The Landsat program has provided an unparalleled record of Earth observations for nearly four decades, offering a unique long-term, global perspective on inland water quality. The advent of Google Earth Engine (GEE) - based platforms has transformed aquatic remote sensing by enabling large-scale and long-term analyses that were previously infeasible due to limitations in data access and computational capacity. Despite this advance, the atmospherically corrected data officially available on GEE, i.e., the Landsat surface reflectance products (SR), were primarily developed for terrestrial applications, raising concerns about their suitability for aquatic studies. In this work, we systematically evaluated SR over global inland waters and identified two major limitations. (1) artifacts from aerosol overcorrection were widespread and appeared as unphysical remote sensing reflectance (R-rs) with persistently negative values in the coastal aerosol band (CA, 443 nm). Globally, up to 13.00% of water pixels were affected, and errors introduced to downstream products such as suspended particulate matter may exceed 100%. (2) cross-sensor inconsistencies in SR due to differences in atmospheric correction (AC) were pronounced, with median symmetric accuracy (MdSA) up to 443.86%. To obtain accurate cloud-based R-rs products, we further evaluated two alternative AC processors available in GEE: Sensor Invariant Atmospheric Correction (SIAC) and Modified Atmospheric Correction for INland waters (MAIN). Our findings indicated that MAIN, which employs a black-pixel assumption in the shortwave infrared, offers a more robust option with reliable spatial pattern and consistent series R-rs. Alternatively, this study proposes the following practical solutions: when utilizing SR datasets, applying a CA < 0 criterion is recommended as a fundamental preprocessing step to identify artifact-contaminated pixels. Such a method provides an effective means of artifacts detection, but it also results in the loss of valid data. These findings emphasize the importance of incorporating water-specific AC into cloud platforms to advance large-scale and long-term aquatic remote sensing and realize the new paradigm enabled by Landsat and GEE.

Monitoring suspended particulate matter (SPM) concentration is essential for environmental protection and the management of aquatic ecosystem quality. Ocean colour remote sensing enables the monitoring of extensive coastal and marine areas with high spatial and temporal resolution. The challenge lies in selecting SPM retrieval models that represent local optical complexities and integrating them across multiple sensors. This study conducted a bio-optical characterization and evaluated 76 SPM retrieval models for multiple sensors, supporting monitoring in a subtropical estuary of environmental and socioeconomic importance in southern Brazil. Four distinct optical water types were identified, highlighting the optical complexity of the study area. The best-performing models for each sensor (with RMSE and absolute Bias ranging from 4.36 to 6.00 g m-3 and 0.01 to 0.95 g m-3, respectively) were applied to simultaneous images, and the derived concentrations were compared. The observed differences in concentrations retrieved by each sensor reflected the models' characteristics and the distinct spatial, spectral, and radiometric resolutions of the sensors. These differences were resolved through data harmonization after model inversion. As a result, the time series becomes consistent, and integrated images from these constellations can be used for retrospective SPM monitoring in the study area, allowing OLI and MSI to replace the MODIS time series, which already show signs of degradation. The methodology applied in this study can serve as a guideline for implementing satellite-based SPM monitoring in other highly complex aquatic environments.

We evaluated reflectance spectra and spectral features of a red tide event associated with high abundances of Mesodinium rubrum. The bloom was observed on the northern coast of the State of São Paulo between 12 and 25 January 2025, with hyperspectral ocean color satellite images. The diagnostic features of phytoplankton algae were observed near 610 nm and 705 nm, with a peak at 665 nm before decreasing. The Normalized Difference Red Tide (NDRT) was developed to map red tide occurrences. For the Bloom class, NDRT values are approximately 0.90, whereas for the No Bloom class, they range from 0.25 to 0.55. We also estimated chl-a concentration using different models: Normalized Difference Chlorophyll Index (NDCI) (0 – 60 mg/m3), a method based on Two Bands Algorithm (2BDA) (0 – 275 mg/m3), Algae Bloom Monitoring Application (AlgaeMAp) (0 - 1600 mg/m3) and Ocean Color 4 (OC4) (0.35 – 0.65 mg/m3).

