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Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation

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Remote sensing estimation based on the dimidiate pixel model (DPM) using vegetation indices (VIs) is a common approach for mapping fractional vegetation cover (FVC). The major drawback of DPM is that it does not consider real endmember conditions and multiple scattering between soil and vegetation. An analysis of FVC uncertainties caused by these model deficiencies is still lacking. Here, we first calculated the FVC theoretical uncertainty caused by reflectance uncertainties based on the law of prapagation of uncertainty (LPU). Then, we tested the performance of DPM using six VIs over 3-D forest scenes. We simulated both Aqua-MODIS and Landsat-OLI surface reflectance (SR) at their corresponding spatial resolutions and spectral response functions (SRFs) using a well-validated 3-D radiative transfer (RT) model which helps to separate the model and input uncertainties. We found that ratio vegetation index (RVI)- and enhanced vegetation index (EVI)-based models were most affected by sensors, followed by the normalized difference vegetation index (NDVI)-, enhanced vegetation index 2 (EVI2)-, renormalized difference vegetation index (RDVI)-, and difference vegetation index (DVI)-based models. Without considering SR uncertainties, the DVI-based model performed best (FVC absolute difference < 0.1); however, the commonly used NDVI model reached a maximum difference of 0.35. At the same time, input uncertainty increased the uncertainty of FVC retrieval. We noticed that the increase of solar zenith angle (SZA) resulted in a clear increase of retrieved FVC under the uniform distribution, which can be explained by the increased shadow proportion. Besides, model accuracy was dominated by the purity of soil (vegetation) endmember in low (high) vegetation cover area. This study provides a reference for the selection of the optimal VI for FVC retrieval based on the DPM.
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1
Evaluation of the Vegetation-Index-Based Dimidiate
Pixel Model for Fractional Vegetation
Cover Estimation
Kai Yan , Si Gao, Haojing Chi, Jianbo Qi, Wanjuan Song , Yiyi Tong ,XihanMu ,
and Guangjian Yan ,Senior Member, IEEE
Abstract Remote sensing estimation based on the dimidiate
pixel model (DPM) using vegetation indices (VIs) is a common
approach for mapping fractional vegetation cover (FVC). The
major drawback of DPM is that it does not consider real
endmember conditions and multiple scattering between soil and
vegetation. An analysis of FVC uncertainties caused by these
model deficiencies is still lacking. Here, we first calculated the
FVC theoretical uncertainty caused by reflectance uncertainties
based on the law of prapagation of uncertainty (LPU). Then,
we tested the performance of DPM using six VIs over 3-D
forest scenes. We simulated both Aqua-MODIS and Landsat-OLI
surface reflectance (SR) at their corresponding spatial resolutions
and spectral response functions (SRFs) using a well-validated
3-D radiative transfer (RT) model which helps to separate the
model and input uncertainties. We found that ratio vegetation
index (RVI)- and enhanced vegetation index (EVI)-based models
were most affected by sensors, followed by the normalized
difference vegetation index (NDVI)-, enhanced vegetation index 2
(EVI2)-, renormalized difference vegetation index (RDVI)-, and
difference vegetation index (DVI)-based models. Without con-
sidering SR uncertainties, the DVI-based model performed best
(FVC absolute difference <0.1); however, the commonly used
NDVI model reached a maximum difference of 0.35. At the
same time, input uncertainty increased the uncertainty of FVC
retrieval. We noticed that the increase of solar zenith angle (SZA)
resulted in a clear increase of retrieved FVC under the uniform
distribution, which can be explained by the increased shadow
proportion. Besides, model accuracy was dominated by the purity
of soil (vegetation) endmember in low (high) vegetation cover
area. This study provides a reference for the selection of the
optimal VI for FVC retrieval based on the DPM.
Manuscript received July 28, 2020; revised December 13, 2020; accepted
December 21, 2020. This work was supported in part by the National Natural
Science Foundation of China under Grant 41901298 and Grant 41901273;
in part by the Open Fund of the State Key Laboratory of Remote Sensing
Science under Grant OFSLRSS201924; in part by the Open Research Fund
of the Key Laboratory of Digital Earth Science, Institute of Remote Sensing
and Digital Earth, Chinese Academy of Sciences, under Grant 2018LDE002;
and in part by the Fundamental Research Funds for the Central Universities
under Grant 2652018031. (Corresponding authors: Kai Yan; Haojing Chi.)
Kai Yan, Si Gao, and Haojing Chi are with the School of Land Science
and Techniques, China University of Geosciences, Beijing 100083, China
(e-mail: kaiyan@cugb.edu.cn; gaosi_gs@163.com; chihaojing99@163.com).
Jianbo Qi is with the College of Forestry, Beijing Forestry University,
Beijing 100083, China (e-mail: jianboqi@126.com).
Wanjuan Song is with the Aerospace Information Research Institute, China
Academy of Sciences, Beijing 100083, China (e-mail: songwj@aircas.ac.cn).
Yiyi Tong, Xihan Mu, and Guangjian Yan are with the State Key Labo-
ratory of Remote Sensing Science, Faculty of Geographical Science, Beijing
Normal University, Beijing 100101, China (e-mail: tongyiyi0311@163.com;
muxihan@bnu.edu.cn; gjyan@bnu.edu.cn).
Color versions of one or more figures in this article are available at
https://doi.org/10.1109/TGRS.2020.3048493.
Digital Object Identifier 10.1109/TGRS.2020.3048493
Index Terms—3-D radiative transfer (RT), dimidiate pixel
model (DPM), fractional vegetation cover (FVC), model eval-
uation, vegetation indices (VIs).
I. INTRODUCTION
FRACTIONAL vegetation cover (FVC), an important vari-
able for describing the quality and changes of vege-
tation in terrestrial ecosystems, refers to the proportion of
the vertically projected area of vegetation (including leaves,
stems, and branches) within the statistical area [1]–[3]. It is
commonly used in vegetation change analysis [4], ecological
environment research [5], [6], climate prediction [7], soil
erosion assessment [8], [9], among others. Therefore, accurate
estimates of FVC are of great importance.
There are two methods for measuring FVC: ground mea-
surement and remote sensing estimation. Ground measurement
is a flexible mean of extracting FVC at the point scale, inde-
pendent of atmosphere condition and model assumptions, and
with high accuracy and spatial resolution [10]. However, this
method is time-consuming, costly, and may not allow extrapo-
lating FVC to a large area quickly [11]. Moreover, vegetation
cover has obvious spatiotemporal variability, making it difficult
to monitor FVC dynamically at large spatial scales using
ground measurements. Remote sensing estimation of FVC
based on the correlation between FVC and canopy reflectance
can provide long time series and large ground coverage for
global and regional ecological monitoring [12], [13].
Remote sensing-based FVC estimation methods mainly
include: 1) empirical models; 2) physical-based models; and
3) pixel unmixing models [14]–[16].
1) Empirical models (e.g., regression models [17]) are
generally descriptive with a relatively simple form to char-
acterize the statistical relationships between a certain band,
band combination, or vegetation indices (VIs) and FVC [15].
