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

Shifting Patterns of Summer Lake Color Phenology in Over 26,000 US Lakes

Lakes are often defined by seasonal cycles. The seasonal timing, or phenology, of many lake processes are changing in response to human activities. However, long-term records exist for few lakes, and extrapolating patterns observed in these lakes to entire landscapes is exceedingly difficult using the limited number of available in situ observations. Limited landscape-level observations mean we do not know how common shifts in lake phenology are at macroscales. Here, we use a new remote sensing data set, LimnoSat-US, to analyze U.S. summer lake color phenology between 1984 and 2020 across more than 26,000 lakes. Our results show that summer lake color seasonality can be generalized into five distinct phenology groups that follow well-known patterns of phytoplankton succession. The frequency with which lakes transition from one phenology group to another is tied to lake and landscape level characteristics. Lakes with high inflows and low variation in their seasonal surface area are generally more stable, while lakes in areas with high interannual variations in climate and catchment population density show less stability. Our results reveal previously unexamined spatiotemporal patterns in lake seasonality and demonstrate the utility of LimnoSat-US, which, with over 22 million remote sensing observations of lakes, creates novel opportunities to examine changing lake ecosystems at a national scale

Multi-decadal improvement in US Lake water clarity

Across the globe, recent work examining the state of freshwater resources paints an increasingly dire picture of degraded water quality. However, much of this work either focuses on a small subset of large waterbodies or uses in situ water quality datasets that contain biases in when and where sampling occurred. Using these unrepresentative samples limits our understanding of landscape level changes in aquatic systems. In lakes, overall water clarity provides a strong proxy for water quality because it responds to surrounding atmospheric and terrestrial processes. Here, we use satellite remote sensing of over 14 000 lakes to show that lake water clarity in the U.S. has increased by an average of 0.52 cm yr-1 since 1984. The largest increases occurred prior to 2000 in densely populated catchments and within smaller waterbodies. This is consistent with observed improvements in water quality in U.S. streams and lakes stemming from sweeping environmental reforms in the 1970s and 1980s that prioritized point-source pollution in largely urban areas. The comprehensive, long-term trends presented here emphasize the need for representative sampling of freshwater resources when examining macroscale trends and are consistent with the idea that extensive U.S. freshwater pollution abatement measures have been effective and enduring, at least for point-source pollution controls

The Color of Rivers

Rivers are among the most imperiled ecosystems globally, yet we do not have broad-scale understanding of their changing ecology because most are rarely sampled. Water color, as perceived by the human eye, is an integrative measure of water quality directly observed by satellites. We examined patterns in river color between 1984 and 2018 by building a remote sensing database of surface reflectance, RiverSR, extracted from 234,727 Landsat images covering 108,000 kilometers of rivers > 60 m wide in the contiguous USA. We found 1) broad regional patterns in river color, with 56% of observations dominantly yellow and 38% dominantly green; 2) river color has three distinct seasonal patterns that were synchronous with flow regimes; 3) one third of rivers had significant color shifts over the last 35 years. RiverSR provides the first map of river color and new insights into macrosystems ecology of rivers

AquaSat: A Data Set to Enable Remote Sensing of Water Quality for Inland Waters

Satellite estimates of inland water quality have the potential to vastly expand our ability to observe and monitor the dynamics of large water bodies. For almost 50 years, we have been able to remotely sense key water quality constituents like total suspended sediment, dissolved organic carbon, chlorophyll a, and Secchi disk depth. Nonetheless, remote sensing of water quality is poorly integrated into inland water sciences, in part due to a lack of publicly available training data and a perception that remote estimates are unreliable. Remote sensing models of water quality can be improved by training and validation on larger data sets of coincident field and satellite observations, here called matchups. To facilitate model development and deeper integration of remote sensing into inland water science, we have built AquaSat, the largest such matchup data set ever assembled. AquaSat contains more than 600,000 matchups, covering 1984–2019, of ground-based total suspended sediment, dissolved organic carbon, chlorophyll a, and SDDSecchi disk depth measurements paired with spectral reflectance from Landsat 5, 7, and 8 collected within ±1 day of each other. To build AquaSat, we developed open source tools in R and Python and applied them to existing public data sets covering the contiguous United States, including the Water Quality Portal, LAGOS-NE, and the Landsat archive. In addition to publishing the data set, we are also publishing our full code architecture to facilitate expanding and improving AquaSat. We anticipate that this work will help make remote sensing of inland water accessible to more hydrologists, ecologists, and limnologists while facilitating novel data-driven approaches to monitoring and understanding critical water resources at large spatiotemporal scales