Sun and sky glint pose significant challenges in remote sensing when evaluating water quality via satellite images in aquatic systems. These glint effects raise the water-leaving radiance, resulting in an overestimation of biogeochemical and optical parameters. Since conventional techniques, mainly designed for ocean and coastal waters, are less effective with high-spectral-resolution satellite data like Sentinel-2/MSI, it is crucial to enhance methods for masking and correcting these effects in satellite products. This study developed a glint-detection classifier using machine learning-based models and two new spectral indices with Sentinel-2 MSI imagery. The proposed spectral indices are called Normalized Difference Glint Index (NDGI) and Normalized Difference Glint Index 2 (NDGI2), and they are based on deep-blue (443 nm) and shortwave infrared (> 1600 nm) ranges. The machine learning model used a Random Forest Classifier (RF) and was trained on a total of 10,000 samples, achieving an accuracy of approximately 80% for glint detection. A comparison of four different glint correction algorithms was conducted and compared with in-situ measurements. The glint algorithms included SWIR-Subtraction (SubSWIR), Atmospheric Correction for OLI 'lite' (ACOLITE), POLYnomial-based algorithm applied to MERIS (POLYMER), and Sun Glint Removal of Sentinel-2-like images (GRS). ACOLITE and GRS performed the best, reducing MAPE by about 59.86%. Among the glint algorithms, ACOLITE demonstrated a strong ability to preserve regions free of glint in the Sentinel-2/MSI imagery. The combined use of the mask and glint-correction algorithms can enhance time-series analysis, providing an effective solution for inland water monitoring using satellite images and supporting various applications.

Inland waters comprise various aquatic systems, including rivers, lakes, lagoons, reservoirs, and others, and satellite data play a crucial role in providing holistic and dynamic observations of these complex ecosystems. However, available medium-spatial resolution satellite sensors, such as Sentinel-2 Multi-Spectral Instrument (MSI), are typically designed for land monitoring and lack suitable spectral bands and radiometric quality for water applications. This study developed a novel synthetic band generation method, called Sentinel-2/3 Synthetic Aquatic Reflectance Bands (S2/3Aqua), for computing eight 10-m synthetic spectral bands from multivariate regression analysis between Sentinel-2 MSI and Sentinel-3 OLCI image pair. Three multivariate regressor models, Multivariate Linear Regressor (MLR), Multivariate Quadratic Regressor (MQR), and Random Forest Regressor (RFR), were applied and assessed to replicate the Sentinel-3 spectral consistency on 10-m Sentinel-2 images. A cyanobacteria modeling was developed based on in-situ observations (n = 54), and we demonstrated, for the first time, the application of 10-m harmful algal bloom mapping over two eutrophic tropical urban reservoirs (Promissao and Billings, Brazil). Additionally, the generalization of S2/3Aqua was assessed by comparing its spectral signatures across different water optical types. Overall, the comparison between S2/3Aqua and Sentinel-3 bands achieved a mean absolute error of 6 % and a mean difference close to zero. We found that MLR exhibited a higher accuracy with in-situ observations (with a 28 % bias) and was more suitable than other tested models. S2/3Aqua also performed satisfactorily across all eight spectral bands, including at 620 and 681 nm, with a mean difference of less than 0.003 reflectance units. The cyanobacteria mapping showed a high level of agreement between S2/3Aqua and Sentinel-3 for low concentrations of Phycocyanin (less than 50 mg m-3 ), and S2/3Aqua effectively captured the spatial variability of narrower and smaller blooms. Finally, S2/3Aqua provides reliable synthetic spectral bands that can effectively be used in several aquatic system studies, including monitoring potentially harmful algal blooms.

Understanding water quality (WQ) is essential for grasping biogeochemical cycles and assessing human impacts such as deforestation, climate change, dam construction, illegal mining, and urbanization. However, monitoring WQ across Brazil’s vast aquatic systems requires significant resources. Remote sensing improves this process by offering high-resolution and large-scale observations. Nevertheless, to produce reliable remote-sensing WQ products based on remote sensing demands comprehensive datasets with concurrent aquatic reflectance and in situ measurements (e.g., Chlorophyll-a, Secchi Disk Depth, Suspended Sediments). Such data enables advanced semi-analytical and machine learning models to capture Brazil’s bio-optical water complexity. Collaborative efforts and open-data sharing are essential for building these datasets. Here, we introduce a new curated, high-quality bio-optical dataset across different aquatic systems in Brazil, called BRAZA (Bio-optical aquatic database for remote sensing of water quality in BRAZil coAstal and inland waters). By leveraging data from 17 institutions, covering 2,895 stations across + 128 lakes, rivers, reservoirs, and coastal areas of Brazil’s five administrative regions our dataset presents an important contribution to support remote sensing WQ-based analysis in Brazil.

