Use of spectral reflectance from a compact spectrometer to assess chlorophyll content in <i>Zizania latifolia</i>

Sonobe, Rei; Yamashita, Hiroto; Nofrizal, Adenan Yandra; Seki, Hiroya; Morita, Akio; Ikka, Takashi

doi:10.1080/10106049.2021.1914747

Cited by 10 publications

(6 citation statements)

References 53 publications

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“…The inclusion of UV predictors in vegetation property models other than LAI could be beneficial because UV reflectance can be related to the chlorophyll, nitrogen [15]- [17] and phosphorus [58] content in the vegetation. On the other hand, some studies report a weak correlation between the chlorophyll content and UV reflectance [59]. Therefore, such UV reflectance applications still have to be tested in a separate remote sensing field study.…”

Section: Significance Uv Reflectance For Remote Sensing Of Vegetationmentioning

confidence: 99%

UAV-Based Hyperspectral Ultraviolet-Visible Interpolated Reflectance Images for Remote Sensing of Leaf Area Index

Berezowski,

Kulawiak,

Kulawiak

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Despite its relation to a number of environmental parameters, ultraviolet (UV) reflectance is rarely used in remote sensing. In this study, we investigate the applicability of UV-vis reflectance for vegetation monitoring with unmanned aerial vehicles (UAV). We measure point reflectance over the study area using a UAV-borne spectrometer, project the points onto the Earth's surface, and interpolate them to obtain continuous reflectance images. We use the leaf area index (LAI) to demonstrate the applicability of UV reflectance for vegetation monitoring. Our results show that the UAV reflectance images match the Sentinel-2 reflectance. Our validation shows that the inclusion of UV reflectance to the visible reflectance in LAI models leads to the r 2 increase of up to 29.2% and RMSE decrease of up to 18.9% in comparison to the LAI models using visible reflectance only. We have shown that measurement of UV reflectance is feasible in the 320-400 nm range using UAV remote sensing and that hyperspectral UV-vis reflectance imaging is useful for vegetation monitoring. Moreover, the obtained results lead us to believe that improvement of our measurement system, or conducting the experiments in a different location should make it possible to measure the reflectance at a wavelength of 290 nm. Finally, we discuss other potential applications of UV in remote sensing.

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Section: Significance Uv Reflectance For Remote Sensing Of Vegetationmentioning

confidence: 99%

UAV-Based Hyperspectral Ultraviolet-Visible Interpolated Reflectance Images for Remote Sensing of Leaf Area Index

Berezowski,

Kulawiak,

Kulawiak

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…This procedure was repeated 100 times to increase the robustness of the results before the regression models were generated. After running the preprocessing and variable selection procedures, we generated regression models based on Cubist, which is a tool that has been shown globally to perform best in studies related to machine learning algorithms [55,56]. Cubist is a rule-based model tree approach, and its leaves are expressed as multivariate linear regression models.…”

Section: Regression Models Based On Machine Learning Algorithmsmentioning

confidence: 99%

“…When a C12880MA-10 hyperspectral sensor was used to measure the reflectance of Zizania latifolia with the sun as the light source and without a plant probe, there was a decrease in reflectance at 800 nm as a result of changes in sun altitude [56]. This may contribute to uncertainty in reflectance data acquired using this sensor.…”

Section: Relationship Between Reflectance Recorded Using the Compact ...mentioning

confidence: 99%

Applying Variable Selection Methods and Preprocessing Techniques to Hyperspectral Reflectance Data to Estimate Tea Cultivar Chlorophyll Content

Sonobe

Hirono²

2022

Remote Sensing

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Tea is second only to water as the world’s most popular drink and it is consumed in various forms, such as black and green teas. A range of cultivars has therefore been developed in response to customer preferences. In Japan, farmers may grow several cultivars to produce different types of tea. Leaf chlorophyll content is affected by disease, nutrition, and environmental factors. It also affects the color of the dried tea leaves: a higher chlorophyll content improves their appearance. The ability to quantify chlorophyll content would therefore facilitate improved tea tree management. Here, we measured the hyperspectral reflectance of 38 cultivars using a compact spectrometer. We also compared various combinations of preprocessing techniques and 14 variable selection methods. According to the ratio of performance to deviation (RPD), detrending was effective at reducing the influence of additive interference of scattered light from particles and then regression coefficients was the best variable selection method for estimating the chlorophyll content of tea leaves, achieving an RPD of 2.60 and a root mean square error of 3.21 μg cm−2.

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“…Recently, affordable, compact, fingertip-sized spectrometers (such as the C12880MA [Hamamatsu Photonics]) have been developed. These small devices can be attached to unmanned aerial vehicles for conducting measurements over large areas [23,24]. However, the measurements from these sensors contain various degrees of noise and may exhibit shifts.…”

Section: Introductionmentioning

confidence: 99%

Carotenoid Content Estimation in Tea Leaves Using Noisy Reflectance Data

Sonobe,

Hirono

2023

Remote Sensing

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Quantifying carotenoid content in agriculture is essential for assessing crop nutritional value, improving crop quality, promoting human health, understanding plant stress responses, and facilitating breeding and genetic improvement efforts. Hyperspectral reflectance imaging is a nondestructive and rapid tool for estimating the carotenoid content. In spectrometer reflectance measurements, there are various sources of noise that can compromise the accuracy of carotenoid content estimations. Recently, various machine learning algorithms have been identified as robust against various types of noise, eliminating the need for denoising processes. Specifically, Cubist and the one-dimensional convolutional neural network (1D-CNN) have been used in evaluating vegetation properties based on reflectance data. We used regression models based on Cubist and 1D-CNN to estimate carotenoid content from reflectance data (the spectral resolution was resampled in 5 nm bands across the entire wavelength domain from 400 to 850 nm) with various degrees of Gaussian and spike noise added. The Cubist-based model was the most robust for this purpose: it achieved a ratio of performance to deviation of 1.41, a root mean square error of 1.11 µg/cm2, and a coefficient of determination (R2) of 0.496 when applied to reflectance data with a combination of Gaussian (mean: 0; variance: 0.04) and spike noise (density: 0.05; amplitude: 0.05).

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Use of spectral reflectance from a compact spectrometer to assess chlorophyll content in Zizania latifolia

Cited by 10 publications

References 53 publications

UAV-Based Hyperspectral Ultraviolet-Visible Interpolated Reflectance Images for Remote Sensing of Leaf Area Index

UAV-Based Hyperspectral Ultraviolet-Visible Interpolated Reflectance Images for Remote Sensing of Leaf Area Index

Applying Variable Selection Methods and Preprocessing Techniques to Hyperspectral Reflectance Data to Estimate Tea Cultivar Chlorophyll Content

Carotenoid Content Estimation in Tea Leaves Using Noisy Reflectance Data

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