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At least 955 records · Page 53Linked to original sources
USGS2023

High-frequency time series comparison of Sentinel-1 and Sentinel-2 for open and vegetated water across the United States (2017-2021)

Frequent observations of surface water at fine spatial scales will provide critical data to support the management of aquatic habitat, flood risk and water quality. Sentinel-1 and Sentinel-2 satellites can provide such observations, but algorithms are still needed that perform well across diverse climate and vegetation conditions. We developed surface inundation algorithms for Sentinel-1 and Sentinel-2, respectively, at 12 sites across the conterminous United States (CONUS), covering a total of >536,000 km 2 and representing diverse hydrologic and vegetation landscapes. Each scene in the 5-year (2017–2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables from Sentinel-1 and Sentinel-2, as well as variables derived from topographic and weather datasets. The Sentinel-1 algorithm was developed distinct from the Sentinel-2 model to explore if and where the two time series could potentially be integrated into a single high-frequency time series. Within each model, open water and vegetated water (vegetated palustrine, lacustrine, and riverine wetlands) classes were mapped. The models were validated using imagery from WorldView and PlanetScope. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for the Sentinel-1 algorithm and 3.1% and 0.5% for the Sentinel-2 algorithm, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. The Sentinel-2 algorithm showed higher accuracy (10.7% omission and 7.9% commission error) relative to the Sentinel-1 algorithm (28.4% omission and 16.0% commission error). Patterns over time in the proportion of area mapped as open or vegetated water by the Sentinel-1 and Sentinel-2 algorithms were charted and correlated for a subset of all 12 sites. Our results showed that the Sentinel-1 and Sentinel-2 algorithm open water time series can be integrated at all 12 sites to improve the temporal resolution, but sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for mixed-pixel, vegetated water. The methods developed here provide inundation at 5-day (Sentinel-2 algorithm) and 12-day (Sentinel-1 algorithm) time steps to improve our understanding of the short- and long-term response of surface water to climate and land use drivers in different ecoregions.

continental United States↗
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

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↗
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↗
USGS2020

Estimation of nonlinear water-quality trends in high-frequency monitoring data

Recent advances in high-frequency water-quality sensors have enabled direct measurements of physical and chemical attributes in rivers and streams nearly continuously. Water-quality trends can be used to identify important watershed-scale changes driven by natural and anthropogenic influences. Statistical methods to estimate trends using high-frequency data are lacking. To address this gap, an evaluation of the generalized additive model (GAM) approach to test for trends in high-frequency data was conducted. Our proposed framework includes methods for handling serial correlation, trend estimation and slope-change detection, and trend interpretation at arithmetic scale for log-transformed variables. Water-temperature and turbidity data, representing two analytes with different temporal patterns, collected from the James River at Cartersville, Virginia, USA, were chosen for this analysis. Results indicated that the model, including flow, season, time covariates, and interaction between flow and season performed well for both analytes. The same model structure was applied to specific conductance data, collected from a small highly urbanized watershed, with satisfactory model performance. The water temperature GAM results indicated that the significant decreasing-then-increasing patterns after 2012 were mainly driven by air temperature changes. The turbidity trend was not significant over time. The specific conductance results showed a consistently upward trend over the last decade due to ever-increasing urbanization in the small watershed. This study suggests that the GAM method has great potential as a useful tool for trend analysis on high-frequency data, and for informing watershed managers of hydro-climatic and human influences on water quality by detecting crucial signal variation over time.

Chesapeake Bay watershed↗
USGS2021

Evaluation of a satellite-based cyanobacteria bloom detection algorithm using field-measured microcystin data

Widespread occurrence of cyanobacterial harmful algal blooms (CyanoHABs) and the associated health effects from potential cyanotoxin exposure has led to a need for systematic and frequent screening and monitoring of lakes that are used as recreational and drinking water sources. Remote sensing-based methods are often used for synoptic and frequent monitoring of CyanoHABs. In this study, one such algorithm – a sub-component of the Cyanobacteria Index called the CI cyano , was validated for effectiveness in identifying lakes with toxin-producing blooms in 11 states across the contiguous United States over 11 bloom seasons (2005–2011, 2016–2019). A matchup data set was created using satellite data from MEdium Resolution Imaging Spectrometer (MERIS) and Ocean Land Colour Imager (OLCI), and nearshore, field-measured Microcystins (MCs) data as a proxy of CyanoHAB presence. While the satellite sensors cannot detect toxins, MCs are used as the indicator of health risk, and as a confirmation of cyanoHAB presence. MCs are also the most common laboratory measurement made by managers during CyanoHABs. Algorithm performance was evaluated by its ability to detect CyanoHAB ‘Presence’ or ‘Absence’, where the bloom is confirmed by the presence of the MCs. With same-day matchups, the overall accuracy of CyanoHAB detection was found to be 84% with precision and recall of 87 and 90% for bloom detection. Overall accuracy was expected to be between 77% and 87% (95% confidence) based on a bootstrapping simulation. These findings demonstrate that CI cyano has utility for synoptic and routine monitoring of potentially toxic cyanoHABs in lakes across the United States.

