Introduction
Agricultural landscape in most Asia monsoon regions is featured by
multicultural cropping systems comprising relatively small land holdings
under 2 ha (Devendra, 2007). Changes in phenology of those crop ecosystems,
where rice makes up a larger portion and exerts a rapid completion of the life
cycle in a short period of time with marked changes in canopy dynamics,
are of significant importance in regional controls of carbon balance and
biogeochemical processes (Kwon et al., 2010; Lindner et al., 2015; Xue et
al., 2017) and tend to be one of the drivers causing seasonal fluctuations
of atmospheric CO2 concentration in the Northern Hemisphere (Forkel et al.,
2016). To better understand their ecological implications under current
climate and environmental changes, one of the main concerns lies in the
spatiotemporal aspects of ecosystem photosynthetic productivity in the
staple crop that is subject to different methods of field management and
anthropogenic interventions, and underlying physiological mechanisms that
are responsible for such spatiotemporal dimensions.
The stability, repeat measurement capability, and landscape to global
coverage of remote sensing from satellites have triggered widespread use of
such measurements to obtain spatial patterns of biophysical and biochemical
variables in studies of land surface and atmospheric processes (Richardson et
al., 2013). A recent study on flux modeling of agroecosystems introduced
satellite products as input parameters (Adiku et al., 2006) and reported
pixel-size dependency of prediction accuracy. A better prediction could be
obtained if satellite products were applied at finer resolution. Accordingly,
attempts made to assimilate the products into process-based crop growth
models have been increasingly concerned (Tenhunen et al., 2009; Lee, 2014; Alton,
2017), due to resulting overestimations and/or under estimations in plant
functional traits. Satellite images collected during plant growing seasons have been used to monitor crop growth
and to predict yield production. However, their use has been limited by poor
revisit times, coarse spatial resolution, and/or cloudy weather. They
technically conceal delicate fluctuations of ecosystem productivity tightly
associated with per-field ecological conditions on which plants survival and
dispersal depend (Seo et al., 2014). Applications of spatially coarse
satellite products generate considerable spatiotemporal uncertainties in
evaluating strength of daily carbon fluxes among microsites of the same
plant function type at principle growth stages. Multi-pragmatic solutions are
suggested to develop spatial/temporal data fusions that integrate spatially
hierarchical remote-sensing networks and in situ ground surface observations
(Lausch et al., 2016; Pause et al., 2016), aiming to better monitor canopy
dynamics and environmental impacts on them.
Of the various means that can aid the understanding of per-field ecological
processes, the close-range remote-sensing technique is a realistically
convenient measure that can provide timely temporal information of ecosystem
dynamics at high spatial resolution. Recent applications in agronomy studies
(Zhang and Kovacs, 2012; Ko et al., 2015; Jeong et al., 2016) have
reinforced the feasibility of resolving the research gaps in terms of
capturing spatiotemporal aspects of intra- and inter-field ecosystem photosynthetic productivity.
To best interpret spatiotemporal variations of ecosystem photosynthetic
productivity captured by the close-range remote sensing, conventional
physiological studies on canopy leaves remain essential (Sinclair and Horie,
1989; Niinemets and Tenhunen, 1997). As leaves are the small and basic units
that constitute rice canopy volume, their functioning could change with
canopy development and changing habitat conditions (Xue et al., 2016a, b),
contributing to fluctuations in strength of seasonal canopy photosynthesis.
Traditional ecophysiology approaches are very limited in comparing
neighboring plants and tend to neglect spatial dimensions. Landscape ecology
can resolve ecosystem functioning at a broad scale but tends to be
restricted to regional analysis at a higher hierarchical level beyond
individual organisms. The central aims of this research are to construct a
spatially integrative concept that assimilates quantitatively abundant data
sets collected from a close-range remote-sensing system applied at field
level and from traditional ecophysiology approaches at plot level, and to
capture and interpret effects of different field management practices
including nutrient application and water treatments on temporal and spatial
aspects of ecosystem photosynthetic productivity according to their
influences on canopy leaf physiology and structure.
The study evaluates two hypotheses. The first posits that the temporal course
of canopy carbon gain capacity is driven primarily by leaf area index (LAI)
development and solar radiation intensity at the reproductive stage (Xue et
al., 2016a, 2017). Canopy leaf physiology is a primary factor that determines
efficient use of canopy light use and therefore carbon gain capacity
(Sinclair and Horie, 1989). Hence, spatial variability of ecosystem gross
primary productivity (GPP) could be concurrently driven by canopy structure
(i.e., LAI) and canopy leaf physiology (i.e., light use efficiency,
LUEcabs). The second hypothesis posits that shifts of planting
culture from flooded to rainfed (RF) conditions mean that water availability tends
to be a primary factor determining ecosystem photosynthetic productivity.
Growth of rainfed rice suffers from multiple uncertainties regarding
timing/strength of precipitation and uptake of nutrient availability in soil
(Kato et al., 2016). Significant changes in leaf and root anatomies, and
canopy structure and function in rainfed rice could occur (Yoshida, 1981;
Steudle, 2000). Greater variations in spatial aspects of ecosystem GPP, LAI,
and LUEcabs in rainfed lowland rice than flooded rice are therefore
anticipated.
Materials and methods
Study site
The field campaign was carried out at the agricultural field station of
Chonnam National University, Gwangju, South Korea, which is located at
35∘10′ N, 126∘53′ E, at an altitude of 33 m (Fig. 1).
The mean annual air temperature and precipitation over past 2 decades
averaged 13.8 ∘C and 1400 mm yr-1, respectively. The
East Asian monsoon climate prevails from June to October in this region,
during which time more than half of the annual precipitation occurs. The top
layer of soil is categorized as loam with sand of 388 g kg-1, silt of
378 g kg-1, clay of 234 g kg-1, pH 5.5, organic carbon (C)
content of 12.3 g kg-1, available phosphorus (P) of
13.1 g kg-1, and total nitrogen (N) before fertilization of
1.0 g kg-1. Thirty-day-old seedlings of a new breeding line,
Oryza sativa cv. Unkwang (Kim et al., 2006), were transplanted in
flooded paddy rice (PD) fields on 20 May 2013 (day of year, DOY, 140). N, P,
and potassium (K) were mixed at a mass ratio of 11:5:6 to generate
fertilizer application rates of 0 kg N ha-1 (no supplementary
fertilizer, plot size ∼ 511 m2; low-nutrient group), 115 kg
N ha-1 (plot size ∼ 1387 m2; normal-nutrient group), and
180 kg N ha-1 (plot size ∼ 511 m2; high-nutrient group)
(Fig. 1). The nutrient treatment groups were isolated by 35 cm wide cement
walls and inserted 1 m into the soil. N-based fertilizer was added to
80 % of total N by hand spreading 2 days before transplanting. The
remaining 20 % was added at the active tillering phase of the vegetative
stage. P-based fertilizer was applied as 100 % of the basal dosage.
