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NASA NTRS1977

Monitoring surface albedo change with Landsat

A pronounced decrease of the surface albedo (reflectivity) has been observed in an area in the Northern Sinai, fenced-in in the summer of 1974. Analysis of the Landsat Multispectral Scanner System digital data from an April 1977 pass indicates a reduction in the albedo in the exclosure by 13%, as compared to the outside, which continues to be subjected to overgrazing and anthropogenic pressures. The reduction of reflectivity is approximately the same in all the spectral bands, and is therefore attributable to accumulation of dead plants and plant debris, and not directly to live vegetation.

Otterman, J.↗

NASA NTRS2018

Spatially-Explicit Monitoring of Crop Photosynthetic Capacity Through the Use of Space-Based Chlorophyll Fluorescence Data

Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, V(sub cmax) at a reference temperature of 25 deg C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, V(sub cmax), in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007-2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to V(sub cmax) at the seasonal scale (R(squared)=0.70 for C4 and R(squared)=0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking V(sub cmax) with SIF that was used to estimate spatially and temporally varying crop V(sub cmax) for the US Corn Belt region. The resulting V(sub cmax) maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite- based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal V(sub cmax) from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional V(sub cmax) using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.

Drought↗

NASA NTRS1982

Extension of laboratory-measured soil spectra to field conditions

Spectral responses of two glaciated soils, Chalmers silty clay loam and Fincastle silt loam, formed under prairie grass and forest vegetation, respectively, were measured in the laboratory under controlled moisture equilibria using an Exotech Model 20C spectroradiometer to obtain spectral data in the laboratory under artificial illumination. The same spectroradiometer was used outdoors under solar illumination to obtain spectral response from dry and moistened field plots with and without corn residue cover, representing the two different soils. Results indicate that laboratory-measured spectra of moist soil are directly proportional to the spectral response of that same field-measured moist bare soil over the 0.52 micrometer to 1.75 micrometer wavelength range. The magnitudes of difference in spectral response between identically treated Chalmers and Fincastle soils are greatest in the 0.6 micrometers to 0.8 micrometer transition region between the visible and near infrared, regardless of field condition or laboratory preparation studied.

Stoner, E. R.↗

NASA NTRS2018

An Interplay between Photons, Canopy Structure, and Recollision Probability: A Review of the Spectral Invariants Theory of 3D Canopy Radiative Transfer Processes

Earth observations collected by remote sensors provide unique information to our ever-growing knowledge of the terrestrial biosphere. Yet, retrieving information from remote sensing data requires sophisticated processing and demands a better understanding of the underlying physics. This paper reviews research efforts that lead to the developments of the stochastic radiative transfer equation (RTE) and the spectral invariants theory. The former simplifies the characteristics of canopy structures with a pair-correlation function so that the 3D information can be succinctly packed into a 1D equation. The latter indicates that the interactions between photons and canopy elements converge to certain invariant patterns quantifiable by a few wavelength independent parameters, which satisfy the law of energy conservation. By revealing the connections between plant structural characteristics and photon recollision probability, these developments significantly advance our understanding of the transportation of radiation within vegetation canopies. They enable a novel physically-based algorithm to simulate the “hot-spot” phenomenon of canopy bidirectional reflectance while conserving energy, a challenge known to the classic radiative transfer models. Therefore, these theoretical developments have a far-reaching influence in optical remote sensing of the biosphere.

vegetation remote sensing; stochastic radiative tr↗

NASA NTRS1993

Detection of soil erosion within pinyon-juniper woodlands using Thematic Mapper (TM) data

Multispectral measurements collected by Landsat Thematic Mapper (TM) were correlated with field measurements, direct soil loss estimates, and Universal Soil Loss Equation (USLE) estimates to determine the sensitivity of TM data to varying degrees of soil erosion in pinyon-juniper woodland in central Utah. TM data were also evaluated as a predictor of the USLE Crop Management C factor for pinyon-juniper woodlands. TM spectral data were consistently better predictors of soil erosion factors than any combination of field factors. TM data were more sensitive to vegetation variations than the USLE C factor. USLE estimates showed low annual rates of erosion which varied little among the study sites. Direct measurements of rate of soil loss using the SEDIMENT (Soil Erosion DIrect measureMENT) technique, indicated high and varying rates of soil loss among the sites since tree establishment. Erosion estimates from the USLE and SEDIMENT methods suggest that erosion rates have been severe in the past, but because significant amounts of soil have already been eroded, and the surface is now armored by rock debris, present erosion rates are lower. Indicators of accelerated erosion were still present on all sites, however, suggesting that the USLE underestimated erosion within the study area.

Price, Kevin P.↗

NASA NTRS2001

A New "Cloud Mode" for the AERONET

A key to predicting climate change is to observe an understand the global distribution of clouds and their physical properties such as optical thickness and droplet size. Since clouds change rapidly over short time and space intervals, they are difficult to simulate in computer models. But it is essential that global climate models predict realistic spatial and temporal distribution of cloud optical depth. The best way to verify these distributions is to infer optical depth from global coverage satellite data. However, satellite methods have many sources of uncertainty; thus, independent and reliable ground-based estimates are essential for validation. For aerosol, there is the AERONET - a ground based monitoring network that consists of identical multi-channel radiometers for assessing aerosol optical properties and validating their satellite retrievals. In addition to AEROSOL, we want the network monitoring CLOUD optical properties. It will use AERONET "time" (inappropriate for aerosol studies) to make basic new measurements related to cloud physics. In the presentation we will report on a new technique that retrieves cloud optical thickness for even broken clouds above green vegetation from surface measurements of zenith radiance in the visible (VIS) and near-IR (NIR) spectral regions. The idea of the method is simple: since green vegetation reflects 40-50% of incoming radiation in the NIR and only 5-10% in the VIS region, ground measurements under thin clouds have little spectral contrast between VIS and NIR, while thick clouds reflect much more of the surface-reflected radiation in the NIR than in VIS. Based on this idea, we use a combination of measurements (spectral indices) in VIS and NIR to estimate cloud optical thickness. By analogy with NDVI, the simplest index that can be defined is the Normalized Difference Cloud Index (NDCI) which is a ratio between the difference and the sum of two radiances measured for two narrow spectral bands in VIS and NIR.

Marshak, Alexander↗

NASA NTRS1984

An overview of Landsat-4 status and results

Analyses of Landsat-4 satellite Thematic Mapper (TM) data indicate good spatial, spectral, and radiometric performance. Land cover features such as residential areas and field boundaries, as well as geomorphological features, are more sharply defined with the 30-m nominal resolution of the TM than with the Multispectral Scanner. The new TM spectral bands in the 1.6 and 2.2-micron regions are noted to be useful in the delineation of vegetation types and geological conditions involving hydrothermal alteration.

Salomonson, V. V.↗

NASA NTRS1971

The NASA earth resources spectral information system: A data compilation

The NASA Earth Resources Spectral Information System and the information contained therein are described. It contains an ordered, indexed compilation of natural targets in the optical region from 0.3 to 45.0 microns. The data compilation includes approximately 100 rock and mineral, 2600 vegetation, 1000 soil, and 60 water spectral reflectance, transmittance, and emittance curves. Most of the data have been categorized by subject, and the curves in those subject areas have been plotted on a single graph. Those categories with too few curves and miscellaneous categories have been plotted as single-curve graphs. Each graph, composite of single, is fully titled to indicate curve source and is indexed by subject to facilitate user retrieval.

Leeman, V.↗

NASA NTRS1988

Measuring and modeling near surface reflected and emitted radiation fluxes at the FIFE site

Research was conducted during the four Intensive Field Campaigns (IFC) of the FIFE project in 1987. The research was done on a tall grass prairie with specific measurement sites on and near the Konza Prairie in Kansas. Measurements were made to help meet the following objectives: determination of the variability in reflected and emitted radiation fluxes in selected spectral wavebands as a function of topography and vegetative community; development of techniques to account for slope and sun angle effects on the radiation fluxes; estimation of shortwave albedo and net radiation fluxes using the reflected and emitted spectral measurements described; estimation of leaf and canopy spectral properties from calculated normalized differences coupled with off-nadir measurements using inversion techniques; estimation of plant water status at several locations with indices utilizing plant temperature and other environmental parameters; and determination of relationships between estimated plant water status and measured soil water content. Results are discussed.

