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USGS2018

Hyperspectral remote sensing of wetland vegetation

Chapter 11 by Ramsey and Rangoonwala provides an overview of how hyperspectral imaging (HSI) advances the mapping of coastal wetlands that comprise a unique variety of plant species, forms, and associations. Each description begins by seeking to uncover the relationship between canopy hyperspectral reflectance and one or more of the aggregated biophysical properties of the wetland canopy: leaf spectral properties, canopy structure, or background reflectance. Examples incorporate the application of radiative transfer equations, direct measurements, and above the top of canopy reflectance and photography. First demonstrated is how HSI can elucidate relations observed with broadband mapping of mangroves as well as enhance change detection. Similar uses of HSI are suggested for the mapping of cypress and bottomland hardwood forests. Next, the mapping of invasive plants is used to demonstrate how HSI can be used to transfer high-spatial-resolution broadband information to spatial resolutions amenable to regional mapping. The demonstration also includes the fusion of HSI and broadband mapping for added-value regional risk assessment of invasive establishment. Illustrations and discussion then show that the proper interpretation of HSI canopy reflectance can require measurement of both the marsh canopy structure and its compositional biomass. The final HSI application demonstrates the capability to detect and track abnormal marsh change at the leaf and canopy levels within variable backgrounds. It further shows how necessary HSI spectral information can be identified and captured in a limited number of narrowbands for advancing operational mapping.

Book chapter↗

USGS1999

Early evolution of a stratospheric volcanic eruption cloud as observed with TOMS and AVHRR

This paper is a detailed study of remote sensing data from the total ozone mapping spectrometer (TOMS) and the advanced very high resolution radiometer (AVHRR) satellite detectors, of the 1982 eruption of El Chichón, Mexico. The volcanic cloud/atmosphere interactions in the first four days of this eruption were investigated by combining ultraviolet retrievals to estimate the mass of sulfur dioxide in the volcanic cloud [ Krueger et al. , 1995] with thermal infrared retrievals of the size, optical depth, and mass of fine-grained (1–10 μm radius) volcanic ash [ Wen and Rose , 1994]. Our study provides the first direct evidence of gravitational separation of ash from a stratospheric, gas-rich, plinian eruption column and documents the marked differences in residence times of volcanic ash and sulfur dioxide in volcanic clouds. The eruption column reached as high as 32 km [ Carey and Sigurdsson , 1986] and was injected into an atmosphere with a strong wind shear, which allowed for an observation of the separation of sulfur dioxide and volcanic ash. The upper, more sulfur dioxide-rich part of the cloud was transported to the west in the stratosphere, while the fine-grained ash traveled to the south in the troposphere. The mass of sulfur dioxide released was estimated at 7.1 × 10 9 kg with the mass decreasing by approximately 4% 1 day after the peak. The mass of fine-grained volcanic ash detected was estimated at 6.5 × 10 9 kg, amounting to about 0.7% of the estimated mass of the ash which fell out in the mapped ash blanket close to the volcano. Over the following days, 98% of this remaining fine ash was removed from the volcanic cloud, and the effective radius of ash in the volcanic cloud decreased from about 8 μm to about 4 μm.

Journal of Geophysical Research D: Atmospheres↗

USGS2006

The USGS Land Cover Institute

The U.S. Geological Survey (USGS) Land Cover Institute (LCI) is located at the Center for Earth Resources Observation and Science (EROS) in Sioux Falls, South Dakota. It provides a focal point for advancing USGS land cover studies and applications. Satellite images and other remotely sensed data play an important role in this research. Land Cover scientists investigate new ways to use satellite images and other data to map land cover. They assess national and global land cover characteristics and monitor how - and how rapidly - land cover changes. They also study the economic impacts of land cover as well as its effects on water quality, the spread of invasive species, habitats and biodiversity, climate variability, and other environmental factors.

Fact Sheet↗

USGS2010

The role of C 3 and C 4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

Time integrated normalized difference vegetation index (ΣNDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989–1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C 3 and C 4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ΣNDVI and the ΣNDVI coefficient of variation (CV ΣNDVI) used as a proxy for interannual climate variability is analysed. Results suggest that the differences in the long-term climatic control over ecosystem performance approximately coincide with changes between C 3 - and C 4 -dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primarily C 4 grasslands), whereas the C 3 -dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ΣNDVI values.

International Journal of Remote Sensing↗

USGS2026

Lava flow emplacement dynamics during the Mauna Loa 2022 eruption

On November 27, 2022, Mauna Loa erupted for the first time in 38 years, threatening a major highway on the Island of Hawaiʻi. The opening phases of the eruption were focused at the volcano’s summit, before magma propagated into the upper Northeast Rift Zone to erupt at four fissure segments that localized at fissure 3 by December 2. This fissure produced a braided ʻaʻā flow that traversed the steep north flank at >1 km/hr, before slowing as slopes shallowed. Effusion rates peaked in the early hours of the eruption and then steadied at around 100 m3/s at fissure 3. Lava effusion became variable and dropped abruptly on December 7. The eruption ended on December 10 with a final flow length of 18 km. We tracked lava effusion rates, flow advance, channel dynamics, and 3-D flow field development with field and remote sensing observations throughout this eruption to inform lava flow forecasts and investigate ʻaʻā flow dynamics. Flows were mapped using optical and infrared imagery, uncrewed aircraft systems, and airborne single-pass InSAR. Video- and time-lapse cameras were deployed to record lava fountaining and channelized flow at frequent intervals, supplemented by field measurements. Analyses of these data built a detailed record of effusion rates and associated flow dynamics. We combine these high frequency observations of the proximal flow field with broader thermal and 3-D mapping of the evolution in the medial and distal flow field to examine ʻaʻā flow dynamics from source to toe. Within the steep, medial section of the flow field, channel blockages caused back-ups and overflows as the eruption progressed. Disrupted lava supply to the distal flow field and low slopes resulted in stalling, widening, and thickening at the toe. Overall, 142 million m3 of lava covered 36 km2 over 13 days. Our results inform the controls on development and evolution of lava flow fields, with implications for forecasting primary flow advance and secondary flow field growth.

Hawaii↗

USGS2021

System characterization report on Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor

Executive Summary This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16 launch vehicle. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management. Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Advanced Wide Field Sensor and LISS–3, as well as the Linear Imaging Self Scanning-4 for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that LISS–3 has an interior geometric performance in the range of −4.620 (−0.154 pixel) to 13.230 meters (m; 0.441 pixel) in easting and −12.360 (−0.412 pixel) to 1.500 m (0.050 pixel) in northing in band-to-band registration, an exterior geometric error of −27.805 (−0.927 pixel) to 26.578 m (0.886 pixel) in easting and −35.341 (−1.178 pixel) to −6.286 m (−0.210 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.096 to 0.036 in offset and 0.585–0.946 in slope, and a spatial performance in the range of 1.87–1.95 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.045–0.070.

Open-File Report↗

USGS2025

RUSH: Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models

Coastal vegetated ecosystems, including tidal marshes, vegetated dunes, and shrub- and forest-dominated wetlands, can mitigate hurricane impacts such as coastal flooding and erosion by increasing surface roughness and reducing wave energy. Land cover maps can be used as input to improve simulations of surface roughness in advanced hydro-morphological models. Consequently, there is a need for efficient tools to develop up-to-date land cover maps that include the accurate distribution of vegetation types prior to an extreme storm. In response, we developed the RUSH tool (Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models). RUSH delivers high-resolution maps of coastal vegetation for near-real-time or historical conditions via a Jupyter Notebook application and a graphical user interface (GUI). The application generates 3 m spatial resolution land cover maps with classes relevant to coastal settings, especially along mainland beaches, headlands, and barrier islands, as follows: (1) open water; (2) emergent wetlands; (3) dune grass; (4) woody wetlands; and (5) bare ground. These maps are developed by applying one of two seasonal random-forest machine learning models to Planet Labs SuperDove multispectral imagery. Cool Season and Warm Season Models were trained on 665 and 594 reference points, respectively, located across study regions in the North Carolina Outer Banks, the Mississippi Delta in Louisiana, and a portion of the Florida Gulf Coast near Apalachicola. Cool Season and Warm Season Models were tested with 666 and 595 independent points, with an overall accuracy of 93% and 94%, respectively. The Jupyter Notebook application provides users with a flexible platform for customization for advanced users, whereas the GUI, designed with user-experience feedback, provides non-experts access to remote sensing capabilities. This application can also be used for long-term coastal geomorphic and ecosystem change assessments.

