Remote Sensing

Research

27 pages, 5450 KiB

Open AccessArticle

Optimization of Landsat Chl-a Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types

by Michael A. Dallosch and Irena F. Creed

Remote Sens. 2021, 13(22), 4607; https://doi.org/10.3390/rs13224607 - 16 Nov 2021

Cited by 6 | Viewed by 3338

The application of remote sensing data to empirical models of inland surface water chlorophyll-a concentrations (chl-a) has been in development since the launch of the Landsat 4 satellite series in 1982. However, establishing an empirical model using a chl-a retrieval [...] Read more.

The application of remote sensing data to empirical models of inland surface water chlorophyll-a concentrations (chl-a) has been in development since the launch of the Landsat 4 satellite series in 1982. However, establishing an empirical model using a chl-a retrieval algorithm is difficult due to the spatial heterogeneity of inland lake water properties. Classification of optical water types (OWTs; i.e., differentially observed water spectra due to differences in water properties) has grown in favour in recent years over traditional non-turbid vs. turbid classifications. This study examined whether top-of-atmosphere reflectance observations in visible to near-infrared bands from Landsat 4, 5, 7, and 8 sensors can be used to identify unique OWTs using a guided unsupervised classification approach in which OWTs are defined through both remotely sensed reflectance and surface water chemistry data taken from samples in North American and Swedish lakes. Linear regressions of algorithms (Landsat reflectance bands, band ratios, products, or combinations) to lake surface water chl-a were built for each OWT. The performances of chl-a retrieval algorithms within each OWT were compared to those of global chl-a algorithms to test the effectiveness of OWT classification. Seven unique OWTs were identified and then fit into four categories with varying degrees of brightness as follows: turbid lakes with a low chl-a:turbidity ratio; turbid lakes with a mixture of high chl-a and turbidity measurements; oligotrophic or mesotrophic lakes with a mixture of low chl-a and turbidity measurements; and eutrophic lakes with a high chl-a:turbidity ratio. With one exception (r² = 0.26, p = 0.08), the best performing algorithm in each OWT showed improvement (r² = 0.69–0.91, p < 0.05), compared with the best performing algorithm for all lakes combined (r² = 0.52, p < 0.05). Landsat reflectance can be used to extract OWTs in inland lakes to provide improved prediction of chl-a over large extents and long time series, giving researchers an opportunity to study the trophic states of unmonitored lakes. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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17 pages, 2497 KiB

Open AccessArticle

Multiple Images Improve Lake CDOM Estimation: Building Better Landsat 8 Empirical Algorithms across Southern Canada

by Talia Koll-Egyed, Jeffrey A. Cardille and Eliza Deutsch

Remote Sens. 2021, 13(18), 3615; https://doi.org/10.3390/rs13183615 - 10 Sep 2021

Cited by 6 | Viewed by 2442

Abstract

Coloured dissolved organic matter (CDOM) is an important water property for lake management. Remote sensing using empirical algorithms has been used to estimate CDOM, with previous studies relying on coordinated field campaigns that coincided with satellite overpass. However, this requirement reduces the maximum [...] Read more.

Coloured dissolved organic matter (CDOM) is an important water property for lake management. Remote sensing using empirical algorithms has been used to estimate CDOM, with previous studies relying on coordinated field campaigns that coincided with satellite overpass. However, this requirement reduces the maximum possible sample size for model calibration. New satellites and advances in cloud computing platforms offer opportunities to revisit assumptions about methods used for empirical algorithm calibration. Here, we explore the opportunities and limits of using median values of Landsat 8 satellite images across southern Canada to estimate CDOM. We compare models created using an expansive view of satellite image availability with those emphasizing a tight timing between the date of field sampling and the date of satellite overpass. Models trained on median band values from across multiple summer seasons performed better (adjusted R² = 0.70, N = 233) than models for which imagery was constrained to a 30-day time window (adjusted R² = 0.45). Model fit improved rapidly when incorporating more images, producing a model at a national scale that performed comparably to others found in more limited spatial extents. This research indicated that dense satellite imagery holds new promise for understanding relationships between in situ CDOM and satellite reflectance data across large areas. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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23 pages, 7626 KiB

