Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Soil water content inversion method based on multi-model ensemble learning

A technology of soil water content and integrated learning, which is applied in soil material testing, measuring devices, scientific instruments, etc., can solve problems such as underfitting and overfitting, and achieve improved accuracy and spatial continuity, improved accuracy and interpretability effect

Active Publication Date: 2020-09-18
UNIV OF ELECTRONICS SCI & TECH OF CHINA +2
View PDF9 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The machine learning method has strong nonlinear expression ability, is suitable for solving various nonlinear problems, and does not need to consider simplifying the model and ignoring the parameters, and realizes the fusion of multi-source remote sensing data by introducing various parameters. Overfitting and underfitting often occur in high-dimensional data

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
  • Soil water content inversion method based on multi-model ensemble learning
  • Soil water content inversion method based on multi-model ensemble learning
  • Soil water content inversion method based on multi-model ensemble learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0045] For the convenience of description, the relevant technical terms appearing in the specific implementation are explained first:

[0046] NDVI (Normalized Difference Vegetation Index): Normalized Difference Vegetation Index

[0047] SASI (Shortwave Angle Slope Index): shortwave angle slope index

[0048] MSAVI (Modified Soil-Adjusted Vegetation Index): Modified soil-adjusted vegetation index

[0049] SIMI (Shortwave Infrared Soil Moisture Index): Shortwave Infrared Soil Moisture Index

[0050] NMDI (Normalized Multi-band Drought Index): normalized multi-band drought index

[0051] DDI (Distance Drought Index): distance drought index

[0052] GVMI (Global Vegetation Moisture Index): Global Vegetation Moisture Index

[0053] EVI (Enhanced Vegetation Index): Enhanced Vegetation Index

[0054] MSI (Moisture Stress Index): Moisture Stress Index

[0055] NDII6 (Normalized Difference Infrared Index 6): the 6th band normalized difference near-infrared index

[0056] NDII7 ...

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 discloses a soil water content inversion method based on multi-model ensemble learning. Firstly, extracting initial input characteristics of a soil water content inversion model by fusing multi-source remote sensing data; then, based on a Stacking framework, training an extreme random tree and an XGBoost model in the framework through the initial input features; obtaining different soil water content prediction values, extracting a temperature-vegetation drought index inversion result, finally building a linear regression model through a fitting tool, and inputting the soil watercontent predicted by an extreme random tree and an XGBoost model and a TVDI inversion result into the linear regression model, thereby outputting a soil water content value.

Description

technical field [0001] The invention belongs to the field of environmental remote sensing technology and machine learning technology, and more specifically relates to a soil water content inversion method based on multi-model integrated learning. Background technique [0002] Soil water content is closely related to the survival of surface organisms. It not only plays an important role in many fields such as agriculture, hydrology, and meteorology, but is even closely related to extreme disasters such as landslides, floods, and fires. Considering the spatial and temporal heterogeneity of soil water content, its precise prediction remains challenging. The soil moisture content can be accurately measured by using TDR soil moisture analyzer or soil weighing method, but such field sampling method is inefficient, expensive in manpower and material resources, and is not suitable for real-time measurement in large areas. Remote sensing technology has the advantages of wide coverag...

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): G01N21/17G01N21/55G01N33/24G06F30/27G06K9/62
CPCG01N21/17G01N21/55G01N33/246G06F30/27G01N2021/1793G06F18/24323G06F18/214
Inventor 李玉霞程渊李凡何磊李玉珍
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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
Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com