Differential Spectral Index Research Articles

Estimation of Soil Moisture Content Based on Fractional Differential and Optimal Spectral Index

Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy’s original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model’s accuracy follows the order: 1.5 order > 2.0 order > 1.0 order > 0.5 order > original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF > SVM > BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models.

Estimation of the Leaf Area Index of Winter Rapeseed Based on Hyperspectral and Machine Learning

Leaf area index (LAI) is essential for evaluating crop growth and development. Destructive manual measurement methods mainly achieve traditional crop LAI acquisition. Due to the advantages of being fast and non-destructive, spectroscopy technology provides a feasible method for obtaining crop LAI. In order to achieve efficient acquisition of winter oilseed rape LAI, this study collected hyperspectral data and LAI data at the full-bloom stage of winter oilseed rape. It calculated the spectral indexes related to the LAI of the original spectrum and the first-order differential spectrum, respectively. The index with the highest correlation with the LAI of winter oilseed rape at the flowering stage was selected as the optimal spectral index for input. Subsequently, three machine learning methods, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF), were used to construct the LAI model of winter oilseed rape, and the model was tested. The results show that the correlation coefficient between the spectral index calculated by the first-order differential processing of the original spectral data and the LAI of winter rapeseed is significantly improved compared with the original data. Among them, the spectral index NDVI with the best correlation coefficient with LAI can be obtained under the first-order differential: the correlation coefficient is 0.734, and the wavelength combination is 716 nm and 724 nm. At the same time, we found that when the input variables are the same, the RF model has higher estimation accuracy than the other models. The best estimation accuracy is obtained when the input variable is the first-order differential spectral index. The R2 of the model validation set is 0.810, RMSE is 0.455 cm2/cm2, MRE is 10.465%, and the model accuracy is high. The results of this study can provide a theoretical basis for crop monitoring based on spectral technology and provide a theoretical basis for crop growth.

VERITAS Discovery of Very High Energy Gamma-Ray Emission from S3 1227+25 and Multiwavelength Observations

We report the detection of very high energy gamma-ray emission from the blazar S3 1227+25 (VER J1230+253) with the Very Energetic Radiation Imaging Telescope Array System (VERITAS). VERITAS observations of the source were triggered by the detection of a hard-spectrum GeV flare on 2015 May 15 with the Fermi-Large Area Telescope (LAT). A combined 5 hr VERITAS exposure on May 16 and 18 resulted in a strong 13σ detection with a differential photon spectral index, Γ = 3.8 ± 0.4, and a flux level at 9% of the Crab Nebula above 120 GeV. This also triggered target-of-opportunity observations with Swift, optical photometry, polarimetry, and radio measurements, also presented in this work, in addition to the VERITAS and Fermi-LAT data. A temporal analysis of the gamma-ray flux during this period finds evidence of a shortest variability timescale of τ obs = 6.2 ± 0.9 hr, indicating emission from compact regions within the jet, and the combined gamma-ray spectrum shows no strong evidence of a spectral cutoff. An investigation into correlations between the multiwavelength observations found evidence of optical and gamma-ray correlations, suggesting a single-zone model of emission. Finally, the multiwavelength spectral energy distribution is well described by a simple one-zone leptonic synchrotron self-Compton radiation model.

Estimation of Chlorophyll Content in Soybean Crop at Different Growth Stages Based on Optimal Spectral Index

