Fact-checked by Grok 2 weeks ago

Red edge

The red edge is a distinctive feature in the spectral reflectance signature of healthy vegetation, characterized by a sharp increase in reflectance from low values in the red wavelengths (around 680 nm) to high values in the near-infrared wavelengths (up to 750 nm), reflecting the transition where chlorophyll absorption diminishes and leaf internal scattering dominates.^[1] This phenomenon, first quantitatively described in the early 1980s through derivative spectroscopy of diverse plant species, serves as a sensitive indicator of plant physiological status in remote sensing applications.^[1] In environmental monitoring and precision agriculture, the red edge is pivotal for detecting subtle changes in vegetation health, such as variations in chlorophyll content and leaf area index, often outperforming traditional broadband indices like the Normalized Difference Vegetation Index (NDVI) under dense canopies or early stress conditions.^[2] Satellite sensors such as those on Sentinel-2 incorporate red edge bands to derive specialized vegetation indices like the Normalized Difference Red Edge (NDRE), which enhance the estimation of crop nitrogen levels and biomass without saturation effects seen in red-NIR ratios.^[3] These indices leverage the red edge's responsiveness to photosynthetic efficiency, enabling early identification of stressors such as nutrient deficiencies, water scarcity, or disease in crops and forests.^[4] Furthermore, hyperspectral imaging exploits the red edge's position and slope—key parameters like the red edge position (REP) shifting toward longer wavelengths with increasing chlorophyll—to map ecosystem productivity and biodiversity at fine scales.^[5] Ongoing research integrates red edge data with machine learning for improved predictive modeling of vegetation dynamics amid climate change.^[6]

Fundamentals

Definition

The red edge refers to the abrupt transition in the reflectance spectrum of healthy vegetation, where reflectance rises sharply from low values (approximately 5%) in the red wavelengths to high values (approximately 50%) in the near-infrared (NIR), occurring over a narrow wavelength band. This phenomenon manifests as a steep increase in reflected light, distinguishing it from the relatively flat or gradual spectral profiles observed in other materials. Unlike the broader reflectance patterns of vegetation, which show overall low absorption in the green and higher in blue and red regions, the red edge is a pronounced, step-like feature characteristic of photosynthetically active plants. It is absent in non-vegetated surfaces, such as bare soil or water bodies, which lack this rapid shift and instead exhibit smoother transitions or consistently low NIR reflectance. Positioned at the boundary between the visible and near-infrared portions of the electromagnetic spectrum, the red edge acts as a key diagnostic marker for identifying and monitoring vegetation cover on Earth's surface.^[7]

Spectral Characteristics

The red edge in vegetation reflectance spectra is characterized by a rapid increase in reflectance within the near-infrared (NIR) transition region, typically spanning wavelengths from 680 to 750 nm. This abrupt shift marks the boundary between the visible red wavelengths, where chlorophyll absorption dominates, and the NIR plateau, where internal leaf scattering causes high reflectance.^[8] The reflectance transition across the red edge is pronounced, often rising from approximately 5% at 680 nm to around 50% at 730 nm in healthy vegetation. The steepest portion of this rise, corresponding to the maximum slope of the reflectance curve, generally occurs between 700 and 720 nm, reflecting the point of highest sensitivity to vegetation properties.^[8] This profile can be modeled as a sigmoidal curve, with the inflection point—identified via the wavelength of maximum first derivative in the spectrum—serving as a key parameter for quantifying the edge's position and sharpness.^[9] In contrast, non-vegetation surfaces exhibit markedly different spectral behavior in this region, lacking the sharp reflectance increase characteristic of vegetation. Bare soil typically shows low and gradually increasing reflectance through 680–750 nm due to mineral absorption and scattering, without a distinct edge.^[8] Water bodies display even lower reflectance overall, with minimal change across these wavelengths owing to strong absorption in the NIR.^[10] Urban surfaces, composed of impervious materials like concrete and asphalt, present variable but generally flat or slowly rising reflectance profiles, absent the sigmoidal transition seen in plants.^[11]

Biological and Physical Causes

Chlorophyll Absorption

Chlorophyll a and chlorophyll b, the primary pigments in plant photosynthesis, exhibit strong absorption in the blue (approximately 400-500 nm) and red (600-700 nm) regions of the spectrum.^[12] Specifically, in vivo within leaves, chlorophyll a has absorption maxima at around 430 nm and 680 nm, while chlorophyll b peaks at approximately 460 nm and 650 nm, enabling efficient capture of light energy for photochemical reactions.^[13] This absorption results in low reflectance in the red wavelengths, forming the basis for the sharp transition at the red edge, where reflectance begins to increase rapidly beyond 680-700 nm due to chlorophyll's transparency to longer wavelengths. The red edge effectively marks the upper boundary of photosynthetically active radiation (PAR), defined as the 400-700 nm range where chlorophyll molecules efficiently convert light into chemical energy.^[14] Beyond this PAR limit, absorption by chlorophyll diminishes sharply, allowing incident light to penetrate deeper into leaf tissues without being utilized for photosynthesis.^[15] Variations in chlorophyll concentration directly influence the depth of the red absorption trough and the sharpness of the red edge onset. Higher concentrations enhance absorption in the red region, deepening the reflectance minimum around 680 nm and typically shifting the red edge position toward longer wavelengths, thereby steepening the reflectance rise.^[16] This effect underscores the red edge's utility as an indicator of pigment levels, as greater chlorophyll content intensifies the contrast between absorbed red light and reflected near-infrared.^[17] The internal leaf structure complements this absorption drop-off by promoting scattering in the near-infrared, further amplifying the edge's steepness.

