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USGS2021

The optical river bathymetry toolkit

Spatially distributed information on water depth is essential for many applications in river research and management and, under certain circumstances, can be inferred from remotely sensed data. Although fluvial remote sensing has emerged as a rapidly developing subdiscipline of the riverine sciences, more widespread adoption of these techniques has been hindered by a lack of accessible software. The Optical River Bathymetry Toolkit (ORByT) fills this void by providing a standalone package for mapping water depth from passive optical image data. The ORByT interface enables end users to import images and field‐based depth measurements, create and refine water masks, and perform spectrally based depth retrieval via an Optimal Band Ratio Analysis algorithm. The resulting bathymetric map can be exported as an image file, point cloud, and/or cross section; a thorough accuracy assessment also is incorporated into the workflow. In addition, image‐derived depth estimates can be subtracted from water surface elevations to obtain bed elevations suitable for input to a hydrodynamic model. Potential users of ORByT must bear in mind the inherent limitations of passive optical remote sensing: reliable bathymetry can only be inferred in clear‐flowing, shallow streams; this approach is not appropriate for more turbid, deeper rivers.

River Research and Applications↗

USGS2007

Southern California Bight 2003 Regional Monitoring Program: V. water quality

More than $30 million is expended annually on environmental monitoring in the Southern California Bight (SCB), yet only 5% of the Bight is monitored on an ongoing basis. Therefore, environmental managers in the SCB decided to expand their monitoring program and, starting in 1994, decided to conduct periodic regional assessments of ecosystem condition and assess the overall health of the SCB. Sixty-five different organizations collaborated in 2003 to create the third SCB Regional Monitoring Program (Bight '03). Bight '03 was designed to be integrated regional monitoring program that encompasses regulatory, academic, and non-governmental agencies. Bight '03 had three components: Coastal Ecology, Shoreline Microbiology, and Water Quality. This report addresses the purpose, approach, findings, and recommendations from the Water Quality component, which focused on contamination-laden stormwater runoff, in particularly its variability in time and space as well as its short-term ecological impacts. Specifically, the Bight '03 Water Quality component had three primary goals, the first of which was to described the temporal evolution of stormwater plumes produced by the major southern California rivers. Specifically, the study was intended to determine how far offshore the plumes extended, how rapidly they advected, how long before the plumes dispersed and how these properties differed among storms and river systems. The second goal was to describe how the physical properties (e.g., turbidity, temperature, salinity) of the plume related to biogeochemical and ecological properties that are of more direct concern to the water quality management community. Accomplished primarily through ship-based sampling of water quality parameters, this second goal was to describe how far offshore, and for how ;long after the storm, elevated bacterial concentrations, toxicity, and nutrients could be detected. Similar to the fist goal, the study also addressed how these answers differed among storms and river systems. The final goal was to determine whether relationships between environmental indicators derived from coincident satellite remote sensing and in situ data sets are sufficiently robust for remote sensing to become a routine water quality monitoring programs. Remote sensing data potentially provide coastal managers with synoptic near-real time regional information about prevailing ocean conditions and hazards that would complement existing field-based sampling protocols, but only if there is a thorough understanding of how to interpret and utilize the proxy measures, such as ocean color. The understanding of these priorities through Bight '03 sampling is intended to provide the basis for developing more efficient, widespread and coast-effective coastal ocean monitoring techniques. Water quality data were collected across eight major river systems within four geographic regions of southern California. Field measurements included the primary contaminants of interest, i.e., bacterial concentrations, water toxicity, and nutrients, as well as related parameters such as temperatures, salinity, total suspended solids, transmissivity, chlorophyll, and colored dissolved organic material (CDOM) concentrations. For each of the four major regions, i.e., Santa Clara/Ventura Rivers, Ballona Creek/Santa Monica Bay, San Pedro Shelf, and the San Diego, Tijuana Rivers, two stormwater events were sampled for up to three days by ship resulting in 574 water column CTD+ profiles and 705 discrete water samples during 36 ship-days. These data were analyzed in combination with MODIS ocean color satellite remote sensing, buoy meteorological observations, drifters, and HF radar current measurements to evaluate the dispersal patterns, dynamics, and impacts of the freshwater runoff plumes. Based on these data and resulting analyses, the principal conclusions were as follow: - Stormwater runoff turbidity plumes were found to be spatially extensive, covering up to 2500 km 2 within the Southern California Bight nearshore zone, and persisting over the entire duration of the post-storm sampling period (at least 3 days). - The spatial and temporal extent of the portion of the plume with contaminants was far less than that of the turbidity plume, typically representing <10% of its area (30-70% off Tijuana); however, with contaminant impacts generally greatly reduced or absent by the third or fourth day of sampling - Pseudo-nitzschia , a harmful algae that produces domoic acid, was found to be more abundant than previously reported. - Accurately describing stormwater runoff plumes requires a combination of in situ and remote sensing assessment tools, with satellite data providing valuable synoptic information. From these conclusions, the following recommendations are provided: - Future studies designed to describe stormwater plumes should include a combination of ship - and remote sensing-based methods. - CDOM is a good proxy of the freshwater runoff plume and should be added as a standard measurement parameter on water quality instrument packages. - Investigations are needed that assess on a local basis the spatial extent of ecological effects of stormwater plumes early in the storm, ideally accompanied by airborne imagery to provide improved temporal & spatial resolution, to fill in knowledge gaps. The next Bight regional monitoring program should focus on quantifying nutrient loadings and dynamics in association with stormwater runoff and other sources, and characterize their attendant ecosystem impacts such as phytoplankton blooms.

