Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice

2007 ◽  
Vol 58 (11) ◽  
pp. 1077 ◽  
Author(s):  
Yan Zhu ◽  
Dongqin Zhou ◽  
Xia Yao ◽  
Yongchao Tian ◽  
Weixing Cao
Keyword(s):  
Spectral Reflectance ◽  
Vegetation Indices ◽  
Leaf Nitrogen ◽  
Canopy Reflectance ◽  
Rice Cultivars ◽  
Canopy Spectral Reflectance ◽  
Mean Square Errors ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

Non-destructive and quick methods for assessing leaf nitrogen (N) status are helpful for precision N management in field crops. The present study was conducted to determine the quantitative relationships of leaf N concentration on a leaf dry weight basis (LNC) and leaf N accumulation per unit soil area (LNA) to ground-based canopy spectral reflectance in rice (Oryza sativa L.). Time-course measurements were taken on canopy spectral reflectance, LNC, and leaf dry weights, with 4 field experiments under different N application rates and rice cultivars across 4 growing seasons. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalised difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC, LNA, and canopy reflectance spectra all markedly varied with N rates, with consistent change patterns among different rice cultivars and experiment years. There were highly significant linear correlations between LNC and canopy reflectance in the visible region from 560 to 710 nm (|r| > 0.85), between LNA and canopy reflectance from 760 to 1100 nm (|r| > 0.79), and from 460 to 710 nm wavelengths (|r| > 0.70). Among all possible RVI, DVI, and NDVI of key wavebands from the MSR16 radiometer, NDVI of 1220 and 710 nm was most highly correlated to LNC, and RVI of 950 and 660 nm and RVI of 950 and 680 nm were the best spectral indices for quantitative monitoring of LNA in rice. The average relative root mean square errors (RRMSE) between the predicted LNC and LNA and the observed values with independent data were no more than 11% and 25%, respectively. These results indicated that the canopy spectral reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in rice.

Monitoring leaf nitrogen in wheat using canopy reflectance spectra

2006 ◽  
Vol 86 (4) ◽  
pp. 1037-1046 ◽  
Author(s):  
Yan Zhu ◽  
Yingxue Li ◽  
Wei Feng ◽  
Yongchao Tian ◽  
Xia Yao ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Reflectance Spectra ◽  
Nutrition Status ◽  
Nitrogen Accumulation ◽  
Canopy Reflectance ◽  
Leaf Nitrogen Concentration ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

Non-destructive monitoring of leaf nitrogen (N) status can assist in growth diagnosis, N management and productivity forecast in field crops. The objectives of this study were to determine the relationships of leaf nitrogen concentration on a leaf dry weight basis (LNC) and leaf nitrogen accumulation per unit soil area (LNA) to ground-based canopy reflectance spectra, and to derive regression equations for monitoring N nutrition status in wheat (Triticum aestivum L.). Four field experiments were conducted with different N application rates and wheat cultivars across four growing seasons, and time-course measurements were taken on canopy spectral reflectance, LNC and leaf dry weights under the various treatments. In these studies, LNC and LNA in wheat increased with increasing N fertilization rates. The canopy reflectance differed significantly under varied N rates, and the pattern of response was consistent across the different cultivars and years. Overall, an integrated regression equation of LNC to normalized difference index (NDI) of 1220 and 710 nm of canopy reflectance spectra described the dynamic pattern of change in LNC in wheat. The ratios of several near infrared (NIR) bands to visible light were linearly related to LNA, with the ratio index (RI) of the average reflectance over 760, 810, 870, 950 and 1100 nm to 660 nm having the best index for quantitative estimation of LNA in wheat. When independent data were fit to the derived equations, the average root mean square error (RMSE) values for the predicted LNC and LNA relative to the observed values were no more than 15.1 and 15.2%, respectively, indicating a good fit. Our relationships of leaf N status to spectral indices of canopy reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in wheat. Key words: Wheat, leaf nitrogen concentration, leaf nitrogen accumulation, canopy reflectance, spectral index, nitrogen monitoring

scholarly journals The Use of Diagnostic Optical Tools to Assess Nitrogen Status and to Guide Fertilization of Vegetables

2011 ◽  
Vol 21 (3) ◽  
pp. 287-292 ◽  
Author(s):  
Giorgio Gianquinto ◽  
Francesco Orsini ◽  
Paolo Sambo ◽  
Matilde Paino D'Urzo
Keyword(s):  
Nutritional Status ◽  
Vegetation Indices ◽  
Precise Determination ◽  
Optical Instruments ◽  
Status Monitoring ◽  
Optical Tools ◽  
Leaf Level ◽  
Plant Monitoring ◽  
Non Destructive ◽  
N Status

