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NASA NTRS2004

A GEO Hyperspectral Mission For Continental-Scale Carbon Cycle Observations

For both terrestrial and ocean carbon cycle science objectives, a hyperspectral geostationary sensor should enable the development of new remote sensing measurements for important but as yet unobservable variables, and with the overall goal of linking both terrestrial and ocean carbon cycle processes to climate variability. For terrestrial research, accurate estimates of carbon, water and energy (CWE) exchange between the terrestrial biosphere and atmosphere are needed to identify the geographical locations of carbon sources/sinks and to improve regional climate models and global climate change assessments. It is an enormous challenge to estimate CWE exchange from the infrequent temporal coverage provided by most polar-orbiting satellites, and without benefit of spectral indices that capture vegetation responses to stress conditions that down-regulate photosynthesis. Physiological status can be better assessed with spectral indices based on continuous, narrow (5 nm) bands, as can seasonal and annual terrestrial productivity. For coastal and ocean constituents, narrow-band observations in the ultraviolet and visible are essential to investigate the variability, dynamics and biogeochemical cycles of the world s coastal and open ocean regions, which will in turn help in measuring ocean productivity and predicting the variability of Ocean carbon uptake and its role in climate change scenarios. The GSFC Carbon Team has been pursuing a geostationary hyperspectral instrument, which would revolutionize our knowledge of biological processes on land, in the ocean, and along the coast by providing multiple, diurnal coverage. Preliminary studies in Goddard's Instrument Synthesis and Analysis Laboratory (ISAL) indicate that we can meet many of our science requirements: full spectral coverage (360-1000 nm); narrow bandwidths (5-10 nm); adequate ground resolution (100-200 m); and continental-scale coverage 4-6 times per day; all the while achieving a signal to noise ratio of between 500 and 1000 to 1. However, an innovative and bold focal plane design and a large mirror (1.8 meter diameter) would be required. The development of our science requirements and the results of the initial design study will be presented as well as our most recent technological developments.

Gervin, Janette C.↗

NASA NTRS2004

A LEO Hyperspectral Mission Implementation for Global Carbon Cycle Observations

For both terrestrial and ocean carbon cycle science objectives, high resolution (less than l0 nm) imaging spectrometers capable of acquiring multiple regional to global scale observations per day should enable the development of new remote sensing measurements for important but as yet unobservable variables, with the overall goal of linking both terrestrial and ocean carbon cycle processes to climate variability. For terrestrial research, accurate estimates of carbon, water and energy (CWE) exchange between the terrestrial biosphere and atmosphere a needed to id- the geographical locations and temporal dynamics of carbon sources/sinks and to improve regional climate models and climate change assessments. It is an enormous challenge to estimate CWE exchange from the infrequent temporal coverage and sparse spectral information provided by most single polar-orbiting, earth-looking satellite. The available satellite observations lack a sufficient number of well-placed narrow bands from which to derive spectral indices that capture vegetation responses to stress conditions associated with down-regulation of photosynthesis. Physiological status can best be assessed with spectral indices based on continuous, narrow bands in the visible/near infrared spectra, as can seasonal and annual terrestrial productivity. For coastal and ocean constituents, narrow-band observations in the ultraviolet and visible are essential to investigate the variability, dynamics and biogeochemical cycles of the world's coastal and open ocean regions, which will in turn help in measuring ocean productivity and predicting the variability of ocean carbon uptake and its role in climate change.

