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At least 1,279 records · Page 71Linked to original sources
USGS2022

The applicability of time-integrated unit stream power for estimating bridge pier scour using noncontact methods in a gravel-bed river

In near-field remote sensing, noncontact methods (radars) that measure stage and surface water velocity have the potential to supplement traditional bridge scour monitoring tools because they are safer to access and are less likely to be damaged compared with in-stream sensors. The objective of this study was to evaluate the use of radars for monitoring the hydraulic conditions that contribute to bridge–pier scour in gravel-bed channels. Measurements collected with a radar were also leveraged along with minimal field measurements to evaluate whether time-integrated stream power per unit area (Ω) was correlated with observed scour depth at a scour-critical bridge in Colorado. The results of this study showed that (1) there was close agreement between radar-based and U.S. Geological Survey streamgage-based measurements of stage and discharge, indicating that radars may be viable tools for monitoring flow conditions that lead to bridge pier scour; (2) Ω and pier scour depth were correlated, indicating that radar-derived Ω measurements may be used to estimate scour depth in real time and predict scour depth based on the measured trajectory of Ω. The approach presented in this study is intended to supplement, rather than replace, existing high-fidelity scour monitoring techniques and provide data quickly in information-poor areas.

Colorado↗
USGS2007

Postfire soil burn severity mapping with hyperspectral image unmixing

Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after the 2002 Hayman Fire in Colorado to assess the application of high resolution imagery for burn severity mapping and to compare it to standard burn severity mapping methods. Mixture Tuned Matched Filtering (MTMF), a partial spectral unmixing algorithm, was used to identify the spectral abundance of ash, soil, and scorched and green vegetation in the burned area. The overall performance of the MTMF for predicting the ground cover components was satisfactory (r2 = 0.21 to 0.48) based on a comparison to fractional ash, soil, and vegetation cover measured on ground validation plots. The relationship between Landsat-derived differenced Normalized Burn Ratio (dNBR) values and the ground data was also evaluated (r2 = 0.20 to 0.58) and found to be comparable to the MTMF. However, the quantitative information provided by the fine-scale hyperspectral imagery makes it possible to more accurately assess the effects of the fire on the soil surface by identifying discrete ground cover characteristics. These surface effects, especially soil and ash cover and the lack of any remaining vegetative cover, directly relate to potential postfire watershed response processes. ?? 2006 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2023

Crop water use dynamics over arid and semi-arid croplands in the lower Colorado River Basin

Numerous studies have evaluated the application of Remote Sensing (RS) techniques for mapping actual evapotranspiration (ETa) using Vegetation-Index-based (VI-based) and surface energy balance methods (SEB). SEB models computationally require a large effort for application. VI-based methods are fast and easy to apply and could therefore potentially be applied at high resolution; however, the accuracy of VI-based methods in comparison to SEB-based models remains unclear. We tested the ETa computed with the modified 2-band Enhanced Vegetation Index (METEVI2) implemented in the Google Earth Engine – for mapping croplands’ water use dynamics in the Lower Colorado River Basin. We compared METEVI2 with the well-established RS-based products of OpenET (Ensemble, eeMETRIC, SSEBop, SIMS, PT_JPL, DisALEXI and geeSEBAL). METEVI2 was then evaluated with measured ETa from four wheat fields (2017–2018). Results indicated that the monthly ETa variations for METEVI2 and OpenET models were comparable, though of varying magnitudes. On average, METEVI2 had the lowest difference rate from the average observed ETa with 17 mm underestimation, while SIMS had the highest difference rate (82 mm). Findings show that METEVI2 is a cost-effective ETa mapping tool in drylands to track crop water use. Future studies should test METEVI2’s applicability to croplands in more humid regions.