Chlorophyll is an important component of crop photosynthesis as it is necessary for the material exchange between crops and the atmosphere. The amount of chlorophyll present reflects the growth and health status of crops. Spectral technology is a feasible method for obtaining crop chlorophyll content. The first-order differential spectral index contains sufficient spectral information related to the chlorophyll content and has a high chlorophyll prediction ability. Therefore, in this study, the hyperspectral index data and chlorophyll content of soybean canopy leaves at different growth stages were obtained. The first-order differential transformation of soybean canopy hyperspectral reflectance data was performed, and five indices, highly correlated with soybean chlorophyll content at each growth stage, were selected as the optimal spectral index input. Four groups of model input variables were divided according to the following four growth stages: four-node (V4), full-bloom (R2), full-fruit (R4), and seed-filling stage (R6). Three machine learning methods, support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were used to establish an inversion model of chlorophyll content at different soybean growth stages. The model was then verified. The results showed that the correlation coefficient between the optimal spectral index and chlorophyll content of soybean was above 0.5, the R2 period correlation coefficient was above 0.7, and the R4 period correlation coefficient was above 0.8. The optimal estimation model of soybean and chlorophyll content is established through the combination of the first-order differential spectral index and RF during the R4 period. The optimal estimation model validation set determination coefficient (R2) was 0.854, the root mean square error (RMSE) was 2.627, and the mean relative error (MRE) was 4.669, demonstrating high model accuracy. The results of this study can provide a theoretical basis for monitoring the growth and health of soybean crops at different growth stages.

Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index

Leaf area index (LAI) and above-ground biomass are both vital indicators for evaluating crop growth and development, while rapid and non-destructive estimation of crop LAI and above-ground biomass is of considerable significance for crop field management. Owing to the advantages of repeatable and high-throughput observations, spectral technology provides a feasible method for obtaining LAI and above-ground biomass of crops. In the present study, the spectral, LAI and above-ground biomass data of winter wheat were collected, and 7 species (14 in total) were calculated based on the original and first-order differential spectrum correlation spectral indices with LAI. Then, the correlation matrix method was used for correlation with LAI. The optimal wavelength combination was extracted, and the results were calculated as the optimal spectral index related to LAI. The calculation process of the optimal spectral index related to above-ground biomass was the same as that aforementioned. Finally, the optimal spectral index was divided into three groups of model input variables, winter wheat LAI and above-ground biomass estimation models were constructed using support vector machine (SVM), random forest (RF) and a back propagation neural network (BPNN), and the models were verified. The results show that the correlation coefficient between the highest of the optimal spectral indices, the LAI, and the above-ground biomass of winter wheat exceeded 0.6, and the correlation was good. The methods for establishing the optimal estimation models for LAI and above-ground biomass of winter wheat are all modeling methods in which the input variables are the combination of the first-order differential spectral index (combination 2) and RF. The R2 of the LAI estimation model validation set was 0.830, the RMSE was 0.276, and the MRE was 6.920; the R2 of the above-ground biomass estimation model validation set was 0.682, RMSE was 235.016, MRE was 4.336, and the accuracies of both models were high. The present research results can provide a theoretical basis for crop monitoring based on spectral technology and provide an application reference for the rapid estimation of crop growth parameters.

Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content

BackgroundThe leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation.MethodsIn this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties) in 2018–2020 were performed to obtain the canopy spectra reflectance and leaf water content (LWC) data. The characteristic bands related to LWC were extracted from correlation coefficient method (CA) and x-Loading weight method (x-Lw). Five modeling methods, spectral index and four other methods (Partial Least-Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Random Trees (ERT), and K-Nearest Neighbor (KNN)) based characteristic bands, were employed to construct LWC estimation models.ResultsThe results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750–1350 nm). The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R2 of 0.85 and R2 of 0.78 for the calibration and validation, respectively. Due to a large amount of hyperspectral data, the correlation coefficient method (CA) and x-Loading weight (x-Lw) were used to select the water characteristic bands (100 and 28 characteristic bands, respectively) from the full spectrum. We found that the accuracy of the model based on the characteristic bands was not significantly lower than that of the full spectrum-based models. Among these models, the ERT- x-Lw model performed the best (R2 and RMSE of 0.88 and 1.46; 0.84 and 1.62 for the calibration and validation, respectively). In addition, the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307).ConclusionThe two models based on ERT-x-Lw and DVI (R1185, R1307) can effectively predict wheat leaf water content. The results provide a technical reference and a basis for crop water monitoring and diagnosis under similar production conditions.

Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance

ABSTRACT Introduction: High mercury (Hg) concentrations affect the chlorophyll in leaves, thereby modifying leaf spectra. Hyperspectra is a promising technique for the rapid, nondestructive evaluation of leaf Hg content. In this study, we investigated Hg contents and reflective hyperspectra of reed leaves (Phragmites communis) in a gold mining (Jilin province, China). Spectral parameters sensitive to Hg content were identified through basic spectral transformations, continuous wavelet transformation (CWT), and spectral indices techniques. Leaf Hg inversion models were developed using stepwise multiple linear regression, partial least squares regression, and random forest algorithms.Outcomes: The results indicated that: 1) leaf Hg content decreased with increasing distance from the mine: Jiapigou (JPG) > Erdaocha (EDC) > Laojingchang (LJC) > Erdaogou (EDG) > Lingqian (LQ) > Weishahe (WSH). 2) Hg–sensitive wavelengths were primarily in the visible region; CWT increased the correlation between hyperspectral data and leaf Hg content, and improved the regression and accuracy of inversion; 3) the continuum removal–CWT–stepwise multiple linear regression was better for estimating low leaf Hg content; while the differential spectral index–partial least squares regression was better for estimating high leaf Hg content.Conclusion: These hyperspectral inversion methods could be used for rapid, nondestructive monitoring of wetland plants.

Separation of galactic and extragalactic radio emission at decameter wavelengths

We present the results of separation of observed radio emission at decameter wavelengths into extragalactic background and halo components of the Galaxy. We have used a multifrequency UTR-2 (decametric wave radio telescope) survey to perform multifrequency temperature-versus-temperature (T-T) plots. The low-frequency radio maps of the area near the minimum of Galactic radiation $(41^{\circ } < \delta_{2000} < 46^{\circ }, 7^{\mathrm{h}} 45^{\mathrm{m}} < \alpha_{2000} < 8^{\mathrm{h}} 35^{\mathrm{m}}) $ has been investigated in detail. The differential spectral index of the Galaxy halo at the frequencies 14.7, 16.7, 20, and 25 MHz was determined in this direction using pairwise of all the above frequencies. Its value was found to be $\beta_{G} = 2.81 \pm 0.03 $ . In addition, using the multifrequency T-T plots, the integral extragalactic brightness temperatures of isotropic background were determined to range 12 kK at 25 MHz to 42 kK at 14.7 MHz, and the extragalactic spectral index $\beta_{E} = 2.41 \pm 0.1 $ .

Gamma-ray flare of PKS 1222+216 in 2010: effect of jet dynamics at the recollimation zone

The $\gamma$-ray flare of PKS 1222+216, observed in June 2010, is interpreted as an outcome of jet dynamics at recollimation zone. We obtained the $\gamma$-ray light-curves in three different energy bands, namely, 100--300 MeV, 300 MeV--1 GeV and 1--3 GeV from observations by the \emph{Fermi} Large Area Telescope (LAT). We also use the \emph{Swift}--XRT flux from 0.3--10 keV obtained from archival data. We supplement these with the 0.07--0.4 TeV observations with MAGIC telescope, available in the literature. The detection of source at very high energy (VHE, $E>100$ GeV) with a differential photon spectral index of $2.7\pm0.3$ and the rapid variability associated with it suggests that the emission arises from a compact region located beyond the broad line emitting region. The plausible $\gamma$-ray emission mechanism can then be inverse Compton scattering of IR photons from obscuring torus. Further, the decay time of LAT flare cannot be explained by considering simple radiative loss mechanisms. Hence, to interpret the LAT light curves, we develop a model where the broadband emission originates from a compact region, arising plausibly from the compression of jet matter at the recollimation zone. The flare is then expressed as an outcome of jet deceleration probably associated with this focusing effect. The parameters of the model are further constrained by reproducing the broadband spectral energy distribution of the source obtained during the flare episode. Our study suggests that the particle energy density exceeds magnetic energy density by a large factor which in turn may cause rapid expansion of the emission region. However, near equipartition can be achieved towards the end of LAT flare during which the compact emission region would have expanded to the size of jet cross-section.

