Remote sensing data combined with crop model is an important application and development trend of current
agricultural information technology, it can solve the problem that remote sensing or crop model cannot solve alone. In
order to simulate crop growth and yield prediction in large scale, this paper using field test data to calibrate and
validation the model parameters before apply to the winter wheat WOFOST model, than according to the actual
environment of Xinxiang, simulate the growth in 3 different condition in the 2002-2003 growing season. Contrast the
simulation value WOFOST model, using the Landsat-7 ETM retrieving leaf area index, define winter wheat’s growth
condition in each pixel, the remote sensing information combined with crop model is accomplished at pixel scale. Based
on the actual production of Xinxiang winter wheat in 2003,compare the simulate results with the corresponding
parameter, results shows that the method of this study method is feasible.
The land surface temperature (LST) plays an important role in the process of interaction between surface and atmosphere.
It is widely need in meteorology, geology, hydrology, ecological and many other fields. This article uses the ETM+ data
of February 16th, 2002 and August 27th, 2002, using the single window algorithm to retrieve the LST in the southern area
of Gansu province. First step is removing cloud for image. Secondly, classifies the type of surface by dividing into three
types of water surface, snow surfaces (winter) and natural surface. Then, estimate the emissivity according to the
classification in order to calculate surface temperature. Through the analysis of spatial distribution of land surface
temperature in the study area, the result shows QinZhiHao's single window algorithm is consistent with the reality.
Crop model is a powerful tool in crop growth monitoring and yield forecasting, however crop model is developed based
on single point scale, due to regional differentiation、field variation and other reasons lead to input parameters and initial
conditions which required by crop model simulation are hard to obtain, the application of crop model has been greatly
limited in the regional scale, the introduction of remote sensing will solve this problem, remote sensing is combined with
the crop model WOFOST, using the state variable retrieved by remote sensing to optimize crop model simulation,
revaluing the sensitive parameters and initial conditions which needed in crop model on the region scale, in order to take
the advantage of crop model in the area.This study is on the basis of adaptive adjustment and amendment of crop model
WOFOST, build a winter wheat growth simulation model which is suitable for Yucheng, Shandong; Using the field
experiment data calibration and validation the WOFOST model, discussed the method which combined crop simulation
model and remote sensing under water stress level, using remote sensing calibrated some key processes of crop
simulation or reinitialize、parameterize the crop simulation model in order to achieve the optimization model; Explored
some reasonable and practical method of remote sensing information application in crop simulation at regional scale,
with more research, make it possible to monitor regional crop growth and forecast the output.
Based on medification of crop model WOFOST, a winter wheat growth model was applied in Yucheng region of Shandong Province in the North China Plain. Combination method of remote sensing information with crop model in water stress production level was studied. Through coupling remote sensing information, crop model was optimized by reestimating its parameters and initial conditions. A new method of regional remote sensing combining crop model was established and its application was studied. This method has highly potential application in crop growth monitoring and yield forecasting.
Every year there are ice disasters in the Bohai Sea, which bring serious effect on the human’s life and production. So
how to monitor the ice disaster becomes an important issue. The remote sensing technology has a good advantage of
monitoring ice disaster over others. In this paper, NOAA/AVHRR data is used to retrieval each parameter of sea surface.
Firstly, according to the worked brightness temperature difference can separate sea from land. Then taking advantage of
the difference of the albedo of visible light and near infrared band to get rid of clouds and seawater, however, due to the
thin ice albedo there exists some errors, thus we can use the multi channel split window method (MCSST) further
extraction of sea ice in accordance with sea surface temperature, then we can get the sea ice area on the basis of number
of pixels and satellite spatial resolution. Secondly, after getting the region of sea ice, sea ice thickness can be obtained
through the empirical formula between ice thickness and near infrared band albedo. Lastly, after solving the extraction of
the ice information within mixed pixels, ice concentration also can be calculated.