Science for the Total Environment↗
USGS2024

A brief note on substantial sub-daily arsenic variability in pumping drinking-water wells in New Hampshire

Large variations in redox-related water parameters, like pH and dissolved oxygen (DO), have been documented in New Hampshire (United States) drinking-water wells over the course of a few hours under pumping conditions. These findings suggest that comparable sub-daily variability in dissolved concentrations of redox-reactive and toxic arsenic (As) also may occur, representing a potentially critical public-health data gap and a fundamental challenge for long-term As-trends monitoring. To test this hypothesis, discrete groundwater As samples were collected approximately hourly during one day in May and again in August 2019 from three New Hampshire drinking-water wells (2 public-supply, 1 private) under active pumping conditions. Collected samples were assessed by laboratory analysis (total As [As Tot ], As(III), As(V)) and by field analysis (As Tot ) using a novel integrated biosensor system. Laboratory analysis revealed sub-daily variability (range) in As Tot concentrations equivalent to 16 % – 36 % of that observed in the antecedent 3-year bimonthly trend monitoring. Thus, the results indicated that, along with previously demonstrated seasonality effects, the timing and duration of pumping are important considerations when assessing trends in drinking-water As exposures and concomitant risks. Results also illustrated the utility of the field sensor for monitoring and management of As Tot exposures in near-real-time.

Science of the Total Environment↗
USGS2025

Phytoplankton biomass dynamics in wet (2019) and dry (2023) years in Lake Pontchartrain estuary, Louisiana from Sentinel 2-MSI and PACE-OCI observations

This study provides a comprehensive assessment of phytoplankton biomass dynamics in Lake Pontchartrain, Louisiana, by combining monthly water quality data with multispectral and hyperspectral satellite observations using a machine learning algorithm. A machine learning model based on Variational Autoencoder (VAE), globally applicable, was used to estimate phytoplankton biomass via chlorophyll- a (Chl- a ) from Sentinel 2-MSI and NASA's new hyperspectral mission, PACE-OCI, enabling the first direct comparison between the two sensors. The model performed well in this complex estuarine system, with higher accuracy from PACE-OCI (MAE: 1.48, RMSE: 10.40, slope: 0.87) than Sentinel 2-MSI (MAE: 1.57, RMSE: 11.08, slope: 0.83). This approach enabled continuous high-resolution monitoring of phytoplankton biomass across space and time. Comparative analysis of 2019, a wet year with Bonnet Carré Spillway (BCS) openings, and 2023, a dry year with extremely low riverine inputs, revealed distinct biomass dynamics. In 2019, BCS discharge initially suppressed Chl- a within turbid waters (<5 mg Chl- a m −3 ) but later acted as a nutrient and hydrodynamic driver, transporting nutrients toward the lake outlet and Mississippi coast, promoting high biomass (25–45 mg Chl- a m −3 ) near the entrance. In contrast, dry conditions in 2023 led to more frequent-than-expected high biomass (>35 mg Chl- a m −3 ), persisting in the lake center. Similar spatial patterns were observed again in 2024, revealed for the first time by PACE-OCI. This study demonstrates the value of satellite-derived observations for capturing transient phytoplankton biomass events and highlights the potential of PACE-OCI's hyperspectral capabilities to better distinguish phytoplankton communities and improve understanding of their responses to freshwater inflows and associated processes driving pulses into estuaries.

Louisiana↗
USGS2008

Landsat still contributing to environmental research

Landsat data have enabled continuous global monitoring of both human-caused and other land cover disturbances since 1972. Recently degraded performance and intermittent service of the Landsat 7 and Landsat 5 sensors, respectively, have raised concerns about the condition of global Earth observation programs. However, Landsat imagery is still useful for landscape change detection and this capability should continue into the foreseeable future.

