Monitoring Forest Phenology and Leaf Area Index
with the Autonomous, Low-Cost Transmittance
Benjamin Brede 1,*, Jean-Philippe Gastellu-Etchegorry 2, Nicolas Lauret 2, Frederic Baret 3,
Jan G. P. W. Clevers 1, Jan Verbesselt 1and Martin Herold 1
1Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research,
Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands; email@example.com (J.G.P.W.C.);
firstname.lastname@example.org (J.V.); email@example.com (M.H.)
2Centre d’Etudes Spatiales de la BIOsphere, Toulouse University, CNES, CNRS, IRD, UPS, (Toulouse),
31401 Toulouse, France; firstname.lastname@example.org (J.-P.G.-E.); email@example.com (N.L.)
3Institut National de la Recherche Agronomique–Université d’Avignon et des Pays du Vaucluse
(INRA-UAPV), 228 Route de l’Aérodrome, 84914 Avignon, France; firstname.lastname@example.org
Received: 15 May 2018; Accepted: 27 June 2018; Published: 30 June 2018
Land Surface Phenology (
) and Leaf Area Index (
) are important variables that
describe the photosynthetically active phase and capacity of vegetation. Both are derived on the
global scale from optical satellite sensors and require robust validation based on in situ sensors at high
temporal resolution. This study assesses the PAI Autonomous System from Transmittance Sensors at
) instrument as a low-cost transmittance sensor for simultaneous monitoring of
in forest ecosystems. In a ﬁeld experiment, spring leaf ﬂush and autumn senescence in a
Dutch beech forest were observed with
and illumination independent, multi-temporal
Terrestrial Laser Scanning (
) measurements in ﬁve plots. Both time series agreed to less than
a day in Start Of Season (
) and End Of Season (
magnitude was strongly correlated
with a Pearson correlation coefﬁcient of 0.98.
summer and winter
were on average
. This can be explained by previously reported
was implemented in the Discrete Anisotropic
Radiative Transfer (
) Radiative Transfer Model (
) model for sensitivity analysis. This
conﬁrmed the robustness of the retrieval with respect to non-structural canopy properties and
illumination conditions. Generally,
fulﬁlled the CEOS LPV requirement of 20% accuracy
for a wide range of biochemical and illumination conditions for turbid medium canopies.
However, canopy non-randomness in discrete tree models led to strong biases. Overall,
demonstrated the potential of autonomous devices for monitoring of phenology and
temporal resolution as required for validation of satellite products that can be derived from ESA
Copernicus’ optical missions, Sentinel-2 and -3.
Land Surface Phenology; Leaf Area Index; ground-based; forest; validation;
radiative transfer model; DART model
Vegetation phenology describes the “timing of seasonal developmental stages in plant life cycles
including bud burst, canopy growth, ﬂowering, and senescence [...]” [
]. This includes the start, end and
length of the photosynthetically active phase in the year. A mechanistic understanding of controls on these
events is still lacking for most biomes [
]. This leads to a general misrepresentation of vegetation temporal
Remote Sens. 2018,10, 1032; doi:10.3390/rs10071032 www.mdpi.com/journal/remotesensing
Remote Sens. 2018,10, 1032 2 of 19
behaviour in global circulation models and uncertainty in vegetation-climate feedbacks. Standardised,
wide-spread observations are paramount for the quantification of phenology and climate feedbacks.
Proximal sensing techniques such as phenocams or webcams allow high revisit frequency and
objective analysis techniques [
]. They are based on the principle that changes in canopy biophysical
and -chemical composition, which go along with leaf development and senescence, alter its radiative
regime. Most often exploited is the decrease of reﬂectance in the visible wavelengths due to absorption
for photosynthesis and the increase in the Near-Infrared (
) due to reﬂecting properties of leaves.
The latter effect can be exploited when sensors also record
]. This can be done for example
with tower-based proximal sensing. However, these sensors are often not standardised in terms of
measurement protocols .
Apart from this, the change in reﬂective behaviour is also utilised to detect phenological events
over large areas with satellite-borne sensors [
]. The spatially aggregated, temporal behaviour of
plants over larger areas that are observed from space is referred to as Land Surface Phenology (
In contrast to ground-based systems, space-borne missions have the advantage to use only single or
few sensors, which makes it easier to derive comparable products.
The case is similar for another quantitative vegetation property, the Leaf Area Index (
). It is
deﬁned as the one-sided leaf area per unit of ground surface area [
]. Hence, it quantiﬁes the amount
of leaves that are available for photosynthesis during the photoperiod. Similar to
inferred from ground-based and space-borne instruments, but also from air-borne sensors [
Unmanned Aerial Vehicless (
observations are typically performed
with consumer-grade cameras equipped with ﬁsh-eye lenses, referred to as Digital Hemispherical
]. This method relies on gap fraction theory to infer
]. Most often,
observations with a viewing zenith angle of
, the so-called hinge angle, are analysed, where
impact of the Leaf Angle Distribution (
) on gap fraction is minimal. Other hand-held instruments
are also available. However, in general, ground-based instruments for
are used manually and in
campaigns that cover larger areas to capture the extent of satellite scenes [
]. Regular re-sampling of
the same locations is possible, but labour-intensive [19,20].
