Accurate crop growth monitoring and yield predicting is very important to food security and agricultural sustainable
development. Crop models can be forceful tools for monitoring crop growth status and predicting yield over
homogeneous areas, however, their application to a larger spatial domains is hampered by lack of sufficient spatial
information about model inputs, such as the value of some of their parameters and initial conditions, which may have
great difference between regions even fields. The use of remote sensing data helps to overcome this problem. By
incorporating remote sensing data into the WOFOST crop model (through LAI), it is possible to incorporate remote
sensing variables (vegetation index) for each point of the spatial domain, and it is possible for this point to re-estimate
new values of the parameters or initial conditions, to which the model is particularly sensitive. This paper describes the
use of such a method on a local scale, for winter wheat, focusing on the parameters describing emergence and early crop
growth. These processes vary greatly depending on the soil, climate and seedbed preparation, and affect yield
significantly. The WOFOST crop model is calibrated under standard conditions and then evaluated under test conditions
to which the emergence and early growth parameters of the WOFOST model are adjusted by incorporating remote
sensing data. The inversion of the combined model allows us to accurately monitoring crop growth status and predicting yield on a regional scale.
The characteristics of canopy spectrum and growth status of winter wheat under different soil moisture levels were studied in the field. Correlations between FMC and EWT of leaf and spectral reflectance of canopy were calculated and analysed quantitatively, and the sensitive bands to leaf water were found. Simple ratio water index(SWI)and normalized difference water index(NDWI) were constructed with the sensitive bands. Simple statistical models at different growth stages were established using spectral indices data and FMC and EWT of leaf. Bands centered at 469, 645, 700 and 710nm of VIS region, bands centered at 760, 815, 855, 930, 1075, 1100nm of NIR region and bands centred 550, 1600, 1640, 1750, 2130nm of SWIR were defined as sensitive bands to estimate leaf water content. These bands centered atmosphere windows had the potential to be applied in monitoring canopy leaf content of crop. The SWI and NDWI constructed with the sensitive bands could estimate leaf content more accurately than single band. The four band MODIS combined index: R (1640,2130) / ND (855,555) showed a good indicator to detect canopy water content of winter wheat.