Gervin, Janette C.↗

NASA NTRS2010

Utilizing In Situ Directional Hyperspectral Measurements to Validate Bio-Indicator Simulations for a Corn Crop Canopy

Two radiative transfer canopy models, SAIL and the two-layer Markov-Chain Canopy Reflectance Model (MCRM), were coupled with in situ leaf optical properties to simulate canopy-level spectral band ratio vegetation indices with the focus on the photochemical reflectance index in a cornfield. In situ hyperspectral measurements were made at both leaf and canopy levels. Leaf optical properties were obtained from both sunlit and shaded leaves. Canopy reflectance was acquired for eight different relative azimuth angles (psi) at three different view zenith angles (Theta (sub v)), and later used to validate model outputs. Field observations of photochemical reflectance index (PRI) for sunlit leaves exhibited lower values than shaded leaves, indicating higher light stress. Canopy PRI expressed obvious sensitivity to viewing geometry, as a function of both Theta (sub v) and psi . Overall, simulations from MCRM exhibited better agreements with in situ values than SAIL. When using only sunlit leaves as input, the MCRM-simulated PRI values showed satisfactory correlation and RMSE, as compared to in situ values. However, the performance of the MCRM model was significantly improved after defining a lower canopy layer comprised of shaded leaves beneath the upper sunlit leaf layer. Four other widely used band ratio vegetation indices were also studied and compared with the PRI results. MCRM simulations were able to generate satisfactory simulations for these other four indices when using only sunlit leaves as input; but unlike PRI, adding shaded leaves did not improve the performance of MCRM. These results support the hypothesis that the PRI is sensitive to physiological dynamics while the others detect static factors related to canopy structure. Sensitivity analysis was performed on MCRM in order to better understand the effects of structure related parameters on the PRI simulations. Leaf area index (LAI) showed the most significant impact on MCRM-simulated PRI among the parameters studied. This research shows the importance of hyperspectral and narrow band sensor studies, and especially the necessity of including the green wavelengths (e.g., 531 nm) on satellites proposing to monitor carbon dynamics of terrestrial ecosystems.

Cheng, Yen-Ben↗

DOE OSTI2026

Hyperspectral traits (TSWIFT) UC Davis Populus trichocarpa Common Garden

This dataset provides tower-based hyperspectral remote sensing measurements of individualPopulustrees collected with the TSWIFT system to support genetic analyses of canopy photosynthetic traits over time under drought. From 2022-08-18 to 2022-10-18, spectra were repeatedly acquired from the same targeted canopy area of each tree using fixed pointing coordinates. The dataset includes hyperspectral measurements from 400–900 nm and ultraspectral measurements from 730–780 nm. These spectra enable calculation of reflectance-based vegetation indices and other spectral traits, including solar-induced fluorescence (SIF) retrievals from the ultraspectral region. Because measurements were collected exclusively over a drought treatment plot, derived phenotypes are intended for drought-context genetic association and prediction analyses.

09 BIOMASS FUELS↗

NASA NTRS1982

Association of spectral development patterns with development stages of corn

Association is made between the development stages of corn as defined by Hanway and the temporal-spectral development pattern of corn in a transformed data space derived from Landsat-MSS band reflectance values, using field-collected reflectance and associated data. Results indicate that the spectral vegetation index used (a reflectance equivalent of Tasseled Cap Greenness) reaches a maximum well before the stage at which corn is expected to achieve its peak leaf area index. Possible physiological and canopy geometry related causes for this and other results are presented.

Crist, E. P.↗

NASA NTRS2010

Diurnal and Directional Responses of Chlorophyll Fluorescence and the PRI in a Cornfield