In the context of Earth observation satellite missions and programmes, such as NASA’s
Moderate-Resolution Imaging Spectroradiometer (
) or ESA’s Sentinel-3 mission [
spatial products require robust quality ground-based validation. This demands
monitoring devices that match land product’s temporal resolution, potentially able to record
high resolution so that
can be inferred from the time series. Low-cost devices would be preferred
to allow deployment over larger areas and at many sites.
The PAI Autonomous System from Transmittance Sensors at
) is a candidate to fulﬁl
these requirements. It was developed by Institut national de la recherche agronomique (INRA)-Hiphen
(Avignon, France) during the FP7 ImagineS project (http://fp7-imagines.eu). Its main application
was to support multi-day calibration and validation ﬁeld campaigns for retrieval of
of Absorbed Photosynthetically-Active Radiation (
) with hectometric resolution space-borne
]. Recently, it was compared with seasonal measurements of LAI-2200 and
agricultural ﬁelds [
]. Its measurement principle is based on gap fraction theory. Similar to
it exploits the
quasi-invariance at the hinge angle. However, a more detailed assessment of its
performance characteristics especially with respect to changing illumination conditions and plant
biochemical properties has not been presented yet.
In this context, vegetation Radiative Transfer Models (
s) can support sensitivity analysis,
the deﬁnition of new sensors and the development of inversion procedures to translate the radiative
signal into canopy variables [
]. Generally, vegetation
s model the interaction of sun radiation
with canopy elements based on the canopy’s biophysical and -chemical properties. In this way, they
can be exploited to assess the sensors sensitivity to canopy parameters in idealised conditions, i.e.,
without measurement noise, and for a wide range of possible canopy conditions. For example, the
widely used PROSAIL model, a combination of the Scattering by Arbitrarily Inclined Leaves (SAIL)
Remote Sens. 2018,10, 1032 3 of 19
canopy and PROSPECT leaf radiative models, has been exploited to design vegetation indices [
for biophysical parameter retrieval via inversion [
]. However, SAIL represents the canopy as a
homogeneous layer of scatterers. This assumption does not hold for clumped canopies such as forests.
In these cases, a heterogeneous representation that can take into account canopy clumping and
non-random structure is more appropriate. The Discrete Anisotropic Radiative Transfer (
model implements this paradigm [
applications include canopy biophysical and -chemical
parameter retrieval [
], surface energy budget studies [
] and recently chlorophyll ﬂuorescence
incorporates the option to implement in-scene sensors such as
hemispherical or pinhole cameras . This provides the option to test arbitrary sensor designs.
The aim of this study has been twofold: (i) testing the ground-based transmittance sensor
in a forest stand with daily frequency; and (ii) assessing
the sensor’s sensitivity to canopy properties other than
and their interactions by means of
experiments, thereby testing the robustness of the measurement principle to different canopy
conditions. This study is structured as follows: The
sensor is presented in detail in
Section 2.1. Section 2.2 describes the ﬁeld data collection and analysis. Section 2.3 elaborates on
implementation of the sensor in
, the set up of the synthetic canopy and how sensitivity analysis
was conducted. Results are presented in Section 3and discussed in Section 4. Section 5summarises
the results and lists implications for future sensor design.
2. Materials and Methods
2.1. PASTiS-57 Instrument
consists of a weather proof, battery powered datalogger with 6 photodiode-based
sensors (Figure 1). The sensors are ﬁxed to a viewing zenith angle of
, sensitive in the blue spectral
region to minimise canopy multiple scattering and have different lengths of wire for sensor distribution
around the data-logger. The logger is battery powered and can autonomously collect data at
interval for up to four months. Intervals of
are also possible. Radiation is recorded with
uncalibrated Digital Number (
) in the interval 0–4000, whereas larger
s are treated as unreliable
due to saturation effects. The signal can be calibrated with dedicated Photosynthetically-Active
) sensors [
]. However, a more practical approach is to utilise the relative signal by
installing one device above the canopy, which serves as reference for incoming radiation, and another
device below the canopy, which represents the observations. In this way, many plots can be served by
one reference sensor, as long as it is within a distance where illumination conditions can be assumed
comparable. The observed signal is the spectral directional transmittance τfor each sensor:
τ=DNbelow c ano py
DNabove cano py
2.2. Field Experiment
2.2.1. Study Area and Field Data Collection
sensors have been installed at the Speulderbos Fiducial Reference site in the Veluwe
forest area (N
), The Netherlands [
] (www.wur.eu/fbprv). This site represents a
maturing stand of mixed European beech (Fagus sylvatica), pedunculate oak (Quercus robur) and sessile
oak (Quercus petraea) with few understorey. The trees were initially planted in 1835. Nowadays, the
stand has a density of around 200 trees/ha.
Remote Sens. 2018,10, 1032 4 of 19
instrument installed in the Speulderbos site: (
) With the opened data-logger
box, the six sensors can be seen on top, a spare cable is curled up at the back of the data-logger, and
two D-cell batteries for power supply in the box; and (
installed at the centre of Plot C
(centre marker not visible).
instruments were installed in the centres of ﬁve plots. For redundancy, each plot was
equipped with two devices (Figure 2). Contrary to previous studies where sensors were put onto the
ground, the individual sensors in this study have been mounted on top of the data logger on a plastic
board to face the NE, E, SE, SW, W and NW directions. Each
unit was ﬁxed at
ground at an iron rod, aligned to north with a magnetic compass and levelled with a bubble level.
