〈一般研究課題〉 東海地方における緑地面積定量を目的とした環境衛星データ解析アルゴリズムの研究
助 成 研 究 者

愛知県立大学吉岡博貴

東海地方における緑地面積定量を目的とした
環境衛星データ解析アルゴリズムの研究
吉岡 博貴（愛知県立大学）

Development of a parameter retrieval algorithm
from data of environmental satellites for estimation ofgreen coverage in Tokai area
Hiroki Yoshioka（Aichi Prefectural University）

キーワード：緑地面積，衛星画像，ASTER， LAI，被植率，放射伝達ABSTRACT
One important measure of living environment related to comfortableness is a fraction of area
covered by green vegetation. This research is to develop a technique to retrieve such parameters
related to amount of green vegetation over Tokai area Japan using space born satellite data from
ASTER sensor on board Terra platform. The major challenge is an accurate estimation of fractional
area covered by green vegetation within a single pixel at 15 m resolution. In this study, we took an
approach of inverting a set of numerical models which simulates physical processes of interactions
between solar radiation and vegetation. The results indicate that the proposed inversion techniqueperforms quite well for retrieval of both one and two vegetation parameters with a few exceptions atsome extreme conditions.

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I. INTRODUCTIONVegetation monitoring on land surface ecology can be addressed through study of a time series
satellite data. One requirement of such studies is the consistency of parameter estimations of land
surface among sensor generations and sensor types. Although several satellite sensors are currently
on orbit, there are a few discrepancies in observations among those sensors. These differences mainly
come from differences in both spatial and spectral characteristics of satellite sensor. For this reason,
the continuity and compatibility of data product from satellite sensors become important [1].
Considering vegetation monitoring in Tokai region, Japan, data from a sensor with high spatial
resolution are needed. ASTER sensor on board Terra platform has its spatial resolution of 15 m, hence
it is suitable for this purpose despite its relatively course radiometric resolution of 8 bit. The challengeof using data from ASTER sensor is associated with its band configuration: it has three bands in visible
and near infrared (NIR) wavelength ranges. Therefore, techniques of vegetation monitoring developed
for other sensors need to be modified by only using these three bands. One objective of this researchis to develop a technique to retrieve two vegetation biophysical parameters in addition to one
parameter related to its background soil brightness for the purpose of processing the data from ASTER
sensor.

II. VEGETATION ISOLINE EQUATIONS
A set of vegetation isoline equations, which describes relationships between red and NIR bands, wasderived analytically by our early work [2]. The usefulness of these expressions can be understood fromthe fact that the coefficients of the isoline equation are functions of biophysical parameters and soil line
parameters (slope and offset) written in the following form.

where ρR and ρN are red and NIR reflectances, respectively, which are functions of both vegetation
biophysical parameters and soil brightness. L represents values of LAI, a and b are the soil line slope
and offset, respectively, and Tν2 s and ρν s are the canopy-layer two-way transmittances andreflectances, respectively. Although all the variables in the above equations depend on view andillumination angles as illustrated in Fig. 1, we omit those angular variables for simplicity.

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Fig.1IIIustration of transmittances and reflectances of leaf canopy

The above equations indicate the possibilities of retrieving biophysical parameters such as LAI, byrelating a pair of ρR and ρN observed by a satellite sensor to the slope and offsets of (1). Morespecifically, our approach is to find an LAI value which results in the slope and offset of the vegetation
isoline that goes through an observation point, (ρR , ρN ), in red-NIR reflectance space.
The above set of equations were farther modified for the purpose of estimating area of green
coverage within a pixel [3]. The extension was done by introducing a parameter,β, which represents a
fraction of area covered by green vegetation within a single pixel. Using the parameter, the equationsof vegetation isoline become as follows.

whereFigures 2 and 3 shows the behavior of isoline slope and offsets. The purpose of using these
expressions is to retrieve L and β, from a given set of reflectance values (reflectance spectrum). Since
the ASTER sensor provides reflectance values at three wavelengths one sample from green, red andNIR wavelength ranges, the idea of our inversion technique is to retrieve L and β, by implicitly
eliminating the effect of vegetation background brightness. Therefore, three unknowns, L ,β, and thebackground brightness (soil brightness) will be retrieved from these three observations (by the green,red and NIR channels). The key information for this inversion technique is the slope, Eq. (5), andoffset, Eq. (6), of the vegetation isoline represented by Eq. (4). We will find a pair of L and β which
satisfies Eq. (4) with a given set of observed reflectances by the sensor.

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Fig.2

Fig.3

Vegetation isolines under 100% and 50% green cover

Isoline slope and offset for various combinations of wavelength− 24 −

III. LAI RETRIEVAL
Several parameters need to be prepared prior to the inversion process. We first discretize L into N
bins, denoted by subscript i , so that the entire red-NIR reflectance space can be divided into (N+1)
sub-regions. γ and D are then computed at those discrete points of L using a numerical canopy model[2]. The estimation of LAI value, νi (ρR , ρN ), for sub-region-i is obtained bywith the definitions of mi and θt,i ,The LAI retrieval is done using the coefficients demoted by 'i' where the sub-region-i includes thereflectance point, (ρR , ρN ).
The inversion procedure consists of the following three steps.
Step-1: Find the sub-region in the reflectance space where the reflectance point, (ρR , ρN ), is included.
Step-2: Obtain from (12) to (15).
Step-3: Compute νi (ρR , ρN ) from (9).
This inversion process can be optimized based on the spectral band-pass filters (BPFs) of the targetsensors by specifically preparing the vegetation isoline parameters, γ and D.

