Growth tracking of basil by proximal remote sensing of chlorophyll fluorescence in growth chamber and greenhouse environments

Bånkestad, Daniel; Wik, Torsten

doi:10.1016/j.compag.2016.08.004

Cited by 17 publications

(12 citation statements)

References 39 publications

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“…Furthermore, various spectral indices can be extracted including indices based on shortwave infrared bands (SWIR), which are influenced by plant constituents such as pigments, leaf water contents and biochemicals 7,8 . Furthermore, vegetation indices derived from reflectance data acquired from optical sensors have been widely used to assess variations in the physiological states and biophysical properties of vegetation [9][10][11] . Specifically, the Normalized Difference Vegetation Index (NDVI) 12 , Soil-Adjusted Vegetation Index (SAVI) 13 and Enhanced Vegetation Index (EVI) 14 have been used for monitoring vegetation systems or ecological responses to environmental change 15 .…”

Section: Introductionmentioning

confidence: 99%

Crop classification from Sentinel-2-derived vegetation indices using ensemble learning

Sonobe

Yamaya

Tani

et al. 2018

J. Appl. Rem. Sens.

133

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The identification and mapping of crops are important for estimating potential harvest as well as for agricultural field management. Optical remote sensing is one of the most attractive options because it offers vegetation indices and some data have been distributed free of charge. Especially, Sentinel-2A, which is equipped with a multispectral sensor (MSI) with blue, green, red and near-infrared-1 bands at 10 m; red edge 1 to 3, nearinfrared-2 and shortwave infrared 1 and 2 at 20 m; and 3 atmospheric bands (Band 1, Band 9 and Band 10) at 60 m, offers some vegetation indices calculated to assess vegetation status. However, sufficient consideration has not been given to the potential of vegetation indices calculated from MSI data. Thus, 82 published indices were calculated and their importance were evaluated for classifying crop types. In this study, the two most common classification algorithms, random forests (RF) and support vector machine (SVM), were applied to conduct cropland classification from MSI data. Additionally, super learning was applied for more improvement, achieving overall accuracies of 90.2-92.2%. Of the two algorithms applied (RF and SVM), the accuracy of SVM was superior and 89.3-92.0% of overall accuracies were confirmed. Furthermore, stacking contributed to higher overall accuracies (90.2-92.2%) and significant differences were confirmed with the results of SVM and RF. Our results showed that vegetation indices had the greatest contributions in identifying specific crop types.

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Section: Introductionmentioning

confidence: 99%

Crop classification from Sentinel-2-derived vegetation indices using ensemble learning

Sonobe

Yamaya

Tani

et al. 2018

J. Appl. Rem. Sens.

133

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“…However, when individual leaves are submitted to the radiative transfer conditions that prevail in the canopy, the problem was not yet addressed. Most current VIs have been designed for leaf and canopy levels, and may therefore be suboptimal to handle the above-mentioned variations in leaf reflectance (Bånkestad and Wik, 2016).…”

Section: Introductionmentioning

confidence: 99%

Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery

Jay

Gorretta²,

Morel³

et al. 2017

Remote Sensing of Environment

123

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Accurate estimation of leaf chlorophyll content (Cab) from remote sensing is of tremendous significance to monitor the physiological status of vegetation or to estimate primary production. Many vegetation indices (VIs) have been developed to retrieve Cab at the canopy level from meter-to decameter-scale reflectance observations. However, most of these VIs may be affected by the possible confounding influence of canopy structure. The objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level. Hyperspectral images were acquired over sugar beet canopies from a ground-based platform in the 400-1000 nm range, concurrently to Cab, green fraction (GF), green area index (GAI) ground measurements. The original image spatial resolution was successively degraded from 1 mm to 35 cm, resulting in eleven sets of hyperspectral images. Vegetation and soil pixels were discriminated, and for each spatial resolution, measured Cab values were related to various VIs computed over four sets of reflectance spectra extracted from the images (soil and vegetation pixels, only vegetation pixels, 50% darkest and brightest vegetation pixels). The selected VIs included some classical VIs from the literature as well as optimal combinations of spectral bands, including simple ratio (), modified