Open AccessArticle

Absence of Surface Water Temperature Trends in Lake Kinneret despite Present Atmospheric Warming: Comparisons with Dead Sea Trends

by Pavel Kishcha, Boris Starobinets, Yury Lechinsky and Pinhas Alpert

Remote Sens. 2021, 13(17), 3461; https://doi.org/10.3390/rs13173461 - 1 Sep 2021

Cited by 7 | Viewed by 3205

Abstract

This study was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km × 1 km resolution records on board Terra and Aqua satellites and in-situ measurements during the period (2003–2019). In spite of the presence of increasing atmospheric warming, in summer when [...] Read more.

This study was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km × 1 km resolution records on board Terra and Aqua satellites and in-situ measurements during the period (2003–2019). In spite of the presence of increasing atmospheric warming, in summer when evaporation is maximal, in fresh-water Lake Kinneret, satellite data revealed the absence of surface water temperature (SWT) trends. The absence of SWT trends in the presence of increasing atmospheric warming is an indication of the influence of increasing evaporation on SWT trends. The increasing water cooling, due to the above-mentioned increasing evaporation, compensated for increasing heating of surface water by regional atmospheric warming, resulting in the absence of SWT trends. In contrast to fresh-water Lake Kinneret, in the hypersaline Dead Sea, located ~100 km apart, MODIS records showed an increasing trend of 0.8 °C decade⁻¹ in summer SWT during the same study period. The presence of increasing SWT trends in the presence of increasing atmospheric warming is an indication of the absence of steadily increasing evaporation in the Dead Sea. This is supported by a constant drop in Dead Sea water level at the rate of ~1 m/year from year to year during the last 25-year period (1995–2020). In summer, in contrast to satellite measurements, in-situ measurements of near-surface water temperature in Lake Kinneret showed an increasing trend of 0.7 °C decade⁻¹. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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23 pages, 7088 KiB

Open AccessArticle

Using Multisource Satellite Data to Investigate Lake Area, Water Level, and Water Storage Changes of Terminal Lakes in Ungauged Regions

by Chuanhui Zhang, Aifeng Lv, Wenbin Zhu, Guobiao Yao and Shanshan Qi

Remote Sens. 2021, 13(16), 3221; https://doi.org/10.3390/rs13163221 - 13 Aug 2021

Cited by 14 | Viewed by 2856

Abstract

Lake area, water level, and water storage changes of terminal lakes are vital for regional water resource management and for understanding local hydrological processes. Nevertheless, due to the complex geographical conditions, it is difficult to investigate and analyze this change in ungauged regions. [...] Read more.

Lake area, water level, and water storage changes of terminal lakes are vital for regional water resource management and for understanding local hydrological processes. Nevertheless, due to the complex geographical conditions, it is difficult to investigate and analyze this change in ungauged regions. This study focuses on the ungauged, semi-arid Gahai Lake, a typical small terminal lake in the Qaidam Basin. In addition to the scant observed data, satellite altimetry is scarce for the excessively large fraction of outlier points. Here, we proposed an effective and simple algorithm for extracting available lake elevation points from CryoSat-2, ICESat-2 and Sentinel-3. Combining with the area data from Landsat, Gaofen (GF), and Ziyuan (ZY) satellites, we built an optimal hypsographic curve (lake area versus water level) based on the existing short-term data. Cross-validation was used to validate whether the curve accurately could predict the lake water level in other periods. In addition, we used multisource high-resolution images including Landsat and digital maps to extract the area data from 1975 to 2020, and we applied the curve to estimate the water level for the corresponding period. Additionally, we adopted the pyramidal frustum model (PFM) and the integral model (IM) to estimate the long-term water storage changes, and analyzed the differences between these two models. We found that there has been an obvious change in the area, water level, and water storage since the beginning of the 21st century, which reflects the impact of climate change and human activities on hydrologic processes in the basin. Importantly, agricultural activities have caused a rapid increase in water storage in the Gahai Lake over the past decade. We collected as much multisource satellite data as possible; thus, we estimated the long-term variations in the area, water level, and water storage of a small terminal lake combining multiple models, which can provide an effective method to monitor lake changes in ungauged basins. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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16 pages, 3708 KiB