Florida, Louisiana, North Carolina↗

USGS1993

Integration of environmental simulation models with satellite remote sensing and geographic information systems technologies: case studies

Environmental modelers are testing and evaluating a prototype land cover characteristics database for the conterminous United States developed by the EROS Data Center of the U.S. Geological Survey and the University of Nebraska Center for Advanced Land Management Information Technologies. This database was developed from multi temporal, 1-kilometer advanced very high resolution radiometer (AVHRR) data for 1990 and various ancillary data sets such as elevation, ecological regions, and selected climatic normals. Several case studies using this database were analyzed to illustrate the integration of satellite remote sensing and geographic information systems technologies with land-atmosphere interactions models at a variety of spatial and temporal scales. The case studies are representative of contemporary environmental simulation modeling at local to regional levels in global change research, land and water resource management, and environmental simulation modeling at local to regional levels in global change research, land and water resource management and environmental risk assessment. The case studies feature land surface parameterizations for atmospheric mesoscale and global climate models; biogenic-hydrocarbons emissions models; distributed parameter watershed and other hydrological models; and various ecological models such as ecosystem, dynamics, biogeochemical cycles, ecotone variability, and equilibrium vegetation models. The case studies demonstrate the important of multi temporal AVHRR data to develop to develop and maintain a flexible, near-realtime land cover characteristics database. Moreover, such a flexible database is needed to derive various vegetation classification schemes, to aggregate data for nested models, to develop remote sensing algorithms, and to provide data on dynamic landscape characteristics. The case studies illustrate how such a database supports research on spatial heterogeneity, land use, sensitivity analysis, and scaling issues involving regional extrapolations and parameterizations of dynamic land processes within simulation models.

Pecora 12 Symposium↗

USGS2026

Advances and applications of Unoccupied Aerial Systems (UAS) research in landscape ecology

Landscape ecologists have long depended on satellite and aerial remote sensing to address questions about landscape pattern and process, structure, and change (Foody 2023 ). Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) technology is becoming an increasingly popular research tool in environmental sciences allowing scientists to generate low-cost, high-quality, and high-resolution imagery on demand that can be tailored to specific research questions. While satellite data are of a fixed resolution and temporal interval, UAS offer researchers control and flexibility to design studies and collect data at resolutions and scales that provide ecologically relevant information at finer spatial resolutions (e.g., < 30 cm) than what is currently available from satellite platforms (typically > 3m), thus helping capture objects such as individual plant canopies, micro-topography, and individual animals. Unlike satellites with fixed orbits, UAS can be deployed at more optimal temporal frequencies for ecological monitoring. We organized the special collection “Advances and Applications of Unoccupied Aerial Systems (UAS) Research in Landscape Ecology” to showcase the many ways that UAS tools and technologies are currently applied to advance landscape ecological research. When we announced the collection in 2023, only 11 papers published in the journal Landscape Ecology used UAS data, which was a notably small number compared to many other general ecology, environmental science and remote sensing journals. In an attempt to understand why UAS were not more widely used in landscape ecology and provide possible solutions, we published a review article (Villarreal et al. 2025) that identified the challenges, knowledge gaps, and obstacles for the adoption of UAS technologies in landscape ecology research. The main issues we identified include: (1) an abundance of UAS methods papers in the existing literature, with comparatively few studies demonstrating how UAS can be applied to address ecological questions; (2) a perceived scale mismatch between the geographic extent of UAS data collection (local) compared to larger study areas (landscapes) and a need to design robust scaling approaches to connect fine-scale UAS data with broader ecological patterns; and (3) a need for improved integration of UAS data with other commonly used remote sensing datasets including historical high resolution aerial imagery. Additionally, researchers new to UAS remote sensing may be discouraged or overwhelmed by the general lack of scientific consensus and standardized protocols for typical tasks such as data collection, vegetation classification, and change detection, as well as restrictive and/or confusing policy, regulatory, and legal issues surrounding UAS operations (Villarreal et al. 2025).