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“…As shown in this study, though the ChlF re-emitted by Chl molecules is very weak, F 760 can represent the information on leaf Chl content, LAI, and biomass. Many previous studies also found that F 760 was strongly correlated to LAI and biomass (Bånkestad and Wik, 2016; Van der Tol et al, 2016) and generally increased with increasing chlorophyll content (Buschmann, 2007). The fluorescent in-filling effects on reflectance results in the peak of the first derivative spectra in the O 2 −A band, and two indices—REArea 760 and REA 760 —were proposed to estimate CCC of corn canopy in this study.…”

Section: Discussionmentioning

confidence: 76%

“…ChlF originates only from chlorophyll in the photosynthetic apparatus and materials such as soil, wood, and dead biomass also absorb PAR but do not contribute to photosynthesis. ChlF therefore is less sensitive to soil, wood, and dead biomass interference (Daughtry et al, 2000; Porcar-Castell et al, 2014; Bånkestad and Wik, 2016). Therefore, it can improve the accuracy of CCC estimation.…”

Section: Discussionmentioning

confidence: 99%

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Estimation of Corn Canopy Chlorophyll Content Using Derivative Spectra in the O2–A Absorption Band

Zhang

Wang

et al. 2019

Front. Plant Sci.

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Chlorophyll (Chl) is one of the most important classes of light-absorbing pigments in photosynthesis, and the proportion of Chl in leaves is closely related to vegetation nutrient status. Remote sensing-based estimation of Chl content holds great potential for evaluating crop growth status in agricultural management, precision farming and ecosystem monitoring. Recent studies have shown that steady-state fluorescence contributed up to 2% on the apparent reflectance in the 750-nm spectral region of plant and also provided additional evidence for fluorescence in-filling of the atmospheric oxygen absorption band at a central wavelength of 760 nm (O 2 –A band). In this study, an in situ hyperspectral remote sensing approach zwas employed to estimate corn Chl content at the canopy level by using chlorophyll fluorescence (ChlF) signals in the O 2 –A absorption band. Two new spectral indices, REArea 760 (sum of first derivative reflectance between 755 and 763 nm) and REA 760 (maximum of first derivative reflectance between 755 and 763 nm), derived from the first derivative spectra in the O 2 –A band, were proposed for estimating the corn canopy Chl content (CCC). They were compared with the performance of published indices measured at ground level, including the MERIS Terrestrial Chlorophyll Index (MTCI), Optimized Soil-Adjusted Vegetation Index 2 (OSAVI2), Modified Chlorophyll Absorption Ratio Index 2 (MCARI2), SR710, REArea (sum of first derivative reflectance between 680 and 780 nm), REA (maximum value of first derivative reflectance between 680 and 780 nm), and mND 705 . The results indicated that corn Chl content at the canopy level was better predicted by the new indices (with R 2 = 0.835) than the published indices (with R 2 ranging from 0.676 to 0.826). The two new indices ranked in the top four according to their summed ranks by integrating the ranks of RMSE and R 2 of CCC linear regression models. ChlF originates only from chlorophyll in the photosynthetic apparatus and therefore is less sensitive to soil, wood, and dead biomass interference. Moreover, due to the fluorescence in-filling of the O 2 –A band and the amplified effect on spectrum signals by derivative operation, the spectral derivative indices in the O 2 –A band have great potential for estimating the CCC.

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Growth tracking of basil by proximal remote sensing of chlorophyll fluorescence in growth chamber and greenhouse environments

Cited by 17 publications

References 39 publications

Crop classification from Sentinel-2-derived vegetation indices using ensemble learning

Crop classification from Sentinel-2-derived vegetation indices using ensemble learning

Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery

Estimation of Corn Canopy Chlorophyll Content Using Derivative Spectra in the O2–A Absorption Band

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