Internal Leaf Structure

The internal structure of leaves, particularly the arrangement of mesophyll cells, plays a crucial role in the high near-infrared (NIR) reflectance that defines the red edge transition. In typical dicotyledonous leaves, the mesophyll consists of densely packed palisade cells on the adaxial side and loosely arranged spongy mesophyll on the abaxial side, with the latter featuring extensive intercellular air spaces and irregularly shaped cells. These air spaces and cell walls serve as primary scattering centers for NIR light (700–1100 nm), where the refractive index mismatch between air (n ≈ 1) and cell walls (n ≈ 1.42) causes refraction and diffuse reflection at interfaces, effectively trapping and redirecting photons multiple times within the leaf.^[18]^[19]^[20] This scattering mechanism operates akin to corner reflectors, with cell walls and air pockets promoting repeated internal reflections that prevent light from escaping or being transmitted, resulting in elevated NIR albedo typically ranging from 40% to 60% in healthy green leaves. Unlike visible wavelengths, which are strongly absorbed by chlorophyll in the red region (as the biochemical counterpart enabling the sharp red edge slope), NIR radiation experiences negligible biochemical absorption within the leaf, allowing it to penetrate deeply and undergo extensive multiple scattering without significant energy loss.^[20]^[19]^[18] The evolutionary development of this mesophyll architecture provides terrestrial plants with a key adaptive advantage, optimizing photosynthetic efficiency by maximizing visible light absorption for energy conversion while reflecting excess NIR to dissipate heat and avoid photodamage or overheating under full sunlight. This structure, refined over millions of years in land-adapted vascular plants, balances light harvesting with thermal regulation, contributing to the resilience of vegetation in diverse terrestrial environments.^[20]

Applications

Vegetation Health and Stress Detection

The red edge serves as a sensitive indicator for detecting vegetation stress, often manifesting as a blue shift in its position toward shorter wavelengths, typically by 10-20 nm, which precedes visible symptoms of nutrient deficiency, drought, or disease.^[21]^[22] This shift occurs due to reduced chlorophyll content and altered leaf internal structure under stress conditions, allowing early intervention before significant yield losses.^[23] For instance, in coniferous woodlands, red edge indices like the Normalized Difference Red-Edge (NDRE) enable detection of stress up to 16 days earlier than traditional indices like the Normalized Difference Vegetation Index (NDVI), facilitating timely management in response to girdling or drought.^[24] In precision agriculture, the red edge is integrated with multispectral sensors on drones and satellites, such as those in the Sentinel-2 mission, to map stress at field scales and guide targeted applications of fertilizers, water, or pesticides.^[25] This approach supports variable-rate technologies that optimize resource use, with studies showing potential yield improvements through enhanced stress mitigation and resource efficiency.^[26] By analyzing red edge reflectance, farmers can delineate stressed zones for precise interventions, reducing overall input costs while boosting productivity.^[27] Ecological monitoring leverages the red edge's sensitivity to canopy closure and chlorophyll dynamics for assessing forest health, detecting invasive species, and evaluating post-wildfire recovery.^[28] In boreal forests, red edge data from satellites improves early stress detection, aiding in the identification of declining stands affected by pests or drought.^[24] For invasive species like sericea lespedeza in grasslands, red edge-based indices enhance mapping accuracy by distinguishing spectral signatures from native vegetation.^[29] Post-wildfire, monitoring with red edge-normalized difference vegetation index (NDVI705) tracks regeneration, revealing recovery patterns in burned areas where canopy regrowth alters reflectance.^[30]

Chlorophyll and Biomass Estimation

The red edge region of vegetation reflectance spectra is particularly valuable for estimating chlorophyll content due to its sensitivity to pigment concentrations without the saturation limitations common in broader red and near-infrared bands. One widely used index is the Chlorophyll Index based on the red edge (CI_{red edge}), defined as CI_{red edge} = (ρ_NIR / ρ_{red edge}) - 1, where ρ_NIR is the reflectance in the near-infrared band (typically around 850 nm) and ρ_{red edge} is the reflectance in the red edge band (typically around 730 nm). This index exploits the steep rise in reflectance across the red edge, which shifts with increasing chlorophyll levels, enabling accurate quantification of leaf chlorophyll a+b concentrations. Studies have shown that CI_{red edge} correlates strongly with chlorophyll content, achieving R² values exceeding 0.8 for concentrations up to 60 µg/cm², as it remains responsive even in moderately dense canopies where traditional indices like NDVI saturate. Recent advances as of 2025 include methods to reconstruct red-edge bands for Landsat imagery, improving leaf area index (LAI) estimation using historical data by leveraging consistency with Sentinel-2 bands.^[31]^[17]^[32] Another effective index incorporating red edge characteristics is the Modified Triangular Vegetation Index 2 (MTVI2), formulated as:

\text{MTVI2} = \frac{1.5 \times (1.2 \times (R_{\text{NIR}} - R_{\text{Green}}) - 2.5 \times (R_{\text{Red}} - R_{\text{Green}}))}{\sqrt{(2 \times R_{\text{NIR}} + 1)^2 - (6 \times R_{\text{NIR}} - 5 \times \sqrt{R_{\text{Red}}}) - 0.5}}

where R_{\text{NIR}}, R_{\text{Red}}, and R_{\text{Green}} represent reflectances in the near-infrared, red, and green bands, respectively. Although primarily using green, red, and NIR bands, MTVI2 integrates red edge sensitivity through its triangular structure, which enhances accuracy for chlorophyll estimation and leaf area index (LAI) at moderate LAI levels (around 2-4), outperforming NDVI by reducing soil background interference and canopy saturation effects in dense vegetation. This improvement stems from the index's design to account for chlorophyll-driven reflectance changes near the red edge, yielding correlations with chlorophyll content that surpass those of broadband indices in crop canopies.^[33]^[34] In biomass applications, the position of the red edge—defined as the wavelength of maximum slope in the reflectance spectrum (typically 690-740 nm)—serves as a robust proxy for estimating LAI and green biomass, particularly in agricultural settings. A shift in red edge position toward longer wavelengths correlates with higher LAI and biomass accumulation, as greater leaf layering and chlorophyll density broaden the transition. For instance, empirical models using red edge position have demonstrated strong predictive power for green LAI in crops like wheat and corn, with R² values around 0.85, enabling reliable crop yield forecasting by integrating these estimates with growth models. Validation in field studies across diverse cropping systems confirms that red edge-based approaches mitigate saturation in high-biomass scenarios (LAI > 4), providing more stable estimates than NIR-red ratios for precision agriculture applications.^[35]^[36]^[27]

Exoplanet Biosignatures

The red edge's sharp spectral discontinuity represents a promising biosignature for detecting vegetation on exoplanets, as it manifests as a distinct rise in planetary reflectance spectra, enabling differentiation between biologically active surfaces and abiotic ones. This feature, characterized by a steep increase in albedo from visible to near-infrared wavelengths, has been explored for detectability in Earth-like exoplanet reflection spectra.^[37] Theoretical models simulate the red edge's visibility in exoplanet spectra under Earth-like atmospheric conditions. Radiative transfer simulations indicate that the edge feature remains robust even with partial cloud cover. The red edge has been proposed as a potential sign of oxygenic photosynthesis, with models for habitable exoplanets orbiting M-dwarf stars predicting possible shifts in the red edge position to longer wavelengths (e.g., 900–1100 nm) due to adaptations in photosynthetic processes to stellar spectra, yet the discontinuity persists as a clear marker.^[37]^[38]

Measurement Techniques

Ground-Based Methods

Ground-based methods for measuring the red edge primarily involve spectroradiometry using portable or laboratory spectrometers to capture high-resolution reflectance spectra directly from plant leaf samples. Instruments like the ASD FieldSpec 4 Standard-Res spectroradiometer are commonly employed, providing spectral coverage from 350 to 2500 nm with resolutions of 3 nm in the visible-near-infrared (VNIR) region and 10 nm in the short-wave infrared (SWIR) region, allowing precise characterization of the red edge transition.^[39] Standard protocols for these measurements entail clipping individual leaves and securing them in a leaf clip attachment equipped with an internal halogen light source to ensure consistent illumination and exclude ambient light. Reflectance is computed as the ratio of the leaf's radiance to that of a calibrated white reference panel, with spectra averaged from multiple (typically four) 1 cm diameter spots on the adaxial leaf surface to account for intra-leaf variability.^[40] This approach enables detailed spectral profiles suitable for laboratory analysis or field deployment. To identify the red edge inflection point, first-derivative analysis is performed on the acquired reflectance spectra, highlighting the wavelength of steepest slope in the 680-750 nm region. Tools such as ENVI software automate this by calculating the maximum derivative value within the red edge band (0.69-0.74 μm), yielding the precise inflection wavelength, often around 700-730 nm for healthy vegetation.^[41] These techniques offer high precision for calibrating instruments and validating broader-scale remote sensing data, while their controlled setup supports experiments on interspecies variability and environmental influences on red edge features.^[42]

Remote Sensing Platforms

Satellite-based remote sensing platforms play a crucial role in capturing red edge data over large areas, enabling global-scale monitoring of vegetation characteristics. The European Space Agency's Sentinel-2 mission, equipped with the MultiSpectral Instrument (MSI), features four dedicated red edge bands centered at approximately 705 nm (B5), 740 nm (B6), 783 nm (B7), and 865 nm (B8A), which provide enhanced sensitivity to chlorophyll content and vegetation health. These bands operate at a spatial resolution of 20 meters, while broader visible and near-infrared bands achieve 10 meters, with the constellation of two satellites ensuring a revisit time of 5 days at the equator.^[43]^[44] The retired RapidEye constellation, formerly operated by Planet Labs until 2020, included a single red edge band spanning 690-730 nm (centered at 710 nm), offering 5-meter spatial resolution and daily revisit capabilities for targeted agricultural and environmental applications.^[45]^[46]^[47] Current Landsat missions, such as Landsat 8 and 9, lack native red edge bands but support extensions through data harmonization with Sentinel-2 or reconstruction techniques to simulate these wavelengths, improving compatibility for long-term vegetation studies. As of 2025, the planned Landsat Next mission, targeted for launch in the 2030s and currently under architectural assessment, will incorporate red edge bands to enhance agroecosystem monitoring, with proposed resolutions of 10-20 meters and a 6-day revisit via a triplet constellation.^[48]^[49] Airborne and unmanned aerial vehicle (UAV) platforms complement satellite data by providing higher spatial resolution for detailed red edge mapping. The NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) captures hyperspectral data across 224 contiguous bands from 400 to 2500 nm with approximately 10 nm spectral sampling, allowing precise delineation of the red edge inflection point at sub-meter to 4-meter resolutions depending on flight altitude. UAV-mounted hyperspectral imagers, often adapted from similar technologies, enable flexible, on-demand surveys with sub-meter resolution over smaller areas, supporting validation and fine-scale analysis of vegetation structure.^[50] Data processing for red edge measurements from these platforms involves atmospheric correction to remove scattering and absorption effects, with tools like FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) widely used for hyperspectral and multispectral imagery in the visible to shortwave infrared range. FLAASH employs MODTRAN-based radiative transfer modeling to retrieve surface reflectance, particularly critical for accurate red edge positioning in vegetation spectra. Additionally, fusion of multispectral data from satellites like Landsat and Sentinel-2 with hyperspectral sources enhances global coverage by combining high temporal revisit with detailed spectral information, as demonstrated in harmonized datasets that achieve near-daily observations at 30-meter resolution.^[51]^[52]

Variations and Parameters

Red Edge Position

The red edge position (REP) is defined as the wavelength corresponding to the point of maximum slope or inflection point in the transition region of vegetation reflectance spectra, where reflectance sharply increases from low values in the red wavelengths to high values in the near-infrared, typically spanning 690–740 nm in healthy vegetation.^[53] This position serves as a key parameter in spectral analysis, reflecting the boundary between chlorophyll absorption dominance and internal leaf scattering effects.^[9] Several methods are employed to calculate the REP from reflectance spectra. Linear extrapolation involves fitting straight lines to the red (e.g., around 670–700 nm) and near-infrared (e.g., 740–780 nm) portions of the spectrum and determining the wavelength at their intersection, often using four discrete points for approximation.^[53] Gaussian fitting and inverted Gaussian models provide more robust curve-fitting approaches; the latter uses a four-parameter function—shoulder reflectance (R_s), minimum reflectance (R_0), minimum wavelength (λ_0), and inflection wavelength (λ_a)—to model the red edge profile in the 670–800 nm range, with the REP derived as the wavelength of maximum slope (λ_a).^[54] These techniques enable precise estimation, with relative errors typically under 1% for well-resolved spectra.^[53] Baseline REP values vary by plant species and physiological state but average approximately 715 nm for broadleaf plants under standard conditions (e.g., 50 mg/m² chlorophyll content).^[53] For instance, maize leaves exhibit an REP of 727 nm, while banana leaves show 708 nm, highlighting species-specific differences influenced by leaf anatomy and pigment distribution.^[53]

Shifts in Stressed or Varied Conditions

Under physiological stress, the red edge position (REP) typically undergoes a blue shift toward shorter wavelengths due to chlorophyll degradation, which reduces absorption in the red region and alters the transition to near-infrared reflectance. For instance, severe stress from pests, drought, or pollution can cause shifts of 5-30 nm, as observed in coniferous trees under heavy metal contamination and in broadleaf species exposed to pollutants. This blue shift is particularly pronounced during leaf senescence at the end of the growing season, where declining chlorophyll levels lead to a similar repositioning of the edge. Conversely, in conditions of high biomass and robust chlorophyll content, such as during peak vegetative growth, the REP exhibits a red shift to longer wavelengths, enhancing the edge's steepness. Environmental factors further modulate the red edge's amplitude (the reflectance difference across the edge) and width (the spectral range of the transition). Elevated leaf water content, often linked to adequate hydration, increases amplitude by strengthening the NIR reflectance plateau while maintaining a narrow width, whereas drought-induced water loss broadens the edge and reduces amplitude through weakened cellular structure scattering. Soil type influences these parameters in sparse canopies, where clay-rich soils enhance edge amplitude via higher moisture retention compared to sandy soils that promote quicker drying and edge broadening. Atmospheric conditions, such as aerosol loading or humidity, indirectly affect measurements by scattering light, potentially dampening amplitude in humid environments, though corrections mitigate this in remote sensing. A representative quantitative example is nitrogen deficiency in crops, where the REP shifts from approximately 720 nm in healthy vegetation to around 700 nm, reflecting reduced chlorophyll synthesis and a blunted transition. In derivative spectra, stress conditions like pollutant exposure or nutrient limitation flatten the slope at the REP, decreasing the first derivative value and indicating diminished photosynthetic efficiency.^[55] ^[56] ^[57] ^[58] ^[59] ^[60] ^[61] ^[62] ^[63]

History and Development

Early Observations

The foundational observations of the red edge phenomenon emerged from laboratory measurements of plant leaf reflectance in the mid-20th century, revealing a distinct rise in near-infrared (NIR) reflectance beyond the visible red wavelengths. As early as 1946, researchers documented the absorption and reflection spectra of leaves using an Ulbricht sphere, showing low reflectance in the red region (around 650-700 nm) due to chlorophyll absorption, followed by a sharp increase to high reflectance in the NIR (beyond 700 nm), characteristic of healthy vegetation.^[64] This "step-like" transition in spectra was consistently observed in subsequent plant physiology studies during the 1950s and 1960s, such as those examining reflectance curves across various leaf types and species, which highlighted the NIR rise as a universal feature linked to internal leaf structure and minimal absorption by plant tissues. The emergence of remote sensing in the 1960s extended these laboratory findings to aerial platforms, where NASA's development of multispectral scanners enabled the detection of vegetation contrasts through red and NIR channels. In 1964, the first experimental multispectral scanner flight over agricultural fields in Indiana, funded by NASA and conducted at Purdue University, captured imagery demonstrating pronounced differences in red-NIR reflectance between vegetated areas and bare soil, with vegetation appearing bright in NIR due to the reflectance surge.^[65] Early airborne campaigns in the late 1960s and early 1970s, using prototype scanners on aircraft, further confirmed this differentiation in multispectral data, allowing initial mapping of crop health and land cover based on the spectral contrast. A pivotal early insight from these observations was the recognition of the spectral "step" as a reliable discriminator for vegetation, distinguishing it from non-vegetated surfaces like soil or water, which lack the NIR reflectance increase. This feature, observed in both lab spectra and airborne imagery prior to the formal coining of the term "red edge" in the 1980s, laid the groundwork for using multispectral data to identify and monitor plant cover without relying on visible color alone.

Key Milestones and Research Advances

In 1983, the term "red edge" was formally coined and characterized in detail through laboratory measurements of plant leaf reflectance spectra, establishing its strong correlation with chlorophyll content and photosynthetic activity.^[1] This foundational work by Horler, Dockray, and Barber highlighted the red edge's steep reflectance transition as a key indicator for vegetation health assessment in remote sensing applications.^[1] Building on this, the red edge position (REP)—defined as the inflection point of the reflectance curve—was developed as a precise metric for estimating chlorophyll concentration, biomass, and hydric status in plants.^[16] Filella and Peñuelas introduced this parameter in 1994, demonstrating through field spectroscopy that REP shifts linearly with chlorophyll levels, offering improved sensitivity over broader spectral indices for detecting subtle physiological changes.^[16] The integration of red edge bands into satellite sensors marked a significant advancement for global-scale monitoring, beginning with the RapidEye constellation launched in 2008, which included a dedicated red edge channel (710 nm) to enhance chlorophyll detection in agricultural and forestry applications.^[66] This was followed by the European Space Agency's Sentinel-2 mission in 2015, incorporating three narrow red edge bands (B5 at 705 nm, B6 at 740 nm, and B7 at 783 nm) at 20-meter resolution, enabling routine high-precision vegetation mapping and early stress identification worldwide.^[67] In astrobiology, the red edge was proposed as a potential biosignature for detecting vegetation on exoplanets, leveraging its distinct spectral step-function as a marker of photosynthetic life under oxygen-rich atmospheres. Seager et al. outlined this concept in 2005, modeling how future telescopes could observe the feature in reflected planetary light, influencing subsequent mission designs for extraterrestrial habitability searches. Recent hyperspectral missions, such as Germany's EnMAP satellite launched in 2022, have extended these capabilities with 242 contiguous spectral channels covering the red edge region, supporting advanced Earth-based validation of biosignature models through detailed surface composition analysis.^[68] During the 2010s, research addressed limitations in REP estimation under varying environmental conditions, particularly for stress detection, by incorporating machine learning algorithms to refine accuracy and robustness. Techniques such as Gaussian process regression and neural networks improved REP retrieval from noisy or coarse-resolution data, as demonstrated in studies integrating Sentinel-2 observations. As of 2025, ongoing research continues to integrate red edge data with advanced machine learning for predictive modeling of vegetation dynamics amid climate change, with new hyperspectral satellites like Planet Labs' Tanager-1, launched in August 2024, providing high-resolution data (5 nm spectral resolution from 400-1000 nm) to further enhance these applications.^[69]

