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262 records · Page 15

CO2 and CH4 leaf-level fluxes and soil porewater concentrations from common vegetation patches in Louisiana’s coastal wetlands

This dataset contains leaf-level flux and soil porewater concentration measurements of carbon dioxide (CO2) and methane (CH4 ) in plots in the footprint of Ameriflux sites US-LA2 and US-LA3. Leaf fluxes in US-LA2 were measured on patches dominated by Sagittaria lancifolia and co-dominated by Sagittaria lancifolia and Typha latifolia. In US-LA3, fluxes were measured from distinct Juncus roemerianus and Spartina alterniflora patches. The porewater concentrations were collected across a vertical profile (~50 cm depth) at centric locations within 25 m2 plots where we measured the leaf fluxes. US-LA3 included an additional set of measurements in open water spots. We aimed to evaluate differences in leaf fluxes and porewater pools of CO2 and CH4 of representative ecohydrological patches across a salinity gradient. We also used this dataset to help develop ELM-Wet, a more realistic representation of wetland carbon biogeochemical processes within the U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM) Land Model version 1 (ELM v.1). The files can be opened with regular text editors or spreadsheet programs. Version 2.0 (8/26/2025): This is the latest version of this dataset. The update includes additional samples of soil porewater CH4/CO2 concentrations from June-2021 to November-2022, as well as minor adjustments made to V1 samples via changing Henry's solubility to account for porewater salinity. Additionally leaf-level measurments of spectral indices, PSRI, NDVI, and PRI have been added to complement Leaf-level flux measurements. All V1 data sets have been integrated into V2 sheets, ensuring data from the previous version is contained with the additional samples and consistent with V2 metadata.

54 ENVIRONMENTAL SCIENCES↗

Evaluating an Automated Approach for Monitoring Forest Disturbances in the Pacific Northwest from Logging, Fire and Insect Outbreaks with Landsat Time Series Data

Forests are the largest aboveground sink for atmospheric carbon (C), and understanding how they change through time is critical to reduce our C-cycle uncertainties. We investigated a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 1991 in Pacific Northwest forests, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometers (AVHRRs). To understand the causal factors of this decline, we evaluated an automated classification method developed for Landsat time series stacks (LTSS) to map forest change. This method included: (1) multiple disturbance index thresholds; and (2) a spectral trajectory-based image analysis with multiple confidence thresholds. We produced 48 maps and verified their accuracy with air photos, monitoring trends in burn severity data and insect aerial detection survey data. Area-based accuracy estimates for change in forest cover resulted in producer's and user's accuracies of 0.21 +/- 0.06 to 0.38 +/- 0.05 for insect disturbance, 0.23 +/- 0.07 to 1 +/- 0 for burned area and 0.74 +/- 0.03 to 0.76 +/- 0.03 for logging. We believe that accuracy was low for insect disturbance because air photo reference data were temporally sparse, hence missing some outbreaks, and the annual anniversary time step is not dense enough to track defoliation and progressive stand mortality. Producer's and user's accuracy for burned area was low due to the temporally abrupt nature of fire and harvest with a similar response of spectral indices between the disturbance index and normalized burn ratio. We conclude that the spectral trajectory approach also captures multi-year stress that could be caused by climate, acid deposition, pathogens, partial harvest, thinning, etc. Our study focused on understanding the transferability of previously successful methods to new ecosystems and found that this automated method does not perform with the same accuracy in Pacific Northwest forests. Using a robust accuracy assessment, we demonstrate the difficulty of transferring change attribution methods to other ecosystems, which has implications for the development of automated detection/attribution approaches. Widespread disturbance was found within AVHRR-negative anomalies, but identifying causal factors in LTSS with adequate mapping accuracy for fire and insects proved to be elusive. Our results provide a background framework for future studies to improve methods for the accuracy assessment of automated LTSS classifications.

Landsat↗

Directional infrared temperature and emissivity of vegetation: Measurements and models

Directional thermal radiance from vegetation depends on many factors, including the architecture of the plant canopy, thermal irradiance, emissivity of the foliage and soil, view angle, slope, and the kinetic temperature distribution within the vegetation-soil system. A one dimensional model, which includes the influence of topography, indicates that thermal emissivity of vegetation canopies may remain constant with view angle, or emissivity may increase or decrease as view angle from nadir increases. Typically, variations of emissivity with view angle are less than 0.01. As view angle increases away from nadir, directional infrared canopy temperature usually decreases but may remain nearly constant or even increase. Variations in directional temperature with view angle may be 5C or more. Model predictions of directional emissivity are compared with field measurements in corn canopies and over a bare soil using a method that requires two infrared thermometers, one sensitive to the 8 to 14 micrometer wavelength band and a second to the 14 to 22 micrometer band. After correction for CO2 absorption by the atmosphere, a directional canopy emissivity can be obtained as a function of view angle in the 8 to 14 micrometer band to an accuracy of about 0.005. Modeled and measured canopy emissivities for corn varied slightly with view angle (0.990 at nadir and 0.982 at 75 deg view zenith angle) and did not appear to vary significantly with view angle for the bare soil. Canopy emissivity is generally nearer to unity than leaf emissivity may vary by 0.02 with wavelength even though leaf emissivity. High spectral resolution, canopy thermal emissivity may vary by 0.02 with wavelength even though leaf emissivity may vary by 0.07. The one dimensional model provides reasonably accurate predictions of infrared temperature and can be used to study the dependence of infrared temperature on various plant, soil, and environmental factors.

