Understanding the link between vegetation characteristics and tree transpiration is a critical need
to facilitate satellite-based transpiration estimation. Many studies use the
Normalized Difference Vegetation Index (NDVI), a proxy for tree biophysical
characteristics, to estimate evapotranspiration. In this study, we
investigated the link between sap velocity and 30 m resolution
Landsat-derived NDVI for 20 days during 2 contrasting precipitation years in
a temperate deciduous forest catchment. Sap velocity was measured in the
Attert catchment in Luxembourg in 25 plots of
Evapotranspiration (ET) is estimated globally as 60 % of the total precipitation (Oki and Kanae, 2006) and 80 % of total surface net radiation (Wild et al., 2013). This makes ET the second largest component of the water and energy balance. Changes in ET due to climate or land-use change have a major influence on the catchment water balance. Deforestation for example reduces ET (de Oliveira et al., 2018), leading to lower precipitation (Bagley et al., 2014) and higher streamflow (Dos Santos et al., 2018). Teuling et al. (2009) showed that changes in incoming radiation and water availability impact regional ET and runoff. In order to predict these changes, a comprehensive understanding of “what controls ET” is an important look forward.
The transpiration component of ET, i.e. water loss through stomata, is the
largest contributor to total terrestrial ET (Wang et al., 2014; Wei et al.,
2017), and therefore transpiration plays a major role in the global
hydrological and biogeochemical cycle. Transpiration is controlled by complex
interactions between climate (Awada et al., 2013; e.g. Hasler and Avissar,
2007), soil moisture content (Mitchell et al., 2012), topographic variables
such as slope position and aspect (Mitchell et al., 2012), and vegetation
characteristics (Williams et al., 2012). With respect to the vegetation
biophysical characteristics, it has been shown that tree transpiration
differs with leaf area index (LAI) (Wang et al., 2014; Granier et al., 2000),
tree height (Ford et al., 2011; Waring and Landsberg, 2011), tree diameter
(Jung et al., 2011; Chiu et al., 2016), tree age (Baret et al., 2018), and
phenological stage (Sobrado, 1994). With the advancements of remote sensing
and free data availability, there have been many efforts to link
The link between NDVI and transpiration or evapotranspiration (
The above-mentioned studies often derive the NDVI from MODIS or AVHRR data which have a spatial resolution of 250 m and 1 km (except for Reyes-González et al., 2018; Kim et al., 2006; Rahman et al., 2001; Su, 2002, who used airborne data or high-resolution satellite data; Landsat or IKONOS). The NDVI is often compared with ET derived from different flux towers with a footprint length of 100 to 1000 m (Kim et al., 2006), or a water balance model. Therefore, these studies encompass large spatial areas, with a larger variation in vegetation cover and sometimes multiple land-use types. Despite the availability of high spatial resolution satellite products increasing rapidly (e.g. Sentinel series), there is a lack of studies that investigate the link between satellite-derived NDVI and the water balance on the scale of forest patches or smaller. At the same time, there is a trend towards hyper-resolution land surface modelling and monitoring (Bierkens et al., 2015), where for example 30 m Landsat-derived NDVI data are used as a proxy for land cover in a continental land-surface model (Chaney et al., 2016). For many processes or parameters it is, however, unknown whether they can be applied at such high resolutions. Therefore, in this study we aim to understand whether the relation between NDVI and transpiration is also valid on the scale of forest patches by using 30 m resolution NDVI data.
Investigating the link between transpiration and NDVI requires high-resolution satellite data as well as a dense network of in situ transpiration observations. In the Attert catchment, a dense network of sensor clusters with – among others – sap velocity sensors allows for a detailed study of the link between tree transpiration and NDVI. For this catchment Hassler et al. (2018) showed that variability in sap velocity is mainly controlled by tree characteristics, such as tree diameter and tree height and site characteristics, such as geology and aspect. The aim of our study is to investigate the link between transpiration and NDVI using measurements of sap velocity combined with 30 m resolution NDVI data. Hassler et al. (2018) showed that small-scale variability in sap velocity was related to tree structural characteristics, and therefore we expect sap velocity and NDVI to be correlated. We hypothesize this correlation to be positive, because we expect that forest stands with a higher leaf biomass (higher NDVI) will have a larger sap velocity.