References

[1]
The red edge of plant leaf reflectance - Taylor & Francis Online
The red edge is the sharp change in leaf reflectance between 680 and 750 nm and has been measured on leaves of a variety of species by first derivative ...
[2]
Understanding the temporal dimension of the red-edge spectral ...
In this context, the red-edge spectral region (RESR) was proposed in the past due to its combined sensitivity to chlorophyll content and leaf area variation. In ...
[3]
NDRE: Normalized Difference Red Edge Index - EOS Data Analytics
Mar 20, 2024 · NDRE is a vegetation index used in remote sensing for measuring the chlorophyll content in plants (read more about precision agriculture).
[4]
Crop Classification Based on Red Edge Features Analysis of GF-6 ...
The red edge is the region where the spectral reflectance of green vegetation rises rapidly within a certain band range, and it is the sensitive spectral band ...Missing: definition | Show results with:definition
[5]
Red edge - Oxford Reference
In remote sensing, the sharp increase in spectral reflectance of wavelengths in the red and very-near infrared (700–750 nm) part of the spectrum associated ...
[6]
Moderate Red-Edge vegetation index for High-Resolution ...
This study developed a new vegetation extraction method, the moderate red-edge vegetation index (MREVI), to enhance the extraction accuracy of UGS vegetation.
[7]
The Vegetation Red Edge Biosignature Through Time on Earth and ...
The high reflection of land vegetation in the near-infrared, the vegetation red edge (VRE), is often cited as a spectral biosignature for surface vegetation ...
[8]
A practical approach for estimating the red edge position of plant ...
The point of maximum slope on the reflectance spectrum of a plant leaf between red and near‐infrared wavelengths is known as the red edge position (REP).Missing: range | Show results with:range
[9]
A new technique for extracting the red edge position from ...
The position of the inflexion point in the red edge region (680 to 780 nm) of the spectral reflectance signature, termed the red edge position (REP), is ...
[10]
Spectral Signature Cheatsheet in Remote Sensing - GIS Geography
Near-infrared reflectance is one of the most powerful ways to classify healthy vegetation. Separating classes like water and vegetation is always easier in the ...
[11]
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13596/135960T/Comparison-of-built-up-indices-for-urban-features-extraction/10.1117/12.3064061.full
[12]
Photosynthesis - Absorption Spectra - Chlorophyll a and b
The absorption maxima of chlorophyll a are lambda= 430 and lambda= 662 nm, that of chlorophyll b are at 453 and 642 nm.Missing: peaks | Show results with:peaks<|control11|><|separator|>
[13]
Chlorophyll a, b and carotenoids absorbance spectra. - ResearchGate
These peaks Chlorophyll has a distinctive absorption spectrum containing several peaks appearing at wavelengths 662 nm,430 nm and 410 for chlorophyll a and at ...
[14]
Photosynthetically Active Radiation | NASA Earthdata
PAR refers to the wavelength range of incoming sunlight (roughly 400-700 nanometers) that can be absorbed by plants for photosynthesis. Knowing the spatial and ...
[15]
Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll a+b ...
The reflectance in the main pigment absorption regions in the blue (400–500 nm) and red (660–690 nm) wavelengths proved to be insensitive to variation in ...
[16]
The red edge position and shape as indicators of plant chlorophyll ...
Red edge was found valuable for assessment of plant chlorophyll concentration and LAI, and therefore nutritional status.
[17]
Detection of Red Edge Position and Chlorophyll Content by ...
Reflectance near 700 nm was found to be a very sensitive indicator of the red edge position as well as of chlorophyll concentration.
[18]
Light scattering in stacked mesophyll cells results in similarity ...
Mar 22, 2023 · The red edge locates in the range from 678 to 765 nm. In this range, the absorption of incident radiation by chlorophyll is gradually weakened, ...
[19]
A robust vegetation index for remotely assessing chlorophyll content ...
Feb 14, 2018 · For abaxial illumination, the scattering by the spongy mesophyll cells occurs before light can be guided into the leaf interior, which ...
[20]
[PDF] MODELING LEAF OPTICAL PROPERTIES
Leaf optical properties are modeled using light absorption and scattering, with reflectance and transmission influenced by compounds like cellulose and lignin. ...<|control11|><|separator|>
[21]
[PDF] estimation of heavy metal and radionuclide contamination of soils ...
The results showed a similar 10-20 nm,blue shift" for the. „red edge" in the reflectance spectra for wheat, corn, barley, alfalfa, and fodder grass under ...<|control11|><|separator|>
[22]
The red edge position and shape as indicators of plant chlorophyll ...
Red edge was found valuable for assessment of plant chlorophyll concentration and LAI, and therefore nutritional status and water stress ...
[23]
Spectroscopic remote sensing of plant stress at leaf and canopy ...
