Estimating heat storage in urban areas using multispectral satellite data and machine learning

Hrisko, Joshua; Ramamurthy, Prathap; Gonzalez, Jorge E.

doi:10.1016/j.rse.2020.112125

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Estimating heat storage in urban areas using multispectral satellite data and machine learning

2021
By Hrisko, Joshua ; Ramamurthy, Prathap ; Gonzalez, Jorge E.
Source: Remote Sensing of the Environment 252: 112125

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Journal Title:

Remote Sensing of Environment
Personal Author:

Hrisko, Joshua ; Ramamurthy, Prathap ; Gonzalez, Jorge E.

Hrisko, Joshua ; Ramamurthy, Prathap ; Gonzalez, Jorge E. Less -
NOAA Program & Office:

CESSRST (Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies)
Description:

A satellite-derived hysteresis model is presented for estimate heat storage in urban areas. Storage heat flux, one of the dominant terms in the urban surface energy budget (USEB), is largely unknown despite its critical relationship to various urban environmental processes. This study introduces a novel technique for quantifying heat storage by relating multispectral satellite radiances and geophysical properties to ground-truth residual heat storage computed with flux instruments. Gradient-boosted regression trees serve as the method of maximizing the relationship between satellite data and flux measurements. Several flux networks are used to train and validate the model over varying land cover types, which strengthens the robustness of the model. The model performs well under variable weather conditions such as cloudy rainy days. In comparison with other studies, the RMSE and MAE values were found to be lower than some ground-to-ground studies, and is one of few satellite-derived methods that computes direct comparison over a range of different land cover types.
Keywords:

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Heat Storage Machine Learning Remote Sensing
Source:

Remote Sensing of the Environment 252: 112125
DOI:

https://doi.org/10.1016/j.rse.2020.112125
Format:

PDF
Document Type:

Journal Article
Funding:

Grant no. NA16SEC4810008
Rights Information:

Accepted Manuscript
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Submitted
Main Document Checksum:

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urn:sha256:3322b1a8f708a2876575896841ce1df4dc3a47a2384791288a7acdbbdb893fd9
Download URL:

https://repository.library.noaa.gov/view/noaa/48706/noaa_48706_DS1.pdf
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