Another two devices have been mounted at the top of a
high scaffold tower approximately
west of Plot A to record above canopy reference downward radiation. Campaigns were conducted in
spring 2016 during leaf ﬂush, autumn 2016 during leaf senescence, and summer and autumn 2017.
Campaigns were programmed with 2min interval in 2016 and 1 min interval in 2017.
0 300150 m
Map of the study site with the scaffold tower where reference instruments were placed and
the ﬁve sampling plots. Background is an airborne false-colour composite of 2013. The location of the
study site within The Netherlands is marked on the inset.
Remote Sens. 2018,10, 1032 5 of 19
2.2.2. Plant Area Index (PAI) Retrieval
It should be noted that many proximal sensing techniques cannot distinguish between foliage and
woody canopy elements. The
includes both classes, while
refers only to photosynthetically
active plant tissue [
]. In the following,
refers to observed plant area, while
refers to the
actual quantity of green leaf area. Typically,
retrieval from below canopy sensors such as
gap fraction theory [
]. For this, the canopy is assumed as a uniform cloud of randomly oriented,
black facets (
0). In this case, the gap fraction is related to
based on Beer–Lambert’s law:
P(θ) = e−G(θ)Ω(θ)L/ cos θ(2)
is the viewing zenith angle,
the canopy gap fraction in direction
the projection of
unit foliage in the
direction, which characterizes the foliage angular distribution,
index that describes the non-randomness of the canopy and
has been variably
interpreted as gap fraction in the case of
], hit probability in the case of lidar sensors [
transmittance in the case of PASTiS-57 [24,26].
For canopy clumping estimation, the method of Lang and Xiang (
] and the six different
viewing directions of each
instrument were exploited. The
method assumes that within
a segment the foliage is random and it contains gaps. In the case of
the instantaneous ﬁeld
of view of a single sensor can be interpreted as a segment. In that case, canopy clumping can be
Ω(θ) = ln P(θ)
is the mean gap fraction of all segments and
the logarithm of the mean gap
fraction of all segments. “The reasoning behind this technique is that since the [
] is related to the
natural logarithm of the gap fraction, the average LAI should follow the logarithm average of the
. In this sense, the denominator in Equation
normalises the gap fraction by the
logarithm. Using this deﬁnition for clumping, replacing
and exploiting the near constant value of the
Equation (2) to
L=−1.075 ln(τPAST IS)
The goal of this study was to produce daily observations of
, from which phenological
parameters can be derived. Earlier studies in forests showed that sub-daily retrievals with
based on Equation
produce results with strong, high frequency noise with
Therefore, raw readings require appropriate quality ﬁlters to be applied. Here, these ﬁlters were
based on experience gathered while investigating the raw time series. The primary result was that
high frequency noise stems from changing illumination conditions that violate the assumptions for
Firstly, broken cloud cover can result in different sky illumination conditions at the location of the
reference and the observation sensors. Here, this was counteracted by aggregating via averaging of the
transmission readings to a daily time series. Secondly, strong cloudiness reduces the radiation
reaching the forest ﬂoor, especially at the north-facing sensors, resulting in below canopy
which are interpreted as inﬁnite
) and do not match the assumptions behind the
clumping appraisal. Therefore,
readings of 0 for the below canopy sensors were removed from the
time series. Thirdly, large canopy gaps result in direct illumination on the below canopy sensor, which
violates the assumption of diffuse illumination and produces high
readings. These conditions
are only of short duration when the sun moves over the speciﬁc gap. Even the NW and NE sensors
experience these conditions when canopy elements are overly strong illuminated through canopy gaps
and result in recorded high transmission. To counteract this effect, all
exceeding the 95th daily
percentile were removed.
Remote Sens. 2018,10, 1032 6 of 19
2.2.3. Reference Datasets
records, a multi-temporal campaign with a RIEGL VZ-400 Terrestrial Laser
) (RIEGL LMS GmbH, Horn, Austria) was conducted at the Speulderbos site. This
scanner has shown good results for monitoring phenology [
]. The main advantage of
gap fraction sensor is its independence from illumination conditions [
]. This results in high
precision time series, i.e., with low noise in the temporal domain. On the other side, partial hits lead
to underestimation of gap fraction by these kind of sensors [
]. Partial hits result from objects
that only partially cover the laser instantaneous ﬁeld of view, but are registered as full interceptions
by the waveform analysis methods of commercial suppliers to maximise point cloud density. In this
way, gap fraction is underestimated and, consequently,
is overestimated. In addition, wet canopy
conditions have to be avoided for the sampling, because water droplets on canopy elements absorb
the laser beam, thereby apparently increasing gap fraction.
In total 45 sampling events were conducted. The sampling strategy was to focus efforts during
change periods, i.e., Start Of Season (
) and End Of Season (
), and to avoid rain conditions.
For each sampling event, the scanner was mounted on a surveying tripod in each centre of the ﬁve
plots, at a maximum distance of
from the respective
was derived from the
hemispherical scans by taking into account the multi-return capability of the scanner [
]. The hinge
angle was approximated with the 55◦to 60◦region, which is a typical strategy [39,42,43].
Apart from the ground-based
Collection 6 MCD15A3H
were retrieved [
]. MCD15A3H is a four-day composite product based on inversion of a vegetation
. Its eight-day companion product was validated to Stage 2 according to the Land Product
) subgroup (https://lpvs.gsfc.nasa.gov/). However, the four-day product was
preferred over the eight-day product as the goal here included estimating temporal metrics, thus
denser samples were important.