Under water-stressed conditions, stomatal closure reduces tree transpiration to limit the risks of hydraulic failure. Among others, leaf area and leaf shedding play a role in mitigating these risks. To study the effect of water stress on the link between transpiration and NDVI, two growing seasons with above- and below-average precipitation are compared.
The geology of the Attert catchment and its location in Luxembourg. Sandstone in the catchment is a combination of Buntsandstein sandstone in the north and Lower Jurassic sandstone in the south. Also shown are the main streams, sensor clusters, meteorological stations Roodt and Useldange, and the location of the CAOS sensor cluster where wind speed and relative humidity measurements were taken from.
The study was carried out in the Attert catchment in midwestern Luxembourg.
This area was chosen because of its small-scale diversity in geology and soil
hydrological conditions. The 288 km
Within the CAOS research unit, a monitoring network was set up in the Attert
catchment including 29 sensor clusters in a forest (of which 25 are used in
this study) in order to provide a new framework for hydrological models for
catchments at the lower meso-scale (Zehe et al., 2014). A sensor cluster covers
Meteorological characteristics and their unit.
Soil moisture content was measured in three soil profiles in each cluster
site using Decagon 5TE sensors at three depths (10, 30, and 50 cm). For this
study, the average
Meteorological conditions in 2014 and 2015. Daily average
temperature (
Cumulative precipitation surplus (
In this study, 2 meteorologically contrasting years were analysed: 2014, a
growing season with above-average precipitation, and 2015, a growing season
with below-average precipitation. For the months May and June, meteorological
conditions were not significantly different between 2014 and 2015, but for
July and August, mean daily temperature, vapour pressure deficit (
Sap velocity is used as a measure of tree transpiration (e.g. Smith and Allen, 1996). In summary, in this method, heat is applied to the water in the xylem of the tree trunk, and this heat is carried upwards with the water. Temperature sensors monitor the time it takes before the heat pulse reaches the sensor. This time is related to the velocity of the water in the xylem. More information about sap velocity measurements can be found in e.g. Smith and Allen (1996).
Sensor cluster characteristics.
At each sensor cluster (all located in deciduous forest stands), four trees
roughly representative of the sensor cluster were selected for the sap velocity
measurements. The main deciduous tree species in the area are beech
(
Sap velocity measurements can be scaled up to whole tree transpiration from the total sapwood area for each tree (Smith and Allen, 1996), but these data were not available within our study area. Alternatively, a species- and site-specific allometric equation between tree diameter at breast height and sapwood area can be used to calculate tree total sap flow, but this conversion introduces uncertainties (Gebauer et al., 2012; Ford et al., 2004). Therefore, we used sap velocity directly in our study.
The vegetation indices were calculated from Landsat-7 (ETM
To study the effect of static vegetation and environmental characteristics on sap velocity and NDVI, correlations with tree and environmental characteristics were calculated. Information on semi-static tree and cluster site characteristics is available from Hassler et al. (2018). For every cluster, the total number of stems was counted, and the DBH was measured for each tree with a circumference of more than 4 cm (Table 2). The tree height was estimated for every tree where sap velocity was measured, and for each cluster site, aspect was noted. Elevation and geology are derived from a digital elevation model and a geological map.
Mean daily sap velocity for beech and oak trees in the three different geologies. The drop in sap velocity in August 2014 (blue arrow) is related to a lower incoming radiation, while the drop in August 2015 (red arrow) is not related to a lower incoming radiation, but falls into a period of below-average precipitation and low soil moisture content. The min, mean, and max values are calculated for July and August in both years, indicated by the grey box.
The seasonality in sap velocity is clearly visible, with a steep increase in
April and a decrease in October (Fig. 4). Mean
daily sap velocity for July and August was highest for beech trees in the
sandstone area (8.9 cm h
The phenological cycle is clearly visible in the temporal dynamics of NDVI
with a rapid green-up in April (Fig. 5). In April, the mean Landsat-derived
NDVI over the clusters was 0.62 (
Observed NDVI dynamics during the growing seasons of 2014 and 2015. The grey line and dots represent the mean NDVI over the forested clusters derived from the MOD13Q1 product of MODIS. It provides a better overview of the seasonal course. The 20 boxplots (in black) show the variability in Landsat-derived NDVI over the studied clusters for each studied day.
