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USGS2026

Monitoring changes in Landsat thermal features in urban and non-urban interfaces from 1986 to 2023 in two international urban centers: Implications for climate and global issues

Rapid urbanization is reshaping thermal environments worldwide, with the strongest impacts occurring at the interface between urban and non-urban areas. Impervious surfaces, as key indicators of urban expansion, are critical for monitoring urban growth and assessing surface urban heat island (SUHI) effects. Land use and land cover change (LULCC) provides an essential link between urban dynamics and their environmental and societal consequences. Here, we integrated the U.S. Geological Survey (USGS) Climate Global Issues (CGI) Land Cover Product with Landsat thermal time-series to investigate SUHI evolution in two contrasting metropolitan regions: Wuhan, China, and Brasília, Brazil. Using data spanning 1986–2023, we analyzed the relationships between land cover, Landsat-based land surface temperature (LST), and SUHI intensity, and identified persistent thermal hotspots. Results demonstrate that the land cover data utilized increases the accuracy of impervious surface mapping along urban–rural gradients. Average SUHI intensities were 3.4 °C in Wuhan and 3.3 °C in Brasília, with statistically significant warming trends of 0.04 °C/year and 0.01 °C/year, respectively. Maximum temperature proved to be a robust indicator of SUHI intensification, capturing long-term upward trends. Our findings highlight the important role of urban land cover dynamics in shaping temporal SUHI variability and hotspot emergence. This prototype framework demonstrates the scientific and policy value of combining long-term land cover monitoring information with satellite thermal monitoring to quantify and track SUHI at city scale, supporting sustainable urban planning and climate adaptation strategies.

Remote Sensing↗

USGS2026

Is satellite-derived bathymetry vertical accuracy dependent on satellite mission and processing method?

This research focusses on three satellite-derived bathymetry methods and optical satellite instruments: (1) a stereo photogrammetry bathymetry module (SaTSeaD) developed for the NASA Ames stereo pipeline open-source software (version 3.6.0) using stereo WorldView data; (2) physics-based radiative transfer equations (PBSDB) using Landsat data; and (3) a modified composite band-ratio method for Sentinel-2 (SatBathy) with an initial simplified calibration, followed by a more rigorous linear regression against in situ bathymetry data. All methods were tested in three different areas with different geological and environmental conditions, Cabo Rojo, Puerto Rico; Key West, Florida; and Cocos Lagoon and Achang Flat Reef Preserve, Guam. It is demonstrated that all satellite derived bathymetry (SDB) methods have increased accuracy when the results are aligned with higher-accuracy ICESat-2 ATL24 track bathymetry data using the iterative closest point (ICP). SDB vertical accuracy depends more on location characteristics than the method or optical satellite instrument used. All error metrics considered (mean absolute error, median absolute deviation, and root mean square error) can be less than 5% of the maximum bathymetry depth penetration for at least one method, although not necessarily for the same method for all sites. The SDB error distribution tends to be bimodal irrespective of method, satellite instrument, alignment, site, or maximum bathymetry depth, leading to the potential ineffectiveness of traditional error metrics, such as the root mean square error. However, our analysis demonstrates that performing detrending where possible can achieve an error distribution as close to normality as possible for which error metrics are more diagnostic.

Florida↗

USGS2025

Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km 2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.

California↗

USGS2026

Comparative crop yield forecasting using satellite-derived biophysical and agro-climatic predictors in Sub-Saharan Africa

Timely and accurate crop yield forecasting is central to food security early warning systems, particularly in climate-vulnerable regions. While operational forecasting frameworks commonly rely on precipitation and vegetation indices such as NDVI, their ability to provide actionable lead time remains limited. Here, we evaluate the added value of satellite-derived biophysical Essential Climate Variables (ECVs): Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FAPAR), for forecasting millet yield in Burkina Faso (BF) and maize yield in South Africa (ZA) and Malawi (MW). Using Random Forest models, we quantify forecast skill across the growing season at both national and sub-national scales. Results show that LAI and FAPAR provide effective forecast lead times of approximately 4 months in BF, 2 months in ZA, and up to 6 months in MW relative to harvest. At peak performance, Mean Absolute Percentage Error (MAPE) reaches 19.8% (LAI) and 23.8% (FAPAR) in BF, 12.0% and 9.8% in ZA, and 21.8% and 20.8% in MW, respectively. Across countries, biophysical parameters often outperform NDVI and precipitation, particularly in arid and semi-arid regions. At the sub-national level, LAI and FAPAR enable classification of administrative units into high and moderate-skill forecast units, revealing strong spatial heterogeneity linked to crop dominance. However, forecast skill declines where the target crop is not the dominant type, highlighting an important limitation for operational deployment. Overall, the findings suggest that satellite-derived biophysical parameters can provide earlier and more spatially resolved yield signals than commonly used predictors, with potential to improve the timeliness and effectiveness of food security early warning systems.

Remote Sensing Applications: Society and Environme↗

USGS2024

On-orbit calibration and performance of the EMIT imaging spectrometer

The Earth surface Mineral dust source InvesTigation (EMIT) is a remote visible to shortwave infrared (VSWIR) imaging spectrometer that has been operating onboard the International Space Station since July 2022. This article describes EMIT's on-orbit spectroradiometric calibration and validation. Accurate spectroscopy is vital to achieve consistent mapping results with orbital imaging spectrometers. EMIT takes a unique approach to this challenge, with just six optical elements, no shutter, and no onboard calibration systems. Its simple design focuses on uniformity and stability to enable vicarious spectroradiometric calibration. Our experiments demonstrate that this approach is successful, approaching the fidelity of manual field spectroscopy in some cases, and enabling new and more accurate products across diverse Earth science disciplines. EMIT achieves several notable firsts for an instrument of its class. It demonstrates successful on-orbit adjustments of Focal Plane Array (FPA) alignment with sub-micron precision. It offers spectral uniformity better than 98%. Optical artifacts in the measurement channels are at least three orders of magnitude below the primary solar-reflected surface signals. Its noise performance enables percent-level discrimination in the depths of mineral absorption features. In these aspects, EMIT satisfies the stringent performance needs for the next generation of VSWIR imaging spectrometers to observe the Earth's ecosystems, geology , and water resources.

Remote Sensing of Environment↗

USGS2026

A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping

Land cover information is essential for understanding Earth’s surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD’s temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)—a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD. LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consisting of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency—critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics. Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 ± 1.60% overall agreement, compared to 71.1 ± 1.7% agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 ± 1.22% agreement, compared to 84.6 ± 1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.

Remote Sensing of Environment↗

USGS2024

A global assessment of SAOCOM-1 L-band stripmap data for InSAR characterization of volcanic, tectonic, cryospheric, and anthropogenic deformation

SAOCOM-1 is an L-band (23.5 cm) synthetic aperture radar (SAR) constellation made up of two satellites launched in 2018 and 2020 by Comisión Nacional de Actividades Espaciales (CONAE, Argentina). In this contribution, we present a global summary of interferometric SAR (InSAR) observations of ground deformation with SAOCOM-1 stripmap data for tracking volcanic, tectonic, glacier, and anthropogenic deformation. These examples include: 1) episodes of unrest at volcanoes in the Aleutian Islands, Southern Andes, and Italy, with line-of-sight (LOS) deformation from 4 cm/yr in InSAR time series to ~70 cm in interferograms; 2) dike intrusions in Hawai’i; 3) earthquakes in the Andean fold and thrust belt and the East Anatolian fault; 4) ice flow of the Southern Patagonia icefield; and 5) subsidence due to lithium brine extraction in the Salar de Atacama basin (northern Chile). Comparisons between SAOCOM-1, ALOS-2 SM3, Sentinel-1, and TerraSAR-X/ TanDEM-X/PAZ (TSX/TDX/PAZ) mean velocities from InSAR time series show a 1:1 ± 3% correlation in the LOS velocity, which highlights the high accuracy of SAOCOM-1 data. The minimum deformation that we measured in individual interferograms is 4 ± 0.6 cm. One limitation of SAOCOM-1 is the lack of a global acquisition program, which reduces its global and broader applications. Considering the repeat periods, background observation program, and lack of a controlled orbital tube, the best suited targets for SAOCOM-1 InSAR are two. First, volcanoes that deform with secular rates located in vegetated regions in mid- and high-latitudes, and/or that undergo transient episodes of fast deformation in which C-band coherence is lost quickly. Second, glaciers where coherence can be sustained during the repeat period of eight days.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2026

Climate-adaptive urban planning: Quantitative assessment of drought impacts and practical strategies for climate-resilient urban green spaces

Urban green spaces (UGSs) are vital for enhancing a city’s resilience and livability; however, their functionality is increasingly jeopardized by drought, particularly in water-scarce regions. This study evaluates drought impact on UGSs in Metropolitan Adelaide, Australia, a representative semi-arid urban system, using satellite-derived Normalized Difference Vegetation Index (NDVI) time-series data spanning 2000–2020. Vegetation dynamics were analyzed through Seasonal-Trend decomposition using Loess (STL), standardized anomaly assessment, lagged Pearson correlation, Ordinary Least Squares (OLS) regression, and Mann–Kendall trend analysis. To isolate climatically sensitive signals, 29 urban lawn patches were examined separately from mixed urban canopy, given their shallow root systems and direct dependence on surface moisture. NDVI declined by approximately 0.09 units during the Millennium Drought (2001–2009), with summer greenness deficits reaching 24% below the 20-year benchmark. Temperature was the dominant driver of lawn NDVI variability (r = −0.863, R 2 = 74.5%), substantially exceeding the effect of rainfall (r = 0.156, R 2 = 2.4%). El Niño–Southern Oscillation (ENSO) cycles modulated vegetation responses, with La Niña years supporting recovery and El Niño years amplifying decline. Post-drought recovery remained incomplete, with NDVI deficits of 8–20% persisting through 2020; full recovery was observed only in 2017, coinciding with the highest recorded summer rainfall. No significant directional trend was detected over the full study period (Mann–Kendall τ = 0.005, p = 0.908). These findings demonstrate that heat, rather than water limitation alone, is the primary driver of vegetation stress in urban systems, highlighting the benefits of integrated management strategies that address both warming and moisture deficits to sustain urban green infrastructure under future climate conditions. We introduce the concept of “urban greenery drought,” referring to a form of vegetation stress in managed urban landscapes where greenness is reduced primarily by elevated temperature and atmospheric demand despite water availability.

Adelaide↗

USGS2024

The EnMAP spaceborne imaging spectroscopy mission: Initial scientific results two years after launch

Imaging spectroscopy has been a recognized and established remote sensing technology since the 1980s, mainly using airborne and field-based platforms to identify and quantify key bio- and geo-chemical surface and atmospheric compounds, based on characteristic spectral reflectance features in the visible-near infrared (VNIR) and short-wave infrared (SWIR). Spaceborne missions, a leap in technology, were sparse, starting with the CHRIS/PROBA and EO1/Hyperion missions in the early 2000s, and providing spectroscopy data with limited spectral coverage and/or low data quality in the SWIR. Since 2019, several countries and agencies have successfully launched a number of spaceborne imaging spectroscopy systems into orbit or deployed them on the International Space Station (ISS) such as DESIS, PRISMA, HISUI, GF-5, EnMAP and EMIT. Among these recent missions, the German Environmental Mapping and Analysis Program (EnMAP) stands for its long-term development, sophisticated design with on-board calibration, high data quality requirements, and extensive accompanying science program. EnMAP was launched in April 2022 and, following a successful commissioning phase, started its operational activities in November 2022. The EnMAP mission encompasses global coverage from 80° N to 80° S through on-demand data acquisitions. Data are free and open access with 30 m spatial resolution, a high spectral resolution with a spectral sampling distance of 6.5 nm and 10 nm in the VNIR and SWIR regions respectively, and a high signal-to-noise ratio. In this paper, we aim to present the mission's current status, coverage, science capabilities and performance two years after launch. We show the potential of EnMAP for space-based imaging spectroscopy to operate in various environments, including high and low light levels, dense forests, Antarctic glaciers, and arid agricultural areas. EnMAP enables various applications in fields such as agriculture and forestry, soil compositional, raw materials, and methane mapping, as well as water quality assessment, and snow and ice properties. The results show that EnMAP's performance exceeds the mission requirements, and highlights the significant potential for contribution to scientific exploitation in various geo- and biochemical sciences. EnMAP is also expected to serve as a key tool for the development and testing of data processing algorithms for upcoming global operational missions.