Lower Colorado River basin↗
USGS2021

Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud

Advances in spaceborne hyperspectral (HS) remote sensing, cloud-computing, and machine learning can help measure, model, map and monitor agricultural crops to address global food and water security issues, such as by providing accurate estimates of crop area and yield to model agricultural productivity. Leveraging these advances, we used the Earth Observing-1 (EO-1) Hyperion historical archive and the new generation DLR Earth Sensing Imaging Spectrometer (DESIS) data to evaluate the performance of hyperspectral narrowbands in classifying major agricultural crops of the U.S. with machine learning (ML) on Google Earth Engine (GEE). EO-1 Hyperion images from the 2010–2013 growing seasons and DESIS images from the 2019 growing season were used to classify three world crops (corn, soybean, and winter wheat) along with other crops and non-crops near Ponca City, Oklahoma, USA. The supervised classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB), and the unsupervised clustering algorithm WekaXMeans (WXM) were run using selected optimal Hyperion and DESIS HS narrowbands (HNBs). RF and SVM returned the highest overall producer’s, and user’s accuracies, with the performances of NB and WXM being substantially lower. The best accuracies were achieved with two or three images throughout the growing season, especially a combination of an earlier month (June or July) and a later month (August or September). The narrow 2.55 nm bandwidth of DESIS provided numerous spectral features along the 400–1000 nm spectral range relative to smoother Hyperion spectral signatures with 10 nm bandwidth in the 400–2500 nm spectral range. Out of 235 DESIS HNBs, 29 were deemed optimal for agricultural study. Advances in ML and cloud-computing can greatly facilitate HS data analysis, especially as more HS datasets, tools, and algorithms become available on the Cloud.

Remote Sensing↗
USGS2024

Greenness and actual evapotranspiration in the unrestored riparian corridor of the Colorado River Delta in response to in-channel water deliveries in 2021 and 2022

Natural resource managers may utilize remotely sensed data to monitor vegetation within their decision-making frameworks for improving habitats. Under binational agreements between the United States and Mexico, seven reaches were targeted for riparian habitat enhancement. Monitoring was carried out using Landsat 8 16-day intervals of the two-band enhanced vegetation index 2 (EVI2) for greenness and actual evapotranspiration (ETa). In-channel water was delivered in 2021 and 2022 at four places in Reach 4. Three reaches (Reaches 4, 5 and 7) showed no discernable difference in EVI2 from reaches that did not receive in-channel water (Reaches 1, 2, 3 and 6). EVI2 in 2021 was higher than 2020 in all reaches except Reach 3, and EVI2 in 2022 was lower than 2021 in all reaches except Reach 7. ET(EVI2) was higher in 2020 than in 2021 and 2022 in all seven reaches; it was highest in Reach 4 (containing restoration sites) in all years. Excluding restoration sites, compared with 2020, unrestored reaches showed that EVI2 minimally increased in 2021 and 2022, while ET(EVI2) minimally decreased despite added water in 2021–2022. Difference maps comparing 2020 (no-flow year) to 2021 and 2022 (in-channel flows) reveal areas in Reaches 5 and 7 where the in-channel flows increased greenness and ET(EVI2).

Colorado River and Delta↗
USGS2004

Identification of spectrally similar materials using the USGS Tetracorder algorithm: The calcite-epidote-chlorite problem

A scheme to discriminate and identify materials having overlapping spectral absorption features has been developed and tested based on the U.S. Geological Survey (USGS) Tetracorder system. The scheme has been applied to remotely sensed imaging spectroscopy data acquired by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) instrument. This approach was used to identify the minerals calcite, epidote, and chlorite in the upper Animas River watershed, Colorado. The study was motivated by the need to characterize the distribution of calcite in the watershed and assess its acid-neutralizing potential with regard to acidic mine drainage. Identification of these three minerals is difficult because their diagnostic spectral features are all centered at 2.3 ??m, and have similar shapes and widths. Previous studies overestimated calcite abundance as a result of these spectral overlaps. The use of a reference library containing synthetic mixtures of the three minerals in varying proportions was found to simplify the task of identifying these minerals when used in conjunction with a rule-based expert system. Some inaccuracies in the mineral distribution maps remain, however, due to the influence of a fourth spectral component, sericite, which exhibits spectral absorption features at 2.2 and 2.4 ??m that overlap the 2.3-??m absorption features of the other three minerals. Whereas the endmember minerals calcite, epidote, chlorite, and sericite can be identified by the method presented here, discrepancies occur in areas where all four occur together as intimate mixtures. It is expected that future work will be able to reduce these discrepancies by including reference mixtures containing sericite. ?? 2004 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2005

