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At least 127 records · Page 7

Producing Global Science Products for the Moderate Resolution Imaging Spectroradiometer (MODIS) in MODAPS

The MODerate resolution Imaging Spectroradiometer (MODIS) was launched on NASA's EOS-Terra spacecraft December 1999. With 36 spectral bands covering the visible, near wave and short wave infrared. MODIS produces over 40 global science data products, including sea surface temperature, ocean color, cloud properties, vegetation indices land surface temperature and land cover change. The MODIS Data Processing System (MODAPS) produces 400 GB/day of global MODIS science products from calibrated radiances generated in the Earth Observing System Data and Information System (EOSDIS). The science products are shipped to the EOSDIS for archiving and distribution to the public. An additional 200 GB of products are shipped each day to MODIS team members for quality assurance and validation of their products. In the sections that follow, we will describe the architecture of the MODAPS, identify processing bottlenecks encountered in scaling MODAPS from 50 GB/day backup system to a 400 GB/day production system and discuss how these were handled.

Masuoka, Edward J.↗

Development of the Ames Global Hyperspectral Synthetic Data Set: Surface Bidirectional Reflectance Distribution Function

This study introduces the Ames Global Hyperspectral Synthetic Data set (AGHSD), in particular the surface bidirectional reflectance distribution function (BRDF) product, to support the NASA Surface Biology and Geology (SBG) mission development. The data set is generated based on the corresponding multispectral BRDF products from NASA's MODIS satellite sensor. Based on theories of radiative transfer in vegetation canopies, we derive a simple but robust relationship that indicates that the hyperspectral surface BRDF can be accurately approximated as a weighted sum of the soil surface reflectance, the leaf single albedo, and the canopy scattering coefficient, where the weights or coefficients are spectrally invariant and thus readily estimated from the multispectral MODIS products. We validate the algorithm with simulations by a Monte Carlo Ray Tracing model and find the results highly consistent with the theoretic derivation. Using reflectance spectra of soil and vegetation derived from existing spectral libraries, we apply the algorithm to generate the AGHSD BRDF product at 1 km and 8-day resolutions for the year of 2019. The data set is biogeochemically and biogeophysically coherent and consistent, and serves the goal to support the SBG community in developing sciences and applications for the future global imaging spectroscopy mission.

Hyperspectral Remote Sensing↗

The red edge in arid region vegetation: 340-1060 nm spectra

The remote sensing study of vegetated regions of the world has typically been focused on the use of broad-band vegetation indices such as NDVI. Various modifications of these indices have been developed in attempts to minimize the effect of soil background, e.g., SAVI, or to reduce the effect of the atmosphere, e.g., ARVI. Most of these indices depend on the so-called 'red edge,' the sharp transition between the strong absorption of chlorophyll pigment in visible wavelengths and the strong scattering in the near-infrared from the cellular structure of leaves. These broadband indices tend to become highly inaccurate as the green canopy cover becomes sparse. The advent of high spectral resolution remote sensing instrument such as the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) has allowed the detection of narrow spectral features in vegetation and there are reports of detection of the red edge even for pixels with very low levels of green vegetation cover by Vane et al. and Elvidge et al., and to characterize algal biomass in coastal areas. Spectral mixing approaches similar to those of Smith et al. can be extended into the high spectral resolution domain allowing for the analysis of more endmembers, and potentially, discrimination between material with narrow spectral differences. Vegetation in arid regions tends to be sparse, often with small leaves such as the creosote bush. Many types of arid region vegetation spend much of the year with their leaves in a senescent state, i.e., yellow, with lowered chlorophyll pigmentation. The sparseness of the leaves of many arid region plants has the dual effect of lowering the green leaf area which can be observed and of allowing more of the sub-shrub soil to be visible which further complicates the spectrum of a region covered with arid region vegetation. Elvidge examined the spectral characteristics of dry plant materials showing significant differences in the region of the red edge and the diagnostic ligno-cellulose absorptions at 2090 nm and 2300 nm. Ray et al. detected absorption at 2100 nm in AVIRIS spectra of an abandoned field known to be covered by a great deal of dead plant litter. In order to better study arid region vegetation remote sensing data, it is necessary to better characterize the reflectance spectra of in situ, living, arid region plants.