Remote Sensing of Environment↗

USGS2026

Subsurface water ice mapping on Mars: A probabilistic approach

Subsurface water ice deposits on Mars are an important resource for potential future human exploration. They are also an indicator of the planet’s past climate. However, the distribution of subsurface water ice in Mars’s midlatitudes is uncertain because spacecraft imagery cannot directly observe subsurface ice in most cases. Various spacecraft remote sensing instruments are sensitive to subsurface water ice, including thermal imaging spectrometers, radar sounders, and neutron spectrometers. Geomorphic analyses of images can also implicate subsurface ice. Building upon the data products from the Mars Subsurface Water Ice Mapping project, we provide a probabilistic framework to jointly interpret existing data and estimate the likelihood of subsurface water ice in the Martian midlatitudes between 60 ∘ S and 60 ∘ N with uncertainty. Broadly, we find that near-surface ice is likely present poleward of ∼45 ∘ in both the northern and southern hemispheres. However, closer to the equator, existing remote sensing data cannot uniquely constrain the presence of subsurface water ice. Our probabilistic results provide a framework for quantifying the abundance of ice on Mars, and our uncertainty estimates allow future analysis and exploration to target regions of high uncertainty.

Planetary Science Journal↗

USGS2025

Enhanced hydrologic monitoring and characterization of groundwater drainage features

Groundwater drains to the land surface, generating the baseflow of streams, lakes, and wetlands. The hydrologic resilience of baseflow during prolonged dry periods and after disturbance can be assessed with evolving remote sensing analysis paired with localized monitoring of groundwater drainage features and creative model calibration strategies.

Nature Water↗

USGS2026

Spectral characteristics and mapping of lithium-rich playas in the Basin and Range Province, western United States

This report presents a comprehensive set of prospectivity maps for lithium in brines located within the playas of the Basin and Range Province of western United States. The prospectivity assessment is based on criteria established in the descriptive deposit model outlined in a previous U.S. Geological Survey report, with a focus on regional remote sensing methodologies. The playas within the study area have been systematically ranked according to several key factors, including size, the presence and abundance of source rocks, vegetation as an indicator of water availability, documented prospects, and data products derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Notably, the deposits in the playa of Clayton Valley make it stand out as the sole lithium brine-producing playa in the United States. The ASTER sensor provides optimal regional coverage, characterized by a 60-kilometer swath width and unique multispectral capabilities that are not available with other remote sensing instruments. This report and the accompanying maps are intended to serve as resources for further multidisciplinary assessments of lithium or other elements or commodities where prospectivity can be inferred from the presence of proxies such as alteration minerals.

Arizona, Baja California, California, Chihuahua, I↗

USGS2026

Groundwater budget for the Mountain Home area, southern Idaho, 2022–23

The U.S. Geological Survey, with funding from the Idaho Department of Water Resources, developed a groundwater budget for the Mountain Home area in southern Idaho for irrigation year 2023 (November 1, 2022–October 31, 2023). This study focused on the water balance across the Cinder Cone Butte Critical Groundwater Area (CGWA), Mountain Home Groundwater Management Area (GWMA), and the rest of the study area (RoSA), compiling data from various sources, including precipitation records, groundwater level measurements, metered groundwater pumpage data, surface water diversions and evapotranspiration (ET) estimates derived from remote sensing satellite imagery, and ground-based reference data. Key inflow components included recharge from applied surface water irrigation (which incorporates incidental recharge from irrigation practices and conveyance losses), estimated tributary streamflow, and estimated mountain block recharge. The key outflow components were groundwater pumpage for irrigation, municipal, industrial, and domestic uses, and ET. Recharge from applied irrigation and mountain block recharge were the largest inflows, and groundwater pumpage for irrigation was the largest outflow. The CGWA had a positive groundwater budget residual of 2,170 acre-feet (acre-ft), which contrasts with observed long-term groundwater level declines and historical trends of storage depletion. This positive residual is likely associated with unquantified outflows, including lateral groundwater flow out of the subregion, or other complexities, such as overestimated tributary contributions relative to the actual recharge for the 2023 water budget. The GWMA exhibited a positive residual of 56,563 acre-ft, primarily owing to recharge from applied surface water irrigation and areal recharge during a wetter-than-average year, which allowed irrigation entities to deliver more water from in-basin and out-of-basin reservoirs. The RoSA showed a large positive residual of 124,933 acre-ft. The interpretation of these positive residuals must account for significant uncertainties, including estimations of areal recharge, tributary streamflow (particularly losses and diversions), ET, the volume of surface water loss to the Snake River, lateral groundwater flows between subregions and across study area boundaries, and the unquantified groundwater discharge to the Snake River. These uncertainties, in combination with the complex hydrogeologic controls on water movement and limitations of remotely sensed data, directly affect the accuracy of water availability assessments. Future data collection efforts would help reduce these uncertainties and support water resource management decisions in the Mountain Home area. Key efforts could include installing additional streamflow gaging stations (particularly to quantify tributary losses and gains and surface water losses to the Snake River), improving groundwater pumpage metering, and validating remotely sensed ET data with ground-based measurements. Furthermore, to better quantify unrepresented or highly uncertain fluxes, focused investigations on groundwater discharge to the Snake River, lateral groundwater flows between subregions and across study area boundaries, and a more robust determination of the actual influence and volume of mountain block recharge would help refine future water availability assessments for the Mountain Home area.

