An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries

Correction of Thin Cirrus Absorption Effects in Landsat 8 Thermal Infrared Sensor Images Using the Operational Land Imager Cirrus Band on the Same Satellite Platform

Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help address this issue, a cirrus detection channel (Band 9) centered within the 1.375-μm water vapor absorption region was implemented on OLI, with a spatial resolution of 30 m. However, this band has not yet been fully utilized in the Collection 2 Landsat 8/9 Level 2 surface temperature data products that are publicly released by U.S. Geological Survey (USGS). The temperature products are generated with a single-channel algorithm. During the surface temperature retrievals, the effects of absorption of infrared radiation originating from the warmer earth's surfaces by ice clouds, typically located in the upper portion of the troposphere and re-emitting at much lower temperatures (approximately 220 K), are not taken into consideration. Through an analysis of sample Level 1 TOA and Level 2 surface data products, we have found that thin cirrus cloud features present in the Level 1 1.375-μm band images are directly propagated down to the Level 2 surface data products. The surface temperature errors resulting from thin cirrus contamination can be 10 K or larger. Previously, we reported an empirical and effective technique for removing thin cirrus scattering effects in OLI images, making use of the correlations between the 1.375-μm band image and images of any other OLI bands located in the 0.4-2.5 μm solar spectral region. In this article, we describe a variation of this technique that can be applied to the thermal bands, using the correlations between the Level 1 1.375-μm band image and the 11-μm BT image for the effective removal of thin cirrus absorption effects. Our results from three data sets acquired over spatially uniform water surfaces and over non-uniform land/water boundary areas suggest that if the cirrus-removed TOA 11-μm band BT images are used for the retrieval of the Level 2 surface temperature (ST) data products, the errors resulting from thin cirrus contaminations in the products can be reduced to about 1 K for spatially diffused cirrus scenes.

Lake Surface Water Temperature in high altitude lakes in the Pyrenees: Combining satellite with monitoring data to assess recent trends

Lake Surface Water Temperature (LSWT) influences critical bio-geological processes in lake ecosystems, and there is growing evidence of rising LSWT over recent decades worldwide and future shifts in thermal patterns are expected to be a major consequence of global warming. At a regional scale, assessing recent trends and anticipating impacts requires data from a number of lakes, but long term in situ monitoring programs are scarce, particularly in mountain areas. In this work, we propose the combined use of satellite-derived temperature with in situ data for a five-year period (2017-2022) from 5 small (<0.5km2) high altitude (1880-2680 masl) Pyrenean lakes. The comparison of in situ and satellite-derived data in a common period (2017-2022) during the summer season showed a notably high (r = 0.94, p < 0.01) correlation coefficient, indicative of a robust relationship between the two data sources. The root mean square errors ranged from 1.8 °C to 3.9 °C, while the mean absolute errors ranged from 1.6 °C to 3.6 °C. We applied the obtained in situ-satellite eq. (2017-2022) to Landsat 5, 7 and 8/9 data since 1985 to reconstruct the summer surface temperature of the five studied lakes with in situ data and to four additional lakes with no in situ monitoring data. Reconstructed LSWT for the 1985-2022 showed an upward trend in all lakes. Moreover, paleolimnological reconstructions based on sediment cores studies demonstrate large changes in the last decades in organic carbon accumulation, sediment fluxes and bioproductivity in the Pyrenean lakes. Our research represents the first comprehensive investigation conducted on high mountain lakes in the Pyrenees that compares field monitoring data with satellite-derived temperature records. The results demonstrate the reliability of satellite-derived LSWT for surface temperatures in small lakes, and provide a tool to improve the LSWT in lakes with no monitoring surveys.

Assessing the suitability of lakes and reservoirs for recreation using Landsat 8

Water clarity has long been used as a visual indicator of the condition of water quality. The clarity of waters is generally valued for esthetic and recreational purposes. Water clarity is often assessed using a Secchi disk attached to a measured line and lowered to a depth where it can be no longer seen. We have applied an approach which uses atmospherically corrected Landsat 8 data to estimate the water clarity in freshwater bodies by using the quasi-analytical algorithm (QAA) and Contrast Theory to predict Secchi depths for more than 270 lakes and reservoirs across the continental US. We found that incorporating Landsat 8 spectral data into methodologies created to retrieve the inherent optical properties (IOP) of coastal waters was effective at predicting in situ measures of the clarity of inland water bodies. The predicted Secchi depths were used to evaluate the recreational suitability for swimming and recreation using an assessment framework developed from public perception of water clarity. Results showed approximately 54% of the water bodies in our dataset were classified as "marginally suitable to suitable" with approximately 31% classed as "eminently suitable" and approximately 15% classed as "totally unsuitable-unsuitable". The implications are that satellites engineered for terrestrial applications can be successfully used with traditional ocean color algorithms and methods to measure the water quality of freshwater environments. Furthermore, operational land-based satellite sensors have the temporal repeat cycles, spectral resolution, wavebands, and signal-to-noise ratios to be repurposed to monitor water quality for public use and trophic status of complex inland waters.

Performance and validation of water surface temperature estimates from Landsat 8 of the Itaipu Reservoir, State of Paraná, Brazil

Studies on water surface temperature (WST) from thermal infrared remote sensing are still incipient in Brazil, and for many water resources, they do not exist. Many algorithms have been developed to estimate surface temperature in satellite images. There are also many difficulties in implementing these algorithms due to their complexity, especially in free software, which restricts the satisfactory processing of these data by users of the technique. Thus, this work aimed to validate an algorithm used to estimate land surface temperature (LST) when applied to the surface of inland water bodies. Water surface temperature estimates (WSTe) were generated from Itaipu State of Paraná (PR) reservoir, Brazil, calculated from Landsat 8 - TIRS satellite images (WSTs) and water surface temperature data from 37 in situ stations (WSTi). A linear regression model of the WSTe was generated in 60% of the samples and its validation with the remaining 40%, subject to prior evaluation of some statistical indicators. The model was considered significant since the coefficient of determination (r²) was 0.90 (95% of confidence), root mean square deviation (RMSD) 0.8 °C, Willmott Index (d) = 0.97, and Nash-Sutcliffe efficiency coefficient (NSE) = 0.89. The methodology used to extract WSTs from the Python QGIS plugin was relatively quick to apply, easy to understand, and had a better performance of the estimates than those presented in the literature review.

Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis

Seagrasses are globally recognized for their contribution to blue carbon sequestration. However, accurate quantification of their carbon storage capacity remains uncertain due, in part, to an incomplete inventory of global seagrass extent and assessment of its temporal variability. Furthermore, seagrasses are undergoing significant decline globally, which highlights the urgent need to develop change detection techniques applicable to both the scale of loss and the spatial complexity of coastal environments. This study applied a deep learning algorithmto a 30-year time series of Landsat 5 through 8 imagery to quantify seagrass extent, leaf area index (LAI), and belowground organic carbon (BGC) in St. Joseph Bay, Florida, between 1990 and 2020. Consistent with previous field-based observations regarding stability of seagrass extent throughout St. Joseph Bay, there was no temporal trend in seagrass extent (23 ± 3 km2, τ = 0.09, p = 0.59, n = 31), LAI (1.6 ± 0.2, τ = -0.13, p = 0.42, n = 31), or BGC (165 ± 19 g C m-2, τ = - 0.01, p = 0.1, n = 31) over the 30-year study period. There were, however, six brief declines in seagrass extent between the years 2004 and 2019 following tropical cyclones, from which seagrasses recovered rapidly. Fine-scale interannual variability in seagrass extent, LAI, and BGC was unrelated to sea surface temperature or to climate variability associated with the El Niño-Southern Oscillation or the North Atlantic Oscillation. Although our temporal assessment showed that seagrass and its belowground carbon were stable in St. Joseph Bay from 1990 to 2020, forecasts suggest that environmental and climate pressures are ongoing, which highlights the importance of the method and time series presented here as a valuable tool to quantify decadal-scale variability in seagrass dynamics. Perhaps more importantly, our results can serve as a baseline against which we can monitor future change in seagrass communities and their blue carbon.