The empirical method has great application potential due to
its high computational efficiency for large remote sensing
data sets [18]. However, this approach lacks sufficient under-
standing of physical mechanisms and highly relies on ground
sampling [19].
2) Physical-based models (e.g., radiative transfer (RT)
model [20] and geometric optical (GO) model [21]) offer
an explicit connection between canopy reflectance and
vegetation biophysical and biochemical parameters [22].
Thus, this method has a solid theoretical basis and is accurately
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2IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
applicable to FVC estimation at large scales [18]. Nonetheless,
owing to the extreme complexity and “ill-posed problem”
of physical-based models, it is difficult to directly retrieve
FVC [23], [24].
3) Pixel unmixing models with a certain degree of physical
basis are simple and convenient, which can be regarded as
an improvement on the basis of physical models [25], [26].
Based on the assumption that each pixel is composed of several
components, pixel unmixing models estimate FVC at the
subpixel level as the proportion of vegetation component [27].
Among the existing pixel unmixing models, the dimidiate
pixel model (DPM) is the simplest one which assumes that
the pixel has only two components (i.e., pure vegetation and
nonvegetation) [28]. This method minimizes the influence
of the atmosphere, soil background, and other factors to
a large extent, and has been widely and successfully used
in vegetation cover extraction at both regional and global
scales [25], [27]–[29].
In addition, with the constant technical developments
in computer science, spatial data mining and knowledge
discovery (SDMKD) and machine learning have shown
their advantages in recognitions of remote sensing signals
(e.g., decision trees [30] and artificial neural networks [31]).
These approaches can accurately extract the FVC value by
training with precomputed remote sensing data sets and have
become popular for studies of FVC estimation [32], while its
practical applications are still insufficient [33].
VIs, as indirect indicators of vegetation growth characteris-
tics, make use of multiband information and are sensitive to
soil types, humidity, and temporal and spatial variations of the
atmosphere [34]. The VI-based DPM is widely used for FVC
estimation given its good accuracy and simplicity [3]. Previous
studies have shown that different VIs perform differently on
FVC calculations. The normalized difference vegetation index
(NDVI), the most widely used VI, has been used in DPM for
a long time [28], [34]. However, some studies have shown
that NDVI-based DPM may overestimate FVC, especially for
very dense canopies or open canopies with bright or dark
bare ground [18], [22]. NDVI-based models appear also to
be sensitive to soil background and scale effects [35], [36].
To solve this problem, Jiang et al. [29] proposed to use
the difference vegetation index (DVI) instead of NDVI in
DPM. Johnson et al. [37] found that modified soil adjusted
vegetation index (MSAVI) is less sensitive to changes in soil
brightness than NDVI. Li et al. [38] combined NDVI and ratio
vegetation index (RVI) to minimize saturation biases in an
NDVI-based model [38]. The main limitation of DPM is the
influence of background spectra variability derived from the
model assumption [22], [33]. Thus, the selection of the optimal
VI for DPM (e.g., most sensitive to vegetation coverage and
least sensitive to background factors) is the key issue in the
application of this method.
Ground-based validation is essential as the benchmark to
evaluate DPM accuracy [9], [18], [33], [38]. However, the
accuracy of this validation approach includes the uncertainty
of the ground measurement, the spatial heterogeneity-caused
uncertainty in the upscaling process from the point to the
pixel scale, and the model uncertainty caused by the DPM
with theoretical deficiency [39]. Therefore, this obviously
hampered the evaluation of the DPM uncertainty and progress
in the understanding of its deficiency. In view of the draw-
backs of ground-based validation, recent developments in
computer simulations have provided an alternative approach
and have been used for validating the retrieval of physical
and structural characteristics of vegetation, such as leaf area
index (LAI) [40], [41], fraction of photosynthetically active
radiation (fPAR) [42], surface albedo [43], solar downward
radiation [44], and so on. RT-based scene simulation can
accurately simulate multispectral and multiangle images and
radiation properties of realistic landscapes with spatial het-
erogeneity and can reduce the uncertainties of scale trans-
formations. To distinguish the input uncertainty and model
uncertainty, we employed the well-validated 3-D RT model
LargE-Scale remote sensing data and image Simulation frame-
work (LESS) [45] to simulate real forest scenes. Further-
more, this article investigated the effects of different factors
[e.g., solar zenith angle (SZA), solar azimuth angle (SAA)] on
the DPM accuracy by controlling the parameters of the simula-
tion experiment and quantitatively evaluated the performance
of different VI-based DPMs. Specifically, we: 1) calculated
the theoretical uncertainty of FVC retrieval caused by input
uncertainties based on the law of propagation of uncertainty
(LPU); 2) evaluated six VI-based DPMs (i.e., NDVI-, RVI-,
DVI-, EVI-, EVI2-, and RDVI-based model) over simulated
3-D forest scenes influenced by vegetation coverage, solar
position, vegetation clumping type, and endmember purity;
and 3) discussed the comparison of FVC uncertainty between
LPU calculation and LESS simulation experiment.
II. DATA AND METHODS
A. DPM and VI
The DPM assumes that the surface of a pixel is either vege-
tated or nonvegetated, and the spectral response of the pixel is
also linearly synthesized by these two components [28]. FVC,
as the weight, exhibits a linear relationship with the spectral
response of a mixed pixel [25]. Thus, VI as the most practical
spectral response can be represented by
VI =VIsoil ×(1FVC)+VIveg ×FVC (1)
where VI is the vegetation index of a specific mixed pixel, and
VIveg and VIsoil are the VI values of pure vegetation and bare
soil pixels, respectively. There are many ways to derive VIveg
and VIsoil from remote sensing images [46], and they can be
considered as known parameters.
Based on (1) and considering the physical constraint, we can
easily express FVC as
FVC =VI VIsoil
VIveg VIsoil
0FVC 1.
(2)
During the last decades, several VIs have been proposed.
In this article, we selected the six commonly used VIs [NDVI,
RVI, DVI, EVI, EVI2, and RDVI (see Table I)] to test the
performance of the DPM.
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YA N et al.: EVALUATION OF THE VI-BASED DPM FOR FVC ESTIMATION 3
TAB LE I
SIX VISUSED IN THIS STUDY
B. Propagation of Input Uncertainty
The uncertainty of the DPM is not only related to the
model assumption but also related to the input uncertainty.
We utilized the LPU to study the propagation process of the
reflectance uncertainties into the FVC retrieval.
In the LPU, the measurement uncertainty is obtained by
taking into consideration the relative uncertainty component
of each input quantity in the model equation relating the
input quantities to the output. If x1,x2,x3,···xnare the input
quantities and the output Zis a function of the input quantities:
Z=f(x1,x2,x3,···
,xn).
The uncertainty of Z(designated by uz)represents the
estimated standard deviation of the dependent variable Z.
