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DOE OSTI2025

Assessing methods in fusion and fitting for time series construction in remote sensing-based earth observations

This study evaluates the comparative performance of spatiotemporal fusion and time-series fitting methods for constructing high-spatiotemporal-resolution remote sensing time-series data. Due to in-class similarity of fusion methods and fitting methods, we employ the Fit-FC (Fitting, spatial Filtering, and residual Compensation) model as a representative fusion method and the linear harmonic fitting model as a representative fitting method. Both Fit-FC and the linear harmonic fitting are widely used for high-spatiotemporal-resolution time-series data construction, and we modify the original Fit-FC model to enable automatic time-series fusion. To ensure data representativeness, we use 3 years (2019–2021) of Harmonized Landsat and Sentinel-2 surface reflectance datasets and Terra MCD43A4 products. Eight experimental regions are selected worldwide to guarantee generalization of the comparative performance between fusion and fitting methods, covering diverse land-use types (cropland, developed land, forest, and grassland) and varying climatological conditions. Time-series of NDVI and surface reflectance are analyzed under both actual observations and simulated data-missing scenarios. The constructed time-series data reveals that (1) the modified Fit-FC and linear harmonic fitting model achieve excellent performance in constructing high-resolution time-series images; (2) the fusion method outperforms the fitting method in constructing time-series of NDVI and surface reflectance images in cropland-, forest-, and grassland-dominated regions; (3) both methods achieve comparable performance in developed-dominated regions; (4) the fusion method is more robust to missing data, and better captures abrupt phenological transitions under conditions of continuous missing data; (5) the fitting method is computationally more efficient, making it suitable for large-scale time-series image reconstruction. This study provides valuable insights for selecting optimal strategies to generate high-resolution time-series images across diverse application scenarios and lays a foundation for extensions to other vegetation indices or land surface variables.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

2024 Workshop - Remote Sensing and Fluxes Upscaling for Real-world Impact - Tutorial v1

The software-tutorial was developed within the 2024 Remote Sensing and Fluxes Upscaling for Real-world Impact workshop as part of the hands-on session. The workshop was supported by AmeriFlux, National Ecological Observatory Network (NEON) and CarbonDew. The software provides basic tools to perform the following tasks: - gather remote sensing images using Google Earth Engine API; - gather flux data; - perform basic functions, such as plotting time-series, perform QA of the data, compute vegetation indices; - perform correlation analysis between flux data and remote sensing data; - perform flux predictions based on remote sensing data integrated in different modalities.

Falco, Nicola [Lawrence Berkeley National Laborato↗

DOE OSTI2022

Data for Aerial Imagery Can Detect Nitrogen Fertilizer Effects on Biomass and Stand Health of Miscanthus × giganteus

Miscanthus × giganteus ( Miscanthus ) is a warm-season perennial grass grown for bioenergy feedstock production. Nitrogen (N) fertilizer management is crucial for the sustainability of Miscanthus production. In our two-year study (2018 and 2019), we investigated the role of vegetation indices (VIs) in evaluating N fertilization (0 N, 56 N, 112 N, and 168 N kg ha−1) impacts on Miscanthus biomass yield and stand health. The flight campaigns were conducted early, middle, and late during the summer growing season. Among the VIs, mid-summer growing season NDRE provided the best prediction of fresh biomass (R2 = 0.87 and 0.97) and dry biomass (R2 = 0.89 and 0.97) in 2018 and 2019, respectively. The VIs generally showed that it was possible to distinguish between 0 N and 168 N treatments, but neither 0 N and 56 N kg ha−1 nor 112 N and 168 N kg ha−1 could be separated. The results from this study highlight the importance of moderate application of N (112 kg N ha−1) in improving and maintaining the stand health and biomass yield of Miscanthus over time and suggest that mid-summer growing season VIs, NDRE in particular, can be useful for assessment of Miscanthus stand health and biomass yield.

Biomass Analytics↗

DOE OSTI2021

Semi-automated Design of Artificial Intelligence Earth Systems Models

Prediction and observation of water cycles at various scales involve not only patterns isolated in space and time, but also modeling of complex spatio-temporal relationships across multiple domains. For instance, evapotranspiration (ET) and leaf area indexes (LAI) are two parameters that are needed to accurately model and understand land-atmosphere processes. Accurate assessments of ET and LAI are critical for understanding hydrological processes, deforestation, crop yield, and irrigation impacts. However, ET estimates for global simulations are available at very coarse spatial resolution. They are usually derived from satellite data based on broad plant functional types (PFTs), which fail to capture fine-scale variations because of changes in vegetation type across the globe. Similarly LAI estimates have typically been derived from vegetation indices at global scales or estimated locally using physical models, both of which suffer from a range of uncertainties that impact model sensitivity. The new era of AI model development for Earth systems (ES) calls for data-driven methods that provide domain scientists with uncertainty-aware estimations of biophysical parameters such as ET and LAI in a generalizable, interpretable, and discoverable manner.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery

Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.