Blad, Blaine L.↗

NASA NTRS1977

Application of LANDSAT data to delimitation of avalanche hazards in Montane Colorado

The author has identified the following significant results. Many avalanche hazard zones can be identified on LANDSAT imagery, but not consistently over a large region. Therefore, regional avalanche hazard mapping, using LANDSAT imagery, must draw on additional sources of information. A method was devised that depicts three levels of avalanche hazards according to three corresponding levels of certainty that active avalanches occur. The lowest level, potential avalanche hazards, was defined by delineating slopes steep enough to support avalanches at elevations where snowfall was likely to be sufficient to produce a thick snowpack. The intermediate level of avalanche hazard was interpreted as avalanche hazard zones. These zones have direct and indirect indicators of active avalanche activity and were interpreted from LANDSAT imagery. The highest level of known or active avalanche hazards was compiled from existing maps. Some landslides in Colorado were identified and, to a degree, delimited on LANDSAT imagery, but the conditions of their identification were highly variable. Because of local topographic, geologic, structural, and vegetational variations, there was no unique landslide spectral appearance.

Knepper, D. H., Jr.↗

NASA NTRS1984

Granitic terrains viewed remotely by Shuttle infrared radiometry - Some compositional predictions

Ratios of intensities of near-infrared spectral bands, measured in reflectance, quantitatively predict the iron content of granitic rocks. Such data from the 1.60- and 1.05-micron bands of the Shuttle multispectral infrared radiometer have been used to study granitic rocks along orbits crossing Baja California, Mexico, and the Eastern Desert of Egypt. Both regions are arid and relatively to essentially free of vegetation, and there is no indication in the spectra that surface chemical alteration masks the true composition of the unweathered rock. Predicted values of iron are consistent with known compositional types of most of the rocks along the orbital paths. Results demonstrate that the near-infrared method used is a powerful technique to average the variability of these rocks on the scales of meters to kilometers.

Baird, A. K.↗

NASA NTRS1989

Forest and grassland ecosystem studies using the advanced solid-state array spectroradiometer

The advanced solid-state array spectroradiometer (ASAS) is an airborne, off-nadir pointing imaging spectroradiometer used to acquire bidirectional radiance data for terrestrial targets. As its platform aircraft flies over a target the sensor can image the target through a sequence of at least seven fore-to-aft view directions ranging up to 45 deg on either side of nadir. ASAS acquires data for 29 spectral bands in the visible and near-infrared portions of the spectrum with a resolution of 15 nm. ASAS data were recently acquired for a prairie ecosystem and a northern forest ecosystem. The data demonstrate the combined effects of reflectance anisotropy and increased atmospheric path length on off-nadir observations. One result of these effects is a variation in vegetation indices as a function of view direction. Normalized-difference-vegetation-indices for prairie grass, coniferous, and deciduous canopies varied up to 14 percent, 23 percent, and 6 percent, respectively, relative to nadir as a function of view zenith angle along the solar principal plane.

Irons, James R.↗

NASA NTRS2017

GEONEX: Land Monitoring From a New Generation of Geostationary Satellite Sensors

The latest generation of geostationary satellites carry sensors such as ABI (Advanced Baseline Imager on GOES-16) and the AHI (Advanced Himawari Imager on Himawari) that closely mimic the spatial and spectral characteristics of Earth Observing System flagship MODIS for monitoring land surface conditions. More importantly they provide observations at 5-15 minute intervals. Such high frequency data offer exciting possibilities for producing robust estimates of land surface conditions by overcoming cloud cover, enabling studies of diurnally varying local-to-regional biosphere-atmosphere interactions, and operational decision-making in agriculture, forestry and disaster management. But the data come with challenges that need special attention. For instance, geostationary data feature changing sun angle at constant view for each pixel, which is reciprocal to sun-synchronous observations, and thus require careful adaptation of EOS algorithms. Our goal is to produce a set of land surface products from geostationary sensors by leveraging NASA's investments in EOS algorithms and in the data/compute facility NEX. The land surface variables of interest include atmospherically corrected surface reflectances, snow cover, vegetation indices and leaf area index (LAI)/fraction of photosynthetically absorbed radiation (FPAR), as well as land surface temperature and fires. In order to get ready to produce operational products over the US from GOES-16 starting 2018, we have utilized 18 months of data from Himawari AHI over Australia to test the production pipeline and the performance of various algorithms for our initial tests. The end-to-end processing pipeline consists of a suite of modules to (a) perform calibration and automatic georeference correction of the AHI L1b data, (b) adopt the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce surface spectral reflectances along with compositing schemes and QA, and (c) modify relevant EOS retrieval algorithms (e.g., LAI and FPAR, GPP, etc.) for subsequent science product generation. Initial evaluation of Himawari AHI products against standard MODIS products indicate general agreement, suggesting that data from geostationary sensors can augment low earth orbit (LEO) satellite observations.

