Satellite remote sensing precipitation refined space estimation method and system
A satellite remote sensing and fine-grained technology, applied in the field of remote sensing image processing, can solve problems such as limited ability to describe spatial precipitation and complex causes of precipitation, and achieve the effect of improving spatial resolution, increasing spatial resolution, and improving accuracy
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Embodiment 1
[0032] This embodiment discloses a refined spatial estimation method of precipitation based on remote sensing precipitation products of Fengyun-4 satellite.
[0033] Such as figure 1 Shown, the present embodiment method comprises the following steps:
[0034] Step 1: Analyze the correlation coefficient between the annual precipitation (spatial resolution 5km×5km, target variable) and each influencing factor (spatial resolution 1km×1km) of the precipitation products of the regional Fengyun-4 satellite in Hunan Province, using stepwise linear regression method Filter out the set of explanatory variables. Wherein, the influencing factors of this embodiment include seven influencing factors such as longitude, latitude, altitude, slope, aspect, terrain relief and normalized difference vegetation index NDVI; except NDVI, other influencing factors can be obtained from the digital elevation model DEM respectively. The corresponding terrain factors are extracted from the data.
[00...
Embodiment 2
[0064] This embodiment discloses a satellite remote sensing precipitation refinement spatial estimation system, including a memory, a processor, and a computer program stored in the memory and operable on the processor, and the processor implements the following steps when executing the computer program :
[0065] Step S1, analyze the correlation coefficient between the satellite annual precipitation data and each influencing factor, and use the stepwise linear regression method to screen out the explanatory variable set. The influencing factors include longitude, latitude, altitude, slope, slope aspect, terrain relief and normalized difference vegetation index.
[0066] Step S2. Taking satellite precipitation as the dependent variable and the selected explanatory variable set as the explanatory variable, the regression analysis is performed using a hybrid geographically weighted regression model that integrates least squares global regression and geographically weighted regre...
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