scholarly journals Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery

2019 ◽  
Vol 21 (4) ◽  
pp. 856-880 ◽  
Author(s):  
Holly Croft ◽  
Joyce Arabian ◽  
Jing M. Chen ◽  
Jiali Shang ◽  
Jiangui Liu
Keyword(s):  
Chlorophyll Content ◽  
Satellite Data ◽  
Nitrogen Fertilizer ◽  
Vegetation Index ◽  
Growing Season ◽  
Fertilizer Application ◽  
Landsat 8 ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
Normalised Difference Vegetation Index

AbstractSpatial information on crop nutrient status is central for monitoring vegetation health, plant productivity and managing nutrient optimization programs in agricultural systems. This study maps the spatial variability of leaf chlorophyll content within fields with differing quantities of nitrogen fertilizer application, using multispectral Landsat-8 OLI data (30 m). Leaf chlorophyll content and leaf area index measurements were collected at 15 wheat (Triticum aestivum) sites and 13 corn (Zea mays) sites approximately every 10 days during the growing season between May and September 2013 near Stratford, Ontario. Of the 28 sites, 9 sites were within controlled areas of zero nitrogen fertilizer application. Hyperspectral leaf reflectance measurements were also sampled using an Analytical Spectral Devices FieldSpecPro spectroradiometer (400–2500 nm). A two-step inversion process was developed to estimate leaf chlorophyll content from Landsat-8 satellite data at the sub-field scale, using linked canopy and leaf radiative transfer models. Firstly, at the leaf-level, leaf chlorophyll content was modelled using the PROSPECT model, using both hyperspectral and simulated mulitspectral Landsat-8 bands from the same leaf sample. Hyperspectral and multispectral validation results were both strong (R2 = 0.79, RMSE = 13.62 μg/cm2 and R2 = 0.81, RMSE = 9.45 μg/cm2, respectively). Secondly, leaf chlorophyll content was estimated from Landsat-8 satellite imagery for 7 dates within the growing season, using PROSPECT linked to the 4-Scale canopy model. The Landsat-8 derived estimates of leaf chlorophyll content demonstrated a strong relationship with measured leaf chlorophyll values (R2 = 0.64, RMSE = 16.18 μg/cm2), and compared favourably to correlations between leaf chlorophyll and the best performing tested spectral vegetation index (Green Normalised Difference Vegetation Index, GNDVI; R2 = 0.59). This research provides an operational basis for modelling within-field variations in leaf chlorophyll content as an indicator of plant nitrogen stress, using a physically-based modelling approach, and opens up the possibility of exploiting a wealth of multispectral satellite data and UAV-mounted multispectral imaging systems.

scholarly journals Estimating Leaf Chlorophyll Content of Buffaloberry Using Normalized Difference Vegetation Index Sensors

2021 ◽  
pp. 1-7
Author(s):  
Ji-Jhong Chen ◽  
Shuyang Zhen ◽  
Youping Sun
Keyword(s):  
Chlorophyll Content ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Red Light ◽  
Leaf Chlorophyll Content ◽  
Nir Light ◽  
Leaf Chlorophyll ◽  
Chlorophyll Extraction ◽  
Relative Chlorophyll Content

Commercial optical chlorophyll meters estimate relative chlorophyll content using the ratio of transmitted red light and near-infrared (NIR) light emitted from a red light-emitting diode (LED) and an NIR LED. Normalized difference vegetation index (NDVI) sensors have red and NIR light detectors and may be used to estimate chlorophyll content by detecting the transmitted red and NIR light through leaves. In this study, leaf chlorophyll content of ‘Torrey’ buffaloberry (Shepherdia ×utahensis) plants treated with 0 mm [zero nitrogen (N)], 2 mm (medium N), or 4 mm (ample N) ammonium nitrate for 3 weeks were evaluated using two commercial chlorophyll meters and NDVI sensors. The absolute chlorophyll content was determined using chlorophyll extraction. Our results showed that plants receiving ample N and medium N had decreased transmitted red light (i.e., greater absorption in red light). Measurements of optical chlorophyll meters, NDVI sensors, and chlorophyll extraction similarly showed that plants receiving medium N and ample N had greater leaf chlorophyll content than those receiving zero N. Relative leaf chlorophyll content estimated using NDVI sensors correlated positively with those from the chlorophyll meters (P < 0.0001; r2 range, 0.56–0.82). Therefore, our results indicate that NDVI measurements are sensitive to leaf chlorophyll content. These NDVI sensors, or specialized sensors developed using similar principles, can be used to estimate the relative chlorophyll content of nursery crops and help growers adjust fertilization to improve plant growth and nutrient status.

