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

(4 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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“…Hyperspectral reflectance has been used for estimating chlorophyll content in various plant species and hyperspectral remote-sensing can be an effective tool for measuring chlorophyll content in the field (Golhani et al, 2019;Sonobe et al, 2021). Some previous studies were based on the datasets composed of measurements taken under relatively low light-stress conditions (Feret et al, 2008); however, some stresses have been used to improve the qualities of crops and it can change the chlorophyll a/b ratio (Terashima & Hikosaka, 1995).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral wavelength selection for estimating chlorophyll content of muskmelon leaves

Sonobe

Sugimoto

Kondo

et al. 2021

European Journal of Remote Sensing

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Quantifying chlorophyll content, an effective indicator of disease as well as nutritional and environmental stresses on plants, may enable optimal fertilization while managing crops. Hyperspectral remote-sensing is commonly used to estimate chlorophyll content. In this context, the process of variable selection is crucial since it is necessary to identify variables relevant to chlorophyll and eliminate redundant variables. In this study, 14 wavelength selection methods based on partial least squares (PLS; namely, backward variable elimination, backward and forward interval-PLS, competitive adaptive reweighted sampling, genetic algorithm, iterative predictive weighting, loading-weights, PLS with Martens' uncertainty test, regression coefficient, regularized elimination procedure, sparse-PLS, sub-window permutation analysis, uninformative variable elimination and variable importance in projection) were combined with one of five machine learning algorithms (Cubist, deep belief nets, random forests, stochastic gradient boosting and support vector machine) and then evaluated. According to the ratio of performance to deviation (RPD), the best combination of variable selection method and machine learning algorithm was regularized elimination procedure and Cubist achieving an RPD of 1.76 and an RMSE of 2.42 μg cm −2 .

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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

Hyperspectral wavelength selection for estimating chlorophyll content of muskmelon leaves

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