K-based fertilizer was applied as 65 % of the basal dosage, with the
remaining 35 % applied during the tillering phase. Seeds of the same rice
cultivar were directly sown in an adjacent upland field that was being
treated as RF rice (∼ 64 m2) on 22 April (DOY 112). The
same fertilizer compound containing 115 kg N ha-1 (PD normal-nutrient
group) was applied to the RF Unkwang rice field twice, with
80 % applied before seeding and the rest applied at the tillering phase.
The RF field was not irrigated during the whole growing season. All field
management practices conformed to local planting cultures. The life history
of the Unkwang rice is generally aligned to a proposed classification of
phenology in temperate rice (Yoshida, 1981), in which the rice spends about
30 days in the vegetative stage after transplanting, 30 days in the
reproductive stage, and 30 days in the ripening period.
Illustration of the Gwangju study site where field data collection was
carried out. Yellow square and white circles represent sites of paddy fields
and those marked for measurements of ground reflectance by one handheld miltispectral radiometer
(MSR) to validate UAV imagery. T1: PD rice under low-nutrient conditions (no
supplementary nitrogen applied); T2: PD rice under high-nutrient conditions
(180 kg N ha-1); T3: PD rice under normal-nutrient conditions (115 kg
N ha-1); and T4: RF rice (115 kg N ha-1). PD: paddy; RF:
rainfed.
To better understand the physiological mechanisms that may contribute to the
spatial patters of per-field photosynthetic productivity, a pair of
experiments involving the PD and RF Unkwang rice in a controlled growth
chamber at the University of Bayreuth (11∘34′ N,
49∘56′ E) were conducted in September 2014. Thirty-day-old
seedlings were transplanted into plastic containers with a top diameter of
25.4 cm and a height of 25 cm with similar plant spacing to the planting
practice in the 2013 field experiment. The equivalent fertilizer containing
115 kg N ha-1 was applied two times for both the PD and RF rice,
before transplanting/sowing and at the tillering phase. All plants were then
acclimated in the growth chamber to daytime air temperature of 30 ∘C,
relative humidity of 60 %, night temperature of 25 ∘C, and light
intensity of 900 µmol m-2 s-1
(35.64 MJ m-2 d-1). Soil water content (SWC) in the RF rice
containers was maintained between 0.2 and 0.4 m3 m-3 using soil
moisture sensors (EC-5, Decagon, WA, USA).
Field measurements of meteorological factors and SWC
Meteorological factors including air temperature, relative humidity, wind
speed, precipitation, and global radiation were continuously measured with a
2 m high WS-GP1 automatic weather station (AWS) installed at a margin of the
RF field (Delta-T Devices Ltd., Cambridge, UK). Weather data were recorded
every 5 min, and were averaged and logged every 30 min. Additionally,
values of SWC at depths of 10, 30, and 60 cm at three sites in the RF field
were continuously measured every 15 min using the soil moisture sensors. SWC
data recorded by the sensors were calibrated by actual SWC measurements
conducted in the laboratory with the same soil. SWC was then converted to
soil water potential (ψs) with standard soil water retention curves
of Van Genuchten (1980) as modified by Xue et al. (2016b).
Field measurements of diurnal courses of leaf and canopy carbon
dioxide (CO2) exchange
Diurnal gas exchange and chlorophyll fluorescence measurements in fully
expanded uppermost, second, third, and fourth leaves of canopy profiles for
the PD high-nutrient group were conducted on day after transplanting (DAT) 57
and 73 (DOY 197 and 213, respectively) using a GFS-3000 portable gas exchange
and PAM-Fluorometer 3050-F chlorophyll fluorescence system (Heinz Walz GmbH,
Effeltrich, Germany) to track ambient environmental conditions external to
the leaf cuvette. Repeated measurements of diurnal courses of leaf gas
exchange were carried out in the uppermost leaves in the PD low-nutrient
group on DOY 171, 172, 179, 180, and 199 (DAT 31, 32, 39, 40, and 59,
respectively); in the PD normal-nutrient group on DOY 175, 177, 195, and 211
(DAT 35, 37, 55, and 71, respectively); in the PD high-nutrient group on DOY 170
and 178 (DAT 30 and 38, respectively); and in the RF rice on DOY 157, 181,
201, 205, 222, 223, 227, 231, 235, and 238. The midportions of two or
three leaves were enclosed in the leaf chamber from sunrise to sunset. The
photosynthetic rate and momentary micrometeorological factors just above the
plant canopies were recorded every 5 min, and automatic calibration was done
by a user-defined program every 15 min. Leaf light use efficiency based on
incident photosynthetically active radiation (PAR; LUEleaf) was
estimated using photosynthesis data recorded at incident
PAR < 200 µmol m-2 s-1.
The diurnal course of canopy gas exchange was conducted in a custom-built
transparent chamber (L 39.5 × W 39.5 × H 50.5 cm) used
for net ecosystem gas exchange (NEE) measurement and in an opaque chamber
(L 39.5 × W 39.5 × H 50.5 cm) designed for ecosystem
respiration (Reco) measurement (Lindner et al., 2016; Xue et al.,
2016a) on ∼ DOY 159, 167, 175, 200, 220, and 240. Measurements on
DOY 240 were only available at the PD normal-nutrient group and RF rice. Four white
frames, with three filled with healthy plants and one set on bare soil
without any plants, were randomly deployed in each PD nutrient group and in
the RF field (Lindner et al., 2016). They were inserted into the soil at a
depth of 10 cm before transplanting/sowing to block air leak at the
interface between the frame and soil surface, and kept in the fields until
plants were harvested. Diurnal courses of NEE and Reco per square
meter were monitored each hour from sunrise to sunset. Differences of air
temperature between the inside and outside of the chamber were controlled to
< 1 ∘C using ice packs positioned at the back side of the
chamber to avoid shadow effects of the ice packs. Incident PAR inside the
transparent chamber was measured with a LI-190 quantum sensor (LI-COR,
Lincoln, NB, USA). GPP estimation was derived using the equation
GPP=-NEE+Reco,
where Reco rates at times when NEE rates were measured were
determined from an exponential regression with respect to chamber air
temperature (Tair) (Xue et al., 2016a). A classical hyperbolic
light response function (Eq. 5; see below) was fit to estimate GPP (sum of NEE and Reco), yielding canopy light use
efficiency (LUEcint), defined as the initial slope of the response,
and an estimate of maximum GPP rate (GPPmax) at a relatively
high PAR level (Lindner et al., 2016).
Seasonal courses of (a) normalized difference vegetation
index (NDVI), (b) leaf area index (LAI), (c) canopy light
use efficiency based on incident PAR (LUEcint), and (d)
maximum gross primary production (GPPmax) measured at plot level in
the PD low-, normal-, and high- nutrient groups, and in the RF rice. Statistic
analysis showed significance at the 0.05 level (small letters) and at the 0.01 level
(capital letters). Mean ± SD, n=3 to 6. DOY: day of year. PD:
paddy; RF: rainfed.