California↗

USGS2020

Effects of huisache removal on rangeland evapotranspiration in Victoria County, south-central Texas, 2015–18

The U.S. Geological Survey and Desert Research Institute, in cooperation with the Natural Resources Conservation Service, Texas State Soil and Water Conservation Board, Victoria County Groundwater Conservation District, Victoria Soil and Water Conservation District, and the San Antonio River Authority, evaluated the hydrologic effects of Vachellia farnesiana var. farnesiana (huisache) removal on rangeland evapotranspiration in Victoria County, Texas. Measurements of evapotranspiration, rainfall, and related properties were made at two sites during March 2015 through August 2018. One site was predominantly grassland. The other site was dominated by dense huisache vegetation that was removed about halfway through the study period. The resulting evapotranspiration data were examined for differences between the locations and differences between the pre-removal (2015–16) and post-removal (2017–18) periods to assess the effects of huisache removal on evapotranspiration. Evapotranspiration measurements were made using the eddy-covariance technique and were supplemented by remote-sensing estimates of evapotranspiration derived from thermal and optical satellite images. A map of remotely sensed evapotranspiration was generated for the area surrounding the study sites for 2015 and demonstrates the capability of remote sensing to evaluate land-management effects on evapotranspiration for larger scale areas, such as a county or stream-basin area. During the pre-removal period (March 2015–December 2016), evapotranspiration was greater at the huisache site than at the grassland site. Evapotranspiration at the grassland site (average of the eddy-covariance evapotranspiration and average remotely sensed evapotranspiration) was 87.6 millimeters per month (mm/mo) and at the huisache site was 100.8 mm/mo, with the differences in evapotranspiration rates being attributed to the difference in site vegetation. After huisache was removed in January 2017, evapotranspiration at the huisache site was substantially lower than at the grassland site, the changes in evapotranspiration rates being attributed not only to removal of huisache vegetation but also to possible disruption of soil runoff and infiltration characteristics. During the post-removal period (February 2017–August 2018), evapotranspiration was 88.5 mm/mo at the grassland site and 72.9 mm/mo at the huisache site (average of the eddy-covariance and average remotely sensed evapotranspiration). The monthly differences in evapotranspiration between the grassland and huisache sites, determined by eddy-covariance and remote-sensing methods, were statistically significant between the pre-removal and post-removal periods. Also, the pre-removal period provided the best conditions to evaluate the differences between huisache site and grassland site evapotranspiration. During the pre-removal period, evapotranspiration from the huisache site as measured by the eddy-covariance method was, on average, 10.7 mm/mo greater than evapotranspiration measured at the grassland site. As determined by the average of the remotely sensed methods, huisache site evapotranspiration was 15.8 mm/mo greater than grassland site evapotranspiration. These average differences in evapotranspiration rates by the two methods indicate that evapotranspiration at the grassland site was, on average, 13.2 mm/mo less than that at the huisache site during the pre-removal period. This average difference in evapotranspiration rates also indicates potential increased groundwater recharge and (or) surface-water runoff at the grassland site.

Texas↗

USGS2021

A new remote sensing-based Carbon Sequestration Potential Index (CSPI): A tool to support land carbon management

Integrating remote sensing into assessments of carbon stocks and fluxes has advanced our understanding of how global change affects landscapes and our capacity to support decision making about forest management. However, there remains a lack of detailed and actionable analyses conducted across widely ranging environmental conditions that are appropriate for tactical planning. We used airborne laser scanning data and multi-source satellite imagery to estimate forest aboveground carbon density and gross primary production, and to map forest cover across the main Hawaiian Islands. We used these measures to develop the Carbon Sequestration Potential Index (CSPI), which identifies where the potential for carbon sequestration following afforestation would be highest within a complex landscape of 304 management units. Variation in CSPI was high across islands and between ecosystems, with low values for cool, dry and largely intact forest systems and high values for warm, wet and largely non-forested systems. The CSPI provided a rapid, spatially-explicit and actionable assessment of Hawaiian forest reserves, which can help stewardship agencies contribute to state carbon neutrality goals through climate-smart and science-driven prescriptions that encompass conservation to restoration.

Hawaii↗

USGS2022

Can we accurately estimate sediment budgets on Mars?

Sediment budgets are fundamentally important for planetary science. However, only one primary method, based on remote sensing, is currently available for determining extraterrestrial sediment budgets. For determining sediment budgets on Earth, both in-situ and remote sensing methods are available. Despite the widespread use of the two methods, there has been surprisingly little research on how well the sediment budgets produced by these two approaches reconcile with one another, which highlights the lack of quantitative understanding of errors for sediment budgets measured with remote sensing in planetary research. Therefore, there is a general need to expand our knowledge of sediment budgets. Here we use a background review and analog case study of an aeolian dunefield in Grand Canyon, Earth to frame a path forward for addressing shortcomings of remote sensing sediment budgets on Mars. We estimate a 53% percent difference in the sediment budget determined with remote sensing relative to in-situ methods for a simple endmember scenario of a dunefield within a unimodal wind directional regime and no external sediment supply. However, when we incorporated key sources of uncertainty in remote sensing change detection following methods commonly used by geomorphologists on Earth, the estimates of sediment budget differences relative to the in-situ method spanned a much larger range, from 3% to 138%. Our case study also suggests that sediment budget errors could be much larger under more complex wind direction, sediment supply, and physiographic settings, and that variability in those landscape characteristics might be used to better estimate errors for dunefield sediment budgets. We conclude that by comparing sediment budgets derived from in-situ measurements of sediment fluxes and from remote sensing measurements at many more analog sites on Earth, the aeolian research community, and the geomorphology discipline, could gain an understanding of the errors of the remote sensing method, which is used by investigators on other planetary bodies such as Mars. This could improve the ability to quantify sediment budgets on Mars – and, in the future, other planetary environments where high-resolution topographic data are available – as well as directly improve our ability to interpret extraterrestrial landscape evolution related to climate, weather, and geologic history.

Earth and Planetary Science Letters↗

USGS2012

Producing fractional rangeland component predictions in a sagebrush ecosystem, a Wyoming sensitivity analysis