ASTER and USGS EROS disaster response: emergency imaging after Hurricane Katrina

The value of remotely sensed imagery during times of crisis is well established, and the increasing spatial and spectral resolution in newer systems provides ever greater utility and ability to discriminate features of interest (International Charter, Space and Major Disasters, 2005). The existing suite of sensors provides an abundance of data, and enables warning alerts to be broadcast for many situations in advance. In addition, imagery acquired soon after an event occurs can be used to assist response and remediation teams in identifying the extent of the affected area and the degree of damage. The data characteristics of the Advanced Spaceborne Thermal Emission and Refl ection Radiometer (ASTER) are well-suited for monitoring natural hazards and providing local and regional views after disaster strikes. For this reason, and because of the system fl exibility in scheduling high-priority observations, ASTER is often tasked to support emergency situations. The Emergency Response coordinators at the United States Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) work closely with staff at the National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (LP DAAC) at EROS and the ASTER Science Team as they fulfi ll their mission to acquire and distribute data during critical situations. This article summarizes the role of the USGS/EROS Emergency Response coordinators, and provides further discussion of ASTER data and the images portrayed on the cover of this issue

Photogrammetric Engineering and Remote Sensing↗
USGS2005

Field determination of optimal dates for the discrimination of invasive wetland plant species using derivative spectral analysis

Mapping invasive plant species in aquatic and terrestrial ecosystems helps to understand the causes of their progression, manage some of their negative consequences, and control them. In recent years, a variety of new remote-sensing techniques, like Derivative Spectral Analysis (DSA) of hyperspectral data, have been developed to facilitate this mapping. A number of questions related to these techniques remain to be addressed. This article attempts to answer one of these questions: Is the application of DSA optimal at certain times of the year? Field radiometric data gathered weekly during the summer of 1999 at selected field sites in upstate New York, populated with purple loosestrife ( Lythrum salicaria L.), common reed ( Phragmites australis (Cav.)) and cattail ( Typha L.) are analyzed using DSA to differentiate among plant community types. First, second and higher-order derivatives of the reflectance spectra of nine field plots, varying in plant composition, are calculated and analyzed in detail to identify spectral ranges in which one or more community types have distinguishing features. On the basis of the occurrence and extent of these spectral ranges, experimental observations suggest that a satisfactory differentiation among community types was feasible on 30 August, when plants experienced characteristic phenological changes (transition from flowers to seed heads). Generally, dates in August appear optimal from the point of view of species differentiability and could be selected for image acquisitions. This observation, as well as the methodology adopted in this article, should provide a firm basis for the acquisition of hyperspectral imagery and for mapping the targeted species over a broad range of spatial scales.

Photogrammetric Engineering and Remote Sensing↗
USGS2020

Calibrating geosynchronous and polar orbiting satellites: Sharing best practices

Earth remote sensing optical satellite systems are often divided into two categories—geosynchronous and sun-synchronous. Geosynchronous systems essentially rotate with the Earth and continuously observe the same region of the Earth. Sun-synchronous systems are generally in a polar orbit and view differing regions of the Earth at the same local time. Although similar in instrument design, there are enough differences in these two types of missions that often the calibration of the instruments can be substantially different. Thus, respective calibration teams develop independent methods and do not interact regularly or often. Yet, there are numerous areas of overlap and much to learn from one another. To address this issue, a panel of experts from both types of systems was convened to discover common areas of concern, areas where improvements can be made, and recommendations for the future. As a result of the panelist’s efforts, a set of eight recommendations were developed. Those that are related to improvements of current technologies include maintaining sun-synchronous orbits (not allowing orbital decay), standardization of spectral bandpasses, and expanded use of well-developed calibration techniques such as deep convective clouds, pseudo invariant calibration sites, and lunar methodologies. New techniques for expanded calibration capability include using geosynchronous instruments as transfer radiometers, continued development of ground-based prelaunch calibration technologies, expansion of RadCalNet, and development of space-based calibration radiometer systems.