Field measurements of canopy reflectance
In situ reflectance measurements were carried out with a model MSR4
handheld multispectral radiometer with four spectral bands (CROPSCAN Inc.,
Rochester, MN, USA). Incident radiation was measured with a view angle of
180∘, and that reflected by rice canopies was measured with a view
angle of 28∘. Weekly reflectance measurements conducted around plants
sampled for canopy gas exchange were repeated six times in each PD nutrient
treatment and three times in the RF field at solar noon midday, when the sky was
clear without clouds. The normalized difference vegetation index (NDVI) was a
product of differences of reflectance in the field of red (the central
bandwidth of 660.9 nm) and near infrared (the central bandwidth of
813.2 nm). Estimations of ground-based NDVI were made on the days of
canopy gas exchange measurements (Xue et al., 2016a).
Spectral reflectance at fine spatial resolution ≈ 10 cm for the
whole PD field and RF field was measured on 21 June (DOY 172, vegetative
stage), 11 July (DOY 192, early reproductive stage), 25 July (DOY 206, middle
reproductive stage), 8 August (DOY 220, early ripening stage), and 21 August
(DOY 233, middle ripening stage) using an unmanned aerial vehicle (UAV)
system (details of the construction of the UAV system are given in Jeong et
al., 2016). The UAV images were acquired at approximately local noon
±30 min (i.e., KST 12:10 to 13:10), when there were clear skies or
homogenous cloudy skies. The camera exposure was set at its minimum value
(0.5 µm s-1) under clear-sky conditions and ranged between
1.0 to 2.0 µm s-1 under homogenous cloudy skies to
obtain the best images. When recording UAV images, the mini-MCA6
multispectral camera (Tetracam Inc., Chatsworth, CA, USA) loaded on board the
UAV – which detected ground reflectance with the wavelength bands of 450, 550,
650, 800, 830, and 880 nm – was always positioned vertically to the ground.
Pseudo-invariant targets (PITs) at three different colors (white, black, and
gray) were placed adjacent to the PD field prior to each UAV flight.
At-surface reflectance values of two selected wavebands at 800 and 650 nm
from those PITs were obtained using the other handheld spectrometer (MSR16
with 16 wave bands; CROPSCAN). Linear regression correlations were made
between mini-MCA6 digital values and the reflectance from the MSR16 at each
corresponding waveband, with a correlation coefficient ranging from 0.98 to
0.99 (detailed descriptions are provided in Ko et al., 2015; Jeong et al.,
2016). Camera measurements were then calibrated based on at-surface
measurements by applying each linear regression to the field imagery.
Evaluation of the radiometrically corrected UAV images was carried out by
comparisons with measurements of 16 ground point reflectance values, which
comprised 12 points in paddy fields and 4 points in bright cement, dark
asphalt, bare soil, and tilled soil. There were close correspondences between
reflectance derived from the radiometrically corrected UAV images and those
measured at the ground over all UAV flight dates, with correction efficiency
(E) up to 0.99 and root mean square error (RMSE) ranging between 0.01 and
0.05 (Appendix Fig. A1). Radiometrically calibrated reflectance at red, green,
and blue bands (450, 550, and 650 nm, respectively) on 21 June/DOY 172
(clear sky), when low-density vegetation canopies with large
exposure of water surface were consistently lower than at-surface
measurements (Fig. A1a), resulting in risks of overestimating the field NDVI (a
product of differences in reflectance of the red (650 nm) and near infrared
(800 nm)) and therefore biased estimation of GPPday and LUEcabs.
For the sake of brevity the radiometrically calibrated camera reflectance of red
waveband on 21 June/DOY 172 were recalibrated by a linear regression line
against at-surface measurements (Fig. A1a; ρred ground
meas= 1.761 × ρred_UAV, R2=0.76,
p<0.01).
Measurements of leaf area, N content, and leaf water potential
After conducting leaf and canopy gas exchange measurements, leaf samples were
collected to estimate leaf area and N content. Three bundles consisting of 15
plants from each treatment were harvested on DAT 26, 33, 54, 72, and 86, and
total plant area (leaf and stem) was determined with a LI-3100 leaf area
meter (LI-COR, Lincoln, Nebraska, USA). Leaves of the PD and RF rice grown in
the growth chamber were harvested on DAT 33 and 55. All plant materials were
dried at ∼ 60 ∘C for at least 2 days before measurements of
leaf nitrogen content. Leaf nitrogen content was quantified using a C : N
analyzer (Model 1500, Carlo Erba Instruments, Milan, Italy). Weekly
measurements of LAI were conducted before DOY 220 using a LI-2000 portable
plant canopy analyzer (LI-COR) at the same locations where at-surface canopy
reflectance values were sampled using the CROPSCAN. These were calibrated
using those obtained by the harvest method. LAI measurements on DOY 240 were
supplemented referring to Lindner et al. (2016). At the same measuring times
as leaf gas exchange was conducted in August, daily courses of leaf water
potential in the RF rice were collected with a pressure chamber (PMS
Instruments, Corvallis, OR, USA). Healthy and well-expanded leaves in plant
canopies were enclosed in a plastic bag before cutting and rapidly
transferred into a pressure chamber.
Data assimilation
Assessment of influences of field management practices (i.e., nutrient and
water availability) in crop photosynthetic traits and interpretation of the
presence of such spatiotemporal fluctuations require development of a data
assimilation process capable of linking in situ observations of leaf and
canopy photosynthetic traits and vegetation information at field level. Here,
a simple concept model aiming to resolve the objective stated above was
developed, up-scaling application of the classical light response model of
leaf photosynthesis to canopy and field dimensions using hyperspectral
reflectance of ground surface collected at corresponding scales in
Eqs. (2–8):
LUEcint=a1×LAI+b1,GPPmax=a2×LAI+b2,LAI=a3×NDVI2+b3×NDVI+c3,GPPday=∑j=1NLUEcint×GPPmax×PARjLUEcint×PARj+GPPmax,fAPAR=fAPARmax1-NDVImax-NDVINDVImax-NDVIminε,fAPAR=a4×NDVI+b4,LUEcabs=GPPdayfAPAR×PARday,
where, in Eq. (2), a1 and b1 are regression coefficients for the
LUEcint–LAI correlation based on plot measurements (Table 1). In
Eq. (3), a2 and b2 are regression coefficients for the
GPPmax–LAI correlation based on plot measurements (Table 1),
consistent with previous reports (Lindner et al., 2015, 2016). In Eq. (4),
a3, b3, and c3 are regression coefficients for the LAI–NDVI
mathematic correlation across all data sets based on plot measurements
(Table 1), which is consistent with a 3-year report in rice in terms of
LAI–NDVI trajectory by Jo et al. (2015). In Eq. (5) GPPday is daily
integrated GPP per pixel, a product of light use efficiency based on incident
PAR (LUEcint), GPPmax, and half-hourly
averaged PARj obtained from the AWS. N is the number of observations of
incident PAR during daytime. In Eq. (6), fAPARmax is the maximum fraction of absorbed
photosynthetically active radiation, NDVImax is the maximum
NDVI of the fAPAR–NDVI relationship, NDVImin is the minimum
NDVI, and ε is a coefficient for green crop canopies,
referring to Table 1 and Xue et al. (2016a). a4 and b4 in Eq. (7)
are regression coefficients for the fAPAR–NDVI correlation in senescing canopies
(Table 1, referring to the stage after the middle ripening stage in rice),
derived from Inoue et al. (2008). Light use efficiency based on daily canopy
light interception per pixel (LUEcabs) in Eq. (8) is a product of
GPPday, fAPAR, and PARday (daily integrated incident PAR).