Idaho↗

USGS2024

Earthquake-triggered ground-failure inventory associated with the M7.1 2018 Southcentral Alaska earthquake

The 30 November 2018, magnitude (Mw) 7.1 earthquake in Southcentral Alaska triggered substantial landslides, liquefaction, and ground cracking throughout the region, resulting in widespread geotechnical damage to buildings and infrastructure. Despite a challenging reconnaissance and remote-sensing environment, we constructed a detailed digital inventory of ground failure associated with the event from several sources. Sources included information derived from remotely sensed data, and data compiled from literature, social media postings, and earthquake damage information compiled by local, state, and federal agencies. Each instance of ground failure within the inventory contains information on the location and type of observed ground failure, and the methods and data used to document the occurrence. Where high-quality data, such as LIDAR or satellite imagery, were available and showed the ground-failure instance clearly, the extent is mapped as a polygon or polyline. All other locations are mapped as points. There are a total of 886 ground-failure instances documented within the inventory (400 landslides, 286 liquefaction features, and 200 features unattributed to specific processes). A semi-quantitative confidence scheme is used to describe mapping certainty associated with each ground-failure feature. This inventory represents a relatively moderate ground-failure-triggering event that occurred in a subarctic environment. This data paper describes the content within the inventory, the inventory data collection procedures, and limitations of the data. Events of this type are not often documented in detail; thus, adding the inventory data to the US Geological Survey Open Repository of Earthquake-Triggered Ground-Failure Inventories further diversifies the datasets available to the scientific community to be used to better understand and model earthquake-triggered ground failure.

Alaska↗

USGS2026

Reassessing water availability in the Nile River Basin using satellite-based irrigation water use accounting

Existing estimates of irrigation areas and water use in the Nile River Basin vary widely and are based on inconsistent data and methodologies. This study leverages advances in remote sensing to provide an up-to-date and consistent basin-wide reassessment of water use in the Nile River Basin. Land cover and actual evapotranspiration data from 2013 to 2022 were used to quantify irrigated areas, irrigation water use, and the average naturalized water yield. Across the basin, 7.1 million hectares of irrigated land consume an average of 72.5 ± 2.6 billion cubic meters (BCM) of blue water annually. Egypt and Sudan together account for 92% of the irrigated area and 97% of the irrigation water use. The basin’s average naturalized yield, evaluated at Dongola station, is determined to be between 108 and 114.4 ± 3.2 BCM. Although conservative, this updated analysis indicates the basin’s average naturalized yield is 14%–20% higher than the previous naturalized yield estimate (95 BCM) and 29%–36% higher than the commonly cited observed flow of 84 BCM, which is frequently treated as the total basin-wide available water. Importantly, the study underscores that the 84 BCM represents flow at the Aswan gauging station after upstream consumptive uses, not the total water available in the basin under naturalized conditions. This distinction is critical for accurate water accounting, planning, and governance in this water-scarce basin. This study demonstrates the value of open-source remote sensing resources in data-scarce regions while emphasizing the need for region specific validation and bias correction for improved accuracy.