Remote Sensing↗
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↗
USGS2014

An analysis of the global spatial variability of column-averaged CO 2 from SCIAMACHY and its implications for CO 2 sources and sinks

Satellite observations of carbon dioxide (CO 2 ) are important because of their potential for improving the scientific understanding of global carbon cycle processes and budgets. We present an analysis of the column-averaged dry air mole fractions of CO 2 (denoted XCO 2 ) of the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) retrievals, which were derived from a satellite instrument with relatively long-term records (2003–2009) and with measurements sensitive to the near surface. The spatial-temporal distributions of remotely sensed XCO 2 have significant spatial heterogeneity with about 6–8% variations (367–397 ppm) during 2003–2009, challenging the traditional view that the spatial heterogeneity of atmospheric CO 2 is not significant enough (<4%) to have any large effect on terrestrial ecosystem carbon cycles. By comparison with surface measurements from the NOAA Earth System Research Laboratory (ESRL) GLOBALVIEW network, significant statistical relationships between XCO 2 and surface CO 2 were found for major ecosystems, with the exception of tropical forest. In addition, when compared with a simulated terrestrial carbon uptake from the Integrated Biosphere Simulator (IBIS) and the Emissions Database for Global Atmospheric Research (EDGAR) carbon emission inventory, the latitudinal gradient of XCO 2 seasonal amplitude was influenced by the combined effect of terrestrial carbon uptake, carbon emission, and atmospheric transport, suggesting no direct implications for terrestrial carbon sinks. From the investigation of the growth rate of XCO 2 we found that the increase of CO 2 concentration was dominated by temperature in the northern hemisphere (20–90°N) and by precipitation in the southern hemisphere (20–90°S), with the major contribution to global average occurring in the northern hemisphere. These findings indicated that the satellite measurements of atmospheric CO 2 improve not only the estimations of atmospheric inversion, but also the understanding of the terrestrial ecosystem carbon dynamics and its feedback to atmospheric CO 2 .

International Journal of Remote Sensing↗
USGS2018

Assessing cropland area in west Africa for agricultural yield analysis

Accurate estimates of cultivated area and crop yield are critical to our understanding of agricultural production and food security, particularly for semi-arid regions like the Sahel of West Africa, where crop production is mainly rain-fed and food security is closely correlated with the inter-annual variations in rainfall. Several global and regional land cover products, based on satellite remotely-sensed data, provide estimates of the agricultural land use intensity, but the initial comparisons indicate considerable differences among them, relating to differences in the satellite data quality, classification approaches, and spatial and temporal resolutions. Here, we quantify the accuracy of available cropland products across Sahelian West Africa using an independent, high-resolution, visually interpreted sample dataset that classifies all points across West Africa using a 2-km sample grid (~500,000 points for the study area). We estimate the “quantity” and “allocation” disagreements for the cropland class of eight land cover products in five Western Sahel countries (Burkina Faso, Mali, Mauritania, Niger, and Senegal). The results confirm that coarse spatial resolution (300 m, 500 m, and 1000 m) land cover products have higher disagreements in mapping the fragmented agricultural landscape of the Western Sahel. Earlier products (e.g., GLC2000) are less accurate than recent products (e.g., ESA CCI 2013, MODIS 2013 and GlobCover 2009). We also show that two of the finer spatial resolution maps (GFSAD30, and GlobeLand30) using advanced classification approaches (random forest, decision trees, and pixel-object combined) are currently the best available products for cropland identification. However, none of the eight land cover databases examined is consistent in reaching the targeted 75% accuracy threshold in the five Sahelian countries. The majority of currently available land cover products overestimate cultivated areas by an average of 170% relative to the cropland area in the reference data.

Remote Sensing↗
USGS2015

Automated integration of lidar into the LANDFIRE product suite

Accurate information about three-dimensional canopy structure and wildland fuel across the landscape is necessary for fire behaviour modelling system predictions. Remotely sensed data are invaluable for assessing these canopy characteristics over large areas; lidar data, in particular, are uniquely suited for quantifying three-dimensional canopy structure. Although lidar data are increasingly available, they have rarely been applied to wildland fuels mapping efforts, mostly due to two issues. First, the Landscape Fire and Resource Planning Tools (LANDFIRE) program, which has become the default source of large-scale fire behaviour modelling inputs for the US, does not currently incorporate lidar data into the vegetation and fuel mapping process because spatially continuous lidar data are not available at the national scale. Second, while lidar data are available for many land management units across the US, these data are underutilized for fire behaviour applications. This is partly due to a lack of local personnel trained to process and analyse lidar data. This investigation addresses these issues by developing the Creating Hybrid Structure from LANDFIRE/lidar Combinations (CHISLIC) tool. CHISLIC allows individuals to automatically generate a suite of vegetation structure and wildland fuel parameters from lidar data and infuse them into existing LANDFIRE data sets. CHISLIC will become available for wider distribution to the public through a partnership with the U.S. Forest Service’s Wildland Fire Assessment System (WFAS) and may be incorporated into the Wildland Fire Decision Support System (WFDSS) with additional design and testing. WFAS and WFDSS are the primary systems used to support tactical and strategic wildland fire management decisions.