Remote sensing information has been widely used to monitor vegetation condition and variations in a variety of ecosystems, including shrublands. Careful application of remotely sensed imagery can provide additional spatially explicit, continuous, and extensive data on the composition and condition of shrubland ecosystems. Historically, the most widely available remote sensing information has been collected by Landsat, which has offered large spatial coverage and moderate spatial resolution data globally for nearly three decades. Such medium-resolution satellite remote sensing information can quantify the distribution and variation of terrestrial ecosystems. Landsat imagery has been frequently used with other high-resolution remote sensing data to classify sagebrush components and quantify their spatial distributions (Ramsey and others, 2004; Seefeldt and Booth, 2004; Stow and others, 2008; Underwood and others, 2007). Modeling algorithms have been developed to use field measurements and satellite remote sensing data to quantify the extent and evaluate the quality of shrub ecosystem components in large geographic areas (Homer and others, 2009). The percent cover of sagebrush ecosystem components, including bare-ground, herbaceous, litter, sagebrush, and shrub, have been quantified for entire western states (Homer and others, 2012). Furthermore, research has demonstrated the use of current measurements with historical archives of Landsat imagery to quantify the variations of these components for the last two decades (Xian and others, 2012). The modeling method used to quantify the extent and spatial distribution of sagebrush components over a large area also has required considerable amounts of training data to meet targeted accuracy requirements. These training data have maintained product accuracy by ensuring that they are derived from good quality field measurements collected during appropriate ecosystem phenology and subsequently maximized by extrapolation on high-resolution remote sensing data (Homer and others, 2012). This method has proven its utility; however, to develop these products across even larger areas will require additional cost efficiencies to ensure that an adequate product can be developed for the lowest cost possible. Given the vast geographic extent of shrubland ecosystems in the western United States, identifying cost efficiencies with optimal training data development and subsequent application to medium resolution satellite imagery provide the most likely areas for methodological efficiency gains. The primary objective of this research was to conduct a series of sensitivity tests to evaluate the most optimal and practical way to develop Landsat scale information for estimating the extent and distribution of sagebrush ecosystem components over large areas in the conterminous United States. An existing dataset of sagebrush components developed from extensive field measurements, high-resolution satellite imagery, and medium resolution Landsat imagery in Wyoming was used as the reference database (Homer and others, 2012). Statistical analysis was performed to analyze the relation between the accuracy of sagebrush components and the amount and distribution of training data on Landsat scenes needed to obtain accurate predictions.

Wyoming↗

USGS2013

Detecting annual and seasonal changes in a sagebrush ecosystem with remote sensing-derived continuous fields

Climate change may represent the greatest future risk to the sagebrush ecosystem. Improved ways to quantify and monitor gradual change resulting from climate influences in this ecosystem are vital to its future management. For this research, the change over time of five continuous field cover components including bare ground, herbaceous, litter, sagebrush, and shrub were measured on the ground and by satellite across six seasons and four years. Ground-measured litter and herbaceous cover exhibited the highest variation annually and herbaceous cover the highest variation seasonally. Correlation of ground measurements to corresponding remote-sensing predictions indicated that annual predictions tracked ground measurements more closely than seasonal ones, and QuickBird predictions tracked ground measurements more closely than Landsat predictions. When annual linear slope values from ground plots and sensor predictions were correlated by component, the direction of ground-measured change was tracked better with QuickBird components than with Landsat components. Component predictions were correlated to annual and seasonal DAYMET precipitation. QuickBird components on average had the best response to precipitation patterns, followed by Landsat components. Overall, these results demonstrate the ability of sagebrush ecosystem components as predicted by regression trees to incrementally measure changing components of a sagebrush ecosystem.

Wyoming↗

USGS2015

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls ( Strix occidentalis caurina ), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map. Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors. We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the performance of the Landsat-based map was within acceptable limits (AUC = 0.717 ± 0.021). As is common with photo-interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map produced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat = 9962 ha, nesting habitat only = 6036 ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679–9592 ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453–7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped. We concluded that both Landsat- and lidar-based SDMs produced reasonable maps and area estimates for northern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to what local wildlife biologists considered spotted owl nesting habitat. The Landsat-based map provided a less precise spatial representation of habitat within the relatively small geographic confines of the study area, but habitat area estimates were similar to both the photo-interpreted and lidar-based maps. Photo-interpreted maps are time consuming to produce, subjective in nature, and difficult to replicate. SDMs provide a framework for efficiently producing habitat maps that can be replicated as habitat conditions change over time, provided that comparable remotely sensed data are available. When the SDM uses predictor variables extracted from lidar data, it can produce a habitat map that is both accurate and useful at large and small spatial scales. In comparison, SDMs using Landsat-based data are more appropriate for large scale analyses of amounts and general spatial patterns of habitat at regional scales.