Dynamic fertilization management is a way of bringing nutrients to the plant when they are crucial for its development. However, destructive measurements of crop nitrogen (N) status are still too costly and time consuming to justify their use, and the implementation of methodologies based on non-destructive, quick, and easy to use tools for plant nutritional status monitoring appears as an appealing opportunity. Several optical tools for plant monitoring have been developed in recent years, and many studies have assessed their ability to discriminate plant N status. Such tools can measure at leaf level (hand-held optical instruments) or may consider the canopy of a plant or few plants (portable radiometers) or even measure areas, such as a field, a farm, or a region (aerial photography). The application of vegetation indices, which combine the readings at different wavelengths, may improve the reliability of the collected data, giving a more precise determination of the plant nutritional status. In this article, we report on the state of the art of the available optical tools for plant N status monitoring.

scholarly journals Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5579
Author(s):  
Jie Jiang ◽  
Cuicun Wang ◽  
Hui Wang ◽  
Zhaopeng Fu ◽  
Qiang Cao ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Optical Sensors ◽  
Accurate Estimation ◽  
Growth Stages ◽  
N Accumulation ◽  
N Nutrition ◽  
N Concentration ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

The accurate estimation and timely diagnosis of crop nitrogen (N) status can facilitate in-season fertilizer management. In order to evaluate the performance of three leaf and canopy optical sensors in non-destructively diagnosing winter wheat N status, three experiments using seven wheat cultivars and multi-N-treatments (0–360 kg N ha−1) were conducted in the Jiangsu province of China from 2015 to 2018. Two leaf sensors (SPAD 502, Dualex 4 Scientific+) and one canopy sensor (RapidSCAN CS-45) were used to obtain leaf and canopy spectral data, respectively, during the main growth period. Five N indicators (leaf N concentration (LNC), leaf N accumulation (LNA), plant N concentration (PNC), plant N accumulation (PNA), and N nutrition index (NNI)) were measured synchronously. The relationships between the six sensor-based indices (leaf level: SPAD, Chl, Flav, NBI, canopy level: NDRE, NDVI) and five N parameters were established at each growth stages. The results showed that the Dualex-based NBI performed relatively well among four leaf-sensor indices, while NDRE of RS sensor achieved a best performance due to larger sampling area of canopy sensor for five N indicators estimation across different growth stages. The areal agreement of the NNI diagnosis models ranged from 0.54 to 0.71 for SPAD, 0.66 to 0.84 for NBI, and 0.72 to 0.86 for NDRE, and the kappa coefficient ranged from 0.30 to 0.52 for SPAD, 0.42 to 0.72 for NBI, and 0.53 to 0.75 for NDRE across all growth stages. Overall, these results reveal the potential of sensor-based diagnosis models for the rapid and non-destructive diagnosis of N status.

Monitoring Leaf Nitrogen Status in Rice with Canopy Spectral Reflectance

2004 ◽  
Vol 96 (1) ◽  
pp. 135 ◽  
Author(s):  
Lihong Xue ◽  
Weixing Cao ◽  
Weihong Luo ◽  
Tingbo Dai ◽  
Yan Zhu
Keyword(s):  
Spectral Reflectance ◽  
Leaf Nitrogen ◽  
Nitrogen Status ◽  
Canopy Spectral Reflectance

scholarly journals Retrieval of Hyperspectral Information from Multispectral Data for Perennial Ryegrass Biomass Estimation

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7192
Author(s):  
Gustavo Togeiro de Alckmin ◽  
Lammert Kooistra ◽  
Richard Rawnsley ◽  
Sytze de Bruin ◽  
Arko Lucieer
Keyword(s):  
Perennial Ryegrass ◽  
Cross Validation ◽  
Parametric Method ◽  
Vegetation Indices ◽  
Performance Comparison ◽  
Hyperspectral Data ◽  
Biomass Estimation ◽  
Canopy Reflectance ◽  
Multispectral Data ◽  
Non Destructive

The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass (Lolium perenne) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors.

scholarly journals Application of a Combined Transmittance/Fluorescence Leaf Clip Sensor for the Nondestructive Determination of Nitrogen Status in White Cabbage Plants

