Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance

Tian, Yongchao; Yao, Xia; Yang, Jiayu; Cao, Weixing; Hannaway, D. B.; Zhu, Yan

doi:10.1016/j.fcr.2010.11.002

Cited by 238 publications

(126 citation statements)

References 52 publications

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“…Low concentrations of chlorophyll are able to saturate absorption in the blue region (Lichtenthaler, 1987), which mainly affected the estimation of higher N concentration (Figures 2A, B). However, for some species using this region in the VIs provided better estimates of N concentrations and chlorophyll contents than reNDVI (Hansen and Schjoerring, 2003;Sims and Gamon, 2002;Tian et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

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Estimating foliar nitrogen in Eucalyptus using vegetation indexes

Oliveira

Gomes

et al. 2017

Sci. agric. (Piracicaba, Braz.)

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Nitrogen (N) has commonly been applied in Eucalyptus stands in Brazil and it has a direct relation with biomass production and chlorophyll content. Foliar N concentrations are used to diagnose soil and plant fertility levels and to develop N fertilizer application rates. Normally, foliar N is obtained using destructive methods, but indirect analyses using Vegetation Indexes (VIs) may be possible. The aim of this work was to evaluate VIs to estimate foliar N concentration in three Eucalyptus clones. Lower crown leaves of three clonal Eucalyptus plantations (25 months old) were classified into five color patterns using the Munsell Plant Tissue Color Chart. ).

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Section: Discussionmentioning

confidence: 99%

“…Based on the close relationship between chlorophyll and N, these VIs have been used to estimate N concentration in rice (Tian et al, 2011;Yao et al, 2013), maize (Schlemmer et al, 2013), grasses and potatoes (Clevers and Gitelson, 2013). However, the use of such indexes to estimate N concentration in Eucalyptus is rare in the literature.…”

Section: Introductionmentioning

confidence: 99%

Estimating foliar nitrogen in Eucalyptus using vegetation indexes

Oliveira

Gomes

et al. 2017

Sci. agric. (Piracicaba, Braz.)

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“…Therefore, the real-time, nondestructive and accurate monitoring of the nitrogen (N) concentration in crops has become a key technique for timely diagnosis of problems, precise fertilization and productivity estimation [2][3][4][5][6][7][8][9][10]. Remote sensing has been widely applied in recent decades to determine the biophysical and chemical parameters of crops [2,11,12]. Many forthcoming hyperspectral satellite missions will be dedicated to land and crop monitoring.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

et al. 2015

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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...

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“…Furthermore, MTCI has been used to discriminate between C3 and C4 grasses (Foody and Dash, 2007) and to monitor vegetation phenology at the sub-regional (Boyd et al, 2011) and continental scales (Rodriguez-Galiano et al, 2015;Crabbe et al, 2016). Regarding canopy N detection, most applications were aimed at agricultural crops using MTCI values computed from in situ hyperspectral reflectance data (Tian et al, 2011;Clevers and Gitelson, 2013;Li et al, 2014). A few were directed towards sensing N concentration in natural environments using airborne data, e.g.…”

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confidence: 99%

Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Loozen

Rebel

Karssenberg

et al. 2017

Preprint

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) from a Mediterranean forests inventory in the region of Catalonia, NE of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20 km, 15 km, 10 km and 5 km) and at the initial higher spatial resolution of 1 km. The results at the higher spatial resolution yielded significant relationships between MTCI and both canopy N concentration and content, r 2 = 0.32 and r 2 = 0.17, respectively.We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N 20 concentration was strongest for deciduous broadleaf and mixed plots (r 2 = 0.25 and r 2 = 0.47, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r 2 = 0.20). At the species level, canopy N concentration was strongly related to MTCI for European Beech plots (r 2 = 0.71). These results present a new perspective on the application of MTCI time series for canopy N detection, ultimately leading towards the generation of canopy N maps that can be used to constrain global vegetation models. capacity (Evans, 1989;Reich et al., 1995;Reich et al., 1997;Reich et al., 1999;Wright et al., 2004), specific leaf area, leaf life span (Reich et al., 1999;Wright et al., 2004) and light use efficiency (Kergoat et al., 2008). Leaf N concentration expressed on a leaf area basis, also called leaf N content (N g m -2 ) has also been linked with chlorophyll content, Rubisco content (Evans, 35 1989) and photosynthetic capacity (Evans, 1989;Reich et al., 1995). At stand scale, canopy nitrogen concentration, which represents the leaf N concentration averaged over the stand canopy, has also been found to correlate with above ground Net Primary Productivity (NPP) (Reich, 2012), while canopy N content has been linked with the canopy light use efficiency (Green et al., 2003).Given their links to many vegetation processes, leaf and canopy N variables could be used to constrain N cycle modules in 40 global vegetation models. At the global scale, ample data is available to constrain models for the C cycle; however, data to constrain the N cycle are limited. Currently, canopy N data is not widely available and canopy N sampling campaigns are timeconsuming and thus expensive tasks. Moreover, upscaling from local sampling campaign measurements represents an additional limitation. In this perspective, local, regional or even global remotely sensed canopy N estimates will be a valuable addition, enabling us to collect information in a less time intensive and expensive manner than traditional on-field sampling 45 campaigns. Such near global canopy N estimates will be beneficial as input in global vegetation models or to calibrate and validate these models.Currently, different remote sensing techniques have been applied to detect canopy N in terrestrial vegetation. Imaging spectrometry from either airborne or spaceborne sensors coupled with different analysis methods, including partial least squares regression (PLS), continuu...

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Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance

Cited by 238 publications

References 52 publications

Estimating foliar nitrogen in Eucalyptus using vegetation indexes

Estimating foliar nitrogen in Eucalyptus using vegetation indexes

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

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