Final report for the prelaunch phase ()

Title: Research on Extracting of BRDF Information of Ground Target Simulated VEGETATION Data and
Validation of BRDF Model
Principal Investigator:
Prof. LI Xiaowen
Institute of Remote Sensing Application, Chinese Academy of Sciences
Beijing 100101, P.R. China
Tel: +86-10-64879382 Fax: +86-10-64915035
Email: [email protected], [email protected]
After launching of SPOT-4, the VEGETATION will be available to have the remote sensing data of vegetation with
large FOV. The VGT data provide the opportunity to develop the researches on vegetation monitoring and correspondent
studies using the newest multiangular remote sensing data. For the methodology study of the VGT data applications, our
approaches include BRDF modeling, the model validation and the model inversion for dynamic monitoring on possible
changing of vegetation coverage. The special preprocessing methods for the remote sensing images with large FOV are
also developed, such as the images registration, atmospheric correction, radiative correction in rugged terrain.
During the past two years, supported by the vegetation preparatory programme, we got some new approaches in the
research on extracting of BRDF information of ground target from simulated VEGETATION data, BRDF modeling,
validation and inversion. According to our investigation and technical plan, our main research works include the following
I. Acquiring the VGT simulation data and the data preprocessing
1) Airborne simulation data in Beijing, images geometric-correction and registration
To obtain the simulated VGT data set, we designed our airborne bi-camera imaging system. The system consists of a
visible CCD camera with three bands, and a NIR CCD camera. The FOV of both cameras is about 90 degree. The digital
image matrix is 512*512. When the flight high is 500 meters, the space resolution is about 2.3 m.
The first airborne flight of the bi-camera system was in the Oct. of 1995. The selected ground target is the Xishatun
orchard in Changping county, the north of Beijing city. By careful design of the flight paths, we got several images of the
given targets with different viewing directions. To get BRDF of the targets, the images was preprocessed by our digital
image processing and analysis system. System correction and precise geometric correction were made by ground control
points (GCP) based on the local map. The images of the given targets were registered, so that the BRDF of the targets can
be extracted. In our practice, for example, for an apple tree site, we got reflectance samples in 75 directions[1]. The result
tells that our system is able to obtain the BRDF from the multiangular remote sensing images. These BRDF data then was
used in our BRDF model inversion studies, which is the basic research for retrieving some ground truth parameters, such
as vegetation canopy structure (refer to the part III of this report in detail).
2) Images registration of AVHRR Images in Three-Gorges reservoir area and Namco lake area to get directional
reflectance and emission data
The AVHRR images is also taken as the simulated VGT data in our researches. Our typical target is the Three-Gorges
Reservoir Area (TGRA) and Namco lake. In the coming years, as the Three-Gorges Dam, the world's biggest water control
project, is established, the water level of TGRA will gradually get higher, from now about 80 meters to 175 meters in 2014.
The water body will be narrow and more than 600 km long in shape. During this period, vegetation coverage in the area
will be changed obviously. To monitor the changing by remote sensing method is very important for the regional
ecosystem balance, and also for global ecosystem.
Namco Lake locates in Tibet, between 90.27-91.05E, and 30.50-30.92N. It is about 4718 meters high above sea level,
with about 1920 square km. So this is a very good place for remote sensing sensor in-orbit Calibration.
AVHRR images in Three-Gorges reservoir area and the Namco lake area was received in Aug. and Sept. of 97
respectively. In the period of the satellite path shifting, several images of the given targets were obtained. The images
automatic registration method was developed to process these images. The obvious corner points can be extracted directly
from two gray-level pictures as the interest points. The algorithm of weighted correction is used to match the interest point
pairs[2]. After geometric correction by GCP and registration, we got the directional reflectance and brightness temperature
of the targets in several directions from the AVHRR images. The same method of the images registration can be used in
the other simulated VGT data.
3) BRDF based atmospheric correction, the approach on the convergence of the algorithm
Quantitative remote sensing requires atmospheric correction, which requires directional reflectance or emission of the
earth surface as the boundary condition of the radiative transfer problem. But the actual reflectance or emittance
characteristics of the earth surface are what the quantitative remote sensing aims at. This presents as a deadlock. In order
to solve the problem, we suggested a BRDF-based correction loop. The convergence of such an iteration loop is studied in
theory. Through the study of the mechanism of the BRDF-based atmospheric correction, we analyzed theoretically the
loop's convergence and its conditions, proved that such a correction loop can also be applied in the atmospheric correction
of the off-nadir remote sensed data of thermal infrared bands[3].
4) Radiative correction in rugged terrain region
In rugged terrain, irradiance received by different pixels may differ significantly due to various slopes and aspects. As
a result, the same targets can show different brightness values in different slopes, on the other hand, different targets may
have the close values. The topographic effects make the quantitative remote sensing more difficult. It is urgent to develop
an effective method to remove the topographic effects, that is, to retrieve the true reflectance of the ground surface .
