Improved Automated Detection of Subpixel-Scale Inundation—Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests

Climate change will reduce North American inland wetland areas and disrupt their seasonal regimes

Climate change can alter wetland extent and function, but such impacts are perplexing. Here, changes in wetland characteristics over North America from 25° to 53° North are projected under two climate scenarios using a state-of-the-science Earth system model. At the continental scale, annual wetland area decreases by ~10% (6%-14%) under the high emission scenario, but spatiotemporal changes vary, reaching up to ±50%. As the dominant driver of these changes shifts from precipitation to temperature in the higher emission scenario, wetlands undergo substantial drying during summer season when biotic processes peak. The projected disruptions to wetland seasonality cycles imply further impacts on biodiversity in major wetland habitats of upper Mississippi, Southeast Canada, and the Everglades. Furthermore, wetlands are projected to significantly shrink in cold regions due to the increased infiltration as warmer temperature reduces soil ice. The large dependence of the projections on climate change scenarios underscores the importance of emission mitigation to sustaining wetland ecosystems in the future.

Spatiotemporal monitoring of climate change impacts on water resources using an integrated approach of remote sensing and Google Earth Engine

In this study, a data-driven approach employed by utilizing the product called JRC-Global surface water mapping layers V1.4 on the Google Earth Engine (GEE) to map and monitor the effects of climate change on surface water resources. Key climatic variables affecting water bodies, including air temperature (AT), actual evapotranspiration (ETa), and total precipitation, were analyzed from 2000 to 2021 using the temperature-vegetation index (TVX) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The findings demonstrate a clear association between global warming and the shrinking of surface water resources in the LUB. According to the results, an increase in AT corresponded to a decrease in water surface area, highlighting the significant influence of AT and ETa on controlling the water surface in the LUB (partial rho of - 0.65 and - 0.68, respectively). Conversely, no significant relationship was found with precipitation and water surface area (partial rho of + 0.25). Notably, the results of the study indicate that over the past four decades, approximately 40% of the water bodies in the LUB remained permanent. This suggests a loss of around 30% of the permanent water resources, which have transitioned into seasonal water bodies, accounting for nearly 13% of the total. This research provides a comprehensive framework for monitoring surface water resource variations and assessing the impact of climate change on water resources. It aids in the development of sustainable water management strategies and plans, supporting the preservation and effective use of water resources.

Climate change causes declines and greater extremes in wetland inundation in a region important for wetland birds

Wetland ecosystems are vital for maintaining global biodiversity, as they provide important stopover sites for many species of migrating wetland-associated birds. However, because weather determines their hydrologic cycles, wetlands are highly vulnerable to effects of climate change. Although changes in temperature and precipitation resulting from climate change are expected to reduce inundation of wetlands, few efforts have been made to quantify how these changes will influence the availability of stopover sites for migratory wetland birds. Additionally, few studies have evaluated how climate change will influence interannual variability or the frequency of extremes in wetland availability. For spring and fall bird migration in seven ecoregions in the south-central Great Plains of North America, we developed predictive models associating abundance of inundated wetlands with a suite of weather and land cover variables. We then used these models to generate predictions of wetland inundation at the end of the century (2069-2099) under future climate change scenarios. Climate models predicted the average number of inundated wetlands will likely decline during both spring and fall migration periods, with declines being greatest in the eastern ecoregions of the southern Great Plains. However, the magnitude of predicted declines varied considerably across climate models and ecoregions, with uncertainty among climate models being greatest in the High Plains ecoregion. Most ecoregions also were predicted to experience more-frequent extremely dry years (i.e., years with extremely low wetland abundances), but the projected change in interannual variability of wetland inundation was relatively small and varied across ecoregions and seasons. Because the south-central Great Plains represents an important link along the migratory routes of many wetland-dependent avian species, future declines in wetland inundation and more frequent periods of only a few wetlands being inundated will result in an uncertain future for migratory birds as they experience reduced availability of wetland stopover habitat across their migration pathways.

Climate Change Will Impact Surface Water Extents and Dynamics Across the Central United States

Climate change is projected to impact river, lake, and wetland hydrology, with global implications for the condition and productivity of aquatic ecosystems. We integrated Sentinel-1 and Sentinel-2 based algorithms to track monthly surface water extent (2017-2021) for 32 sites across the central United States (U.S.). Median surface water extent was highly variable across sites, ranging from 3.9% to 45.1% of a site. To account for landscape-based differences (e.g., water storage capacity, land use) in the response of surface water extents to meteorological conditions, individual statistical models were developed for each site. Future changes to climate were defined as the difference between 2006-2025 and 2061-2080 using MACA-CMIP5 (MACAv2-METDATA) Global Circulation Models. Time series of climate change adjusted surface water extents were projected. Annually, 19 of the 32 sites under RCP4.5 and 22 of the 32 sites under RCP8.5 were projected to show an average decline in surface water extent, with drying most consistent across the southeast central, southwest central, and midwest central U.S. Projected declines under surface water dry conditions at these sites suggest greater impacts of drought events are likely in the future. Projected changes were seasonally variable, with the greatest decline in surface water extent expected in summer and fall seasons. In contrast, many north central sites showed a projected increase in surface water in most seasons, relative to the 2017-2021 period, likely attributable to projected increases in winter and spring precipitation exceeding increases in projected temperature.

National-scale remotely sensed lake trophic state from 1984 through 2020

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.

Practical Guide to Measuring Wetland Carbon Pools and Fluxes

Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We first define each of the major C pools and fluxes and provide rationale for their importance to wetland C dynamics. For each approach, we clarify what component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as where and when an approach is typically used, who can conduct the measurements (expertise, training requirements), and how approaches are conducted, including considerations on equipment complexity and costs. Finally, we review key covariates and ancillary measurements that enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13157-023-01722-2.

Data integration reveals dynamic and systematic patterns of breeding habitat use by a threatened shorebird

Incorporating species distributions into conservation planning has traditionally involved long-term representations of habitat use where temporal variation is averaged to reveal habitats that are most suitable across time. Advances in remote sensing and analytical tools have allowed for the integration of dynamic processes into species distribution modeling. Our objective was to develop a spatiotemporal model of breeding habitat use for a federally threatened shorebird (piping plover, Charadrius melodus). Piping plovers are an ideal candidate species for dynamic habitat models because they depend on habitat created and maintained by variable hydrological processes and disturbance. We integrated a 20-year (2000-2019) nesting dataset with volunteer-collected sightings (eBird) using point process modeling. Our analysis incorporated spatiotemporal autocorrelation, differential observation processes within data streams, and dynamic environmental covariates. We evaluated the transferability of this model in space and time and the contribution of the eBird dataset. eBird data provided more complete spatial coverage in our study system than nest monitoring data. Patterns of observed breeding density depended on both dynamic (e.g., surface water levels) and long-term (e.g., proximity to permanent wetland basins) environmental processes. Our study provides a framework for quantifying dynamic spatiotemporal patterns of breeding density. This assessment can be iteratively updated with additional data to improve conservation and management efforts, because reducing temporal variability to average patterns of use may cause a loss in precision for such actions.

High-frequency time series comparison of Sentinel-1 and Sentinel-2 satellites for mapping open and vegetated water across the United States (2017-2021)

Frequent observations of surface water at fine spatial scales will provide critical data to support the management of aquatic habitat, flood risk and water quality. Sentinel-1 and Sentinel-2 satellites can provide such observations, but algorithms are still needed that perform well across diverse climate and vegetation conditions. We developed surface inundation algorithms for Sentinel-1 and Sentinel-2, respectively, at 12 sites across the conterminous United States (CONUS), covering a total of >536,000 km2 and representing diverse hydrologic and vegetation landscapes. Each scene in the 5-year (2017-2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables from Sentinel-1 and Sentinel-2, as well as variables derived from topographic and weather datasets. The Sentinel-1 algorithm was developed distinct from the Sentinel-2 model to explore if and where the two time series could potentially be integrated into a single high-frequency time series. Within each model, open water and vegetated water (vegetated palustrine, lacustrine, and riverine wetlands) classes were mapped. The models were validated using imagery from WorldView and PlanetScope. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for the Sentinel-1 algorithm and 3.1% and 0.5% for the Sentinel-2 algorithm, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. The Sentinel-2 algorithm showed higher accuracy (10.7% omission and 7.9% commission error) relative to the Sentinel-1 algorithm (28.4% omission and 16.0% commission error). Patterns over time in the proportion of area mapped as open or vegetated water by the Sentinel-1 and Sentinel-2 algorithms were charted and correlated for a subset of all 12 sites. Our results showed that the Sentinel-1 and Sentinel-2 algorithm open water time series can be integrated at all 12 sites to improve the temporal resolution, but sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for mixed-pixel, vegetated water. The methods developed here provide inundation at 5-day (Sentinel-2 algorithm) and 12-day (Sentinel-1 algorithm) time steps to improve our understanding of the short- and long-term response of surface water to climate and land use drivers in different ecoregions.

Large increases in methane emissions expected from North America's largest wetland complex

Natural methane (CH₄) emissions from aquatic ecosystems may rise because of human-induced climate warming, although the magnitude of increase is highly uncertain. Using an exceptionally large CH₄ flux dataset (~19,000 chamber measurements) and remotely sensed information, we modeled plot- and landscape-scale wetland CH₄ emissions from the Prairie Pothole Region (PPR), North America's largest wetland complex. Plot-scale CH₄ emissions were driven by hydrology, temperature, vegetation, and wetland size. Historically, landscape-scale PPR wetland CH₄ emissions were largely dependent on total wetland extent. However, regardless of future wetland extent, PPR CH₄ emissions are predicted to increase by two- or threefold by 2100 under moderate or severe warming scenarios, respectively. Our findings suggest that international efforts to decrease atmospheric CH₄ concentrations should jointly account for anthropogenic and natural emissions to maintain climate mitigation targets to the end of the century.

Convolutional neural network for high-resolution wetland mapping with open data: Variable selection and the challenges of a generalizable model

Landscape scale wetland conservation requires accurate, up-to-date wetland maps. The most useful approaches to creating such maps are automated, spatially generalizable, temporally repeatable, and can be applied at large spatial scales. However, mapping wetlands with predictive models is challenging due to the highly variable characteristics of wetlands in both space and time. Currently, most approaches are limited by coarse resolution, commercial data, and geographic specificity. Here, we trained a deep learning model and evaluated its ability to automatically map wetlands at landscape scale in a variety of geographies. We trained a U-Net architecture to map wetlands at 1-meter spatial resolution with the following remotely sensed covariates: multispectral data from the National Agriculture Imagery Program and the Sentinel-2 satellite system, and two LiDAR-derived datasets, intensity and geomorphons. The full model mapped wetlands accurately (94 % accuracy, 96.5 % precision, 95.2 % AUC) at 1-meter resolution. Post hoc model evaluation showed that the model correctly predicted wetlands even in areas that had incorrect label/training data, which penalized the recall rate (90.2 %). Applying the model in a new geography resulted in poor performance (precision = ~80 %, recall = 48 %). However, limited retraining in this geography improved model performance substantially, demonstrating an effective means to create a spatially generalizable model. We demonstrate wetlands can be mapped at high-resolution (1 m) using free data and efficient deep-learning models that do not require manual feature engineering. Including LiDAR and geomorphons as input data improved model accuracy by 2 %, and where these data are unavailable a simpler model can efficiently map wetlands. Given the dynamic nature of wetlands and the important ecosystem services they provide, high-resolution mapping can be a game changer in terms of informing restoration and development decisions.

Headwater streams and inland wetlands: Status and advancements of geospatial datasets and maps across the United States

Headwater streams and inland wetlands provide essential functions that support healthy watersheds and downstream waters. However, scientists and aquatic resource managers lack a comprehensive synthesis of national and state stream and wetland geospatial datasets and emerging technologies that can further improve these data. We conducted a review of existing United States (US) federal and state stream and wetland geospatial datasets, focusing on their spatial extent, permanence classifications, and current limitations. We also examined recent peer-reviewed literature for emerging methods that can potentially improve the estimation, representation, and integration of stream and wetland datasets. We found that federal and state datasets rely heavily on the US Geological Survey's National Hydrography Dataset for stream extent and duration information. Only eleven states (22%) had additional stream extent information and seven states (14%) provided additional duration information. Likewise, federal and state wetland datasets primarily use the US Fish and Wildlife Service's National Wetlands Inventory (NWI) Geospatial Dataset, with only two states using non-NWI datasets. Our synthesis revealed that LiDAR-based technologies hold promise for advancing stream and wetland mapping at limited spatial extents. While machine learning techniques may help to scale-up these LiDAR-derived estimates, challenges related to preprocessing and data workflows remain. High-resolution commercial imagery, supported by public imagery and cloud computing, may further aid characterization of the spatial and temporal dynamics of streams and wetlands, especially using multi-platform and multi-temporal machine learning approaches. Models integrating both stream and wetland dynamics are limited, and field-based efforts must remain a key component in developing improved headwater stream and wetland datasets. Continued financial and partnership support of existing databases is also needed to enhance mapping and inform water resources research and policy decisions.

High-resolution surface water dynamics in Earth's small and medium-sized reservoirs

Small and medium-sized reservoirs play an important role in water systems that need to cope with climate variability and various other man-made and natural challenges. Although reservoirs and dams are criticized for their negative social and environmental impacts by reducing natural flow variability and obstructing river connections, they are also recognized as important for social and economic development and climate change adaptation. Multiple studies map large dams and analyze the dynamics of water stored in the reservoirs behind these dams, but very few studies focus on small and medium-sized reservoirs on a global scale. In this research, we use multi-annual multi-sensor satellite data, combined with cloud analytics, to monitor the state of small (10–100 ha) to medium-sized (> 100 ha, excluding 479 large ones) artificial water reservoirs globally for the first time. These reservoirs are of crucial importance to the well-being of many societies, but regular monitoring records of their water dynamics are mostly missing. We combine the results of multiple studies to identify 71,208 small to medium-sized reservoirs, followed by reconstructing surface water area changes from satellite data using a novel method introduced in this study. The dataset is validated using 768 daily in-situ water level and storage measurements (r2 > 0.7 for 67% of the reservoirs used for the validation) demonstrating that the surface water area dynamics can be used as a proxy for water storage dynamics in many cases. Our analysis shows that for small reservoirs, the inter-annual and intra-annual variability is much higher than for medium-sized reservoirs worldwide. This implies that the communities reliant on small reservoirs are more vulnerable to climate extremes, both short-term (within seasons) and longer-term (across seasons). Our findings show that the long-term inter-annual and intra-annual changes in these reservoirs are not equally distributed geographically. Through several cases, we demonstrate that this technology can help monitor water scarcity conditions and emerging food insecurity, and facilitate transboundary cooperation. It has the potential to provide operational information on conditions in ungauged or upstream riparian countries that do not share such data with neighboring countries. This may help to create a more level playing field in water resource information globally.

How Have Global River Widths Changed Over Time?

Changes in a river's width reflect natural and anthropogenic impacts on local and upstream/downstream hydraulic and hydrologic processes. Temporal variation of river width also impacts biogeochemical exchange and reflects geomorphologic evolution. However, while global maps of mean river width and dynamic water surface extent exist, there is currently no standardized global assessment of river widths that documents changes over time. Therefore, we made repeated width measurements from Landsat images for all rivers wider than 90 m collected from 1984 to 2020 (named Global LOng-term river Width, GLOW), which consists of ∼1.2 billion cross-sectional river width measurements, with an average of 3,000 width measurements per 10-km reach. With GLOW, we investigated the temporal variations of global river width, quantified by the interquartile range (IQR) and temporal trend. We found that 85% of global rivers have a width IQR <150 m. We also found that 37% of global river segments show significant temporal trends in width over the past 37 years, and this number is higher (46%) for human-regulated rivers. Further, we leveraged machine learning to identify the most important factors explaining river width variations and revealed that these driving factors are significantly different between free-flowing and non-free-flowing rivers. Specifically, the most important factor driving temporal variations in river width is the climate for free-flowing rivers, and is soil condition for human-impacted rivers. Finally, we anticipate that this study and the public release of GLOW will improve the understanding of river dynamics and catalyze additional interdisciplinary studies.

Monitoring Surface Water Inundation of Poyang Lake and Dongting Lake in China Using Sentinel-1 SAR Images

High-temporal-resolution inundation maps play an important role in surface water monitoring, especially in lake sites where water bodies change tremendously. Synthetic Aperture Radar (SAR) that guarantees a full time-series in monitoring surface water due to its cloud-penetrating capability is preferred in practice. To date, the methods of extracting and analyzing inundation maps of lake sites have been widely discussed, but the method of extracting surface water maps refined by inundation frequency map and the distinction of inundation frequency map from different datasets have not been fully explored. In this study, we leveraged the Google Earth Engine platform to compare and evaluate the effects of a method combining a histogram-based algorithm with a temporal-filtering algorithm in order to obtain high-quality surface water maps. Both algorithms were conducted on Sentinel-1 images over Poyang Lake and Dongting Lake, the two largest lakes in China, respectively. High spatiotemporal time-series analyses of both lakes were implemented between 2017 and 2021, while the inundation frequency maps extracted from Sentinel-1 data were compared with those extracted from Landsat images. It was found that Sentinel-1 can monitor water inundation with a substantially higher accuracy, although minor differences were found between the two sites, with the overall accuracy for Poyang Lake (95.38–98.69%) being higher than that of Dongting Lake (95.05–97.5%). The minimum and maximum water areas for five years were 1232.96 km2 and 3828.36 km2 in Poyang Lake, and 624.7 km2 and 2189.17 km2 in Dongting Lake. Poyang Lake was frequently inundated with 553.03 km2 of permanent water and 3361.39 km2 of seasonal water while Dongting Lake was less frequently inundated with 320.09 km2 of permanent water and 2224.53 km2 of seasonal water. The inundation frequency maps from different data sources had R2 values higher than 0.8, but there were still significant differences between them. The overall inundation frequency values of the Sentinel-1 inundation frequency maps were lower than those of the Landsat inundation frequency maps due to the severe contamination from cloud cover in Landsat imagery, which should be paid attention in practical application.

