# Learning Radiative Transfer Models for Climate Change Applications in Imaging Spectroscopy

## TL;DR

This paper introduces a neural network-based algorithm that efficiently emulates radiative transfer models, significantly speeding up spectroscopy data processing for climate change applications, enabling more scalable and timely environmental analysis.

## Contribution

The work develops a neural network approach to emulate radiative transfer models, reducing computational costs and facilitating large-scale climate-related remote sensing analysis.

## Key findings

- Achieved a multifold speedup in RTM processing time.
- Demonstrated effective emulation accuracy of radiative transfer models.
- Enabled scalable analysis of spectroscopy data for climate change studies.

## Abstract

According to a recent investigation, an estimated 33-50% of the world's coral reefs have undergone degradation, believed to be as a result of climate change. A strong driver of climate change and the subsequent environmental impact are greenhouse gases such as methane. However, the exact relation climate change has to the environmental condition cannot be easily established. Remote sensing methods are increasingly being used to quantify and draw connections between rapidly changing climatic conditions and environmental impact. A crucial part of this analysis is processing spectroscopy data using radiative transfer models (RTMs) which is a computationally expensive process and limits their use with high volume imaging spectrometers. This work presents an algorithm that can efficiently emulate RTMs using neural networks leading to a multifold speedup in processing time, and yielding multiple downstream benefits.