Surface temperature downscaling method, system and device based on XGBoost learning algorithm

A technology of surface temperature and learning algorithm, applied in the field of geographic information, can solve problems such as unsatisfactory accuracy, and achieve the effect of improving accuracy

Active Publication Date: 2020-05-26
GUANGZHOU INST OF GEOGRAPHY GUANGDONG ACAD OF SCI +1
View PDF6 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] At present, the method of spatial downscaling of remote sensing surface temperature is mainly to spatially downscale the surface temperature with low spatial resolution such as AMSR-E, MODIS, FY-3, etc., to obtain the surface temperature at 1km or 30m resolution, and its accuracy can no longer meet the needs of people. needs

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Surface temperature downscaling method, system and device based on XGBoost learning algorithm
  • Surface temperature downscaling method, system and device based on XGBoost learning algorithm
  • Surface temperature downscaling method, system and device based on XGBoost learning algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0058] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with aspects of the invention as recited in the appended claims.

[0059] The terminology used in the present invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein and in the appended claims, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as use...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a surface temperature downscaling method, system and device based on an XGBoost learning algorithm. The method includes carrying out region division on the research region according to the impermeable surface coverage; combining different influence degrees of low-resolution urban impervious surface coverage, vegetation coverage and road density in different regions on thesurface temperature, combining the influence of the spatial heterogeneity of the urban underlying surface on the surface temperature, and combining low-resolution thermal infrared image data for establishing a low-resolution nonlinear regression model; according to the high-resolution earth surface parameters and the nonlinear regression model, calculating the earth surface parameters. Compared with the prior art, the method has the advantages that high-resolution prediction of the surface temperature of the urban environment with complex spatial heterogeneity is achieved, the surface temperature of roads, buildings, vegetation and water can be distinguished more meticulously, and the accuracy of urban surface temperature prediction is improved.

Description

technical field [0001] The invention relates to the technical field of geographic information, in particular to a method, system and equipment for downscaling surface temperature based on an XGBoost learning algorithm. Background technique [0002] Surface temperature is a very important physical parameter of the land surface system, and it has a wide range of application requirements in the research fields of surface evapotranspiration estimation, soil moisture estimation, and urban thermal environment. However, with economic development, population increase, and accelerated urbanization, urban land use and coverage types have undergone significant changes, and the increase in artificial buildings has made the original natural vegetation and bare land covered by buildings, asphalt, cement and other impermeable materials. These impermeable underlying surfaces store heat during the day and release heat at night. Their good thermal conductivity and high heat capacity are one o...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/20G06K9/62
CPCG06V20/182G06V20/188G06V10/143G06F18/24323Y02A30/60
Inventor 许剑辉周成虎邓应彬杨骥张菲菲姜浩
Owner GUANGZHOU INST OF GEOGRAPHY GUANGDONG ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap