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Related Concept Videos

Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...
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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
Radiation: Applications01:17

Radiation: Applications

The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
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Related Experiment Video

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Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
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Published on: December 12, 2025

Estimating high-resolution albedo for urban applications.

David Fork1, Elizabeth J Wesley2, Salil Banerjee3

  • 1Google, Mountain View, CA, USA. fork@google.com.

Nature Communications
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

New satellite data fusion creates detailed 30-cm urban albedo maps. This allows precise planning for cool roof interventions, significantly reducing city temperatures and mitigating extreme urban heat impacts.

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Urban Planning

Background:

  • Extreme urban heat is a significant global health concern.
  • Effective cooling strategies like cool roofs require high-resolution data for planning, which is currently lacking.
  • Existing satellite albedo data has coarse resolution, limiting building-level analysis.

Purpose of the Study:

  • To develop a method for high-resolution urban albedo mapping at the building level.
  • To enable targeted cool roof interventions for urban heat mitigation.
  • To validate the use of Sentinel-2 imagery for city-scale albedo estimation.

Main Methods:

  • Fusing 10-m Sentinel-2 imagery with high-resolution commercial satellite data.
  • Achieving a 30-cm resolution for urban albedo mapping.
  • Validating the method against airborne hyperspectral measurements (RMSE = 0.04).

Main Results:

  • Successfully mapped urban albedo at 30-cm resolution across diverse urban environments.
  • Demonstrated that prioritizing large-footprint buildings for cool roof retrofits can reduce citywide temperatures by up to 0.5 °C.
  • Validated Sentinel-2's capability for city-scale albedo estimation.

Conclusions:

  • The developed method provides actionable, building-scale rooftop albedo data.
  • This approach enables globally deployable cool roof interventions.
  • The findings support targeted climate action to combat extreme urban heat.