Regionally and Locally Adaptive Models for Retrieving Chlorophyll-a Concentration in Inland Waters From Remotely Sensed Multispectral and Hyperspectral Imagery
Publication Date
2-14-2019
Document Type
Article
Publication Title
IEEE Transactions on Geoscience and Remote Sensing
Volume
57
Issue
7
DOI
10.1109/TGRS.2019.2892899
Abstract
Various empirical algorithms have been developed to retrieve chlorophyll-a (Chl-a) from multispectral and hyperspectral images as a proxy variable for algal blooms in inland waters. In most previous studies, a single empirical model (global model) was calibrated for the entire water body under study. Our analysis shows that the performance of a global model is limited for optically complex inland waters. We discovered that the global model tends to overestimate in some regions and underestimate in other regions, and that the model residuals (errors) display an apparent spatial autocorrelation pattern. To address the inadequacy of the global empirical model, this paper presents regionally or locally adaptive models to better estimate Chl-a concentrations for the first time. We collected two dense sets of Chl-a measurements over Harsha Lake in Ohio during a Sentinel-2A satellite overpass and a dedicated airborne hyperspectral flight. Based on the atmospherically corrected multispectral and hyperspectral images and concurrent in situ measurements, we implemented and evaluated the performance of regionally and locally adaptive models in comparison with the single global model. Among a number of candidate empirical algorithms, the two-band algorithm produces the best global model for Chl-a retrievals for both the multispectral and hyperspectral image sources. By subdividing the water body under investigation into several regions or a set of local areas, we demonstrate that regionally and locally adaptive models can improve Chl-a estimate accuracy by 13%-28% for the multispectral image and by 33%-47% for the hyperspectral image, in comparison with the best global Chl-a model.
Keywords
Lakes, Adaptation models, Hyperspectral imaging, Atmospheric modeling, Water resources, Airborne hyperspectral, chlorophyll-a (Chl-a), regionally and locally adaptive models, Sentinel-2 multispectral
Department
Urban and Regional Planning
Recommended Citation
Min Xu, Hongxing Liu, Richard Beck, John Lekki, Bo Yang, Song Shu, Yang Liu, Teresa Benko, Robert Anderson, Roger Tokars, Richard Johansen, Erich Emery, and Molly Reif. "Regionally and Locally Adaptive Models for Retrieving Chlorophyll-a Concentration in Inland Waters From Remotely Sensed Multispectral and Hyperspectral Imagery" IEEE Transactions on Geoscience and Remote Sensing (2019). https://doi.org/10.1109/TGRS.2019.2892899