09 BIOMASS FUELS↗

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↗

DOE OSTI2024

Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton

Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R2 = 0.66), enabling better detection of phenotypic variations (p < 0.05) among the varieties compared to physiological-related traits (from R2 = 0.21 to R2 = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions.

Agriculture↗

NASA NTRS1980

Hand-held radiometry: A set of notes developed for use at the Workshop of Hand-held radiometry

A set of notes was developed to aid the beginner in hand-held radiometry. The electromagnetic spectrum is reviewed, and pertinent terms are defined. View areas of multiband radiometers are developed to show the areas of coincidence of adjacent bands. The amounts of plant cover seen by radiometers having different fields of view are described. Vegetation indices are derived and discussed. Response functions of several radiometers are shown and applied to spectrometer data taken over 12 wheat plots, to provide a comparison of instruments and bands within and among instruments. The calculation of solar time is reviewed and applied to the calculation of the local time of LANDSAT satellite overpasses for any particular location in the Northern Hemisphere. The use and misuse of hand-held infrared thermometers are discussed, and a procedure for photographic determination of plant cover is described. Some suggestions are offered concerning procedures to be followed when collecting hand-held spectral and thermal data. A list of references pertinent to hand-held radiometry is included.

Jackson, R. D.↗

NASA NTRS1982

Diurnal patterns of wheat spectral reflectances and their importance in the assessment of canopy parameters from remotely sensed observations

Spectral reflectances of Produra wheat were measured at 13 different times of the day at Phoenix, Arizona, during April 1979 using a nadir-oriented hand-held 4-band radiometer which had bandpass characteristics similar to those on LANDSAT satellites. Different Sun altitude and azimuth angles caused significant diurnal changes in radiant return in both visible and near-IR regions of the spectrum and in several vegetation indices derived from them. The magnitude of these changes were related to different canopy architecture, percent cover and green leaf area conditions. Spectral measurements taken at each time period were well correlated with green leaf area index but the nature of the relationship changed significantly with time of day. Thus, a significant bias in the estimation of the green leaf area index from remotely sensed spectral data could occur if sun angles are not properly accounted for.

Pinter, P. J.↗

NASA NTRS1983

The effect of irradiation and reflectance variability on vegetation condition assessment

Surface reflectance and global irradiance variations across an imaged area determine the minimum signal difference from groups of pixels in different targets needed to discriminate them. The factors affecting such variations and their magnitudes are considered. The sensor output also depends upon the interaction of the spectral response of the sensor and the spectral upwelling radiance from the target. From a consideration of the spectral instrument responses of detectors in the Landsat multispectral scanners (MS), together with measured reflectance factors of a vegetative canopy stressed to different levels of severity, the feasibility of mapping and quantifying disease stress witlh Landsat vegetative indices (VINs) is demonstrated.

Duggin, M. J.↗

NASA NTRS1984

Spectral estimators of absorbed photosynthetically active radiation in corn canopies

Most models of crop growth and yield require an estimate of canopy leaf area index (LAI) or absorption of radiation. Relationships between photosynthetically active radiation (PAR) absorbed by corn canopies and the spectral reflectance of the canopies were investigated. Reflectance factor data were acquired with a LANDSAT MSS band radiometer. From planting to silking, the three spectrally predicted vegetation indices examined were associated with more than 95% of the variability in absorbed PAR. The relationships developed between absorbed PAR and the three indices were evaluated with reflectance factor data acquired from corn canopies planted in 1979 through 1982. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50% of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR were associated with up to 73% of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index. Absorbed PAR may be estimated reliably from spectral reflectance data of crop canopies.

Gallo, K. P.↗

NASA NTRS1985

Leaf-area estimates from spectral measurements over various planting dates of wheat

Several vegetative indices were analyzed for their sensitivity and stability to green-leaf-area index (LAI) changes over various planting dates and irrigation frequencies of wheat grown at Phoenix, AZ, from 1978 to 1980. Seasonal patterns of greenness showed that values saturated at LAI values above 4.0 did not return to the pre-emergence bare-soil value at senescence, and were not uniquely related to LAI over the various planting dates. Regressions of individual MSS band reflectances against LAI also showed that there was not a unique relation between any of the bands and LAI. However, the near-infrared/red reflectance ratio was stable over all planting dates and could be used successfully over a number of years and locations.

