| Meteorological data is the basic data for variety of scientific researches, and plays an important role in the study of agronomy, earth science, ecology, especially climate change. Climate change is a common phenomenon in nature, which is affected significantly by the nature environment and human life. At present, the frequent of extreme weather disasters caused by global climate change and a series of changes such as global warming, rising sea levels, reduced precipitation have a far-reaching impact on the natural environment and the economic and social development of a region, particularly its agriculture, ecosystems, water resources and the socio-economic of coastal zone. Agricultural production is dependent strongly on the climate and influenced by climatic conditions, so climate change will directly affect the security of food and sustainable development of agriculture. China is a large agricultural country, in which agriculture is an important part of the national economy, so the reconstruction research of climate change data in the agricultural development area is very important.Currently the station measured data of climate (such as:humidity, average temperature, precipitation, extremely high or low temperature, wind direction, etc), which are affected by many factors, are used in the regional climate change research. The data are with differences of time and space due to different underlying surface properties, altitude and latitude and longitude, solar radiation, and atmospheric circulation. In the usually study, area is divided according to administrative area or ecological geographic area. It is very important for areas extraction to rebuild the temperature changing data, as the crop planting areas largely effect on agriculture production. The zone can be accurately divided by remote sensing technology; owing to the crops have the definite characteristics and show different regular patterns in different periods. This study rebuilds the temperature data based on objected-oriented classification method to extract the agricultural areas using timing MODIS NDVI data.Meteorological data usually acquire with discrete data measured by meteorological, and the regional weather change data is mainly obtained through three methods:spatial approach method, alternative sites method and remote sensing method, in which spatial approach method is more usually used, but it is not conducive to quick access of regional climate change data because of its large amount of calculation. In this paper, the replacement of space methods with regional meteorological station is achieved based on multiple regression analysis method to obtain regional climate change data.Henan is a major grain producing area in China, where the agricultural production is influenced by climate change. Henan Province is selected as the study area in this paper, and then the agricultural areas in Henan Province are obtained by the object-oriented classification method based on MODIS NDVI timing data, which is superimposed with monthly mean temperature, monthly precipitation, and sunshine hour data in grid type. The average grid meteorological data of each city in the agricultural district is calculated and analyzed the correlation with the meteorological stations around to identify the meteorological stations near each city. The different weights of each meteorological station are endowed through multiple regression method, so as to rebuild climate change data of agricultural areas.Through analysis of the reconstruction results it can draw conclusions that: Monthly values of meteorological data in agro-regions of Henan province reconstruction effect overall is better, the monthly precipitation reconstruction data is less effective in the summer. Using remote sensing technology to extract the range of agro-regions is very important for the agricultural weather analysis. The reconstruction method rearch can provide data support for climate change research in agricultural areas. It is significant for the study of climate change on food production and agricultural production in Henan Province. |
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