Landscape and vegetation traits of urban green space can predict local surface temperature

Societal and technological advances have triggered demands to improve urban environmental quality. Urban green space (UGS) can provide effective cooling service and thermal comfort to alleviate warming impacts. We investigated the relative influence of a comprehensive spectrum of UGS landscape and vegetation factors on surface temperature in arid Urumqi city in northwest China. Built-up area range was extracted from Luojia 1-01 (LJ1-01) satellite data, and within this range, the landscape metric information and vegetation index information of UGS were obtained based on PlanetScope data, and a total of 439 sampling grids (1 km × 1 km) were generated. The urban surface temperature of built-up areas was extracted from Landsat8-TIRS images. The 12 landscape metrics and 14 vegetation indexes were assigned as independent variables, and surface temperature the dependent variable. Support Vector Machine (SVM), Gradient Boost Regression Tree (GBRT) and Random Forest (RF) were enlisted to establish numerical models to predict surface temperature. The results showed that: (1) It was feasible to predict local surface temperature using a combination of landscape metrics and vegetation indexes. Among the three models, RF demonstrated the best accuracy. (2) Collectively, all the factors play a role in the surface-temperature prediction. The most influential factor was Difference Vegetation Index (DVI), followed by Green Normalized Difference Vegetation Index (GNDVI), Class Area (CA) and AREA. This study developed remote sensing techniques to extract a basket of UGS factors to predict the surface temperature at local urban sites. The methods could be applied to other cities to evaluate the cooling impacts of green infrastructures. The findings could provide a scientific basis for ecological spatial planning of UGS to optimize cooling benefits in the arid region.

A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across U.S. lakes

Cyanobacteria harmful algal blooms (cyanoHABs) negatively affect ecological, human, and animal health. Traditional methods of validating satellite algorithms with data from water samples are often inhibited by the expense of quantifying cyanobacteria indicators in the field and the lack of public data. However, state recreation advisories and other recorded events of cyanoHAB occurrence reported by local authorities can serve as an independent and publicly available dataset for validation. State recreation advisories were defined as a period delimited by a start and end date where a warning was issued due to detections of cyanoHABs over a state's risk threshold. State reported events were defined as any event that was documented with a single date related to cyanoHABs. This study examined the presence-absence agreement between 160 state reported cyanoHAB advisories and 1,343 events and cyanobacteria biomass estimated by a satellite algorithm called the Cyanobacteria Index (CIcyano). The true positive rate of agreement with state recreation advisories was 69% and 60% with state reported events. CIcyano detected a reduction or absence in cyanobacteria after 76% of the recreation advisories ended. CIcyano was used to quantify the magnitude, spatial extent, and temporal frequency of cyanoHABs; each of these three metrics were greater (r > 0.2) during state recreation advisories compared to non-advisory times with effect sizes ranging from small to large. This is the first study to quantitatively evaluate satellite algorithm performance for detecting cyanoHABs with state reported events and advisories and supports informed management decisions with satellite technologies that complement traditional field observations.

Satellite-derived cyanobacteria frequency and magnitude in headwaters & near-dam reservoir surface waters of the Southern U.S

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300 m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016-April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

Merging of the Case 2 Regional Coast Colour and Maximum-Peak Height chlorophyll-a algorithms: validation and demonstration of satellite-derived retrievals across US lakes

Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll-a (chl-a, µg L-1) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl-a with in-water algorithms. Validation of chl-a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl-a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH(P)) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl-a data. The best performance based on mean absolute multiplicative error (MAEmult) was demonstrated by the merged algorithm referred to as C15-M10 (MAEmult = 1.8, biasmult = 0.97, n = 836). In the C15-M10 algorithm, the MPH(P) chl-a value was retained if it was > 10 µg L-1; if the MPH(P) value was ≤ 10 µg L-1, the C2RCC value was selected, as long as that value was < 15 µg L-1. Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl-a algorithms previously reported independently. Additionally, it contributes to the transition of chl-a algorithm maturity by quantifying error statistics for a number of locations and times.

