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.

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.

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.

Land use impacts are commonly quantified and compared using 2D maps, limiting the scale of their reported impacts to surface area estimates. Yet, nearly all land use involves disturbances below the land surface. Incorporating this third dimension into our estimates of land use impact is especially important when examining the impacts of mining. Mountaintop mining is the most common form of coal mining in the Central Appalachian ecoregion. Previous estimates suggest that active, reclaimed, or abandoned mountaintop mines cover ∼7% of Central Appalachia. While this is double the areal extent of development in the ecoregion (estimated to occupy <3% of the land area), the impacts are far more extensive than areal estimates alone can convey as the impacts of mines extend 10s to 100s of meters below the current land surface. Here, we provide the first estimates for the total volumetric and topographic disturbance associated with mining in an 11 500 km² region of southern West Virginia. We find that the cutting of ridges and filling of valleys has lowered the median slope of mined landscapes in the region by nearly 10 degrees while increasing their average elevation by 3 m as a result of expansive valley filling. We estimate that in southern West Virginia, more than 6.4km³ of bedrock has been broken apart and deposited into 1544 headwater valley fills. We used NPDES monitoring datatsets available for 91 of these valley fills to explore whether fill characteristics could explain variation in the pH or selenium concentrations reported for streams draining these fills. We found that the volume of overburden in individual valley fills correlates with stream pH and selenium concentration, and suggest that a three-dimensional assessment of mountaintop mining impacts is necessary to predict both the severity and the longevity of the resulting environmental impacts.

Weathering is the ultimate source of solutes for ecosystems, controls chemical denudation of landscapes, and drives the geologic carbon cycle. Mining and other land‐moving operations enhance physical weathering by bringing large volumes of shattered bedrock to the surface. Yet, the relative influence of these activities on chemical weathering remains poorly constrained. Here we show that catchments impacted by mountaintop removal coal mining have among the highest rates of chemical weathering ever reported. Mined catchments deliver more than 7,600 kg·ha⁻¹·year⁻¹ of dissolved solids downstream. The chemical signatures of these exceptionally high weathering rates reflect the product of sulfuric acid weathering of carbonate‐bearing rock, driven by the oxidation of pyritic materials. As this strong acid rapidly weathers surrounding carbonate materials, H⁺ ions are consumed and Ca²⁺, Mg²⁺, and HCO₃⁻ ions are exported to balance the elevated SO₄²⁻ exports, generating alkaline mine drainage. The sulfate exports from pyrite oxidation in mountaintop‐mined catchments account for ~5–7% of global sulfate derived from pyrite, despite occupying less than 0.006% of total land area. Further, the suite of weathering reactions liberate 100–450 kg of rock‐derived C·ha⁻¹·year⁻¹ as CO₂, with an additional 90–150 kg C·ha⁻¹·year⁻¹ of C released when HCO₃⁻ reaches the ocean. This rock C release contributes to the high carbon costs of coal combustion.