Determining the health and vigor of vegetation using high spectral resolution remote sensing is an important goal which has application to monitoring agriculture and ecosystem productivity and carbon exchange. Two spectral indices used to assess whether vegetation is performing near-optimally or exhibiting symptoms of environmental stress (e.g., drought or nutrient deficiency, non-optimal temperatures, etc.) are the Photochemical Reflectance Index (PRI) and solar-induced red and far-red Chlorophyll Fluorescence (Fs). Both the PRI and Fs capture the dynamics of photoprotection mechanisms within green foliage: the PRI is based on the association of the reflected radiation in the green spectrum with the xanthophyll cycle, whereas Fs measures the emitted radiation in the red and far-red spectrum. Fs was determined from retrievals in the atmospheric oxygen absorption features centered at 688 and 760 nm using a modified Fraunhofer Line Depth (FLD) method. We previously demonstrated diurnal and seasonal PRI differences for sunlit vs. shaded foliage in a conifer forest canopy, as expressed in the hotspot and darkspot of the Bidirectional Reflectance Function (BRF). In a USDA-ARS experimental field site located in Beltsville, MD, USA, measurements were acquired over a corn crop from a nadir view in 2008 with an ASD FieldSpec Pro (Analytical Spectral Devices, Inc., Boulder, CO, USA) to study the behavior of the PRI for sunlit and shaded foliage as captured in reflectance variations associated with the BRF, in a I m tall canopy in the vegetative growth stage. Those observations were compared to simulations obtained from two radiative transfer models. Measurements were then acquired to examine whether the PRI and Fs were influenced by view zenith and azimuth geometries at different times of day. Those measurements were made in 2010 with the Ocean Optics USB4000 Miniature Fiber Optic Spectrometer (Ocean Optics Inc., Dunedin, Florida, USA) at several times during the day on multiple days throughout the growing season. We found that the PRI consistently had higher values, indicating lower stress, in the BRF darkspot associated with shaded foliage than in the hotspot associated with sunlit foliage. We also found that Fs exhibited differences associated with sunlit and shaded canopy sectors, which were most pronounced for the red/far-red Fs ratio. Values indicated greater physiological stress in afternoons compared to mornings, and in the early senescent canopy as compared to the vegetative growth stage, BRFs for both the PRI and the red/far-red Fs ratio were bowl-shaped for the full azimuth sweep of the canopy. These two spectral indices (PRI, Fs ratio) provided complementary information on the photosynthetic function of the corn canopy.

Middleton, Elizabeth↗

DOE OSTI2020

Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize

Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1985

Influence of rock-soil spectral variation on the assessment of green biomass

A comparison of how n-spaced and ratio-based vegetation indices respond to rock and soil spectral variation is made, using a set of ground-based reflectance spectra and airborne Thematic Mapper imagery of the Virginia Range, NV. The influence of variations in rock-soil brightness on ratio-based vegetation indices is also discussed. It is shown that of all the vegetation indices tested, the perperdicular vegetation index is the most appropriate for use in multispectral imagery of arid and semiarid regions where there is a wide variation in substrate characteristics.

Elvidge, C. D.↗

NASA NTRS1990

High resolution derivative spectra in remote sensing

The use of derivative spectra is an established technique in analytical chemistry for the elimination of background signals and for resolving overlapping spectral features. Application of this technique for tackling analogous problems such as interference from soil background reflectance in the remote sensing of vegetation or for resolving complex spectra of several target species within individual pixels in remote sensing is proposed. Methods for generating derivatives of high spectral resolution data are reviewed. Results of experiments to test the use of derivatives for monitoring chlorosis in vegetation show that derivative spectral indices are superior to conventional broad-band spectral indices such as the near-infrared/red reflectance ratio. Conventional broad-band indices are sensitive to both leaf cover as well as leaf color. New derivative spectral indices which were able to monitor chlorosis unambiguously were identified. Potential areas for the application of this technique in remote sensing are considered.

Demetriades-Shah, Tanvir H.↗

NASA NTRS2022

Southern Wyoming Ecological Forecasting: Monitoring Cheatgrass in Southern Wyoming and Northern Colorado to Inform Management Efforts Post-Mullen Fire