Decline in Thermal Habitat Conditions for the Endangered Delta Smelt as Seen from Landsat Satellites (1985-2019)

This study uses Landsat 5, 7, and 8 level 2 collection 2 surface temperature to examine habitat suitability conditions spanning 1985-2019, relative to the thermal tolerance of the endemic and endangered delta smelt (Hypomesus transpacificus) and two non-native fish, the largemouth bass (Micropterus salmoides) and Mississippi silverside (Menidia beryllina) in the upper San Francisco Estuary. This product was validated using thermal radiometer data collected from 2008 to 2019 from a validation site on a platform in the Salton Sea (RMSE = 0.78 °C, r = 0.99, R² = 0.99, p < 0.01, and n = 237). Thermally unsuitable habitat, indicated by annual maximum water surface temperatures exceeding critical thermal maximum temperatures for each species, increased by 1.5 km² yr^-1 for the delta smelt with an inverse relationship to the delta smelt abundance index from the California Department of Fish and Wildlife (r = -0.44, R² = 0.2, p < 0.01). Quantile and Theil-Sen regression showed that the delta smelt are unable to thrive when the thermally unsuitable habitat exceeds 107 km². A habitat unsuitable for the delta smelt but survivable for the non-natives is expanding by 0.82 km² yr^-1. Warming waters in the San Francisco Estuary are reducing the available habitat for the delta smelt.

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Exploring the potential value of satellite remote sensing to monitor chlorophyll-a for US lakes and reservoirs

Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.

Spatio-Temporal Modeling for Forecasting High-Risk Freshwater Cyanobacterial Harmful Algal Blooms in Florida

Due to the occurrence of more frequent and widespread toxic cyanobacteria events, the ability to predict freshwater cyanobacteria harmful algal blooms (cyanoHAB) is of critical importance for the management of drinking and recreational waters. Lake system specific geographic variation of cyanoHABs has been reported, but regional and state level variation is infrequently examined. A spatio-temporal modeling approach can be applied, via the computationally efficient Integrated Nested Laplace Approximation (INLA), to high-risk cyanoHAB exceedance rates to explore spatio-temporal variations across statewide geographic scales. We explore the potential for using satellite-derived data and environmental determinants to develop a short-term forecasting tool for cyanobacteria presence at varying space-time domains for the state of Florida. Weekly cyanobacteria abundance data were obtained using Sentinel-3 Ocean Land Color Imagery (OLCI), for a period of May 2016-June 2019. Time and space varying covariates include surface water temperature, ambient temperature, precipitation, and lake geomorphology. The hierarchical Bayesian spatio-temporal modeling approach in R-INLA represents a potential forecasting tool useful for water managers and associated public health applications for predicting near future high-risk cyanoHAB occurrence given the spatio-temporal characteristics of these events in the recent past. This method is robust to missing data and unbalanced sampling between waterbodies, both common issues in water quality datasets.

Spatio-Temporal Modeling for Forecasting High-Risk Freshwater Cyanobacterial Harmful Algal Blooms in Florida

Due to the occurrence of more frequent and widespread toxic cyanobacteria events, the ability to predict freshwater cyanobacteria harmful algal blooms (cyanoHAB) is of critical importance for the management of drinking and recreational waters. Lake system specific geographic variation of cyanoHABs has been reported, but regional and state level variation is infrequently examined. A spatio-temporal modeling approach can be applied, via the computationally efficient Integrated Nested Laplace Approximation (INLA), to high-risk cyanoHAB exceedance rates to explore spatio-temporal variations across statewide geographic scales. We explore the potential for using satellite-derived data and environmental determinants to develop a short-term forecasting tool for cyanobacteria presence at varying space-time domains for the state of Florida. Weekly cyanobacteria abundance data were obtained using Sentinel-3 Ocean Land Color Imagery (OLCI), for a period of May 2016-June 2019. Time and space varying covariates include surface water temperature, ambient temperature, precipitation, and lake geomorphology. The hierarchical Bayesian spatio-temporal modeling approach in R-INLA represents a potential forecasting tool useful for water managers and associated public health applications for predicting near future high-risk cyanoHAB occurrence given the spatio-temporal characteristics of these events in the recent past. This method is robust to missing data and unbalanced sampling between waterbodies, both common issues in water quality datasets.