Analysing all sensor clusters together for all 20 days, a moderate positive
correlation was found between sap velocity and NDVI (
Temporal correlation between sap velocity and NDVI for all 20
studied days in 2014 and 2015.
Relationship between sap velocity and NDVI for 6 days. Each dot
represents one sensor cluster in the sandstone, schist, and marl area. The dashed
line represents the 95 % confidence interval.
Scatterplots of spatial variability in sap velocity and NDVI show three
different patterns: (1) a significant linear positive correlation (Fig. 7a
and d: Pearson's
Relationship between sap velocity and NDVI and observed soil
moisture content (
Figure 8a shows the dynamic changes in the correlation coefficient between
sap velocity and NDVI. In both years, the correlation coefficient was
positive at the beginning of the growing season (April) and negative or close
to zero during the rest of the year. In the year 2014, no trend was visible
in the variability of the correlation coefficient. In 2015, the correlation
coefficient was initially positive and became negative in May. As the growing
season progressed and
The effects of static vegetation and environmental characteristics on sap velocity and NDVI were calculated. This was also done to check whether dependency on one of these characteristics could explain the negative correlation between sap velocity and NDVI. Assessing individual trees, sap velocity was related to tree DBH and tree height, but at cluster level, sap velocity was not or moderately dependent on these characteristics (Table 3). The number of stems and mean tree DBH per sensor cluster did not correlate with sap velocity. For some days, sap velocity was higher in clusters with higher trees. For most studied days, sap velocity for beech trees was higher than for oak trees, but this difference was usually not significant. Altitude and sap velocity were negatively correlated in April for both years. Geology and aspect explained part of the variability in sap velocity, especially during summer 2015, when sandstone clusters had a higher sap velocity than schist and marl clusters, and north-facing slopes had a higher sap velocity than south-facing slopes. The different cluster characteristics were not independent and, therefore, a relation between two variables could also have been the result of a causal relation with another variable.
Seven (semi-)static sensor cluster characteristics and whether they are
significantly correlated (
Cluster-averaged tree characteristics were usually not related to NDVI, and
their direction of influence was not consistent. Also, the change in NDVI
with altitude was not consistent over the year, but in April of both years,
the correlation was negative. In both years, schist clusters had the lowest
NDVI in April (
In the present study, mean sap velocity was calculated for the two to four trees in each sensor cluster. This is only a small selection of the total number of trees per cluster, which varied from 9 to 346, with a median of 34 trees per cluster. The trees selected for sap velocity measurements are roughly representative of the cluster with respect to species and DBH. But velocity of the sap depends on tree DBH, height, species, and tree age (Gebauer et al., 2012; Ryan et al., 2006), and therefore, making a true representative selection remains challenging.
We looked for a relationship between tree sap velocity and a canopy trait, NDVI. Please note that two scaling steps are required to scale sap velocity up to the canopy level: a first step to scale from sap velocity to whole tree transpiration and a second step from tree to stand transpiration. In this study, measurements of sap velocity were preferred over whole tree or stand transpiration, because scaling introduces uncertainties, especially when sapwood area is not known (Gebauer et al., 2012; Ford et al., 2004). An empirical scaling formula can be used to calculate whole tree transpiration from (1) sap flow, (2) tree DBH, and (3) a species- and site-specific parameter. On an individual tree level, trees with a larger DBH had a higher sap velocity, which is also known from other studies (Jung et al., 2011). Calculating whole tree transpiration from sap velocity would have thus increased the mutual differences among clusters, but usually would have not changed the order of values and direction of correlation with NDVI. The species-specific parameter in the scaling formula would have increased the differences in transpiration between beech and oak trees. This is because beech trees in this study had, on average, a larger sap velocity and, despite the lower DBH, a higher average sapwood area.