The samples were retrieved from the Application for Extracting and Exploring Analysis Ready
) service of the US Geological Survey (https://lpdaacsvc.cr.usgs.gov/appeears/)
for the period 1 January 2016 until 28 February 2018 and for the
pixel centred at the Speulderbos
site. This means that the MCD15A3H samples also included forest patches other than beech, e.g.,
some mixed species stand included in the Speulderbos site. After downloading, the time series was
ﬁltered with the accompanying quality ﬂags to allow only good quality
retrievals. After visual
inspection it was clear that some outliers in summer with
occurred, which have
been excluded as well.
2.2.4. Phenological Model Fitting
can be modelled with logistic functions [
]. These mathematically simple models are
ﬁtted piecewise to time series of vegetation indices or
to describe the spring growth and autumn
senescence periods [
]. From these models
can be derived. Here,
a logistic model was used [9,39]:
PAI(t) = U−L
is time (expressed as Day Of Year (
the upper asymptote (
the growth rate (
the inﬂection point, where
is maximal (
The model was ﬁtted with a non-linear least squares routine implemented in the
function of the R
]. Separate sigmoids were estimated for the spring 2016 and autumn 2017 periods.
In addition to the model parameters’ best estimates, non-linear least squares estimate also produces
prediction intervals. Finally,
were estimated as the time of the year when the ﬁtted
model reached the 95% upper and lower prediction interval for the
Remote Sens. 2018,10, 1032 7 of 19
This ﬁtting strategy was applied separately to the
time series, and results
2.3. Radiative Transfer Model Experiments
In-scene sensors in
can be implemented as frame cameras with arbitrary viewing direction
and properties (see Figure 3for an example), and record at-sensor spectral radiance for any number
of spectral bands with any bandwidths (
). For this study, the frame camera
characteristic was not exploited, but only the integrated radiance over the whole sensor FOV was
regarded similar to the
photo-diodes. As in the ﬁeld set up (Section 2.2), in this simulation
sensors were directed in the NE, E, SE, SW, W and NW directions with a viewing zenith
. In the
scene, one device consisting of six sensors was positioned below, another
above the simulated canopy, so that canopy transmittance could be calculated in the same way as in
the ﬁeld experiment (Equation
spectral response curve is not exactly known, so a
blue waveband centred at
bandwidth (FWHM) was chosen. Additionally, bands
centred in the green (
), red (
) were tested following the speciﬁcations
of the Sentinel-2 Multi Spectral Instrument (
]. These additional bands allowed judging the
instrument performance in the case photo-diodes would have been chosen that are sensitive in another
Figure 3. DART
sample scene: (
) True colour image sample for below-canopy sensor in
The viewing zenith angle is
, but the ﬁeld of view is extended compared to the sensors used in the
modelling to give an overview of the scene. (B) Top view of the created mock-up with 50 trees.
Two scenarios were set up to test different canopy parameters. In both, only the sensitivity of
to change in
was investigated, but not to the temporal evolution. This is justiﬁed with
the direct dependence of temporal sensitivity on the sensitivity to
. The ﬁrst scenario modelled
the canopy as a turbid medium, which is in accordance to gap fraction theory that is underlying the
retrieval (Section 2.2.2). This scenario was intended to test the retrieval robustness to different
illumination conditions as well as variation in biophysical and -chemical canopy composition. Table 1
summarises the parameters and their chosen values. For each case, one typical and two extreme cases
were chosen. All parameters were varied in a full grid approach, resulting in a total of 450 simulations.
For this experiment, clumping was not investigated (
1) because the canopy was homogeneous in
all directions. The solar azimuth angle was kept constant at
. The simulated sensors were analysed
with respect to their prediction performance of the true
. The results were compared with the
Global Climate Observing System (
) requirement for
retrieval accuracy, which is 20% as
Remote Sens. 2018,10, 1032 8 of 19
well as the accuracy goal for agricultural meteorology applications identiﬁed by the WMO , which is
5% . The Relative Error (RE) was chosen as accuracy metric and calculated as:
RE =PAIsi mul ated PAST IS −LAIDART
PAIsimulated PAST I S
derived with the simulated
PASTiS-57. Using this formulation, positive REs meant overestimation of the PASTiS-57-derived PAI.
During analysis of the results of these
simulations, a systematic bias in
been identiﬁed. This could be linearly modelled with the form
PAI =aLA I +b+e
0.01). For assessment of this error’s impact on
was replaced with the linear bias model and solved for
. The comparison of this with
the unbiased estimation of
gave the expected error in
metric. Since the analytic solution was
complex, the impact of the relative error was assessed numerically by testing a range of values for
. In the case of
extreme combinations were tested, i.e.,
1, 2, ...10
In the case of
, estimates from the ﬁeld derived models were used (Section 2.2.4), i.e.,
were not necessary, because it cancels out when only considering the difference between
Biophysical, biochemical and illumination parameters and values used for turbid DART
Parameter Values Unit
Leaf Area Index (LAI) 1, 2, ..., 10 m2m−2
Leaf Angle Distribution (LAD) spherical, erectrophile, planophile, extremophile, plagiophile -
Chlorophyll a and b (Cab) 20, 50, 80 µg cm−2
Solar Zenith Angle (SZA) 0, 57.5, 80 ◦
The second scenario was intended to test the retrieval performance with respect to canopy
non-randomness. For this, discrete trees were modelled with ellipsoid crowns with
height, and trunks with
diameter. The number of trees per scene was varied between
50 and 400 trees on a scene of
80 m×80 m
. Illumination and canopy biochemical parameters were held
constant with a spherical
50 µg cm−2Cab
to extract the effect of clumping alone.
