ABSTRACT
Remote sensing using satellite imagery has been promoted as a method to broaden the scale and frequency of cyanobacterial monitoring. This relies on the ability to establish relationships between the reflectance spectra of water bodies and the abundance of cyanobacteria. A challenge to achieving this comes from a limited understanding of the extent to which the optical properties of cyanobacteria vary according to their physiological state and growth environment. The aim of the present study was to determine how growth stage, nutrient status and irradiance affect pigment concentrations and absorption spectra in two common bloom forming cyanobacterial taxa: Dolichospermum lemmermannii and Microcystis aeruginosa. Each species was grown in laboratory batch culture under a full factorial design of low or high light intensity and low, medium, or high nitrate concentrations. Absorption spectra, pigment concentrations and cell density were measured throughout the growth phases. The absorption spectra were all highly distinguishable from each other, with greater interspecific than intraspecific differences, indicating that both D. lemmermannii and M. aeruginosa can be readily differentiated using hyperspectral absorption spectra. Despite this, each species exhibited different responses in the per-cell pigment concentrations with varying light intensity and nitrate exposure. Variability among treatments was considerably higher in D. lemmermannii than in M. aeruginosa, which exhibited smaller changes in pigment concentrations among the treatments. These results highlight the need to understand the physiology of the cyanobacteria and to take caution when estimating biovolumes from reflectance spectra when species composition and growth stage are unknown.
Subject(s)
Cyanobacteria , Microcystis , Nitrates , Nutrients , Batch Cell Culture TechniquesABSTRACT
The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.
ABSTRACT
Horizontal patchiness of water quality attributes in lakes substantially influences the ability to accurately determine an average condition of a lake from traditional in situ sampling. Monitoring programmes for lake water quality often rely on water samples from one or few locations but the assumption of representativeness is seldomly tested. Satellite observations can support environmental monitoring by detecting horizontal variability of water quality attributes over entire lakes. This article is a co-submission with Lehmann et al. (2021), who present a method to create a regional calibration of a satellite chlorophyll a algorithm and a spatial analysis of an image time series to detect recurring patchiness. Our method was developed on 13 lakes in the central North Island of New Zealand and this publication makes available the data used in our analysis and the spatial fields of results. These data are immediately valuable for practitioners operating within the region of interest providing a five year archive of synoptic water quality data and spatial fields to help optimize in situ monitoring efforts. In addition, there is value to the wider scientific community as the study lakes are a useful 'natural lab' for the development of aquatic remote sensing methods due to the range of trophic conditions and water colour in a single satellite image scene. Together with decades of in situ water quality records, our data is therefore useful for the development and validation of widely applicable methods of water quality retrieval from satellite data.