Landscape Ecology↗

USGS2009

NASA and USGS invest in invasive species modeling to evaluate habitat for Africanized Honey Bees

Invasive non-native species, such as plants, animals, and pathogens, have long been an interest to the U.S. Geological Survey (USGS) and NASA. Invasive species cause harm to our economy (around $120 B/year), the environment (e.g., replacing native biodiversity, forest pathogens negatively affecting carbon storage), and human health (e.g., plague, West Nile virus). Five years ago, the USGS and NASA formed a partnership to improve ecological forecasting capabilities for the early detection and containment of the highest priority invasive species. Scientists from NASA Goddard Space Flight Center (GSFC) and the Fort Collins Science Center developed a longterm strategy to integrate remote sensing capabilities, high-performance computing capabilities and new spatial modeling techniques to advance the science of ecological invasions [Schnase et al., 2002].

The Earth Observer↗

USGS2019

Remote sensing of tracer dye concentrations to support dispersion studies in river channels

In river channels the flow field influences the dispersion of biota, contaminants, and other suspended or dissolved materials. Insight on patterns and rates of dispersion can be gained by injecting a pulse of visible dye and observing spatial and temporal variations in dye concentration as the pulse moves downstream. We evaluated the potential of passive optical remote sensing to enhance such tracer experiments by providing spatially distributed concentration information. During tests performed in both an experimental flume facility and a large natural channel, we made field measurements of Rhodamine WT dye concentration and above-water spectral reflectance. At Korea's River Experiment Center, a small unmanned aircraft system (sUAS) was used to acquire hyperspectral images of a sinuous outdoor flume. On the Kootenai River in northern Idaho, USA, field spectra were collected from a boat and hyperspectral image data and high resolution aerial photographs were obtained from manned aircraft. We modified an Optimal Band Ratio Analysis (OBRA) algorithm to identify wavelength combinations that yielded strong correlations between a spectrally based quantity X and dye concentration C. For both the flume and field tests, we obtained very strong (R^2 from 0.94 to 0.99) relationships between X and C across a broad range of visible wavelengths. On the Kootenai, we found that X vs. C relations derived from field spectra could be applied to airborne hyperspectral images and that dye concentrations could be estimated nearly as reliably from relatively simple three-band images as from hyperspectral data. These results imply that remote sensing could become a powerful tool for mapping dye patterns. Such a capability would advance our understanding of dispersion processes by enabling more rigorous testing of numerical flow models.

Journal of Ecohydraulics↗

USGS2008

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Overview

The National Aeronautics and Space Administration (NASA) launched Terra, the Earth Observing System's (EOS) flagship satellite platform on December 18, 1999. The polar-orbiting Terra contains five remote sensing instruments, which enable the scientific study and analyses of global terrestrial processes and manifestations of global change. One of the five instruments is the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which is built in Japan by a consortium of government, industry, and research groups. It has three spectral bands in the visible near-infrared region (VNIR), six bands in the shortwave infrared region (SWIR), and five bands in the thermal infrared region (TIR), with 15-, 30-, and 90-meter ground resolutions, respectively. This combination of wide spectral coverage and high spatial resolution allows ASTER to discriminate among a wide variety of surface materials. The VNIR subsystem also has a backward-viewing telescope for high-resolution (15-meter) stereoscopic observation in the along-track direction, which facilitates the generation of digital elevation models (DEM).

Fact Sheet↗

USGS2012

An economic value of remote-sensing information—Application to agricultural production and maintaining groundwater quality