Remote Sensing of Environment↗

USGS2021

Evaluation of remote mapping techniques for earthquake-triggered landslide inventories in an urban subarctic environment: A case study of the 2018 Anchorage, Alaska Earthquake

Earthquake-induced landslide inventories can be generated using field observations but doing so can be challenging if the affected landscape is large or inaccessible after an earthquake. Remote sensing data can be used to help overcome these limitations. The effectiveness of remotely sensed data to produce landslide inventories, however, is dependent on a variety of factors, such as the extent of coverage, timing, and data quality, as well as environmental factors such as atmospheric interference (e.g., clouds, water vapor) or snow and vegetation cover. With these challenges in mind, we use a combination of field observations and remote sensing data from multispectral, light detection and ranging (lidar), and synthetic aperture radar (SAR) sensors to produce a ground failure inventory for the urban areas affected by the 2018 magnitude (M w ) 7.1 Anchorage, Alaska earthquake. The earthquake occurred during late November at high latitude (∼61°N), and the lack of sunlight, persistent cloud cover, and snow cover that occurred after the earthquake made remote mapping challenging for this event. Despite these challenges, 43 landslides were manually mapped and classified using a combination of the datasets mentioned previously. Using this manually compiled inventory, we investigate the individual performance and reliability of three remote sensing techniques in this environment not typically hospitable to remotely sensed mapping. We found that differencing pre- and post-event normalized difference vegetation index maps and lidar worked best for identifying soil slumps and rapid soil flows, but not as well for small soil slides, soil block slides and rock falls. The SAR-based methods did not work well for identifying any landslide types because of high noise levels likely related to snow. Some landslides, especially those that resulted in minor surface displacement, were identifiable only from the field observations. This work highlights the importance of the rapid collection of field observations and provides guidance for future mappers on which techniques, or combination of techniques, will be most effective at remotely mapping landslides in a subarctic and urban environment.

Alaska↗

USGS1990

Spectral estimates of net radiation and soil heat flux

Conventional methods of measuring surface energy balance are point measurements and represent only a small area. Remote sensing offers a potential means of measuring outgoing fluxes over large areas at the spatial resolution of the sensor. The objective of this study was to estimate net radiation (R n ) and soil heat flux (G) using remotely sensed multispectral data acquired from an aircraft over large agricultural fields. Ground-based instruments measured R n and G at nine locations along the flight lines. Incoming fluxes were also measured by ground-based instruments. Outgoing fluxes were estimated using remotely sensed data. Remote R n , estimated as the algebraic sum of incoming and outgoing fluxes, slightly underestimated R n measured by the ground-based net radiometers. The mean absolute errors for remote R n minus measured R n were less than 7%. Remote G, estimated as a function of a spectral vegetation index and remote R n , slightly overestimated measured G; however, the mean absolute error for remote G was 13%. Some of the differences between measured and remote values of R n and G are associated with differences in instrument designs and measurement techniques. The root mean square error for available energy (R n - G) was 12%. Thus, methods using both ground-based and remotely sensed data can provide reliable estimates of the available energy which can be partitioned into sensible and latent heat under nonadvective conditions.

Remote Sensing of Environment↗

USGS2025

Understanding abundances and behaviors of shorebirds in coastal Louisiana

Barrier islands provide resources and ecological services that are integral to economic and environmental interests, such as protection of coastal infrastructure and provision of wildlife habitat. Over time, barrier islands may become eroded and experience land loss, which can require management actions to restore island integrity. Barrier island restoration can create or modify habitats, which can impact the organisms depending on them. Our objective was to understand how the abundance and behaviors of a suite of shorebird species responded to restoration and habitat factors at two restored sites in coastal Louisiana (USA). For five focal species, we used abundance from the breeding and non-breeding seasons as well as breeding, foraging, and maintenance behaviors as response variables in boosted regression tree models to determine the importance of various geospatial and remotely sensed predictor variables related to restoration. Across sites and species, remotely sensed variables, particularly a brightness index, tended to be more important than restoration phases as predictors of bird abundance and behavior. Our results suggest that sediment composition, moisture, and vegetative cover are related to shorebird coastal habitat selection, although the direction and strength of relationships differ among these variables and our focal species. Tying these remote sensing metrics to restoration design and management actions can help land managers better understand factors that attract and benefit birds. Additional research can advance understanding in how remote sensing can be used to monitor the availability of functional habitats for shorebirds.