III. SIMULTANEOUS RETREIVAL OF LAI AND FRACTION OF GREEN COVER
Our final goal is to retrieve both L and β simultaneously from observed reflectances regardless thebrightness of vegetation background. For this purpose, two pairs of reflectances (red-NIR and greenNIR) are used to retrieve L independently represented by L r N (β) and L gN (β) by assuming a value ofβ by applying the above procedure. Then, these processes are repeated until the cost function definedby the following equation becomes sufficiently small.

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IV. NUMERICAL EXPERIMENTS
In order to examine the proposed technique, we conduct numerical studies by using models of leaf
and canopy radiative transfer by assuming hypothetical background soils. Leaf reflectance andtransmittance were simulated using PROSPECT [4], and then the results were used as inputs of SAIL
canopy model [5]. When the simulations of the top-of-the-canopy reflectance were conducted, weassumed a flat-soil spectrum , whose reflectance is constant throughout entire wavelength at twomagnitudes, from 0.05 to 0.3 to simulate soil of various brightness. Sample spectra by thosesimulations were plotted in Fig. 4. Sensor outputs were then simulated based on the sensor band-passfilters. Fig. 5 shows the simulated reflectances in red-NIR reflectance space.
The vegetation isoline parameters were obtained for each band pairs (red-NIR and green-NIR) basedon the sensor reflectances. Therefore, two sets of vegetation isoline parameters were prepared prior to
the inversion. The inversion is then conducted using the sensor-specific inversion coefficients against
the reflectances shown in Fig. 5.

Fig.4

Fig.5

Simulated vegetation spectra

Scatter plot of simulated reflectance spectra in red-NIR reflectance space− 26 −

V. RESULTS AND DISCUSSIONS
We first show the retrieval of L under a know value of β. Fig. 6 shows errors in the estimation of L
under two soil brightness. The results indicate two things. L was retrieved quite successfully for mostof the cases except few points. The retrieval tends to be unsuccessful when the cases with high L andlow β. This trend of performance difference comes from the fact that we expect very small differencesin values of reflectance spectra when L becomes large at low β, since this corresponds to thickvegetation within a very small portion of area in a target pixel. Thus, inversion becomes extremely
difficult at this extreme condition for any techniques.
Knowing the tendency of the retrieval difficulties we then move on to the simultaneous retrieval ofboth L and β. Figures 7 and 8 show the errors in L and βat two values of soil brightness, 0.05 and 0.1
in reflectance units, respectively. The tendency of retrieval error becomes more complicated than theestimation of single parameter. However, the trend of difficulty stays almost the same as the previous
cases: the parameter retrieval becomes harder at the combination of higher L and lower β. One
distinct difference between the results of Fig. 6 and Figs. 7 and 8 is that the errors in both L and β
become relatively higher at low LAI throughout entire range of green cover. This may indicates thebreakdown of the uniqueness of solution. Farther studies will be needed to identify the causality of thistrend.

Fig.6

Fig.7

Error in retrieved LAI values under two kinds of soil brightness

Errors in retrieved values of LAI and fraction of green cover under darker soil

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Fig.8Errors in retrieved values of LAI and fraction of green cover under brighter soil

VI. CONCLUDING REMARKS
An inversion technique developed for the use of ASTER sensor based on the vegetation isoline
equation was introduced and demonstrated its performance with a numerical experiment assuming aspectrally flat soil. The results showed good performance of the LAI retrieval for the entire range ofLAI with a few exceptions. We also extend the technique to the simultaneous retrieval of twoparameters, L and β. Numerical experiments indicated that simultaneous retrieval becomes harderwhen LAI is small in addition to the cases where single parameter retrieval was unsuccessful. One
possible reason is that several combinations of these parameters may result in very similar spectra. In
that case, the parameter combinations can not be determined uniquely. Farther studies are needed toclarify this point.

ACKNOWLEDGEMENT
This work was supported by the Hibi research grant.

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EOS Transactions,

AGU, vol. 83, no. 1, pp. 1-7, 2002.
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Derivation of vegetation isoline equations in Red-NIR

IEEE Trans. Geosci. Remote Sensing, vol. 38, pp. 838-848, 2000.

[3] H. Yoshioka, T. Miura, A. R. Huete, and B. D. Ganapol,
reflectance space,Analysis of vegetation isolines in Red-NIR

Remote Sens. Environ. vol. 74, pp. 313-326, 2000.

[4] S. Jacquemoud and F. Baret,

PROSPECT: A model of leaf optical properties spectra,

RemoteSens. Eniron., vol. 3, pp. 43- 47, 1980.
[5] W. Verhoef,
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