Values of coefficients for Eqs. (2–7).
Eqs.
Coef.
Values
Coef.
Values
Coef.
Values
Coef.
Values
Eq. (2)
a1_PD
0.0074
b1_PD
0.0107
a1_RF
0.0211
b1_RF
0.0070
Eq. (3)
a2
8.571
b2
4.081
Eq. (4)
a3
7.398
b3
-1.752
c3
0.452
Eq. (6)
fAPARmax
0.95
NDVImax
0.94
NDVImin
0.11
ε
0.6
Eq. (7)
a4
0.169
b4
0.765
* Values of coefficients for Eq. (7) were derived from Inoue et al. (2008).
PD: paddy rice; RF: rainfed rice.
Geospatial statistic
Regionalized variable theory takes the differences between pairs of values
separated by a certain quantity, usually distance, commonly expressed as
variance (Vieira et al., 1983). A widely used geostatistical analysis to
depict the spatial correlation structure of observations in space such as
field soil fertility and temperature as well as other ecological processes is
semi-variogram (Pierson and Wight, 1991; Loescher et al., 2014), given by
γh=12Nh∑j=1Nhzxj-zxj+h2,CVsill=2×γsillMean,
where z(xj), j=1, 2, …, n denotes the set of
GPPday/LUEcabs data; xj is the vector of spatial
coordinates of the jth observation; h is the pixel distance of sample values
(lag); N(h) is the number of pairs of values separated by lag; and γ(h)
is semi-variance for the lag. CVsill is a coefficient of variance using
the sill and value of the mean for estimation. The semi-variogram simply
describes how the variance of observations changes with the distance in a
given direction, or it is averaged over all directions. The averaged
semi-variance over all directions used in this research looked for an overall
pattern between proximity and the similarity of pixel values, providing a
single value that describes the spatial autocorrelation of the data set as a
whole. Most often, semi-variance values increase until they reach a maximum
approximately equal to the sample variance of the measured variable known as
the “sill”. The lag at which the sill is reached is known as the “range”.
Beyond the range, values of observations are no longer spatially correlated.
Sill values refract magnitude of spatial variability of variables in the
field. Several simple functions are commonly used to model semi-variogram,
which must be proven to be definitely positive. An exponential rise to the maximum
function for approximating a spherical model was used to extrapolate the value
of the sill, listed below:
γh=a×1-exp-b×h,
where b is the sill and a is the nugget value.
Statistical analysis
Descriptive statistics of the data-included computation of the sample mean,
maximum (max), and coefficient of variation (CVtraditional). A nonlinear
least-squares method for GPP/PAR curves was executed using R software (R 3.2.3, R Development Core Team, Austria).
The data fitting that links remote-sensing data and ecophysiological measurements and geostatistical analyses
were processed using IDL 8.0/ENVI 4.8 software (EXELIS Inc., Rochester, NY,
USA).
Results
Seasonal courses of at-surface NDVI, LAI,
LUEcint, and GPPmax
Analysis of variance (ANOVA) for NDVI indicated that NDVI values measured around DOY 170
between the PD normal- and high-nutrient groups were analogous but
significantly higher than the low-nutrient group at the 0.05 level (Fig. 2a; p=0.026).
There was not a statistical difference at the 0.05 significance level between
the RF and PD low-nutrient group. No significant discrepancy existed between the PD
normal- and high-nutrient groups over the growing season (p>0.1). Higher
NDVI at the PD fertilizer addition groups was evident during the vegetative
stage and early in the reproductive stage before DOY 200 (p=0.06). Such a
clear discrepancy in NDVI between the PD low-nutrient and fertilization groups and the
RF rice was not evident after DOY 210 (p=0.10). NDVI values advanced to
decline after plants in the PD field arrived at maximum levels around
DOY 210. However, the RF rice remained green around DOY 240 with
approximately 23 % higher LAI when plants in the PD field started senescence (Fig. 2b),
which resulted in a relatively higher at-surface NDVI that was captured also
by field images of NDVI derived from the UAV system. LAI in the PD
normal-nutrient group was similar to those of the high-nutrient group at the corresponding
growth stages (Fig. 2b), consistent with a seasonal course of NDVI for the
normal/high-nutrient groups. Enhanced LAI development with addition of fertilizer was
evident after DOY 180 (Fig. 2b; p<0.05), and N-related effects
persisted until around DOY 210, consistent with NDVI development among PD
nutrient groups. LAI in the RF rice ranged between the PD low-nutrient and
fertilization groups, while it remained higher on DOY 240. Regression
analysis for the NDVI–LAI relationship in grouped data sets showed a common
trajectory across the PD nutrient groups and RF rice (Fig. 3a; R2=0.95, p<0.001).
A curvilinear response of the GPP rate to incident PAR fit well with the
classical light response model at each measuring date (data not shown), as
previously reported (Eq. 5; Lindner et al., 2016). The resulting
LUEcint on DOY 160 was approximately 0.01 µmol
CO2 µmol-1 PARincident, crossing the PD nutrient
groups and the RF rice, and rapidly increased after DOY 180 (Fig. 2c).
Differences in LUEcint among the PD nutrient groups were relatively
small (< 20 %) on the corresponding dates. Nevertheless, the RF
rice presented dramatically high LUEcint as compared to the PD rice
from DOY 180 to the end of the growing season, showing the highest values at
0.11 and 0.05 µmol
CO2 µmol-1 in the RF and PD rice, respectively. Generally
speaking, PD rice in the fertilization groups had dramatically higher
GPPmax, with a maximum level of 51.60 µmol
CO2 m-2 s-1 compared to 38.90 µmol
CO2 m-2 s-1 of the low-nutrient group (Fig. 2d). Maximum
GPPmax in the RF rice was analogous to that of the PD rice and
remained higher on DOY 240 (p<0.01), which was ascribed to green
LAI (Fig. 2b). Similarities in photosynthetic traits in terms of NDVI, LAI,
GPPmax, and LUEcint between the normal- and high-nutrient
groups at the corresponding growth stages were evident. Hence, comparisons in
those parameters stated below referred to the PD low- and normal-nutrient groups.