The Applicability of LandTrendr to Surface Water Dynamics: A Case Study of Minnesota from 1984 to 2019 Using Google Earth Engine

The means to accurately monitor wetland change over time are crucial to wetland management. This paper explores the applicability of LandTrendr, a temporal segmentation algorithm designed to identify significant interannual trends, to monitor wetlands by modeling surface water presence in Minnesota from 1984 to 2019. A time series of harmonized Landsat and Sentinel-2 data in the spring is developed in Google Earth Engine, and calculated to sub-pixel water fraction. The optimal parameters for modeling this time series with LandTrendr are identified by minimizing omission of known surface water locations, and the result of this optimal model of sub-pixel water fraction is evaluated against reference images and qualitatively. Accuracy of this method is high: overall accuracy is 98% and producer’s and user’s accuracies for inundation are 82% and 88% respectively. Maps summarizing the trendlines of multiple pixels, such as frequency of inundation over the past 35 years, also show LandTrendr as applied here can accurately model long-term trends in surface water presence across wetland types. However, the tendency of omission for more variable prairie pothole wetlands and the under-prediction of inundation for small or emergent wetlands suggests the algorithm will require careful development of the segmented time series to capture inundated conditions more accurately.

Seasonality of inundation in geographically isolated wetlands across the United States

Inundation area is a major control on the ecosystem services provisioned by geographically isolated wetlands. Despite its importance, there has not been any comprehensive study to map out the seasonal inundation characteristics of geographically isolated wetlands over the continental United States (CONUS). This study fills the aforementioned gap by evaluating the seasonality or the long-term intra-annual variations of wetland inundation in ten wetlandscapes across the CONUS. We also assess the consistency of these intra-annual variations. Finally, we evaluate the extent to which the seasonality can be explained based on widely available hydrologic fluxes. Our findings highlight significant intra-annual variations of inundation within most wetlandscapes, with a standard deviation of the long-term averaged monthly inundation area ranging from 15% to 151% of its mean across the wetlandscapes. Stark differences in inundation seasonality are observed between snow-affected vs. rain-fed wetlandscapes. The former usually shows the maximum monthly inundation in April following spring snowmelt (SM), while the latter experiences the maximum in February. Although the magnitude of inundation fraction has changed over time in several wetlandscapes, the seasonality of these wetlands shows remarkable constancy. Overall, commonly available regional hydrologic fluxes (e.g. rainfall, SM, and evapotranspiration) are found to be able to explain the inundation seasonality at wetlandscape scale with determination coefficients greater than 0.57 in 7 out of 10 wetlandscapes. Our methodology and presented results may be used to map inundation seasonality and consequently account for its impact on wetland functions.

PyGEE-SWToolbox: A Python Jupyter Notebook Toolbox for Interactive Surface Water Mapping and Analysis Using Google Earth Engine

Continuous monitoring of surface water resources is often challenging due to the lack of monitoring systems in remote areas and the high spatial distribution of water bodies. The Google Earth Engine (GEE) platform, which houses a large set of remote sensing datasets and geospatial processing power, has been applied in various aspects of surface water resources monitoring to solve some of the challenges. PyGEE-SWToolbox is a freely available Google Earth Engine-enabled open-source toolbox developed with Python to be run in Jupyter Notebooks that provides an easy-to-use graphical user interface (GUI) that enables the user to obtain time series of Landsat, Sentinel-1, and Sentinel-2 satellite imagery, pre-process them, and extract surface water using water indices, such as the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEI), and Dynamic Surface Water Extent (DSWE). The validation of the toolbox is carried out at four reservoir and lake locations: Elephant Butte Lake, Hubbard Creek Reservoir, Clearwater Lake, and Neversink Reservoir in the United States. A time series of the water surface area generated from PyGEE-SWToolbox compared to the observed surface areas yielded good results, with R2 ranging between 0.63 and 0.99 for Elephant Butte Lake, Hubbard Creek Reservoir, and Clearwater Lake except the Neversink Reservoir with a maximum R2 of 0.52. The purpose of PyGEE-SWToolbox is to provide water resource managers, engineers, researchers, and students a user-friendly environment to utilize the GEE platform for water resource monitoring and generation of datasets. The toolbox is accompanied by a step-by-step user manual and Readme documentation for installation and usage.

Recent changes to Arctic river discharge

Arctic rivers drain ~15% of the global land surface and significantly influence local communities and economies, freshwater and marine ecosystems, and global climate. However, trusted and public knowledge of pan-Arctic rivers is inadequate, especially for small rivers and across Eurasia, inhibiting understanding of the Arctic response to climate change. Here, we calculate daily streamflow in 486,493 pan-Arctic river reaches from 1984-2018 by assimilating 9.18 million river discharge estimates made from 155,710 satellite images into hydrologic model simulations. We reveal larger and more heterogenous total water export (3-17% greater) and water export acceleration (factor of 1.2-3.3 larger) than previously reported, with substantial differences across basins, ecoregions, stream orders, human regulation, and permafrost regimes. We also find significant changes in the spring freshet and summer stream intermittency. Ultimately, our results represent an updated, publicly available, and more accurate daily understanding of Arctic rivers uniquely enabled by recent advances in hydrologic modeling and remote sensing.

Uncertainties Involved in the Use of Thresholds for the Detection of Water Bodies in Multitemporal Analysis from Landsat-8 and Sentinel-2 Images

Although the single threshold is still considered a suitable and easy-to-do technique to extract water features in spatiotemporal analysis, it leads to unavoidable errors. This paper uses an enumerative search to optimize thresholds over satellite-derived modified normalized difference water index (MNDWI). We employed a cross-validation approach and treated accuracy as a random variable in order to: (a) investigate uncertainty related to its application; (b) estimate non-optimistic errors involving single thresholding; (c) investigate the main factors that affect the accuracy's model, and (d) compare satellite sensors performance. We also used a high-resolution digital elevation model to extract water elevations values, making it possible to remove topographic effects and estimate non-optimistic errors exclusively from orbital imagery. Our findings evidenced that there is a region where thresholds values can vary without causing accuracy loss. Moreover, by constraining thresholds variation between these limits, accuracy is dramatically improved and outperformed the Otsu method. Finally, the number of scenes employed to optimize a single threshold drastically affects the accuracy, being not appropriate using a single scene once it leads to overfitted threshold values. More than three scenes are recommended.