Ray, Terrill W.↗

Surface Energy Heat Fluxes Using Remotely Sensed Parameters

Realistic estimates of surface energy heat fluxes are needed for the study of water and energy interactions between the land and atmosphere. The primary objective of this work is to study the estimation of surface heat energy fluxes using remote sensing derived parameters under different spatial and temporal conditions. Surface energy fluxes and remote sensing derived data from two sources were analyzed. First, we used surface heat flux, remote sensing, and ancillary data from the International Satellite Land Surface Climatology Project (ISLSCP), mapped at a 1 deg. x 1 deg. grid. Second, we used NOAA AVHRR (1 km), weather station, and ancillary data to derive estimates of surface latent and sensible heat energy fluxes over a 100 sq kilometers area for three test sites: 1) First ISLSCP Field Experiment (FIFE) grassland site, Konza Prairie, Kansas; 2) Howland, Maine Forest Ecosystem Dynamics Site; and 3) Walnut Gulch, scrubland site, surrounding Tombstone, Arizona. Satellite derived estimates of land surface temperature, surface albedo, and spectral vegetation index are used in selected models to provide estimates of surface heat fluxes. Analysis of results from the 1 deg. x 1 deg. grid for North America indicated there were similar, overall correlations between sensible and latent heat energy fluxes versus remotely sensed vegetation index and ground temperature during dry and wet year conditions. However, there were significant differences in correlations between years when stratified by land cover class. Analysis of 100 km x 100 km data (1 km resolution) indicated partitioning the areas in to primary versus secondary cover, with the secondary cover comprising less than 5% of the area, significantly improved surface heat energy flux estimates.

Toll, David L.↗

Canopy Level Solar Induced Fluorescence for Vegetation in Controlled Experiments

Solar induced chlorophyll fluorescence (SIF) was retrieved from high resolution reflectance spectra acquired one meter above saplings of three deciduous tree species during springtime (three weeks after leaf flush) and in late summer when foliage was mature. SIF was determined by application of the Fraunhofer Line Depth (FLD) Principal to above-canopy spectra acquired with an Analytical Spectral Devices (ASD) Fieldspec spectroradiometer (3.2 nm resolution with 1.2 nm sampling interval). SIF retrievals were made at the two atmospheric oxygen (O2) absorption features that occur in the chlorophyll fluorescence (ChlF) region (660 -780 nm). These telluric features are 02V, the broader and deeper feature centered at 760 nm, but located on the shoulder of the far-red ChlF peak at 740 nm; and 023, a narrow feature centered at 688 nm that is positioned near the red ChlF peak at 685 nm. Supporting, coincident leaf level fluorescence, reflectance, photochemical and other measurements were also made. At the leaf level, these measurements included in situ photosynthetic capacity (Pmax) and light adapted total chlorophyll fluorescence (Fs') collected at steady state under high light and controlled chamber conditions (e.g., temperature, PAR, humidity, and COz); optical properties (reflectance, transmittance, absorptance); chlorophyll and carotenoid content; specific leaf mass; carbon (C) and nitrogen (N) content; fluorescence emission spectra at multiple excitation wavelengths; the ChlF contribution to red (R) and far-red (FR) reflectance; fluorescence imagery; and fluorescence excitation-emission matrices (EEMs). The tree species examined were tulip poplar (Liriodendron tulipifera L.), red maple (Acer rubrum L.), and sweetgum (Liquidambar styraczflua L.), and each had been provided four levels of N augmentation (0, 19, 37, and 75 kg Nhectare seasonally) to simulate atmospheric deposition from air pollution. Whole-plant SIF measurements of these species were compared with SIF estimates derived using FluorMOD, a radiative transfer model that includes fluorescence properties of foliage, when provided with our supporting measurements. Simulated values for SIF were also compared with similar estimates made over three years for corn (Zea mays L) crops under N treatments (20, 50,70, and 140 kg Nhectare) and with corn and pepper (Capsicum annuum L.) plants provided dimethyl urea (DCMU, 0 and 5 x 10" M) which were grown in pots and grouped in artificial canopies for spectral measurements. For near-surface measurements of healthy and stressed vegetation, red SIF retrieved at 688 nm (023) varied between 2-7 mW/m(exp 2)/nm/sr while far-red SIF retrieved at 760 nm (O2A) varied between 0.5 and 4.0 mW/m(exp 2)/nm/sr. Typical values for the SIF red/far-red ratio ranged between 1.75 and 4.0. Relationships of SIF to spectral reflectance indices and foliar photochemical indices were examined, such as photosynthetic light use efficiency. Initial investigations of the variability in these measurements at the same leaf temperature for sunlit and shaded foliage showed us that shaded foliage produced higher ChlF and light use efficiency. Other factors that affect SIF determinations are discussed. These results will assist in determine the expected intensity of the SIF signal from vegetation near the surface, as well as to identify enhancements needed for FluorMOD (or other such models), and will assist in determining relationships of SIF to reflectance indices and carbon dynamics.

