial limitations complicating the analysis of tree drought response that do not have immediate solutions. These concern both the multispectral data and thermal data. Canopy segmentation is a first important step that presented a greater challenge in the dense canopy of the Control block (Appendices A, C). The large number of trees that could not be segmented required a statistics-based correction (Appendix C). The dominant trees are more sensitive to climate and weather, being more exposed to wind, VPD stress and solar radiation (Schmied et al., 2022). The UAV data users should therefore be aware of the biases associated with the UAV tree health proxies in stands with non-uniform tree spacing and age.

The separation of canopy and ground pixels in the UAV multispectral and thermal orthomosaics is an issue common for all sensors. In the present experiment, the Normalized Green-Red Difference Index NGRDI was used (Appendix C), and performed well based on visual examination. However, we acknowledge that the greenness threshold segregating spruce needles from ground varies across the survey area due to the differences in tree vs. ground colour, both between the individual trees, and between the Control and Harvest blocks. The relatively open canopies of the Norway spruce pose an additional challenge to segregate the shoots from the woody parts and the ground.

A further two considerations apply to thermal imaging. One is the small-scale variability in aerodynamic conductance in the vicinity of each tree, or, in other words, wind-induced cooling and enhancement of transpiration. The trees that are sheltered from the wind experience a lower VPD and less wind-induced leaf cooling than other trees that are more exposed (e.g. Zweifel et al., 2005). This has direct consequences for the estimation of stomatal conductance based on leaf temperature (Sect. 3.4, Fig. 10, Appendix D). Such tree-scale variations in the field of turbulence are difficult or impossible to measure directly, so that Large Eddy Simulation of wind flow through forest canopy becomes the only feasible tool to tackle this problem. Accounting for inter-tree differences in aerodynamic conductance and amount of received solar radiation will enable more precise interpretation of canopy temperature.

The initial expectation was that the stressed trees would show both low Tree-mean NDVI and low temperature-derived stomatal conductance. A positive relationship is indeed observed in the Control, but the absence of correlation between the individual canopy NDVI and temperature-based stomatal conductance in the Harvest block that is observed in late July 2021 data is difficult to explain (Fig. 12). NDVI showed a positive correlation with tree height in Control, but the correlation was not detectable in Harvest, which could be partly due to the previously observed NDVI_can saturation over dense stands (Vicca et al., 2016). The highly disparate temporal dynamics of leaf temperature and optical indices such as NDVI should be considered as well: while the air drought stress is immediately reflected in leaf temperature via the rapid link between VPD – stomatal conductance – transpiration, NDVI instead reflects the changes in canopy spectral properties tied to the structural and biochemical state of the photosystems, which respond to stress over days to weeks. This time scale mismatch would be more relevant for the UAV surveys in the early drought, such as 2 June 2021 in this study, than for the surveys conducted several weeks into the drought period (14–26 July 2021, Fig. 10)

In contrast with the tree-specific information provided by sap flow sensor data, Eddy-Covariance drought stress proxies are known to be inherently area-averaging, and thus incapable of precisely detecting short-term changes in the physiology of a subset of trees within a stand. In the present study, the EC fluxes are mainly representative of the Harvest block (Appendix B), while the amount of data from Control is deemed too low to serve as reliable basis for drought stress estimation. This further diminishes the possibility of detecting the stress in the minority of non-resilient trees within Control which was identified by sap flow data. While the sequence of Norway spruce drought responses was found to be always the same (drop in stomatal conductance, photosynthesis, leaf potential, chlorophyll), the stress level controlled the timing and magnitude of responses (Ditmarová et al., 2010). The more resilient Norway spruce trees have water reserves to withstand several days of drought stress without showing pronounced reactions (Ditmarová et al., 2010; Čermák et al., 2007). Consequently, trees within a stand would respond to drought with a widely varying lag, complicating the interpretation of EC data.

The upscaling of sap-flow to the Harvest and Control blocks and EC footprint demonstrated a close match between the two independent transpiration measurement techniques (Sect. 3.3, Fig. 9). While tree transpiration comprised a larger fraction of the total ET during the hot June–July period of 2021, this ratio was not altered by the initiation of soil drought in the late June 2021. In general, it seems that the 2021 drought was also not strong enough to affect the seasonality in tree growth (Fig. H1 in the Appendix), carbon uptake (Fig. 4) and transpiration (Fig. 9), although it did induce relative deficits in all these parameters. From the tree viewpoint, the relative moderateness of the 2021 drought follows from the observation that sap flow and transpiration did not decouple from potential evapotranspiration (Fig. 9).

The difference in drought reaction timing between the individual trees in a stand is often ignored in studies based on eddy-covariance technique. While it is a useful tool providing continuous data on CO₂ and H₂O surface exchange, it yields fluxes averaged over a large area, typically a few ha to over 1 km² in size (Rebmann et al., 2018). Because of this averaging nature of the EC method, its users can only detect drought responses as substantial changes in ecosystem-average carbon, water and energy fluxes - when (and if) such changes occur. Even over reasonably homogeneous stands, any change in EC footprint might lead to significant differences in estimated NEE and its components, further highlighting the challenge of EC flux attribution to smaller elements of the landscape (Krasnova et al., 2022). Furthermore, the contributions of the ground and trees to the total 30 min exchange rates in EC data are difficult to estimate (Subke and Tenhunen, 2004; Aslan et al., 2024).

Therefore, the second objective consisting of a broad comparison of ecosystem monitoring tools has been accomplished, however, revealing a complex picture of divergent performance, depending on the temporal and spatial scales in question. In summary, no current observational technique offers the combined benefits of directness, continuity, broad spatial coverage, and low uncertainty. However, the specific task of early warning for the initiation of stand drought response could be carried out by sap flow and stem diameter sensors installed in the less resilient trees. Further studies combining more frequent UAV surveys, eddy-covariance, wide soil sensor networks (WTD, moisture content) and larger numbers of sap-flow and dendrometer sensors resolving the tree drought resilience range would enhance the capacity to reconcile these monitoring techniques and gain deeper insights into the onset and progress of drought.