Variational Autoencoder Framework for Hyperspectral Retrievals (Hyper-VAE) of Phytoplankton Absorption and Chlorophyll a in Coastal Waters for NASA's EMIT and PACE Missions

2025-04-18

AI Summary

In optically complex estuarine and coastal waters, the inversion of phytoplankton absorption coefficients and chlorophyll-a concentration suffers from ill-posedness and non-uniqueness. To address this, we propose the first variational autoencoder (VAE) framework tailored for hyperspectral ocean remote sensing, which maps remote-sensing reflectance (Rrs) to physically consistent, multimodal parameter distributions—overcoming the limitations of conventional deterministic regression. Compared with mixture density networks (MDN), our VAE achieves superior accuracy and robustness on high-dimensional PACE/EMIT spectral data while enabling uncertainty-aware retrieval. Validated against in situ measurements, the model exhibits low bias and high fidelity. This work establishes a scalable, AI-driven paradigm for monitoring phytoplankton community dynamics, directly supporting NASA’s EMIT and PACE missions, as well as future Surface Biology and Geology (SBG) initiatives.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Deep Generative Models & Autoencoders
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Phytoplankton absorb and scatter light in unique ways, subtly altering the color of water, changes that are often minor for human eyes to detect but can be captured by sensitive ocean color instruments onboard satellites from space. Hyperspectral sensors, paired with advanced algorithms, are expected to significantly enhance the characterization of phytoplankton community composition, especially in coastal waters where ocean color remote sensing applications have historically encountered significant challenges. This study presents novel machine learning-based solutions for NASA's hyperspectral missions, including EMIT and PACE, tackling high-fidelity retrievals of phytoplankton absorption coefficient and chlorophyll a from their hyperspectral remote sensing reflectance. Given that a single Rrs spectrum may correspond to varied combinations of inherent optical properties and associated concentrations, the Variational Autoencoder (VAE) is used as a backbone in this study to handle such multi-distribution prediction problems. We first time tailor the VAE model with innovative designs to achieve hyperspectral retrievals of aphy and of Chl-a from hyperspectral Rrs in optically complex estuarine-coastal waters. Validation with extensive experimental observation demonstrates superior performance of the VAE models with high precision and low bias. The in-depth analysis of VAE's advanced model structures and learning designs highlights the improvement and advantages of VAE-based solutions over the mixture density network (MDN) approach, particularly on high-dimensional data, such as PACE. Our study provides strong evidence that current EMIT and PACE hyperspectral data as well as the upcoming Surface Biology Geology mission will open new pathways toward a better understanding of phytoplankton community dynamics in aquatic ecosystems when integrated with AI technologies.

Problem

• Retrieving phytoplankton absorption and chlorophyll a from hyperspectral data
• Improving phytoplankton characterization in coastal waters using machine learning
• Handling multi-distribution prediction problems with Variational Autoencoder (VAE)

Innovation

• Variational Autoencoder for hyperspectral retrievals
• Machine learning for phytoplankton absorption
• High-dimensional data handling with VAE