Norman, J. M.↗

Additional characterization of Libya-4 in support of post-launch vicarious calibration of satellite imagers

Libya-4 (28.55° N and 23.39° E) is one of the most characterized and utilized Pseudo Invariant Calibration Site (PICS) for post-launch radiometer drift monitoring and sensor pair radiometric scaling. Libya-4 is one of the driest and most reflective PICS located in an extensive sand dune region void of any vegetation with an elevation of 118 m. It is positioned near the northeast border with Egypt. With minimal cloud cover and the monthly mean precipitation of ~ 1mm, Libya-4 is one of the temporally, spectrally, and spatially stable CEOS recommended PICS and Cosnefroy et al. 1996 identified sites. The Libya-4 PICS has been extensively used for post-launch radiometric calibration and validation of high-, medium, and low-resolution satellite imagers, including Landsat, MODIS, VIIRS, and Meteosats. The CERES Imager and Geostationary Calibration Group (IGCG) at NASA LaRC utilizes Libya-4 to perform an independent assessment of the radiometric stability of the MODIS and VIIRS L1B products. The site is also used for absolute radiometric scaling between MODIS, VIIRS, and geostationary imagers to ensure consistent cloud and radiative flux retrievals. Multi-year Terra-MODIS, Aqua-MODIS , NPP-VIIRS and Metoesat-7 observations over Libya-4 show very similar TOA reflectance temporal variability. Although, the surface reflectance and atmospheric column varies seasonally, the inter-annual variability of the seasonal cycle should be small. Especially during 2013, the Libya-4 TOA reflectance was found to be greater than usual in all 4 satellite records. Preliminary comparisons with mean wind speed and aerosol optical depth (AOD) indicate that the year 2013 is marked by elevated levels of both conditions. The goal of this study is to tie the Libya-4 visible reflectance inter-annual variability with corresponding meteorological measurements to further improve the characterization of the site.

David R. Doelling↗

Integrating Chlorophyll fapar and Nadir Photochemical Reflectance Index from EO-1/Hyperion to Predict Cornfield Daily Gross Primary Production

The concept of light use efficiency (Epsilon) and the concept of fraction of photosynthetically active ration (PAR) absorbed for vegetation photosynthesis (PSN), i.e., fAPAR (sub PSN), have been widely utilized to estimate vegetation gross primary productivity (GPP). It has been demonstrated that the photochemical reflectance index (PRI) is empirically related to ε. An experimental US Department of Agriculture (USDA) cornfield in Maryland was selected as our study field. We explored the potential of integrating fAPAR(sub chl) (defined as the fraction of PAR absorbed by chlorophyll) and nadir PRI (PRI(sub nadir)) to predict cornfield daily GPP. We acquired nadir or near-nadir EO-1/Hyperion satellite images that covered the cornfield and took nadir in-situ field spectral measurements. Those data were used to derive the PRI(sub nadir) and fAPAR (sub chl). The fAPAR (sub chl) is retrieved with the advanced radiative transfer model PROSAIL2 and the Metropolis approach, a type of Markov Chain Monte Carlo (MCMC) estimation procedure. We define chlorophyll light use efficiency Epsilon (sub chl) as the ratio of vegetation GPP as measured by eddy covariance techniques to PAR absorbed by chlorophyll (Epsilon(sub chl) = GPP/APAR (sub chl). Daily Epsilon (sub chl) retrieved with the EO-1 Hyperion images was regressed with a linear equation of PRI (sub nadir) Epsilon (sub chl) = Alpha × PRI (sub nadir) + Beta). The satellite Epsilon(sub chl- PRI (sub nadir) linear relationship for the cornfield was implemented to develop an integrated daily GPP model [GPP = (Alpha × PRI(sub nadir) + Beta) × fAPAR (sub chl) × PAR], which was evaluated with fAPAR (sub chl) and PRI (sub nadir) retrieved from field measurements. Daily GPP estimated with this fAPAR (sub chl-) PRI (nadir) integration model was strongly correlated with the observed tower in-situ daily GPP (R(sup 2) = 0.93); with a root mean square error (RMSE) of 1.71 g C mol−(sup -1) PPFD and coefficient of variation (CV) of 16.57%. Both seasonal Epsilon (sub chl) and PRI (sub nadir) were strongly correlated with fAPAR (sub chl ) retrieved from field measurements, which indicates that chlorophyll content strongly affects seasonal epsilon (sub chl) and PRI (sub nadir). We demonstrate the potential capacity to monitor GPP with space-based visible through shortwave infrared (VSWIR) imaging spectrometers such as NASA's soon to be decommissioned EO- 1/Hyperion and the future Hyperspectral Infrared Imager (HyspIRI).