Cheatgrass (Bromus tectorum) is a prominent invasive species in the Intermountain West that has the potential to out-compete native plant species, reduce biodiversity, and reduce the quality of habitat for ungulates. Furthermore, because cheatgrass readily establishes in disturbed landscapes, it can potentially increase fuel loads and exacerbate wildfire risk. In 2020, the Mullen Fire burned 176,878 acres in Carbon and Albany Counties, Wyoming and Jackson County, Colorado. Large fires such as this one raise concern for partners at the United States Forest Service and the United States Geological Survey Fort Collins Science Center, who are tasked with rapidly detecting and controlling invasive species in the post-fire environment. We developed a Random Forest model trained by in-situ field data and spectral indices such as Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index, and Enhanced Vegetation Index derived from Landsat 8 Operational Land Imager, Sentinel-2 MultiSpectral Instrument, and Shuttle Radar Topography Mission to detect and map cheatgrass presence during the 2021 growing season. The team successfully created a spectral cheatgrass detection map in the study area (RMSE = 13.71, R2 = 0.34). We also produced a NDVI time-series derived from Sentinel-2 MSI to analyze vegetation recovery patterns.

Dahlia Shahin↗

NASA NTRS2011

Spectral Bio-indicator Simulations for Tracking Photosynthetic Activities in a Corn Field

Accurate assessment of vegetation canopy optical properties plays a critical role in monitoring natural and managed ecosystems under environmental changes. In this context, radiative transfer (RT) models simulating vegetation canopy reflectance have been demonstrated to be a powerful tool for understanding and estimating spectral bio-indicators. In this study, two narrow band spectroradiometers were utilized to acquire observations over corn canopies for two summers. These in situ spectral data were then used to validate a two-layer Markov chain-based canopy reflectance model for simulating the Photochemical Reflectance Index (PRI), which has been widely used in recent vegetation photosynthetic light use efficiency (LUE) studies. The in situ PRI derived from narrow band hyperspectral reflectance exhibited clear responses to: 1) viewing geometry which affects the asset of light environment; and 2) seasonal variation corresponding to the growth stage. The RT model (ACRM) successfully simulated the responses to the variable viewing geometry. The best simulations were obtained when the model was set to run in the two layer mode using the sunlit leaves as the upper layer and shaded leaves as the lower layer. Simulated PRI values yielded much better correlations to in situ observations when the cornfield was dominated by green foliage during the early growth, vegetative and reproductive stages (r = 0.78 to 0.86) than in the later senescent stage (r = 0.65). Further sensitivity analyses were conducted to show the important influences of leaf area index (LAI) and the sunlit/shaded ratio on PRI observations.

Cheng, Yen-Ben↗

NASA NTRS2021

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

Crop yield monitoring is an important component in agricultural assessment. Multispectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 µm), red-edge (0.726 µm), and near-infrared (NIR - 0.865 µm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R^2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R^2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.

agriculture; yield; within-field; corn; soybean; r↗

NASA NTRS1995

AVIRIS spectral trajectories for forested areas of the Gifford Pinchot National Forest

A simple mixing model employing reference endmembers (green vegetation, non-photosynthetic vegetation, soil and shade), and using 180 AVIRIS bands, was used to establish an interpretive framework for a forested area in the Pacific Northwest. A regrowth trend, based on changes in the endmember proportions, was defined for conifers that extends from clearcuts to mature forest, and by implication to old growth. Deciduous species within replanted forest plots caused the fractions to be displaced from the main coniferous regrowth trend and to move toward the green vegetation fraction. The results indicate that the spectral information in AVIRIS can be inverted to estimate approximate stand age and relative proportion of deciduous species in the context of the area studied. Using AVIRIS we measured a 3 to 5 percent increase in woody material in old-growth forest, as distinct from other mature forest. This result is consistent with a predicted increase in NPV in old-growth forest, based on field observations. Previous application of the mixing analysis to a TM image of the same area separated old growth based solely on the shade fraction; however the approach required successful removal of shade introduced by topography. Our new results suggest that with the high spectral resolution and high signal-to-noise of AVIRIS images it may be possible to characterize and map old-growth forests in the Northwest using both the NPV fraction and shade.

