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At least 253 records · Page 14Linked to original sources
USGS2023

Modeling global indices for estimating non-photosynthetic vegetation cover

Non-photosynthetic vegetation (NPV) includes plant litter, senesced leaves, and crop residues. NPV plays an essential role in terrestrial ecosystem processes, and is an important indicator of drought severity, ecosystem disturbance, agricultural resilience, and wildfire danger. Current moderate spatial resolution multispectral satellite systems (e.g., Landsat and Sentinel-2) have only a single band in the 2000–2500 nm shortwave infrared “SWIR2” range where non-pigment biochemical constituents of NPV, including cellulose and lignin, have important spectral absorption features. Thus, these current systems have suboptimal capabilities for characterizing NPV cover. This research used simulated spectral mixtures accounting for variability among NPV and soils to evaluate globally-appropriate hyperspectral and multispectral indices for estimation of fractional NPV cover. The Continuum Interpolated NPV Depth Index (CINDI), a weighted ratio index measuring lignocellulose absorption near 2100 nm, was found to produce the lowest error in estimating NPV cover. CINDI was less sensitive to variability in soil spectra and green vegetation cover than competing indices. While CINDI was sensitive to the relative water content of soil and NPV, this sensitivity allowed for correcting error in estimated NPV cover as water content increased. CINDI bands were less capable than Dual Absorption NPV Index (DANI) bands for maintaining continuity with the heritage Landsat SWIR2 band, but combining multiple CINDI bands demonstrated adequate continuity. Three SWIR2 bands with band centers at 2038, 2108, and 2211 nm can provide superior capabilities for future moderate resolution multispectral/superspectral systems targeting NPV monitoring, including the next generation Landsat mission (Landsat Next). These bands and the associated CINDI index provide potential for global NPV monitoring using a constellation of future superspectral sensors and imaging spectrometers, with applications including improving soil management, preventing land degradation, evaluating impacts of drought, mapping ecosystem disturbance, and assessing wildfire danger.

Remote Sensing of Environment↗
USGS2024

Need and vision for global medium-resolution Landsat and Sentinel-2 data products

Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and policy development. The current constellation of multiple Landsat and Sentinel-2 satellites collecting imagery at predominantly ≤ "> ≤ 30-m spatial resolution affords an opportunity for the generation of global medium- resolution products every few days. Our goal is to both identify the information needs and provide direction towards the generation of a suite of global, high-level, systematically-generated, medium-resolution products designed for both management and science. Our vision builds on the success of the NASA MODIS/VIIRS product suite, while recognizing the unique strengths of medium-resolution satellite data given their higher spatial resolution and longer time series. We propose a suite of 13 essential products that enable the characterization of the current state and changes in the biosphere, cryosphere, and hydrosphere, and would fill information needs identified by the Committee on Earth Observation Satellites for the Global Climate Observing System and the Global Terrestrial Observing System, by the National Research Council of the US National Academies in the decadal survey, and by others. These products are: land cover, land cover change, burned area, forest loss, vegetation indices, phenology, dynamic habitat indices, albedo, land surface temperature, snow cover, ice extent, surface water extent, and evapotranspiration. Furthermore, we provide a list of desirable products poised for addition to the essential products (e.g., crop type, emissivity, and ice sheet velocity). Lastly, we suggest aspirational products requiring further algorithm development (e.g., forest structure and crop yield). For the identified essential products, algorithms are in place, making it feasible to begin generating products systematically. These products should be accompanied by quality and accuracy assessments undertaken following consensus protocols. Five decades after the first Landsat satellite, and two decades after the MODIS products were first produced, it is time now for readily available, standardized, and consistent high-level products built upon medium-resolution imagery, thereby fulfilling the promise and the vision that inspired the Landsat program since its inception.

