scholarly journals Information properties of spectral brightness coefficients and vegetation indexes for the aerospace imagies calibration

2014 ◽  
Vol 2 (2) ◽  
pp. 360 ◽  
Author(s):  
Guseynov ◽  
Smokty
Keyword(s):  
Spectral Brightness ◽  
Vegetation Indexes ◽  
Spectral Brightness Coefficients

Procedures for in-flight calibration of satellite spectral equipment

2020 ◽  
pp. 80-90
Author(s):  
Leonid V. KATKOVSKY ◽  
Boris I. BELYAEV ◽  
Olga O. SILYUK ◽  
Gleb S. LITVINOVICH ◽  
Aleksey A. LAMAKA ◽  
...  
Keyword(s):  
Key Words ◽  
Radiation Transfer ◽  
Field Measurements ◽  
Spectral Brightness ◽  
Direct And Inverse Problems ◽  
Test Range ◽  
Multi Level ◽  
Spectral Equipment ◽  
Sentinel 2 ◽  
Spectral Brightness Coefficients

The paper presents procedures developed for in-flight calibration of satellite spectral equipment (by solving direct and inverse problems in the theory of radiation transfer in the atmosphere) using quasi-synchronous sub-satellite point measurements. Also presented are the results of processing and analysis of several sessions of multi-level measurements conducted at the Zyabrovka test range (Republic of Belarus, Gomel region). Measurements taken from ground and air used spectral instrumentation developed at the Airspace Research department of Sevchenko IAPP of BSU to support satellite test ranges on the ground. Verification results are provided for measurements taken on the ground, in air and in space. Used for verification of the ground and space measurements were data from the multi-spectral spacecraft Sentinel-2. Key words: in-flight calibrations, sub-satellite test range, spectral brightness coefficients, field measurements.

INSTRUMENTS AND METHODS FOR DETERMINING SPECTRAL BRIGHTNESS COEFFICIENTS OF NATURAL FEATURES FROM AERIAL MEASUREMENTS

1998 ◽  
Vol 35 (1) ◽  
pp. 14-21
Author(s):  
A. V. Vasil'yev ◽  
I. N. Mel'nikova ◽  
L. N. Poberovskaya ◽  
I. A. Tovstenko
Keyword(s):  
Spectral Brightness ◽  
Spectral Brightness Coefficients

Maps of spectral brightness coefficients for the U.S.S.R. territory and their statistical analysis

1993 ◽  
Vol 14 (3) ◽  
pp. 521-535 ◽  
Author(s):  
K. Ya. KONDRATYEV ◽  
A. A. GRIGORYEV ◽  
G. A. IVANIAN ◽  
G. A. PUTINTSEVA
Keyword(s):  
Statistical Analysis ◽  
Spectral Brightness ◽  
Spectral Brightness Coefficients

scholarly journals Technique for experimental study of spectral brightness coefficients materials and natural backgrounds.

2021 ◽  
Author(s):  
A.V. Ovchinnikov ◽  
Keyword(s):  
Experimental Study ◽  
Wavelength Range ◽  
Spectral Brightness ◽  
Spectral Brightness Coefficients ◽  
Multispectral Camera

The article describes a technique for experimental study of spectral brightness coefficients in the wavelength range of 470 ... 940 nm of objects, natural and anthropogenic backgrounds using a multispectral camera. Examples of the results of measurements of the spectral brightness coefficients are given.

scholarly journals Linear optics control of sideband instability for improved free-electron laser spectral brightness

2020 ◽  
Vol 23 (11) ◽  
Author(s):  
G. Perosa ◽  
E. M. Allaria ◽  
L. Badano ◽  
N. Bruchon ◽  
P. Cinquegrana ◽  
...  
Keyword(s):  
Free Electron ◽  
Free Electron Laser ◽  
Spectral Brightness ◽  
Linear Optics

scholarly journals Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 956
Author(s):  
Florian Mouret ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Denis Kouamé ◽  
Guillaume Rieu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Experimental Validation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Images ◽  
Vegetation Indexes ◽  
Crop Development ◽  
Different Types ◽  
Selection Of ◽  
Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

Hyperspectral vegetation indexes to monitor wheat plant height under different sowing conditions

2020 ◽  
Vol 53 (3) ◽  
pp. 194-206
Author(s):  
Zhang Zhen ◽  
Lou Yunsheng ◽  
Ojara A Moses ◽  
Li Rui ◽  
Ma Li ◽  
...  
Keyword(s):  
Plant Height ◽  
Wheat Plant ◽  
Vegetation Indexes

Vegetation Canopy Coverage Estimation Using Physical Models

2013 ◽  
Vol 726-731 ◽  
pp. 4709-4713
Author(s):  
Lin Jing Zhang ◽  
Hong Zhang Ma ◽  
Zhu Bo Zhou ◽  
Zhong Liang Ren ◽  
Xiao Bo Zhu ◽  
...  
Keyword(s):  
Regression Model ◽  
Simulated Data ◽  
Hyperspectral Data ◽  
Physical Models ◽  
Vegetation Canopy ◽  
Canopy Coverage ◽  
Vegetation Indexes ◽  
Ground Measurement ◽  
Highly Correlated ◽  
Coverage Estimation

Based on the physical models of PROSPECT, SAIL and porosity model, hyperspectral data and canopy coverage data of different combined scenes were simulated. According to the simulated data, we chose four sensitive bands and four sensitive vegetation indexes highly correlated to vegetation canopy coverage, and analyzed the correlation between sensitive bands, sensitive vegetation indexes and canopy coverage. Then we built a regression model of canopy coverage with EVI highly correlated with canopy coverage. At last, we verified this model by experimental data from ground measurement experiment. It shows that there is a high correlation between EVI and canopy coverage and the regression model built by EVI can produce an effective result and the RMSE is less than 0.09.

An Analysis of Shadow Effects on Spectral Vegetation Indexes Using a Ground-Based Imaging Spectrometer

2015 ◽  
Vol 12 (11) ◽  
pp. 2188-2192 ◽  
Author(s):  
Lifu Zhang ◽  
Xuejian Sun ◽  
Taixia Wu ◽  
Hongming Zhang
Keyword(s):  
Imaging Spectrometer ◽  
Vegetation Indexes
Sign in / Sign up

Export Citation Format

Share Document