The moments of vegetation green-up and leaf senescence are reflected in both sap velocity and NDVI as they increase in April and decrease in October. Comparing the summer (July and August) of 2014 and 2015, the higher potential evapotranspiration in 2015 resulted in a higher sap velocity for beech and oak trees in the sandstone area compared to 2014. For the beech trees in the marl and schist area however, mean sap velocity was lower in summer 2015. This drop in sap velocity in 2015 could not be attributed to a reduction in atmospheric demand or available energy (Fig. 9), and was likely the result of stomatal closure in response to water stress. No drought-related reduction was observed in NDVI, and also no lagged effect. This indicates that trees were conservative with water and closed their stomata to prevent transpirational water loss. Under the relatively mild stress during the summer of 2015 no change in tree canopy structure (leaf area index, leaf angle distribution) and thus no change in structural indices like NDVI can be expected as structural vegetation changes become visible only after a prolonged dry period (Eklundh, 1998).
Relationship between sap velocity and meteorological conditions for
spring and summer 2014 and 2015 for a beech tree in the schist area. The
relationship between mean daily sap velocity and
Considering the spatial variability, Hassler et al. (2018) found that in the Attert catchment, tree characteristics (species, DBH, and tree height) explained 22 % of the variability in sap velocity. Interestingly, our study showed that cluster mean tree characteristics did not explain variability in cluster mean sap velocity during most of the growing season (Table 3). This is likely because of the smaller variability in sap velocity and tree characteristics on the cluster level as compared to individual trees.
Part of the trees showed a water-stress-induced drop in sap velocity in 2015. The statistical analysis revealed that during this period, geology and aspect significantly explained part of this spatial variability in sap velocity (Table 3). The higher sap velocity on north-facing slopes could indicate the effect of a higher water availability compared to south-facing slopes. In the sandstone area, trees maintained high sap velocity during the dry period, but sap velocity was reduced in the schist and marl area. Also, this effect of geology is likely related to water availability. Pfister et al. (2017) and Wrede et al. (2015) showed that in the Attert catchment, sandstone has a high storage capacity, because of the deep permeable soils, while the storage capacity is low in the marl and schist area. Furthermore, trees in the sandstone area were on average taller and had a larger DBH. These trees might have been able to access water from deeper layers because of a more developed root system.
Temporally, sap velocity and NDVI were positively correlated, because both
follow a similar seasonal cycle with lower values in April and October than
in summer. Considering only the full leaf period (May–September), sap
velocity and NDVI were not correlated. Variability in sap velocity during the
full leaf period was to a large extent explained by daily variations
in
Considering spatial correlation, three different patterns were found:
positive, negative, and no correlation. The different patterns are discussed
below. During April in both years, sap velocity and NDVI were positively
correlated. This was before complete leaf-out and the spatial variability in
NDVI was high. In April, elevation of the clusters significantly explained
part of the variability in both sap velocity – Pearson's
The negative correlation between sap velocity and NDVI – a higher sap
velocity for lower leaf biomass – was found during most of the studied
period, though its was sometimes weak and not significant. There is no clear
explanation for this unexpected result, but four probable reasons are
foreseen that could have influenced the correlation. First, for NDVI it is
well known that it saturates at high LAI (Huete et al., 2002), which makes
the index insensitive to vegetation biophysical and biochemical properties
(Gamon et al., 1995). NDVI saturation was found for LAI greater than
On half of the studied days, no correlation was found between sap velocity
and NDVI, which could be due to noise in the data caused by the saturation of
the NDVI signal. Absence of a correlation could also indicate that optical
vegetation characteristics are uncoupled from ET, i.e. that no significant
control of stomata and vegetation structure on ET was apparent in the Attert
catchment. The temporal change in Pearson's
Summer 2015 experienced below-average precipitation, but was not
exceptionally dry. Nevertheless, sap velocity dropped during this dry period.
In 2014, when ample soil water was available, temporal variability in sap
velocity was strongly coupled with
We hypothesized finding a positive correlation between sap velocity and NDVI,
but spatially, this was the case only in April. This means that NDVI
successfully captured the pattern of sap velocity during the phase of
green-up when water was not limited. After green-up, the positive correlation
changed into a negative correlation or no correlation. The inconsistent
correlation between sap velocity and NDVI would also translate into an
inconsistent correlation between transpiration and NDVI, after applying a
scaling equation. Various methods however use NDVI to estimate
Compared to our study, earlier studies that found a positive correlation
between
NDVI lags behind sap velocity in relation to drought and cannot be used to predict transpiration under dry conditions. A water-stress factor has been introduced by several studies to overcome this problem, but this stress factor is not always spatially explicit (e.g. Maselli et al., 2014). Our study showed that, in the studied catchment, a spatially explicit stress factor is required for accurate transpiration prediction under drying conditions, because neither NDVI nor meteorological conditions capture the spatial variability in ET controlled by geologically induced differences in water availability.