Testing a Generalizable Machine Learning Workflow for Aquatic Invasive Species on Rainbow Trout (Oncorhynchus mykiss) in Northwest Montana

Biological invasions are accelerating worldwide, causing major ecological and economic impacts in aquatic ecosystems. The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and data-driven products. Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales. We present a workflow that generates rapid spatial risk assessments on aquatic invasive species using occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, we tested this workflow using extensive spatial and temporal hybridization and occurrence data from a well-studied, ongoing, and climate-driven species invasion in the upper Flathead River system in northwestern Montana, USA. Rainbow Trout (RBT; Oncorhynchus mykiss), an introduced species in the Flathead River basin, compete and readily hybridize with native Westslope Cutthroat Trout (WCT; O. clarkii lewisii), and the spread of RBT individuals and their alleles has been tracked for decades. We used remotely sensed and other geospatial data as key environmental predictors for projecting resultant habitat suitability to geographic space. The ensemble modeling technique yielded high accuracy predictions relative to 30-fold cross-validated datasets (87% 30-fold cross-validated accuracy score). Both top predictors and model performance relative to these predictors matched current understanding of the drivers of RBT invasion and habitat suitability, indicating that temperature is a major factor influencing the spread of invasive RBT and hybridization with native WCT. The congruence between more time-consuming modeling approaches and our rapid machine-learning approach suggest that this workflow could be applied more broadly to provide data-driven management information for early detection of potential invaders.

Automated Water Level Monitoring at the Continental Scale from ICESat-2 Photons

Of the approximately 6700 lakes and reservoirs larger than 1 km2 in the Contiguous United States (CONUS), only ~430 (~6%) are actively gaged by the United States Geological Survey (USGS) or their partners and are available for download through the National Water Information System database. Remote sensing analysis provides a means to fill in these data gaps in order to glean a better understanding of the spatiotemporal water level changes across the CONUS. This study takes advantage of two-plus years of NASA’s ICESat-2 (IS-2) ATLAS photon data (ATL03 products) in order to derive water level changes for ~6200 overlapping lakes and reservoirs (>1 km2) in the CONUS. Interactive visualizations of large spatial datasets are becoming more commonplace as data volumes for new Earth observing sensors have markedly increased in recent years. We present such a visualization created from an automated cluster computing workflow that utilizes tens of billions of ATLAS photons which derives water level changes for all of the overlapping lakes and reservoirs in the CONUS. Furthermore, users of this interactive website can download segmented and clustered IS-2 ATL03 photons for each individual waterbody so that they may run their own analysis. We examine ~19,000 IS-2 derived water level changes that are spatially and temporally coincident with water level changes from USGS gages and find high agreement with our results as compared to the in situ gage data. The mean squared error (MSE) and the mean absolute error (MAE) between these two products are 1 cm and 6 cm, respectively.

Monitoring Drought through the Lens of Landsat: Drying of Rivers during the California Droughts

Water scarcity during severe droughts has profound hydrological and ecological impacts on rivers. However, the drying dynamics of river surface extent during droughts remains largely understudied. Satellite remote sensing enables surveys and analyses of rivers at fine spatial resolution by providing an alternative to in-situ observations. This study investigates the seasonal drying dynamics of river extent in California where severe droughts have been occurring more frequently in recent decades. Our methods combine the use of Landsat-based Global Surface Water (GSW) and global river bankful width databases. As an indirect comparison, we examine the monthly fractional river extent (FrcSA) in 2071 river reaches and its correlation with streamflow at co-located USGS gauges. We place the extreme 2012–2015 drought into a broader context of multi-decadal river extent history and illustrate the extraordinary change between during- and post-drought periods. In addition to river extent dynamics, we perform statistical analyses to relate FrcSA with the hydroclimatic variables obtained from the National Land Data Assimilation System (NLDAS) model simulation. Results show that Landsat provides consistent observation over 90% of area in rivers from March to October and is suitable for monitoring seasonal river drying in California. FrcSA reaches fair (>0.5) correlation with streamflow except for dry and mountainous areas. During the 2012–2015 drought, 332 river reaches experienced their lowest annual mean FrcSA in the 34 years of Landsat history. At a monthly scale, FrcSA is better correlated with soil water in more humid areas. At a yearly scale, summer mean FrcSA is increasingly sensitive to winter precipitation in a drier climate; and the elasticity is also reduced with deeper ground water table. Overall, our study demonstrates the detectability of Landsat on the river surface extent in an arid region with complex terrain. River extent in catchments of deficient water storage is likely subject to higher percent drop in a future climate with longer, more frequent droughts.

Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat

The capability and synergistic use of multisource satellite observations for flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. Here, surface fractional water cover (FW) retrievals derived from Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) brightness temperatures were used for flood assessment over southeast Africa during the Cyclone Idai event. We then focused on five subcatchments of the Pungwe basin and developed a machine learning based approach with the support of Google Earth Engine for daily (24-h) forecasting of FW and 30-m inundation downscaling and mapping. The Classification and Regression Trees model was selected and trained using retrievals derived from SMAP and Landsat coupled with rainfall forecasts from the NOAA Global Forecast System. Independent validation showed that FW predictions over randomly selected dates are highly correlated (R = 0.87) with the Landsat observations. The forecast results captured the flood temporal dynamics from the Idai event; and the associated 30-m downscaling results showed inundation spatial patterns consistent with independent satellite synthetic aperture radar observations. The data-driven approach provides new capacity for flood monitoring and forecasts leveraging synergistic satellite observations and big data analysis, which is particularly valuable for data sparse regions.

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.

Characterizing Wetland Inundation and Vegetation Dynamics in the Arctic Coastal Plain Using Recent Satellite Data and Field Photos