3.1. Field Experiment
Figure 4shows the raw
recordings of two sampling days, one before
, one after leaf ﬂush
in summer. For both days, averages were clearly lower than reference readings above the canopy,
resulting in average canopy transmittance of 29.1% and 0.9% before and after the start of season,
respectively. Another feature was the high number of
readings at early and late hours of the
day after leaf ﬂush, which made up 25.7% of all observations on that day. At these times, the
typically large, so that the direct path through the canopy is long and not sufﬁcient radiation reaches
the below canopy sensors. In contrast to this, the SW sensor on the reference device experienced
saturation in the afternoon, probably due to direct illumination. Overall, the two days showed high
agreement in temporal evolution, indicating similar impact of changes in illumination. These stem
from the course of the sun, resulting in the rise and fall of readings over the course of the day, and
from clouds and stems, causing high frequency changes.
Remote Sens. 2018,10, 1032 9 of 19
Avg = 162.08 N = 2247
Avg = 556.62 N = 2247
Avg = 10.27 N = 1112
Avg = 1119.41 N = 1112
06:00 09:00 12:00 15:00 18:00 06:00 09:00 12:00 15:00 18:00
Recordings of two sampling days for one device in Plot A. Upper panels are the observations
below the canopy, lower panels are reference readings from above the canopy.
axis is on log-scale.
Dotted horizontal line is saturation point at
. Discontinued lines on lower panel reach
saturation. Only pairs for which the observation did not reach 0 DigitalNumber were considered.
time series for all three campaigns derived from a device on Plot C can be seen in
Figure 5. While the difference between the single sensors was only marginal, the impact of the ﬁltering
was clearly visible. The naive retrieval resulted in strong, high frequency noise with positive spikes
and a Lag 1 Auto-Correlation Coefﬁcient (
) of 0.93. The noise after ﬁltering was modest and
evolved equally around a mean course of
of 0.97. The former resulted from situations
under full canopy in summer, when the below-canopy sensors had
readings. This results in
according to Equation
. When comparing the two years, an earlier decrease
could be observed in 2017. This can be explained with the natural variability of
. This results
from different wind loads, which is the main force to defoliate the trees once the leaves have died.
Apr May Jun Sep Oct Nov Aug Sep Oct Nov Dec
All campaigns of one instrument in Plot C before (Naive) and after application of ﬁltering
Remote Sens. 2018,10, 1032 10 of 19
In Figure 6
are plotted together for the dynamic phases of
the yearly phenology, which are spring leaf ﬂush and autumn senescence. Overall,
showed high agreement in temporal development. Both sensors’ time series reﬂect the fast leaf
development during spring and the longer senescence period in autumn. The
were not sufﬁcient to record the fast changes in spring, especially between the sampling events of
4 May 2016
and 12 May 2016 when
on average within eight days.
The PASTiS-57 with their daily interval could closely follow the development.
Considering the magnitude, both sensors agreed strongly with a Pearson correlation coefﬁcient of
retrievals were lower in winter by
. This was
likely due to the different sensing techniques. In case of
, backward scattering from woody
elements increases recorded radiation at the below canopy sensors, thus decreases
In the case of
, the partial hits are mainly responsible for the sensitivity to the recording of canopy
elements. For both instruments,
1.5 in winter pointed to the large inﬂuence of woody material on
the retrievals. In contrast to this,
agreed in summer to within
Parameter estimates for the ﬁtted phenological models are summarised in Tables 2and 3. A total
of 21 and 40 samples for each plot were used for
, respectively. Concerning the upper
and lower asymptotes
showed signiﬁcantly lower estimates compared to
all plots. For both spring and autumn campaigns,
were on average
, reﬂecting the difference in acquisition mechanism. Compared to
higher, respectively. Again,
this reﬂects the different nature of the retrieval algorithms.
makes use of top of canopy
reﬂectance and is stronger utilising the
signal. This makes it less sensitive to woody material in
the canopy, thus MODIS LAI showed generally lower values than PASTiS-57.
Aug Sep Oct Nov Dec Jan Feb Aug Sep Oct Nov Dec Jan Feb Aug Sep Oct Nov Dec Jan Feb Aug Sep Oct Nov Dec Jan Feb Aug Sep Oct Nov Dec Jan Feb
Apr May Jun Apr May Jun Apr May Jun Apr May Jun Apr May Jun
PAI (LAI) [m2m2]
for single sensors (coloured) and Land and
Xiang clumping correction (LX), and
for ﬁve plots during the spring 2016 and summer
Remote Sens. 2018,10, 1032 11 of 19
Phenological model ﬁtting results for the spring 2016 campaign with parameter mean estimates
and their 95% standard error.
is the upper asymptote (
the lower asymptote (
the growth rate (
the inﬂection point (
the Start Of Season (
results refer to all plots and represent LAI in case of Uand L.