Open AccessArticle

Retrieving Water Turbidity in Araucanian Lakes (South-Central Chile) Based on Multispectral Landsat Imagery

by Lien Rodríguez-López, Iongel Duran-Llacer, Lisdelys González-Rodríguez, Rolando Cardenas and Roberto Urrutia

Remote Sens. 2021, 13(16), 3133; https://doi.org/10.3390/rs13163133 - 7 Aug 2021

Cited by 23 | Viewed by 3914

Abstract

Remote sensing was used as an early alert tool for water clarity changes in five Araucanian Lakes in South-Central Chile. Turbidity records are scarce or unavailable over large and remote areas needed to fully understand the factors associated with turbidity, and their spatial-temporal [...] Read more.

Remote sensing was used as an early alert tool for water clarity changes in five Araucanian Lakes in South-Central Chile. Turbidity records are scarce or unavailable over large and remote areas needed to fully understand the factors associated with turbidity, and their spatial-temporal representation remains a limitation. This work aimed to develop and validate empirical models to estimate values of turbidity from Landsat images and determine the spatial distribution of estimated turbidity in the selected Araucanian Lakes. Secchi disk depth measurements were linked with turbidity measurements to obtain a turbidity dataset. This in turn was used to develop and validate a set of empirical models to predict turbidity based on four single bands and 16 combination bands from 15 multispectral Landsat images. The best empirical models predicted turbidity over the range of 0.3–12.3 NTUs with RMSE values around 0.31–1.03 NTU, R² (Index of Agreement IA) around 0.93–0.99 (0.85–0.97) and mean bias error (MBE) around (−0.36–0.44 NTU). Estimation maps to analyze the temporal-spatial turbidity variation in the lakes were constructed. Finally, it was found that the meteorological conditions may affect the variation of turbidity, mainly precipitation and wind speed. The data indicate that the turbidity has slightly increased in winter–spring. These models will be used in the future to reconstruct large datasets that allow analyzing transparency trends in those lakes. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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15 pages, 3252 KiB

Open AccessArticle

Arctic-Boreal Lake Phenology Shows a Relationship between Earlier Lake Ice-Out and Later Green-Up

by Catherine Kuhn, Aji John, Janneke Hille Ris Lambers, David Butman and Amanda Tan

Remote Sens. 2021, 13(13), 2533; https://doi.org/10.3390/rs13132533 - 29 Jun 2021

Cited by 3 | Viewed by 2787

Abstract

Satellite remote sensing has transformed our understanding of Earth processes. One component of the Earth system where large uncertainties remain are Arctic and boreal freshwater lakes. With only short periods of open water due to annual ice cover, lake productivity in these regions [...] Read more.

Satellite remote sensing has transformed our understanding of Earth processes. One component of the Earth system where large uncertainties remain are Arctic and boreal freshwater lakes. With only short periods of open water due to annual ice cover, lake productivity in these regions is extremely sensitive to warming induced changes in ice cover. At the same time, productivity dynamics in these lakes vary enormously, even over short distances, making it difficult to understand these potential changes. A major impediment to an improved understanding of lake dynamics has been sparsely distributed field measurements, in large part due to the complexity and expense of conducting scientific research in remote northern latitudes. This project overcomes that hurdle by using a new set of ‘eyes in the sky’, the Planet Labs CubeSat fleet, to observe 35 lakes across 3 different arctic-boreal ecoregions in western North America. We extract time series of lake reflectance to identify ice-out and green-up across three years (2017–2019). We find that lakes with later ice-out have significantly faster green-ups. Our results also show ice-out varies latitudinally by 38 days from south to north, but only varies across years by ~9 days. In contrast, green-up varied between years by 22 days in addition to showing significant spatial variability. We compare PlanetScope to Sentinel-2 data and independently validate our ice-out estimates, finding an ice-out mean absolute difference (MAD) ~9 days. This study demonstrates the potential of using CubeSat imagery to monitor the timing and magnitude of ice-off and green-up at high spatiotemporal resolution. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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22 pages, 5509 KiB

Open AccessArticle

Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery

by Minqi Hu, Ronghua Ma, Zhigang Cao, Junfeng Xiong and Kun Xue

Remote Sens. 2021, 13(10), 1988; https://doi.org/10.3390/rs13101988 - 19 May 2021

Cited by 22 | Viewed by 4267

Abstract

Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland [...] Read more.

Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland waters. First, the turbid water index was applied for the determination of optical water types on each pixel, and water bodies are divided into two categories: algae-dominated water (Type I) and turbid water (Type II). The algal biomass index (ABI) was then established based on water classification to derive the trophic state index (TSI) proposed by Carlson (1977). The results showed a considerable precision in Type I water (R² = 0.62, N = 282) and Type II water (R² = 0.57, N = 132). The ABI-derived TSI outperformed several band-ratio algorithms and a machine learning method (RMSE = 4.08, MRE = 5.46%, MAE = 3.14, NSE = 0.64). Such a model was employed to generate the trophic state index of 146 lakes (> 10 km²) in eastern China from 2013 to 2020 using Landsat-8 surface reflectance data. The number of hypertrophic and oligotrophic lakes decreased from 45.89% to 21.92% and 4.11% to 1.37%, respectively, while the number of mesotrophic and eutrophic lakes increased from 12.33% to 23.97% and 37.67% to 52.74%. The annual mean TSI for the lakes in the lower reaches of the Yangtze River basin was higher than that in the middle reaches of the Yangtze River and Huai River basin. The retrieval algorithm illustrated the applicability to other sensors with an overall accuracy of 83.27% for moderate-resolution imaging spectroradiometer (MODIS) and 82.92% for Sentinel-3 OLCI sensor, demonstrating the potential for high-frequency observation and large-scale simulation capability. Our study can provide an effective trophic state assessment and support inland water management. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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18 pages, 4432 KiB

Open AccessArticle

Remote Sensing of Lake Water Clarity: Performance and Transferability of Both Historical Algorithms and Machine Learning

by Hannah J. Rubin, David A. Lutz, Bethel G. Steele, Kathryn L. Cottingham, Kathleen C. Weathers, Mark J. Ducey, Michael Palace, Kenneth M. Johnson and Jonathan W. Chipman

Remote Sens. 2021, 13(8), 1434; https://doi.org/10.3390/rs13081434 - 8 Apr 2021

Cited by 20 | Viewed by 4740

Abstract

There has been little rigorous investigation of the transferability of existing empirical water clarity models developed at one location or time to other lakes and dates of imagery with differing conditions. Machine learning methods have not been widely adopted for analysis of lake [...] Read more.

There has been little rigorous investigation of the transferability of existing empirical water clarity models developed at one location or time to other lakes and dates of imagery with differing conditions. Machine learning methods have not been widely adopted for analysis of lake optical properties such as water clarity, despite their successful use in many other applications of environmental remote sensing. This study compares model performance for a random forest (RF) machine learning algorithm and a simple 4-band linear model with 13 previously published empirical non-machine learning algorithms. We use Landsat surface reflectance product data aligned with spatially and temporally co-located in situ Secchi depth observations from northeastern USA lakes over a 34-year period in this analysis. To evaluate the transferability of models across space and time, we compare model fit using the complete dataset (all images and samples) to a single-date approach, in which separate models are developed for each date of Landsat imagery with more than 75 field samples. On average, the single-date models for all algorithms had lower mean absolute errors (MAE) and root mean squared errors (RMSE) than the models fit to the complete dataset. The RF model had the highest pseudo-R² for the single-date approach as well as the complete dataset, suggesting that an RF approach outperforms traditional linear regression-based algorithms when modeling lake water clarity using satellite imagery. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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21 pages, 3769 KiB

Open AccessArticle

Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada

by Eliza S. Deutsch, Jeffrey A. Cardille, Talia Koll-Egyed and Marie-Josée Fortin

Remote Sens. 2021, 13(7), 1257; https://doi.org/10.3390/rs13071257 - 26 Mar 2021

Cited by 13 | Viewed by 3825

Abstract

Water clarity has been extensively assessed in Landsat-based remote sensing studies of inland waters, regularly relying on locally calibrated empirical algorithms, and close temporal matching between field data and satellite overpass. As more satellite data and faster data processing systems become readily accessible, [...] Read more.