Remote Sensing of Environment↗
USGS2024

Co-registration accuracy between Landsat-8 and Sentinel-2 orthorectified products

Landsat orthorectified products use Ground Control Points (GCPs) and Digital Elevation Models (DEM) to improve the geolocation accuracy and temporal consistency, and to account for the relief displacements due to the sensor-target geometry. In Collection-2, to improve the geometric harmonization between Landsat and Sentinel-2 (S2) orthorectified products, the Landsat GCP's absolute and relative accuracies were improved using the S2 Global Reference Image (GRI) dataset through a continent-level bundle adjustment method. The GRI is a highly accurate global image dataset that was developed by the European Space Agency (ESA) to improve the S2 multi-temporal geolocation accuracy. Since late August 2021, ESA has been using the GRI dataset in the geometric refinement process to generate S2 terrain-corrected (L1C) products. This paper presents the co-registration accuracy between the Landsat-8 (L8) Collection-2 terrain-corrected products and the S2 L1C products that were processed with and without the use of the GRI dataset. The image-to-image registration (I2I) analysis performed between the L8 and S2 data products over a set of globally distributed tiles shows a significant improvement in their co-registration accuracy when GRI is used in the S2 L1C product generation. The co-registration error is estimated to be <6 m circular error at 90% probability (CE90) when GRI is used, and >12 m CE90 when GRI is not used in the S2 product generation process. A similar I2I analysis was conducted between S2 L1C products, L8 L1TP products, and L8 and Landsat 9 (L9) L1TP products. The analysis shows that the S2 L1C products are co-registered with each other temporally to better than 5.1 m CE90 when GRI is used. The L8 L1TP products and L8 versus L9 L1TP products are both co-registered temporally to better than 3 m CE90.

Remote Sensing of Environment↗
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↗
USGS2024

Snow-cover remote sensing of conifer tree recovery in high-severity burn patches

The number of large, high-severity wildfires has been increasing across the western United States over the last several decades. It is not fully understood how changes in the frequency of large, severe wildfires may impact the resilience of conifer forests, due to alterations in regeneration success or failure. Our research investigates 30 years of conifer recovery patterns within 34 high-severity wildfire complexes (1988–1991) of the Northern Rocky Mountains. We evaluate the capability of snow-cover Landsat to characterize conifer tree recolonization of high-severity burn patches. Snow-cover images isolate conifer-specific vegetation signals by diminishing spectral contributions from soil and deciduous vegetation. The presence of conifer regeneration was successfully classified by snow-cover Landsat at >10% canopy cover at 98% accuracy and modeled at 3-year intervals post-fire. Spectral detectability of regenerating conifer vegetation began 11–19 years post-fire, varying across forest types. Thirty years post-fire, 65% of the total high-severity burn area had been recolonized by conifer trees, with differences observed between forest types: 72% of lodgepole pine, 77% of Douglas-fir, and 44% of fir-spruce severely burned areas containing conifer regeneration. Projected recovery timelines to pre-fire conifer vegetation also differed between lodgepole pine (29.5 years), Douglas-fir (36.9 years), and fir-spruce forests (48.7 years), as estimated from snow-cover NDVI trends. Although we generally documented patterns of conifer resilience, we also identified reduced likelihoods of recovery within high-severity burn patches exhibiting greater area-to-perimeter ratios, aridity, south-facing aspects, slopes, and elevation. Snow-cover Landsat imagery was shown to improve the characterization of post-fire forest recovery and may be applied to support forest restoration decision-making following high-severity wildfire.

