THE TEMPORAL CHANGING INFORMATION EXTRACTION 
ON SPECTRAL REFLECTANCE AND GROWTH PARAMETERS OF TYPICAL CROPS 
Xiao Yueting, Song Jinling, Wang Jindi 
School of Geography and Remote Sensing Science, State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory of 
Environmental Remote Sensing and Digital City, Beijing Normal University, 100875, Beijing, China 
xiaoyueting2099@163.com, wangjd@bnu.edu.cn, songjl@bnu.edu.cn 
Commission VII, WG VII/3 
KEY WORDS: A Priori Knowledge, Spectral Reflectance, Leaf Area Index, Vegetation Index, Model Inversion 
ABSTRACT: 
When we estimate the land surface variables by remote sensing model inversion, a priori knowledge is needed for given the initial 
values of the model parameters to be retrieved. In this paper, based on the measured datasets accumulated in the Spectrum Database 
of Typical Land Objects, we analyzed the measured data of typical crops and presented the parameters prior knowledge as the 
statistics data on canopy spectral reflectance and leaf spectral reflectance. We calculated the mean values and variances for 
constructing the initial values and uncertainties of model parameters. By taking the winter wheat as the typical crop, with the ground 
measured crop canopy spectral reflectance and leaf spectral reflectance data in the spectrum database, integrating the spectral 
response function of Beijing-1 multi-spectral sensor, we got the needed a priori spectral knowledge in three spectral band (Green: 
523nm—605nm, Red: 630nm—690nm and Near Infrared: 774nm—900nm) and achieved the transformation from narrow band to 
broad band in order to get the corresponding vegetation index, such as RVI,NDVI,SAVI and so on. With this method, we obtained 
the statistical relationship between vegetation index and LAI for estimate LAI using Beijing-1 data. The Beijing-1 multi-spectral data 
with DN value is transformed into different vegetation index. Then we could get LAI data from Beijing-1 multi-spectral image with 
the statistical relationship expressed above. The data and methods presented in this paper could be considered as valuable reference 
while estimating model parameter’s initial value and its associated uncertainty in physical model inversion. 
1. INTRODUCTION 
New views are provided to understand the earth system more 
comprehensively for people via remote sensing ways. Beijing-1 
small satellite was launched in Oct. 10, 2005 in China 
successfully. Two type of sensors were carried on the satellite, 
the 3-band multi-spectral sensor with 32m spatial resolution and 
the the panchromatic sensor with 4m spatial resolution. 
Preliminary processing system has been developed for Beijing-1 
data receiving, preprocessing, and distribution (Han Dong, 
2007). Some relative research works are needed for retrieving 
more information on land surface. 
Taking the remotely sensed data application in Beijing region as 
sample, MU Fengyun concluded the land use characteristic of 
Beijing between 1984-2005 urbanization based on three 
Landsat TM images of 1984, 1996, 2001 and one “Beijing-1” 
data obtained in 2005, which shows that “Beijing-1” data could 
service as the data base for the studies on the city planning, the 
ecological environment monitoring, etc (Mu Fengyun, 2007). 
Yang Shengtian integrated “Beijing-1”, SPOT5 and QuickBird 
images to detect and analyze the vegetation fractional coverage 
in riparian buffer zones of Guanting Reservoir. For reed 
marshes, compared with the result of using vegetation fractional 
coverage calculation equation, the mean absolute error of 
vegetation fractional coverage measured by “Beijing- T’image 
was reduced by 22.7 % after applying statistic models (Yang 
Shengtian, 2006). Wangqian studied “Beijing-1” satellite image 
application potential in land use field, showed that “Beijing-1” 
image quality is fine and land use classification precision is high 
(Wang Qian, 2007). 
For applying broadband and high spatial resolution remotely 
sensed data in agricultural applications at the field measurement 
scale, an improve method was developed to evaluate commonly 
used broadband vegetation indices (Vis) for the estimation of 
LAI with VI-LAI relationships. For the agricultural applications, 
MSAVI-LAI relationships are easy-to-apply and reasonably 
accurate for estimating LAI. (Wu Jindong, 2007). Researchers 
also have found that the broadband SAVI2 index is least 
affected by background reflectance for both LAI and CCD 
estimation, and is also the best predictor of LAI. RVI is 
marginally better than SAVI2 for CCD estimation in terms of 
canopy effects, but it becomes increasingly sensitive to 
atmospheric effects and solar zenith angle with increasing 
vegetation densities. Further, SAVI2 proved to be least affected 
by illumination geometry changes (N.H. Brogea, 2000). 
In this paper, based on the spectrum database of typical land 
surface objects, we first statistically analyze the collected 
data(including five typical crops, such as winter wheat, summer 
maize, cotton, rice and rape) and construct the prior knowledge 
of the canopy and leaf reflectance(obtaining the mean values 
and variances of measured canopy reflectance). Then we 
applied the prior knowledge to estimate LAI by using VI-LAI 
relationship from “Beijing-1” images obtained in 2006. 
2. MATERIALS 
2.1 Spectral Reflectance Datasets 
The five typical crops included in this study are winter wheat, 
summer maize, cotton, rice and rape. These datasets come from 
the Spectral Database System of Typical Objects in China