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Abstract Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

Abstract The US Federal Government supports hundreds of watershed monitoring efforts from which solute fluxes can be calculated. Although instrumentation and methods vary between studies, the data collected and their motivating questions are remarkably similar. Nevertheless, little effort toward their compilation has previously been made. The MacroSheds project has developed a future‐friendly system for harmonizing daily time series of streamflow, precipitation, and solute chemistry from 169+ watersheds, and supplementing each with watershed attributes. Here, we describe the breadth of MacroSheds data, and detail the steps involved in rendering each data product. We provide recommendations for usage and discuss when other datasets might be more suitable. The MacroSheds dataset is an unprecedented resource for watershed science, and for hydrology, as a small‐watershed supplement to existing collections of streamflow predictors, like CAMELS and GAGES‐II. The MacroSheds platform includes a web dashboard for visualization and an R package for data access and analysis.

Abstract Accurately estimating stream discharge is crucial for many ecological, biogeochemical, and hydrologic analyses. As of September 2022, The National Ecological Observatory Network (NEON) provided up to 5 years of continuous discharge estimates at 28 streams across the United States. NEON created rating curves at each site in a Bayesian framework, parameterized using hydraulic controls and manual measurements of discharge. Here we evaluate the reliability of these discharge estimates with three approaches. We (1) compared predicted to observed discharge, (2) compared predicted to observed stage, and (3) calculated the proportion of discharge estimates extrapolated beyond field measurements. We considered 1,523 site-months of continuous streamflow predictions published by NEON. Of these, 39% met our highest quality criteria, 11% fell into an intermediate classification, and 50% of site-months were classified as unreliable. We provided diagnostic metrics and categorical evaluations of continuous discharge and stage estimates by month for each site, enabling users to rapidly query for suitable NEON data.

Abstract In rivers, phytoplankton populations are continuously exported by unidirectional, advective flow. Both transport and growth conditions determine periods of excess phytoplankton growth, or blooms, in a given reach. Phytoplankton abundance, however, has mainly been compared to the state of either growth or transport conditions alone rather than in tandem. Previous studies have not yielded consistent driver–response relationships, limiting our ability to predict the timing of riverine phytoplankton blooms based on environmental factors. Here, we derive a simple joint metric that combines the state of growth and transport conditions, specifically the ratio of temperature and discharge (T/Q$$ T/Q $$). We then compare the metric to biomass abundance data (daily, sensor‐based chlorophyll a [chl a] data) from a mid‐sized Great Plains river (the Kansas). T/Q$$ T/Q $$ was an excellent predictor of low to high biomass, outperforming either variable alone. However, it could not differentiate between very high biomass values, values well above the biomass threshold designating bloom conditions. Our findings of reduced performance at the highest values of T/Q indicated that T/Q$$ T/Q $$ could predict the occurrence (timing) but not magnitude of phytoplankton blooms, and we used T/Q to correctly predict 71% of days when bloom conditions occurred. Analyzing chl a abundance with T/Q$$ T/Q $$ also revealed likely switching from transport and temperature to nutrient control. T/Q$$ T/Q $$ offers a simple tool for (1) predicting the timing of river phytoplankton blooms, (2) forecasting how river ecosystems will respond to surrounding environmental changes, and (3) determining which environmental factors shape phytoplankton blooms at specific locations along a river.

Abstract Although water color is a fundamental property of freshwater ecosystems, the global distribution of lake color remains unknown. Here, we used 5.14 million color records derived from satellite images during 2013–2020 to determine the modal water color of 85,360 representative lakes worldwide. We find that globally, lake modal colors span the visible spectrum with a bimodal distribution of blue versus non‐blue (green‐brown), with 31% of lakes being blue and 69% being non‐blue. Both climate and lake morphology influence lake modal color. Blue lakes are associated with cooler summer air temperature, winter ice cover, and higher precipitation. Under a 3°C increase in summer air temperature, 14% of blue lakes could shift to a non‐blue regime, representing a substantial change to their underlying ecology. As lake ecosystems continue to face a range of stressors, this study provides a critical baseline for understanding lake responses to global environmental change.

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.