A radiative transfer model based on physical analysis is developed in mountainous area, the surrounding environment
effects are computed analytically in the whole test site. We can decompose the received irradiance of a ground point into
three sources: 1) direct irradiacne, after attenuated by atmosphere it may be shadowed by the slope itself or nearby terrain;
2) downward diffuse irradiation which a portion of it may be obstructed; 3) irradiance reflected by the adjacent terrain
towards the studied point. After modeling the three parts respectively, the reflected properties of an actual physical surface
can be described using bi-directional reflectance distribution function (BRDF) .To simplify the equation, an equivalent
perfectly diffuse surface is assumed.
The atmospheric parameters required in the model are obtained from the image itself . A priori knowledge is used to
speed the computation and improve the reliability of the topographic correction. Based on the radiation model, the true
reflectance of the ground surface can be retrieved exclusively by combination of the digital elevation data and the original
TM data.
Our study site is selected in Three-Gorge Reservoir Area, located in Wanxian county of Sichuan province. A digital
elevation model (DEM) with a resolution of 1 meters for elevation and 30 meters for space is used. A Landsat TM scene of
24 November 1995 for the study area is matched to the DEM, and a sub site of 256*256 is selected as our source spectral
data. The center of this sub area is at 30.5 degree north latitude and 108.5 degree east Longitude. At the time of satellite
overpass the center, the sun zenith angle is 31 degree, and the azimuth angle is 144 degree. This area is dominated by
mountains, the elevation is between 329 and 1197m.
In our processing, "Ray Tracing" is used to obtain the caused shadow coefficient. Sky view factor is come form
Dozier's rapid algorithm. However, non-uniform sampling angles are used in the searching process for local horizon, and
adjacent reflected irradiance to each point is considered in the process of calculating sky view factor. The atmospheric
parameters are inverted using POWELL algorithm. The expected values and physical bounders are come from a priori
knowledge, 6S code are used as a reference in the estimation of the bounders and expected mean values.
After all the parameters needed in our radiative model are obtained, we can retrieve the true reflectance based on
radiation equation. The corrected reflectance maps for TM bands 2, 3 and 4 were gained, from which the correction effects
are seen clearly. From the visual comparison to the original TM images, it is obvious that the topographic effects are
removed in most of the rugged area.
II. BRDF information extraction and the study of BRDF model verification
1) An inverting approach of BRDF for wheat in North China using AVHRR images
Because the relationship between LAI and NDVI is not unique and vary with structure of vegetation, we adopted
AVHRR images in the northern great plain of China to map distribution of the leaf area index (LAI), average leaf angle
and so on, instead of using NDVI which is very popularly used in China. We first validated N-K inversion algorithm
(developed by Dr. Nilson and Kuusk) using measurements of SE590 spectra-radiometer in Yucheng Experimental Station.
The optimum conditions for inversion are that viewing zenith ranged from 40 or so, viewing azimuth ranged -10 to 10.
The comparison between inversion result and NDVI shows some advantages of the N-K model inversion algorithm[4].
2) BRDF model validation using field measurements
The radiative transfer of light within vegetation canopy is an important part of the studies on vegetation optical
remote sensing mechanism. Based on the field measurement of reflected radiation distribution on sunlit crown, and crown
structure which is measured using our TCT indirect measuring method[5,6], the analytical approximation model of
path-scattering of light in a homogeneous layer is applied to the calculation of the reflectance of sunlit crown. The
reflectance on the sunlit crown surface is considered as the weighted sum of the direction-to-hemisphere reflectance. The
validation results show that the calculated reflectance signature fits the field measurement very well[7].
3) To calculate Albedo using POLDER data
After 10 months in-orbit running of ADEOS, POLDER data is available now, to provide the satellite borne
multiangular remote sensing data for the first time. As the participant of the ADEOS project, our research group is
working on the application research of POLDER data, which also can be taken as a simulation of VGT. From the data set,
we can have the BRDF of the targets. Taking the Three-Gorges area as one example, we are using these data to study how
to invert the albedo by our BRDF kernel driven models. The land surface structure parameters will be taken into account.
For the rugged terrain, the correction for the photographic effect should be considered at the same time. For the
homogeneous layer, such as plant, we may take the north China as the other target to study the inversion model and
inversion methodology.
III. Retrieving the 3D structure information of the target using VGT simulation data
1) Knowledge based BRDF model inversion
Physical BRDF model are usually very complex and difficulty to invert. We usually need to employ a priori
knowledge in this or that ways, fixed some parameters values and invert some others. Most of us agree that the
non-sensitive parameters should be fixed, but there has not been any consensus on how to define the sensitive and most
uncertain parameters in inversion. We suggested that only those most sensitive and most uncertain should be inverted by
using a subset of observations.
We developed a general frameworks for the inversion, It is based on accumulation of knowledge and an inversion
strategy which we called Multi-stage, Sample-direction Dependent, Target-decision (MSDT). The Uncertainty and
Sensitivity Matrix (USM) was defined as somehow analogous to the partial deviative matrix used in Newton method for
minimization, but there are three significant differences: a) the uncertainty of the initial guess is taken into account; b) it is
less dependent on the initial guess; c) all elements have the same unit and therefore quantitatively comparable.
Changping flight BRDF data set was taken as the example to study the description of the USM and to demonstrate
the MSDT strategy. The 7 parameters of Li-Strahler GOMS model was inverted using all of the samples in the observation.