DISCOVERY OF TeV GAMMA-RAY EMISSION FROM CTA 1 BY VERITAS

We report the discovery of TeV gamma-ray emission coincident with the shell-type radio supernova remnant (SNR) CTA 1 using the VERITAS gamma-ray observatory. The source, VER J0006+729, was detected as a 6.5 standard deviation excess over background and shows an extended morphology, approximated by a two-dimensional Gaussian of semi-major (semi-minor) axis 0.30 degree (0.24 degree) and a centroid 5' from the Fermi gamma-ray pulsar PSR J0007+7303 and its X-ray pulsar wind nebula (PWN). The photon spectrum is well described by a power-law dN/dE = N_0 (E/3 TeV)^(-\Gamma), with a differential spectral index of \Gamma = 2.2 +- 0.2_stat +- 0.3_sys, and normalization N_0 = (9.1 +- 1.3_stat +- 1.7_sys) x 10^(-14) cm^(-2) s^(-1) TeV^(-1). The integral flux, F_\gamma = 4.0 x 10^(-12) erg cm^(-2) s^(-1) above 1 TeV, corresponds to 0.2% of the pulsar spin-down power at 1.4 kpc. The energetics, co-location with the SNR, and the relatively small extent of the TeV emission strongly argue for the PWN origin of the TeV photons. We consider the origin of the TeV emission in CTA 1.

DETECTION OF VERY HIGH ENERGY γ-RAY EMISSION FROM THE PERSEUS CLUSTER HEAD-TAIL GALAXY IC 310 BY THE MAGIC TELESCOPES

We report on the detection with the MAGIC telescopes of very high energy gamma-rays from IC 310, a head-tail radio galaxy in the Perseus galaxy cluster, observed during the interval November 2008 to February 2010. The Fermi satellite has also detected this galaxy. The source is detected by MAGIC at a high statistical significance of 7.6sigma in 20.6 hr of stereo data. The observed spectral energy distribution is flat with a differential spectral index of -2.00 \pm 0.14. The mean flux above 300 GeV, between October 2009 and February 2010, (3.1 \pm 0.5)x10^{-12} cm^{-2} s^{-1}, corresponds to (2.5 \pm 0.4)% of Crab Nebula units. Only an upper limit, of 1.9% of Crab Nebula units above 300 GeV, was obtained with the 2008 data. This, together with strong hints (>3sigma) of flares in the middle of October and November 2009, implies that the emission is variable. The MAGIC results favour a scenario with the very high energy emission originating from the inner jet close to the central engine. More complicated models than a simple one-zone SSC scenario, e.g. multi-zone SSC, external Compton or hadronic, may be required to explain the very flat spectrum and its extension over more than three orders of magnitude in energy.

Spectrum of the Isotropic Diffuse Gamma-Ray Emission Derived from First-Year Fermi Large Area Telescope Data

We report on the first Fermi Large Area Telescope (LAT) measurements of the so-called "extragalactic" diffuse gamma-ray emission (EGB). This component of the diffuse gamma-ray emission is generally considered to have an isotropic or nearly isotropic distribution on the sky with diverse contributions discussed in the literature. The derivation of the EGB is based on detailed modeling of the bright foreground diffuse Galactic gamma-ray emission, the detected LAT sources, and the solar gamma-ray emission. We find the spectrum of the EGB is consistent with a power law with a differential spectral index gamma = 2.41 +/- 0.05 and intensity I(>100 MeV) = (1.03 +/- 0.17) x 10(-5) cm(-2) s(-1) sr(-1), where the error is systematics dominated. Our EGB spectrum is featureless, less intense, and softer than that derived from EGRET data.