Remote Sensing of Environment↗
USGS2024

An accuracy assessment of the surface reflectance product from the EMIT imaging spectrometer

The Earth surface Mineral dust source InvesTigation (EMIT) is an imaging spectrometer launched to the International Space Station in July 2022 to measure the mineral composition of Earth’s dust-producing regions. We present a systematic accuracy assessment of the EMIT surface reflectance product in two parts. First, we characterize the surface reflectance product’s overall performance using multiple independent vicarious calibration field experiments with hand-held and automated field spectrometers. We find that the EMIT surface reflectance product has a standard error of ±1.0 % in absolute reflectance units for temporally coincident observations. Discrepancies rise to ±2.7 % for spectra acquired at different dates and times of day, which we attribute mainly to changes in solar geometry. Second, we develop an error budget that explains the differences between EMIT and in-situ field spectrometer data. We find that uncertainties in spatial footprints, field spectroscopy, and the EMIT-reported measurement were sufficient to explain discrepancies in most cases. Our approach did not detect any systematic calibration or reflectance errors in the timespan considered. Together, these findings demonstrate that a space-based imaging spectrometer can acquire high-quality spectra across a wide range of observational and atmospheric conditions.

Nevada↗
USGS2024

Tracking mangrove condition changes using dense Landsat time series

Mangroves in tropical and subtropical coasts are subject to episodic disturbances, notably from severe storms, leading to potential widespread vegetation mortality. The ability of vegetation to recover varies, and with disturbances becoming more frequent and severe, it is vital to track and project vegetation responses to support management and policy decisions. Prior studies have largely focused on binary mangrove mapping (i.e., presence or absence), while tracking conditions and condition change have not received sufficient attention. In this paper, we demonstrate a method based on dense time series Landsat images for continuous monitoring of mangrove conditions, where we track three kinds of post-disturbance mangrove conditions, including disturbed (disturbed, with rebound to the previous state within one growing season), recovering (undergoing natural recovery in longer than one growing season), and declining (showing long-term decline after disturbance). The method starts with disturbance detection using the DEtection and Characterization Of the tiDal wEtland change (DECODE) algorithm, an existing dense time series model designed to detect disturbances in tidal wetlands with adaptation to tidal fluctuations. This algorithm is well suited for the detection of tidal wetland disturbances but does not provide satisfactory post-disturbance monitoring results, due to the substantial variability in post-disturbance Landsat observations. To better monitor post-disturbance conditions, a new time series fitting approach, DECODER (DECODE and Recovery), is proposed for the recovery stage. Additionally, for temporal segments divided by disturbance events, we built a random forest classifier with temporal-spectral variables derived from the time series model to characterize mangrove conditions. Employing this approach in Florida's mangroves, we generated condition maps, such as dieback and recovery, with an overall accuracy of approximately 97.96 ± 0.86- [95 % confidence intervals]. Comparing post-hurricane conditions in Florida revealed that the increased frequency and severity of disturbances are challenging mangrove resilience, potentially diminishing their ability to recover and sustain ecosystem functions.

Florida↗
USGS2024

Inventorying ponds through novel size-adaptive object mapping using Sentinel-1/2 time series

Ponds are an important source of greenhouse gases (GHGs) to the atmosphere, yet evaluating their role in global biogeochemical cycling is currently hampered by limitations in quantifying their global distribution. Existing satellite-derived estimates of lake distributions have difficulty identifying small lakes (5–10 ha) and ponds (<5 ha) due to limitations in satellite resolution and challenges extracting individual small waterbodies from low-albedo surfaces, vegetated water, and lotic water systems including rivers and streams. In this study, we developed generalizable pond mapping strategies based on their spatial-temporal-spectral characteristics to fully exploit accessible medium-resolution optical and synthetic aperture radar (SAR) time series to identify ponds. Our novel approach entails: (1) making full use of ponds' characteristics from an object-based perspective; (2) extracting pond objects using seeds of prominent water pixels defined by the SAR VH signal; (3) constructing training samples of ponds with high representativeness; and (4) improving inter-class discrimination by combining features from optical and SAR data. We designed a novel Optical-SAR Pond Object Mapper (OptiSAR-POM) to achieve an improved estimate of pond size distribution by incorporating mapping strategies into the object-based image analysis framework. We generated landscape objects through an elaborate water-focused segmentation approach, which adaptively aligned the segmentation parameters with the size and distribution patterns of ponds to identify small waterbodies and increase inter-class variability. We further introduced an interactive learning process to construct random forests for object-based classification, which incorporated adaptive empirical thresholds to identify potential pond objects and select representative training samples of varying sizes. We tested the OptiSAR-POM framework using Sentinel-1/2 time series at three county-level study sites and three supplementary watershed-level study sites in the United States and China. Our approach yielded high overall accuracy (>95 %) for all sites and highlighted the ability of Sentinel-1/2 imagery to accurately detect small ponds (0.1–1 ha) across diverse landscapes. The average producer's accuracy for small ponds at county-level sites improved by ∼45 % compared to that of all other products with a 10-m or higher spatial resolution, addressing the absence of such information in existing regional and global datasets. The generated county-level pond maps revealed the numerical dominance of ponds in lentic waters, their substantial area contribution in human-impacted regions, and the relevance of studying biogeochemical processes in smaller waterbodies.

