Algal blooms pose a considerable threat to both human health and the natural environment, their presence even extending to lakes situated across plateau regions. The geolocation and volatile climate conditions render it quite a challenge for algal bloom detection with single optical satellite across plateau lakes. To address this limitation, this study aims to achieve algal bloom detection through five satellites with high spatial resolution based on machine learning (ML) across nine lakes in Yunnan Province, China. Noteworthy findings from the study include: 1) achieving high accuracy on algal bloom detection over 0.82 based on random forest (RF) across multiple lakes and multisensors; 2) evaluating quantitatively and qualitatively algal bloom outbreaks in five out of nine plateau lakes in 2019; and 3) establishing a severity ranking of algal bloom occurrences, with Lake Dianchi exhibiting the highest severity, followed by Lake Xingyun, Lake Chenghai, Lake Erhai, and Lake Qilu. In general, this work demonstrated the effectiveness in multisource satellites observation with rational precision. These results laid the foundation for implementing a practical technical framework that enables precise algal bloom detection and facilitates comparative analyses among different lakes.

Submerged aquatic vegetation (SAV) is crucial for maintaining a clear-water state in lakes. Tracking the spatiotemporal changes in SAV is crucial for understanding the ecological evolution, particularly in eutrophic lakes. Sentinel imagery offers high-resolution data for detailed SAV mapping. However, existing SAV classification algorithms based on Sentinel-2 multispectral instrument (MSI) require preprocessing to eliminate interference from other types such as floating-leaved aquatic vegetation and algal blooms (ABs), and also heavily rely on field survey data and human interventions, limiting the application for large-scale and long-term SAV monitoring. Here, we developed SSAVI-GMM, an algorithm leveraging a novel index, the Sentinel-based SAV index (SSAVI), derived from Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 MSI data, to automatically map SAV distribution using Gaussian mixture model (GMM) clustering. Testing in 19 lakes in the Yangtze Plain region yielded an average accuracy of 87.15%. This study marks a successful integration of SAR and optical data, addressing challenges in mapping SAV, and the robust GMM clustering method overcomes the limitations of traditional threshold methods. The SSAVI-GMM algorithm demonstrates promising potential for mapping SAV in shallow lakes globally.

Moderate-high resolution satellite missions provide an opportunity to capture subtle spatial variability in lakes; however, the sparsity of time series for individual satellite instruments cannot monitor temporal variation in the lake environment. To date, studies on the joint observations of chlorophyll-a (Chl-a) in inland lakes from multiple missions have been poorly reported. Here, we generated a harmonized Chl-a dataset for the lakes in the Yunnan–Guizhou Plateau in China from 2013 to 2022 by the Landsat 8/9 and Sentinel-2A/B virtual constellation. This study first examined the performance of four atmospheric correction processors to derive remote sensing reflectance (Rrs) from Landsat 8/9 Operational Land Imager (OLI) and Sentinel-2A/B multispectral instrument (MSI) images. We determined that the dark spectral fitting algorithm generated better Rrsthan the other processors, e.g., Rrs(561) mean absolute percentage error (MAPE)=15.2%, Rrs(665) MAPE=27.5%, and Rrs(704) MAPE=25.7%. OLI-derived Rrsat five visible and near-infrared bands showed satisfactory agreement with MSI (slope=0.94, MAPE=11.8%). The mixed density network outperformed the six state-of-the-art algorithms and other two machine learning models in retrieving Chl-a [MSI: MAPE=31.4% (N=109), OLI: MAPE=38.0% (N=74)]. The satisfactory agreement of Chl-a retrievals between the synchronous MSI and OLI images (N=2,293,821, MAPE=34.6%) supported the establishment of the virtual constellation. MSI- and OLI- derived Chl-a in nine major lakes in the studied area exhibited apparent seasonal variability from 2013 to 2022, particularly after 2017. Results highlight a solution to establish the Landsat/Sentinel-2 virtual constellation for improving the spatial and temporal resolutions of a database of lake water quality.