Hatfield, J. L.↗

NASA NTRS1985

Spectral estimators of absorbed photosynthetically active radiation in corn canopies

Most models of crop growth and yield require an estimate of canopy leaf area index (LAI) or absorption of radiation. Relationships between photosynthetically active radiation (PAR) absorbed by corn canopies and the spectral reflectance of the canopies were investigated. Reflectance factor data were acquired with a Landsat MSS band radiometer. From planting to silking, the three spectrally predicted vegetation indices examined were associated with more than 95 percent of the variability in absorbed PAR. The relationships developed between absorbed PAR and the three indices were evaluated with reflectance factor data acquired from corn canopies planted in 1979 through 1982. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50 percent of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR were associated with up to 73 percent of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index. Absorbed PAR may be estimated reliably from spectral reflectance data of crop canopies.

Gallo, K. P.↗

NASA NTRS1986

Sun angle, view angle, and background effects on spectral response of simulated balsam fir canopies

An experiment is described that examines the effects of solar zenith angle and background reflectance on the composite scene reflectance of small balsam fir (Abies balsamea (L.) Mill.) arranged in different densities. In this study, the shape, density, and, consequently, the needle area index and phytomass of the canopies, as well as the background reflectance, were controlled. The effects of sun angle, view angle, and background reflectance on the multispectral response of small balsam fir trees were significant. Regression models relating spectral vegetation indices (i.e., normalized difference (ND) and greenness (GR) to phytomass) showed very poor relationships for balsam fir canopies with a grass background. However, strong linear relationships were found for ND and GR with phytomass for a background that simulated the reflectance of snow. Changing solar zenith angle significantly affected the models relating ND to phytomass for the snow background, but was not significant in the model relating GR to phytomass for the snow background

Ranson, K. J.↗

NASA NTRS1987

Application of Advanced Very High Resolution Radiometer vegetation index to study atmosphere-biosphere exchange of CO2

Normalized difference vegetation indices derived from radiances measured by the Advanced Very High Resolution Radiometer were used to prescribe the phasing of terrestrial photosynthesis. The satellite data were combined with field data on soil respiration and a global map of net primary productivity to obtain the seasonal exchange of CO2 between the atmosphere and the terrestrial biosphere. The monthly fluxes of CO2 thus obtained were employed as source/sink functions in a global three-dimensional atmospheric tracer transport model to simulate the annual oscillations of CO2 in the atmosphere. The results demonstrate that satellite data of high spatial and temporal resolution can be used to provide quantitative information about seasonal and longer-term variations of photosynthetic activity on a global scale.

Fung, I. Y.↗

NASA NTRS1990

Three methods for the absolute calibration of the NOAA AVHRR sensors in-flight

Three techniques for the absolute calibration of two reflective channels of the NOAA AVHRR sensors are described and compared. The technique 1 is based on field measurements and refers to another calibrated satellite sensor; technique 2 utilizes the reflectance-based calibration method; and technique 3 estimates the calibration by reference to another satellite sensor. NOAA-9 and NOAA-10 AVHRR images are analyzed. It is observed that the responsivity of the images degrade compared to prelaunch calibration. The effect of degradation in responsivity of the AVHRR sensors on vegetation indices are studied. It is noted that the uncertainties in the calibration methods is about 7-10 percent making them useful for many applications.

Teillet, P. M.↗

NASA NTRS1990

Bidirectional measurements of surface reflectance for view angle corrections of oblique imagery

An apparatus for acquiring bidirectional reflectance-factor data was constructed and used over four surface types. Data sets were obtained over a headed wheat canopy, bare soil having several different roughness conditions, playa (dry lake bed), and gypsum sand. Results are presented in terms of relative bidirectional reflectance factors (BRFs) as a function of view angle at a number of solar zenith angles, nadir BRFs as a function of solar zenith angles, and, for wheat, vegetation indices as related to view and solar zenith angles. The wheat canopy exhibited the largest BRF changes with view angle. BRFs for the red and the NIR bands measured over wheat did not have the same relationship with view angle. NIR/Red ratios calculated from nadir BRFs changed by nearly a factor of 2 when the solar zenith angle changed from 20 to 50 degs. BRF versus view angle relationships were similar for soils having smooth and intermediate rough surfaces but were considerably different for the roughest surface. Nadir BRF versus solar-zenith angle relationships were distinctly different for the three soil roughness levels. Of the various surfaces, BRFs for gypsum sand changed the least with view angle (10 percent at 30 degs).

Jackson, R. D.↗

NASA NTRS1990

Moderate Resolution Imaging Spectrometer (MODIS) for the NASA Earth Observing System (EOS)

MODIS is intended to provide daily global surveys for the oceans, the atmosphere, and land. To achieve this capability, this instrument requires at least a 1700 km swath width and provides geometric-instantaneous-fields-of-view that are either 214 m, 428 m, or 856 m in size with reference to a 705 km satellite altitude. The range of products to be supported by MODIS includes ocean chlorophyll concentration, ocean primary productivity, dissolved organic matter in the oceans, global SST, total suspended solids, land cover type, land surface temperatures, vegetation indices, and volcanic and fire events. Details of the MODIS-N and MODIS-T instrument concepts are discussed.

Thompson, Leslie L.↗