Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach

Frontiers in Environmental Science

Jeremy Kravitz¹  Mark Matthews²  Sarah Fawcett³  Lisl Lain³  Stewart Bernard⁴ 

[1] Biospheric Science Branch, NASA Ames Research Center, Mountain View, CA, United States;CyanoLakes (Pty) Ltd, Cape Town, South Africa;Department of Oceanography, University of Cape Town, Cape Town, South Africa;Earth Systems Earth Observation Division, CSIR, Cape Town, South Africa;

关键词: eutrophication;    Earth observation;    water quality;    inland waters;    machine learning;    radiative transfer modeling;   

DOI  :  10.3389/fenvs.2021.587660

来源: DOAJ

【 摘 要 】

There is currently a scarcity of paired in-situ aquatic optical and biogeophysical data for productive inland waters, which critically hinders our capacity to develop and validate robust retrieval models for Earth Observation applications. This study aims to address this limitation through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which is the first to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size- and type-specific phytoplankton inherent optical properties (IOPs) for mixed eukaryotic/cyanobacteria assemblages; 2) calculations of mixed assemblage chlorophyll-a (chl-a) fluorescence; 3) modeled phycocyanin concentration derived from assemblage-based phycocyanin absorption; 4) and paired sensor-specific top-of-atmosphere reflectances, including optically extreme cases and the contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships of concentrations and IOPs to those of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, and used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and IOPs over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. The results of this work represent a significant leap forward in our capacity for routine, global monitoring of inland water quality.

【 授权许可】

Unknown