This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.

Cyanobacterial blooms pose a serious threat to the multiple uses of inland waters because of their adverse effects on the environment and human health. Monitoring cyanobacteria concentrations using traditional methods can be expensive and impractical. Recently, alternative efforts using remote sensing techniques have been successful. In particular, semi-analytical modelling approaches have been used to successfully predict chlorophyll (Chl)-a concentrations from remote sensing reflectance. The aims of this study were to test the performance of different semi-analytical algorithms in the estimation of Chl-a concentrations and the applicability of Sentinel-2 multispectral instrument (MSI) imagery, and its atmospheric correction algorithms, in the estimation of Chl-a concentrations. For our dataset, phycocyanin concentration was strongly correlated with Chl-a concentration and the inversion model of inland waters (IIMIW) semi-analytical algorithm was the best performing model, achieving a root mean square error of 4.6 mg m–3 in the prediction of Chl-a. When applying the IIMIW model to MSI data, the use of top-of-atmosphere reflectance performed better than the atmospheric correction algorithm tested. Overall, the results were satisfactory, demonstrating that even without an adequate atmospheric correction pipeline, the monitoring of cyanobacteria can be successfully achieved by applying a semi-analytical bio-optical model to MSI data.

Water and sediment discharges can change rapidly, and low-frequency measurement devices might not be sufficient to elucidate existing dynamics. As such, above-water radiometry might enhance monitoring of suspended particulate matter (SPM) dynamics in inland waters. However, it has been barely applied for continuous monitoring, especially under partially cloudy sky conditions. In this study, an in situ, high-frequency (30 s timestep), above-water radiometric dataset, collected over 18 days in a tropical reservoir, is analyzed for the purpose of continuous monitoring of SPM concentration. Different modalities to retrieve reflectance spectra, as well as SPM inversion algorithms, were applied and evaluated. We propose a sequence of processing that achieved an average unsigned percent difference (UPD) of 10.4% during cloudy conditions and 4.6% during clear-sky conditions for Rrs (665 nm), compared to the respective UPD values of 88.23% and 13.17% when using a simple calculation approach. SPM retrieval methods were also evaluated and, depending on the methods used, we show that the coefficient of variation (CV) of the SPM concentration varied from 69.5% down to 2.7% when using a semi-analytical approach. As such, the proposed processing approach is effective at reducing unwanted variability in the resulting SPM concentration assessed from above-water radiometry, and our work paves the way towards the use of this noninvasive technique for high-frequency monitoring of SPM concentrations in streams and lakes.

Context: The usual strategy for monitoring of eutrophication process is the use of traditional limnological methods, based on laboratory analysis. These procedures involve costly and time-consuming analyses, usually with in vitro methodologies, which can still have limitations in terms of sensitivity and reliability, if poorly managed. Phytoplankton pigments, such as chlorophyll-a (Chl-a), are highly fluorescent and can provide the environmental status of water bodies. Aims: This study aims to analyse, compare and evaluate an estimation of Chl-a through fluorescence in public water sources in the Brazilian cerrado. Exploratory statistical analyses were conducted by using absolute fluorescence units (AFU) and relative fluorescence units (RFU) compared with traditional laboratory data (standard procedure for the determination of Chl-a by spectroscopic methods) to evaluate the significance of differences in estimating Chl-a concentration. Subsequently, empirical models, based on spectral band combinations, were generated to convert fluorescence measurement in Chl-a concentration, by linear regression. Key results: The generated model found a strong correlation and coefficient of determination (r = 0.88; R2 = 0.78). The efficiency of the model was also confirmed by statistical indicators (RMSE = 1.27, MAPE = 26.72 and BIAS = −6.32). Conclusions: We concluded that the estimate of Chl-a through RFU was better than through AFU. Implications: Therefore, based on the results of this study, it is recommended that RFU be used to obtain more precise and accurate estimates of Chl-a concentration through empirical models based on linear regression.