scholarly journals ROMA: a database of rock reflectance spectra for Martian in situ exploration

2021 ◽  
Author(s):  
L. Mandon ◽  
P. Beck ◽  
C. Quantin‐Nataf ◽  
E. Dehouck ◽  
P. Thollot ◽  
...  
Keyword(s):  
Reflectance Spectra

scholarly journals Experimental determination of reflectance spectra of Antarctic krill (Euphausia superba) in the Scotia Sea

2021 ◽  
pp. 1-13
Author(s):  
Anna Belcher ◽  
Sophie Fielding ◽  
Andrew Gray ◽  
Lauren Biermann ◽  
Gabriele Stowasser ◽  
...  
Keyword(s):  
Antarctic Krill ◽  
Euphausia Superba ◽  
Reflectance Spectra ◽  
Spectral Signature ◽  
Absorption Feature ◽  
Scotia Sea ◽  
Surface Ocean ◽  
Measuring Surface ◽  
Ground Truthing

Abstract Antarctic krill are the dominant metazoan in the Southern Ocean in terms of biomass; however, their wide and patchy distribution means that estimates of their biomass are still uncertain. Most currently employed methods do not sample the upper surface layers, yet historical records indicate that large surface swarms can change the water colour. Ocean colour satellites are able to measure the surface ocean synoptically and should theoretically provide a means for detecting and measuring surface krill swarms. Before we can assess the feasibility of remote detection, more must be known about the reflectance spectra of krill. Here, we measure the reflectance spectral signature of Antarctic krill collected in situ from the Scotia Sea and compare it to that of in situ water. Using a spectroradiometer, we measure a strong absorption feature between 500 and 550 nm, which corresponds to the pigment astaxanthin, and high reflectance in the 600–700 nm range due to the krill's red colouration. We find that the spectra of seawater containing krill is significantly different from seawater only. We conclude that it is tractable to detect high-density swarms of krill remotely using platforms such as optical satellites and unmanned aerial vehicles, and further steps to carry out ground-truthing campaigns are now warranted.

scholarly journals Using Optical Water Types to Monitor Changes in Optically Complex Inland and Coastal Waters

2019 ◽  
Vol 11 (19) ◽  
pp. 2297 ◽  
Author(s):  
Kristi Uudeberg ◽  
Ilmar Ansko ◽  
Getter Põru ◽  
Ave Ansper ◽  
Anu Reinart
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Reflectance Spectra ◽  
Water Type ◽  
Colored Dissolved Organic Matter ◽  
Water Color ◽  
Sentinel 2

The European Space Agency’s Copernicus satellites Sentinel-2 and Sentinel-3 provide observations with high spectral, spatial, and temporal resolution which can be used to monitor inland and coastal waters. Such waters are optically complex, and the water color may vary from completely clear to dark brown. The main factors influencing water color are colored dissolved organic matter, phytoplankton, and suspended sediments. Recently, there has been a growing interest in the use of the optical water type (OWT) classification in the remote sensing of ocean color. Such classification helps to clarify relationships between different properties inside a certain class and quantify variation between classes. In this study, we present a new OWT classification based on the in situ measurements of reflectance spectra for boreal region lakes and coastal areas without extreme optical conditions. This classification divides waters into five OWT (Clear, Moderate, Turbid, Very Turbid, and Brown) and shows that different OWTs have different remote sensing reflectance spectra and that each OWT is associated with a specific bio-optical condition. Developed OWTs are distinguishable by both the MultiSpectral Instrument (MSI) and the Ocean and Land Color Instrument (OLCI) sensors, and the accuracy of the OWT assignment was 95% for both the MSI and OLCI bands. To determine OWT from MSI images, we tested different atmospheric correction (AC) processors, namely ACOLITE, C2RCC, POLYMER, and Sen2Cor and for OLCI images, we tested AC processors ALTNNA, C2RCC, and L2. The C2RCC AC processor was the most accurate and reliable for use with MSI and OLCI images to estimate OWTs.