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 482
Author(s):  
Stanisław Kaniszewski ◽  
Artur Kowalski ◽  
Jacek Dysko ◽  
Giovanni Agati
Keyword(s):  
Precision Agriculture ◽  
Leaf Nitrogen ◽  
Economic Advantage ◽  
Vegetable Crops ◽  
Non Invasive ◽  
White Cabbage ◽  
N Rates ◽  
N Status ◽  
Leaf N

The correct fertilization of vegetable crops is commonly determined on the basis of soil and plant costly destructive analyses, demanding more sustainable non-invasive optical detection. Here, we tested the ability of the combined transmittance/fluorescence leaf clip Dualex device for determining the nitrogen (N) status of cabbage plants. Fully developed leaves from plants grown under different N rates of 0; 100; 200; 300 kg N ha−1 in 2018 and 2019 were measured in the field by the Dualex sensor twice a year in July and October. The chlorophyll (Chl) and nitrogen (nitrogen balance index, NBI) indices and the flavonols (Flav) index of the sensor were positively and negatively correlated to leaf nitrogen, respectively. Merging the two-years data, the NBI versus leaf N correlation was less point dispersed in October than July (R2 = 0.76 and 0.64, respectively). NBI was also correlated to cabbage yield, better in July than October. Our results showed that the multiparametric Dualex device can be used as precision agriculture tool for the early prediction of plant N and cabbage yield with economic advantage for the growers and reduced environmental contamination due to nitrate leaching.

scholarly journals Estimation of Cotton Leaf Area Index (LAI) Based on Spectral Transformation and Vegetation Index

2021 ◽  
Vol 14 (1) ◽  
pp. 136
Author(s):  
Yiru Ma ◽  
Qiang Zhang ◽  
Xiang Yi ◽  
Lulu Ma ◽  
Lifu Zhang ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Spectral Reflectance ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Cotton Field ◽  
Growth Monitoring ◽  
Spectral Transformation ◽  
Canopy Spectral Reflectance ◽  
Hyperspectral Sensor

Unmanned aerial vehicles (UAV) has been increasingly applied to crop growth monitoring due to their advantages, such as their rapid and repetitive capture ability, high resolution, and low cost. LAI is an important parameter for evaluating crop canopy structure and growth without damage. Accurate monitoring of cotton LAI has guiding significance for nutritional diagnosis and the accurate fertilization of cotton. This study aimed to obtain hyperspectral images of the cotton canopy using a UAV carrying a hyperspectral sensor and to extract effective information to achieve cotton LAI monitoring. In this study, cotton field experiments with different nitrogen application levels and canopy spectral images of cotton at different growth stages were obtained using a UAV carrying hyperspectral sensors. Hyperspectral reflectance can directly reflect the characteristics of vegetation, and vegetation indices (VIs) can quantitatively describe the growth status of plants through the difference between vegetation in different band ranges and soil backgrounds. In this study, canopy spectral reflectance was extracted in order to reduce noise interference, separate overlapping samples, and highlight spectral features to perform spectral transformation; characteristic band screening was carried out; and VIs were constructed using a correlation coefficient matrix. Combined with canopy spectral reflectance and VIs, multiple stepwise regression (MSR) and extreme learning machine (ELM) were used to construct an LAI monitoring model of cotton during the whole growth period. The results show that, after spectral noise reduction, the bands screened by the successive projections algorithm (SPA) are too concentrated, while the sensitive bands screened by the shuffled frog leaping algorithm (SFLA) are evenly distributed. Secondly, the calculation of VIs after spectral noise reduction can improve the correlation between vegetation indices and LAI. The DVI (540,525) correlation was the largest after standard normal variable transformation (SNV) pretreatment, with a correlation coefficient of −0.7591. Thirdly, cotton LAI monitoring can be realized only based on spectral reflectance or VIs, and the ELM model constructed by calculating vegetation indices after SNV transformation had the best effect, with verification set R2 = 0.7408, RMSE = 1.5231, and rRMSE = 24.33%, Lastly, the ELM model based on SNV-SFLA-SNV-VIs had the best performance, with validation set R2 = 0.9066, RMSE = 0.9590, and rRMSE = 15.72%. The study results show that the UAV equipped with a hyperspectral sensor has broad prospects in the detection of crop growth index, and it can provide a theoretical basis for precise cotton field management and variable fertilization.

scholarly journals Assessment of Narrow-band Canopy Spectral Reflectance and Turfgrass Performance under Drought Stress