Middleton, E. M.↗

Remote sensing of biomass and annual net aerial primary productivity of a salt marsh

Net aerial primary productivity is the rate of storage of organic matter in above-ground plant issues exceeding the respiratory use by the plants during the period of measurement. It is pointed out that this plant tissue represents the fixed carbon available for transfer to and consumption by the heterotrophic organisms in a salt marsh or the estuary. One method of estimating annual net aerial primary productivity (NAPP) required multiple harvesting of the marsh vegetation. A rapid nondestructive remote sensing technique for estimating biomass and NAPP would, therefore, be a significant asset. The present investigation was designed to employ simple regression models, equating spectral radiance indices with Spartina alterniflora biomass to nondestructively estimate salt marsh biomass. The results of the study showed that the considered approach can be successfully used to estimate salt marsh biomass.

Hardisky, M. A.↗

Optimizing Landsat Next Shortwave Infrared Bands for Crop Residue Characterization

This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (f R ) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue field spectra collected in Beltsville, Maryland, USA (n = 916) and computed generalized two- and three-band spectral indices for all wavelength combinations between 2000 and 2350 nm, then used these indices to model field-measured f R . A subset of the full dataset with a Normalized Difference Vegetation Index (NDVI) < 0.3 threshold (n = 643) was generated to evaluate green vegetation impacts on f R estimation. For the two-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized normalized difference using 2226 nm and 2263 nm bands produced the top f R estimation performance (R 2 = 0.8222; RMSE = 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) (R 2 = 0.8145; RMSE = 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset (R 2 = 0.5865 and 0.4144, respectively). For the three-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized ratio-based index with a 2031–2085–2216 nm band combination, closely matching established Cellulose Absorption Index (CAI) bands, was top performing (R 2 = 0.8397; RMSE = 0.1231). Three-band indices with CAI-type wavelengths maintained top f R estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination (R 2 = 0.7581; RMSE = 0.1548). The 2036–2111–2217 nm band combination was also top performing in f R estimation (R 2 = 0.8690; RMSE = 0.0970) for an additional analysis assessing combined green vegetation cover and surface moisture effects. Our results indicate that a three-band configuration with band centers and wavelength tolerances of 2036 nm (±5 nm), 2097 nm (±14 nm), and 2214 (±11 nm) would optimize Landsat Next SWIR bands for f R estimation.