Nile River Basin↗

USGS2026

Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach

Ciénegas are rare wetlands in arid landscapes of the North American Southwest, historically providing critical ecological and hydrological functions but increasingly threatened by changing climate and land use pressures. This study quantifies changes in ciénega condition and floodplain dynamics using a state-and-transition model (STM) informed by expert knowledge and remote sensing. Key factors include woody plant encroachment, water availability, and soil aggradation. We mapped 31 ciénegas with high-resolution imagery and analyzed Landsat data (1985–2023) to assess vegetation health and moisture using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Results show substantial interannual variability in phenology, water stress, and soil moisture, with regional drying and elevation strongly influencing ciénega resilience. We classified ciénegas into three functional states—healthy, desiccated, and dormant—and mapped their 2023 condition. Trend analyses indicate most ciénegas exhibit greening despite drought, though localized variability underscores the need for site-specific management. None are in a stable climax (reference) state; rather, they transition among states in response to external drivers. Increasing woody plant cover and surface drying, likely linked to declining regional water tables, favor deep-rooted species over wetland grasses—a pattern mirrored in adjacent control plots. Spatially explicit analysis revealed intra-ciénega variability often masked by aggregated data, highlighting the importance of high-resolution monitoring. Seasonal and long-term trends provide context for understanding ciénega dynamics, including degradation and restoration pathways. This study emphasizes the importance of groundwater conservation and demonstrates how remote sensing supports long-term monitoring. The STM framework offers a practical tool for adaptive management to sustain freshwater resources in arid environments.

Arizona, New Mexico↗

USGS2026

Tracking baseflow supply dynamics using SWOT data from small groundwater-dominated lakes

In situ surface-water monitoring strategies are biased towards larger perennial streams and lakes and are generally not designed to track mechanisms of baseflow supply contributed by the dynamic storage of aquifers. Additionally, small (< 1 km 2 ) groundwater-influenced lakes and wetlands globally have little in situ monitoring infrastructure. We explored the utility of remotely sensed Surface Water Ocean Topography Satellite (SWOT) data, collected from 2023 onward, to characterise the seasonal and multi-year water-level trends of groundwater flow-through kettle lakes distributed across the permeable sediments of eastern Massachusetts, USA. This analysis indicated that water levels for kettle lakes with areas down to approximately 0.05 km 2 are resolvable in the study area. Our examination of 17 kettle lakes found that SWOT water-surface elevation data closely tracked groundwater levels in adjacent monitoring wells where available, including the timing of seasonal patterns (highest levels generally in late spring), although there was some variation between years and there was a substantial lag in the timing of high water levels for a lake located downgradient from a 30-m-thick vadose zone. Furthermore, SWOT-observed water-level increases in kettle lakes tracked with baseflow increases in two adjacent groundwater-dominated streams, as would be expected from increased hydraulic gradients. Unlike spectral remote sensing, SWOT data are generally not affected by cloud cover, resulting in a potential for groundwater-dominated lakes to be sentinels of dynamic storage patterns, including identification of baseflow drought lags, which are currently ill-defined hydrological processes. SWOT monitoring of groundwater-influenced surface waters shows potential for augmenting existing monitoring wells and streamgages as continuous monitors of groundwater levels and baseflow supply in permeable terrain.

Massachusetts↗

USGS2024

Wildfire, extreme precipitation and debris flows, oh my! Channel response to compounding disturbances in a mountain stream in the Upper Colorado Basin, USA

Compounding changes in climate and human activities stand to increase sediment input to rivers in many landscapes, including via discrete perturbations such as post-fire debris flows. Because sediment supply is a dominant control on river morphology, understanding mountain river responses to sediment regime perturbations is critical to predicting and addressing downstream effects to infrastructure, water security and aquatic habitat. A growing body of literature explores the causes, likelihood, size and composition of post-fire debris flows, but the channel response to these disturbances remains poorly studied. This study used repeat field surveys, time-lapse photographs and pre- and post-disturbance remote sensing datasets to document and analyse space- and time-varying channel response to post-fire debris flows along a steep mountain stream in the Upper Colorado River Basin, USA. Specifically, we evaluated channel morphology and bed composition changes, correlations between channel changes and valley and channel attributes, and the relative importance of spring snowmelt versus summer monsoon events. Several cross-sectional channel change types were observed from lidar a month after post-fire debris-flow events, including channelized and braided incision into deposits, incision into the pre-fire channel bed, bank erosion and no change. Channel changes were most correlated with pre-fire channel width, valley width and unit stream power, and these relationships could be tested in other burned locations to evaluate their transferability. Repeat channel surveys before and after snowmelt indicate rapid recovery and channel narrowing following major sediment disturbances, although sediment deposits remained in the channel margins. Together, these results highlight the importance of field and remote sensing-based channel surveys to improve understanding of, and potential to predict, mountain channel response to compounding climate disturbances.

Earth Surface Processes and Landforms↗