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Related Concept Videos

Temperature Measurement Sites01:14

Temperature Measurement Sites

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A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
Oral: When assessing oral temperature, the thermometer tip should be placed under the tongue in the posterior sublingual pocket. It offers accurate readings and can be...
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Application of Linearization and Approximation01:29

Application of Linearization and Approximation

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A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Precipitation Gravimetry01:03

Precipitation Gravimetry

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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
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GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Regression Analysis01:11

Regression Analysis

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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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A Machine Learning Model Integrating Remote Sensing, Ground Station, and Geospatial Data to Predict Fine-Resolution

Giorgio Limoncella1, Denise Feurer2, Dominic Roye3,4,5

  • 1Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, 50134 Florence, Italy.

Remote Sensing
|September 15, 2025
PubMed
Summary

A new machine learning model estimates daily air temperature at high resolution (100m) using satellite and ground data. This tool helps identify areas vulnerable to extreme heat, crucial for climate adaptation and public health.

Keywords:
Landsat 8MODISair temperaturemachine learningremote sensingurban heat island

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Area of Science:

  • Environmental Science
  • Climate Science
  • Geospatial Analysis

Background:

  • Climate change is increasing heat-related health risks.
  • Accurate, high-resolution temperature data is needed to identify vulnerable populations and areas.
  • Existing methods often lack the spatial detail required for localized heat stress assessments.

Purpose of the Study:

  • To develop and validate a machine learning model for high-resolution (100m x 100m) daily air temperature estimation.
  • To integrate diverse data sources, including remote sensing, ground stations, and geospatial information.
  • To improve heat stress impact assessment and support climate adaptation strategies.

Main Methods:

  • A two-stage machine learning approach was employed.
  • Imputed missing land surface temperature (LST) data using gradient-boosted trees and spatio-temporal predictors.
  • Modeled daily maximum (Tmax) and minimum (Tmin) air temperatures using satellite data (MODIS, Landsat 8), meteorological data (ERA5-land), topography, land cover, and NDVI.

Main Results:

  • The model demonstrated high accuracy for Tmax (R 2: 0.95, RMSE: 1.95 °C) and Tmin (R 2: 0.92, RMSE: 1.96 °C).
  • It effectively captured both temporal (R 2: 0.95 for Tmax, 0.94 for Tmin) and spatial (R 2: 0.92 for Tmax, 0.72 for Tmin) temperature variations.
  • Generated high-resolution temperature maps for Tuscany, Italy.

Conclusions:

  • Integrating Earth Observation and machine learning provides a powerful method for generating high-resolution temperature maps.
  • These maps are valuable for urban planning, climate adaptation, and epidemiological studies.
  • The developed model is replicable and can be applied to other regions to assess heat-related risks.