Sabol, Donald E., Jr.↗

NASA NTRS1987

Comparison of North and South American biomes from AVHRR observations

Previous analysis of the North American continent with AVHRR-derived vegetation index measurements showed a strong relation between known patterns of vegetation seasonality, productivity and the spectral vegetation index measurements. This study extends that analysis to South America to evaluate the degree to which these findings extend to tropical regions. The results show that the spectral vegetation index measurements provide a general indicator of vegetation activity across the major biomes of the Western Hemisphere of the earth, including tropical regions. The satellite-observed patterns are strongly related to the known climatology of the continents and may offer a means to improve understanding of global bioclimatology. For example, South America is shown to have a longer growing season with much earlier spring green-up than North America. The time integral of the measurements, computed from 12 composited monthly values, produces a value that is related to published net primary productivity data. However, limited net primary production data does not allow complete evaluation of satellite-observed contrasts between North and South American biomes. These results suggest that satellite-derived spectral vegetation index measurements are of great potential value in improving knowledge of the earth's biosphere.

Goward, Samuel N.↗

DOE OSTI2020

Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning

Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models.

47 OTHER INSTRUMENTATION↗

NASA NTRS1985

Spectral Characterization of Suspected Acid Deposition Damage in Red Spruce (picea Rubens) Stands from Vermont

In an attempt to demonstrate the utility of remote sensing systems to monitor sites of suspected acid rain deposition damage, intensive field activities, coupled with aircraft overflights, were centered on red spruce stands in Vermont during August and September of 1984. Remote sensing data were acquired using the Airborne Imaging Spectrometer, Thematic Mapper Simulator, Barnes Model 12 to 1000 Modular Multiband Radiometer and Spectron Engineering Spectrometer (the former two flown on the NASA C-130; the latter two on A Bell UH-1B Iroquois Helicopter). Field spectral data were acquired during the week of the August overflights using a high spectral resolution spectrometer and two broad-band radiometers. Preliminary analyses of these data indicate a number of spectral differences in vegetation between high and low damage sites. Some of these differences are subtle, and are observable only with high spectral resolution sensors; others are less subtle and are observable using broad-band sensors.

Vogelmann, J. E.↗

NASA NTRS1985

Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop

The Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on April 8 to 10, 1985. It was attended by 92 people who heard reports on 30 investigations currently under way using AIS data that have been collected over the past two years. Written summaries of 27 of the presentations are in these Proceedings. Many of the results presented at the Workshop are preliminary because most investigators have been working with this fundamentally new type of data for only a relatively short time. Nevertheless, several conclusions can be drawn from the Workshop presentations concerning the value of imaging spectrometry to Earth remote sensing. First, work with AIS has shown that direct identification of minerals through high spectral resolution imaging is a reality for a wide range of materials and geological settings. Second, there are strong indications that high spectral resolution remote sensing will enhance the ability to map vegetation species. There are also good indications that imaging spectrometry will be useful for biochemical studies of vegetation. Finally, there are a number of new data analysis techniques under development which should lead to more efficient and complete information extraction from imaging spectrometer data. The results of the Workshop indicate that as experience is gained with this new class of data, and as new analysis methodologies are developed and applied, the value of imaging spectrometry should increase.

Vane, G.↗

NASA NTRS1983

The response of vegetation to geochemical conditions

An understanding of the factors of vegetation response to changes in the geochemistry of the environment may give exploration geologists and other researchers an additional and effective tool for rock type discrimination. The factors of vegetation response can be grouped into three principal categories: structural or morphological factors, taxonomic factors which include indicator flora as well as vegetation assemblages, and spectral factors which represent the manner in which the vegetation interacts with electromagnetic radiation. The response of these factors over areas of anomalous mineralization is often unique and may be due to nutrient deficiencies and/or imbalances, toxicity and stress caused by anomalous mineral concentrations in the soil, low water retention, and plant competition. The successful use of geobotanical techniques results from the integration of the geobotanical observations with other techniques. The use of remote sensing in such a program must be predicated on those factors which can be discriminated within the constraints of the spatial, spectral, radiometric, and temporal resolutions of the sensing system and with appropriate analytical techniques.

Mouat, D. A.↗