Trends in Ecology and Evolution↗
USGS2001

Soil efflux and total emission rates of magmatic CO2 at the horseshoe lake tree kill, mammoth mountain, California, 1995-1999

We report the results of eight soil CO2 efflux surveys by the closed circulation chamber method at the Horseshoe Lake tree kill (HLTK) - the largest tree kill on Mammoth Mountain. The surveys were undertaken from 1995 to 1999 to constrain total HLTK CO2 emissions and to evaluate occasional efflux surveys as a surveillance tool for the tree kills. HLTK effluxes range from 1 to > 10,000 g m -2 day -1 (grams CO2 per square meter per day); they are not normally distributed. Station efflux rates can vary by 7-35% during the course of the 8- to 16-h surveys. Disturbance of the upper 2 cm of ground surface causes effluxes to almost double. Semivariograms of efflux spatial covariance fit exponential or spherical models; they lack nugget effects. Efflux contour maps and total CO2 emission rates based on exponential, spherical, and linear kriging models of survey data are nearly identical; similar results are also obtained with triangulation models, suggesting that the kriging models are not seriously distorted by the lack of normal efflux distributions. In addition, model estimates of total CO2 emission rates are relatively insensitive to the measurement precision of the efflux rates and to the efflux value used to separate magmatic from forest soil sources of CO2. Surveys since 1997 indicate that, contrary to earlier speculations, a termination of elevated CO2 emissions at the HLTK is unlikely anytime soon. The HLTK CO2 efflux anomaly fluctuated greatly in size and intensity throughout the 1995-1999 surveys but maintained a N-S elongation, presumably reflecting fault control of CO2 transport from depth. Total CO2 emission rates also fluctuated greatly, ranging from 46 to 136 t day-1 (metric tons CO2 per day) and averaging 93 t day-1. The large inter-survey variations are caused primarily by external (meteorological) processes operating on time scales of hours to days. The externally caused variations can mask significant changes occurring at depth; a striking example is the masking of a degassing event generated at depth and detected by a soil gas sensor network in September 1997 while an efflux survey was in progress. Thus, occasional efflux surveys are not an altogether effective surveillance tool for the HLTK, and making them effective by greatly increasing their frequency may not be practical. Published by Elsevier Science B.V.

Chemical Geology↗
USGS2001

Laboratory and field evaluations of the LISST-100 instrument for suspended particle size determinations

Advances in technology have resulted in a new instrument that is designed for in-situ determination of particle size spectra. Such an instrument that can measure undisturbed particle size distributions is much needed for sediment transport studies. The LISST-100 (Laser In-Situ Scattering and Transmissometry) uses the principle of laser diffraction to obtain the size distribution and volume concentration of suspended material in 32 size classes logarithmically spaced between 1.25 and 250 μm. This paper describes a laboratory evaluation of the ability of LISST-100 to determine particle sizes using suspensions of single size, artificial particles. Findings show the instrument is able to determine particle size to within about 10% with increasing error as particle size increases. The instrument determines volume (or mass) concentration using a volume conversion factor C v . This volume conversion factor is theoretically a constant. In the laboratory evaluation C v is found to vary by a factor of about three over the particle size range between 5 and 200 μm. Results from field studies in South San Francisco Bay show that values of mass concentration of suspended marine sediments estimated by LISST-100 agree favorably with estimates from optical backscatterance sensors if an appropriate value of C v , according to mean size, is used and the assumed average particle (aggregate) density is carefully chosen. Analyses of size distribution of suspended materials in South San Francisco Bay over multiple tide cycles suggest the likelihood of different sources of sediment because of different size characteristics during flood and ebb cycles.

Marine Geology↗
USGS2000

Classification methods for monitoring Arctic sea ice using OKEAN passive/active two-channel microwave data

This paper presents methods for classifying Arctic sea ice using both passive and active (2-channel) microwave imagery acquired by the Russian OKEAN 01 polar-orbiting satellite series. Methods and results are compared to sea ice classifications derived from nearly coincident Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) image data of the Barents, Kara, and Laptev Seas. The Russian OKEAN 01 satellite data were collected over weekly intervals during October 1995 through December 1997. Methods are presented for calibrating, georeferencing and classifying the raw active radar and passive microwave OKEAN 01 data, and for correcting the OKEAN 01 microwave radiometer calibration wedge based on concurrent 37 GHz horizontal polarization SSM/I brightness temperature data. Sea ice type and ice concentration algorithms utilized OKEAN's two-channel radar and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter, together with a priori knowledge about the scattering parameters and natural emissivities of basic sea ice types. OKEAN 01 data and algorithms tended to classify lower concentrations of young or first-year sea ice when concentrations were less than 60%, and to produce higher concentrations of multi-year sea ice when concentrations were greater than 40%, when compared to estimates produced from SSM/I data. Overall, total sea ice concentration maps derived independently from OKEAN 01, SSM/I, and AVHRR satellite imagery were all highly correlated, with uniform biases, and mean differences in total ice concentration of less than four percent (sd<15%).