Relatively low LAI in the RF rice during the reproductive stage but higher
LUEcint than the PD at the same growing stage resulted in a
distinction regarding the LAI–LUEcint correlation associated with slope
(Fig. 3c; R2= 0.74, p=0.02 in RF, R2=0.85, p<0.0001 in PD; F=22.16, p=1.398e-05; see Table 1). A common linear
regression for the LAI–GPPmax correlation that interpreted
approximately 88 % of variations in GPPmax across the PD nutrient groups and
RF rice was evident (Fig. 3b; R2=0.88, p<0.0001). Canopy
leaf nitrogen content (Nm, %) collected in the field and
controlled growth chamber was significantly higher in the RF rice after
DOY 180 (Fig. 4a, b; p<0.05). Light use efficiency at leaf level
(LUEleaf) was positively correlated with Nm (Fig. 4b;
R2=0.65, p=0.0007). This implied that the improved
LUEcint in the RF rice observed after DOY 180 could be related to
strengthened capacity of N accumulation in canopy leaves.
Correlations between (a) normalized difference vegetation
index (NDVI) and LAI, (b) GPPmax and LAI, and (c) canopy light use efficiency
(LUEcint) and LAI across the PD low-, normal-, and high-nutrient groups,
and in the RF rice. Mean ± SD, n=3 to 6. PD: paddy; RF: rainfed.
Seasonal courses of leaf nitrogen content (Nm) in
(a) the PD low-, normal-, and high-nutrient groups, and in the RF rice
in the field, and (b) in a controlled growth chamber. (c)
Correlation between leaf light use efficiency (LUEleaf) and
Nm crossing the PD and RF rice. Mean ± SD, n=3 to 6.
Statistic analysis showed significance at the 0.05 level (small letters) and at
the 0.01 level (capital letters). DOY: day of year; DAT: day after
transplanting; PD: paddy; RF: rainfed; GC: growth chamber.
Field mapping of GPPday and
LUEcabs
Field maps of GPPday and LUEcabs at principle growth
stages (Figs. 5 and 6) clearly showed that seasonal change of within-field
GPPday at each nutrient group could be quantitatively mapped using
three colors (yellow, blue, and red) corresponding to low, medium,
and high numerical values (respectively). Pink pixels and bright red pixels
were respectively observed in the PD and RF rice on measuring date 8 August
(DOY 220), during which time most rice plants proceeded to ripen, showing the
highest LAI. However, color distribution in space at a specific growth stage
within nutrient groups, especially in the normal- and low-nutrient groups on 11 July
(DOY 192) and 21 August (DOY 223), seemed to be uneven (Fig. 5b, d).
Furthermore, uneven distribution in the RF rice was intensified as compared
with the PD rice on the corresponding dates. For LUEcabs,
appearance of greater spatial variability in color distribution was seen at
the early growth stage in both PD and RF rice (Fig. 6a, e), which seemed to
contrast with spatial aspects of GPPday over the growing season.
LUEcabs distributions in space over the reproductive stage (11
July, DOY 192) tended to approach homogeneity in either PD nutrient groups or
RF rice (Fig. 6b, c, f, g).
Field mapping of daily integrated gross primary productivity
(GPPday) in the PD rice and RF rice at principle growth stages:
vegetative stage (21 June/DOY 172), middle reproductive stage
(11 July/DOY 192), early ripening stage (8 August/DOY 220), and middle
ripening stage (21 August/DOY 233). Date are expressed as MM DD/DOY. DOY: day
of year; PD: paddy; RF: rainfed.
Field mapping of canopy light use efficiency (LUEcabs) in
the PD rice and RF rice at principle growth stages: vegetative stage
(21 June/172), middle reproductive stage (11 July/192), early ripening stage
(8 August/220), and middle ripening stage (21 August/233). Date is expressed
as MM DD/DOY. DOY: day of year; PD: paddy; RF: rainfed.
Descriptive statistics including mean, max, and CVtraditional in
GPPday and LUEcabs respectively described their mean, their maximum values
at field scale, and within-field variation of mean across the growing season
(Table 2). Max GPPday differed significantly between the normal-
(7.29 g C m-2 d-1) and low-nutrient (3.78 g C m-2 d-1)
groups 4 weeks after transplantation, which was clearly apparent
in the visual display of pixel GPPday as well (Fig. 5a, d).
Nevertheless, field mean values among the three nutrient groups were close to
one another. The enhanced field mean of GPPday in the normal-nutrient group
by approximately 36 % compared to the low-nutrient group appeared on 11 June (DOY 192).
Such a large discrepancy persisted until the end of the growing season.
Except for the early growth stage, the three nutrient groups showed similar
values in the maximum GPPday, which reached 12.49 g
C m-2 d-1 for the normal-nutrient group around 8 August (DOY 220) and then
declined with senescence. The maximum GPPday predicted using a
light use efficiency model in our previous report (Xue et al., 2016a) tended
to be higher than the one shown here for the normal-nutrient
group, which is thought to be due to model sensitivity to changes in ambient
light environment.
Descriptive statistics of GPPday (g C m-2 d-1)
and LUEcabs (g C MJ-1) at each nutrient treatment in
the PD rice and at the RF rice over principle growing stages. Date is
expressed as MM DD/DOY. DOY: day of year; PD: paddy; RF: rainfed.
GPPday
LUEcabs
Low
Normal
High
Rainfed
Low
Normal
High
Rainfed
21 June/172
Mean
2.32
2.56
2.33
4.53
1.16
1.67
1.43
1.3
Max
3.78
7.29
3.51
10.57
∼ 3.50
∼ 3.50
∼ 3.50
3.18
CVtraditional
2.16 %
14.06 %
5.15 %
25.81 %
17.24 %
47.90 %
48.95 %
22.00 %
11 July/192
Mean
6.16
9.57
8.35
10.99
0.68
0.62
0.73
0.86
Max
11.21
12.73
11.97
16.93
1.72
2.75
2.86
2.35
CVtraditional
21.36 %
14.52 %
20.37 %
26.32 %
4.92 %
11.29 %
9.20 %
7.09 %
25 July/206
Mean
7.93
9.74
9.45
14.28
0.7
0.68
0.68
1.08
Max
10.97
11
11.04
17.15
0.87
1.32
1.06
1.79
CVtraditional
13.55 %
9.22 %
10.12 %
16.89 %
4.38 %
8.82 %
4.94 %
4.81 %
8 August/220
Mean
9.56
10.85
10.57
15.41
0.66
0.62
0.63
0.87
Max
12.28
12.49
12.41
18.11
1.58
1.42
1.57
0.95
CVtraditional
8.89 %
7.77 %
8.77 %
15.36 %
4.54 %
4.19 %
4.12 %
4.65 %
21 August/233
Mean
7.13
7.69
7.45
12.14
0.49
0.52
0.52
0.81
Max
9.94
10.73
10.22
15.91
0.66
0.71
0.68
1.05
CVtraditional
9.23 %
8.49 %
8.88 %
19.91 %
6.93 %
7.69 %
6.73 %
19.75 %
Rice plants grown in the RF field showed significantly higher mean and
maximum GPPday than the PD rice at respective growth stages
(Table 2). However, CVtraditional in the RF rice was about 2
times higher than the PD normal- and low-nutrient groups several weeks after
transplantation. The PD normal-nutrient group displayed a higher
CVtraditional quantified on 21 June (DOY 172), followed by the
high- and low-nutrient groups. Differences in CVtraditional among the PD nutrient
groups disappeared over time, consistent with the color display in field map
of GPPday in Fig. 5c and d. The results suggested that although
addition of fertilizer in the traditional way can promote increment of field
average GPPday, it dramatically strengthened field variations of
GPPday during the early growth stage in the paddy field setting. As
we expected, the change in planting culture from flooding to rainfed promoted
the enhancement of field variations in the mean of field GPPday,
probably due to the rising risk of soil water availability when prolonged
drought events occur.