Arctic wetlands play a critical role in the global carbon cycle and are experiencing disproportionate impacts from climate change. Even though Alaska hosts 65% of U.S. wetlands, less than half of the wetlands in Alaska have been mapped by the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) or other high-resolution wetlands protocols. The availability of time series satellite data and the development of machine learning algorithms have enabled the characterization of Arctic wetland inundation dynamics and vegetation types with limited ground data input. In this study, we built a semi-automatic process to generate sub-pixel water fraction (SWF) maps across the Coastal Plain of the Arctic National Wildlife Refuge (ANWR) in Alaska using random forest regression and 139 Sentinel-2 images taken in ice-free seasons from 2016 to 2019. With this, we characterized the seasonal dynamics of wetland inundation and explored their potential usage in determining NWI water regimes. The highest levels of surface water expression were detected in June, resulting from seasonal active layer thaw and snowmelt. Inundation was most variable in riverbeds, lake and pond margins, and depressional wetlands, where water levels fluctuate substantially between dry and wet seasons. NWI water regimes that indicate frequent inundation, such as permanently flooded wetlands, had high SWF values (SWF ≥ 90%), while those with infrequent inundation, such as temporarily flooded wetlands, had low SWF values (SWF < 10%). Vegetation types were also classified through the synergistic use of a vegetation index, water regimes, synthetic-aperture radar (SAR) data, topographic data, and a random forest classifier. The random forest classification algorithms demonstrated good performance in classifying Arctic wetland vegetation types, with an overall accuracy of 0.87. Compared with NWI data produced in the 1980s, scrub-shrub wetlands appear to have increased from 91 to 258 km2 over the last three decades, which is the largest percentage change (182%) among all vegetation types. However, additional field data are needed to confirm this shift in vegetation type. This study demonstrates the potential of using time series satellite data and machine learning algorithms in characterizing inundation dynamics and vegetation types of Arctic wetlands. This approach could aid in the creation and maintenance of wetland inventories, including the NWI, in Arctic regions and enable an improved understanding of long-term wetland dynamics.

Towards global flood mapping onboard low cost satellites with machine learning

Spaceborne Earth observation is a key technology for flood response, offering valuable information to decision makers on the ground. Very large constellations of small, nano satellites- 'CubeSats' are a promising solution to reduce revisit time in disaster areas from days to hours. However, data transmission to ground receivers is limited by constraints on power and bandwidth of CubeSats. Onboard processing offers a solution to decrease the amount of data to transmit by reducing large sensor images to smaller data products. The ESA's recent PhiSat-1 mission aims to facilitate the demonstration of this concept, providing the hardware capability to perform onboard processing by including a power-constrained machine learning accelerator and the software to run custom applications. This work demonstrates a flood segmentation algorithm that produces flood masks to be transmitted instead of the raw images, while running efficiently on the accelerator aboard the PhiSat-1. Our models are trained on WorldFloods: a newly compiled dataset of 119 globally verified flooding events from disaster response organizations, which we make available in a common format. We test the system on independent locations, demonstrating that it produces fast and accurate segmentation masks on the hardware accelerator, acting as a proof of concept for this approach.

Monitoring Variations in Lake Water Storage with Satellite Imagery and Citizen Science

Despite lakes being a key part of the global water cycle and a crucial water resource, there is limited understanding of whether regional or lake-specific factors control water storage variations in small lakes. Here, we study groups of small, unregulated lakes in North Carolina, Washington, Illinois, and Wisconsin, USA using lake level measurements gathered by citizen scientists and lake surface area measurements from optical satellite imagery. We show the lake level measurements to be highly accurate when compared to automated gauges (mean absolute error = 1.6 cm). We compare variations in lake water storage between pairs of lakes within these four states. On average, water storage variations in lake pairs across all study regions are moderately positively correlated (ρ = 0.49) with substantial spread in the degree of correlation. The distance between lake pairs and the extent to which their changes in volume are correlated show a weak but statistically significant negative relationship. Our results indicate that, on regional scales, distance is not a primary factor governing lake water storage patterns, which suggests that other, perhaps lakes-specific, factors must also play important roles.

Multi-Source EO for Dynamic Wetland Mapping and Monitoring in the Great Lakes Basin

Wetland managers, citizens and government leaders are observing rapid changes in coastal wetlands and associated habitats around the Great Lakes Basin due to human activity and climate variability. SAR and optical satellite sensors offer cost effective management tools that can be used to monitor wetlands over time, covering large areas like the Great Lakes and providing information to those making management and policy decisions. In this paper we describe ongoing efforts to monitor dynamic changes in wetland vegetation, surface water extent, and water level change. Included are assessments of simulated Radarsat Constellation Mission data to determine feasibility of continued monitoring into the future. Results show that integration of data from multiple sensors is most effective for monitoring coastal wetlands in the Great Lakes region. While products developed using methods described in this article provide valuable management tools, more effort is needed to reach the goal of establishing a dynamic, near-real-time, remote sensing-based monitoring program for the basin.

Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data

The increasing impact of humans on land and ongoing global population growth requires an improved understanding of land cover (LC) and land use (LU) processes related to settlements. The heterogeneity of built-up areas and infrastructures as well as the importance of not only mapping, but also characterizing anthropogenic structures suggests using a sub-pixel mapping approach for analysing related LC from space. We implement a regression-based unmixing approach for mapping built-up surfaces and infrastructure, woody vegetation and non-woody vegetation for all of Germany and Austria at 10 m resolution to demonstrate the potential of sub-pixel mapping. We map LC fractions for one point in time, using all available Sentinel-2 data from 2017 and 2018 (<70% cloud cover). We combine the concept of synthetically mixed training data with statistical aggregations from spectral-temporal metrics (STM) derived from Sentinel-2 reflectance time series. We specifically examine how STM can be used for creating synthetically mixed training data. STM are known to facilitate large area mapping by being largely independent of image acquisition dates and inherently incorporate phenological information. Vegetation is an important part of settlements and time series information supports its mapping. Synthetically mixed training data facilitates a streamlined training by using pure reference spectra to generate artificial mixtures as input to regression modelling of LC fractions in mixed pixels. We here show how combining both offers great potential for wall-to-wall LC fraction mapping. We further investigate the positive effect of STM on map results by comparing the performance of different subsets of STM combinations. Our results indicate that many STM combinations containing spectral variability and vegetation indices provide suitable input to creating synthetic training data for regression-based fraction mapping. Results for built-up surfaces and infrastructure (MAE 0.13/RMSE 0.18 at 20 m resolution), woody vegetation (0.18, 0.22) and non-woody vegetation (0.14, 0.19) are highly consistent across Germany and Austria. Only a few surface types were not accurately predicted in our nation-wide mapping. Further research is required to optimize mapping of temporally invariant bare soil and rock surfaces that show spectral similarity to built-up surfaces and infrastructure. The proposed methodology combines benefits of both regression-based modelling with synthetically mixed training data and STM, and thus facilitates mapping of LC fractions on a national scale and at high resolution. Such information will allow to better characterize settlements and identifying processes such as densification that are best represented by continuous LC mapping.

Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine

Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near real-time applications. Due to its cloud penetrating capability, many studies have focused on providing efficient and high quality methods for surface water mapping using Synthetic Aperture Radar (SAR). However, few studies have explored the effects of SAR pre-processing steps used and the subsequent results as inputs into surface water mapping algorithms. This study leverages the Google Earth Engine to compare two unsupervised histogram-based thresholding surface water mapping algorithms utilizing two distinct pre-processed Sentinel-1 SAR datasets, specifically one with and one without terrain correction. The resulting surface water maps from the four different collections were validated with user-interpreted samples from high-resolution Planet Scope data. It was found that the overall accuracy from the four collections ranged from 92% to 95% with Cohen’s Kappa coefficients ranging from 0.7999 to 0.8427. The thresholding algorithm that samples a histogram based on water edge information performed best with a maximum accuracy of 95%. While the accuracies varied between methods it was found that there is no statistical significant difference between the errors of the different collections. Furthermore, the surface water maps generated from the terrain corrected data resulted in a intersection over union metrics of 95.8%–96.4%, showing greater spatial agreement, as compared to 92.3%–93.1% intersection over union using the non-terrain corrected data. Overall, it was found that algorithms using terrain correction yield higher overall accuracy and yielded a greater spatial agreement between methods. However, differences between the approaches presented in this paper were not found to be significant suggesting both methods are valid for generating accurate surface water maps. High accuracy surface water maps are critical to disaster planning and response efforts, thus results from this study can help inform SAR data users on the pre-processing steps needed and its effects as inputs on algorithms for surface water mapping applications.

Timing of Landsat Overpasses Effectively Captures Flow Conditions of Large Rivers

Satellites provide a temporally discontinuous record of hydrological conditions along Earth’s rivers (e.g., river width, height, water quality). The degree to which archived satellite data effectively capture the overall population of river flow frequency is unknown. Here, we use the entire archives of Landsat 5, 7, and 8 to determine when a cloud-free image is available over the United States Geological Survey (USGS) river gauges located on Landsat-observable rivers. We compare the flow frequency distribution derived from the daily gauge record to the flow frequency distribution derived from ideally sampling gauged discharge based on the timing of cloud-free Landsat overpasses. Examining the patterns of flow frequency across multiple gauges, we find that there is not a statistically significant difference between the flow frequency distribution associated with observations contained within the Landsat archive and the flow frequency distribution derived from the daily gauge data (α = 0.05), except for hydrological extremes like maximum and minimum flow. At individual gauges, we find that Landsat observations span a wide range of hydrological conditions (97% of total flow variability observed in 90% of the study gauges) but the degree to which the Landsat sample can represent flow frequency distribution varies from location to location and depends on sample size. The results of this study indicate that the Landsat archive is, on average, representative of the temporal frequencies of hydrological conditions present along Earth’s large rivers with broad utility for hydrological, ecologic and biogeochemical evaluations of river systems.

Isolating Anthropogenic Wetland Loss by Concurrently Tracking Inundation and Land Cover Disturbance across the Mid-Atlantic Region, U.S

Global trends in wetland degradation and loss have created an urgency to monitor wetland extent, as well as track the distribution and causes of wetland loss. Satellite imagery can be used to monitor wetlands over time, but few efforts have attempted to distinguish anthropogenic wetland loss from climate-driven variability in wetland extent. We present an approach to concurrently track land cover disturbance and inundation extent across the Mid-Atlantic region, United States, using the Landsat archive in Google Earth Engine. Disturbance was identified as a change in greenness, using a harmonic linear regression approach, or as a change in growing season brightness. Inundation extent was mapped using a modified version of the U.S. Geological Survey's Dynamic Surface Water Extent (DSWE) algorithm. Annual (2015-2018) disturbance averaged 0.32% (1095 km2 year-1) of the study area per year and was most common in forested areas. While inundation extent showed substantial interannual variability, the co-occurrence of disturbance and declines in inundation extent represented a minority of both change types, totaling 109 km2 over the four-year period, and 186 km2, using the National Wetland Inventory dataset in place of the Landsat-derived inundation extent. When the annual products were evaluated with permitted wetland and stream fill points, 95% of the fill points were detected, with most found by the disturbance product (89%) and fewer found by the inundation decline product (25%). The results suggest that mapping inundation alone is unlikely to be adequate to find and track anthropogenic wetland loss. Alternatively, remotely tracking both disturbance and inundation can potentially focus efforts to protect, manage, and restore wetlands.

Gap Fill of Land Surface Temperature and Reflectance Products in Landsat Analysis Ready Data

The recently released Landsat analysis ready data (ARD) over the United States provides the opportunity to investigate landscape dynamics using dense time series observations at 30-m resolution. However, the dataset often contains data gaps (or missing data) because of cloud contamination or data acquisition strategy, which result in different capabilities for seasonality modeling. We present a new algorithm that focuses on data gap filling using clear observations from orbit overlap regions. Multiple linear regression models were established for each pixel time series to estimate stable predictions and uncertainties. The model’s training data came from stratified random samples based on the time series similarity between the pixel and data from the overlap regions. The algorithm was first evaluated using four tiles (5000 × 5000 30-m pixels for each tile) from 2018 land surface temperature data (LST) in Atlanta, Georgia. The accuracy was assessed using randomly masked clear observations with an average Root Mean Square Error (RMSE) of 3.88 and an average bias of −0.37, which were comparable to the product accuracy. We also applied the method on ARD surface reflectance bands at Fairbanks, Alaska. The accuracy assessment suggested a majority RMSE of less than 0.04 and a bias of less than 0.0023. The gap-filled time series can be of help for reliable seasonal modeling and reducing artifacts related to data availability. This approach can also be applied to other datasets, vegetation indexes, or spectral reflectance bands of other sensors.

