&lt;title&gt;Superresolution in millimeter-wave imaging technology&lt;/title&gt;

Pirogov, Yu. A.; Attia, Magdy F.; Gladun, Valeri V.; Dubina, A. I.; Tischenko, Dmitri A.; Terentiev, Evgeni N.

doi:10.1117/12.277087

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…NPV and BS had similar spectral reflectance characteristics in Visible and Near Infrared band and but could be differentiated using the Shortwave Infrared band (1600 nm and 2100 nm) [25]. In this study, we selected several PVIs for testing, including the NDI [23], NDTI [19], NDSAVI [20], STI [19], SWIR32 [3] and DFI [24] (Table 2). PVIs and NPVIs were calculated using ArcGIS 10.5 software.…”

Section: Pinty Et Al 1992mentioning

confidence: 99%

“…normalized difference vegetation index (NDVI) [15], soil adjusted vegetation index (SAVI) [16] and global environment monitoring index (GEMI) [17]) have been developed for PV utilizing spectral reflectance, which is widely used in various satellite platforms. Researchers have also proposed some vegetation indices so as to estimate the f NPV [18][19][20][21][22][23]. According to the spectral resolution of remote sensing data, non-photosynthetic vegetation indices (NPVIs) can be divided into hyperspectral NPVIs and multispectral NPVIs.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

et al. 2021

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Estimating the fractional coverage of the photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) is essential for assessing the growth conditions of vegetation growth in arid areas and for monitoring environmental changes and desertification. The aim of this study was to estimate the fPV, fNPV and the fractional coverage of the bare soil (fBS) in the lower reaches of Tarim River quantitatively. The study acquired field data during September 2020 for obtaining the fPV, fNPV and fBS. Firstly, six photosynthetic vegetation indices (PVIs) and six non-photosynthetic vegetation indices (NPVIs) were calculated from Sentinel-2A image data. The PVIs include normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), reduced simple ratio index (RSR) and global environment monitoring index (GEMI). Meanwhile, normalized difference index (NDI), normalized difference tillage index (NDTI), normalized difference senescent vegetation index (NDSVI), soil tillage index (STI), shortwave infrared ratio (SWIR32) and dead fuel index (DFI) constitutes the NPVIs. We then established linear regression model of different PVIs and fPV, and NPVIs and fNPV, respectively. Finally, we applied the GEMI-DFI model to analyze the spatial and seasonal variation of fPV and fNPV in the study area in 2020. The results showed that the GEMI and fPV revealed the best correlation coefficient (R2) of 0.59, while DFI and fNPV had the best correlation of R2 = 0.45. The accuracy of fPV, fNPV and fBS based on the determined PVIs and NPVIs as calculated by GEMI-DFI model are 0.69, 0.58 and 0.43, respectively. The fPV and fNPV are consistent with the vegetation phonological development characteristics in the study area. The study concluded that the application of the GEMI-DFI model in the fPV and fNPV estimation was sufficiently significant for monitoring the spatial and seasonal variation of vegetation and its ecological functions in arid areas.

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Section: Pinty Et Al 1992mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

et al. 2021

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“…Previous studies have proposed numerous indices to estimate CRC. Table 1 lists several promising spectral-based indices for CRC, such as the simple tillage index (STI) [32], the SRNDI [34], the NDSVI [33], DFI [30], the normalized difference tillage index (NDTI) [32], and the hyperspectral CAI [20]. The hyperspectral SINDRI is calculated from the: (i) hyperspectral bands 2210 and 2260 nm [36], (ii) ASTER SWIR bands 6 and 7 [36], and (iii) from the currently functional Worldview-3 SWIR bands 6 and 7 [14].…”

Section: Traditional Crop Residue Cover Spectral Indicesmentioning

confidence: 99%

“…An SI is a combination of two or more remotely detected reflectance bands. Estimates of CRC based on remote-sensing data are quantified by using SIs, such as the dead fuel index (DFI) [30], the normalized difference index (NDI, NDI5, and NDI7) [31], the normalized difference tillage index [32], the normalized difference senescent vegetation index (NDSVI) [33], the short-wave near-infrared normalized difference residue index (SRNDI) [34], the cellulose absorption index (CAI) [35], the shortwave infrared normalized difference residue index (SINDRI) [36], and lignin cellulose absorption [37]. A linear or exponential empirical CRC-estimation equation can be constructed and applied to remote-sensing data by using SI methods.…”

Section: Introductionmentioning

confidence: 99%

Using Hyperspectral Crop Residue Angle Index to Estimate Maize and Winter-Wheat Residue Cover: A Laboratory Study

et al. 2019

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Crop residue left in the field after harvest helps to protect against water and wind erosion, increase soil organic matter, and improve soil quality, so a proper estimate of the quantity of crop residue is crucial to optimize tillage and for research into environmental effects. Although remote-sensing-based techniques to estimate crop residue cover (CRC) have proven to be good tools for determining CRC, their application is limited by variations in the moisture of crop residue and soil. In this study, we propose a crop residue angle index (CRAI) to estimate the CRC for four distinct soils with varying soil moisture (SM) content and crop residue moisture (CRM). The current study uses laboratory-based tests ((i) a dry dataset (air-dried soils and crop residues, n = 392); (ii) a wet dataset (wet soils and crop residues, n = 822); (iii) a saturated dataset (saturated soils and crop residues, n = 402); and (iv) all datasets (n = 1616)), which allows us to analysis the soil and crop residue hyperspectral response to varying SM/CRM. The CRAI combines two features that reflect the moisture content in soil and crop residue. The first is the different reflectance of soil and crop residue as a function of moisture in the near-infrared band (833 nm) and short-wave near-infrared band (1670 nm), and the second is different reflectance of soils and crop residues to lignin, cellulose, and moisture in the bands at 2101, 2031, and 2201 nm. The effects of moisture and soil type on the proposed CRAI and selected traditional spectral indices ((i) hyperspectral cellulose absorption index; (ii) hyperspectral shortwave infrared normalized difference residue index; and (iii) selected broad-band spectral indices) were compared by using a laboratory-based dataset. The results show that the SM/CRM significantly affects the broad-band spectral indices and all other spectral indices investigated are less correlated with CRC when using all datasets than when using only the dry, wet, or saturated dataset. Laboratory study suggests that the CRAI is promising for estimating CRC with the four soils and with varying SM/CRM. However, because the CRAI was only validated by a laboratory-based dataset, additional field testing is thus required to verify the use of satellite hyperspectral remote-sensing images for different crops and ecological areas.

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Estimating fractional coverage of crop, crop residue, and bare soil using shortwave infrared angle index and Sentinel-2 MSI

Yue

Guo

et al. 2022

International Journal of Remote Sensing

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<title>Superresolution in millimeter-wave imaging technology</title>

Cited by 6 publications

References 2 publications

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

Using Hyperspectral Crop Residue Angle Index to Estimate Maize and Winter-Wheat Residue Cover: A Laboratory Study

Estimating fractional coverage of crop, crop residue, and bare soil using shortwave infrared angle index and Sentinel-2 MSI

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