The general expression for uncertainty propagation is as fol-
lows [47], [48]:
u2
z=
N
i=1f
xi2
+2
N1
i=1
N
j=i+1
f
xi
f
xj
uxi,xj(3)
where uzis the uncertainty (standard deviation) of the depen-
dent variable Z;uxiis the uncertainty associated with the input
quantity xi;uxi,xj is the estimated covariance associated with
xiand xj; the partial derivatives f
xiare equal to that evaluated
at X=xi.
If there is no correlation between the input quantities (we
discuss this hypothesis in the Discussion section), (3) can be
simplified as
u2
z=f
x1
ux12
+f
x2
ux22
+··· +f
xN
uxN2
.(4)
In our study, we took different band reflectance as the input
quantities and regarded FVC as the output quantity. VI is
a quantitative value calculated from surface reflectance (SR)
(Table I). Here, we take NDVI as an example (other VIs are
given in the Appendix) for the derivation of the FVC formula
NDVI =NIR R
NIR +R.(5)
Fig. 1. Uncertainties of Landsat-OLI SR and Aqua-MODIS SR derived from
publications.
Then, the NDVI equation is introduced directly into (2) and
we get
FVC =1
NDVIveg NDVIsoil
×NIR R
NIR +RNDVIsoil.(6)
NDVIsoil and NDVIveg are constants. So, the uncertainty of
FVC (uFVC)depends on the relative uncertainties of the
red and NIR band reflectance. To simplify the approach,
we regarded the multi-band reflectance as independent vari-
ables (detailed in the Discussion section). According to (4),
uFVC is given by
u2
FVC =FVC
NIR uNIR2
+FVC
RuR2
(7)
where
FVC
NIR =1
NDVIveg NDVIsoil
×2R
(NIR +R)2
FVC
R=1
NDVIveg NDVIsoil
×2NIR
(NIR +R)2
.(8)
This article analyzed the FVC uncertainty propagation from
Operational Linear Instrument (OLI) and Moderate Resolution
Imaging Spectroradiometer (MODIS) reflectance based on
LPU and different VI-based models. We digitized the data
from published figures and replotted them in Fig. 1. The OLI
SR uncertainty was derived from [49], and the MODIS SR
uncertainty was from the science team website (https://modis-
land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD09). The
SR uncertainties were obtained by comparing the SR products
with a reference obtained by the Second Simulation of the
Satellite Signal in the Solar Spectrum (6S) model with the
observations from the Aerosol Robotic Network (AERONET)
sites [49].
C. LESS RT Model
The LESS model is a newly proposed ray-tracing-based
3-D RT Model, which can accurately and efficiently simu-
late multispectral and multiangle images and radiation prop-
erties of complex realistic landscapes [45]. The accuracy
of the LESS model is validated in comparison with other
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4IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Fig. 2. Spectra of three types of soil (dark, medium and bright) and one type of vegetation (European birch) used as inputs for the LESS simulation. The
filled and unfilled curves represent the SRFs of the MODIS and Landsat-OLI sensors, respectively; the blue, orange, and crimson colors represent the blue,
red, and NIR spectral bands, respectively.
models (e.g., FLIGHT [50], RAYTYAN [51]) over several
different homogeneous and heterogeneous canopies from the
radiation transfer model intercomparison (RAMI) experiment
(http://rami-benchmark.jrc.ec.europa.eu/HTML), and also val-
idated with a publish field measurement data sets [52], [53].
The basic inputs of the LESS model are the 3-D structure
(e.g., tree height), component spectrum (e.g., leaf reflectance),
sun-sensor geometry, and illumination parameters (e.g., sky
light proportion) of the scene. LESS can simulate the trans-
mission process (absorption, reflection, and transmission) of
incident light in a scene based on ray-tracing strategy, and
output the corresponding simulated variables (such as direc-
tional reflectance, albedo, fPAR, and so on). Due to the high
computation efficiency, solid theoretical foundation, as well
as well-assessed accuracy, the output of LESS model can be
used as benchmarks for various applications in remote sensing,
forestry, photogrammetry, and so on [42], [44], [54]–[56].
Further detailed information on the LESS model can be found
on the website (http://lessrt.org/).
D. 3-D Forest Scene Simulation
The LESS model was run to simulate actual satellite obser-
vations of canopy reflectance based on the spectral response
functions (SRFs) of the MODIS and OLI sensors, and used
to calculate multiband bidirectional reflectance factors (BRFs)
of the simulated scenes. The simulated scenes are covered
by European birch and its component (i.e., leaf) spectra were
downloaded from the LOPEX93 data sets on the OPTICLEAF
website (http://opticleaf.ipgp.fr/). The soil reflectance curves
were selected from the spectral library (soils.sli file) of the
ENVI software (https://www.ittvis.com/). We sorted 25 soil
spectral curves from low to high, taking the mean values of the
first 10%, the middle 10%, and the last 10% as dark, medium,
and bright soils (Fig. 2).
We calculated the broadband reflectance and transmittance
using SRFs of MODIS and OLI sensors with the following
equation:
R=
λmax
λ=λmin
SλRλλmax
λ=λmin
Sλ
T=
λmax
λ=λmin
SλTλλmax
λ=λmin
Tλ
(9)
where Rand Tare the broadband reflectance and transmit-
tance, respectively, λmin and λmax are the wavelength lower
boundary and upper boundary detected by the sensor, and Sλ
is the SRF value of the sensor at the wavelength λ.Rλand
Tλare the narrowband reflectance and transmittance derived
from the spectral curves.
E. Experimental Design
This study focused on the influence of five factors on
FVC retrieval: FVC magnitude (Exp. I), solar position
(Exp. II), vegetation clumping type (Exp. III), endmember
purity (Exp. IV), and input uncertainty (Exp. V). To illustrate
the roles of these factors, we designed five experiments
following the principle of a single variable.
The purpose of Exp. I was to analyze the influence of
vegetation cover on FVC retrieval. The range of FVC truth
designed in LESS was 0.1–0.9 with an increment of 0.2
(Fig. 3). Exp. II was used to analyze the influence of the solar
position, including two key variables, SZA and SAA. Exp. III
served to analyze the influence of vegetation distribution.
We simulated three scenarios (Fig. 4). Exp. IV studied the
influence of endmember purity on DPM accuracy. We assumed
that the scene with FVC truth of 0, 0.1, 0.2, and 0.3 was
soil endmembers with the purity of 100%, 90%, 80%, and
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YA N et al.: EVALUATION OF THE VI-BASED DPM FOR FVC ESTIMATION 5
TAB LE I I
EXPERIMENTAL PARAMETER DESIGN
Fig. 3. 3-D forest scenes with five different FVC magnitudes.
(a) FVC =0.1. (b) FVC =0.3. (c) FVC =0.5. (d) FVC =0.7.
(e) FVC =0.9. They are all in uniform distribution.
