Effects of Earth-atmosphere coupling irradiance modeling on spectral reflectance data accuracy

2018 ◽  
Author(s):  
Cheng Jiang ◽  
Hongyan He
Keyword(s):  
Spectral Reflectance ◽  
Data Accuracy ◽  
Earth Atmosphere ◽  
Reflectance Data

scholarly journals Inhibition of Primary Photosynthesis in Desiccating Antarctic Lichens Differing in Their Photobionts, Thallus Morphology, and Spectral Properties

2021 ◽  
Vol 9 (4) ◽  
pp. 818
Author(s):  
Miloš Barták ◽  
Josef Hájek ◽  
Alla Orekhova ◽  
Johana Villagra ◽  
Catalina Marín ◽  
...  
Keyword(s):  
Spectral Reflectance ◽  
Quantum Yields ◽  
Specific Response ◽  
Variable Fluorescence ◽  
Protective Mechanisms ◽  
Quenching Analysis ◽  
Relative Water ◽  
Species Specific ◽  
Kinetics Of ◽  
Reflectance Data

Five macrolichens of different thallus morphology from Antarctica (King George Island) were used for this ecophysiological study. The effect of thallus desiccation on primary photosynthetic processes was examined. We investigated the lichens’ responses to the relative water content (RWC) in their thalli during the transition from a wet (RWC of 100%) to a dry state (RWC of 0%). The slow Kautsky kinetics of chlorophyll fluorescence (ChlF) that was recorded during controlled dehydration (RWC decreased from 100 to 0%) and supplemented with a quenching analysis revealed a polyphasic species-specific response of variable fluorescence. The changes in ChlF at a steady state (Fs), potential and effective quantum yields of photosystem II (FV/FM, ΦPSII), and nonphotochemical quenching (NPQ) reflected a desiccation-induced inhibition of the photosynthetic processes. The dehydration-dependent fall in FV/FM and ΦPSII was species-specific, starting at an RWC range of 22–32%. The critical RWC for ΦPSII was below 5%. The changes indicated the involvement of protective mechanisms in the chloroplastic apparatus of lichen photobionts at RWCs of below 20%. In both the wet and dry states, the spectral reflectance curves (SRC) (wavelength 400–800 nm) and indices (NDVI, PRI) of the studied lichen species were measured. Black Himantormia lugubris showed no difference in the SRCs between wet and dry state. Other lichens showed a higher reflectance in the dry state compared to the wet state. The lichen morphology and anatomy data, together with the ChlF and spectral reflectance data, are discussed in relation to its potential for ecophysiological studies in Antarctic lichens.

scholarly journals Feasibility Study of Transparency Estimation in Lake Nakaumi by SGLI using in-situ Spectral Reflectance Data

2009 ◽  
Vol 65 (1) ◽  
pp. 1046-1050
Author(s):  
Yuji SAKUNO ◽  
Yasushi MIYAMOTO ◽  
Toshiaki KOZU ◽  
Toyoshi SHIMOMAI ◽  
Tsuneo MATSUNAGA ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Spectral Reflectance ◽  
Lake Nakaumi ◽  
Reflectance Data

scholarly journals Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks

Foods ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 558 ◽  
Author(s):  
Yoshio Makino ◽  
Yumi Kousaka
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spectral Reflectance ◽  
Spatial Information ◽  
Chlorophyll Concentration ◽  
Noninvasive Technique ◽  
Green Color ◽  
Artificial Neural ◽  
A Site ◽  
Reflectance Data

Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in the present research. Using artificial neural networks (ANNs), we demonstrated that the reduction velocity of chlorophyll at a site on a broccoli head was related to the second derivative of spectral reflectance data at 15 wavelengths from 405 to 960 nm. The reduction velocity was predicted using the ANNs model with a correlative coefficient of 0.995 and a standard error of prediction of 5.37 × 10−5 mg·g−1·d−1. The estimated reduction velocity was effective for predicting the chlorophyll concentration of broccoli buds until 7 d of storage, which was established as the maximum time for maintaining marketability. This technique may be useful for nondestructive prediction of the shelf life of broccoli heads.