Remote Sensing of Environment↗
USGS2025

The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset

Frequent multispectral observations of sufficient spatial detail from well-calibrated spaceborne sensors are needed for large-scale terrestrial monitoring. To meet this demand, the NASA Harmonized Landsat and Sentinel-2 (HLS) project was initiated in early 2010s to produce comparable 30-m surface reflectance from the US Landsat 8 Operational Land Imager (OLI) and the European Copernicus Sentinel-2A MultiSpectral Instrument (MSI), and currently from two OLI and two MSI sensors, by applying atmospheric correction to top-of-atmosphere (TOA) reflectance, masking out clouds and cloud shadows, normalizing bi-directional reflectance view angle effects, adjusting for sensor bandpass differences with the OLI as the reference, and providing the harmonized data in a common grid. Several versions of HLS dataset have been produced in the last few years. The recent improvements on almost all the harmonization algorithms had prompted a production of a new HLS dataset, tagged Version 2.0, which was completed in the summer of 2023 and for the first time takes on a global coverage (except for Antarctica). The HLS V2.0 data record starts in April 2013, two months after Landsat 8 launch. For 2022, the first whole year two Landsat and two Sentinel-2 satellites were available, HLS provides a global median of 66 cloud-free observations over land, substantially more than from a single sensor. This paper describes the HLS algorithm improvements and assesses the harmonization efficacy by examining how the reflectance difference between contemporaneous Landsat and Sentinel-2 observations was successively reduced by each harmonization step. The assessment was conducted on 545 pairs of globally distributed same-day Landsat/Sentinel-2 images from 2021 to 2022. Compared to the TOA data, the HLS atmospheric correction slightly increased the reflectance relative difference between Landsat and Sentinel-2 for most of the spectral bands, especially for the two blue bands and the green bands. The subsequent bi-directional reflectance view angle effect normalization effectively reduced the between-sensor reflectance difference present in the atmospherically corrected data for all the spectral bands, and notably to a level below the TOA differences for the red, near-infrared (NIR), and the two shortwave infrared (SWIR) bands. The bandpass adjustment only had a modest effect on reducing the between-sensor reflectance difference. In the final HLS products, the same-day reflectance difference between Landsat and Sentinel-2 was below 4.2% for the red, NIR, and the two SWIR bands, all smaller than the difference in the TOA data. However, the between-sensor differences for the two blue and the green bands remain slightly higher than in TOA data, and this reflects the difficulty in accurately correcting for atmospheric effects in the shorter wavelength visible bands. The data consistency evaluation on a suite of commonly used vegetation indices (VI) calculated from the HLS V2.0 reflectance data showed that the between-sensor VI difference is below 4.5% for most of the indices. These results suggest that the harmonization is robust and the HLS V2.0 data are adequate for quantitative terrestrial applications.