70%, respectively, and the scene with FVC truth of 1, 0.9,
0.8, and 0.7 was vegetation endmembers with the purity of
100%, 90%, 80%, and 70%, respectively. Exp. V was based
on Exp. I, adding OLI and MODIS reflectance uncertainties,
and studying the effect of reflectance uncertainty on the DPM
accuracy. All experiments consider multiple scattering with
simulated reflectance of 400–900 nm, and model of individual
trees was from the 3-D Object of LESS. The scene sizes of
OLI and MODIS were 30 30 m and 500 500 m, respec-
tively. The experimental parameters are shown in Table II, and
the research process is shown in Fig. 5.
III. RESULTS
A. Uncertainty of FVC Based on LPU
To analyze the FVC uncertainty caused by the input
reflectance, we calculated the FVC theoretical uncertainty of
Fig. 4. 3-D forest scenes with three different vegetation distribution
types. (a) Uniform distribution. (b) “Half-half” distribution. (c) Row structure
distribution.
six VI-based models by propagating the SR uncertainty. The
plots of the OLI and MODIS FVC theoretical uncertainty
versus band reflectance were shown in Figs. 6 and 7, respec-
tively. Overall, we found that the FVC theoretical uncertainty
of MODIS was higher than that of OLI. For RVI- and
EVI-based models, the FVC uncertainties of OLI and MODIS
were almost consistent. Besides, we found DVI-based model
performed best with FVC uncertainty ranging from 0 to 0.2 for
OLI [Fig. 6(b)], and the FVC uncertainty was least affected by
changes in reflectance. For NDVI-based model, the FVC theo-
retical uncertainty decreased faster as the red band reflectance
increased, which indicates that the main source of uncertainty
is in the red band. RDVI performed similar to NDVI, but the
overall uncertainty was lower and less affected by changes in
reflectance. For RVI- and EVI-based models, the denominator
of the partial derivative in (4) approached zero for a given
reflectance, leading to very large FVC uncertainty; for this
reason, we put a 0–1 limit on the FVC uncertainty of the
LPU. As shown in Fig. 6, the range of FVC uncertainties
of the RVI- and EVI-based models (colorbar ranging from
0 to 1) was larger than that of NDVI- (colorbar ranging from
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6IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Fig. 5. Flowchart of the designed experiments. SR, VI, LPU, and DPM represent SR VI, LPU, and DPM, respectively. LESS is a 3-D RT model for the
realistic scene simulation. Green indicates the study of SR uncertainty based on LPU, red indicates the study of LESS simulation experiments, and blue
indicates the study of LESS simulation experiments adding input uncertainty.
0 to 0.25), DVI-, RDVI-, and EVI2-based models (colorbar
ranging from 0 to 0.05). For RVI-based model, the FVC
uncertainty was much higher in the case of extremely low
red band reflectance and high NIR band reflectance than
in other cases. The EVI-based model determined by three
bands [Fig. 6(c)] was distinct from the other indices, showing
higher FVC uncertainty in low red reflectance and high blue
reflectance.
B. Uncertainty of FVC Based on LESS simulation
In Exp. I, we simulated scenes with FVC truth of 0.1–0.9
over three soil types and calculated the corresponding FVC
retrievals. The histograms in Fig. 8 show the absolute dif-
ference (AD =FVC retrieval - FVC truth) for different VIs
over different soil types. We observed that the FVC retrieval
variations of OLI and MODIS for most cases were highly
consistent. Overall, DVI-, EVI-, and EVI2-based models per-
formed better, both in AD magnitudes and its trend changed by
different FVC truth (the absolute values of ADs in most cases
were below 0.1). Fig. 8(b), (e), and (f) shows increasing trends
in FVC retrievals with soil brightness, indicating that the DVI-,
EVI-, and EVI2-based models were sensitive to soil brightness.
DVI-, EVI-, and EVI2-based models performed best over dark
soil background (AD 0). NDVI- [Fig. 8(a)] and RDVI-based
[Fig. 8(d)] models performed similarly, both overestimating
FVC and showing the opposite trend of other indices as the
soil brightness changed. The NDVI-based model was very
sensitive to soil brightness, especially in the case of low
vegetation cover (FVC =0.1: AD of dark soil =0.13; AD of
medium soil =0.8; AD of bright soil =0.6). Compared with
the NDVI-based model, the FVC retrievals of the RVI-based
model were smaller and its sensitivity to soil brightness did
not decrease with FVC truth [Fig. 8(c)]. The RVI-based model
generally underestimates under low vegetation cover (FVC
truth <=0.5) and overestimates under high vegetation cover
(FVC truth >=0.7). Besides, RVI-based model was not sen-
sitive to soil brightness under low and high vegetation cover,
while it was significantly sensitive under medium vegetation
cover (FVC truth =0.3 0.7).
Fig. 8 also compares the FVC retrievals considering the
input uncertainties and FVC truth. The result shows that
RVI-based model was most sensitive to reflectance uncertainty;
especially when the FVC truth =0.5, the standard deviation is
up to 0.23. The DVI-based model had the lowest sensitivity to
reflectance uncertainty, and the maximum standard deviation
only can reach 0.067. Besides, we noted that the EVI2-based
model was less sensitive to reflectance uncertainty than EVI
(standard deviation range of EVI: 0.042–0.090; standard
deviation range of EVI2: 0.036–0.071). When FVC truth =
0.9, the standard deviation of the NDVI-based model is small
(about 0.038), which means that the NDVI-based model
is not sensitive to input uncertainty under high vegetation
cover.
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YA N et al.: EVALUATION OF THE VI-BASED DPM FOR FVC ESTIMATION 7
Fig. 6. FVC uncertainty propagated from the SR uncertainties of OLI based on (a) NDVI-based model, (b) RVI-based model, (c) EVI-based model, (d)
DVI-based model, (e) RDVI-based model, and (f) EVI2-based models. Colors correspond to FVC uncertainty magnitudes. Black lines and dots represent soil
lines. For EVI, the reflectance increment is 0.005 in red and blue bands and 0.015 in NIR band. For other VIs, the reflectance increment is 0.02 in red and
NIR bands.
Fig. 7. FVC uncertainty propagated from the SR uncertainties of MODIS based on (a) NDVI-based model, (b) RVI-based model, (c) EVI-based model, (d)
DVI-based model, (e) RDVI-based model, and (f) EVI2-based models. Colors correspond to FVC uncertainty magnitudes. Black lines and dots represent soil
lines. For EVI, the reflectance increment is 0.005 in red and blue bands and 0.015 in NIR band. For other VIs, the reflectance increment is 0.02 in red and
NIR bands.
C. Influence of the Solar Position
We studied the influence of the solar position in Exp. II.