BioRxiv↗

USGS2026

Hyperspectral narrowband imaging spectroscopy: A new paradigm for Earth observation

This editorial introduces the Special Issue entitled “Hyperspectral Narrowband Imaging Spectroscopy: A New Paradigm for Earth Observation” in the August 2026 issue of Photogrammetric Engineering & Remote Sensing (PE&RS), the flagship journal of the American Society for Photogrammetry and Remote Sensing (ASPRS). This volume represents the fourth dedicated hyperspectral special issue published in PE&RS, following earlier contributions by Thenkabail et al. (2025, 2024a, 2024b), and continues ASPRS’s commitment to advancing cutting‑edge imaging spectroscopy research and its applications across Earth system science. Remote sensing is undergoing one of the most profound transformations in its history. The emergence of hyperspectral narrowband (HNB) imaging spectroscopy data, capable of acquiring hundreds of contiguous, narrow spectral bands, has shifted the discipline from observing Earth in a handful of broad spectral windows to capturing continuous spectral signatures of the Planet. This transition marks a decisive break from the multispectral paradigm that has dominated satellite remote sensing for nearly five decades, driven by the advent of new orbital imaging spectrometers such as EnMAP, PRISMA, and NASA’s EMIT, and by the forthcoming Surface Biology and Geology (SBG) mission ((Pires Silva et al., 2026; Bourriz et al., 2025; Thenkabail et al., 2025; Chabrillat et al., 2024; Aneece et al., 2024; Dave et al., 2024; Thenkabail et al., 2024a; Thenkabail et al., 2024b; Thenkabail, 2024a; Thenkabail, 2024b; Thompson et al., 2022; Kokaly et al., 2022; Aneece & Thenkabail, 2022; Cawse Nicholson et al., 2021; Guanter et al., 2021; Vangi et al., 2021; Thenkabail et al., 2021). These missions (e.g., Table 1) deliver unprecedented spectral fidelity, improved signal to noise ratios, and global coverage capabilities, enabling a new era of quantitative, spectroscopy based Earth observation. Where multispectral broadbands (MBBs) provide only a few discrete measurements along the electromagnetic spectrum, HNB systems deliver rich, diagnostic information that enables scientists to characterize Earth’s surface with unprecedented biochemical, biophysical, and structural detail (Figure 1a, 1b). The implications for environmental monitoring, agriculture, water resources, and mineral exploration are profound. Several overarching themes emerge: • Spectral fidelity matters. The ability to preserve subtle absorption features is essential for mineral mapping, vegetation trait retrieval, and biochemical modeling. • AI and deep learning are indispensable. From destriping to classification, modern analytics must be scalable, label‑efficient, and capable of exploiting the full spectral–spatial richness of HNB data. • Physics‑based and data‑driven approaches must converge. Radiative transfer models such as PROSAIL, enhanced with localized soil parameterizations, remain foundational for biophysical retrievals and model‑based inference. • Dimensionality reduction and feature extraction are critical. Techniques such as L1‑ISOMAP demonstrate that intelligent manifold learning can unlock the structure of fused, high‑dimensional datasets. • Next‑generation architectures must be interactive and multimodal. ICTNet exemplifies the future of hyperspectral classification: hybrid, synergistic, and capable of modeling both local textures and global spectral dependencies.