HortScience ◽  
2005 ◽  
Vol 40 (1) ◽  
pp. 242-245 ◽  
Author(s):  
Yiwei Jiang ◽  
Robert N. Carrow
Keyword(s):  
Drought Stress ◽  
Tall Fescue ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Narrow Band ◽  
Coefficient Of Determination ◽  
Canopy Reflectance ◽  
Linear Regression Models ◽  
Turf Quality ◽  
Canopy Spectral Reflectance

Canopy reflectance has the potential to determine turfgrass shoot status under drought stress conditions. The objective of this study was to describe the relationship of turf quality and leaf firing versus narrow-band canopy spectral reflectance within 400 to 1100 nm for different turfgrass species and cultivars under drought stress. Sods of four bermudagrasses (Cynodon dactylon L. × C. transvaalensis), three seashore paspalums (Paspalum vaginatum Swartz), zoysiagrass (Zoysia japonica), and st. augustinegrass (Stenotaphrum secundatum), and three seeded tall fescues (Festuca arundinacea) were used. Turf quality decreased 12% to 27% and leaf firing increased 12% to 55% in 12 grasses in response to drought stress imposed over three dry-down cycles. The peak correlations occurred at 673 to 693 nm and 667 to 687 nm for turf quality and leaf firing in bermudagrasses, respectively. All three tall fescues had the strongest correlation at 671 nm for both turf quality and leaf firing. The highest correlations in the near-infrared at 750, 775, or 870 nm were found in three seashore paspalums, while at 687 to 693 nm in Zoysiagrass and st. augustinegrass. Although all grasses exhibited some correlations between canopy reflectance and turf quality or leaf firing, significant correlation coefficients (r) were only observed in five grasses. Multiple linear regression models based on selected wavelengths for turf quality and leaf firing were observed for 7 (turf quality) and 9 (leaf firing) grasses. Wavelengths in the photosynthetic region at 658 to 700 nm or/and near-infrared from 700 to 800 nm predominated in models of most grasses. Turf quality and leaf firing could be well predicted in tall fescue by using models, evidenced by a coefficient of determination (R2) above 0.50. The results indicated that correlations of canopy reflectance versus turf quality and leaf firing varied with turfgrass species and cultivars, and the photosynthetic regions specifically from 664 to 687 nm were relatively important in determining turf quality and leaf firing in selected bermudagrass, tall fescue, zoysiagrass and st. augustinegrass under drought stress.

Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat

2018 ◽  
Vol 217 ◽  
pp. 82-92 ◽  
Author(s):  
Katherine Frels ◽  
Mary Guttieri ◽  
Brian Joyce ◽  
Bryan Leavitt ◽  
P. Stephen Baenziger
Keyword(s):  
Winter Wheat ◽  
Spectral Reflectance ◽  
Vegetation Indices ◽  
Nitrogen Use ◽  
Canopy Spectral Reflectance

scholarly journals Response of ‘Italian Riesling’ Leaf Nitrogen Status and Fruit Composition (Vitis vinifera L.) to Foliar Nitrogen Fertilization

HortScience ◽  
2016 ◽  
Vol 51 (3) ◽  
pp. 262-267 ◽  
Author(s):  
Danijela Janjanin ◽  
Marko Karoglan ◽  
Mirjana Herak Ćustić ◽  
Marijan Bubola ◽  
Mirela Osrečak ◽  
...  
Keyword(s):  
Amino Nitrogen ◽  
Grape Juice ◽  
Leaf Nitrogen ◽  
Vitis Vinifera L ◽  
Fruit Composition ◽  
Yeast Assimilable Nitrogen ◽  
Leaf N Content ◽  
N Compounds ◽  
N Status ◽  
Leaf N

Two-year study was conducted on Italian Riesling cultivar with the aim to compare the effect of foliar sprays with different nitrogen forms on grapevine leaf N status, yield, and nitrogen compounds in grape juice. Treatments included no fertilization (control), soil NPK treatment, and three foliar treatments [amino acids, urea, ammonia (NH4+)/nitrate] applied four times during season, also treated with soil NPK. The application of 1% w/v urea significantly increased leaf total leaf N content in the second year of study. However, there were no effects on N compounds in grape juice, since changes in free amino nitrogen (FAN), NH4+, and consequently yeast assimilable nitrogen (YAN) were not consistent among the treatments and experimental years. Although increase of vine leaf N status was achieved by 1% w/v urea, additional modifying of application time (by moving it closer to veraison) is needed, with the aim to increase N compounds in grape juice as well.

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