In the case of uniform distribution, the shadow area increased
with the increase in SZA, and the change trends of the FVC
retrievals from different models were approximately the same
as the change of the shadow area (Fig. 9). To further study
the effect of sun incident angle on the accuracy of the model
under different vegetation distributions, we designed Exp. III
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8IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Fig. 8. Absolute difference (AD =FVC retrieval FVC truth) between FVC retrieval and FVC truth. The colorbars represent AD without considering
reflectance uncertainties, and the black dots and vertical lines represent the mean values and standard deviations of the result after adding the reflectance
uncertainties. The green series is for OLI, and the blue series is for MODIS. The same color series from dark to light indicates dark soil, medium soil, and
light soil types, respectively. Panels a, b, c, d, e and f are for NDVI-, DVI-, RVI-, RDVI-, EVI-, and EVI2-based models, respectively.
based on Exp. II and the result was shown in Fig. 10. In the
scene with uniform vegetation distribution, the AD of FVC
calculated by different VI-based models increased with SZA.
Under the same SZA, AD hardly changed with the SAA
[Fig. 10(a)]. In the case of the “half-half” clumped distribution,
the AD of FVC was symmetrical along the 90–270line, and
the FVC was the largest when the SAA was 90and 270,and
the SZA was 70[Fig. 10(b)]. In the case of the row structure
distribution, the AD was symmetrical along the 0–180line,
and the FVC retrieval was the largest when the SAA is 0and
180, and the SZA is 70[Fig. 10(c)]. The change trends of
the FVC retrievals in different vegetation distributions were
also approximately the same as the change of the shadow
area (Fig. 11).
Overall, the uncertainties of VI-based models were sorted
from low to high: DVI <EVI <EVI2 <RDVI <RVI <
NDVI. The NDVI-based model showed a large uncertainty,
but was the least affected by the solar position. Under the
“half-half distribution,” the maximum and minimum differ-
ence of absolute difference was about 0.3; the RVI-based
model was greatly affected by solar angle, and the max-
imum and minimum difference of absolute difference was
the largest (0.65) under “half-half distribution. For DVI-,
RDVI-, EVI-, and EVI2-based models, the ADs were relatively
small, but they were more affected by the solar position
than NDVI.
D. Influence of Endmember Purity
In Exp. IV, we studied the influence of endmember purity on
the model accuracy. The result shows that AD of FVC retrieval
was greatly affected by soil purity in low vegetation coverage
Fig. 9. (a) Variation of the four-component ratio as a function of SZA (FVC
is fixed at 0.5, the solar azimuth is 0). (b) Absolute difference (AD) between
FVC retrieval and FVC truth at different SZA.
areas (Fig. 12), while is greatly affected by vegetation purity
in high vegetation coverage areas, especially for the RVI-based
model. This may be explained by the proportions of vegetation
and soil in the scenes.
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YA N et al.: EVALUATION OF THE VI-BASED DPM FOR FVC ESTIMATION 9
Fig. 10. Variation of absolute difference (AD =FVC retrieval - FVC truth) with the change of SZA for three vegetation distribution types. (a) Uniform
distribution. (b) “Half-half distribution. (c) Row structure distribution.
Fig. 11. Proportion of shadows in the scene changes with the sun position. The radial direction represents the SZA. The axial direction represents SAA.
(a) Uniform distribution. (b) “Half-half” distribution. (c) Row structure distribution.
We noted that for the NDVI-based model, in low and
medium vegetation cover areas, the AD was the smallest
when the soil purities were 82% and 70%, respectively.
In high vegetation cover areas, the AD was positive, and the
minimum value was 0.19, indicating that FVC retrieval was
overestimated. For RVI-based model, FVC retrieval under low
vegetation cover was underestimated. In medium vegetation
cover areas, the AD was the smallest when the vegetation
purity is 70%. Besides, we noted that the error trends of the
DVI-, EVI-, RDVI-, and EVI2-based models were similar.
When the purity of the endmembers was equal, the absolute
differences of these four VI-based models ordered from large
to small were: RDVI >EVI2 >EVI >DVI.
IV. DISCUSSION
A. Discussion About Results
In the present study, we investigated the effect of input
uncertainty on the DPM accuracy (Part A of the Results).
We found that DVI-, RDVI-, and EVI2-based models were
greatly influenced by input uncertainty when NIR SR =0.1.
This striking observation could be explained in Fig. 1: when
SR truth is around 0.1, the absolute uncertainty of the MODIS
NIR band is abruptly elevated. Beyond that, as elaborated
in Part B of Results, RVI-based model was most susceptible
to different vegetation cover among all six VI-based models.
The standard deviation of RVI-based model was significantly
higher under high and middle vegetation cover than under
low vegetation cover. Thus, this characteristic should be taken
note of when RVI-based model is applied for FVC estimation.
This finding is consistent with Guo and Zeng [57]. We also
found that EVI-based model was affected by the reflectance
uncertainty more than EVI2-based model, which is caused by
the fact that EVI2 replaces the blue band with the red band
whose atmosphere correction uncertainty is much smaller (see
Fig. 1) [49].
We also studied the effect of different soil types and
different FVC on the DPM accuracy in Part B of the Results.
The FVC retrieved by DVI-based model increased with soil
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10 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Fig. 12. FVC absolute difference (AD =FVC retrieval FVC truth) as a function of endmember purity. The three layers represent FVC magnitudes for
each VI-based model. Panels a, b, c, d, e and f are for NDVI-, DVI-, RVI-, EVI-, EVI2-, and RDVI-based models, respectively.
brightness, and with larger increments under middle vegetation
cover than low and high vegetation cover. These findings
corroborate the ideas of Ding et al. [3], who found that DVI
increased nearly linearly with an increase in soil reflectance
under low vegetation cover and increases exponentially with
the great levels of vegetation cover due to the nonlinear mixing
of the NIR reflectances of vegetation and soil. Besides, we also
observed that NDVI- and RVI-based models were sensitive to
changes in soil brightness, which is confirmed by Huete and
Jackson [35]. In addition, the NDVI-based model saturated
readily under high vegetation cover can be seen obviously.
Chen et al. [58] have demonstrated that this saturation is
related to the spectral ranges of red and near-infrared channels.
After comprehensive consideration of LPU calculation and
LESS simulation, DVI-based model showed excellent stability
and accuracy than the other five VIs, which is in good
agreement with Xu and Shen [59].
B. Drawbacks of the DPM
The DPM assumes that the surface of a pixel is either
vegetated or nonvegetated, and the spectral response of the
pixel is also linearly synthesized by these two components.
However, this assumption does not hold in the real scenario,
and in turn affected the accuracy of FVC calculated by DPM.
For example, when the incident light is off-nadir, the pixel
is composed of four components, which are sunlit soil,
shadow soil, sunlit vegetation, and shadow vegetation [29].
In Section III, we have evaluated absolute difference of
different VI-based models and their changes with the increase
of SZA from 0to 70, and discovered that the AD of
the NDVI-based model was minimally changed with SZA.
We can induce from this phenomenon that NDVI is least
sensitive to shadow. The ground-based measurement result
of Zhang et al. [60] also evidenced it. Multiple endmember
spectral mixture model (MESMM) has been used to address
the case where the pixel contains multiple endmembers [61].