Landsat Next↗

Applications of ERTS data to coastal wetland ecology with special reference to plant community mapping and typing and impact of man

Complete seasonal ERTS-1 coverage of Atlantic coastal wetlands from Delaware Bay to Georgia provides a basis for assessment of temporal data for wetland mapping, evaluation, and monitoring. Both MSS imagery and digital data have proved useful for gross wetland species delineation and determination of the upper wetland boundary. Tidal effects and (band to band or seasonal) spectral reflectance differences make it possible to type vegetatively coastal wetlands in salinity related categories. Management areas, spoil disposal sites, drainage ditches, lagoon-type developments and highway construction can be detected indicating a monitoring potential for the future. A northern test site (Maryland-Virginia) and a southern test site (Georgia-South Carolina), representing a range of coastal marshes from saline to fresh, were chosen for intensive study. Wetland maps were produced at various scales using both ERTS imagery (bands 5 and 7) and digital data (bands 4, 5 and 7).

Anderson, R. R.↗

The use of spectral data in wheat yield estimation - An assessment of techniques explored in LACIE

The object of the paper is to assess the results of the Large Area Crop Inventory Experiment (LACIE) and closely related research on yield estimation techniques based on remote sensing variables. The exploratory research conducted during LACIE substantiated the hypothesis of yield related information contained in Landsat multispectral scanner data and indicated some of its empirical characteristics. It is noted that leaf area and possibly other foliage features can be derived from spectral data for yield estimation through agrometeorological models and that multiple vegetative and grain related features may be discernable by Landsat derived wheat spectra at different points in the crop development.

Stuff, R. G.↗

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.

Index optimization↗

Early Detection of Drought Stress in Durum Wheat Using Hyperspectral Imaging and Photosystem Sensing

Wheat, being the third largest U.S. crop and the principal food grain, faces significant risks from climate extremes such as drought. This necessitates identifying and developing methods for early water-stress detection to prevent yield loss and improve water-use efficiency. This study investigates the potential of hyperspectral imaging to detect the early stages of drought stress in wheat. The goal is to utilize this technology as a tool for screening and selecting drought-tolerant wheat genotypes in breeding programs. Additionally, this research aims to systematically evaluate the effectiveness of various existing sensors and methods for detecting early stages of water stress. The experiment was conducted in a durum wheat experimental field trial in Maricopa, Arizona, in the spring of 2019 and included well-watered and water-limited treatments of a panel of 224 replicated durum wheat genotypes. Spectral indices derived from hyperspectral imagery were compared against other plant-level indicators of water stress such as Photosystem II (PSII) and relative water content (RWC) data derived from proximal sensors. Our findings showed a 12% drop in photosynthetic activity in the most affected genotypes when compared to the least affected. The Leaf Water Vegetation Index 1 (LWVI1) highlighted differences between drought-resistant and drought-susceptible genotypes. Drought-resistant genotypes retained 43.36% more water in leaves under well-watered conditions compared to water-limited conditions, while drought-susceptible genotypes retained only 15.69% more. The LWVI1 and LWVI2 indices, aligned with the RWC measurements, revealed a strong inverse correlation in the susceptible genotypes, underscoring their heightened sensitivity to water stress in earlier stages. Several genotypes previously classified based on their drought resistance showed spectral indices deviating from expectations. Results from this research can aid farmers in improving crop yields by informing early management practices. Moreover, this research offers wheat breeders insights into the selection of drought-tolerant genotypes, a requirement that is becoming increasingly important as weather patterns continue to change.

Roy, Bishal (ORCID:0000000199125505)↗

The NASA earth resources spectral information system: A data compilation, second supplement

The NASA Earth Resources Spectral Information System (ERSIS) and the information contained therein are described. It is intended for use as a second supplement to the NASA Earth Resources Spectral Information System: A Data Compilation, NASA CR-31650-24-T, May 1971. The current supplement includes approximately 100 rock and mineral, and 375 vegetation directional reflectance spectral curves in the optical region from 0.2 to 22.0 microns. The data were categorized by subject and each curve plotted on a single graph. Each graph is fully titled to indicate curve source and indexed by subject to facilitate user retrieval from ERSIS magnetic tape records.

