Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales

Yoder, Barbara J.; Pettigrew-Crosby, Rita E.

doi:10.1016/0034-4257(95)00135-n

Cited by 578 publications

(351 citation statements)

References 17 publications

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“…RMSEP was also calculated using Equation (4). The ratio of prediction to deviation (RPD) was calculated as follows:…”

Section: Calibration and Validationmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…Mauser and Bach [43] have concluded that derivative spectral indices are very sensitive to LAI. Yoder and Pettigrew-Crosby [4] have found that first-order derivative spectra are the best predictors of the N and chlorophyll contents of big-leaf maples grown under different fertilization treatments. Johnson and Billow [44] have examined Douglas fir needles grown using various fertilization treatments and also found the first-order derivatives of the fresh leaf spectra to be strongly correlated with the total N concentration.…”

Section: Introductionmentioning

confidence: 99%

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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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“…RMSEP was also calculated using Equation (4). The ratio of prediction to deviation (RPD) was calculated as follows:…”

Section: Calibration and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

et al. 2015

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“…Although spatially-explicit data for soil N status are rare, a growing body of research has demonstrated that canopy N concentrations can be estimated using high spectral resolution remote sensing, or imaging spectroscopy (Yoder and Pettigrew-Crosby 1995;Zagolski and others 1996;Martin and Aber 1997;Boegh and others 2002). Imaging spectroscopy differs from more conventional forms of remote sensing in that the full spectrum of reflected light is captured in a series of narrow optical bands, allowing more detailed analysis of vegetation reflectance features than is possible with broad-band sensors.…”

Section: Introductionmentioning

confidence: 99%

Net Primary Production and Canopy Nitrogen in a Temperate Forest Landscape: An Analysis Using Imaging Spectroscopy, Modeling and Field Data

2005

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Understanding spatial patterns of net primary production (NPP) is central to the study of terrestrial ecosystems, but efforts are frequently hampered by a lack of spatial information regarding factors such as nitrogen availability and site history. Here, we examined the degree to which canopy nitrogen can serve as an indicator of patterns of NPP at the Bartlett Experimental Forest in New Hampshire by linking canopy nitrogen estimates from two high spectral resolution remote sensing instruments with field measurements and an ecosystem model. Predicted NPP across the study area ranged from less than 700 g m )2 year )1 to greater than 1300 g m )2 year )1 with a mean of 951 g m )2 year )1 . Spatial patterns corresponded with elevation, species composition and historical forest management, all of which were reflected in patterns of canopy nitrogen. The relationship between production and elevation was nonlinear, with an increase from low-to mid-elevation deciduous stands, followed by a decline in upper-elevation areas dominated by evergreens. This pattern was also evident in field measurements and mirrored an elevational trend in foliar N concentrations. The increase in production from low-to mid-elevation deciduous stands runs counter to the generally accepted pattern for the northeastern U.S. region, and suggests an importance of moisture limitations in lower-elevation forests. Field measurements of foliar N, wood production and leaf litterfall were also used to evaluate sources of error in model estimates and to determine how predictions are affected by different methods of acquiring foliar N input data. The accuracy of predictions generated from remotely sensed foliar N approached that of predictions driven by field-measured foliar N. Predictions based on the more common approach of using aggregated foliar N for individual cover types showed reasonable agreement in terms of the overall mean, but were in poor agreement on a plot-by-plot basis. Collectively, these results suggest that variation in foliar N exerts an important control on landscape-level spatial patterns and can serve as an integrator of other underlying factors that influence forest growth rates.

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“…Many methods based on in situ and laboratory estimations including chemical extraction (Walsh and Beaton, 1982), chlorophyll meter (Fox et al, 1994;Peng et al, 1996;Scharf et al, 2006) and remote sensing (Yoder and Pettigrew-Crosby, 1995;Boegh et al, 2002) have been developed to improve the accuracy of nitrogen diagnosis in soil and crops. For example, