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DOE OSTI2026

Arm and shoulder muscle segmentation in axial MRI with UNet deep learning model

Quantifying individual upper-limb muscle volumes from MRI provides key insight into muscle-specific strength, deficits, and adaptations. Manual delineation is the gold standard but time‑intensive, and the performance of current deep learning approaches, particularly for small or anatomically complex muscles, remains incompletely characterized. We evaluated a state‑of‑the‑art deep learning framework across the entire upper limb and analyzed factors governing segmentation performance, with attention to the forearm. Three previously published MRI datasets (1.5 T, 3D GRE T1‑weighted; total n = 39) spanning young, middle‑aged, and older adults were curated and quality‑checked, including expert manual segmentations for 31 muscles. Following multiclass mask reconstruction, we trained three 3D nnU‑Net multiclass models matched to the muscle subsets present across datasets, using five‑fold cross‑validation and a composite Dice Similarity Coefficient (DSC) + cross entropy loss. Segmentation accuracy was assessed with DSC. Performance varied across muscles (mean DSC = 0.806 ± 0.098), ranging from 0.920 (Deltoid) to 0.461 (Extensor pollicis brevis). In uncertainty‑weighted regressions, muscle volume was positively associated with DSC (R2 = 0.36, p < 0.001), whereas training segmentation count and muscle orientation showed negligible associations (R2 ≤ 0.06). A weighted mixed‑effects model identified volume as the strongest evaluated predictor, explaining 23.9% of variance in DSC; orientation and training count each contributed <1%, leaving 61.5% unexplained. These results indicate that deep learning–based segmentation can accurately quantify muscle volume for many upper‑limb muscles but remains constrained for small, low‑contrast forearm muscles.

Gillespie, Samuel↗

DOE OSTI2025

Estimation of intensity, footprint, and capacity of surface urban heat islands using a direction-enhanced adaptive synchronous extraction (DEASE) method

Here, the surface urban heat island (SUHI) effect, assessed through remotely sensed land surface temperature (LST), remains a focal point in urban climate research. Conventional indicators like SUHI intensity (SUHII) and footprint (SUHIF) capture peak values and spatial extent but fail to account for the cumulative thermal load—a critical dimension reflecting the total heat exposure imposed by spatially continuous warming, which directly limits a holistic assessment of ecological and societal impacts of the SUHI effect. Therefore, this study introduces an indicator termed SUHI capacity (SUHIC), designed to quantify the aggregated SUHI effect by integrating the magnitude of the warming signal across all affected areas, thereby enabling a more comprehensive evaluation of urban thermal environments. Furthermore, a direction-enhanced adaptive synchronous extraction (DEASE) method is proposed for the quantification of SUHIC. This method can dynamically identify the optimal background reference area based on the urban-rural LST gradients in various directions within the city, without relying on predefined mathematical models as previously. The results from 102 European cities first confirm that the directional variations in urban-rural LST gradients, and the DEASE method can effectively capture these distinctions for the simultaneous estimation of SUHII, SUHIF, and SUHIC. Secondly, the spatial patterns of absolute SUHIC values show strong associations with those of SUHIF (R2>0.86), while its relative values (normalized by the area of urban) align more closely with SUHII (R2 > 0.64). More importantly, SUHIC can serve as a crucial reference for assessing the urban thermal signal when SUHII and SUHIF diverge. The proposed method and framework contribute to standardizing the quantification of the SUHI effect.

Indicator↗

DOE OSTI2021

On the Rate Constant for NH 2 +HO 2 and Third-Body Collision Efficiencies for NH 2 +H(+M) and NH 2 +NH 2 (+M)

In low-temperature flash photolysis of NH 3 /O 2 /N 2 mixtures, the NH 2 consumption rate and the product distribution is controlled by the reactions NH 2 + HO 2 → products (R1), NH 2 + H (+M) → NH 3 (+M) (R2), and NH 2 + NH 2 (+M) → N 2 H 4 (+M) (R3). In the present work, published flash photolysis experiments by, among others, Cheskis and co-workers, are re-interpreted using recent direct measurements of NH 2 + H (+N 2 ) and NH 2 + NH 2 (+N 2 ) from Altinay and Macdonald. To facilitate analysis of the FP data, relative third-body collision efficiencies compared to N 2 for R2 and R3 were calculated for O 2 and NH 3 as well as for other selected molecules. We report results were in good agreement with the limited experimental data. Based on reported NH 2 decay rates in flash photolysis of NH 3 /O 2 /N 2 , a rate constant for NH 2 + HO 2 → NH 3 + O 2 (R1a) of $k_{1\text{a}}$ = 1.5(±0.5) × 10 14 cm 3 mol –1 s –1 at 295 K was derived. This value is higher than earlier determinations based on the FP results but in good agreement with recent theoretical work. Kinetic modeling of reported N 2 O yields indicates that NH 2 + HO 2 → H 2 NO + O (R1c) is competing with R1a, but perturbation experiments with addition of CH4 indicate that it is not a dominating channel. Measured HNO profiles indicate that this component is formed directly by NH 2 + HO 2 → HNO + H 2 O (R1b), but theoretical work indicates that R1b is only a minor channel. Based on this analysis, we estimate $k_{1\text{c}}$ = 2.5 × 10 13 cm 3 mol –1 s –1 and $k_{1\text{b}}$ = 2.5 × 10 12 cm 3 mol –1 s –1 at 295 K, with significant uncertainty margins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Re-Examination of the N 2 O + O Reaction

The reaction of N 2 O with O is a key step in consumption of nitrous oxide in thermal processes. It has two product channels, NO + NO (R2) and N 2 + O 2 (R3). The rate constant for R2 has been measured both in the forward and the reverse direction at elevated temperature and is well established. However, the rate constant for the N 2 + O 2 channel (R3) has been difficult to quantify and has significant error limits. Here, the direct reaction on the triplet surface has a barrier of around 40 kcal mol –1 , and it is too slow for the N 2 + O 2 channel to have any practical significance. Recently, Pham and Lin (2022) suggested an alternative low activation energy reaction path that involves intersystem crossing and reaction on the singlet surface. In the present work, we re-examined a wide range of experiments relevant for the N 2 O + O reaction through kinetic modeling, paying attention to the impact of artifacts such as impurities and surface reactions. Experimental results from shock tubes and batch reactors on the final NO yield in N 2 O decomposition, covering temperatures of 973–2200 K and pressures of 0.013–11.5 atm, support k 3 ~ 0, consistent with the high activation energy for reaction on the triplet surface and a low probability of ISC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Si–Cl Bond Activations at Ni(0) to Give Bimetallic Ni(I) μ 1,2 -Cl–SiR 1 R 2 Complexes that Undergo Selective Hydrogenolyses to R 1 R 2 SiH 2 Dihydrosilanes

Chlorosilanes are cheap and abundant raw materials as crucial building blocks in silicon chemistry, yet the metal-mediated activation and functionalization of Si–Cl bonds typically require precious metal sources due to their thermodynamic inertness. Herein, we report the stoichiometric, facile activation, and hydrogenolysis of chlorosilanes mediated by a series of low-valent NHC–Ni (NHC = N-heterocyclic carbene) complexes. Treatment of a Ni(0) complex (IPr)Ni(η 6 -toluene) (IPr = 1,3-bis(2,6-diisopropylphenyl)imidazole-2-ylidene) with chlorosilanes (R 1 R 2 SiCl 2 , R 1 = Cl, R 2 = Cl, Me, Ph, or R 1 = R 2 = Me, Et, Ph, 4-MePh) rapidly afforded di-Ni(I) complexes with a bridging silyl ligand ([(IPr)Ni] 2 (μ-SiR 1 R 2 Cl)(μ-Cl), 1 R1,R2 ) in high yields. Use of a bulkier chlorosilane, Ph 2 SiCl 2 , allowed the isolation of the mono-Ni(II) silyl complex (IPr)Ni(SiPh 2 Cl)Cl (2 Ph ) as an intermediate generated via Si–Cl oxidative addition, which underwent comproportionation with (IPr)Ni(η 6 -toluene) to form 1 Ph,Ph in nearly quantitative yield. Interestingly, 1 R1,R2 was found to react with H 2 at room temperature to form mono- or di-hydrosilanes in moderate to high yields, and the product selectivity was found to be highly dependent on the identity of substituents on Si. In conclusion, these results demonstrate a novel example of facile Si–Cl activation and hydrogenolysis mediated by low-valent mono- and dinuclear NHC–Ni complexes under mild conditions.

Liu, Tianchang [University of California, Berkeley↗

DOE OSTI2020

Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site-level Applications of the CLM-Microbe Model

Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for the realistic projections of soil carbon (C) and climate dynamics. The CLM-Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time-series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes, we parameterized and validated the CLM-Microbe model, and further conducted sensitivity analysis and uncertainty analysis for simulating C cycling. The model performance was evaluated with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) for relative change in FBC and BBC. The CLM-Microbe model is able to reasonably capture the seasonal dynamics of FBC and BBC across biomes, particularly for tropical/subtropical forest, temperate broadleaf forest, and grassland, with MAE < 0.49 for FBC and <0.36 for BBC and RMSE <0.52 FBC and <0.39 for BBC, while R2 values are relatively smaller in some biomes (e.g., shrub) due to small sample sizes. We found good consistencies between simulated and observed FBC (R 2 =0.70, P<0.001) and BBC (R 2 =0.26, P<0.05) on average across biomes, but the model is not able to fully capture the large variation in observed FBC and BBC. Sensitivity analysis shows the most critical parameters are turnover rate, carbon-to-nitrogen ratio of fungi and bacteria, and microbial assimilation efficiency. This study confirms that the explicit representation of soil microbial mechanisms enhances model performance in simulating C variables such as heterotrophic respiration and soil organic C density. The further application of the CLM-Microbe model would deepen our understanding of microbial contributions to the global C cycle.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Representation of Leaf-to-Canopy Radiative Transfer Processes Improves Simulation of Far-Red Solar-Induced Chlorophyll Fluorescence in the Community Land Model Version 5

Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to evaluate and constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and the radiative transfer of SIF to remote sensing sensors is an essential prerequisite for the evaluation and data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy level is usually not well-represented. In this work, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). An efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and a canopy-level model agreed well at sites except one in needle leaf forest (R2>0.91, root-mean-square error < 0.19W m -2 sr -1 um -1 ), and captured the day-to-day variation of tower-measured SIF at temperate forest sites (R 2 >0.68). At the global scale, simulated SIF generally captured the spatial and seasonal pat37 terns of satellite-observed SIF (R2 > 0.76 except for tropical forest). Factors including the fluorescence emission model, clumping, bidirectional effect, and canopy properties (leaf optical properties and leaf area index) had considerable impacts on SIF simulation, and the discrepancies between simulated and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF towards constraining and evaluating GPP simulations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗

DOE OSTI2020

Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device

The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA), partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. Additionally, a boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Electric field dependence of nanoscale cathodoluminescence inside Cr doped β-Ga 2 O 3 interfaces

Chromium (Cr) is a common impurity in β-Ga 2 O 3 crystals, where its characteristic R1 and R2 luminescence lines are susceptible to both the host crystal field and externally applied fields. Here, in this work, we demonstrate that the Cr cathodoluminescence (CL) quenches toward the bulk of the crystal but enhances with applied reverse bias, reflecting the effect of free carrier depletion with increasing electric field. Furthermore, we illustrate that the R1/R2 CL intensity ratio, measured as the integrated area ratio of R1 to R2, can be used as a direct probe of the electric field in a Ni-β-Ga 2 O 3 : Cr Schottky diode. This optical calibration method provides a complementary approach to conventional C–V and I–V measurements for determining electric field strength in the depletion region of β-Ga 2 O 3 -based Schottky diodes and can be extended to other semiconductors and multilayer device structures.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Landsat 8 monitoring of multi-depth suspended sediment concentrations in Lake Erie’s Maumee River using machine learning

Satellite remote sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. However, due to the complexity of sediment-water interactions, it has been difficult to derive linear and non-linear regression equations to reliably predict SSC, especially when trying to estimate depth of integrated sediment. Herein, this study uses Landsat 8 OLI (Operational Land Imager) sensor to map SSC within the Maumee River in Ohio, USA, at multiple depth intervals (15, 61, 91, and 182 cm). Simple linear least squares regression (LLSR), and three common machine learning models: random forest (RF), support vector regression (SVR), and model averaged neural network (MANN) were used to estimate SSC at the depth intervals. All machine learning models significantly outperformed LLSR while RF performed the best. In both RF and MANN, R2 (coefficient of determination) increases with depth with a maximum R2 of 0.89 and 0.83, respectively, at a depth of 0–182 cm. The results show that machine learning models can implement nonlinear relationships that produce better predictions than traditional linear regression methods in estimating depth integrated SSC, especially when samples are limited.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Calibration of ruby and matlockite luminescence lines from the melting of mercury: constraints on the initial slopes

We have measured the luminescence shifts of the ruby’s R1- and R2-lines and the line of 5 D 0 → 7 F 0 from Sm 2+ :SrFCl corresponding to the melting pressure of mercury that is recommended by the AIRAPT task force as an International Practical Pressure Scale (IPPS). The linear coefficients of the pressure dependence of the R1-, R2-lines, and the luminescence line of Sm 2+ :SrFCl are determined to be 0.3722 ± 0.002, 0.3796 ± 0.002, and 1.123 ± 0.002 nm/GPa, respectively. The results not only put tight constraints on the initial slopes of ruby and Sm 2+ :SrFCl gauges, but also link the luminescence-based pressure gauges to the more fundamental primary piston gauges.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Long-term variability of root production in bioenergy crops from ingrowth core measurements

Long-term determination of root biomass production upon land-use conversion to biofuel crops is rare. To assess land-use legacy influences on belowground biomass accumulation, we converted 22-year-old Conservation Reserve Program (CRP) grasslands and 50+-year-old agricultural (AGR) lands to corn (C), switchgrass (Sw) and restored prairie (Pr) biofuel crops. We maintained one CRP grassland as a reference (Ref). We hypothesized that land-use history and crop type have significant effects on root density, with perennial crops on CRP grasslands having a higher root biomass productivity, while corn grown on former agricultural lands produce the lowest root biomass. Methods The ingrowth core method was used to determine in situ ingrowth root biomass, alongside measurements of aboveground net primary productivity (ANPP). Ancillary measurements, including air temperature, growing season length and precipitation were used to examine their influences on root biomass production. Important Findings Root biomass productivity was the highest in unconverted CRP grassland (1716 g m-2 yr-1) and lowest in corn fields (526 g m-2 yr-1). All perennial sites converted from CRP and AGR lands had lower root biomass and ANPP in the first year of planting but peaked in 2011 for switchgrass and a year later for restored prairies. Ecosystem stability was higher in restored prairies (AGR-Pr: 4.3 ± 0.11; CRP-Pr: 4.1 ± 0.10), with all monocultures exhibiting a lower stability. Root biomass production was positively related to ANPP (R2 = 0.40). Overall, attention should be given to root biomass accumulation in large-scale biofuel production as it is a major source of carbon sequestration.

Environmental Sciences & Ecology↗

DOE OSTI2021

Subaru Hyper Suprime-Cam Survey of Cygnus OB2 Complex – I. Introduction, photometry, and source catalogue

ABSTRACT Low-mass star formation inside massive clusters is crucial to understand the effect of cluster environment on processes like circumstellar disc evolution, planet, and brown dwarf formation. The young massive association of Cygnus OB2, with a strong feedback from massive stars, is an ideal target to study the effect of extreme environmental conditions on its extensive low-mass population. We aim to perform deep multiwavelength studies to understand the role of stellar feedback on the IMF, brown dwarf fraction and circumstellar disc properties in the region. We introduce here, the deepest and widest optical photometry of 1.5○ diameter region centred at Cygnus OB2 in r2, i2, z, and Y-filters, using Subaru Hyper Suprime-Cam (HSC). This work presents the data reduction, source catalogue generation, data quality checks, and preliminary results about the pre-main sequence sources. We obtain 713 529 sources in total, with detection down to ∼28, 27, 25.5, and 24.5 mag in r2, i2, z, and Y-band, respectively, which is ∼3 – 5 mag deeper than the existing Pan-STARRS and GTC/OSIRIS photometry. We confirm the presence of a distinct pre-main sequence branch by statistical field subtraction of the central 18 arcmin region. We find the median age of the region as ∼5 ± 2 Myr with an average disc fraction of ∼9 per cent. At this age, combined with A $_V\, \sim$ 6 – 8 mag, we detect sources down to a mass range of ∼0.01–0.17 M⊙. The deep HSC catalogue will serve as the groundwork for further studies on this prominent active young cluster.