scholarly journals Deep Learning Application for Predicting Soil Organic Matter Content by VIS-NIR Spectroscopy

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Zhe Xu ◽  
Xiaomin Zhao ◽  
Xi Guo ◽  
Jiaxin Guo
Keyword(s):  
Organic Matter ◽  
Deep Learning ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Near Infrared Spectroscopy ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Spectral Variation ◽  
Full Spectrum ◽  
Reflectance Data

Deep learning is characterized by its strong ability of data feature extraction. This method can provide unique advantages when applying it to visible and near-infrared spectroscopy for predicting soil organic matter (SOM) content in those cases where the SOM content is negatively correlated with the spectral reflectance of soil. This study relied on the SOM content data of 248 red soil samples and their spectral reflectance data of 400–2450 nm in Fengxin County, Jiangxi Province (China) to meet three objectives. First, a multilayer perceptron and two convolutional neural networks (LeNet5 and DenseNet10) were used to predict the SOM content based on spectral variation and variable selection, and the outcomes were compared with that from the traditional back-propagation neural network (BPN). Second, the four methods were applied to full-spectrum modeling to test the difference to selected feature variables. Finally, the potential of direct modeling was evaluated using spectral reflectance data without any spectral variation. The results of prediction accuracy showed that deep learning performed better at predicting the SOM content than did the traditional BPN. Based on full-spectrum data, deep learning was able to obtain more feature information, thus achieving better and more stable results (i.e., similar average accuracy and far lower standard deviation) than those obtained through variable selection. DenseNet achieved the best prediction result, with a coefficient of determination (R2) = 0.892 ± 0.004 and a ratio of performance to deviation (RPD) = 3.053 ± 0.056 in validation. Based on DenseNet, the application of spectral reflectance data (without spectral variation) produced robust results for application-level purposes (validation R2 = 0.853 ± 0.007 and validation RPD = 2.639 ± 0.056). In conclusion, deep learning provides an effective approach to predict the SOM content by visible and near-infrared spectroscopy and DenseNet is a promising method for reducing the amount of data preprocessing.

scholarly journals Using Hyperspectral and Multispectral Indices to Detect Water Stress for an Urban Turfgrass System

Agronomy ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 439 ◽  
Author(s):  
Badzmierowski ◽  
McCall ◽  
Evanylo
Keyword(s):  
Water Stress ◽  
Spectral Reflectance ◽  
Festuca Arundinacea ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Water ◽  
Management Tool ◽  
N Accumulation ◽  
Spatial Coverage ◽  
Reflectance Data

Spectral reflectance measurements collected from hyperspectral and multispectral radiometers have the potential to be a management tool for detecting water and nutrient stress in turfgrass. Hyperspectral radiometers collect hundreds of narrowband reflectance data compared to multispectral radiometers that collect three to ten broadband reflectance data for a cheaper cost. Spectral reflectance data have been used to create vegetation indices such as the normalized difference vegetation index (NDVI) and the simple ratio vegetation index (RVI) to assess crop growth, density, and fertility. Other indices such as the water band index (WBI) (narrowband index) and green-to-red ratio index (GRI) (both broadband and narrowband index) have been proposed to predict soil moisture status in turfgrass systems. The objective of this study was to compare the value of multispectral and hyperspectral radiometers to assess soil volumetric water content (VWC) and tall fescue (Festuca arundinacea Schreb.) responses. The multispectral radiometer VI had the strongest relationships to turfgrass quality, biomass, and tissue N accumulation during the trial period (April 2017–August 2018). Soil VWC had the strongest relationship to WBI (r = 0.60), followed by GRI and NDVI (both r = 0.54) for the 0% evapotranspiration (ET). Nonlinear regression showed strong relationships at high water stress periods in each year for WBI (r = 0.69–0.79), GRI (r = 0.64–0.75), and NDVI (r = 0.58–0.79). Broadband index data collected using a mobile multispectral sensor is a cheaper alternative to hyperspectral radiometry and can provide better spatial coverage.

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