Plants under stress normally present a blue shift of the red edge position (e.g. Filella and Penuelas, 1994, Jago et al., 1999, Smith et al., 2004, Sanches ...
[24]
Broadband, red-edge information from satellites improves early ...
We conclude that red-edge information has the potential to considerably improve forest stress monitoring from satellites and warrants further investigation in ...
[25]
Multimodal Deep Learning for Rice Yield Prediction Using UAV ...
May 10, 2023 · The aim of this study was to explore the potential of multimodal deep learning on rice yield prediction accuracy using UAV multispectral images at the heading ...2. Materials And Methods · 2.4. Neural Network... · 3. Results
[26]
Precision agriculture for improving crop yield predictions: a literature ...
Jul 20, 2025 · Airborne multispectral sensors generate high-resolution images that feature accurate spatial resolutions and a wide range of spectral bands.
[27]
Assessment of red-edge based vegetation indices for crop yield ...
This study introduces the triple red-edge index (TREI), a novel vegetation index that leverages the three RE bands provided by the Sentinel-2 satellite.Assessment Of Red-Edge Based... · 2. Methodology · 2.2. Data<|control11|><|separator|>
[28]
Multi-Model Estimation of Forest Canopy Closure by Using Red ...
The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees.Missing: wildfire | Show results with:wildfire
[29]
Mapping invasive alien species in grassland ecosystems using ...
Mar 15, 2022 · We demonstrate the capability of airborne imaging spectroscopy to map sericea in a large natural grassland within the Tallgrass Prairie Preserve.
[30]
(PDF) Red-Edge Normalised Difference Vegetation Index (NDVI705 ...
Aug 7, 2025 · For post-fire recovery analysis, the use of spectral VIs provides a foundational tool, as they support temporal assessments of vegetation ...
[31]
Chlorophyll Index In Agriculture
CIred-edge = (ρNIR / ρred-edge) – 1, or specifically CIred-edge = (ρ850 / ρ730) – 1. This index is exceptionally sensitive to moderate-to-high chlorophyll ...
[32]
Spectral Indices by Type
Vegetation¶ ; MTVI2. Modified Triangular Vegetation Index 2 [ref]. ( 1.5 × ( 1.2 × ( N − G ) − 2.5 × ( R − G ) ) ) / ( ( ( ( 2.0 × N + 1 ) 2 ) − ( 6.0 × N − 5 × ...
[33]
Assessment of vegetation indices for regional crop green LAI ...
Haboudane et al. (2004) developed the modified triangular vegetation index (MTVI2), which combines hyperspectral reflectance in the NIR, red and green ...Abstract · Introduction · Remote Sensing DataMissing: formula | Show results with:formula<|separator|>
[34]
Remote estimation of leaf area index and green leaf biomass in ...
Mar 13, 2003 · In this paper we present a new approach to accurately estimate LAI and green leaf biomass using reflectance in the green and red edge regions of the ...
[35]
A red-edge spectral index for remote sensing estimation of green ...
This is the region where a sharp change in reflectance between wavelengths 690 and 750 nm takes place, and characterizes the transition from chlorophyll ...
[36]
https://www.sciencedirect.com/science/article/abs/pii/S1161030112001542
[37]
ASD FieldSpec 4 Standard-Res Spectroradiometer
The ASD FieldSpec 4 Standard-Res Spectroradiometer is ideal for characterizing spectral features with a resolution down to 10 nm. Find out more here.
[38]
4.3. Hyperspectral Reflectance - Bio-protocol
The full range (350–2500 nm) leaf reflectance profiles were collected by an ASD FieldSpec 4 spectroradiometer (Analytical Spectral Devices, Boulder, CO, ...
[39]
Vegetation Analysis: Using Vegetation Indices in ENVI
Results represent the wavelength of the maximum derivative of reflectance in the vegetation red edge region of the spectrum in microns from 0.69 microns to 0. ...
[40]
Evaluating potential of leaf reflectance spectra to monitor plant ...
Oct 14, 2023 · ... NIR, typically falling within the 700–730 nm range for vegetation. ... NIR region of highest absolute reflectance values (Fig. 6c, d). The ...
[41]
S2 Mission - SentiWiki
... Red (~665nm)) and a Near Infra-Red (~833nm) band;. 6 x 20 metre Bands: 4 narrow Bands in the VNIR vegetation red edge spectral domain (~704nm,~740nm, ~783nm ...Overview of Sentinel-2 Mission · MSI Instrument · Acquisition Resolutions
[42]
ESA - Sentinel-2 - European Space Agency
Sentinel-2C - 5 September 2024. Site: Kourou, French Guiana Launcher: Vega rocket. 10 m resolution; 13 spectral bands; 290 km swath; 5-day revisit time.Data products · 10 m · Open · Plant healthMissing: red edge wavelengths
[43]
RapidEye - ESA Earth Online - European Space Agency
Bands, Blue, 440-510 nm ; Bands · Green, 520-590 nm ; Bands · Red, 630-685 nm ; Bands · Red edge, 690-730 nm ; Bands · NIR (Near Infrared), 760-850 nm ...Missing: wavelength | Show results with:wavelength
[44]
Regional estimation of savanna grass nitrogen using the red-edge ...
The RapidEye sensor contains five spectral bands in the visible-to-near infrared (VNIR), including a red-edge band centered at 710 nm. The importance of the ...