In 2023 and 2024, severe droughts affected the Amazon basin, caused by climate change and a strong El Nino event. There is still a need to understand the extent of this drought on the rivers of the Amazon. This research aimed to analyze the water surface dynamics of the Trombetas River using Sentinel-1 and altimetry data to assess the impact of droughts. The methodological procedures involved two stages: i) the mapping of Open Water Extent (OWE) using Sentinel-1 and ii) the use of altimetry satellite data on surface water elevation. The analysis period refers to monthly values between 2017-2024. The results showed that the lowest OWE values occurred in November 2024, with 202.33 km(2), and the second lowest drought was in December 2024, with 204.52 km(2). The third lowest OWE was in November 2023 with 2017.66 km(2). Regarding the water surface elevation, the lowest water levels were observed in October and November 2023; in 2024, the lowest were in October and November. The findings highlight the urgency of tackling remote sensing strategies to ensure water security while providing monitoring subsidies for the Trombetas River management.

Constructing a robust ocean color (OC) record(e.g., water transparency, phytoplankton absorption) for long-term assessments of coastal and inland water ecosystems from past, present, and future missions requires high-quality spectral remote sensing reflectance (R-rs) products. Using the GLORIA dataset (Lehmann et al., 2023), we evaluated the quality of R-rs products from the moderate resolution imaging spectroradiometer (MODIS on Terra and Aqua), medium resolution imaging spectrometer (MERIS), and visible infrared imaging radiometer suite (VIIRS) processed via the two-band heritage atmospheric correction method (a combination of near-infrared and shortwave infrared bands) available in the Sea WiFS Analysis Data Analysis System (SeaDAS). Overall, retrieval residuals are consistent within a few percentages among the four missions. Median residuals ranged from similar to 20% in the similar to 550-nm band to>60% in the similar to 412-nm bands. Spectrally averaged root mean squared differences for all the missions were similar to 0.0024 sr(-1)with one standard deviation of similar to 0.001 sr(-1). The corresponding(median) biases in the visible bands varied from-60% to-3%,with the largest biases identified in MERIS and VIIRS products. Despite the lower sensitivity of band-ratio algorithms to residuals in specific spectral regions [e.g., OC3 chlorophyll-a algorithm is less prone to residuals in R-rs(lambda>600 nm)], other algorithms or downstream products that leverage all the visible bands are highly compromised. We underscore the need to improve the quality of Rrsproducts, thereby enabling the reconstruction of baseline OC products of high caliber in global coastal and inland waters that are often near human activity.