Another drawback in DPM is the absence of consideration
of multiple scattering between vegetation and soil [62]. In real-
world scenarios, most field plots are patchy, of complex spatial
structures, and the effects of multiple scattering can hardly be
ignored [63]. Hence, the actual interactions between endmem-
ber abundances and spectra were inherently nonlinear, which
leads to unavoidable biases of FVC retrieved by DPM. In the
Exp V, we fixed vertical irradiation of sunlight to exclude the
effect of a pixel containing multiple endmembers, and then
examined the influence of endmember purity on the model
accuracy. Our results suggest that the accuracy of DPM does
not always continually increase as the increase in endmember
purity owing to the existence of theoretical deficiency. Among
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YA N et al.: EVALUATION OF THE VI-BASED DPM FOR FVC ESTIMATION 11
Fig. 13. Comparison of FVC uncertainty from LPU calculation and
simulation experiment. The number at the end of the legend represents the
correlation coefficient (R2). The size of the point represents the true FVC.
all six VI-based models, the NDVI-based model was most
affected by multiple scattering. At present, several nonlinear
mixture models have been put toward reducing the impacts of
multiple scattering [64]–[66].
C. Comparison of FVC Uncertainty From LPU and
Simulation Experiment
In the Methods and Results sections, we assumed that
there is no correlation between the input quantities (spec-
tral SR) for the LPU calculation. However, confirming
the study obtained by Jiang et al. [29], the correlation
between different bands certainly exists for most ground
targets. Failure to account for this correlation may result
in a certain deviation of FVC theoretical uncertainty cal-
culated by LPU. Meanwhile, we found that the EVI-based
model was affected by the reflectance uncertainty more than
the EVI2-based model. Moreover, during data processing,
we employed physical constraints on broadband reflectance
(0 <SR <1) and FVC retrieval (0 <=FVC <=1)
for the simulation experiments, but employed a constraint on
FVC uncertainty (0 <=FVC uncertainty <=1) in the LPU
only. Furthermore, it is notable that the LPU calculation only
contains input uncertainty, whereas the simulation experiments
contain both input uncertainty and model uncertainty of LESS.
Here, we discuss the consistency between the two
approaches for FVC uncertainty analysis used in this study.
The results of the comparison are presented in Fig. 13. It can
be seen that the FVC uncertainty from LPU calculation and
that from simulation experiments were very close to each other
for DVI- and EVI2-based models. For NDVI- and RVI-based
models, the results under low vegetation cover are close
between these two approaches; however, the result of LPU
calculation was significantly greater than that of simulation
experiments under high and middle vegetation cover. Espe-
cially for RVI, this difference under high vegetation cover was
even up to about 0.2. This is caused by the uncertainty of the
LESS model itself and the correlation between the reflectance
of different bands. At present, we have not distinguished
between these two possibilities in our study yet, but we are
preparing a study focused on the correlation between different
bands based on ground validation.
V. C ONCLUSION
This study investigated the performance of six VI-based
DPMs for estimating FVC using a 3-D RT model. We first
calculated the theoretical uncertainty caused by reflectance
uncertainties based on the LPU. Then, we tested the per-
formance of the different VI-based models over simulated
3-D forest scenes. The multiband BRFs of these scenes were
calculated by computer simulation which helps to separate the
model uncertainty and other factors. Both MODIS and OLI
SR at their corresponding spatial resolutions were simulated.
In the analysis of theoretical uncertainty, we found that RVI-
and EVI-based models were most affected by sensors (the-
oretical uncertainty can reach 1), followed by NDVI, DVI,
RDVI, and EVI2. In the scene simulation analysis, we found
that the DVI-based model performed best, the FVC absolute
difference was less than 0.1, while the NDVI-based model
showed poor accuracy (the absolute difference can reach a
maximum of about 0.35). At the same time, SR uncertainties
further increased the uncertainty of FVC estimation, and RVI
was more sensitive to them. From the simulation experiments,
MODIS and OLI showed approximately equal uncertainties;
however, from the LPU calculation, MODIS showed larger
uncertainty than OLI. At the same time, the incidence angle
of the sun increases the shadow area, leading to an increase in
the uncertainty of the DPM. When the SZA is 70, the max-
imum bias can reach 95.2% for NDVI-based model, and the
minimum was also as large as 73.2% for RVI-based model, but
it was least affected by the change of the SZA. We also found
that different vegetation distributions affect FVC retrieved
by the DPM. In the case of uniform vegetation distribution,
the absolute difference was greatly affected by the SZA, and
did not vary with the SAA. The absolute differences under the
“half-half” and row structure distributions were approximately
symmetric. From the study of endmember purity, we found
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12 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
TABLE III
PARTIAL DERIVATIVES OF RVI, DVI, EVI, EVI2, AND RDVI
that the accuracy of DPM does not increase with the purity
of the endmember. This might be attributed to the DPM that
its theoretical assumption is unreasonable. Overall, our results
show that based on the propagation of input uncertainty and
computer simulation experiments, DVI performs best both in
accuracy and stability, and RVI performs the worst. These
analyses provide a reference for the selection of the optimal
VI for FVC retrieval based on DPM.
APPENDIX
See Table III.
ACKNOWLEDGMENT
The authors thank the help from Prof. Youtang Hong for his
fruitful discussion about the law of propagation of uncertainty.
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Kai Yan received the B.S. degree in mapping and
surveying from the Beijing University of Civil Engi-
neering and Architecture, Beijing, China, in 2011,
and the Ph.D. degree in GIS/RS from Beijing Nor-
mal University, Beijing, in 2018.
He was a Visiting Scholar with the Department of
Earth and Environment, Boston University, Boston,
MA, USA, from 2014 to 2016. He is an Assistant
Professor with the School of Land Science and Tech-
niques, China University of Geosciences, Beijing.
He was involved in the generation and assessment
of official MODIS/VIIRS global leaf area index (LAI) and fraction of
photosynthetically active radiation absorbed by vegetation (fPAR) products.
His research interests include the bidirectional reflectance distribution func-
tion (BRDF) modeling and LAI&fPAR retrieval.
Si Gao was born in Henan, China. She is a Senior
Student with the China University of Geosciences,
Beijing, China. Her major is surveying and mapping
engineering and her interest is vegetation quantita-
tive remote sensing. She will pursue her master’s
degree in remote sensing under the direction of
Dr. Kai Yan from 2021.
Haojing Chi was born in Henan, China. She is a
Senior Student with the China University of Geo-
sciences, Beijing, China. Her major is in surveying
and mapping engineering and her interest is vege-
tation quantitative remote sensing. She will pursue
her master’s degree in remote sensing with the
Aerospace Information Research Institute, Chinese
Academy of Sciences, Beijing.
Jianbo Qi received the Ph.D. degree from Beijing
Normal University, Beijing, China, in 2019. He was
a joint-Ph.D. student with Paul Sabatier University,
Toulouse, France, from 2016 to 2018.
He is an Assistant Professor with Beijing Forestry
University, Beijing. His research interests include
3-D radiative transfer modeling, realistic forest scene
simulation, and vegetation parameter retrieval.