Parameter A B C D E
UPASTIS 5.31 (±0.04) 5.65 (±0.04) 5.46 (±0.05) 5.09 (±0.05) 5.67 (±0.05)
UTLS 5.82 (±0.03) 6.10 (±0.03) 5.72 (±0.04) 5.62 (±0.04) 5.99 (±0.03)
UMOD IS 5.63 (±0.15) – – – –
LPASTIS 1.63 (±0.03) 1.94 (±0.03) 1.85 (±0.04) 1.57 (±0.03) 1.75 (±0.04)
LTLS 3.10 (±0.02) 3.51 (±0.03) 3.02 (±0.03) 3.03 (±0.03) 3.32 (±0.02)
LMOD IS 0.80 (±0.09) – – – –
kPASTIS 0.43 (±0.03) 0.49 (±0.04) 0.54 (±0.06) 0.48 (±0.04) 0.44 (±0.03)
kTLS 0.41 (±0.03) 0.52 (±0.05) 0.47 (±0.04) 0.43 (±0.04) 0.31 (±0.02)
kMOD IS 0.77 (±0.26) – – – –
tm,PASTIS 129.5 (±0.2) 129.3 (±0.2) 129.3 (±0.2) 129.2 (±0.2) 129.1 (±0.2)
tm,TLS 130.0 (±0.3) 128.6 (±0.4) 129.3 (±0.4) 129.5 (±0.4) 130.9 (±0.3)
tm,MOD IS 126.0 (±0.5) – – – –
SOSPAST I S 117.9 119.5 121.0 119.6 118.4
SOST LS 118.2 119.9 119.7 119.2 115.8
SOSMO DIS 120.9 – – – –
Table 3. Same as Table 2, but for the autumn 2017 campaign referring to the EOS (DOY).
Parameter A B C D E
UPASTIS 5.69 (±0.04) 5.72 (±0.03) 5.64 (±0.05) 5.49 (±0.05) 5.79 (±0.04)
UTLS 6.06 (±0.08) 6.07 (±0.08) 6.00 (±0.13) 6.05 (±0.12) 6.15 (±0.10)
UMOD IS 5.06 (±0.11) – – – –
LPASTIS 1.59 (±0.04) 1.80 (±0.04) 1.69 (±0.05) 1.55 (±0.05) 1.64 (±0.04)
LTLS 3.05 (±0.10) 3.43 (±0.14) 2.74 (±0.17) 2.78 (±0.18) 3.34 (±0.16)
LMOD IS 0.65 (±0.09) – – – –
kPASTIS −0.08 (±0.00) −0.08 (±0.00) −0.07 (±0.00) −0.08 (±0.01) −0.09 (±0.01)
kTLS −0.08 (±0.01) −0.12 (±0.02) −0.07 (±0.01) −0.10 (±0.02) −0.09 (±0.02)
kMOD IS −0.16 (±0.02) – – – –
tm,PASTIS 307.6 (±0.7) 307.8 (±0.7) 307.5 (±1.0) 309.8 (±1.0) 311.7 (±0.7)
tm,TLS 307.2 (±1.9) 317.4 (±2.0) 307.4 (±3.0) 314.4 (±2.6) 316.7 (±2.6)
tm,MOD IS 292.5 (±1.7) – – – –
EOSPAST I S 250.0 250.4 243.2 256.2 262.8
EOST LS 258.8 288.4 263.7 280.8 281.4
EOSMO DIS 269.0 – – – –
agreed very well with
in terms of
with an average difference of
less than a day.
was estimated on average
. However, agreement among
plots was low with a range of
. More samples during winter would have been necessary
to decrease the estimation error. Moreover,
achieved the lowest estimation standard error on
the sigmoid inﬂection points
. This was made possible by the high temporal density of the
time series. Additionally,
agreed well with
. As in the case of
was impaired. Only for
, persistent cloud cover— which is
common in autumn in The Netherlands—prevented frequent observations.
Remote Sens. 2018,10, 1032 12 of 19
3.2. Radiative Transfer Model Experiments
experiments permitted to have control over all canopy and illumination
parameters, and to model abstract canopies. Figure 7summarises the results for the turbid medium
canopy case. Most inﬂuential was the choice of the spectral band. For instance,
generally more than 75% lower than true
. This strong misinterpretation stems from the retrieval
assumption of black leaves, which is not fulﬁlled in the
. In fact, leaves typically transmit around
45% of incoming radiation in this band. This leads to higher recorded radiation below canopy and
. Additionally, the
was larger at small
. This could be explained by a
smaller optical path through the canopy at small
, which leads to increased below-canopy recorded
radiation compared to what would be expected for black leaves. These effects could also be observed
to some degree in the green spectral band, where leaves typically transmit >10%.
0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0
DART LAI [m2m2]
Relative error [%]
Figure 7. DART
model results for turbid canopy representation for west facing sensors. Positive errors
mean over-estimation by the retrieval. Light grey and darker grey areas are the 20% and 5% accuracy
requirement of GCOS .
In contrast, the blue and red bands were less compromised. They both underestimated true LAI
by maximum 40.0% and on average by 14.2% and 14.5%, respectively. Thus, the average accuracy was
threshold accuracy of 20%. Leaf absorption is strongest at these wavelengths due to
absorption by chlorophyll, so that the canopy comes close to the approximation of black facets. Typical
transmittance for these spectral regions is
2% and reﬂectance
4%. This is why blue channels of
digital cameras are recommended for
]. Nonetheless, even these low values in
led to higher detected radiation at the sensor compared to what would be expected with black
leaves, so that canopy transmittance was overestimated, which leads to underestimation of PAI.
