Design of airborne imaging spectrometer based on curved prism

2011 ◽  
Author(s):  
Yunfeng Nie ◽  
Bin Xiangli ◽  
Jinsong Zhou ◽  
Xiaoxiao Wei
Keyword(s):  
Imaging Spectrometer ◽  
Airborne Imaging

High-resolution airborne imaging spectrometer

1998 ◽  
Author(s):  
Andre J. Villemaire ◽  
Serge Fortin ◽  
Claude Lafond ◽  
Marc-Andre A. Soucy ◽  
Jean-Francois Legault ◽  
...  
Keyword(s):  
High Resolution ◽  
Imaging Spectrometer ◽  
Airborne Imaging

Estimation of anthropogenic GHG emission rate for different sources in Japanese megacity by using airborne imaging-spectrometer suites

2019 ◽  
Author(s):  
Takahiro Kawashima ◽  
Fumie Kataoka ◽  
Tetsuya Kaku ◽  
Akihiko Kuze ◽  
Hiroshi Suto ◽  
...  
Keyword(s):  
Emission Rate ◽  
Ghg Emission ◽  
Imaging Spectrometer ◽  
Airborne Imaging ◽  
Different Sources

Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping

Geoderma ◽  
2019 ◽  
Vol 337 ◽  
pp. 607-621 ◽  
Author(s):  
Sanne Diek ◽  
Sabine Chabrillat ◽  
Marco Nocita ◽  
Michael E. Schaepman ◽  
Rogier de Jong
Keyword(s):  
Soil Moisture ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Imaging Spectrometer ◽  
Multi Temporal ◽  
Airborne Imaging ◽  
Spectrometer Data

scholarly journals Use of Airborne Hyperspectral Imagery to Map Soil Properties in Tilled Agricultural Fields

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
W. Dean Hively ◽  
Gregory W. McCarty ◽  
James B. Reeves ◽  
Megan W. Lang ◽  
Robert A. Oesterling ◽  
...  
Keyword(s):  
Soil Properties ◽  
Data Extraction ◽  
Agricultural Fields ◽  
Moisture Regime ◽  
Imaging Spectrometer ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Raster Maps ◽  
Low Pass ◽  
Airborne Imaging

Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm,∼10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n=315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted withR2>0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a3×3low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

scholarly journals Detection and Quantification of Snow Algae with an Airborne Imaging Spectrometer

2001 ◽  
Vol 67 (11) ◽  
pp. 5267-5272 ◽  
Author(s):  
Thomas H. Painter ◽  
Brian Duval ◽  
William H. Thomas ◽  
Maria Mendez ◽  
Sara Heintzelman ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Wavelength Range ◽  
Spectral Reflectance ◽  
Infrared Imaging ◽  
Spectral Signature ◽  
Absorption Feature ◽  
Imaging Spectrometer ◽  
Algal Concentration ◽  
Airborne Imaging ◽  
Spectrometer Data

ABSTRACT We describe spectral reflectance measurements of snow containing the snow alga Chlamydomonas nivalis and a model to retrieve snow algal concentrations from airborne imaging spectrometer data. Because cells of C. nivalis absorb at specific wavelengths in regions indicative of carotenoids (astaxanthin esters, lutein, β-carotene) and chlorophylls a and b, the spectral signature of snow containing C. nivalis is distinct from that of snow without algae. The spectral reflectance of snow containing C. nivalis is separable from that of snow without algae due to carotenoid absorption in the wavelength range from 0.4 to 0.58 μm and chlorophyll a and babsorption in the wavelength range from 0.6 to 0.7 μm. The integral of the scaled chlorophyll a and b absorption feature (I 0.68) varies with algal concentration (Ca ). Using the relationshipCa = 81019.2 I 0.68+ 845.2, we inverted Airborne Visible Infrared Imaging Spectrometer reflectance data collected in the Tioga Pass region of the Sierra Nevada in California to determine algal concentration. For the 5.5-km2 region imaged, the mean algal concentration was 1,306 cells ml−1, the standard deviation was 1,740 cells ml−1, and the coefficient of variation was 1.33. The retrieved spatial distribution was consistent with observations made in the field. From the spatial estimates of algal concentration, we calculated a total imaged algal biomass of 16.55 kg for the 0.495-km2 snow-covered area, which gave an areal biomass concentration of 0.033 g/m2.

scholarly journals “Flex 2018” Cruise: an opportunity to assess phytoplankton chlorophyll fluorescence retrieval at different observative scales

2020 ◽  
pp. 688-697
Author(s):  
Annalisa Di Cicco ◽  
Remika Gupana ◽  
Alexander Damm ◽  
Simone Colella ◽  
Federico Angelini ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Coastal Waters ◽  
Field Measurements ◽  
Tyrrhenian Sea ◽  
Satellite Mission ◽  
Imaging Spectrometer ◽  
Optical Instruments ◽  
Ground Station ◽  
Airborne Imaging

The “FLEX 2018” cruise, organized by the CNR-ISMAR in frame of the ESA “FLEXSense Campaign 2018” and CMEMS project, provided a ground station for several bio-optical instruments that investigated the coastal waters of the Tyrrhenian Sea in June 2018. The field measurements were performed in time synergy with Sentinel 3A and Sentinel 3B satellites and HyPlant airborne imaging spectrometer. Active and passive fluorescence were investigated at different scales in coastal waters to support preparatory activities of the FLuorescence EXplorer (FLEX) satellite mission.

Airborne imaging spectrometer with wide field of view and large relative-aperture

2015 ◽  
Vol 23 (1) ◽  
pp. 15-21
Author(s):  
陈伟 CHEN Wei ◽  
郑玉权 ZHENG Yu-quan ◽  
薛庆生 XUE Qing-sheng
Keyword(s):  
Field Of View ◽  
Wide Field ◽  
Imaging Spectrometer ◽  
Wide Field Of View ◽  
Airborne Imaging

Radiometric restoration of airborne imaging spectrometer data

1998 ◽  
Author(s):  
Xiaofang Guo ◽  
Runsheng Wang ◽  
Jicheng Cheng ◽  
Qinghua Wang
Keyword(s):  
Imaging Spectrometer ◽  
Airborne Imaging ◽  
Spectrometer Data

Study of crop growth parameters using Airborne Imaging Spectrometer data

2001 ◽  
Vol 22 (12) ◽  
pp. 2401-2411 ◽  
Author(s):  
N. K. Patel ◽  
C. Patnaik ◽  
S. Dutta ◽  
A. M. Shekh ◽  
A. J. Dave
Keyword(s):  
Growth Parameters ◽  
Crop Growth ◽  
Imaging Spectrometer ◽  
Airborne Imaging ◽  
Spectrometer Data
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