Remote Sensing of Environment↗
USGS2001

Effects of Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper plus radiometric and geometric calibrations and corrections on landscape characterization

The Thematic Mapper (TM) instruments onboard Landsats 4 and 5 provide high-quality imagery appropriate for many different applications, including land cover mapping, landscape ecology, and change detection. Precise calibration was considered to be critical to the success of the Landsat 7 mission and, thus, issues of calibration were given high priority during the development of the Enhanced Thematic Mapper Plus (ETM+). Data sets from the Landsat 5 TM are not routinely corrected for a number of radiometric and geometric artifacts, including memory effect, gain/bias, and interfocal plane misalignment. In the current investigation, the effects of correcting vs. not correcting these factors were investigated for several applications. Gain/bias calibrations were found to have a greater impact on most applications than did memory effect calibrations. Correcting interfocal plane offsets was found to have a moderate effect on applications. On June 2, 1999, Landsats 5 and 7 data were acquired nearly simultaneously over a study site in the Niobrara, NE area. Field radiometer data acquired at that site were used to facilitate crosscalibrations of Landsats 5 and 7 data. Current findings and results from previous investigations indicate that the internal calibrator of Landsat 5 TM tracked instrument gain well until 1988. After this, the internal calibrator diverged from the data derived from vicarious calibrations. Results from this study also indicate very good agreement between prelaunch measurements and vicarious calibration data for all Landsat 7 reflective bands except Band 4. Values are within about 3.5% of each other, except for Band 4, which differs by 10%. Coefficient of variation (CV) values derived from selected targets in the imagery were also analyzed. The Niobrara Landsat 7 imagery was found to have lower CV values than Landsat 5 data, implying that lower levels of noise characterize Landsat 7 data than current Landsat 5 data. It was also found that following radiometric normalization, the Normalized Difference Vegetation Index (NDVI) imagery and classification products of Landsats 5 and 7 were very similar. This implies that data from the two sensors can be used to measure and monitor the same landscape phenomena and that Landsats 5 and 7 data can be used interchangeably with proper caution. In addition, it was found that difference imagery produced using Landsat 7 ETM+ data are of excellent quality.

Remote Sensing of Environment↗
USGS2001

The Landsat 7 mission: terrestrial research and applications for the 21st century

The Landsat Earth observation approach introduced in 1972 created a new way of monitoring land cover and land use globally. The Landsat 7 mission, successfully launched on April 15, 1999, continues those observations and demonstrates significant progress in precise numerical radiometry, spectral differentiation, and seasonally repetitive monitoring. Substantial improvements in calibration procedures, both prior to launch and during normal operations, have also been made to ensure long-term stability in the acquired spectral radiometry. Landsat 7 data acquisitions are being driven by a long-term data acquisition plan that was designed to ensure that substantially cloud-free, seasonal coverage would be recorded and archived in the US for all land areas of the globe. NASA competitively selected a Landsat Science Team, consisting of representatives from US universities and government agencies, to exploit the Landsat 7 record for global change research. This team is addressing the technical and analytical means to process and analyze the core of this observation record, and for the first time in the history of the Landsat mission, the technical and operational aspects of the mission are being driven by the goals of the US science community. The expected outcome of these efforts is a rapid improvement in understanding the Earth system, as well as conceptual knowledge that will underpin significant advancements in the application of this technology for commercial, operational, educational, and research purposes. Pathways to achieve effective Landsat continuity in the early decades of the 21st century are also being given careful attention, and there is no question that the lessons learned from the Landsat 7 mission will strongly influence these next-generation sensor systems.