Limnology and Oceanography BulletinEarly View Meeting Highlights Hacking Limnology Workshops and DSOS23: Growing a Workforce for the Nexus of Data Science, Open Science, and the Aquatic Sciences Michael F. Meyer, Corresponding Author Michael F. Meyer [email protected] orcid.org/0000-0002-8034-9434 U.S. Geological Survey, Madison, WI, USASearch for more papers by this authorMerritt E. Harlan, Merritt E. Harlan orcid.org/0000-0002-4019-4888 U.S. Geological Survey, Denver, CO, USASearch for more papers by this authorRobert T. Hensley, Robert T. Hensley orcid.org/0000-0001-8542-087X National Ecological Observatory Network, Battelle, Boulder, CO, USASearch for more papers by this authorQing Zhan, Qing Zhan orcid.org/0000-0002-1339-3646 The Netherlands Institute of Ecology, Wageningen, The NetherlandsSearch for more papers by this authorNahit S. Börekçi, Nahit S. Börekçi orcid.org/0000-0003-1124-1013 Mersin University, Mersin, TürkiyeSearch for more papers by this authorTuba Bucak, Tuba Bucak orcid.org/0000-0002-6710-0423 Aarhus University, Aarhus, DenmarkSearch for more papers by this authorAlli N. Cramer, Alli N. Cramer orcid.org/0000-0002-0356-5782 University of Washington, Friday Harbor, WA, USASearch for more papers by this authorJohannes Feldbauer, Johannes Feldbauer orcid.org/0000-0002-8238-5375 Technische Universität Dresden, Dresden, GermanySearch for more papers by this authorRobert Ladwig, Robert Ladwig orcid.org/0000-0001-8443-1999 University of Wisconsin—Madison, Madison, WI, USASearch for more papers by this authorJorrit P. Mesman, Jorrit P. Mesman orcid.org/0000-0002-4319-260X Uppsala University, Uppsala, SwedenSearch for more papers by this authorIsabella A. Oleksy, Isabella A. Oleksy orcid.org/0000-0003-2572-5457 University of Colorado—Boulder, Boulder, CO, USASearch for more papers by this authorRachel M. Pilla, Rachel M. Pilla orcid.org/0000-0001-9156-9486 Oak Ridge National Laboratory, Oak Ridge, TN, USASearch for more papers by this authorJacob A. Zwart, Jacob A. Zwart orcid.org/0000-0002-3870-405X U.S. Geological Survey, San Francisco, CA, USASearch for more papers by this authorElisa Calamita, Elisa Calamita orcid.org/0000-0002-2614-2942 Eawag, Dübendorf, SwitzerlandSearch for more papers by this authorNicholas J. Gubbins, Nicholas J. Gubbins orcid.org/0000-0003-0688-3767 Colorado State University, Fort Collins, CO, USASearch for more papers by this authorMary E. Lofton, Mary E. Lofton orcid.org/0000-0003-3270-1330 Virginia Tech, Blacksburg, VA, USASearch for more papers by this authorDaniel A. Maciel, Daniel A. Maciel orcid.org/0000-0003-4543-5908 National Institute for Space Research, São José dos Campos, São Paulo, BrazilSearch for more papers by this authorNicholas S. Marzolf, Nicholas S. Marzolf orcid.org/0000-0001-9146-1643 Duke University, Durham, NC, USASearch for more papers by this authorFreya Olsson, Freya Olsson orcid.org/0000-0002-0483-4489 Virginia Tech, Blacksburg, VA, USASearch for more papers by this authorAudrey N. Thellman, Audrey N. Thellman orcid.org/0000-0003-3716-6664 Duke University, Durham, NC, USASearch for more papers by this authorR. Quinn Thomas, R. Quinn Thomas orcid.org/0000-0003-1282-7825 Virginia Tech, Blacksburg, VA, USASearch for more papers by this authorMichael J. Vlah, Michael J. Vlah orcid.org/0000-0002-6260-2416 Duke University, Durham, NC, USASearch for more papers by this author Michael F. Meyer, Corresponding Author Michael F. Meyer [email protected] orcid.org/0000-0002-8034-9434 U.S. Geological Survey, Madison, WI, USASearch for more papers by this authorMerritt E. Harlan, Merritt E. Harlan orcid.org/0000-0002-4019-4888 U.S. Geological Survey, Denver, CO, USASearch for more papers by this authorRobert T. Hensley, Robert T. Hensley orcid.org/0000-0001-8542-087X National Ecological Observatory Network, Battelle, Boulder, CO, USASearch for more papers by this authorQing Zhan, Qing Zhan orcid.org/0000-0002-1339-3646 The Netherlands Institute of Ecology, Wageningen, The NetherlandsSearch for more papers by this authorNahit S. Börekçi, Nahit S. Börekçi orcid.org/0000-0003-1124-1013 Mersin University, Mersin, TürkiyeSearch for more papers by this authorTuba Bucak, Tuba Bucak orcid.org/0000-0002-6710-0423 Aarhus University, Aarhus, DenmarkSearch for more