Estimating leaf nitrogen concentration in ryegrass ( Lolium spp.) pasture using the chlorophyll red-edge: Theoretical modelling and experimental observations

Lamb, David; Steyn-Ross, Moira L.; Schaare, Peter; Hanna, M. M.; Silvester, Warwick B.; Steyn-Ross, A.

doi:10.1080/01431160110114529

Cited by 142 publications

(107 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the present study, all possible two-wavelength combinations of hyperspectral indices throughout the entire spectral range of 350-1800 nm were considered in matrix form. Based on the R 2 and RMSE values and the absorption principle, we found that the wavelengths selected by the CR and VI methods were 690/695, 709/710, 700/705, 713/727, 1200, and 1335/1340 nm, which are predominantly located in the red-edge and near-infrared regions, as noted in many previous studies [16,17,26,58,59]. The selected wavelengths differed for the CR and VIs methods, perhaps because CR can be used to determine the absorbing positions of chlorophyll or carotenoids, whereas for the VI-based approach, the much more sensitive wavelength of N can be used because of the different calculation formulas for the two spectral indices.…”

Section: Wavelength Selection For the Six Algorithmsmentioning

confidence: 72%

“…The wavelengths of 492 and 508 nm lie in the visible range and are often strongly absorbed by plant chlorophyll and carotenoids in green plants [42]. The wavelengths of 681, 695, and 722 nm lie in the red range and are sensitive indicators of the LNC and chlorophyll [16,17,26,58,59]. The wavelengths of 960, 1264, 1339, and 1369 nm are located in the shortwave infrared range and are indicators of proteins [27].…”

Section: Wavelength Selection For the Six Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

et al. 2015

View full text Add to dashboard Cite

Abstract:The rapid and non-destructive monitoring of the canopy leaf nitrogen concentration (LNC) in crops is important for precise nitrogen (N) management. Nowadays, there is an urgent need to identify next-generation bio-physical variable retrieval algorithms that can be incorporated into an operational processing chain for hyperspectral satellite missions. We assessed six retrieval algorithms for estimating LNC from canopy reflectance of winter wheat in eight field experiments. These experiments represented variations in the N application rates, planting densities, ecological sites and cultivars and yielded a total of 821 samples from various places in Jiangsu, China over nine consecutive years. Based on the reflectance spectra and their first derivatives, six methods using different numbers of wavelengths were applied to construct predictive models for estimating wheat LNC, including continuum removal (CR), vegetation indices (VIs), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural networks (ANNs), and support vector machines (SVMs). To assess the performance of these six methods, we provided a systematic evaluation of the estimation OPEN ACCESS Remote Sens. 2015, 7 14940 accuracies using the six metrics that were the coefficients of determination for the calibration (R 2 C) and validation (R 2 V) sets, the root mean square errors of prediction (RMSEP) for the calibration and validation sets, the ratio of prediction to deviation (RPD), the computational efficiency (CE) and the complexity level (CL). The following results were obtained: (1) For the VIs method, SAVI(R1200, R705) produced a more accurate estimation of the LNC than other indices, with R² C, R² V, RMSEP, RPD and CE values of 0.844, 0.795, 0.384, 2.005 and 0.10 min, respectively; (2) For the SMLR, PLSR, ANNs and SVMs methods, the SVMs using the first derivative canopy spectra (SVM-FDS) offered the best accuracy in terms of R² C, R² V, RMSEP, RPD, and CE, at 0.96, 0.78, 0.37, 2.02, and 21.17, respectively; (3) The PLSR-FDS, ANN-OS and SVM-FDS methods yield similar accuracies if the CE and CL are not considered, however, ANNs and SVMs performed better on calibration set than the validation set which indicate that we should take more caution with the two methods for over-fitting. Except PLS method, the performance for most methods did not enhance when the spectrum were operated by the first derivative. Moreover, the evaluation of the robustness demonstrates that SVM method may be better suited than the other methods to cope with potential confounding factors for most varieties, ecological site and growth stage; (4) The prediction accuracy was found to be higher when more wavelengths were used, though at the cost of a lower CE. The findings are of interest to the remote sensing community for the development of improved inversion schemes for hyperspectral applications concerning other types of vegetation. The examples provided in this paper may also serve to illustrate the advantages and shortcomings...

show abstract

Section: Wavelength Selection For the Six Algorithmsmentioning

confidence: 72%

Section: Wavelength Selection For the Six Algorithmsmentioning

confidence: 99%

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Since plant chlorophyll concentration is highly correlated with nitrogen status (Hansen & Schjoerring, 2003;Haboudane et al, 2004), REP has also been used for evaluating plant nitrogen status (Lamb et al, 2002;Jongschaap & Booij, 2004). However, as most often encountered, REP is not adequate to track the variations in chlorophyll concentration due to its doublepeak feature (Clevers et al, 2004;Cho and Skidmore, 2006).…”

Section: Discussionmentioning

confidence: 99%

Extracting Red Edge Position Parameters from Ground- and Space-Based Hyperspectral Data for Estimation of Canopy Leaf Nitrogen Concentration in Rice

Tian

Yao

Yang

et al. 2011

Plant Production Science

View full text Add to dashboard Cite

“…The red-edge slope is of particular relevance for leaf chlorophyll content (LCC) because of its enhanced sensitivity to varied and higher chlorophyll levels while circumventing saturation problems as observed in the blue and red due to vast chlorophyll-induced absorption (Gitelson, 2012;Kooistra and Clevers, 2016). Reliance on wavelengths at the onset of the near-infrared follows from the gradual stabilization of reflectance beyond the red-edge, which settles at higher values for increased chlorophyll levels (Lamb et al, 2002). Largely similar spectral regions were structurally highlighted for N content, resulting from the inherent biochemical linkage between leaf N, chlorophyll molecules and photosynthetic capacity (Sellers et al, 1992;Weiss et al, 2001;Netto et al, 2005;Wu et al, 2008).…”

Section: Relevant Wavelengths For Plant Trait Predictionsmentioning

confidence: 99%

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

et al. 2017

View full text Add to dashboard Cite

Abstract. Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant-soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are shortterm and small-scale due to practical constraints for fieldscale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R 2 = 0.79), chlorophyll content (RMSE = 0.11 g m −2 , R 2 = 0.80), Ncontent (RMSE = 1.94 g m −2 , R 2 = 0.68), and fresh biomass (RMSE = 0.72 kg m −2 , R 2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m −2 , respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m −2 , respectively), while the lowest values (76 cm, 0.41 g m −2 , respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAVbased optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.

show abstract

Estimating leaf nitrogen concentration in ryegrass ( Lolium spp.) pasture using the chlorophyll red-edge: Theoretical modelling and experimental observations

Cited by 142 publications

References 14 publications

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

Extracting Red Edge Position Parameters from Ground- and Space-Based Hyperspectral Data for Estimation of Canopy Leaf Nitrogen Concentration in Rice

Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors

Contact Info

Product

Resources

About