Gupta, Saumya (ORCID:0000000161843958)↗

DOE OSTI2024

AstroCLIP: a cross-modal foundation model for galaxies

ABSTRACT We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used – without any model fine-tuning – for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estimation, (3) galaxy property estimation from both images and spectra, and (4) morphology classification. Our approach to implementing AstroCLIP consists of two parts. First, we embed galaxy images and spectra separately by pre-training separate transformer-based image and spectrum encoders in self-supervised settings. We then align the encoders using a contrastive loss. We apply our method to spectra from the Dark Energy Spectroscopic Instrument and images from its corresponding Legacy Imaging Survey. Overall, we find remarkable performance on all downstream tasks, even relative to supervised baselines. For example, for a task like photometric redshift prediction, we find similar performance to a specifically trained ResNet18, and for additional tasks like physical property estimation (stellar mass, age, metallicity, and specific-star-formation rate), we beat this supervised baseline by 19 per cent in terms of R2. We also compare our results with a state-of-the-art self-supervised single-modal model for galaxy images, and find that our approach outperforms this benchmark by roughly a factor of two on photometric redshift estimation and physical property prediction in terms of R2, while remaining roughly in-line in terms of morphology classification. Ultimately, our approach represents the first cross-modal self-supervised model for galaxies, and the first self-supervised transformer-based architectures for galaxy images and spectra.

Parker, Liam (ORCID:0009000749521674)↗

DOE OSTI2024

Geographical Insights into Suicide Mortality Through Spatial Machine Learning

Suicide mortality is a leading cause of death in the United States, with an upward trend that emphasizes its significance as a public health issue. Previous research has employed global models like ordinary least squares (OLS) regression and local models such as geographically weighted regression (GWR). While local models are useful for analyzing spatial variations in suicide mortality, they share limitations with traditional global models, particularly about their inability to handle multi-collinearity and non-linear relationships. Machine learning approaches, like random forests (RF), can address some of these limitations but often fail to account for spatial variability. This gap highlights the need for spatial ML models specifically designed to tackle suicide mortality. This research seeks to fill this void by using a geographically weighted random forest model (GWRF) to examine the associations between county-level suicide mortality in the U.S. from 2010 to 2020 and various social and environmental determinants of health. A key aspect of our methodology is disciplined feature selection, which reduces the pool of explanatory variables by about 90%. This refinement enhances the explanatory power of both global (R2 improved from 0.59 to 0.67) and local (R2 improved from 0.64 to 0.67) RF models while reducing their run times. An analysis of the importance scores for these selected features reveals that the drivers of suicide mortality vary by context. Thus, to effectively address regional disparities and inform targeted public health interventions, a holistic approach that incorporates multiple county-level characteristics is essential.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗

DOE OSTI2026

What Shapes Transportation Charging Infrastructure Availability? Evidence from Tennessee

This study examines how community, travel, and freight characteristics relate to public charging infrastructure availability across Tennessee ZIP codes. We link Alternative Fuels Data Center station locations with traffic, socioeconomic, demographic, commuting, and freight employment data to build a ZIP code-level dataset. Ordinary least squares regression captures variation in chargers per 10,000 residents (R2=0.311). Quantile regressions at the 25th, 50th, and 75th percentiles, with pseudo R2 values up to 0.099, show that the determinants of infrastructure availability differ across low-, medium-, and high-availability areas. Percent female, percent car commuters, average household size, and median age are negatively associated with charging availability across much of the distribution. Truck traffic is positively associated only in lower-availability ZIP codes, while vehicle miles traveled shifts from a negative association at the lower end of the distribution to a positive association at the upper end. The results provide insight into how public charging deployment aligns with community characteristics, mobility demand, and freight activity across Tennessee. Future work can distinguish charger types and power levels, incorporate land-use and temporal rollout patterns, and examine how charging infrastructure needs differ across urban and rural contexts.

Calderón, Oriana [University of Tennessee, Knoxvil↗

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↗