papers by this authorAlli N. Cramer, Alli N. Cramer orcid.org/0000-0002-0356-5782 University of Washington, Friday Harbor, WA, USASearch for more papers by this authorJohannes Feldbauer, Johannes Feldbauer orcid.org/0000-0002-8238-5375 Technische Universität Dresden, Dresden, GermanySearch for more papers by this authorRobert Ladwig, Robert Ladwig orcid.org/0000-0001-8443-1999 University of Wisconsin—Madison, Madison, WI, USASearch for more papers by this authorJorrit P. Mesman, Jorrit P. Mesman orcid.org/0000-0002-4319-260X Uppsala University, Uppsala, SwedenSearch for more papers by this authorIsabella A. Oleksy, Isabella A. Oleksy orcid.org/0000-0003-2572-5457 University of Colorado—Boulder, Boulder, CO, USASearch for more papers by this authorRachel M. Pilla, Rachel M. Pilla orcid.org/0000-0001-9156-9486 Oak Ridge National Laboratory, Oak Ridge, TN, USASearch for more papers by this authorJacob A. Zwart, Jacob A. Zwart orcid.org/0000-0002-3870-405X U.S. Geological Survey, San Francisco, CA, USASearch for more papers by this authorElisa Calamita, Elisa Calamita orcid.org/0000-0002-2614-2942 Eawag, Dübendorf, SwitzerlandSearch for more papers by this authorNicholas J. Gubbins, Nicholas J. Gubbins orcid.org/0000-0003-0688-3767 Colorado State University, Fort Collins, CO, USASearch for more papers by this authorMary E. Lofton, Mary E. Lofton orcid.org/0000-0003-3270-1330 Virginia Tech, Blacksburg, VA, USASearch for more papers by this authorDaniel A. Maciel, Daniel A. Maciel orcid.org/0000-0003-4543-5908 National Institute for Space Research, São José dos Campos, São Paulo, BrazilSearch for more papers by this authorNicholas S. Marzolf, Nicholas S. Marzolf orcid.org/0000-0001-9146-1643 Duke University, Durham, NC, USASearch for more papers by this authorFreya Olsson, Freya Olsson orcid.org/0000-0002-0483-4489 Virginia Tech, Blacksburg, VA, USASearch for more papers by this authorAudrey N. Thellman, Audrey N. Thellman orcid.org/0000-0003-3716-6664 Duke University, Durham, NC, USASearch for more papers by this authorR. Quinn Thomas, R. Quinn Thomas orcid.org/0000-0003-1282-7825 Virginia Tech, Blacksburg, VA, USASearch for more papers by this authorMichael J. Vlah, Michael J. Vlah orcid.org/0000-0002-6260-2416 Duke University, Durham, NC, USASearch for more papers by this author First published: 20 October 2023 https://doi.org/10.1002/lob.10607 Michael F. Meyer, Merritt E. Harlan, Robert T. Hensley, and Qing Zhan contributed equally and are listed as co-first authors. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat References Lehmann, M. K., and others. 2023. GLORIA—A globally representative hyperspectral in situ dataset for optical sensing of water quality. Sci. Data 10: 100. doi:10.1038/s41597-023-01973-y. Meyer, M. F., and Zwart, J. A. 2020. Virtual summit: Incorporating data science and open science in aquatic research. Limnol. Oceanogr. Bull. 29: 144–146. doi:10.1002/lob.10411 Meyer, M. F., and others. 2021a. Virtual growing pains: initial lessons learned from organizing virtual workshops, summits, conferences, and networking events during a global pandemic. Limnol. Oceanogr. Bull. 30: 1–11. doi:10.1002/lob.10431. Meyer, M.F., and others, 2021b. The AEMON-J “Hacking Limnology” workshop series & virtual summit: Incorporating data science and open science in aquatic research. Limnol. Oceanogr. Bull. 30, 140–143. doi:10.1002/lob.10475 Meyer, M., and others. 2021c. AEMON-J/DSOS archive: “Hacking Limnology” workshop + virtual summit in data science & open science in aquatic research. doi:10.17605/OSF.IO/682V5. Meyer, M. F., and others. 2022. Hacking Limnology Workshop and DSOS22: Creating a community of practice for the nexus of data science, open science, and the aquatic sciences. Limnol. Oceanogr. Bull. 31: 123–126. doi:10.1002/lob.10525. Thomas, R. Q., and others. 2023. The NEON ecological forecasting challenge. Front. Ecol. Environ. 21: 112–113. doi:10.1002/fee.2616. Vlah, M. J., S. Rhea, E. S. Bernhardt, W. Slaughter, N. Gubbins, A. G. DelVecchia, A. Thellman, and M. R. V. Ross. 2023. MacroSheds: A synthesis of long-term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies. Limnol. Oceanogr. Lett. 8: 419–452. doi:10.1002/lol2.10325. Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation

In 2023, an intense drought impacted the Amazon basin triggered by climate change and a strong El Ni & ntilde;o event, with the Negro River reaching its lowest water level in 120 years. However, the spatiotemporal open water extent (OWE) during this drought remains unclear. This study comprehensively evaluates OWE variability in the central Amazon using Sentinel-1 synthetic aperture radar (SAR) data since 2017. Monthly OWE masks were generated through an empirical threshold classification with accuracy >95%. Overall, the central Amazon experienced a reduction of similar to 8% in OWE in the 2023 dry season months (November and December) when compared to monthly-average. However, reductions of up to 80% in OWE were observed in several specific lakes. Our analysis underscores the unprecedented severity of the 2023/2024 drought on rivers and floodplains. Utilizing SAR remote sensing technologies, this study emphasizes the urgent need for proactive conservation measures to safeguard the Amazon's ecological integrity amid escalating environmental challenges. Monthly water masks from January/2017 to September/2024 are available here: https://doi.org/10.5281/zenodo.12751783.

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Land use and land cover (LULC) analysis provides valuable information to understand environmental changes and their effects on landslide occurrence. However, LULC time series can be affected by errors in classifications that lead to invalid transitions and, therefore, to misinterpretations. One solution is to include temporal approaches that reduce the effects of invalid transitions. Here, we aimed to evaluate how such methods can improve the LULC analysis for a landslide-affected area. For that, we integrated the Random Forest (RF) class likelihoods with the temporal approach provided by the Compound Maximum a Posteriori (CMAP) algorithm, named here as RF-CMAP. Results from RF-CMAP were compared to those obtained from the traditional RF in a post-classification comparison approach. Although both methods presented high performance, with overall accuracy (OA) values greater than 0.87, RF-CMAP reached higher OA than RF for all the analysed years and corrected 99.92 km2 (12% of the total area) of invalid transitions presented by the traditional RF. Furthermore, RF-CMAP was capable of correctly classifying more areas than RF in landslides (e.g., 66% and 21% for RF-CMAP and RF in 2000, respectively). Finally, this study contributes to exploring the integration between RF and CMAP algorithms to avoid invalid transitions and to assess how the existence of LULC invalid transitions can impact subsequent analyses.

Secchi Disk Depth (Z(sd)) is one of the most fundamental and widely used water-quality indicators quantifiable via optical remote sensing. Despite decades of research, development, and demonstrations, currently, there is no operational model that enables the retrieval of Z(sd) from the rich archive of Landsat, the long-standing civilian Earth-observation program (1972 - present). Devising a robust Z(sd) model requires a comprehensive in situ dataset for testing and validation, enabling consistent mapping across optically varying global aquatic ecosystems. This study utilizes Mixture Density Networks (MDNs) trained with a large in situ dataset (N = 5689) from 300+ water bodies to formulate and implement a global Z(sd) algorithm for Landsat sensors, including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) aboard Landsat-5, -7, -8, and -9, respectively. Through an extensive Monte Carlo cross-validation with in situ data, we showed that MDNs improved Z(sd) retrieval when compared to other commonly used machine-learning (ML) models and recently developed semi-analytical algorithms, achieving a median symmetric accuracy (epsilon) of similar to 29% and median bias (beta) of similar to 3%). A fully trained MDN model was then applied to atmospherically corrected Landsat data (i.e., remote sensing reflectance; R-rs) to both further validate our MDN-estimated Z(sd) products using an independent global satellite-to-in situ matchup dataset (N = 3534) and to demonstrate their utility in time-series analyses (1984 - present) via selected lakes and coastal estuaries. The quality of R-rs products rigorously assessed for the Landsat sensors indicated sensor-/band-dependent epsilon ranging from 8% to 37%. For our Z(sd) products, we found epsilon similar to 39% and beta similar to 8% for the Landsat-8/OLI matchups. We observed higher errors and biases for TM and ETM+, which are explained by uncertainties in R-rs products induced by uncertainties in atmospheric correction and instrument calibration. Once these sources of uncertainty are, to the extent possible, characterized and accounted for, our developed model can then be employed to evaluate long-term trends in water transparency across unprecedented spatiotemporal scales, particularly in poorly studied regions of the world in a consistent manner.