scholarly journals Effect of Container Design on Plant Growth and Root Deformation of Littleleaf Linden and Field Elm

HortScience ◽  
2010 ◽  
Vol 45 (12) ◽  
pp. 1824-1829 ◽  
Author(s):  
Gabriele Amoroso ◽  
Piero Frangi ◽  
Riccardo Piatti ◽  
Francesco Ferrini ◽  
Alessio Fini ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Dry Weight ◽  
Growing Season ◽  
Dry Mass ◽  
Shoot Biomass ◽  
Root Mass ◽  
Leaf Chlorophyll Content ◽  
Growing Seasons ◽  
Leaf Chlorophyll ◽  
Root Deformation

This experiment investigated the effect of different container design on growth and root deformation of littleleaf linden (Tilia cordata Mill.) and field elm (Ulmus minor Mill.). The trial was carried out over two growing seasons (2008 to 2009). In April 2008, 1-year-old bare-root seedlings of the two species were potted in three types of 1-L containers: Superoots® Air-Cell™ (The Caledonian Tree Company, Pathhead, UK), Quadro fondo rete (Bamaplast, Massa e Cozzile, Italy), and smooth-sided containers. At the beginning of the second growing season, the same plants were repotted in the following 3-L containers: Superoots® Air-Pot™ (The Caledonian Tree Company), Quadro antispiralizzante (Bamaplast), and smooth-sided containers. At the end of each growing season, a subset of the plants from each container type was harvested to determine shoot and root dry mass and root deformation (by dry weight of root deformed mass relative to the whole root mass). Chlorophyll fluorescence and leaf chlorophyll content were measured during the second growing season. For both species, at the end of first growing season, the poorest root architecture was observed in the smooth-sided containers, whereas Superoots® Air-Cell™ and Quadro fondo rete both reduced the percentage of deformed root mass. At the end of the second growing season, plants of both species grown in Superoots® Air-Pot™ showed less deformed root mass, whereas Quadro antispiralizzante provided good results only in littleleaf linden. A reduction of field elm root biomass and littleleaf linden shoot biomass was observed at the end of the trial in plants grown in Superoots® Air-Pot®. Plants grown in these containers showed less leaf chlorophyll content compared with plants grown in smooth-sided containers at the end of the second year.

scholarly journals Assessment of Leaf Chlorophyll Content Models for Winter Wheat Using Landsat-8 Multispectral Remote Sensing Data

2020 ◽  
Vol 12 (16) ◽  
pp. 2574
Author(s):  
Xianfeng Zhou ◽  
Jingcheng Zhang ◽  
Dongmei Chen ◽  
Yanbo Huang ◽  
Weiping Kong ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Dynamic Change ◽  
Vegetation Indices ◽  
Growth Conditions ◽  
Crop Growth ◽  
Lookup Table ◽  
Physiological Status ◽  
Landsat 8 ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll

The leaf chlorophyll content (LCC) is a critical index to characterize crop growth conditions, photosynthetic capacity, and physiological status. Its dynamic change characteristics are of great significance for monitoring crop growth conditions and understanding the process of material and energy exchange between crops and the environment. Extensive research has focused on LCC retrieval with hyperspectral data onboard various sensor platforms. Nevertheless, limited attention has been paid to LCC inversion from multispectral data, such as the data from Landsat-8, and the potentials and capabilities of the data for crop LCC estimation have not been fully explored. The present study made use of Landsat-8 Operational Land Imager (OLI) imagery and the corresponding field experimental data to evaluate their capabilities and potentials for LCC modeling using four different retrieval methods: vegetation indices (VIs), machine learning regression algorithms (MLRAs), lookup-table (LUT)-based inversion, and hybrid regression approaches. The results showed that the modified triangular vegetation index (MTVI2) exhibited the best estimate accuracy for LCC retrieval with a root mean square error (RMSE) of 5.99 μg/cm2 and a relative RMSE (RRMSE) of 10.49%. Several other vegetation indices that were established from red and near-infrared (NIR) bands also exhibited good accuracy. Models established from Gaussian process regression (GPR) achieved the highest accuracy for LCC retrieval (RMSE = 5.50 μg/cm2, RRMSE = 9.62%) compared with other MLRAs. Moreover, red and NIR bands outweighed other bands in terms of GPR modelling. LUT-based inversion methods with the “K(x) = −log (x) + x” cost function that belongs to the “minimum contrast estimates” family showed the best estimation results (RMSE = 8.08 μg/cm2, RRMSE = 14.14%), and the addition of multiple solution regularization strategies effectively improved the inversion accuracy. For hybrid regression methods, the use of active learning (AL) techniques together with GPR for LCC modelling significantly increased the estimation accuracy, and the combination of entropy query by bagging (EQB) AL and GPR had the best accuracy for LCC estimation (RMSE = 12.43 μg/cm2, RRMSE = 21.77%). Overall, our study suggest that Landsat-8 OLI data are suitable for crop LCC retrieval and could provide a basis for LCC estimation with similar multispectral datasets.