Water clarity has been extensively assessed in Landsat-based remote sensing studies of inland waters, regularly relying on locally calibrated empirical algorithms, and close temporal matching between field data and satellite overpass. As more satellite data and faster data processing systems become readily accessible, new opportunities are emerging to revisit traditional assumptions concerning empirical calibration methodologies. Using Landsat 8 images with large water clarity datasets from southern Canada, we assess: (1) whether clear regional differences in water clarity algorithm coefficients exist and (2) whether model fit can be improved by expanding temporal matching windows. We found that a single global algorithm effectively represents the empirical relationship between in situ Secchi disk depth (SDD) and the Landsat 8 Blue/Red band ratio across diverse lake types in Canada. We also found that the model fit improved significantly when applying a median filter on data from ever-wider time windows between the date of in situ SDD sample and the date of satellite overpass. The median filter effectively removed the outliers that were likely caused by atmospheric artifacts in the available imagery. Our findings open new discussions on the ability of large datasets and temporal averaging methods to better elucidate the true relationships between in situ water clarity and satellite reflectance data. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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18 pages, 4838 KiB

Open AccessArticle

Spatial Heterogeneity in Dead Sea Surface Temperature Associated with Inhomogeneity in Evaporation

by Pavel Kishcha and Boris Starobinets

Remote Sens. 2021, 13(1), 93; https://doi.org/10.3390/rs13010093 - 30 Dec 2020

Cited by 4 | Viewed by 2926

Abstract

Spatial heterogeneity in Dead Sea surface temperature (SST) was pronounced throughout the daytime, based on METEOSAT geostationary satellite data (2005–2015). In summer, SST peaked at 13 LT (local time), when SST reached 38.1 °C, 34.1 °C, and 35.4 °C being averaged over the [...] Read more.

Spatial heterogeneity in Dead Sea surface temperature (SST) was pronounced throughout the daytime, based on METEOSAT geostationary satellite data (2005–2015). In summer, SST peaked at 13 LT (local time), when SST reached 38.1 °C, 34.1 °C, and 35.4 °C being averaged over the east, middle, and west parts of the lake, respectively. In winter, daytime SST heterogeneity was less pronounced than that in summer. As the characteristic feature of the diurnal cycle, the SST daily temperature range (the difference between daily maxima and minima) was equal to 7.2 °C, 2.5 °C, and 3.8 °C over the east, middle, and west parts of the Dead Sea, respectively, in summer, compared to 5.3 °C, 1.2 °C, and 2.3 °C in winter. In the presence of vertical water mixing, the maximum of SST should be observed several hours later than that of land surface temperature (LST) over surrounding land areas due to thermal inertia of bulk water. However, METEOSAT showed that, in summer, maxima of SST and LST were observed at the same time, 13 LT. This fact is evidence that there was no noticeable vertical water mixing. Our findings allowed us to consider that, in the absence of water mixing and under uniform solar radiation in the summer months, spatial heterogeneity in SST was associated with inhomogeneity in evaporation. Maximal evaporation (causing maximal surface water cooling) took place at the middle part of the Dead Sea, while minimum evaporation took place at the east side of the lake. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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21 pages, 27329 KiB

Open AccessArticle

Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2

by Milad Niroumand-Jadidi, Francesca Bovolo and Lorenzo Bruzzone

Remote Sens. 2020, 12(23), 3984; https://doi.org/10.3390/rs12233984 - 6 Dec 2020

Cited by 72 | Viewed by 8898

Abstract

A new era of spaceborne hyperspectral imaging has just begun with the recent availability of data from PRISMA (PRecursore IperSpettrale della Missione Applicativa) launched by the Italian space agency (ASI). There has been pre-launch optimism that the wealth of spectral information offered by [...] Read more.