Remote Sensing of Environment↗
USGS2025

Toward a near-lossless image compression strategy for the NASA/USGS Landsat Next mission

As orbiting Earth imaging platforms carry more complex and capable instruments, efficient methods are needed to reduce the time and cost associated with storing and downlinking greater volumes of image data. The upcoming NASA/USGS Landsat Next mission, with an increase in spatial and spectral resolution over previous Landsat missions, is no exception. Landsat Next will produce nearly six times the amount of image data per day over either of the current Landsat 8 or Landsat 9 observatories. Near-lossless compression, where the image after compression is not identical to the original image, allows for the efficient storage and transmission of all image data while meeting the mission’s global coverage, temporal revisit frequency, and science measurement and performance requirements. Although the Landsat user community is understandably cautious about lossy compression, it is possible to constrain the maximum loss, or error, introduced during compression, ensuring that any added error remains within the intrinsic noise level of the instrument. The Consultative Committee for Space Data Systems image compression standard, CCSDS 123.0-B-2, was chosen for the Landsat Next mission because it is an internationally supported standard suited for integration with space hardware, and it allows control over the magnitude and distribution of compression error. Using several proxy datasets as a surrogate for Landsat Next image data, an investigation was performed to determine a preliminary set of parameter values that would keep the added compression error within acceptable limits. The results of these studies demonstrate that near-lossless image compression can be utilized by the Landsat Next instruments to store and downlink all science data without compromising image quality or mission requirements.

Remote Sensing of Environment↗
USGS2026

Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data

We introduce a fully automated and scalable method for mapping surface water extents from single-acquisition Sentinel-1 synthetic aperture radar (SAR) imagery. This approach integrates adaptive thresholding of radiometric terrain-corrected SAR backscatter data, fuzzy-logic classification, region growing, dark land estimation, and a bimodality test to minimize false positives in low-backscattering areas and false negatives in high-backscattering areas. By combining these steps, the algorithm achieves classification accuracies exceeding 85% in detecting surface water extents across diverse environmental conditions. Accuracy was first assessed at meter scale using 52 PlanetScope scenes acquired worldwide in September–October 2019; the algorithm achieved 93% overall accuracy, 86% user's accuracy, and 94% producer's accuracy. Global robustness was then evaluated by processing every Sentinel-1 acquisition from 1 to 12 November 2023 and cross-comparing the resulting maps with 6561 temporally matched observational products for end-users from remote sensing analysis (OPERA) dynamic surface water extent from Harmonized Landsat and Sentinel-2 (DSWx-HLS) products. This large-scale test yielded 90% user's and 94% producer's accuracies, confirming reliable performance at continental extent. Additional case studies demonstrate the algorithm's ability to handle surface water extent in sand-dominated deserts, to track seasonal amplitude in Folsom Lake (California), drought-induced loss in Cerro Prieto Reservoir (Mexico), and rapid filling of the Grand Ethiopian Renaissance Dam. These results show that the method scales across local to global domains and maintains high accuracy, providing a practical tool for near-real-time monitoring of floods, droughts, and water-resource management. Because the approach is sensor-agnostic, it can be ported to forthcoming L- and S-band missions such as NASA-ISRO synthetic aperture radar (NISAR), broadening its applicability to future hydrologic observations.

Remote Sensing of Environment↗
USGS2026

Hyperspectral retrieval of phytoplankton absorption and community composition from NASA’s PACE-OCI in estuarine–coastal waters using a hybrid framework combining mixture-of-experts and Variational Autoencoder