Estimation of satellite-derived lake water surface temperatures in the western Mediterranean: Integrating multi-source, multi-resolution imagery and a long-term field dataset using a time series approach

Lake surface water temperature (LSWT) is a key parameter to help study the environmental and ecological impacts of climate change. In this work, we measured the LSWT of 1 natural and 23 artificial lakes located on the island of Sardinia in the western Mediterranean, which is a region where changes in climate are projected to have significant impacts. By integrating multi-source and multi-resolution datasets of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat and long-term in situ temperature observations, we detected, measured, and analysed the LSWT trends during the period of 2000-2018 across all the investigated lakes. Methodologically, we demonstrated that a simplified approached based on Planck's equation for Landsat thermal infrared (TIR) data could be a valid alternative to radiative transfer equation retrieval methods for the retrieval of LSWT without loss of accuracy. Moreover, we demonstrated that rescaled and independently validated MOD112A-derived LSWT showed good accuracy, efficiently filled the spatial and temporal gaps in long-term in situ LSWT, and could be used for long-term LSWT trend detection and measurement. All 24 lakes showed an annual warming trend of +0.010 °C/y, warming winter trend of +0.013 °C/y, and cooling summer trend of -0.038 °C/y during the period of 2000-2018. This study demonstrated that the measured trend rates could be explained by and were strongly correlated with the climatology of Italy for the 2000-2018 period. Finally, we demonstrated the key role and the importance of the availability of long-term in situ temperature datasets. The approach used in this study is up-scalable to other medium to low-resolution TIR sensors as well as to other long-term monitoring sites, such as LTER-Italy, LTER-Europe, or ILTER sites.

Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications

Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10–15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters.

Mapping of spatial and temporal variation of water characteristics through satellite remote sensing in Lake Panguipulli, Chile

Central-southern Chile is characterized by a series of large lakes that originate in the Andes Mountains. This region is facing increasing anthropogenic impact, which threatens the oligotrophic status of these lakes. While monitoring programs are often based on a limited spatial and temporal coverage, remote sensing offers promising tools for large-scale observations improving our capacity to study comprehensively indicators of lake properties. Seasonal trends (long-term means) and intra-lake variation of surface water temperature (SWT), turbidity and chlorophyll a in Lake Panguipulli were studied through satellite imagery from Landsat 5 TM, 7 ETM+ and 8 OLI (1998-2018; SWT, turbidity), and Sentinel-2A/B MSI (2016-2017; chlorophyll). Remotely sensed data were validated against in situ data from monitoring database. Satellite-derived SWT (representing the surface skin layer of water, so-called skin temperature) showed good similarity with in situ (bulk) temperature (RRMSD 0.17, R² = 0.86), although was somewhat lower (RMSD of 2.77 °C; MBD of -2.10 °C). Seasonal long-term means of turbidity from satellite imagery corresponded to those from in situ data, while satellite-derived predictions (based on OC2v2 algorithm) overestimated chlorophyll a levels slightly in summer-spring. SWT ranged from 8.0 °C in winter to 17.5 °C in summer. Mean turbidity (1.6 FNU) and chlorophyll a (1.1 μg L^-1) levels were at their lowest in summer. Spatial and seasonal patterns reflected the bathymetry and previously described mixing patterns of this monomictic lake: warming of shallow bays in spring extended to wider area along with summer stratification period, while mixing of the water column was reflected in spatially more homogenous SWT in fall-winter. Spatial heterogeneity in summer was confirmed by a clear separation of different lake areas based on SWT, turbidity and chlorophyll a using 3-D plot. Mapping of spatial and seasonal variation using satellite imagery allowed identifying lake areas with different characteristics, improving strategies for water resource management.