A new era of spaceborne hyperspectral imaging has just begun with the recent availability of data from PRISMA (PRecursore IperSpettrale della Missione Applicativa) launched by the Italian space agency (ASI). There has been pre-launch optimism that the wealth of spectral information offered by PRISMA can contribute to a variety of aquatic science and management applications. Here, we examine the potential of PRISMA level 2D images in retrieving standard water quality parameters, including total suspended matter (TSM), chlorophyll-a (Chl-a), and colored dissolved organic matter (CDOM) in a turbid lake (Lake Trasimeno, Italy). We perform consistency analyses among the aquatic products (remote sensing reflectance (R_rs) and constituents) derived from PRISMA and those from Sentinel-2. The consistency analyses are expanded to synthesized Sentinel-2 data as well. By spectral downsampling of the PRISMA images, we better isolate the impact of spectral resolution in retrieving the constituents. The retrieval of constituents from both PRISMA and Sentinel-2 images is built upon inverting the radiative transfer model implemented in the Water Color Simulator (WASI) processor. The inversion involves a parameter (g_dd) to compensate for atmospheric and sun-glint artifacts. A strong agreement is indicated for the cross-sensor comparison of R_rs products at different wavelengths (average R ≈ 0.87). However, the R_rs of PRISMA at shorter wavelengths (<500 nm) is slightly overestimated with respect to Sentinel-2. This is in line with the estimates of g_dd through the inversion that suggests an underestimated atmospheric path radiance of PRISMA level 2D products compared to the atmospherically corrected Sentinel-2 data. The results indicate the high potential of PRISMA level 2D imagery in mapping water quality parameters in Lake Trasimeno. The PRISMA-based retrievals agree well with those of Sentinel-2, particularly for TSM. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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18 pages, 3767 KiB

Open AccessArticle

Characterization of SWOT Water Level Errors on Seine Reservoirs and La Bassée Gravel Pits: Impacts on Water Surface Energy Budget Modeling

by Catherine Ottlé, Anthony Bernus, Thomas Verbeke, Karine Pétrus, Zun Yin, Sylvain Biancamaria, Anne Jost, Damien Desroches, Claire Pottier, Charles Perrin, Alban de Lavenne, Nicolas Flipo and Agnès Rivière

Remote Sens. 2020, 12(18), 2911; https://doi.org/10.3390/rs12182911 - 8 Sep 2020

Cited by 5 | Viewed by 3533

Abstract

The Surface Water and Ocean Topography (SWOT) space mission will map surface area and water level changes in lakes at the global scale. Such new data are of great interest to better understand and model lake dynamics as well as to improve water [...] Read more.

The Surface Water and Ocean Topography (SWOT) space mission will map surface area and water level changes in lakes at the global scale. Such new data are of great interest to better understand and model lake dynamics as well as to improve water management. In this study, we used the large-scale SWOT simulator developed at the French Space National Center (CNES) to estimate the expected measurement errors of the water level of different water bodies in France. These water bodies include five large reservoirs of the Seine River and numerous small gravel pits located in the Seine alluvial plain of La Bassée upstream of the city of Paris. The results show that the SWOT mission will allow to observe water levels with a precision of a few tens of centimeters (10 cm for the largest water reservoir (Orient), 23 km²), even for the small gravel pits of size of a few hectares (standard deviation error lower than 0.25 m for water bodies larger than 6 ha). The benefit of the temporal sampling for water level monitoring is also highlighted on time series of pseudo-observations based on real measurements perturbed with the simulated noise errors. Then, the added value of these future data for the simulation of lake energy budgets is shown using the FLake lake model through sensitivity experiments. Results show that the SWOT data will help to model the surface temperature of the studied water bodies with a precision better than 0.5 K and the evaporation with an accuracy better than 0.2 mm/day. These large improvements compared to the errors obtained when a constant water level is prescribed (1.2 K and 0.6 mm/day) demonstrate the potential of SWOT for monitoring the lake energy budgets at global scale in addition to the other foreseen applications in operational reservoir management. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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21 pages, 11546 KiB

Open AccessArticle

Spatial Variability and Detection Levels for Chlorophyll-a Estimates in High Latitude Lakes Using Landsat Imagery

by Filipe Lisboa, Vanda Brotas, Filipe Duarte Santos, Sakari Kuikka, Laura Kaikkonen and Eduardo Eiji Maeda

Remote Sens. 2020, 12(18), 2898; https://doi.org/10.3390/rs12182898 - 7 Sep 2020

Cited by 7 | Viewed by 3754

Abstract

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. While satellite imagery can monitor phytoplankton biomass using [...] Read more.