Vincent, R. K.↗

A Landsat Thematic Mapper investigation of the geobotanical relationships in the northern spruce-fir forest, Mt. Moosilauke, New Hampshire

This investigation, in the northern spruce-fir forest at Mt. Moosilauke, NH, indicates that Landsat TM data can be used to distinguish between and map major vegetation zones. Principal components analysis can be used to reduce the dimensionality of the TM data; and in this simpler spectral space, it is easier to visualize the discrimination between major vegetation zones: the northern hardwoods zone, spruce-fir zone, fir zone, and alpine tundra zone. The moisture stress index highlights areas of heavy forest damage (fir waves), but does not correlate with low levels of damage in the mixed, background forest at Mt. Moosilauke. Care must be taken to avoid confusion between high-elevation climatically-stressed vegetation (normal krummholz forest) and damaged lower elevation forests, both of which have similar TM5/TM4 ratio values.

Torcoletti, Paul J.↗

Analysis of hyper-spectral AVIRIS image data over a mixed-conifer forest in Maine

An introduction to some of the potential uses of hyperspectral data for ecosystem analysis is presented. The examples given are derived from a digital dataset acquired over a sub-boreal forest in central Maine in 1990 by the NASA-JPL Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) instrument gathers data from 400 to 2500 nm in 224 channels at bandwidths of approximately 10 nm. As a preview to the uses of the hyperspectral data, several products from this dataset were extracted. They range from the traditional false color composite made from simulated Thematic Mapper bands and the well known normalized difference vegetation index to much more exotic products such as fractions of vegetation, soil and shade based on linear spectral mixing models and estimates of the leaf water content at the landscape level derived using spectrum-matching techniques. Our research and that of many others indicates that the hyperspectral datasets carry much important information which is only beginning to be understood. This analysis gives an initial indication of the utility of hyperspectral data. Much work still remains to be done in algorithm development and in understanding the physics behind the complex information signal carried in the hyperspectral datasets. This work must be carried out to provide the fullest science support for high spectral resolution data to be acquired by many of the instruments to be launched as part of the Earth Observing System program in the mid-1990's.

Lawrence, William T.↗

Obtaining spectral reflectance factor measurements of stressed forest vegetation

Techniques and instruments used to collect spectral reflectance data on Vermont spruce and fir stands subjected to stress (due to acid rain and ozone) are described and illustrated. The healthy and stressed sites selected are characterized; the data-quality requirements are indicated; and particular attention is given to (1) a helicopter-mounted instrument package (radiometer, spectrometer, cameras, and video camera) for canopy-level remote sensing of reflectance and (2) a portable hemispherical light source for use in taking laboratory reflectance measurements from branches or needles. Typical data are presented in graphs and briefly discussed.

Case, David W.↗

Hyperspectral Remote Sensing of Foliar Nitrogen Content

A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact - it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423-855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710-790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.

Knyazikhin, Yuri↗

Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation

The author has identified the following significant results. The Great Plains Corridor rangeland project utilizes natural vegetation systems as phenological indicators of seasonal development and climatic effects upon regional growth conditions. A method has been developed for quantitative measurement of vegetation conditions over broad regions using ERTS-1 MSS data. Radiance values recorded in ERTS-1 spectral bands 5 and 7, corrected for sun angle, are used to compute a band ratio parameter which is shown to be correlated with green biomass and vegetation moisture content. This report details the progress being made toward determining factors associated with the transformed vegetation index (TVI) and limitations on the method. During the first year of ERTS-1 operation (cycles 1-20), an average of 50% usable ERTS-1 data was obtained for the ten Great Plains Corridor test sites.

Rouse, J. W., Jr.↗

Monitoring vegetation systems in the Great Plains with ERTS

The Great Plains Corridor rangeland project utilizes natural vegetation systems as phenological indicators of seasonal development and climatic effects upon regional growth conditions. A method has been developed for quantitative measurement of vegetation conditions over broad regions using ERTS-1 MSS data. Radiance values recorded in ERTS-1 spectral bands 5 and 7, corrected for sun angle, are used to compute a band ratio parameter which is shown to be correlated with aboveground green biomass on rangelands.

Rouse, J. W., Jr.↗