Constraints on Energy Spectra of Blazars based on Recent EBL Limits from Galaxy Counts

We combine the recent estimate of the contribution of galaxies to the 3.6 μm intensity of the extragalactic background light (EBL) with optical and near-infrared (IR) galaxy counts to set new limits on intrinsic spectra of some of the most distant TeV blazars, 1ES 0229+200, 1ES 1218+30.4, and 1ES 1101–232, located at redshifts 0.1396, 0.182, and 0.186, respectively. The new lower limit on the 3.6 μm EBL intensity is significantly higher than the previous one set by the cumulative emission from resolved Spitzer galaxies. Correcting for attenuation by the revised EBL, we show that the differential spectral index of the intrinsic spectrum of the three blazars is -->1.28 ± 0.20 or harder. These results present blazar emission models with the challenge of producing extremely hard intrinsic spectra in the sub-TeV to multi-TeV regime. These results also question the reliability of recently derived upper limits on the near-IR EBL intensity that are solely based on the assumption that intrinsic blazar spectra should not be harder than 1.50.

VERITAS Discovery of &gt;200 GeV Gamma-Ray Emission from the Intermediate-Frequency-Peaked BL Lacertae Object W Comae

We report the detection of very high-energy gamma-ray emission from the intermediate-frequency-peaked BL Lacertae object W Comae (z=0.102) by VERITAS. The source was observed between January and April 2008. A strong outburst of gamma-ray emission was measured in the middle of March, lasting for only four days. The energy spectrum measured during the two highest flare nights is fit by a power-law and is found to be very steep, with a differential photon spectral index of Gamma = 3.81 +- 0.35_stat +- 0.34_syst. The integral photon flux above 200GeV during those two nights corresponds to roughly 9% of the flux from the Crab Nebula. Quasi-simultaneous Swift observations at X-ray energies were triggered by the VERITAS observations. The spectral energy distribution of the flare data can be described by synchrotron-self-Compton (SSC) or external-Compton (EC) leptonic jet models, with the latter offering a more natural set of parameters to fit the data.

TeV Gamma‐Ray Observations of the Perseus and Abell 2029 Galaxy Clusters

Galaxy clusters might be sources of TeV gamma rays emitted by high-energy protons and electrons accelerated by large-scale structure formation shocks, galactic winds, or active galactic nuclei. Furthermore, gamma rays may be produced in dark matter particle annihilation processes at the cluster cores. We report on observations of the galaxy clusters Perseus and A2029 using the 10 m Whipple Cerenkov telescope during the 2003-2004 and 2004-2005 observing seasons. We apply a two-dimensional analysis technique to scrutinize the clusters for TeV emission. In this paper we first determine flux upper limits on TeV gamma-ray emission from point sources within the clusters. Second, we derive upper limits on the extended cluster emission. We subsequently compare the flux upper limits with EGRET upper limits at 100 MeV and theoretical models. Assuming that the gamma-ray surface brightness profile mimics that of the thermal X-ray emission and that the spectrum of cluster cosmic rays extends all the way from thermal energies to multi-TeV energies with a differential spectral index of -2.1, our results imply that the cosmic-ray proton energy density is less than 7.9% of the thermal energy density for the Perseus Cluster.

Spectrum of Very High Energy Gamma‐Rays from the blazar 1ES 1959+650 during Flaring Activity in 2002

The blazar 1ES 1959+650 was observed in a flaring state with the Whipple 10 m Imaging Atmospheric Cerenkov Telescope in 2002 May. A spectral analysis has been carried out on the data from that time period, and the resulting very high energy gamma-ray spectrum (E ≥ 316 GeV) can be well fitted by a power law of differential spectral index α = 2.78 ± 0.12stat ± 0.21sys. On 2002 June 4, the source flared dramatically in the gamma-ray range without any coincident increase in the X-ray emission, providing the first unambiguous example of an "orphan" gamma-ray flare from a blazar. The gamma-ray spectrum for these data can also be described by a simple power-law fit with α = 2.82 ± 0.15stat ± 0.30sys. There is no compelling evidence for spectral variability or for any cutoff to the spectrum.