[45]
Potential of Red Edge Spectral Bands in Future Landsat Satellites ...
Sep 12, 2018 · The red edge is the sharp change in leaf reflectance between 680 and 750 nm [3]. It is a key wavelength range in remote sensing that is ...2. Materials And Methods · 2.1. Experimental Dataset · 3. Results And Discussion
[46]
Next up to Continue NASA/USGS's Landsat Legacy
Dec 20, 2022 · Red edge and shortwave bands will help identify stress in forests or croplands. New near infrared and shortwave bands will provide ...
[47]
AVIRIS - Airborne Visible / Infrared Imaging Spectrometer
May 22, 2024 · It is a unique optical sensor that delivers calibrated images of the upwelling spectral radiance in 224 contiguous spectral channels (also ...
[48]
[PDF] ENVI Atmospheric Correction Module User's Guide
The Atmospheric Correction Module provides two atmospheric correction modeling tools for retrieving spectral reflectance from multispectral and hyperspectral ...
[49]
[PDF] The Harmonized Landsat and Sentinel-2 surface reflectance data set
Oct 14, 2018 · The Sentinel-2 constellation provides open-access optical imagery of the global land surface with a 5-day revisit period.
[50]
[PDF] A practical approach for estimating the red edge position of plant ...
The point of maximum slope on the reflectance spectrum of a plant leaf between red and near-infrared wavelengths is known as the red edge position (REP).
[51]
Quantitative characterization of the vegetation red edge reflectance ...
An inverted-Gaussian model for the vegetation red edge reflectance is evaluated with respect to its applicability as a simple four-parameter descriptor.
[52]
A Case Study for Norway Spruce Forests Using HyMap and ... - MDPI
Oct 9, 2016 · In this forest-oriented study, it was demonstrated that it is possible to calculate the red-edge-position (REP) using the simulated Sentinel-2 ( ...<|control11|><|separator|>
[53]
Use of hyperspectral derivative ratios in the red-edge region to ...
Rock et al. (1988) detected a shift in λp, towards the blue, of approximately 5 nm when measuring severe foliage stress on spruce trees due to ...
[54]
[PDF] Assessing Metal-Induced Changes in the Visible and Near-Infrared ...
These changes also involved a blue-shift feature of red-edge position in the first ... Shifting of red-edge inflection points by. * 20 nm toward blue wavelength ...
[55]
[PDF] red edge shift as vitality indicator for plants
HORLER et al. (1983) described the shifting to longer wave- length mainly to be induced by an increase in leaf scattering. As more leaves were added to ...
[56]
[PDF] Hyperspectral Remote Sensing of Vegetation Using Red Edge ...
Experimental results of both interpolation techniques indicate that the wavelength and reflectance of. REP for younger plants (higher chlorophyll content) shift ...
[57]
Red Edge in Remote Sensing: Applications and Advantages
Mar 3, 2025 · Red edge refers to the 680-750nm wavelength region between red and near-infrared light where vegetation reflectance changes dramatically.
[58]
based technologies to measure plant water stress - SciELO SA
Furthermore, the importance of the red edge defined as the region between 690 and 740 nm has gained increasingly more attention over the years, and is seen as ...
[59]
[PDF] A Phenological Approach to Spectral Differentiation of Low ... - EPIC
red-edge wavelength region (~20–30 nm width at ... 800 nm known as the “blue shift” of stressed ... Carotenoids and stress. In Stress Responses in ...<|control11|><|separator|>
[60]
Press Release: Using a Field Spectroradiometer to Measure ...
The red edge is usually situated around 720nm. For nitrogen deficient plants, the red edge may be closer to 700nm and the actual slope of the red edge ...
[61]
Monitoring plant response to phenanthrene using the red edge of ...
Sep 15, 2014 · The spectral response to phenanthrene included a flattened red edge, with decreased first derivative of reflectance. The red edge slope and ...
[62]
the absorption and reflection spectra of leaves, chloroplast ...
THE ABSORPTION AND REFLECTION SPECTRA OF LEAVES, CHLOROPLAST SUSPENSIONS ... French, C. S., G. S. Rabideau, and A. S. Holt. 1946. The construction and ...
[63]
[PDF] computer-aided analysis of multispectral scanner data ... - ASPRS
In 1964, a remote sensing project was initiated at Purdue University. That year, for the first time ever, a multispectral scanner was flown over a ...
[64]
RapidEye Earth Observation Constellation - eoPortal
Launched in August 2008 and deactivated in March 2020, the RapidEye Earth observation system was a commercial constellation of five minisatellites, ...
[65]
Copernicus: Sentinel-2 - eoPortal
The Sentinel-2 satellites have a swath width of 290 km, and together achieve a five-day revisit time. The two satellites follow the same sun-synchronous orbit ...<|separator|>
[66]
EnMAP (Environmental Monitoring and Analysis Program) - eoPortal
Scheduled for launch aboard a SpaceX Falcon 9 in April 2022, EnMAP will operate in a solar-synchronous orbit around the Earth, capturing spectrally high- ...