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched to continue the legacy of the MODerate Resolution Imaging Spectroradiometer (MODIS). Despite recent studies demonstrating the use of VIIRS observations over inland waters, VIIRS has not been widely used to generate water quality products (e.g., chlorophyll-a (Chl-a), suspended particulate matter (SPM)) in relatively large turbid lakes. This study examines the quality of VIIRS-derived remote sensing reflectance (Rrs) from four different atmospheric-correction processors with matchups from 13 lakes sized between 107 km2and 2573 km2across the eastern plain of China. NOAA’s operational Rrsoutperforming Rrsretrieved by other state-of-the-art algorithms were shown to contain mean uncertainties of 57%, 33%, 20%, 28% for Rrs(486), Rrs(551), Rrs(671), and Rrs(745), respectively, which induced ~55% uncertainty in satellite-retrieved SPM and Chl-afrom recently developed algorithms in the studied lakes. A deep neural network was developed for simultaneous retrievals of Chl-aand SPM from VIIRS Rayleigh-corrected reflectance to improve accuracy. The model with satisfactory accuracy (mean uncertainty of 28% for Chl-aand 20% for SPM) outperformed other machine learning approaches and nearly halved uncertainties compared to those obtained from satellite-derived Rrsproducts. Within the 2012-2020 period, high-quality VIIRS-derived Chl-aand SPM across 61 lakes in eastern China had evident interannual variability in SPM but insignificant temporal variations in Chl-a. This study provides validated, high-quality, basin-scale VIIRS-derived Chl-aand SPM products in eastern China during the past decade. Our results offer a strategy for improving regional water quality products from VIIRS data.

Significant progress has been made in spatiotemporal fusion for remote sensing images; however, most models require inputs to be free of clouds and without missing data, considerably confining their applications in practice. Due to recent advances in deep learning technologies, powerful modeling capabilities could be leveraged to bring potential solutions to this problem. This article proposes a novel architecture named the robust spatiotemporal fusion network (RSFN) based on the generative adversarial network and attention mechanism with dual temporal references to automatically handle input noise. The RSFN only needs one coarse-resolution image on the prediction date and two referential fine-resolution images before and after the prediction date as model inputs. Most notably, there is no special restriction attached on the data quality of referential images. The comparison with other models demonstrates the effectiveness of the RSFN model quantitatively and visually in four study areas using MODIS and Landsat images. Two main conclusions can draw from the experiments. First, the input data noise hardly affects the prediction results of the RSFN, and the RSFN can gain a comparable or even higher accuracy; conversely, the other methods only show limited resistance to input noise. Second, the RSFN with cloud-contaminated references outperforms the other models with cloud-free references after data filtering in the same study area during the same period. The satellite data quality usually varies significantly; the model robustness and fault tolerance are considered critical for actual applications. The RSFN is a simple end-to-end deep model with high accuracy and fault tolerance designed for spatiotemporal fusion with imperfect data inputs, showing promising prospects in practical applications.

Using data collected from two eutrophic lakes located in eastern China (Taihu Lake, 2330 km2and Chaohu Lake, 760 km2), the influence of variable particle composition on remote sensing reflectance (Rrs, in sr-1) properties and on the Medium Resolution Imaging Spectrometer (MERIS) maximum chlorophyll index (MCI) algorithm for estimating near-surface chlorophyll-a concentrations (Chla, in μg · L-1) is demonstrated. Although separated by a distance of only ~200 km, the two lakes showed dramatic differences in particle composition, with Taihu Lake dominated by inorganic particles and Chaohu Lake dominated by organic particles. Such differences led to variable Rrs spectral slopes in the red and near-IR bands and perturbations to the MCI algorithm. A modified MCI algorithm (MCIT) was then developed to reduce the impact of turbidity caused by inorganic particles. Root-mean-square errors in Chla retrievals decreased from 129.5% to 43.5% when using this new approach compared with the MCI algorithm in Taihu Lake for Chla ranging between ~5 and 100 μg · L-1. Application of this approach to other turbid water bodies, on the other hand, requires validation and possibly further tuning.