The scaling of water flux measurements across scales is a main challenge in ecohydrology (Asbjornsen et al., 2011; Hatton and Wu, 1995). Scaling in situ measurements over a larger area, for example flux tower or sap velocity measurements, is traditionally done by scaling over in situ measured biometric parameters such as DBH, basal area, or sapwood area (Čermák et al., 2004). Obtaining these characteristics from satellite images is less resource demanding, can be applied over larger areas, and provides the opportunity to study both spatial and temporal patterns simultaneously. Satellite-derived scaling parameters have another advantage over the conventional ones: (semi-)static characteristics are unreliable under the changing conditions that we face for the future, with among others more intense droughts (Cleverly et al., 2016; IPCC, 2012).
This study shows that, in a temperate forest with high LAI and low
variability in NDVI and EVI, these indices cannot be used to estimate
transpiration or scale sap flux measurements to the stand level. The benefits
that satellite-derived scaling parameters provide makes it worth exploring
other possibilities using remote data to characterize vegetation and
The aim of this study was to investigate the link between sap velocity and
satellite-derived NDVI in a temperate forest catchment. We focussed on
small-scale variability, in both space and time. A positive correlation
between sap velocity and NDVI was expected. Data analysis for 2 consecutive
years led us to the following conclusions.
Temporally, a correlation between sap velocity and NDVI was only found when
the entire growing season was considered. Spatially, a positive correlation
was found in April, when spatial variability in sap velocity and NDVI was
large and reflected an altitude-dependent difference in green-up. This means
that NDVI did capture the spatial pattern in leaf-out which also affected sap
velocity. During the rest of the growing season, a negative correlation was
found between sap velocity and NDVI. This negative correlation was
significant during half of the studied days. The likely saturation of the
NDVI signal in combination with the small spatial variability in NDVI could
explain the absence of a positive correlation, but does not explain this
negative correlation. In 2015, during the dry summer period, the spatial correlation between sap
velocity and NDVI changed. Variability in sap velocity could not be captured
by NDVI. Instead, sap velocity was controlled by geology and aspect, likely
through their effect on water availability. This shows that a stress factor,
used to estimate transpiration during dry periods, cannot always be based on
meteorology only, but should include information that reflects the water
availability. The time-variable and inconsistent spatial correlation between sap velocity
and NDVI would also translate into an inconsistent correlation between
transpiration and NDVI. From this we conclude that NDVI alone cannot describe
small-scale temporal and spatial variability in sap velocity and
transpiration in a temperate forest ecosystem. Only for temporal scales that
cover the whole phenological cycle was NDVI a significant predictor of
transpiration processes. The EVI, which is less sensitive to saturation
effects, was also unsuitable as a predictor of transpiration under the
studied conditions. Therefore, we suggest that the use of vegetation indices
to predict transpiration should be limited to ecosystems and scales where the
correlation was confirmed.
The satellite images are available from
KM, MM, MS and AJT initialized the study. SKH and TB provided the sap flow and meteorological data. AJHvD performed the data analysis in consultation with KM, MS, and AJT and wrote the paper. All the authors contributed to interpreting results, discussing findings and improving the paper through joint editing.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Linking landscape organisation and hydrological functioning: from hypotheses and observations to concepts, models and understanding (HESS/ESSD inter-journal SI)”. It is not associated with a conference.
This work was supported by the Luxembourg National Research Fund (FNR) (PRIDE15/10623093/HYDRO-CSI). We acknowledge the DFG for funding CAOS research unit FOR 1598 and Britta Kattenstroth and Tobias Vetter for the maintenance of the sensor network. Partial support for Kaniska Mallick and Martin Schlerf also came through the HiWET consortium sponsored by BELSPO – FNR (STEREOIII: INTER/STEREOIII/13/03/HiWET; contract no. SR/00/301) and FNR-DFG (CAOS-2; INTER/DFG/14/02).
This paper was edited by Patricia Saco and reviewed by Jozsef Szilagyi and one anonymous referee.