Wanjuan Song received the B.S. degree in geo-
graphic information science and the Ph.D. degree
in cartography and geography information system
from Beijing Normal University, Beijing, China,
in 2013 and 2019, respectively.
She was a Visiting Scholar with the Department of
Earth and Environment, Boston University, Boston,
MA, USA, from 2016 to 2017. She is with the
State Key Laboratory of Remote Sensing Science,
Aerospace Information Research Institute, Chinese
Academy of Sciences, Beijing. Her research interests
include the vegetation structure parameter estimation, vegetation remote
sensing, and the application of DSCOVR EPIC data.
Yiyi Tong received the B.S. degree in physi-
cal geography and M.S. degree in remote sensing
from Beijing Normal University, Beijing, China,
in 2017 and 2020, respectively.
Her research interests include the estimation of
radiation budget and validation of remote sensing
models in rugged terrains.
Xihan Mu received the B.S. degree in computer
science and technology from the College of Informa-
tion Science and Technology, Beijing Normal Uni-
versity, Beijing, China, in 1999, and the Ph.D. degree
in remote sensing from the School of Geography,
Beijing Normal University, in 2009.
He was a Visiting Student with the Laboratoire
des Sciences de l’Images, de l’Informatique et de la
Télédétection, Louis Pasteur University, Strasbourg,
France, in 2007, and a Visiting Scientist with the
Commonwealth Scientific and Industrial Research
Organization, Canberra, ACT, Australia, in 2016. He is with the State Key
Laboratory of Remote Sensing Science, Faculty of Geographical Science,
Beijing Normal University, Beijing. His research interest includes multian-
gular remote sensing, particularly in the retrieval/measurement of vegetation
structural parameters.
Guangjian Yan (Senior Member, IEEE) received
the Ph.D. degree from the Institute of Remote Sens-
ing Applications, Chinese Academy of Sciences,
Beijing, China, in 1999.
He is a Professor with the State Key Laboratory
of Remote Sensing Science, Faculty of Geographical
Science, Beijing Normal University, Beijing. He has
published more than 200 articles. His research inter-
ests include multiangular remote sensing, vegetation
remote sensing, radiation budget, and scale correc-
tion of remote sensing.
Authorized licensed use limited to: China University of Geosciences. Downloaded on February 08,2021 at 15:17:42 UTC from IEEE Xplore. Restrictions apply.
... Optical satellite data is mostly used for FVC estimation (Allies et al., 2021;Jia et al., 2015;Liu et al., 2021a;Tu et al., 2020Tu et al., , 2020Wang et al., 2017). Empirical models (Dong et al., 2015;Maurya et al., 2018), linear un-mixing models (Wu et al., 2021;Yan et al., 2022), physical based models (Song et al., 2017;Tu et al., 2020;Wang et al., 2017) and machine learning (ML) based models Liu et al., 2021a;Yang et al., 2016) are used for FVC estimation using optical data. In the empirical model, a statistical relationship is established between FVC and surface reflectance or vegetation indices (Dong et al., 2015). ...
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... The support vector machine was applied for image classification to extract vegetation parts, and then, the FVC data can be obtained via the division of the plant pixel count by the overall. Moreover, the NDVI-based DPM was employed in the FVC estimation (Yan et al., 2022). Equation (4) shows the calculation principle. ...
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Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R2) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m2, 51.27 g/m2, and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening.
... While it is a challenge to directly observe V s in some forests. However, the mixture model is very sensitive to the values of V v and V s (Donohue et al., 2014;Montandon and Small, 2008;Mu et al., 2021;Yan et al., 2021). Mu et al. (2018) improved the VI-based model by developing a Multi-angle VI method (MultiVI) to estimate V v , V s , and FVC. ...
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Fractional Vegetation Cover (FVC) represents the planar fraction of the land-surface covered by green foliage, and its dynamics are important for an enhanced understanding of ecosystems especially how they respond to climate change. The lack of global near-real-time satellite-based products restricts the application of FVC in ecosystem modeling, climate change, and vegetation phenology studies. Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR) spacecraft provides daily spectral reflectance of the entire sunlit Earth in the near Hotspot directions. Hotspot observations (i.e., observation in Hotspot direction which has the peak backscattering reflected radiation) with only sunlit vegetation and sunlit soil components are more suitable for FVC estimation with a two-endmember mixture model as such observations exclude contributions from shaded vegetation and soil components. In this study, an algorithm for retrieving quasi-daily FVC from EPIC based on two-endmember mixture and gap fraction models is developed. Analyses of its performance predict that the average Root-Mean-Square Deviations (RMSDs) of retrievals in FVC units is below 0.050 when compared with reference values. The RMSD is 0.043 when compared to field-based Landsat reference FVC, which confirms lower retrieval uncertainty than FVC retrieved from Low-Earth-Orbit (LEO) satellite products such as MODIS, VIIRS, and GEOV2 with RMSDs 0.049– 0.087. The comparison analyses suggest a good consistency between EPIC FVC and FVC products from LEO and geostationary (GEO) satellites sensor, SEVIRI, with RMSD values less than 0.129. EPIC allows for quasi-daily FVC estimation across the global terrestrial surface at 10 km resolution, which is an important development for numerous biophysical applications.
... However, RTMs always simplify the 3D structures and radiative transfer processes to a certain degree, which may reduce the estimation accuracy. Also, based on a physical assumption that pixel spectrum is a mixture of spectra from different components, spectral mixture analysis and pixel dichotomy model can be used [5,8,18,72]. However, the drawback is that it is difficult to obtain pure vegetation and background pixels that are required in these models. ...
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Forest canopy cover (FCC) plays an important role in many ecological, hydrological and forestry applications. For large-scale applications, FCC is usually estimated from remotely sensed data by inverting radiative transfer models (RTMs) or using data-driven regressions. In this study, we proposed a hybrid model, which combines 3D RTMs and transfer learning-based convolutional neural network (T-CNN), to estimate FCC from very high-resolution satellite images (e.g., Chinese GaoFen-2, 1 m resolution with 4 bands). Unlike common hybrid models that are purely trained with simulation data, T-CNN combines simulation data-based pre-training and actual data-based transfer learning, which is a widely used technique in artificial intelligence for fine-tuning models. The performance of T-CNN was compared with a random forest (RF) model and two general CNN models, including CNN trained with actual dataset only (Data-CNN) and CNN trained with RTM simulation data only (RTM-CNN). Results on the independent validation dataset (not used in training stage) showed that T-CNN had higher accuracy (RMSE = 0.121, R2 = 0.83), compared with RF (RMSE = 0.26, R2 = 0.61), Data-CNN (RMSE = 0.142, R2 = 0.81), and RTM-CNN (RMSE = 0.144, R2 = 0.73), which indicates that T-CNN has a strong transferability. Tests on different training sizes showed that T-CNN (0.084<RMSE<0.108) provided constantly better performances than RF (0.116<RMSE<0.122) and Data-CNN (0.103<RMSE<0.128), which demonstrates the potential of T-CNN as an alternative to RTM-based inversion and data-driven regressions to estimate FCC, especially when training data is imbalanced and inadequate.