Coefficient of variation (CVtraditional) calculated
by dividing the standard deviation by the mean versus coefficient of
variation (CVsill) calculated using the semi-variogram sill across the
PD nutrient groups and the RF rice for variables (a) GPPday, (b) LUEcabs,
and (c) LAI. Subplot (d) shows the sillGPP–sillLAI
relationship in the PD and RF rice. RF: rainfed; PD: paddy.
LUEcabs appeared to be higher early in the growing season; rapidly
declined after plant growth and development advanced to the reproductive stage;
and gradually decreased to approximately 0.52 and 0.81 g C MJ-1 at the senescence
stage in the PD and RF rice, respectively (Table 2). The RF rice had clearly
high values of average LUEcabs as compared to the PD by approximately 21,
35, 26, and 36 % on 11 July, 25 July, 8 August, and 21 August,
respectively, apart from 21 June, when the PD and RF showed similar
LUEcabs of around 1.4 g C MJ-1. Enhanced LUEcabs
in the RF rice over the growing season was likely due to higher leaf
nitrogen content as shown in Fig. 4a.
The seasonal course of CVtraditional of LUEcabs among the PD
nutrient groups exerted a similar tendency, assembling the mean of
GPPday (Table 2). CVtraditional in the normal- and
high-nutrient groups was analogous over time, while appearing to be higher on
21 June (DOY 172) and 11 July (DOY 192) by approximately 62 and 50 %,
respectively, than the low-nutrient group. Interestingly,
CVtraditional in the fertilization groups (normal- and high-nutrient groups)
displayed approximately 53 and 30 % higher values, respectively, than the RF
rice at the early growth stage (21 June/DOY 172 and 11 July/DOY 192). Similar
to drought impacts in amplifying CVtraditional in GPPday
on 21 August (DOY 233) in the RF rice, amplified CVtraditional in
LUEcabs was also observed. Lower CVtraditional and
similarities in LUEcabs over field space on 25 July (DOY 206) and
8 August (DOY 220) corresponded well to the field map of LUEcabs at
corresponding dates, meaning that field mapping in proper ways also could
visibly deliver distribution information of ecosystem photosynthetic traits
in space.
Semi-variograms of GPPday,
LUEcabs, and LAI
Semi-variogram analysis is a widely used geostatistical parameter to
quantitatively evaluate spatial variation. Sill values were derived from
exponential rise to maximum function, which fit the values of semi-variogram
at each nutrient and/or water treatment (R2 >0.83, p<0.01). Values of CVsill in GPPday were
significantly and positively correlated with CVtraditional (R2=0.83, p<0.001; Fig. 7a), demonstrating that the
semi-variogram accurately captured patterns of spatial variability in those
ecophysiological traits among the nutrient treatments and RF rice. Estimates
of CVsill among the nutrient groups were generally close to those
of CVtraditional, approaching the 1:1 line (Fig. 7a). However,
CVtraditional values in the RF rice were commonly lower by
approximately 20 % than CVsill at the principle growth stages. This occurred
because the traditional method of calculating CV does not account for
spatial correlation in data, suggesting that spatial heterogeneity in the RF
field associated with water availability and resulting crop growth was
greater than in the PD rice. This was also proven by averaged
CVsill in the RF that was greater by about 50 % than that of
the PD rice averaged across the nutrient groups (Table 3).
A significantly positive correlation between CVsill and
CVtraditional was observed in LUEcabs as well (R2=0.89, p<0.001; Fig. 7b). All CVsill sampled
across the PD nutrient groups and RF rice resided at the right side of the
1:1 line, being higher than CVtraditional but analogous between
the PD and RF rice, which was different from the significant difference in
CVsill of GPPday between the PD and RF rice shown in
Fig. 7a. It was also evident by average CVsill of 11.66 (%) in
the RF rice, which was close to the value of 14.37 of the PD rice averaged
across nutrient groups (Table 3), meaning that spatial variability of
LUEcabs in the PD rice exerted great amplitude that tended to be
similar to the RF rice. A positively linear correlation between
CVsill and CVtraditional was evident in LAI (R2=0.80, p<0.001; Fig. 7c). Data points collected over the PD
nutrient groups oscillated closely around the 1:1 line; an exception was
observed in the RF rice, which reassembled the phenomena observed in the
CVsill–CVtraditional relationship for GPPday
but differed from that for LUEcabs. Given the tight correlation
between CVsill and sill values, sill instead of CVsill
was used in spatial analysis for GPPday and LUEcabs as
discussed below.
Sill values of semi-variograms and CVsill for
GPPday (g C m-2 d-1, upper part), LAI
(m2 m-2, middle part), and LUEcabs (g C MJ-1,
lower part) at the PD rice subject to low, normal, and high nitrogen gradients
and at the RF rice over the principle growing seasons: vegetative stage
(21 June), reproductive stage (11 and 25 July), ripening stage (8 and
21 August). Date is expressed as MM DD/DOY. DOY: day of year; PD: paddy;
RF: rainfed.