Remote Sensing Letters↗
USGS2008

Quantile regression applied to spectral distance decay

Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.01), considering both OLS and quantile regressions. Nonetheless, the OLS regression estimate of the mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when the spectral distance approaches zero, was very low compared with the intercepts of the upper quantiles, which detected high species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.

IEEE Geoscience and Remote Sensing Letters↗
USGS2009

Spectral distance decay

Remotely sensed data represents key information for character-izing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance may allow us to quantitatively estimate how beta-diversity in species changes with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological datasets are characterized by a high number of zeroes that can add noise to the regression model. Quantile regression can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this paper, we used ordinary least square (OLS) and quantile regression to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.05) considering both OLS and quantile regression. Nonetheless, OLS regression estimate of mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when spectral distance approaches zero, was very low compared with the intercepts of upper quantiles, which detected high species similarity when habitats are more similar. In this paper we demonstrated the power of using quantile regressions applied to spectral distance decay in order to reveal species diversity patterns otherwise lost or underestimated by ordinary least square regression.

Photogrammetric Engineering and Remote Sensing↗
USGS2019

Phenology patterns indicate recovery trajectories of ponderosa pine forests after high-severity fires

Post-fire recovery trajectories in ponderosa pine (Pinus ponderosa Laws.) forests of the US Southwest are increasingly shifting away from pre-burn vegetation communities. This study investigated whether phenological metrics derived from a multi-decade remotely sensed imagery time-series could differentiate among grass, evergreen shrub, deciduous, or conifer-dominated replacement pathways. We focused on 10 fires that burned ponderosa pine forests in Arizona and New Mexico, USA before the year 2000. A total of 29 sites with discernable post-fire recovery signals were selected within high-severity burn areas. At each site, we used Google Earth Engine to derive time-series of normalized difference vegetation index (NDVI) signals from Landsat Thematic Mapper, Enhanced Thematic Mapper+, and Operational Line Imager data from 1984 to 2017. We aggregated values to 8- and 16-day intervals, fit Savitzy-Golay filters to each sequence, and extracted annual phenology metrics of amplitude, base value, peak value, and timing of peak value in the Timesat analysis package. Results show that relative to post-fire conditions, pre-burn ponderosa pine forests exhibit significantly lower mean NDVI amplitude (0.14 vs. 0.21), higher mean base NDVI (0.47 vs. 0.22), higher mean peak NDVI (0.60 vs. 0.43), and later mean peak NDVI (day of year 277 vs. 237). Vegetation succession exhibits distinct phenometric characteristics as early as year five (amplitude) and as late as year 20 (timing of peak NDVI). This study confirms the feasibility of leveraging phenology metrics derived from long-term imagery time series to identify and monitor ecological outcomes. This information may be of benefit to land resource managers who seek indicators of future landscape composition to inform management strategies.

Arizona, New Mexico↗
USGS2023

The EnMAP imaging spectroscopy mission towards operations

EnMAP (Environmental Mapping and Analysis Program) is a high-resolution imaging spectroscopy remote sensing mission that was successfully launched on April 1st, 2022. Equipped with a prism-based dual-spectrometer, EnMAP performs observations in the spectral range between 418.2 nm and 2445.5 nm with 224 bands and a high radiometric and spectral accuracy and stability. EnMAP products, with a ground instantaneous field-of-view of 30 m x 30 m at a swath width of 30 km, allow for the qualitative and quantitative analysis of surface variables from frequently and consistently acquired observations on a global scale. This article presents the EnMAP mission and details the activities and results of the Launch and Early Orbit and Commissioning Phases until November 1st, 2022. The mission capabilities and expected performances for the operational Routine Phase are provided for existing and future EnMAP users.

Remote Sensing of Environment↗
USGS2024

A multi-sensor approach to characterize winter water-level drawdown patterns in lakes

Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices.

Massachusetts↗