geostationary↗

NASA NTRS1978

A Determination of the Optimum Time of Year for Remotely Classifying Marsh Vegetation from LANDSAT Multispectral Scanner Data

The author has identified the following significant results. A technique was used to determine the optimum time for classifying marsh vegetation from computer-processed LANDSAT MSS data. The technique depended on the analysis of data derived from supervised pattern recognition by maximum likelihood theory. A dispersion index, created by the ratio of separability among the class spectral means to variability within the classes, defined the optimum classification time. Data compared from seven LANDSAT passes acquired over the same area of Louisiana marsh indicated that June and September were optimum marsh mapping times to collectively classify Baccharis halimifolia, Spartina patens, Spartina alterniflora, Juncus roemericanus, and Distichlis spicata. The same technique was used to determine the optimum classification time for individual species. April appeared to be the best month to map Juncus roemericanus; May, Spartina alterniflora; June, Baccharis halimifolia; and September, Spartina patens and Distichlis spicata. This information is important, for instance, when a single species is recognized to indicate a particular environmental condition.

Butera, M. K.↗

NASA NTRS2013

Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion-EO-1 Data

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy approx. 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using approx. 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was approx. 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or "the curse of high dimensionality") in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. Index Terms-Hyperion, field reflectance, imaging spectroscopy, HyspIRI, biophysical parameters, hyperspectral vegetation indices, hyperspectral narrowbands, broadbands.

Vegetation↗

NASA NTRS1987

Vegetation spatial variability and its effect on vegetation indices

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification program. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12.7 per cent in determining these areas. NDVI values less than 0.3 represented fractional vegetated areas of 5 per cent or less, while a value of 0.7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0.89 and 0.95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.

Ormsby, J. P.↗

NASA NTRS1982

Study of sensor spectral responses and data processing algorithms and architectures for onboard feature identification

A computational model of the deterministic and stochastic processes involved in remote sensing is used to study spectral feature identification techniques for real-time onboard processing of data acquired with advanced earth-resources sensors. Preliminary results indicate that: Narrow spectral responses are advantageous; signal normalization improves mean-square distance (MSD) classification accuracy but tends to degrade maximum-likelihood (MLH) classification accuracy; and MSD classification of normalized signals performs better than the computationally more complex MLH classification when imaging conditions change appreciably from those conditions during which reference data were acquired. The results also indicate that autonomous categorization of TM signals into vegetation, bare land, water, snow and clouds can be accomplished with adequate reliability for many applications over a reasonably wide range of imaging conditions. However, further analysis is required to develop computationally efficient boundary approximation algorithms for such categorization.

Huck, F. O.↗

NASA NTRS1976

Application of satellite data and LARS's data processing techniques to mapping vegetation of the Dismal Swamp

The feasibility of using digital satellite imagery and automatic data processing techniques as a means of mapping swamp forest vegetation was considered, using multispectral scanner data acquired by the LANDSAT-1 satellite. The site for this investigation was the Dismal Swamp, a 210,000 acre swamp forest located south of Suffolk, Va. on the Virginia-North Carolina border. Two basic classification strategies were employed. The initial classification utilized unsupervised techniques which produced a map of the swamp indicating the distribution of thirteen forest spectral classes. These classes were later combined into three informational categories: Atlantic white cedar (Chamaecyparis thyoides), Loblolly pine (Pinus taeda), and deciduous forest. The subsequent classification employed supervised techniques which mapped Atlantic white cedar, Loblolly pine, deciduous forest, water and agriculture within the study site. A classification accuracy of 82.5% was produced by unsupervised techniques compared with 89% accuracy using supervised techniques.

Messmore, J. A.↗