Airborne detection of southern pine beetle damage using key spectral bands

1998 ◽  
Vol 28 (7) ◽  
pp. 1040-1045 ◽  
Author(s):  
Gregory A Carter ◽  
Michael R Seal ◽  
Tim Haley
Keyword(s):  
Chlorophyll Content ◽  
Vegetation Index ◽  
Dendroctonus Frontalis ◽  
Southern Pine Beetle ◽  
Pinus Echinata ◽  
Shortleaf Pine ◽  
Southern Pine ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
Pine Beetle

Damage by the southern pine beetle (SPB) (Dendroctonus frontalis Zimm.) occurs frequently in the southeastern United States and can result in tree death over large areas. A new technique for detection of SPB activity was tested for shortleaf pine (Pinus echinata Mill.) in the Caney Creek Wilderness, Ouachita National Forest, Arkansas. Digital images with 1-m pixel resolution were acquired from a light aircraft in 6- to 10-nm bandwidths centered at wavelengths of 675, 698, and 840 nm. The 675-nm band was selected to yield a maximum contrast between yellow or brown versus green foliage. The 698-nm band was selected based on its high sensitivity to leaf chlorophyll content to enable detection of less severe chlorosis in more recently damaged trees. The 840-nm band was used as a reference band that is not sensitive to chlorophyll. Images acquired within each band were calibrated to percent reflectance based on the known reflectances of a gray scale placard located on the ground. Individual trees with yellow to brown foliage were easily located in the 675- and 698-nm images. Milder chlorosis in more recently damaged pines was detected by a normalized difference vegetation index (NDVI) that was derived from 698- and 840-nm reflectances. Although statistically significant, the contrast of recently infested trees versus undamaged trees was generally visually poor in NDVI or color composite images. This was apparently a result of the inherent variability in leaf chlorophyll content throughout the forest. The increased reflectance near 700 nm characteristic of recent damage likely would be resolved more easily in pine plantations of low species diversity. Images of a NDVI that was based on 675- and 840-nm reflectances produced the strongest contrast between heavily damaged and undamaged trees.

Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach

2019 ◽  
Vol 224 ◽  
pp. 60-73 ◽  
Author(s):  
Mingzhu Xu ◽  
Ronggao Liu ◽  
Jing M. Chen ◽  
Yang Liu ◽  
Rong Shang ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Vegetation Index ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
Combination Approach ◽  
Index Combination

scholarly journals RIL-StEp: epistasis analysis of rice recombinant inbred lines (RILs) reveals candidate interacting genes that control seed hull color and leaf chlorophyll content

2021 ◽  
Author(s):  
Toshiyuki Sakai ◽  
Akira Abe ◽  
Motoki Shimizu ◽  
Ryohei Terauchi
Keyword(s):  
Chlorophyll Content ◽  
Recombinant Inbred ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Gene Interactions ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
Seed Hull

Abstract Characterizing epistatic gene interactions is fundamental for understanding the genetic architecture of complex traits. However, due to the large number of potential gene combinations, detecting epistatic gene interactions is computationally demanding. A simple, easy-to-perform method for sensitive detection of epistasis is required. Due to their homozygous nature, use of recombinant inbred lines (RILs) excludes the dominance effect of alleles and interactions involving heterozygous genotypes, thereby allowing detection of epistasis in a simple and interpretable model. Here, we present an approach called RIL-StEp (recombinant inbred lines stepwise epistasis detection) to detect epistasis using single nucleotide polymorphisms in the genome. We applied the method to reveal epistasis affecting rice (Oryza sativa) seed hull color and leaf chlorophyll content and successfully identified pairs of genomic regions that presumably control these phenotypes. This method has the potential to improve our understanding of the genetic architecture of various traits of crops and other organisms.

Variability of Leaf Chlorophyll Content in a Population of Sassafras

1990 ◽  
Vol 117 (2) ◽  
pp. 167 ◽  
Author(s):  
Amrita G. de Soyza ◽  
Dwight T. Kincaid ◽  
Carlos R. Ramirez
Keyword(s):  
Chlorophyll Content ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll
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