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. While satellite imagery can monitor phytoplankton biomass using chlorophyll a (Chl) as a proxy over large areas, detection of Chl in small lakes is hindered by the low spatial resolution of conventional ocean color satellites. The short time-series of the newest generation of space-borne sensors (e.g., Sentinel-2) is a bottleneck for assessing long-term trends. Although previous studies have evaluated the use of high-resolution sensors for assessing lakes’ Chl, it is still unclear how the spatial and temporal variability of Chl concentration affect the performance of satellite estimates. We discuss the suitability of Landsat (LT) 30 m resolution imagery to assess lakes’ Chl concentrations under varying trophic conditions, across extensive high-latitude areas in Finland. We use in situ data obtained from field campaigns in 19 lakes and generate remote sensing estimates of Chl, taking advantage of the long-time span of the LT-5 and LT-7 archives, from 1984 to 2017. Our results show that linear models based on LT data can explain approximately 50% of the Chl interannual variability. However, we demonstrate that the accuracy of the estimates is dependent on the lake’s trophic state, with models performing in average twice as better in lakes with higher Chl concentration (>20 µg/L) in comparison with less eutrophic lakes. Finally, we demonstrate that linear models based on LT data can achieve high accuracy (R² = 0.9; p-value < 0.05) in determining lakes’ mean Chl concentration, allowing the mapping of the trophic state of lakes across large regions. Given the long time-series and high spatial resolution, LT-based estimates of Chl provide a tool for assessing the impacts of environmental change. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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20 pages, 6133 KiB

Open AccessArticle

Spatio-Temporal Variability of Phytoplankton Primary Production in Baltic Lakes Using Sentinel-3 OLCI Data

by Tuuli Soomets, Kristi Uudeberg, Kersti Kangro, Dainis Jakovels, Agris Brauns, Kaire Toming, Matiss Zagars and Tiit Kutser

Remote Sens. 2020, 12(15), 2415; https://doi.org/10.3390/rs12152415 - 28 Jul 2020

Cited by 8 | Viewed by 3160

Abstract

Phytoplankton primary production (PP) in lakes play an important role in the global carbon cycle. However, monitoring the PP in lakes with traditional complicated and costly in situ sampling methods are impossible due to the large number of lakes worldwide (estimated to be [...] Read more.

Phytoplankton primary production (PP) in lakes play an important role in the global carbon cycle. However, monitoring the PP in lakes with traditional complicated and costly in situ sampling methods are impossible due to the large number of lakes worldwide (estimated to be 117 million lakes). In this study, bio-optical modelling and remote sensing data (Sentinel-3 Ocean and Land Colour Instrument) was combined to investigate the spatial and temporal variation of PP in four Baltic lakes during 2018. The model used has three input parameters: concentration of chlorophyll-a, the diffuse attenuation coefficient, and incident downwelling irradiance. The largest of our studied lakes, Võrtsjärv (270 km²), had the highest total yearly estimated production (61 Gg C y⁻¹) compared to the smaller lakes Lubans (18 Gg C y⁻¹) and Razna (7 Gg C y⁻¹). However, the most productive was the smallest studied, Lake Burtnieks (40.2 km²); although the total yearly production was 13 Gg C y⁻¹, the daily average areal production was 910 mg C m⁻² d⁻¹ in 2018. Even if lake size plays a significant role in the total PP of the lake, the abundance of small and medium-sized lakes would sum up to a significant contribution of carbon fixation. Our method is applicable to larger regions to monitor the spatial and temporal variability of lake PP. Full article

(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)

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