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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Microwave Backscatter and Attenuation Dependence of Leaf Area Index for Flooded Rice Fields

Wetlands are important for their role in global climate as a source of methane and other reduced trace gases. As part of an effort to determine whether radar is suitable for wetland vegetation monitoring, we have studied the dependence of microwave backscatter and attenuation on leaf area index (LAI) for flooded rice fields. We find that the radar return from a flooded rice field does show dependence on LAI. In particular, the C-band VV cross section per unit area decreases with increasing LAI. A simple model for scattering from rice fields is derived and fit to the observed HH and VV data. The model fit provides insight into the relation of backscatter to LAI and is also used to calculate the canopy path attenuation as a function of LAI.

Durden, Stephen L.↗
NASA NTRS1988

Selecting a spatial resolution for estimation of per-field green leaf area index

For any application of multispectral scanner (MSS) data, a user is faced with a number of choices concerning the characteristics of the data; one of these is their spatial resolution. A pilot study was undertaken to determine the spatial resolution that would be optimal for the per-field estimation of green leaf area index (GLAI) in grassland. By reference to empirically-derived data from three areas of grassland, the suitable spatial resolution was hypothesized to lie in the lower portion of a 2-18 m range. To estimate per-field GLAI, airborne MSS data were collected at spatial resolutions of 2 m, 5 m and 10 m. The highest accuracies of per-field GLAI estimation were achieved using MSS data with spatial resolutions of 2 m and 5 m.

Curran, Paul J.↗
NASA NTRS2022

Estimating Species-Specific Leaf Area Index and Basal Area Using Optical and SAR Remote Sensing Data in Acadian Mixed Spruce-Fir Forests, USA

This study combined Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral, and site variable datasets to model leaf area index (LAI) and basal area per ha (BAPH) of two economically important tree species in Northeast, USA; red spruce (Picea rubens Sarg.; RS), and balsam fir (Abies balsamea (L.) Mill.; BF). We used Random Forest (RF), and Multi-Layer Perceptron (MLP) algorithms for LAI and BAPH modeling. The results showed that RF outperformed MLP by reducing the normalized root mean square error (nRMSE) by 0.01 and 0.06 for LAI and BAPH, respectively. The final variables selected for modeling of both LAI and BAPH indicated the superiority of Sentinel-2 variables over the Sentinel-1 SAR with minor contributions of site variables (mainly elevation). The red-edge spectral vegetation indices played a significant role in both LAI and BAPH estimation. We attained the lowest nRMSEs of 0.12, and 0.16 for the final LAI model of RS, and BF, respectively using Sentinel-2 and site variables. The lowest nRMSE for both RS and BF BAPH models was 0.12. As RS and BF are the primary host species for a cyclically occurring and most destructive pest of the region, eastern spruce budworm (Choristoneura fumiferana; SBW), these estimations will be useful to evaluate SBW dynamics in the region.

Forest inventory↗
NASA NTRS1985

Atmospheric effects on the remote sensing estimation of forest leaf area index

An analysis is presented of the magnitude and variability of the effect of the atmosphere on high-altitude Daedalus Airborne Thematic Mapper data. By regressing ATM radiances against ground radiances (from measurements by a helicopter-mounted Barnes Modular Multiband Radiometer), it was possible to account for atmospheric conditions and variability across a 250 km transect in Oregon to estimate coniferous forest leaf area index. The technique permitted scene contrast to be increased, providing an improved capability for measurement of ground feature radiance.

Spanner, M. A.↗
NASA NTRS1986

Estimation of canopy parameters for inhomogeneous vegetation canopies from reflectance data. II - Estimation of leaf area index and percentage of ground cover for row canopies

The canopy reflectance (CR) model for row-planted vegetation proposed earlier has been tested for soybean canopies in three different stages of growth and for corn canopies at early and full growth stages. The model fits the field-measured bidirectional CR data quite well. It is shown that, by inverting this model, one could estimate the leaf area index as well as the percentage of ground cover quite accurately from measured canopy reflectances.

Goel, N. S.↗
NASA NTRS2019

Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental U.S.

Accurate representation of vegetation states is required for the modeling of terrestrial water-energy-carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing-based Leaf Area Index (LAI) retrievals over the Continental U.S. in the Noah-MP land surface model, during a time period of 2000 to 2017. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms such as soil moisture, evapotranspiration, snow depth, terrestrial water storage and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of Gross Primary Production (GPP) and Net Ecosystem Exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the U.S., where assimilation improves the representation of vegetation seasonality impacted by cropping schedules. The systematic, added improvements from assimilation in a configuration that employs high quality boundary conditions highlight the significant utility of LAI data assimilation in capturing the impacts of vegetation changes.

anthropogenic vegetation↗
NASA NTRS2020

Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershedin the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’sLAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

Adnan Rajib↗
NASA NTRS1981

Crop identification and leaf area index calculations with Landsat multitemporal data

Formulas are given to convert land-based vegetative canopy spectral reflectance measurements into Landsat digital counts in the four channels for a clear standard atmosphere. These formulas are shown to predict the curves found by plotting multitemporal Landsat trajectories. With correct interpretation, plots of channel 3 versus channel 4 Landsat data for crops are shown to yield information on both crop identification and crop leaf area index.