HyspIRI↗

Remote Sensing of Cloud, Aerosol, and Water Vapor Properties from MODIS

MODIS is an earth-viewing cross-track scanning spectroradiometer launched on the Terra satellite in December 1999. MODIS scans a swath width sufficient to provide nearly complete global coverage every two days from a polar-orbiting, sun-synchronous, platform at an altitude of 705 km, and provides images in 36 spectral bands from 0.415 to 14.235 microns with spatial resolutions of 250 m (2 bands), 500 m (5 bands) and 1000 m (29 bands). These bands have been carefully selected to enable advanced studies of land, ocean, and atmospheric processes. In this presentation I will review the comprehensive set of remote sensing algorithms that have been developed for the remote sensing of atmospheric properties using MODIS data, placing primary emphasis on the principal atmospheric applications of: (1) developing a cloud mask for distinguishing clear sky from clouds, (2) retrieving global cloud radiative and microphysical properties, including cloud top pressure and temperature, effective emissivity, cloud optical thickness, thermodynamic phase, and effective radius, (3) monitoring tropospheric aerosol optical thickness over the land and ocean and aerosol size distribution over the ocean, (4) determining atmospheric profiles of moisture and temperature, and (5) estimating column water amount. The physical principles behind the determination of each of these atmospheric products will be described, together with an example of their application using MODIS observations. All products are archived into two categories: pixel-level retrievals (referred to as Level-2 products) and global gridded products at a latitude and longitude resolution of 1 deg (Level-3 products). An overview of the MODIS atmosphere algorithms and products, status, validation activities, and early level-2 and -3 results will be presented. Finally, I will present some highlights from the land and ocean algorithms developed for processing global MODIS observations, including: (1) surface reflectance, (2) vegetation indices, leaf area index, and FPAR, (3) albedo and nadir BRDF-adjusted reflectance, (4) normalized water-leaving radiance, (5) chlorophyll-a concentration, and (6) sea surface temperature.

King, Michael D.↗

Flood Monitoring and Crop Damage Assessment

In recent years, the occurrence and impact of inland and coastal flood events have become more frequent and damaging, especially within agricultural fields, due to the global climate change and consistent sea level rise. Monitoring and measuring the magnitude of flood events in a timely manner and assessing the subsequent crop damages accurately are precursors in minimizing detrimental consequences that could potentially lead to a global food security crisis. Traditional gauge-based measurements with sophisticated hydrological models are capable of monitoring flood events precisely but limited within the smaller spatial extent, time-consuming, and costly. In recent decades, advancement in airborne- and satellite-based remote sensing technologies offering products at a daily global spatial extent with various spectral resolution helps address the shortcomings of the traditional in situ approaches in flood monitoring. Furthermore, the methods such as classification and band ratioing using remote sensing products are simple and effective in assessing flood-induced agricultural damages. The combination of remote sensing products and geographic information systems along with the current development in web mapping, users now can get near real-time flood monitoring and crop damage assessments, albeit dependent upon the quality of available data. A case study to quantify the impact of the 2011 Missouri Mississippi River flooding on the surrounding cornfield was performed through a regression model. The model was trained using historical daily NDVI and corn yield across Nebraska and Missouri, and the overall accuracy in estimating corn yield was about 90%. The method implemented in this localized case study could be extended at a larger geographical scale.

Flood monitoring↗

Assessing the Transferability of Statistical Predictive Models for Leaf Area Index Between Two Airborne Discrete Return LiDAR Sensor Designs Within Multiple Intensely Managed Loblolly Pine Forest Locations in the South-Eastern USA

Leaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices was assessed for correlation with each other, and was used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all returns produced the strongest correlations, such as the Above and Below Ratio Index (ABRI) and Laser Penetration Index 1 (LPI1). The regression model produced from a combination of three metrics did not improve correlations greatly (R2 0.90; S.E. 0.35). The results indicate that LAI can be predicted over a range of intensively managed pine plantation forest environments accurately when using different LiDAR sensor designs. Those indices which incorporated counts of specific return numbers (e.g. first returns) or return intensity correlated poorly with field measurements. There were disparities between the number of different types of returns and intensity values when comparing the results from two LiDAR sensors, indicating that predictive models developed using such metrics are not transferable between datasets with different acquisition parameters. Each of the indices were significantly correlated with one another, with one exception (LAI proxy), in particular those indices calculated from all returns, which indicates similarities in information content for those indices. It can then be argued that LiDAR indices have reached a similar stage in development to those calculated from optical-spectral sensors, but which offer a number of advantages, such as the reduction or removal of saturation issues in areas of high biomass.