Does remote-sensing information provide economic benefits to society, and can a value be assigned to those benefits? Can resource management and policy decisions be better informed by coupling past and present Earth observations with groundwater nitrate measurements? Using an integrated assessment approach, the U.S. Geological Survey (USGS) applied an established conceptual framework to answer these questions, as well as to estimate the value of information (VOI) for remote-sensing imagery. The approach uses moderate-resolution land-imagery (MRLI) data from the Landsat and Advanced Wide Field Sensor satellites that has been classified by the National Agricultural Statistics Service into the Cropland Data Layer (CDL). Within the constraint of the U.S. Environmental Protection Agency's public health threshold for potable groundwater resources, the USGS modeled the relation between a population of the CDL's land uses and dynamic nitrate (NO3-) contamination of aquifers in a case study region in northeastern Iowa. Employing various multiscaled, multitemporal geospatial datasets with MRLI to maximize the value of agricultural production, the approach develops and uses multiple environmental science models to address dynamic nitrogen loading and transport at specified distances from specific sites (wells) and at landscape scales (for example, across 35 counties and two aquifers). In addition to the ecosystem service of potable groundwater, this effort focuses on the use of MRLI for the management of the major land uses in the study region-the production of corn and soybeans, which can impact groundwater quality. Derived methods and results include (1) economic and dynamic nitrate-pollution models, (2) probabilities of the survival of groundwater, and (3) a VOI for remote sensing. For the northeastern Iowa study region, the marginal benefit of the MRLI VOI (in 2010 dollars) is $858 million ±$197 million annualized, which corresponds to a net present value of $38.1 billion ±$8.8 billion for that flow of benefits in perpetuity. Given that these economic estimates are derived from one case study in a part of only one State, the estimates provide a lower estimate related to the potential value of the Landsat Data Continuity Mission.

Professional Paper↗

USGS1981

The Earth Resources Observation Systems data center's training technical assistance, and applications research activities

The Earth Resources Observation Systems (EROS) Data Center (EDO, administered by the U.S. Geological Survey, U.S. Department of the Interior, provides remotely sensed data to the user community and offers a variety of professional services to further the understanding and use of remote sensing technology. EDC reproduces and sells photographic and electronic copies of satellite images of areas throughout the world. Other products include aerial photographs collected by 16 organizations, including the U.S. Geological Survey and the National Aeronautics and Space Administration. Primary users of the remotely sensed data are Federal, State, and municipal government agencies, universities, foreign nations, and private industries. The professional services available at EDC are primarily directed at integrating satellite and aircraft remote sensing technology into the programs of the Department of the Interior and its cooperators. This is accomplished through formal training workshops, user assistance, cooperative demonstration projects, and access to equipment and capabilities in an advanced data analysis laboratory. In addition, other Federal agencies, State and local governments, universities, and the general public can get assistance from the EDC Staff. Since 1973, EDC has contributed to the accelerating growth in development and operational use of remotely sensed data for land resource problems through its role as educator and by conducting basic and applied remote sensing applications research. As remote sensing technology continues to evolve, EDC will continue to respond to the increasing demand for timely information on remote sensing applications. Questions most often asked about EDC's research and training programs include: Who may attend an EDC remote sensing training course? Specifically, what is taught? Who may cooperate with EDC on remote sensing projects? Are interpretation services provided on a service basis? This report attempts to define the goals and objectives of and policies on the following EDC services: Training Program. User Assistance. Data Analysis Laboratory. Cooperative Demonstration Projects. Research Projects.

Open-File Report↗

USGS2005

A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced classification algorithms.

Remote Sensing of Environment↗

USGS2020

Scenarios for valuing sample information in natural resources

Uncertainty is ubiquitous in natural resource systems, science and management. Sample data are obtained in order to reduce uncertainty, thereby increasing knowledge and improving resource management, but sampling always comes at a cost of some sort. Is that cost worthwhile? Analysis of the value of sample information ( VSI ) addresses this question. In this paper we develop the valuation of sample information in terms of five elements: (a) a system whose attributes are the focus of analysis; (b) a range of management actions that affect the system's status; (c) uncertainty about system status or structure, as characterized by initial (prior) probabilities of possible system states or structures; (d) an experiment or other information source that produces new data points and updated (posterior) probabilities; and (e) a value measure that is a function of the management action taken, conditional on either the system state or structure. We describe five scenarios for analysing the VSI under uncertainty about system structure and state. Scenarios 1–3 comprise analyses of conditional, expected and optimal expected values of sample information. They focus primarily on choice of management adaptations with new information. Scenarios 4 and 5 involve pre-selected management actions, and are useful for comparing designs of data collection rather than for choosing a management action. These last scenarios expand the framework for VSI to include actions that have been selected independently of the updating of uncertainty. We discuss other extensions of VSI analysis, which include spatial applications, hybrid scenarios, applications involving dynamic systems, and a focus on costs rather than net benefits. Value of sample information analysis holds promise in emerging areas of ecology such as ecological forecasting and the use of remote sensing in conservation, where potential new data from models and satellites can be evaluated in advance, thereby allowing more efficient prioritization of scientific efforts. More generally, VSI can contribute to better ecological understanding and more effective management in a wide range of ecological situations.