Accurate remote estimation of chlorophyll-a (CHL) concentration for turbid inland waters is a challenging task due to their optical complexity. In situ spectra (n=666) measured with ASD and Ocean Optics spectrometers from three drinking water sources in Indiana, USA, were used to calibrate the partial least squares model (PLS), artificial neural network model (ANN), and the three-band model (TBM) for CHL estimates; model performances are validated with three independent datasets (n=360) from China. The PLS-ANN model resulted in accurate model calibration ( R2=0.94; Range=0.2-296.6 μg/l of CHL), outperforming the PLS (R2=0.87), ANN (R2=0.91), and TBM (R2=0.86). With an independent validation dataset, the PLS-ANN yielded relatively high accuracy (RMSE: 6.12 μg/l; rRMSE=42.12%; range=0.45-97.2 μg/l of CHL), while TBM yielded acceptable accuracy (RMSE: 8.85 μg/l; rRMSE=63.21%). With simulated ESA/MERIS and EO-1/Hyperion spectra, the PLS-ANN also (MERIS: R2=0.84; Hyperion: R2=0.88) outperforms the TBM (MERIS: R2=0.69; Hyperion: R2=0.76) for model calibration. For validation, the PLS-ANN achieves good performance with simulated spectra (MERIS: RMSE=7.83 μg/l, rRMSE=48.79%; Hyperion: RMSE=6.98 μg/l, rRMSE=45.57%) as compared to the TBM (MERIS: RMSE=10.39 μg/l, rRMSE=68.92%; Hyperion: RMSE=9.54 μg/l, rRMSE=65.35%). Nevertheless, considering the large and diverse datasets, the TBM is a robust semiempirical algorithm. Based on our observations, both the PLS-ANN and TBM are effective approaches for CHL estimation in turbid waters.

We have presented an improved short-wave infrared (SWIR)-based iterative algorithm for the atmospheric correction (AC) of Moderate Resolution Imaging Spectroradiometer (MODIS) data over Lake Taihu, China. The algorithm was validated by means of matchup comparison between MODIS-retrieved and in situ remote sensing reflectances (Rrs). Four examples of the matchup comparison were first carried out for the observation stations within a ±5-min time window of MODIS overpass and field measurements. It is shown in the examples that the retrieved Rrsspectra compare reasonably well with the in situ measurements not only over relatively clear waters (with Rrs(859) about 0.0014 sr-1) but also over turbid waters (with Rrs(859) about 0.013 sr-1). The matchup comparison was further carried out for a total of 54 observation stations within a ±2-h time window, indicating that the AC algorithm has good performance for producing water spectra from MODIS data over Lake Taihu. The development of an algal bloom event has been monitored using MODIS-measured Rrs(443) and Rrs(859), showing that MODIS data, combined with the AC algorithm, can be a useful tool for monitoring the water quality of Lake Taihu. The SWIR iterative algorithm, along with the chlorophyll-a concentration (Chl-a) retrieval model using red to near-infrared bands, has the potential of monitoring Chl-a quantitatively and providing useful information for decision makers to manage the water environment and to prepare for events as algal blooms.

IEEE, IEEE Geosci & Remote Sensing Soc, NASA, NOAA, USN Off Res, Japan Aerosp Explorat Agcy, Natl Polar orbiting Operat Environm Satellite Syst, Ball Aerosp & Technologies Corp, Int Union Radio Sci, Elect & Telecommun Res Inst, Korea Sci & Engn Fdn, Korea Natl Tourism Org, Korea Telecommun

IEEE, IEEE Geosci & Remote Sensing Soc, NASA, NOAA, USN Off Res, Japan Aerosp Explorat Agcy, Natl Polar orbiting Operat Environm Satellite Syst, Ball Aerosp & Technologies Corp, Int Union Radio Sci, Elect & Telecommun Res Inst, Korea Sci & Engn Fdn, Korea Natl Tourism Org, Korea Telecommun