Chance, J. E.↗
NASA NTRS2022

Towards Effective Drought Monitoring in the Middle East and North Africa (Mena) Region: Implications From Assimilating Leaf Area Index and Soil Moisture Into the Noah-Mp Land Surface Model for Morocco

The Middle East and North Africa (MENA) region has experienced more frequent and severe drought events in recent decades, leading to increasingly pressing concerns over already strained food and water security. An effective drought monitoring and early warning system is thus critical to support risk mitigation and management by countries in the region. Here we investigate the potential for assimilation of leaf area index (LAI) and soil moisture observations to improve the representation of the overall hydrological and carbon cycles and drought by an advanced land surface model. The results reveal that assimilating soil moisture does not meaningfully improve model representation of the hydrological and biospheric processes for this region, but instead it degrades the simulation of the interannual variation in evapotranspiration (ET) and carbon fluxes, mainly due to model weaknesses in representing prognostic phenology. However, assimilating LAI leads to greater improvement, especially for transpiration and carbon fluxes, by constraining the timing of simulated vegetation growth response to evolving climate conditions. LAI assimilation also helps to correct for the erroneous interaction between the prognostic phenology and irrigation during summertime, effectively reducing a large positive bias in ET and carbon fluxes. Independently assimilating LAI or soil moisture alters the categorization of drought, with the differences being greater for more severe drought categories. We highlight the vegetation representation in response to changing land use and hydroclimate as one of the key processes to be captured for building a successful drought early warning system for the MENA region.

Wanshu Nie↗
NASA NTRS2016

Detection of Chlorophyll and Leaf Area Index Dynamics from Sub-weekly Hyperspectral Imagery

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense time series of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Houborg, Rasmus↗
NASA NTRS1980

Spectral assessment of soybean leaf area and leaf biomass

Red and photographic infrared spectral radiance have been correlated with soybean total leaf area index, green leaf area index, chlorotic area index, green leaf biomass, chlorotic leaf biomass, and total biomass. The most significant correlations were found to exist between the spectral data and green leaf area index and/or green leaf biomass. These findings demonstrate that ground based remote sensing data can supply information basic to soybean canopy growth, development, and status by non-destructive determination of the green leaf area or green leaf biomass.

Holben, B. N.↗
NASA NTRS2000

BOREAS TF-11 SSA-Fen 1995 Leaf Area Index Data

The BOREAS TF-11 team gathered a variety of data to complement its tower flux measurements collected at the SSA-Fen site. These data are LAI measurements made by the TF-11 team throughout the 1995 growing season. The data include the LAI of plants that fall into six categories: total, Carex spp., Betula pumila, Menyanthes trifoliata, Salix spp., and other vascular plants. The data are stored in tabular ASCII files.

Arkebauer, Timothy J.↗
NASA NTRS1985

Coupled atmosphere/canopy model for remote sensing of plant reflectance features

Solar radiative transfer through a coupled system of atmosphere and plant canopy is modeled as a multiple-scattering problem through a layered medium of random scatterers. The radiative transfer equation is solved by the discrete-ordinates finite-element method. Analytic expressions are derived that allow the calculation of scattering and absorption cross sections for any plant canopy layer form measurable biophysical parameters such as the leaf area index, leaf angle distribution, and individual leaf reflectance and transmittance data. An expression for a canopy scattering phase function is also given. Computational results are in good agreement with spectral reflectance measurements directly above a soybean canopy, and the concept of greenness- and brightness-transforms of Landsat MSS data is reconfirmed with the computed results. A sensitivity analysis with the coupled atmosphere/canopy model quantifies how satellite-sensed spectral radiances are affected by increased atmospheric aerosols, by varying leaf area index, by anisotropic leaf scattering, and by non-Lambertian soil boundary conditions. Possible extensions to a 2-D model are also discussed.

Gerstl, S. A.↗
NASA NTRS1981

Azimuthal radiometric temperature measurements of wheat canopies

The effects of azimuthal view angle on the radiometric temperature of wheat canopies at various stages of development are investigated. Measurements of plant height, total leaf area index, green leaf area index and Feeks growth stage together with infrared radiometric temperature measurements at 12 azimuth intervals with respect to solar azimuth and at different solar zenith angles were obtained for four wheat canopies at various heights. Results reveal a difference on the order of 2 C between the temperatures measured at azimuths of 0 and 180 deg under calm wind conditions, which is attributed to the time-dependent transfer of heat between canopy component surfaces. The azimuthal dependence must thus be taken into account in the determination of radiometric temperatures.

Kimes, D. S.↗