Sumnall, Matthew↗

Verification Testing and Veg-05 Tomato Crop Production on the International Space Station

Production of fresh, nutritious, and tasty produce for astronauts during spaceflight may provide health-promoting, bioavailable nutrients and enhance the dietary experience as we move into longer-duration missions. Growing and caring for plants may also reduce the psychological stresses associated with spaceflight and enhance connections to Earth. Requirements to consistently grow a diversity of crops under spaceflight environmental conditions remain poorly defined. The VEG-05 experiment is part of a series of experiments with pick-and-eat salad crops to better define best practices for crop production and handling in space. VEG-05 and predecessor experiments VEG-04A and VEG-04B, use the Veggie vegetable production facilities on the International Space Station to grow salad crops under different spectral compositions. In VEG-04A and B, mizuna mustard was cultivated with two different red: blue light treatments, and in VEG-05 we are cultivating ‘Red Robin’ dwarf cherry tomatoes under the same light spectra. Light can impact the growth habit, yield, nutritional composition, microbial levels, and even flavor attributes within crops, and our team will assess these characteristics for this crop during VEG-05. Prior to launch and installation on ISS in late 2022, both a science verification test (SVT), and an experiment verification test (EVT) were conducted at Kennedy Space Center in ISS Environment Simulator Chambers. Science verification testing, and a previous fertilizer test, grew plants in both plant pillows and PONDS (Passive Orbital Nutrient Delivery System) units and tested two different fertilizer treatments in both sets of hardware, with each test under only one of the light conditions. Because of challenges validating the PONDS hardware on ISS, and with good crop production in plant pillows, the EVT moved forward using only plant pillows with the highest fertilizer composition tested during SVT, and two Veggie units were utilized. One Veggie had light settings consisting of equal levels of red: blue light (150 µmol/m2/s for each color) plus green light (30 µmol/m2/s) while the second Veggie had a 90:10 ratio of red: blue light (270 µmol/m2/s red and 30 µmol/m2/s blue) plus green, so each unit provided 330 µmol/m2/s of photosynthetically active radiation to the tomato crops on average. Our original plan, based on prior ground testing, was to grow the crop for 104 days and harvest at 80, 90 and 104 days after initiation. For SVT, under the equal red: blue light treatment, fruit ripening in plant pillows was delayed and fruit were not ripe by day 80, so actual harvest days were days 90, 97, and 104. For EVT we saw fruit ripening earlier, especially in the high red treatment, and so we harvested at days 83, 90, and 99 days after initiation. In SVT we had mostly daily watering, and this led to excess water in plant pillows, which leaked out. This excess water also caused fungus to grow on one leaf and a couple of plant stems. To reduce this excess moisture, we throttled back the watering for EVT, and used the root mat reservoir more frequently. This led to watering only every other day, reducing crew time needed for plant care, however, two wilting events occurred during this EVT, at days 51 and 75. Plants recovered from these wilting events, but these events may have influenced the rate of fruit ripening and flower formation. Regardless, more than 10 fruit were produced from each plant on average, with the high red treatment producing slightly heavier fruit. Microbial testing from fruit during SVT indicated that fruit were safe for consumption with microbial levels below detection limits. VEG-05 flight and ground operations are expected to run between December 2022 and March 2023. This research was co-funded by the Human Research Program and Space Biology (MTL#1075) in the ILSRA 2015 NRA call.

Gioia D. Massa↗

Landscape-scale Characterization of Arctic Tundra Vegetation Composition, Structure, and Function with a Multi-sensor Unoccupied Aerial System: Supporting Data.

Airborne remote sensing data collected using the Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform, the Osprey, operated by the Terrestrial Ecosystem Science and Technology (TEST) group (https://www.bnl.gov/testgroup). This package includes data from three flights flown over the NGEE-Arctic Council, Kougarok and Teller sites in July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface 'skin' temperature imagery, as well as surface reflectance at 1 nm intervals in the visible to near-infrared spectral range 350 - 1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model (CHM). In addition, a VNIR surface reflectance file is provided for the trigger locations collected during each flight campaign. Ancillary aircraft data, flight mission parameters, and general flight conditions provided by the onboard flight and data collection computers are also included. Unprocessed and processed data products are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *.kml, *hdr, *.enp), tabular (*.dat, *.csv, *.waypoint, ENVI format (no extension)), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. This metadata document contains flight campaign, instrument and file metadata, along with a description of data processing levels, data products and file naming scheme.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