Growth stage
Date/DOY
Low
Normal
High
Rainfed
GPPday
Sill
CVsill
Sill
CVsill
Sill
CVsill
Sill
CVsill
Vegetative
21 June/172
0.01
2.86 %
0.09
16.57 %
0.01
6.10 %
0.98
30.91 %
Reproductive
11 July/192
0.45
15.40 %
0.78
13.05 %
0.79
15.05 %
6.15
31.91 %
25 July/206
0.37
10.85 %
0.31
8.08 %
0.37
9.10 %
6.03
24.32 %
Ripening
8 August/220
0.42
9.59 %
0.25
6.52 %
0.43
8.77 %
2.57
14.71 %
21 August/233
0.20
8.87 %
0.23
8.82 %
0.22
8.90 %
4.77
25.44 %
LAI
Vegetative
21 June/172
0.0015
14.19 %
0.0219
42.48 %
0.0026
18.40 %
0.1079
43.75 %
Reproductive
11 July/192
0.1111
20.81 %
0.2869
19.96 %
0.2076
18.91 %
0.6915
37.36 %
25 July/206
0.1866
14.68 %
0.2924
14.76 %
0.1535
10.71 %
0.6127
22.07 %
Ripening
8 August/220
0.4306
23.99 %
0.1148
10.10 %
0.1174
10.37 %
0.4050
18.83 %
21 August/233
0.0910
12.02 %
0.2015
16.72 %
0.0879
11.14 %
0.6622
27.59 %
LUEcabs
Vegetative
21 June/172
0.0302
21.19 %
0.5478
62.68 %
0.3506
58.56 %
0.0633
27.37 %
Reproductive
11 July/192
0.0190
28.67 %
0.0065
18.39 %
0.0073
16.55 %
0.0041
10.53 %
25 July/206
0.0008
5.71 %
0.0031
11.58 %
0.0011
6.90 %
0.0070
10.96 %
Ripening
8 August/220
0.0011
7.11 %
0.0010
7.21 %
0.0007
5.94 %
0.0032
9.20 %
21 August/233
0.0009
8.66 %
0.0024
13.32 %
0.0008
7.69 %
0.0142
20.81 %
Spatial patterns of GPPday,
LUEcabs, and LAI
Seasonal development in sill values of GPPday exhibited a similar
tendency across the PD nutrient groups and RF rice, with an increase from the
vegetative stage to the early reproductive stage followed by a decline (Table
3, upper part). A paired t test across the range of DOY showed that difference
of sill in the RF rice was significantly different from the PD nutrient
groups (p<0.05). Nevertheless, significant differences were not
repeatedly observed among the PD nutrient groups. Early in the growth season
(i.e., 21 June/DOY 172 and especially on 11 July/DOY 192), the normal- and
high-nutrient groups had relatively high sill by an average of 44 % as
compared to the low-nutrient group, suggesting that fertilizer addition could
contribute to spatial variability of GPPday, which conforms to
differences in CVtraditional (Table 2). As expected, sill of the RF
rice measured on 21 August/DOY 233 increased in contrast to observed seasonal
tendency of sill that was supposed to decline, due to occurrence of a
prolonged drought event in mid-August during which time leaf water potential
around solar noon declined to -2.0 MPa and severe leaf rolling occurred
(data not shown). Significant impacts by drought on GPPday were
observed. Seasonal courses of sill in LAI across the PD nutrient groups and
the RF rice were similar to those of GPPday (Table 3, middle part).
Sills of LAI in the RF rice were generally higher than the PD rice at
corresponding growth stages.
Sill of LUEcabs showed a seasonal trend that was similar to
GPPday (Table 3, lower part). The prolonged drought event occurring
in August contributed to spatial variability in the RF rice as indicated by
greater sill of 0.0142 compared with 0.0032 on 8 August (DOY 220). ANOVA
indicated no difference at the 0.05 significance level among the
three PD nutrient groups over the growing season (p=0.67), whereas the
mean sill value of 0.4492 on 21 June (DOY 172) was improved by approximately
94 % for the normal- and high-nutrient groups compared to the value of
0.03 for the low-nutrient group, resembling comparisons in sill of
GPPday and field maps shown in Fig. 6a. The results implied that
fertilizer addition can enhance spatial variability of LUEcabs
especially early in the growing season. Interestingly, at the early growth stage,
especially on 21 June (DOY 172) and 11 July (DOY 192), the PD nutrient
addition groups had average sill values that were approximately 85 %
than the RF rice. Thereafter the values of RF rice became greater,
meaning that spatial variability of LUEcabs in the PD rice
amplified by field nutrient application could be even greater than the RF
rice, in contrast with aforementioned GPPday spatial variability
between the PD and RF rice.
Spatial correlation for GPPday,
LUEcabs, and LAI
LUEcabs was calculated by Eq. (8), consisting of GPPday
and fAPAR variables, meaning that spatial variations of LUEcabs may
greatly influence GPPday. Sill values or CVsill for
GPPday and LUEcabs were not significantly correlated with
one another when all data sets were grouped across the PD nutrient groups and
RF rice over the growing season (R2 < 0.14, p>0.01). Significantly positive correlations were found for the
sillGPP day–sillLAI relationship in the PD nutrient groups (Fig. 7d;
R2=0.36, p=0.012) and in the RF rice (Fig. 7d; R2=0.85, p=0.015), suggesting that the primary factor that mediated GPPday
spatial variation in the PD nutrient groups, especially in the RF rice, was
LAI development.
Implied ecological implications of canopy leaf physiology
Ecological implications of canopy leaf physiology (i.e., LUEcabs)
in monitoring of spatial variation and strength of GPPday for the
same plant function type (PD and RF rice) were analyzed using scenario
analysis. The analysis applied LUEcabs of the PD rice on 8 August
(DOY 220) in the estimation of the RF rice GPPday on the same date,
yielding comparisons in field map of GPPday (Fig. 8a, b) and
quantitative assessment (Fig. 8c). The field map of predicted
GPPday using PD LUEcabs indicated blue as the prevailing
color as compared to the prevailing red color in the field map of the initial
estimation, indicating a significant underestimation of GPPday
especially at the sites showing high LAI (Fig. 8c). The results suggested
that delicate variations in canopy leaf physiology among the same plant
function type across various habitat conditions are vital.
Effects of light use efficiency (LUEcint) on estimation of
daily integrated photosynthetic productivity (GPPday) at ripening
stage in the RF rice. GPPday estimation using (a) observed
LUEcint in the RF rice, (b) LUEcint of the PD
rice (GPPday_LUEcint_PD); i.e., GPPday
estimation of the RF rice was carried out by adopting the LUEcint
value of the PD rice at the ripening stage. (c) Quantitative
comparisons between GPPday and
GPPday_LUEcint_PD based on pixel LAI. PD: paddy; RF: rainfed.
Discussions
A series of successive effects regarding rice growth and environment
perspectives from the leaf to the ecosystem have been revealed in our
research group, with the aim of clarifying the physiological mechanisms
responsible for optimal carbon gain and water use at the leaf level as well
as their plastic acclimation to changing ambient environments (Xue et al.,
2016b, c); discerning the roles of canopy structure and function in
determining canopy carbon gain in individual organisms in different field
management conditions and anthropogenic interventions (Lindner et al., 2016;
Xue et al., 2016a); increasing the understanding of the influences of climate
change, phenology, and rice ecosystem photosynthetic productivity (Xue et
al., 2017); and facilitating a discussion of the ecological implications of
the life history of rice crops in controlling regional carbon fluxes in the
agriculture landscape (Lindner et al., 2015). There are large fluctuations of
ecosystem photosynthetic productivity at different geographic sites. However,
the fluctuations have not been statistically correlated with the rate of N
application, which does significantly contribute to rice growth at the individual
level. This is thought to be due to various factors, including inter- and
intra-field variations of ecosystem photosynthetic productivity. This
highlights the need for field/microsite-directed research to gain new
insights into how water and N availability affect photosynthetic productivity
at individual and microsite scales.