Retrieving the phytoplankton absorption coefficient (a phy ; m−1), one of the most spectrally rich inherent optical properties, remains challenging in optically complex coastal waters worldwide. Leveraging NASA's new hyperspectral mission, PACE, we introduce Hyper-MoE-VAE, a deep-learning architecture that integrates a Mixture-of-Experts with a Variational Autoencoder to retrieve high-dimensional a phy and subsequent estimation of phytoplankton community composition (PCC) from PACE-OCI hyperspectral remote sensing reflectance (R rs ). Pre-trained on global hyperspectral bio-optical datasets and fine-tuned using regional field R rs –a phy pairings from inland– estuarine–coastal waters, Hyper-MoE-VAE demonstrated strong transferability and effective adaptation across regions. Validation with in-situ Rrs showed accurate aphy retrievals in Lake Erie (NRMSE = 0.12, ε = 17.10), Lake Pontchartrain (NRMSE = 0.11, ε = 37.12), and the Barataria–Terrebonne Estuary (NRMSE = 0.14, ε = 38.89). Using same-day PACE-OCI Level 2 Rrs, the model achieved comparable performance in Lake Erie (NRMSE = 0.19, ε = 55.19), Lake Pontchartrain (NRMSE = 0.14, ε = 51.39), and the Barataria–Terrebonne Estuary (NRMSE = 0.17, ε = 47.92). Hyper-MoE-VAE derived PACE-OCI hyperspectral aphy was further decomposed against mass-specific absorption spectra to estimate group-specific contributions to total chlorophyll a. The resulting PCC showed strong agreement with HPLC–CHEMTAX in Lake Erie ( R 2 = 0.692) and Gulf estuarine–coastal systems ( R 2 = 0.732). Monte Carlo noise experiments further revealed group-dependent sensitivities, with diatoms and dinoflagellates showing moderate susceptibility to noise, while cyanobacteria and cryptophytes exhibited narrow uncertainty distributions. These results demonstrate Hyper-MoE-VAE's capability for regional, operational water-quality monitoring with PACE-OCI and its adaptability to current and future hyperspectral missions.

Great Lakes, Lake Pontchartrain↗
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↗
USGS2026

Advances in volcano monitoring driven by the first decade of Sentinel-1 observations

Sentinel-1 has transformed how satellite radar data (SAR and InSAR) are used in volcanology. The systematic, long-term archive and open-access policy means that volcano observatories and research organisations have invested in integrating Sentinel-1 datasets into their monitoring systems. We identify 233 high priority volcanoes and estimate that Sentinel-1 data has been used in peer-reviewed publications for 90 of them. We examine a global archive of 3.3 million automatically processed Sentinel-1 interferograms of volcanoes and use machine learning methods to identify eruptions and periods of unrest. We then review the ways in which InSAR data are being used in different contexts. At frequently erupting basaltic systems in Iceland, Hawaiʻi, the Galápagos , and Piton de la Fournaise, InSAR has become an effective monitoring tool and is integrated with other datasets and models to forecast magma pathways. For large explosive eruptions, deformation measurements often remain challenging, but SAR backscatter is increasingly used to map damaging flows and measure the changing shape of ocean islands. Sentinel-1's long archive provides critical baseline measurements that are vital for measuring slow deformation, capturing new periods of unrest and providing fresh insights into subsurface dynamics. Understanding the drivers of deformation remains challenging and typically relies on integration with external datasets. Future European Space Agency missions have the potential to improve both resolution and coverage providing an even richer dataset to further enhance global volcano monitoring

