Evaporation remote sensing inversion application based on reflectivity-vegetation coverage two-dimensional space

A technology for remote sensing of evapotranspiration and vegetation coverage, applied in instruments, biological neural network models, character and pattern recognition, etc., can solve the problems of poor intelligence, high server configuration, and large server memory occupancy, and achieve accurate surname high. Effect

Pending Publication Date: 2020-07-10
INST OF KARST GEOLOGY CAGS
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Problems solved by technology

However, the traditional estimation model has the following limitations: (1) It requires matching data of visible light and thermal infrared, which directly excludes the application of satellite remote sensing sensors that have no thermal infrared band but only visible light band; (2) because the surface temperature is not only affected by soil The impact of water content is also affected by meteorological factors such as solar radiation, air temperature, relative humidity, and wind speed. Therefore, it is impossible to carry out the dry edge of the surface temperature-vegetation inde

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  • Evaporation remote sensing inversion application based on reflectivity-vegetation coverage two-dimensional space
  • Evaporation remote sensing inversion application based on reflectivity-vegetation coverage two-dimensional space
  • Evaporation remote sensing inversion application based on reflectivity-vegetation coverage two-dimensional space

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Embodiment 1

[0049] The application of remote sensing retrieval of evapotranspiration based on the albedo-vegetation coverage two-dimensional space, including:

[0050] S1. Construct evapotranspiration remote sensing inversion network based on convolutional neural network;

[0051]In this embodiment, the convolutional neural network includes an input layer, a convolutional layer, and a pooling layer. The number of convolutional layers is 16, the convolutional kernel size is 3*3, and the maximum pooling layer is 5. layer.

[0052] S2. Use training samples to train the evapotranspiration remote sensing inversion network; the training samples include satellite remote sensing images, meteorological data corresponding to satellite remote sensing image areas, and remote sensing short-wave infrared reflectance data.

[0053] In this embodiment, the evapotranspiration remote sensing inversion network obtains the evapotranspiration corresponding to the satellite remote sensing image area based on ...

Embodiment 2

[0075] The evapotranspiration remote sensing retrieval device based on the albedo-vegetation coverage two-dimensional space, including:

[0076] Data collection module 1, is used for collecting the meteorological data of target area;

[0077] Satellite remote sensing image identification module 2, used to identify the satellite remote sensing image of the target area;

[0078] The interpretation module 3 is used to obtain reflectance data by interpreting the long-term sequence of remote sensing short-wave infrared reflectance data;

[0079] The data storage module 4 is connected with the data collection module 1, the satellite remote sensing image recognition module 2 and the interpretation module 3, and is used to store the meteorological data collected by the data collection module 1, the satellite remote sensing image of the target area identified by the satellite remote sensing image recognition module 2 and Interpretation module 3 interprets the obtained reflectance data...

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Abstract

The invention discloses an evapotranspiration remote sensing inversion application based on a reflectivity-vegetation coverage two-dimensional space, and belongs to the technical field of surface evapotranspiration remote sensing estimation, and the method comprises the steps: S1, constructing an evapotranspiration remote sensing inversion network based on a convolutional neural network; S2, training the evapotranspiration remote sensing inversion network by using the training sample; S3, inputting the meteorological data and the satellite remote sensing image of the target area in the presettime scale into the trained evapotranspiration remote sensing inversion network to obtain evapotranspiration data of the target area in the preset time scale. According to the evapotranspiration remote sensing inversion application based on the reflectivity-vegetation coverage two-dimensional space, the neural network and the method for accurately obtaining the surface evapotranspiration of the target time scale through the remote sensing means are perfectly combined, the evapotranspiration calculation speed is increased, the calculation period is shortened, operation is easy, and intelligenceis good.

Description

technical field [0001] The invention belongs to the technical field of remote sensing estimation of surface evapotranspiration, and in particular relates to an application of remote sensing inversion of evapotranspiration based on the two-dimensional space of albedo-vegetation coverage. Background technique [0002] Evapotranspiration (Evapotransspiration, ET) is an important part of the surface water cycle and energy balance, which determines the water and heat transfer in the soil-vegetation-atmosphere system. , play an important role. Remote sensing technology is considered to be the most effective method to obtain regional scale evapotranspiration distribution on the earth's surface from the aspects of technology, economy and effectiveness. [0003] At present, remote sensing evapotranspiration estimation methods have one-source model, two-source model and surface temperature-vegetation index triangle / trapezoidal model. However, the traditional estimation model has the...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06N3/045
Inventor 王永程洋杨妍妨
Owner INST OF KARST GEOLOGY CAGS
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