Learning Radiative Transfer Models for Climate Change Applications in Imaging Spectroscopy
This work addresses a bottleneck in remote sensing for climate change applications, enabling more efficient analysis of environmental impacts like coral reef degradation, though it is incremental as it improves existing methods.
The paper tackles the computational expense of radiative transfer models (RTMs) in processing spectroscopy data for climate change analysis by developing a neural network algorithm that emulates RTMs, achieving a multifold speedup in processing time.
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.