Within the blue and red spectral bands most variation was across the different
by an average of 2.8% and a maximum of 11.0% in the
blue, simulated canopies with planophile
s resulted in average and maximum underestimation of
38.2% and 40.0%, respectively. In the latter case, the deviation of the
-function value from 0.5 and
transmitting properties of the leaves probably interacted to increase the deviation from true LAI.
Apart from this, underestimation was generally increasing with true
. This means that
more radiation was reaching the sensor than expected by the model, i.e., canopy
transmittance is larger than expected. This effect could be created by multiple scattering in the canopy.
According to the model assumption there are no scattering processes within the canopy. Radiation is
only absorbed or transmitted without interaction. However, the
simulated leaves had
which allows radiation to go through leaves. The more leaves there are, the stronger the mismatch
between gap fraction and DART model.
Remote Sens. 2018,10, 1032 13 of 19
, pairwise Student’s
-tests between any of the
levels showed no signiﬁcant
0.95) and differences were below 0.1%. This showed that the direct
inﬂuence of Cab on LAI estimation was very low.
had minor overall impact on the retrievals. This was on average 0.9% in the blue
. However, the difference was larger for spherical and erectophile
s. The extreme case was at
10, where the relative error for
9.7◦for spherical and erectophile LADs, respectively.
When translating the impact of the bias in
delivered the largest error with
later estimation of
. Spherical, planophile, extremophile
s resulted in
estimation showed lower errors with on average
. This means
as the generally slower
process experiences larger errors in
metric retrieval based on the
bias error. It should be noted
that this difference was based on the particular phenological model used here (Section 2.2.4), but models
based on sigmoid functions in general should experience errors on the same order of magnitude.
results for the heterogeneous scenario are presented in Figure 8. Tree density was
signiﬁcantly altering retrieval performance in scenarios with
200 trees and
. This led
to underestimation of up to 69.4% at true
LAI 10 m2m−2
and a scene with 50 trees. For these scenarios
the present leaf mass was concentrated in few crowns, so that the assumption of a homogeneous
canopy did not hold and
was underestimated. The clumping correction after Lang and Xiang [
could account for some of these effects, but could only reduce the underestimation to 55.1% in the case
of 50 trees. In those cases, the assumption of a random foliage distribution within the sensor FOV
was violated. Actually, the horizontal FOV of the
is large compared to the solid angles that
camera pixels represent. The clumping correction after Lang and Xiang [
] corresponds rather to the
small FOVs represented by camera pixels.
Trees = 50
Trees = 100
Trees = 200
Trees = 300
Trees = 400
0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0
DART LAI [m2m2]
Retrieved PAI [m2m2]
Figure 8. DART
model results of discrete canopy representation for ﬁve different tree densities
(horizontal panels in number of trees). Retrieval without (None) and with clumping correction after
Lang and Xiang (LX) .
require high temporal density canopy observations. This
study explored the
instrument for autonomous monitoring of phenology and
Dutch beech forest.
experiments helped to evaluate sensing mechanism of the
in relation to changes in canopy biochemical and structural properties other than LAI.
The ﬁeld experiments showed very good temporal agreement with illumination independent
products when temporal density of these reference products was high. Biases in
PAI magnitude were attributed to differences in sensing mechanism. The ﬁeld observations required
ﬁltering and aggregating the readings to daily time series to reduce high frequency noise, especially
during full canopy coverage in summer. This noise can be partially tracked back to the sensor’s
radiometric resolution of
. Considering Equation
, the change in
, which is
the sensitivity to signal digitisation, is inversely proportional to the
. This is because the ﬁrst
derivative of Equation
with respect to
is proportional to the inverse of
. This can result
Remote Sens. 2018,10, 1032 14 of 19
in differences as large as
observation readings of 1 and 2 when the reference
sensor is close to saturation (Figure 9). Radiometric sensitivity also impacts the maximum
can be recorded. In the case of
, it lies at
with a single measurement. Modern
digital cameras typically offer digitisation up to
for raw images, resulting in
so that theoretically
can be retrieved. Therefore, a higher signal bit depth improves
the sensitivity to high
as well as maximum retrievable
. In the case of the ﬁeld experiment,
the maximum summer PAI was 6 m2m−2(Figure 5), which was within the theoretical range.
DN Reference = 2000
DN Reference = 4000
0 10 20 30 40 50 0 10 20 30 40 50
Retrieved PAI [m2m2]
due to digitisation at low observation readings (
) for two
levels of reference readings.
experiments conﬁrmed the principles underlying the retrievals. In particular, below
canopy transmittance measurements in blue and red spectral bands have a high sensitivity to canopy
structure and are robust against variation in biochemical composition and illumination conditions. This
is not the case for top of canopy reﬂectance measuring sensors, e.g., tower based or satellite sensors,
which often exploit the NIR. These are much more dependent on Cab and illumination angle .
However, heterogeneous scenarios conﬁrmed the strong effect of canopy non-randomness on
estimation. In particular in case of low tree density scenarios, which violate the homogeneous
canopy assumption more than dense canopies with closed cover,
was strongly underestimated.