Feasible application of the UAV system to capture spatiotemporal
variations of GPPday
Applications of close-range remote sensing in studies of vegetation dynamics
regarding plant growth and phenology have been increasingly explored,
partially due to small-scale pixel-to-pixel detection that eliminates the
averaging involved in larger pixels. It compensates for the regional
observations of a satellite remote-sensing system. UAV-based applications in
agronomical studies has been tested and include evaluation of spatial
variability of soil N content in a winter wheat field (Cao et al., 2012),
detection of canopy N status in irrigated maize (Bausch and Khosla, 2010),
and mapping of cereal yield using field vegetation indices (Fisher et al.,
2009; Swain et al., 2010; Tubaña et al., 2012; Zhang and Kovacs, 2012)
with rice growth and yield included (Ko et al., 2015). Recent attempts were
made to apply narrow-band multispectral imagery derived at the plot level in
monitoring of whole field C content of lucerne plants (Wehrhan et al., 2016).
Furthermore, an applicable crop information delivery system tested in rice
ecosystems by Ko et al. (2015) and Jeong et al. (2016), which took several
valuable high-spatial-resolution vegetation indices into account, captured
delicate changes in crop growth and yield among the pixels. In this research,
diagnostic information derived from high-spatial-resolution images could be
strongly linked to canopy biophysical traits in the paddy and rainfed rice,
allowing seasonally zonal maps of GPPday and LUEcabs to
be made (Figs. 5 and 6), and assisting in the evaluation of spatial variation
of those functional traits.
Practical application of the UAV technique in the field requires a number of
procedural steps. They include image pre-processing, image interpretation,
and data extraction. Integration of the data with agronomic data in expert
systems still needs to be developed and improved before end products can be
germane for decision making (Zhang and Kovacs, 2012). An empirical
calibration method adopting spectral reflectance from three types of PITs was
applied to process radiometric correction of UAV images on each measuring
date. Although calibrated UAV reflectance and at-surface measurements usually
closely corresponded during the middle and late growing seasons, the
empirical calibration tended to underestimate ground reflectance especially
in red reflectance at the early growth stage. This was probably due to water
scattering effects. The UAV flight schedule that is always scheduled at solar
noon may not be the best option to obtain a close correspondence between
camera reflectance and ground surface measurements at the early growth stage.
Another empirical regression linking radiometrically calibrated UAV images and
plot measurements was applied instead of considering complex mechanisms of
light scattering in the area of physical category. The methods used to
recalibrate UAV images on 21 June (DOY 172) may yield biased estimations of
field reflectance due to the limited number of ground reflectance swatches that
were deployed in the limited space. Leaves of plants grown under fertilizer
addition conditions had higher N content at the early growth stage, resulting
in greater LUEcabs (Sinclair and Horie, 1989; Xue et al., 2017). On
average, LUEcabs in the fertilization groups where plants
accumulated more N in leaves on 21 June (Fig. 4a) was higher than in
the low-nutrient group (Table 2), which implies the pragmatic feasibility of
adopting the recalibration routine to acquire correct UAV products.
The data fitting concept that integrates traditional physiology approaches at
the plot level and close-range remote-sensing information requires reliable
establishments regarding correlations between ground surface measurements of
VIs and LAI, LAI and LUEcint, and GPPmax. Reliable
relationships between those biophysical traits were inferred across the paddy
and rainfed rice (Fig. 3). Nevertheless, data sets for the LAI–LUEcint
correlation in the rainfed rice were limited mainly due to the difficulty in
physically performing measurements of diurnal courses of leaf and canopy gas
exchange and measurements of other plant parameters in the paddy nutrient
groups and rainfed rice. Supplement data sets in terms of the
LAI–LUEcint correlation in the rainfed rice, as well as other main
crops, will be conducted as the technical barriers are overcome.
Spatial variability of photosynthetic trait in the
rainfed field is not always greater than in the paddy field
The burgeoning global population continues to increase the demand for water
and food staples, including rice. Furthermore, the looming specter of water
scarcity in the coming decades in some regions now capable of flooding of
crop fields highlights the need for turning flooding culture into
multicultural management, including the paddy and rainfed systems. The
expansion of rice planting to different geographic sites, particularly in
regions lacking the capability of irrigation and/or flooding of crop fields
is always featured by large variations of seasonal photosynthetic
productivity (Serraj et al., 2008). Therefore, a critical concern related to
the reported observation is how water availability in the rainfed fields
could influence spatiotemporal variations of ecosystem photosynthetic
productivity as compared to paddy fields. In the present study, spatial
variations of GPPday and LAI in the rainfed field were amplified
compared to the paddy nutrient groups at corresponding growth stages
(Table 3). However, spatial variation of LUEcabs at the early
growth stage (21 June/DOY 172 and 11 July/DOY 192) in the paddy fertilization
groups was significantly greater than the rainfed rice at the same times,
suggesting that spatial variability of the photosynthetic trait in the rainfed
field does not always exceed that of paddy fields, depending on nutrient
availability. Furthermore, nutrient addition at the early growth stage could
amplify spatial heterogeneity of GPPday and LUEcabs in
the paddy field, while such nutritional effects are dismissed at reproductive and
ripening stages.
Implied ecological implications of field niche in a spatially
hierarchical remote-sensing network
In situ plot data are important for the accurate interpretation of ecosystem
carbon dynamics in response to different field management methods and
anthropogenic interventions that involve influences on plant structure and
physiology. While plot data provide the most detailed information on rice
carbon and water vapor gas exchange, applying this understanding to broader spatial
and temporal domains requires scaling approaches. As mentioned before, the
field niche between in situ plot and regional dimension is supposed to be a
key chain of a spatially hierarchical remote-sensing network (Masek et al.,
2015; Pause et al., 2016). Applications of the data fusion at the
microsite/field scale that combine observations of in situ canopy structure
and function with field crop information derived from the UAV system capture
critical growth information of rice crop in space.
Spatial variations in GPPday over the paddy nutrient groups and
rainfed rice tend to be primarily mediated by LAI. Canopy structure (i.e.,
LAI) is the main biotic factor in paddy rice ecosystems, yielding a great
impact on the seasonal course of ecosystem photosynthetic productivity (Xue
et al., 2017). Variations of the overall growing season photosynthetic
productivity are significantly mediated by fluctuations of daily GPP at the
ripening stage when canopy LAI is maximized. The scenario analysis in Fig. 8
documented marked underestimations of GPPday in the rainfed rice at
the beginning of the ripening stage when applying LUEcabs of the paddy
rice in spatial monitoring of GPPday in the rainfed field. Enhanced
LUEcabs after DOY 180 in the rainfed rice could be primarily
ascribed to greater capacity of N accumulation (Fig. 4) and/or to efficient P
uptake (Kato et al., 2016) that was not quantified here. Changes in leaf N
allocation within leaves that relate to photosynthetic activity of individual
leaves may also have important implications like plant biomass production
(Karaba et al., 2007; Wang et al., 2014) or may not affect biomass (Tanaka et
al., 2013; Dow and Bergmann, 2014), and they must be investigated along with
canopy structure. There is a need to consider variations in canopy leaf
physiology for the same plant function type across various habitat
conditions. The results will hopefully assist in better monitoring of
per-field photosynthetic productivity and biological interpretation of its
spatial patterns using the remote-sensing technique.