Implementation of a Surface Water Extent Model in Cambodia using Cloud-Based Remote Sensing

Mapping surface water over time provides the spatially explicit information essential for hydroclimatic research focused on droughts and flooding. Hazard risk assessments and water management planning also rely on accurate, long-term measurements describing hydrologic fluctuations. Stream gages are a common measurement tool used to better understand flow and inundation dynamics, but gage networks are incomplete or non-existent in many parts of the world. In such instances, satellite imagery may provide the only data available to monitor surface water changes over time. Here, we describe an effort to extend the applicability of the USGS Dynamic Surface Water Extent (DSWE) model to non-US regions. We leverage the multi-decadal archive of the Landsat satellite in the Google Earth Engine (GEE) cloud-based computing platform to produce and analyze 372 monthly composite maps and 31 annual maps (January 1988–December 2018) in Cambodia, a flood-prone country in Southeast Asia that lacks a comprehensive stream gage network. DSWE relies on a series of spectral water indices and elevation data to classify water into four categories of water inundation. We compared model outputs to existing surface water maps and independently assessed DSWE accuracy at discrete dates across the time series. Despite considerable cloud obstruction and missing imagery across the monthly time series, the overall accuracy exceeded 85% for all annual tests. The DSWE model consistently mapped open water with high accuracy, and areas classified as “high confidence” water correlate well to other available maps at the country scale. Results in Cambodia suggest that extending DSWE globally using a cloud computing framework may benefit scientists, managers, and planners in a wide array of applications across the globe.

A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

The airborne AirSWOT instrument suite, consisting of an interferometric Ka-band synthetic aperture radar and color-infrared (CIR) camera, was deployed to northern North America in July and August 2017 as part of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE). We present validated, open (i.e., vegetation-free) surface water masks produced from high-resolution (1 m), co-registered AirSWOT CIR imagery using a semi-automated, object-based water classification. The imagery and resulting high-resolution water masks are available as open-access datasets and support interpretation of AirSWOT radar and other coincident ABoVE image products, including LVIS, UAVSAR, AIRMOSS, AVIRIS-NG, and CFIS. These synergies offer promising potential for multi-sensor analysis of Arctic-Boreal surface water bodies. In total, 3167 km2 of open surface water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude and broad environmental gradients. Detected water body sizes range from 0.00004 km2 (40 m2) to 15 km2. Power-law extrapolations are commonly used to estimate the abundance of small lakes from coarser resolution imagery, and our mapped water bodies followed power-law distributions, but only for water bodies greater than 0.34 (±0.13) km2 in area. For water bodies exceeding this size threshold, the coefficients of power-law fits vary for different Arctic-Boreal physiographic terrains (wetland, prairie pothole, lowland river valley, thermokarst, and Canadian Shield). Thus, direct mapping using high-resolution imagery remains the most accurate way to estimate the abundance of small surface water bodies. We conclude that empirical scaling relationships, useful for estimating total trace gas exchange and aquatic habitats on Arctic-Boreal landscapes, are uniquely enabled by high-resolution AirSWOT-like mappings and automated detection methods such as those developed here.

Mapping Crop Residue by Combining Landsat and WorldView-3 Satellite Imagery

A unique, multi-tiered approach was applied to map crop-residue cover on the Eastern Shore of the Chesapeake Bay, United States. Field measurements of crop-residue cover were used to calibrate residue mapping using shortwave infrared (SWIR) indices derived from WorldView-3 imagery for a 12-km × 12-km footprint. The resulting map was then used to calibrate and subsequently classify crop residue mapping using Landsat imagery at a larger spatial resolution and extent. This manuscript describes how the method was applied and presents results in the form of crop-residue cover maps, validation statistics, and quantification of conservation tillage implementation in the agricultural landscape. Overall accuracy for maps derived from Landsat 7 and Landsat 8 were comparable at roughly 92% (+/− 10%). Tillage class-specific accuracy was also strong and ranged from 75% to 99%. The approach, which employed a 12-band image stack of six tillage spectral indices and six individual Landsat bands, was shown to be adaptable to variable soil-moisture conditions—under dry conditions (Landsat 7, 14 May 2015) the majority of predictive power was attributed to SWIR indices, and under wet conditions (Landsat 8, 22 May 2015) single band reflectance values were more effective at explaining variability in residue cover. Summary statistics of resulting tillage class occurrence matched closely with conservation tillage implementation totals reported by Maryland and Delaware to the Chesapeake Bay Program. This hybrid method combining WorldView-3 and Landsat imagery sources shows promise for monitoring progress in the adoption of conservation tillage practices and for describing crop-residue outcomes associated with a variety of agricultural management practices.

Hydrologic Lag Effects on Wetland Greenhouse Gas Fluxes

Hydrologic margins of wetlands are narrow, transient zones between inundated and dry areas. As water levels fluctuate, the dynamic hydrology at margins may impact wetland greenhouse gas (GHG) fluxes that are sensitive to soil saturation. The Prairie Pothole Region of North America consists of millions of seasonally-ponded wetlands that are ideal for studying hydrologic transition states. Using a long-term GHG database with biweekly flux measurements from 88 seasonal wetlands, we categorized each sample event into wet to wet (W→W), dry to wet (D→W), dry to dry (D→D), or wet to dry (W→D) hydrologic states based on the presence or absence of ponded water from the previous and current event. Fluxes of methane were 5-times lower in the D→W compared to W→W states, indicating a lag ‘ramp-up’ period following ponding. Nitrous oxide fluxes were highest in the W→D state and accounted for 20% of total emissions despite accounting for only 5.2% of wetland surface area during the growing season. Fluxes of carbon dioxide were unaffected by transitions, indicating a rapid acclimation to current conditions by respiring organisms. Results of this study highlight how seasonal drying and re-wetting impact GHGs and demonstrate the importance of hydrologic transitions on total wetland GHG balance.

Inundation Extent Mapping by Synthetic Aperture Radar: A Review

Recent flood events have demonstrated a demand for satellite-based inundation mapping in near real-time (NRT). Simulating and forecasting flood extent is essential for risk mitigation. While numerical models are designed to provide such information, they usually lack reference at fine spatiotemporal resolution. Remote sensing techniques are expected to fill this void. Unlike optical sensors, synthetic aperture radar (SAR) provides valid measurements through cloud cover with high resolution and increasing sampling frequency from multiple missions. This study reviews theories and algorithms of flood inundation mapping using SAR data, together with a discussion of their strengths and limitations, focusing on the level of automation, robustness, and accuracy. We find that the automation and robustness of non-obstructed inundation mapping have been achieved in this era of big earth observation (EO) data with acceptable accuracy. They are not yet satisfactory, however, for the detection of beneath-vegetation flood mapping using L-band or multi-polarized (dual or fully) SAR data or for urban flood detection using fine-resolution SAR and ancillary building and topographic data.