Clumping correction after Lang and Xiang [
] counteracted this effect somewhat. Other clumping
correction strategies exist, but these usually require estimation of gap size distribution [
]. This is
, but not with pointing devices, such as
. Therefore, a strategy for ﬁeld
measurements would be to employ multiple
instruments per plot. Alternatively, a new
sensor design based on low-cost micro-computers equipped with ﬁsh-eye cameras could be tested.
Such an imaging sensor could also retrieve LAD concurrently with LAI .
Another disadvantage of the single-band, pointing device design of the
is the lack of
options to distinguish woody and foliage canopy elements. Gower et al. [
] list ranges of 7% to 34% of
wood area index contribution to
based on a literature review. Previous studies proposed solutions
to this problem with multi-band imaging sensors, including NIR [
] or imaging sensors combined
with radiative transfer modelling [
]. Another way is multi-temporal estimation by using the winter
measured PAI as branch area index and subtract it from the summer measured PAI. However, this
neglects radiative interaction processes when both elements are present in the canopy (e.g., occlusion
of leaves by branches) and is not agreed on [
]. The lack of consolidated correction methods has
also led to a prevalent neglect of correction [
]. This topic needs to be addressed with dedicated
devices, e.g., dual-wavelength lidar .
In the context of sensor simulation,
proved to be a versatile tool. Especially the option to
simulate arbitrary sensors allowed the implementation of the
sensor in this study. Although
Remote Sens. 2018,10, 1032 15 of 19
sensor simulation with
s is not new, below canopy sensor simulations have been restricted to
]. Another advantage of
was the option to simulate heterogeneous
canopies, which is crucial for forest radiative transfer modelling.
Next to considerations concerning the retrieval principle, thoughts should be given to practical
instrument design choices. For instance, the power supply with batteries is a good choice for remote
sites and proved to provide electricity for ~1 year. However, close to ﬁeld stations constant power
could be supplied via the electricity grid or from centrally organised solar cells to prevent power loss
and missing observations. A permanent data-link to the logger and upload to cloud servers could help
to identify sensor problems and monitor results in real-time. Furthermore, the contamination of the
sensors with water, falling leaves or needles, or with insects should be taken care of in a long-term
deployment. In sites with substantial understorey, sensors could be deployed at different heights and
below understorey plants to sample the vertical proﬁle. In addition, sensors at larger heights might be
able to focus on the foliage and prevent large stems to be in the FOV.
In the context of a set-up in larger, permanent sample sites, the representative area of
should be considered to determine the number of required devices. In this respect,
comparable to other below-canopy sensors such as
and Licor LAI-2000 that measure
hinge angle. Therefore, the diameter of the measurement area is 2
results in a diameter of
high canopy, as is the case for Speulderbos. Considering
geo-location error of 1 pixel [
] this would be representative for Sentinel-2
However, replicates need to be installed per plot to improve precision in the case of
In the case of Sentinel-3—when considering geo-location error—a footprint of
would need to
be covered. Locations should be sampled to account for the site heterogeneity, i.e., number of species,
differences in canopy structure and presence/variability of understorey.
Furthermore, low-cost, passive sensors such as
can be combined with
light-independent monitoring. For instance, Culvenor et al. [
] presented a monitoring lidar system
that samples the hinge angle, similar to the
used in this study. These systems are more cost
and maintenance intense, but offer opportunities for inter-comparison and benchmarking, also with
traditional manual sampling methods. Such a combination of sensors would offer the option of high
precision light-independent sensors for site central areas and low-cost sensors for covering larger areas.
This would provide the instrument infrastructure necessary for continuous validation of
products, as required by validation Stage 4 of the GCOS LPV group .
Robust tracking of the phenological cycle and thereby connected canopy biophysical conditions
requires sampling techniques with high temporal resolution. This study assessed the ground-based,
instrument in both ﬁeld and
experiments for its
performance in forest
estimation. The instrument design supported acquisition
of yearly time series at up to
raw data resolution with low maintenance effort. The choice of
the blue spectral region and a viewing angle of
was found to be robust for a range of canopy
biochemical and illumination conditions, thereby focussing on changes in canopy structure, mainly
. However, clumping assessment in irregular canopies was limited by the low number of sensors
per instrument and the sensors’ pointing measurements. In addition, the restriction to a viewing
alone does not allow retrieval of
, as is possible with
Future studies should compare
with other phenology monitoring devices and develop
combinations of instruments as site concepts. Other sensor designs could be tested, e.g., based on
B.B. designed and conducted the experiments, and wrote the paper. J.-P.G.-E. and N.L.
experiments. N.L. implemented the sensor option in
. F.B. designed and built the
PASTiS-57 instrument. J.G.P.W.C., J.V. and M.H. scientiﬁcally supported and reviewed the paper.
Funding: This work was carried out as part of the IDEAS+ contract funded by ESA-ESRIN.
Remote Sens. 2018,10, 1032 16 of 19
Data analysis was supported by the Research and User Support (RUS) Service. The RUS
Service is funded by the European Commission, managed by the European Space Agency, and operated by CSSI
and its partners. The authors thank the Dutch Forestry Service (Staatsbosbeheer) for granting access to the site,
and Murat Üçer and Christiaan van der Tol for access to the scaffold tower. The ﬁeldwork design of this study
was inspired by the M.Sc. thesis of Tom Schenkels (http://edepot.wur.nl/333531). Further thanks go to Marcello
Novani, Alvaro Lau Sarmiento and Mathieu Decuyper for help during the ﬁeldwork.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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