Photogrammetric Engineering and Remote Sensing (PE↗

USGS2024

Learning from a high-severity fire event—Conditions following the 2018 Carr Fire at Whiskeytown National Recreation Area

The 2018 Carr Fire burned more than 90 percent of Whiskeytown National Recreation Area, with much of the park burning at high severity. California yellow pine and mixed conifer forests are not well adapted to large, high-severity fires, and forest recovery after these events may be problematic. Large, high-severity fire patches pose difficulties for recruitment with interiors that are long distances from potential seed trees and may develop fuel structures that can promote further high-severity fire. This report details patterns of forest structure derived from field plots measured 2–3 years after the Carr Fire, providing a characterization of immediate fire effects. We coupled these observations with remotely sensed information, including data collected from unoccupied aircraft system surveys. The remotely sensed data were used to depict erosion after the Carr Fire as well as to create a high-resolution land cover classification map, a debris flow risk map and hazard assessment, and a post-fire canopy vegetation loss map. Results indicated high levels of tree mortality after the Carr Fire, including high-value old growth forest stands, supporting remotely sensed assessments of fire severity. The high-resolution tree mortality model also aligned well with other remotely sensed estimates of immediate burn severity. Results of the land cover classification illustrated the high percentage of dead vegetation remaining in the understory and canopy 8 months post-fire. Changes in vegetation height identified areas with canopy vegetation loss from 1- to 8-months post-fire. Pairing the post-fire debris accumulation with debris flow probabilities may identify high-risk debris flow areas. The results of this study will help inform future decisions concerning wildland fire and vegetation management strategies at Whiskeytown National Recreation Area and are broadly relevant for management in the aftermath of large, high-severity fires in mixed, dry coniferous forests in the western United States.

California↗

USGS2024

The spatially adaptable filter for error reduction (SAFER) process: Remote sensing-based LANDFIRE disturbance mapping updates

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.

Fire↗

USGS2010

Joint Agency Commercial Imagery Evaluation (JACIE)

Remote sensing data are vital to understanding the physical world and to answering many of its needs and problems. The United States Geological Survey's (USGS) Remote Sensing Technologies (RST) Project, working with its partners, is proud to sponsor the annual Joint Agency Commercial Imagery Evaluation (JACIE) Workshop to help understand the quality and usefulness of remote sensing data. The JACIE program was formed in 2001 to leverage U.S. Federal agency resources for the characterization of commercial remote sensing data. These agencies sponsor and co-chair JACIE: U.S. Geological Survey (USGS) National Aeronautics and Space Administration (NASA) National Geospatial-Intelligence Agency (NGA) U.S. Department of Agriculture (USDA) JACIE is an effort to coordinate data assessments between the participating agencies and partners and communicate the knowledge and results of the quality and utility of the remotely sensed data available for government and private use.

Fact Sheet↗

USGS2021

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Methods in Ecology and Evolution↗

USGS2025

Small waterbodies of large conservation concern: Towards an integrated approach to more accurately measuring surface water dynamics

Millions of small waterbodies are dispersed throughout the middle of the North American continent, and billions of dollars have been invested to conserve, restore, and manage these waterbodies in the 20th and 21st centuries. Small waterbody conservation has been supported by different stakeholders aiming at improving water quality, enhancing floodwater storage, and supporting migratory bird breeding habitat. Conservation agencies are using hydrological and biological monitoring, modeling, and mapping to adaptively manage small waterbodies in the face of stressors such as invasive species and climate change. As remote sensing estimates of small waterbody surface water extent have become easier to access, understanding the capabilities and limitations of using remote sensing, especially in areas lacking surface water monitoring, is important for conservation decision making. Here, we used in situ monitoring and process-based hydrological modeling to explore remote sensing accuracy, especially related to waterbody size, emergent aquatic vegetation cover, and climatic conditions. Overall, we found that the accuracy of satellite and aerial imagery surface water mapping approaches vastly decreased for waterbodies smaller than 2 ha. We also found emergent vegetation could be masking surface water in waterbodies larger than 2 ha and that accuracy of some remote sensing estimates may decrease during wetter climatic periods. These results indicate that sensors commonly used for surface water applications alone may not be able to accurately detect small waterbody surface water, which supports the need for combining monitoring and modeling to understand how small waterbodies may respond to future changes in climate and land use.

Minnesota↗

USGS2021

Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.

Alaska, Minnesota↗