Observation of Multi‐TeV Diffuse Gamma Rays from the Galactic Plane with the Tibet Air Shower Array

Data from the Tibet III air shower array (with energies around 3 TeV) and from the Tibet II array (with energies around 10 TeV) have been searched for diffuse gamma rays from the Galactic plane. These arrays have an angular resolution of about 09. The sky regions searched are the inner Galaxy, 20? ? l ? 55?, and outer Galaxy, 140? ? l ? 225?, and |b| ? 2? or ? 5?. No significant Galactic-plane gamma-ray excess was observed. The 99% confidence level upper limits for gamma-ray intensity obtained are (for |b| ? 2?) 1.1 ? 10-15 cm-2 s-1 sr-1 MeV-1 at 3 TeV and 4.1 ? 10-17 cm-2 s-1 sr-1 MeV-1 at 10 TeV for the inner Galaxy, and 3.6 ? 10-16 cm-2 s-1 sr-1 MeV-1 at 3 TeV and 1.3 ? 10-17 cm-2 s-1 sr-1 MeV-1 at 10 TeV for the outer Galaxy, assuming a differential spectral index of 2.4. The upper limits are significant in the multi-TeV region when compared to those from Cerenkov telescopes in the lower energy region and other air shower arrays in the higher energy region; however, the results are not sufficient to rule out the inverse Compton model with a source electron spectral index of 2.0.

Detection of the BL Lac object 1ES 1426+428 in the Very High Energy gamma-ray band by the CAT Telescope from 1998–2000

The BL Lac Object lES 1426+428, at a red-shift of z = 0.129, has been monitored by the CAT telescope from February 1998 to June 2000. The accumulation of 26 h of observations shows a γ-ray signal of 321 events above 250 GeV at 5.2 standard deviations, determined using data analysis cuts adapted to a weak, steep-spectrum source. The source emission has an average flux of Φ d i f f (400 GeV) = 6.73 ′ 1.27 s t a t ′ 1.45 s y s t × 10 - 1 1 cm - 2 s - 1 TeV - 1 , and a very steep spectrum, with a differential spectral index of y = -3.60 ′ 0.57 which can be refined to y = -3.66 ′ 0.41 using a higher flux data subset. If, as expected from its broad-band properties, the Very High Energy emission is hard at the source, these observations support a strong absorption effect of γ-rays by the Intergalactic Infrared field.

Evidence for TeV gamma ray emission from Cassiopeia A

232 hours of data were accumulated from 1997 to 1999, using the HEGRA Stereoscopic Cherenkov Telescope System to observe the supernova remnant Cassiopeia A. TeV gamma ray emission was detected at the 5 sigma level, and a flux of (5.8 +- 1.2(stat) +- 1.2(syst)) 10^(-9) ph m^(-2) s^(-1) above 1 TeV was derived. The spectral distribution is consistent with a power law with a differential spectral index of -2.5 +- 0.4(stat) +- 0.1(syst) between 1 and 10 TeV. As this is the first report of the detection of a TeV gamma ray source on the "centi-Crab" scale, we present the analysis in some detail. Implications for the acceleration of cosmic rays depend on the details of the source modeling. We discuss some important aspects in this paper.

Two-parametric representation of the solar and heliospheric protons

Abstract The dynamics of the interplanetary proton spectra in the energy range 1–40 MeV have been investigated using the data obtained on board the IMP-8 (1973–1992) and Meteor (1970–1996) satellites. Phase diagrams of the proton events are constructed in the two-parametric space { Y,g }, where Y=g −1 lg( I p ), g is the differential energy spectral index, I p represents the >1 MeV proton intensity. The results show that the phase trajectories allow separation of the background proton fluxes, proton populations accelerated mainly in the solar flares, at the propagating interplanetary shocks associated with coronal mass ejections and also in the corotating interaction regions between fast and slow solar wind streams. The estimations of the maximal and minimal >1 MeV proton fluxes in the near Earth heliosphere are obtained.