Derivation of optical constants for nanophase hematite and application to modeled abundances from in-situ Martian reflectance spectra

Icarus ◽  
2018 ◽  
Vol 300 ◽  
pp. 167-173 ◽  
Author(s):  
Paul G. Lucey ◽  
David Trang ◽  
Jeffrey R. Johnson ◽  
Timothy D. Glotch
Keyword(s):  
Optical Constants ◽  
Reflectance Spectra

scholarly journals Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification

2020 ◽  
Vol 12 (19) ◽  
pp. 3233
Author(s):  
Ran Meng ◽  
Zhengang Lv ◽  
Jianbing Yan ◽  
Gengshen Chen ◽  
Feng Zhao ◽  
...  
Keyword(s):  
Severity Index ◽  
Plant Diseases ◽  
Reflectance Spectra ◽  
Support Vector ◽  
Leaf Water Content ◽  
Leaf Reflectance ◽  
Measurement Capability ◽  
Southern Corn Rust ◽  
Severity Classification

Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting corn quality and yields globally. Field-based fast, nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety. The development of spectral disease indices (SDIs), based on in situ leaf reflectance spectra, has proven to be an effective method in detecting plant diseases in the field. However, little is known about leaf spectral signatures that can assist in the accurate diagnosis of SCR, and no SDIs-based model has been reported for the field-based SCR monitoring. Here, to address those issues, we developed SDIs-based monitoring models to detect SCR-infected leaves and classify SCR damage severity. In detail, we first collected in situ leaf reflectance spectra (350–2500 nm) of healthy and infected corn plants with three severity levels (light, medium, and severe) using a portable spectrometer. Then, the RELIEF-F algorithm was performed to select the most discriminative features (wavelengths) and two band normalized differences for developing SDIs (i.e., health index and severity index) in SCR detection and severity classification, respectively. The leaf reflectance spectra, most sensitive to SCR detection and severity classification, were found in the 572 nm, 766 nm, and 1445 nm wavelength and 575 nm, 640 nm, and 1670 nm wavelength, respectively. These spectral features were associated with leaf pigment and leaf water content. Finally, by employing a support vector machine (SVM), the performances of developed SCR-SDIs were assessed and compared with 38 stress-related vegetation indices (VIs) identified in the literature. The SDIs-based models developed in this study achieved an overall accuracy of 87% and 70% in SCR detection and severity classification, 1.1% and 8.3% higher than the other best VIs-based model under study, respectively. Our results thus suggest that the SCR-SDIs is a promising tool for fast, nondestructive diagnosis of SCR in the field over large areas. To our knowledge, this study represents one of the first few efforts to provide a theoretical basis for remote sensing of SCR at field and larger scales. With the increasing use of unmanned aerial vehicles (UAVs) with hyperspectral measurement capability, more studies should be conducted to expand our developed SCR-SDIs for SCR monitoring at different study sites and growing stages in the future.

In situ estimation of foliar nitrogen in wild blueberry using reflectance spectra

2012 ◽  
Vol 92 (6) ◽  
pp. 1155-1161 ◽  
Author(s):  
R. Maqbool ◽  
D. C. Percival ◽  
M. S. Adl ◽  
Q. U. Zaman ◽  
D. Buszard
Keyword(s):  
Nutrient Management ◽  
Reflectance Spectra ◽  
Management Tool ◽  
Biochemical Basis ◽  
Foliar Nitrogen ◽  
Foliar N ◽  
Spectral Ranges ◽  
Mean Square Errors ◽  
Wild Blueberry

Maqbool, R., Percival, D. C., Adl, M. S., Zaman, Q. U. and Buszard, D. 2012. In situ estimation of foliar nitrogen in wild blueberry using reflectance spectra. Can. J. Plant Sci. 92: 1155–1161. Remote sensing techniques have the potential to serve as an important nutrient management tool in wild blueberry. The potential of visible (VIS), near infrared (NIR) and shortwave infrared (SWIR) spectroscopy was evaluated during 2006 (sprout/vegetative phase of production) to estimate foliar nitrogen (N). Canopy reflectance measurements were taken from two nutrient management experimental sites located in Nova Scotia (NS) and New Brunswick (NB). Partial least squares regression (PLSR) estimated foliar N, giving the coefficients of determination (R 2) values ranging from 0.69 to 0.85, and root mean square errors of cross validation (RMSECV) from 0.16% (±8.29% of mean) to 0.24% (±12.43% of mean) for different spectral ranges used in this study. The green peak region located in the VIS region best estimated foliar N. The tested spectral ranges differed in their predictive ability, but generally followed the biochemical basis. Variable importance in projection scores (VIP), regression vector coefficients and PLSR loading weights (LWs) plots highlight the importance of wavebands (∼550 nm, ∼610 nm, 1510 nm, ∼1690 nm, ∼1730 nm, ∼1980 nm and ∼2030 nm) for in situ foliar N estimations. Thus, it was concluded that reflectance spectra may be used to estimate and ultimately map foliar N in wild blueberry production. The results illustrated the ability of multivariate techniques, such as PLSR to explore hyperspectral data and estimate leaf tissue nutrient content.

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