The inversion is step by step depending on the USM-based MSDT, the effect is encouraging [1].
After that, our research is on the utilization of prior knowledge in BRDF model inversion. Because a physical BRDF
model with reasonable precision needs several parameters. Without a priori knowledge of these parameters, the model
inversion requires the number of independent observations great then the number of unknown parameters, this
requirement can hardly be satisfied even in the coming EOS era. So the inversion problems in geoscience are always
underdetermined problems in some sense. In order to make good use of every kind of a priori knowledge including
auxiliary information from other sensors, the right question to set for quantitative remote sensing is: given a imperfect
model and a certain amount of a priori information on model parameters, in which sense should one modify the a priori
information, given the actual observation with noise? When such a priori knowledge can be expressed as joint
probability density, Bayessian theorem can be used in the inversion to obtain posterior probability densities of parameters
using newly acquired observations. There is no pre-requirement on how many independent observations must be made,
and the knowledge gained merely depends on the information content of the new observations.
To show how the approach works, we simulated a forest site using GOMS model as an example. The crown coverage
parameters nR2 and the spectral component signature on sunlit ground G are inverted using the simulated BRDF on the
principal plane. The prior knowledge of these two parameters was given by the peak at their initial values, with a big
uncertainty. In the inversion procedure, the posterior 2D probability density distribution of the parameters shows that even
such knowledge is inaccurate and vague, the peak of posterior probability density converges to the correct value and is
stable, 31 observations are used in this case. And the posterior PDFs of the two parameters show less uncertainty when
more observations are used in inversion, the results using 1, 5 and 31 observations are given respectively. Our WSSE
(weighted sum of square error) algorithm was also taken to compare with the new approach.
Note that even a single observation changes the a priori PDF's of both parameters significantly. This does not present
as a problem for this case, since a 2.5 of standard deviation is almost null information. But if a priori PDF is real
knowledge, there will be a problem of weights for the new observations. We present suggest that if a priori knowledge is
global statistics for land cover, then a priori knowledge should have the same weight as the new observations. But if the a
priori knowledge is target specific, the sample size (i.e., the total numbers of previous observations from which the
knowledge is gained) should be kept to estimate the confidence level of the posterior PDF. Further study on this is still
2) The new approach on the directionality correction of thermal images
New sensor will provide directional thermal infrared remote sensing images. Using such images to estimate the land
surface temperature is a very interesting field for research. We plan to extend our GO model to the TIR bands, then to
develop our inversion method in this field for LST estimation to assist the dynamic monitoring of environment changing.
For this work done, first we need to calibrate the remote sensing TIR data on ground.
To find a ideal radiometric calibration field for the thermal remote sensing research, satellite data calibration and
atmosphere-ground energy exchange study, where the atmospheric affection is less, Prof. Li Xiaowen and his group visited
Namco lake , Dang Xiong county of Tibet in September 1997. During that time, work was undertaken on the inspect
around the Namco lake. The environment background, atmospheric conditions, lake temperature changes, typical objects
spectrum reflectance were evaluated and measured.
AVHRR images was obtained at the same time, the retrieved data shows the more stable brightness temperature on
the surface of the water body. Based on this first time survey, we preliminary consider that the lake area fits the need of
radiation calibration field. Further field measurement and TIR directionality modeling are going to continue now.
In our research works, several papers had been completed by investigators who are taken part in the implementation
of the project, listed in the references as following.
[1] Li Xiaowen, Yan Gangjian, Liu Yi et al., Uncertainty and sensitivity matrix of parameters in inversion of physical
BRDF models, Journal of Remote Sensing, 1(Suppl.):113-122, 1997.
[2] Zhu Chongguang, Guo Jun, Yu Fangfang, Procedures for registering POLDER airborne simulated multiangulsr images,
Journal of Remote Sensing, 1(Suppl.):198-202, 1997.
[3] Li Xiaowen, On convergence of BRDF-based atmospheric correction loop, Journal of Remote Sensing, 2(1), 1998.
[4] Zhang Renhua, Andres, Chen Gang et al., An inverting approach of BRDF for wheat in north China using
NOAA-AVHRR images, Journal of Remote Sensing, 1(uppl.):171-180, 1997.
[5] Li Xiaowen, Wang Jindi, Xiang Yueqin and Alan Strahler, Measuring leaf angle distribution and leaf area index of
conifer canopies simultaneously on wide-angle image, Journal of Remote Sensing, 1(Suppl.):54-61, 1997.
[6] Wang Jindi, Li Xiaowen and Xiang Yueqin, TCT algorithm validation using measurement data of coniferous and
deciduous crowns, Journal of Remote Sensing, 1(Suppl.):62-70, 1997.
[7] Wang Jindi, Li Xiaowen, Xiang Yueqin, Reflectance signature on sunlit crown of conifers, Science in China (Series E),
40(6):623-630, 1997.
[8] Li Xiaowen, Wang Jindi, Hu Baoxin and Alan Strahler, on utilization of a priori knowledge in inversion of remote
sensing models, Science in China (Series D), in press, 1998.