Special Issue "Remote Sensing of Lake Properties and Dynamics"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editor

Dr. Jonathan Chipman
E-Mail Website
Guest Editor
Department of Geography, Dartmouth College, Hanover, NH 03755, USA
Interests: remote sensing of terrestrial and aquatic systems; geographic information science (GIS); applied spatial analysis; cartography and geovisualization

Special Issue Information

Dear Colleagues,

Our planet is home to over 100 million lakes. These lakes play multiple important roles in the Earth’s environmental systems and local- to global-scale economies. A short list of their contributions would include water and carbon cycling, wildlife habitat, navigation, fisheries, recreation, and the provision of water for domestic consumption, agriculture, and industry. At the same time, lakes are under increasing threat from climate change, water withdrawals, point- and nonpoint-source pollution, invasive species, and other factors.

Remote sensing has been used for decades to monitor the properties and dynamics of lakes. With the proliferation of new sensors (optical and thermal imaging, active and passive microwave, laser altimeters, and others) and new sensing platforms—from UAVs to multi-satellite constellations—the opportunities for novel applications of remote sensing in lake research have never been more promising.

In this Special Issue, we will highlight research on the use of remote sensing systems for characterizing the properties of lakes and monitoring lake dynamics over space and time. Potential subjects of investigation include the dynamics of water storage in lakes (including surface area, water level, and volume); optical properties such as water clarity and the quantification of various color-producing agents; harmful algal blooms (HABs); water temperature; lake ice; lake bathymetry and geomorphology; shoreline processes and lake/land interactions; and the ecological dynamics of lakes. It is hoped that the papers in this Special Issue will contribute to the wider and more effective adoption of remote sensing methods by limnologists, lake managers, and others concerned with the state and fate of the world’s lakes.

Dr. Jonathan Chipman
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Lakes
  • Limnology
  • Water resources
  • Aquatic systems
  • Ecology
  • Hydrology

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Remote Sensing of Lake Water Clarity: Performance and Transferability of Both Historical Algorithms and Machine Learning
Remote Sens. 2021, 13(8), 1434; https://doi.org/10.3390/rs13081434 - 08 Apr 2021
Viewed by 248
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)
Show Figures

Figure 1

Open AccessArticle
Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada
Remote Sens. 2021, 13(7), 1257; https://doi.org/10.3390/rs13071257 - 26 Mar 2021
Cited by 1 | Viewed by 398
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)
Show Figures

Graphical abstract

Open AccessArticle
Spatial Heterogeneity in Dead Sea Surface Temperature Associated with Inhomogeneity in Evaporation
Remote Sens. 2021, 13(1), 93; https://doi.org/10.3390/rs13010093 - 30 Dec 2020
Viewed by 457
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)
Show Figures

Graphical abstract

Open AccessArticle
Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2
Remote Sens. 2020, 12(23), 3984; https://doi.org/10.3390/rs12233984 - 06 Dec 2020
Cited by 1 | Viewed by 1489
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 (Rrs) 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 (gdd) to compensate for atmospheric and sun-glint artifacts. A strong agreement is indicated for the cross-sensor comparison of Rrs products at different wavelengths (average R ≈ 0.87). However, the Rrs of PRISMA at shorter wavelengths (<500 nm) is slightly overestimated with respect to Sentinel-2. This is in line with the estimates of gdd 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)
Show Figures

Graphical abstract

Open AccessArticle
Characterization of SWOT Water Level Errors on Seine Reservoirs and La Bassée Gravel Pits: Impacts on Water Surface Energy Budget Modeling
Remote Sens. 2020, 12(18), 2911; https://doi.org/10.3390/rs12182911 - 08 Sep 2020
Cited by 1 | Viewed by 777
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)
Show Figures

Graphical abstract

Open AccessArticle
Spatial Variability and Detection Levels for Chlorophyll-a Estimates in High Latitude Lakes Using Landsat Imagery
Remote Sens. 2020, 12(18), 2898; https://doi.org/10.3390/rs12182898 - 07 Sep 2020
Viewed by 911
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)
Show Figures

Graphical abstract

Open AccessArticle
Spatio-Temporal Variability of Phytoplankton Primary Production in Baltic Lakes Using Sentinel-3 OLCI Data
Remote Sens. 2020, 12(15), 2415; https://doi.org/10.3390/rs12152415 - 28 Jul 2020
Viewed by 770
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)
Show Figures

Graphical abstract

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Optical characterization of Upper Midwestern lakes to support applications of remote sensing for water quality monitoring
Authors: Daniela Gurlin 1,*; Leif Olmanson 2,; Steven Greb 3; Benjamin Page 4; Patrick Brezonik 5; Marvin Bauer 2
Affiliation: 1 Wisconsin Department of Natural Resources, Madison, WI, USA; [email protected] 2 Dept. of Forest Resources, University of Minnesota, St. Paul, MN, USA; [email protected] (L.O.); [email protected] (M.B.) 3 Space Science and Engineering, University of Wisconsin, Madison, WI, USA; [email protected] 4 Water Resources Center, University of Minnesota, St. Paul, MN, USA; [email protected] 5 Dept. of Civil, Environmental, and Geo- Engineering, University of Minnesota, Minneapolis, MN, USA; [email protected]
Abstract: Abstract: Remote sensing presents a cost efficient complementary approach for lake assessments across the Upper Midwest and provides water quality data with a high spatial and temporal resolution for thousands of lakes at a time. Satellite derived water clarity data products assist natural resource management agencies in the Upper Midwestern States of Minnesota and Wisconsin with trophic state assessments of lakes. These lakes comprise a wide range of optical water types (OWTs), which enables the exploration of the accuracy of satellite derived water quality products for specific OWTs. The results presented here address limitations in optical data and describe the diversity of OWTs through an analysis of a 6-year dataset of optically active constituents (OACs), inherent optical properties (IOPs), and apparent optical properties (AOPs) across different ecoregions in these states. The measurements include two AOPs (total suspended matter and chlorophyll-a concentrations), one IOP (chromophoric dissolved organic matter absorption), and one AOP (remote sensing reflectance) at most stations in addition to an expanded set of IOPs at a subset of the stations. The quality of the measurements is assessed through the relationships of OACs, IOPs, and AOPs and optical closure analysis for an example dataset from four lakes sampled in 2015. This research is expected to contribute to international algorithm calibration, refinement, and validation efforts for the development of more accurate satellite water quality products that can be readily adapted by water quality managers.

Back to TopTop