A Participatory Science Approach to Expanding Instream Infrastructure Inventories

Over the past decade, remote sensing data have improved in resolution and become more widely available, bringing new opportunities for its use in environmental science and conservation. One potential application is to identify and map instream infrastructure across the world, with important implications for fisheries, hydrology, flooding, and more. To date, databases of instream infrastructure focus on larger dams with reservoirs that are comparatively easy to detect with remotely sensed imagery. Despite their impact on freshwater ecosystems, smaller infrastructure is often overlooked. To overcome these challenges, we require more systematic approaches, such as the Global River Obstruction Database (GROD) presented here, to map instream infrastructure. We present a participatory approach to identify, map, and validate infrastructure and provide an initial data set for the contiguous United States (n = 4,197). We highlight the value of participatory methods that include the public and suggest ways they could be fused with machine learning for future applications

Mapping Flow-Obstructing Structures on Global Rivers

To help store water, facilitate navigation, generate energy, mitigate floods, and support industrial and agricultural production, people have built and continue to build obstructions to natural flow in rivers. However, due to the long and complex history of constructing and removing such obstructions, we lack a globally consistent record of their locations and types. Here, we used a consistent method to visually locate and classify obstructions on 2.1 million km of large rivers (width ≥30 m) globally. We based our mapping on Google Earth Engine’s high resolution images, which for many places have meter-scale resolution. The resulting Global River Obstruction Database (GROD) consists of 30,549 unique obstructions, covering six different obstruction types: dam, lock, low head dam, channel dam, and two types of partial dams. By classifying a subset of the obstructions multiple times, we are able to show high classification consistency (87% mean balanced accuracy) for the three types of obstructions that fully intersect rivers: dams, low head dams, and locks. The classification of the three types of partial obstructions are somewhat less consistent (61% mean balanced accuracy). Overall, by comparing GROD to similar datasets, we estimate GROD likely captured >90% of the obstructions on large rivers. We anticipate that GROD will be of wide interest to the hydrological modeling, aquatic ecology, geomorphology, and water resource management communities

AquaSat: A Data Set to Enable Remote Sensing of Water Quality for Inland Waters

Satellite estimates of inland water quality have the potential to vastly expand our ability to observe and monitor the dynamics of large water bodies. For almost 50 years, we have been able to remotely sense key water quality constituents like total suspended sediment, dissolved organic carbon, chlorophyll a, and Secchi disk depth. Nonetheless, remote sensing of water quality is poorly integrated into inland water sciences, in part due to a lack of publicly available training data and a perception that remote estimates are unreliable. Remote sensing models of water quality can be improved by training and validation on larger data sets of coincident field and satellite observations, here called matchups. To facilitate model development and deeper integration of remote sensing into inland water science, we have built AquaSat, the largest such matchup data set ever assembled. AquaSat contains more than 600,000 matchups, covering 1984–2019, of ground-based total suspended sediment, dissolved organic carbon, chlorophyll a, and SDDSecchi disk depth measurements paired with spectral reflectance from Landsat 5, 7, and 8 collected within ±1 day of each other. To build AquaSat, we developed open source tools in R and Python and applied them to existing public data sets covering the contiguous United States, including the Water Quality Portal, LAGOS-NE, and the Landsat archive. In addition to publishing the data set, we are also publishing our full code architecture to facilitate expanding and improving AquaSat. We anticipate that this work will help make remote sensing of inland water accessible to more hydrologists, ecologists, and limnologists while facilitating novel data-driven approaches to monitoring and understanding critical water resources at large spatiotemporal scales

A Participatory Science Approach to Expanding Instream Infrastructure Inventories

Over the past decade, remote sensing data have improved in resolution and become more widely available, bringing new opportunities for its use in environmental science and conservation. One potential application is to identify and map instream infrastructure across the world, with important implications for fisheries, hydrology, flooding, and more. To date, databases of instream infrastructure focus on larger dams with reservoirs that are comparatively easy to detect with remotely sensed imagery. Despite their impact on freshwater ecosystems, smaller infrastructure is often overlooked. To overcome these challenges, we require more systematic approaches, such as the Global River Obstruction Database (GROD) presented here, to map instream infrastructure. We present a participatory approach to identify, map, and validate infrastructure and provide an initial data set for the contiguous United States (n = 4,197). We highlight the value of participatory methods that include the public and suggest ways they could be fused with machine learning for future applications