... such as discrete anisotropic radiative transfer (DART) (Gastellu-Etchegorry et al., 2015) and large scale remote sensing data and image simulation framework (LESS) (Qi et al., 2019). Since computer simulation models describe clear physical process and have high accuracy, they are always used as a reference to validate other models (Pu et al., 2020;K. Yan et al., 2021a). However, 3D simulations involve a large number of computational steps and detailed information about scene structure (Jacquemoud et al., 1997), thus computer simulation models are not convenient for parameter retrieval. To reduce the computational cost, the RT problem has been solved using the average characteristics obtained by averag ...
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Chlorophyll is a significant indicator of plant vigor and plant health which enables to estimate final crop yield. Current experiment was conducted to acquire high resolution multispectral image from UAV and accurately estimate chlorophyll contents using spectral chlorophyll indices in vertical crop plantation. Ground truthing was performed by taking chlorophyll values from citrus leaves using (SPAD-502 Minolta) chlorophyll meter. Five vegetation indices including DVI (difference vegetation index), RDVI (renormalized difference vegetation index), MTVI2 (modified triangular vegetation index 2), SARVI (soil and atmospherically resistant vegetation index) and Iron Oxide index were derived to determine most robust index by developing linear regression models among VIs and citrus leaves chlorophyll contents. Triangular indices (MTVI2) expressed highest correlation coefficient R² = 0.81 with high accuracy and precision for ground truthed citrus leaves chlorophyll contents. MTVI2 and SARVI were proven as most robust indices to map spatial differences in chlorophyll content with R² = 0.81 and R² = 0.76, respectively. DVI, RDVI and Iron Oxide index resulted in less efficient VIs in vertical plantations with coefficient of determination (R²) values 0.57, 0.59 and 0.69, respectively. Moreover, in this study low cost multispectral UAV (DJI Phantom4 Pro) was used to monitor biophysical status of crop in real time and develop an on-field cost efficient, precise and accurate agricultural monitoring system against conventional techniques. This article has covered the aspect of chlorophyll detection using spectrally modified VIs in vertical hierarchy that was a citrus field and manual efforts were used by taking the help of UAV. The novel aspect of this study covers the chlorophyll detection in the trees which is not studied by the scientists as a part of Remotesensing.
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Fractional vegetation cover (FVC) plays an important role in the study of vegetation growth state, and the key issue is accurately segmenting and extracting green vegetation from the background. However, the shadows generated by natural lights produce extreme illuminance differences in images, which greatly reduces the vegetation extraction accuracy. The polarization information for ground objects is independent of the physical state of the reflectivity of ground objects, and it can be used to eliminate the influence of strong reflections in images to a certain extent, reduce the illuminance differences under extreme sunlight conditions, and help improve the vegetation recognition effect under shadow conditions. To improve the accuracy of vegetation segmentation under shadow conditions, this study introduces polarized reflection information for vegetation and an improved semantic segmentation network, notably a double input residual network based on DeepLabv3plus (DIR_DeepLabv3plus), with fusion strategies based on concatenation and addition is proposed. The network extracts low-level features and high-level features at different spatial scales from both light intensity (red-greenblue (RGB)) images and degree of linear polarization (DoLP) images independently by a deep residual network and atrous spatial pyramid pooling (ASPP) structure, effectively improving the accuracy of vegetation segmentation in shadow situations. The results show that the mean intersection over union (mIoU) values of vegetation without shadows, with light shadows and with shadows are 94.01%, 92.508% and 90.969%, respectively. Compared with the color index method and green fractional vegetation cover extraction from digital images using a shadow-resistant algorithm (SHAR-LABFVC), the proposed method provides a greatly improved extraction accuracy, and it has 0.18%, 1.00% and 1.49% higher mIoU values for vegetation under different shadow conditions than the method without polarization information. This study provides a new approach for vegetation segmentation and improves the accuracy of FVC calculations under shadow conditions.
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Crop residues left on the field after harvest increase soil organic matter content and improve soil quality. Linear spectral mixture analysis (LSMA) is an important technique for calculating crop residue cover. Traditionally, farmland has been considered to be composed solely of soil and crop residue endmembers. But rice paddy fields are often more complex than other fields. In the decomposition of pixels reflectance, leaving out potential endmembers greatly increases the variability of existing endmember reflectance. The error is then transferred to the rice residue endmember. In this paper, a dynamic-quadripartite pixel model (DQPM) is proposed to adapt LSMA to calculate rice residue cover (RRC) in complex paddy fields. This method considers that pixels in paddy fields are composed of four endmembers: soil, rice residues, green moss and white moss. With the approach, soil moisture can be calculated to automatically correct the reflectance of the soil endmembers of each pixel. The calculation results of our model were verified with field data and compared with those from the static-quadripartite pixel model (SQPM) without considering soil moisture (SM) content and with the dynamic-dimidiate pixel model (DDPM). Results confirm the feasibility of DQPM. The results show that with four endmembers, RRC has a large range of improved computational accuracy with DQPM compared with SQPM and DDPM. DDPM has a large error under 1% < SM < 3% (gravimetric water content) and 60% < RRC < 70%. Only when SM is near 0.3, SQPM can achieve good accuracy. Among the three models, DQPM has the best robustness under different soil moisture and RRC scenarios. Therefore, our proposed method is useful for calculating RRC in complex paddy field scenarios.
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Uncertainty assessment of the moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) retrieval algorithm can provide a scientific basis for the usage and improvement of this widely-used product. Previous evaluations generally depended on the intercomparison with other datasets as well as direct validation using ground measurements, which mix the uncertainties from the model, inputs, and assessment method. In this study, we adopted the evaluation method based on three-dimensional radiative transfer model (3D RTM) simulations, which helps to separate model uncertainty and other factors. We used the well-validated 3D RTM LESS (large-scale remote sensing data and image simulation framework) for a grassland scene simulation and calculated bidirectional reflectance factors (BRFs) as inputs for the LAI/FPAR retrieval. The dependency between LAI/FPAR truth and model estimation serves as the algorithm uncertainty indicator. This paper analyzed the LAI/FPAR uncertainty caused by inherent model uncertainty, input uncertainty (BRF and biome classification), clumping effect, and scale dependency. We found that the uncertainties of different algorithm paths vary greatly (−6.61% and +84.85% bias for main and backup algorithm, respectively) and the “hotspot” geometry results in greatest retrieval uncertainty. For the input uncertainty, the BRF of the near-infrared (NIR) band has greater impacts than that of the red band, and the biome misclassification also leads to nonnegligible LAI/FPAR bias. Moreover, the clumping effect leads to a significant LAI underestimation (−0.846 and −0.525 LAI difference for two clumping types), but the scale dependency (pixel size ranges from 100 m to 1000 m) has little impact on LAI/FPAR uncertainty. Overall, this study provides a new perspective on the evaluation of LAI/FPAR retrieval algorithms.
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