Hydrological connectivity based on water surface connectivity controls the water exchange between large rivers and their floodplain lakes, which occurs by channelized flow through floodplain areas and overbank flow, and it is relevant to sustain the ecosystem's health and biodiversity of floodplain waterbodies. Given the climate change impacts on floodplain aquatic habitats, further studies are needed to understand and quantify the river-lake connectivity and its temporal dynamics. However, few studies are dedicated to objectively estimating hydrological connectivity, and new commercial satellite datasets and machine learning approaches can advance the understanding of this important topic. This paper proposes a new framework for computing the hydrological connectivity of small floodplain lakes (river-Lake CONNECTivity or L-CONNECT) in the Amazon Juru & aacute; River. The L-CONNECT framework consists of spectral similarity analysis with machine learning in the river-lake system and three steps were implemented: (i) sampling process based on independent satellite-imagery visual interpretation; (ii) automated similarity features extraction from river-lake system; and (iii) training and validation of machine learning algorithm. A total of 552 3-m PlanetScope SuperDove imagery were acquired in 2020 and 2021 to perform our approach. In general, the L-CONNECT framework achieved 88% overall accuracy. However, not-connected lakes were not easily estimated. We found that the L-CONNECT framework managed to perform accurately over all lakes investigated independent on their distance to Juru & aacute; River channel (average accuracy of similar to 86%), and that there was a low discordance (less than 30%) between the lakes' hydrological connectivity responses acquired by Sentinel-2 against PlanetScope data. The mapping results showed that lakes were more connected to the Juru & aacute; River during the 2021 (not-connected lakes rate of 28%) compared to 2020, which was explained by higher cumulative precipitation during January, February, and March of 2021. Finally, the new L-CONNECT framework proposed here can support hydrological connectivity mapping of small floodplain lakes by considering assumptions relative to the similarity between river and water spectra as a proxy for that.

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 Originally developed for terrestrial science and applications, the US Geological Survey Landsat surface reflectance (SR) archive spanning ~ 40 yr of observations has been increasingly utilized in large‐scale water‐quality studies. These products, however, have not been rigorously validated using in situ measured reflectance. This letter quantifies and demonstrates the quality of the SR products by harnessing a sizeable global dataset (N = 1100). We found that the Landsat 8/9 SR in the green and red bands marginally meet the targeted accuracy requirements (30%), whereas the uncertainties in the blue and coastal‐aerosol bands ranged from 48% to 110%. We further observed > +25% biases in the visible bands of Landsat 5/7 SR, which can introduce an apparent downward trend when applied in time‐series analyses combined with Landsat 8/9. Users must exercise caution when using this archive for trend analyses, and progress in atmospheric correction is required to foster advanced applications of the Landsat archive for aquatic science.

Urban reservoirs are important for drinking water services and urban living. However, potentially toxic cyanobacteria blooms are frequently present due to human pollution and might threaten the urban water supply. Conveniently, cyanobacteria can be monitored by remote sensing-based approaches based on the spectral features of C-Phycocyanin (PC). Furthermore, methods leveraging Machine Learning Algorithms (MLA) for PC estimation from hyperspectral data have highlighted the potential to estimate PC more accurately - even at low concentrations. Since relatively few methodologies for PC retrieval in tropical environments have been developed or validated, this research evaluated PRISMA hyperspectral data processed with three MLA (Random Forest, Extreme Gradient Boost, and Support Vector Machines) to estimate PC concentrations in the Billings reservoir, Brazil. The same MLA were used to generate PC models using Wordview-3 and Landsat-8/OLI simulated data to assess the potential gain of using hyperspectral over multispectral data. A PRISMA image was processed with three atmospheric correction methods and validated with co-located in-situ data, where the best atmospherically corrected product was used to generate synthetic Landsat-8/OLI and Worldview-3 images. The PC models were calibrated and validated through Monte Carlo simulation using field radiometric and biological data (Chlorophyll-a, PC, and phytoplankton taxonomy) collected in eight field campaigns (N = 115). The PRISMA and the synthetic multispectral images were used for a second round of models' validation using colocated PC measurements (match-up window +/- 4 h). The global PC Mixture Density Network was also applied to the PRISMA data, and the estimates were compared with the other MLA. The results showed that the standard PRISMA surface reflectance product provided the best atmospheric correction (MAE < 20% for the 500-700 nm bands), while ACOLITE and 6SV underperformed it from two to more than ten-fold. Cyanobacteria species were abundant in 96% of the taxonomical samples, even though relatively low PC concentrations were found (PC from 0 to 301.81 mu g/L and median PC = 2.9 mu g/L). The global Mixture Density Network sharply overestimated PC (MAE = 280% and Bias = 280%), potentially due to Billings reservoir's low PC:Chlorophyll-a ratio relative to the original training dataset. PRISMA/Random Forest (MAE = 45%) achieved the lowest error for orbital PC estimate, while Extreme Gradient Boost outperformed the other MLA using Worldview-3 (MAE = 49%) and Landsat-8 (MAE = 74%) synthetic imagery. Therefore, the results suggest hyperspectral and multispectral orbital data aligned with MLA are feasible for monitoring PC, even for waters containing low PC concentrations and reduced PC:Chlorophyll-a ratios.