Erta Ale volcano↗
USGS2026

Mapping winter cover crops using remote sensing of crop phenology

Winter cover crops (WCC) are an effective agricultural conservation practice for improving soil health and water quality. Several U.S. states have established incentive programs to promote planting WCC between cash crop rotations. In recent years, the area planted with WCC has steadily increased due to these efforts, although the planting area varies among states. Since these programs are largely led by individual counties or state programs, there is no centralized data repository that quantifies the total WCC planted annually or their locations. Therefore, there is a practical need to identify fields that have been planted with WCC, as well as to quantify the total planted area both during and after the growing season. Furthermore, incentive programs often require verification that WCC were planted to release payments to participating growers in a timely manner. Satellite remote sensing time series can provide information on WCC growth status. However, it remains unclear what kind of WCC the remote sensing data detects. For example, separating WCC from weeds and winter commodity crops remains a challenge. Additionally, a binary classification of WCC presence and absence cannot accurately describe the category and performance of the WCC. This paper presents a new remote sensing phenology-based approach to map WCC likelihood. We assessed model predictions of WCC likelihood using WCC planting records and ground observations over fields at the Beltsville Agricultural Research Center (BARC) from 2018 to 2024. Regional WCC maps were assessed using the Maryland Department of Agriculture's records of WCC fields enrolled in the 2019 and 2020 incentive programs. Our results show that the balanced overall accuracy for WCC and non-WCC ranges from 75% to 85% but could exceed 90%, with limited training samples. However, the high accuracy of WCC corresponds with low accuracy for non-WCC due to confusion with weeds and perennial grasses. Early-season detection yields promising results, with overall accuracies comparable to late-season detection. The after-season detection can differentiate between winter cereal grain cash crops and incentive WCC based on the ending date of the growing season. With a small set of WCC samples and management information, the phenology-based mapping approach offers a rapid and scalable solution for mapping WCC over a large region during or after each WCC growing season.

Maryland↗
USGS2026

Basal area loss from fire using field-calibrated remote sensing refines western US fire severity measurements

The spatial patterns of fire effects and tree mortality have profound consequences for forest resilience. Cost-effective, medium-resolution, and spatiotemporally extensive fire severity measurements are essential for informing post-fire restoration and improving our understanding of wildfires—from forest stands to continents and from days to decades. Remote sensing advancements have improved burn severity mapping, but methods vary in interpretability, scalability, generalizability, and alignment with field measurements. One meaningful metric of fire effects on forests is proportion basal area loss, but existing methods are limited by a lack of region-specific field reference data and a scalable mapping framework. To address these issues, we compiled 3280 field reference plots from 123 fires in forests across the Western US to calculate the proportion of fire-induced basal area loss. We then used spatially cross-validated machine learning models with concurrent hyperparameter tuning to select a skillful, parsimonious model from a large candidate set of remotely-sensed, climatic, and topographic predictors. Spectral-only measures of severity over- or underestimated basal area loss in dry versus wet years and across aspects, demonstrating the value of incorporating climatic and topographic context. We also tested model performance on a separate holdout dataset in the Southwest US as a demonstration of reproducibility and transparency. We provide a Google Earth Engine tool for estimating proportional basal area loss for any fire perimeter in the Western US, enabling rapid map creation for land management and ecological modeling. All code, model parameters, and training data are released to support reproducibility, community adoption, regional refinement, and adaptation to new regions.

western United States↗
USGS2026

Enhancements to the USGS Landsat Level 2 surface temperature and emissivity product for Collection 3 reprocessing

The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations.

Remote Sensing of Environment↗
USGS2026

Methodology for assessing the accuracy of long-term, annual land cover monitoring at national scale: Experiences from the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative

Annual land cover monitoring using medium-resolution satellite imagery represents a transition from periodic mapping campaigns to continuous observation systems. This transition introduces accuracy assessment challenges not encountered in one-time mapping efforts, particularly when the initial monitoring period is extended forward in time. These challenges are not addressed in existing guidance, which remains largely framed around the evaluation of maps of a single date or maps showing changes between two dates. We present a set of methodological adaptations to adjust to this evolving paradigm, using as a case study the U.S. Geological Survey (USGS) Land Change Monitoring, Assessment, and Projection (LCMAP) initiative, which generated annual land cover and land cover change products across the historical Landsat archive. Assessing the accuracy of these products was an essential component of LCMAP and resulted in an extensive reference sample dataset that may be used for accuracy estimation, area estimation, or support in the development of new algorithms or models. We review the LCMAP methodology and highlight challenges encountered and lessons learned that are transferable to other long-term, temporally dense monitoring programs. This framework demonstrates that statistically rigorous assessment can scale to operational annual land cover production, while the experiences of the project clarify where existing good practice guidance should be extended to support the needs of ongoing, continuous land cover monitoring.

Remote Sensing of Environment↗