Humans have drastically disrupted the global sediment cycle. Suspended sediment flux and concentration are key controls over both river morphology and river ecosystems. Our ability to understand sediment dynamics within river corridors is limited by observations. Here, we present RivSed, a database of satellite observations of suspended sediment concentration (SSC) from 1984 to 2018 across 460 large (>60 m wide) US rivers that provides a new, spatially explicit view of river sediment. We found that 32% of US rivers have a declining temporal trend in sediment concentration, with a mean reduction of 40% since 1984, whereas only 2% have an increasing trend. Most rivers (52%) show decreasing sediment concentration longitudinally moving downstream, typically due to a few large dams rather than the accumulated effect of many small dams. Comparing our observations with modeled ‘pre-dam’ longitudinal SSC, most rivers (53%) show different patterns. However, contemporary longitudinal patterns in concentration are remarkably stable from year to year since 1984, with more stability in large, highly managed rivers with less cropland. RivSed has broad applications for river geomorphology and ecology and highlights anthropogenic effects on river corridors across the US.

Global change may contribute to ecological changes in high-elevation lakes and reservoirs, but a lack of data makes it difficult to evaluate spatiotemporal patterns. Remote sensing imagery can provide more complete records to evaluate whether consistent changes across a broad geographic region are occurring. We used Landsat surface reflectance data to evaluate spatial patterns of contemporary lake color (2010–2020) in 940 lakes in the U.S. Rocky Mountains, a historically understudied area for lake water quality. Intuitively, we found that most of the lakes in the region are blue (66%) and were found in steep-sided watersheds (>22.5°) or alternatively were relatively deep (>4.5 m) with mean annual air temperature (MAAT)

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.

Mountaintop mining, like all forms of surface mining, fundamentally alters the landscape to extract resources that lie 10–100 ms below the land surface. Despite these deep, critical zone alterations, post-mining landscapes are required by United States law to be restored to ecosystems of equal or greater value than the ones they replace. Yet, remote sensing of vegetation across more than 1000 km ^2 of reclaimed surface mines in WV, USA reveals little evidence that these habitats are returning to the diverse Appalachian forests that were removed by mining. Instead, even decades after reclamation, mined landscapes are dominated by shorter and sparser trees. Based on detailed field studies and literature synthesis, we suggest that part of these widespread failures in re-establishing native forest result from the fundamental changes in critical zone processes on the post-mining landscape. Former surface mines have substantially altered topography, hydrology and chemistry. In these post-mining, synthetic landscapes, water moves more slowly through piles of exploded bedrock, changing the system from one dominated by stormflow in unmined catchments, to one dominated by baseflow after mining. This slow-moving water, travelling through high surface-area debris and pyrite-rich bedrock, creates ideal conditions for highly elevated weathering in mines both old and new. These foundational changes to the critical zone set ecosystem recovery along a novel trajectory, in which the legacy of past disturbance is likely to constrain the establishment of native forest for many decades.

Surface mining for coal has taken place in the Central Appalachian region of the United States for well over a century, with a notable increase since the 1970s. Researchers have quantified the ecosystem and health impacts stemming from mining, relying in part on a geospatial dataset defining surface mining's extent at a decadal interval. This dataset, however, does not deliver the temporal resolution necessary to support research that could establish causal links between mining activity and environmental or public health and safety outcomes, nor has it been updated since 2005. Here we use Google Earth Engine and Landsat imagery to map the yearly extent of surface coal mining in Central Appalachia from 1985 through 2015, making our processing models and output data publicly available. We find that 2,900 km2 of land has been newly mined over this 31-year period. Adding this more-recent mining to surface mines constructed prior to 1985, we calculate a cumulative mining footprint of 5,900 km2. Over the study period, correlating active mine area with historical surface mine coal production shows that each metric ton of coal is associated with 12 m2 of actively mined land. Our automated, open-source model can be regularly updated as new surface mining occurs in the region and can be refined to capture mining reclamation activity into the future. We freely and openly offer the data for use in a range of environmental, health, and economic studies; moreover, we demonstrate the capability of using tools like Earth Engine to analyze years of remotely sensed imagery over spatially large areas to quantify land use change.