Remote Sensing of Environment↗
USGS2002

Achieving sub-pixel geolocation accuracy in support of MODIS land science

The Moderate Resolution Imaging Spectroradiometer (MODIS) was launched in December 1999 on the polar orbiting Terra spacecraft and since February 2000 has been acquiring daily global data in 36 spectral bands—29 with 1 km, five with 500 m, and two with 250 m nadir pixel dimensions. The Terra satellite has on-board exterior orientation (position and attitude) measurement systems designed to enable geolocation of MODIS data to approximately 150 m (1 σ ) at nadir. A global network of ground control points is being used to determine biases and trends in the sensor orientation. Biases have been removed by updating models of the spacecraft and instrument orientation in the MODIS geolocation software several times since launch and have improved the MODIS geolocation to approximately 50 m (1 σ ) at nadir. This paper overviews the geolocation approach, summarizes the first year of geolocation analysis, and overviews future work. The approach allows an operational characterization of the MODIS geolocation errors and enables individual MODIS observations to be geolocated to the sub-pixel accuracies required for terrestrial global change applications.

Remote Sensing of Environment↗
USGS2003

Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices

The Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor moisture-related vegetation condition. The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data. To better understand this relationship, an analysis was conducted on time series of monthly NDVI (1989&ndash;2000) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI&ndash;SPI relationship occurred in areas with low soil water-holding capacity. Our most important finding is that NDVI is an effective indicator of vegetation-moisture condition, but seasonal timing should be taken into consideration when monitoring drought with the NDVI.

Remote Sensing of Environment↗
USGS2003

Observations of large-amplitude cross-shore internal bores near the shelf break, Santa Monica Bay, CA

Two sets of moorings were deployed along a cross-shelf transect in central Santa Monica bay for four months in the winter of 1998-1999. Both sites had an array of instruments attached to tripods set on the seafloor to monitor currents over the entire water column, surface waves, near-bed temperature, water clarity and suspended sediment. A companion mooring had temperature sensors spaced approximately 10 m apart to measure temperature profiles between the surface and the seafloor. One array was deployed in 70 m of water at a site adjacent to the shelf break, just northwest of a major ocean outfall. The other was deployed on the mid shelf in 35 m of water approximately 6 km from the shelf break site. The subtidal currents in the region flowed parallel to the isobaths with fluctuating time scales around 10 days, a typical coastal-ocean pattern. However, during the falling phase of the barotropic spring tide, sets of large-amplitude, sheared cross-shore current pulses with a duration of 2-5 h were observed at the shelf break site. Currents in these pulses flowed exclusively offshore in a thin layer near the bed with amplitudes reaching 30-40 cm/s. Simultaneously, currents with amplitudes around 15-20 cm/s flowed exclusively onshore in the thicker layer between the offshore flow layer and the sea surface. The net offshore transport was about half the onshore transport. Near-surface isotherms were depressed 30-40 m. These pulses were likely internal bores generated by tidal currents. Bed stresses associated with these events exceeded 3 dynes/cm2. These amplitudes are large enough to resuspend and transport not only fine-grained material, but also medium to coarse sands from the shelf toward the slope. Consequently, the seafloor over the shelf break was swept clear of fine sediments. The data suggest that the internal bores dissipate and are reduced in amplitude as they propagate across this relatively narrow shelf. There is evidence that they reach the 35 m site, but other coastal ocean processes obscure their distinctive characteristics.

Marine Environmental Research↗
USGS2003

Airflows and turbulent flux measurements in mountainous terrain: Part 1. Canopy and local effects

We have studied the effects of local topography and canopy structure on turbulent flux measurements at a site located in mountainous terrain within a subalpine, coniferous forest. Our primary aim was to determine whether the complex terrain of the site affects the accuracy of eddy flux measurements from a practical perspective. We observed displacement heights, roughness lengths, spectral peaks, turbulent length scales, and profiles of turbulent intensities that were comparable in magnitude and pattern to those reported for forest canopies in simpler terrain. We conclude that in many of these statistical measures, the local canopy exerts considerably more influence than does topographical complexity. Lack of vertical flux divergence and modeling suggests that the flux footprints for the site are within the standards acceptable for the application of flux statistics. We investigated three different methods of coordinate rotation: double rotation (DR), triple rotation (TR), and planar-fit rotation (PF). Significant variability in rotation angles at low wind speeds was encountered with the commonly used DR and TR methods, as opposed to the PF method, causing some overestimation of the fluxes. However, these differences in fluxes were small when applied to large datasets involving sensible heat and CO 2 fluxes. We observed evidence of frequent drainage flows near the ground during stable, stratified conditions at night. Concurrent with the appearance of these flows, we observed a positive bias in the mean vertical wind speed, presumably due to subtle topographic variations inducing a flow convergence below the measurement sensors. In the presence of such drainage flows, advection of scalars and non-zero bias in the mean vertical wind speed can complicate closure of the mass conservation budget at the site.

Agricultural and Forest Meteorology↗