Methods in Ecology and Evolution↗

USGS2009

Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies

For two decades, remotely sensed data from imaging spectrometers have been used to estimate non-pigment biochemical constituents of vegetation, including water, nitrogen, cellulose, and lignin. This interest has been motivated by the important role that these substances play in physiological processes such as photosynthesis, their relationships with ecosystem processes such as litter decomposition and nutrient cycling, and their use in identifying key plant species and functional groups. This paper reviews three areas of research to improve the application of imaging spectrometers to quantify non-pigment biochemical constituents of plants. First, we examine recent empirical and modeling studies that have advanced our understanding of leaf and canopy reflectance spectra in relation to plant biochemistry. Next, we present recent examples of how spectroscopic remote sensing methods are applied to characterize vegetation canopies, communities and ecosystems. Third, we highlight the latest developments in using imaging spectrometer data to quantify net primary production (NPP) over large geographic areas. Finally, we discuss the major challenges in quantifying non-pigment biochemical constituents of plant canopies from remotely sensed spectra.

Remote Sensing of Environment↗

USGS2025

Absolute radiometric calibration evaluation of Uncrewed Aerial System (UAS) Headwall and MicaSense sensors and improving data quality using the Empirical Line Method

The use of Uncrewed Aerial Systems (UASs) for remote sensing applications has increased significantly in recent years due to their low cost, operational flexibility, and rapid advancements in sensor technologies. In many cases, UAS platforms are considered viable alternatives to conventional satellite and crewed airborne platforms, offering very high spatial, spectral, and temporal resolution data. However, the radiometric quality of UAS-acquired data has not received equivalent attention, particularly with respect to absolute calibration. In this study, we (1) evaluate the absolute radiometric performance of two commonly used UAS sensors: the Headwall Nano-Hyperspec hyperspectral sensor and the MicaSense RedEdge-MX Dual Camera multispectral system; (2) assess the effectiveness of the Empirical Line Method (ELM) in improving the radiometric accuracy of reflectance products generated by these sensors; and (3) investigate the influence of calibration target characteristics—including size, material type, reflectance intensity, and quantity—on the performance of ELM for UAS data. A field campaign was conducted jointly by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the USGS National Uncrewed Systems Office (NUSO) from 15 to 18 July 2023, at the USGS EROS Ground Validation Radiometer (GVR) site in Sioux Falls, South Dakota, USA, over a 160 m × 160 m vegetated area. Absolute calibration accuracy was evaluated by comparing UAS sensor-derived reflectance to in situ measurements of the site. Results indicate that the Headwall Nano-Hyperspec and MicaSense sensors underestimated reflectance by approximately 0.05 and 0.015 reflectance units, respectively. While the MicaSense sensor demonstrated better inherent radiometric accuracy, it exhibited saturation over bright targets due to limitations in its automatic gain and exposure settings. Application of the ELM using just two calibration targets reduced discrepancies to within 0.005 reflectance units. Reflectance products generated using various target materials—such as felt, melamine, or commercially available validation targets—showed comparable agreement with in situ measurements when used with the Nano-Hyperspec sensor. Furthermore, increasing the number of calibration targets beyond two did not yield measurable improvements in calibration accuracy. At a flight altitude of 200 ft above ground level (AGL), a target size of 0.6 m × 0.6 m or larger was sufficient to provide pure pixels for ELM implementation, whereas smaller targets (e.g., 0.3 m × 0.3 m) posed challenges in isolating pure pixels. Overall, the standard manufacturer-recommended calibration procedures were insufficient for achieving high radiometric accuracy with the tested sensors, which may restrict their applicability in scenarios requiring greater accuracy and precision. The use of the ELM significantly improved data quality, enhancing the reliability and applicability of UAS-based remote sensing in contexts requiring high precision and accuracy.

Remote Sensing↗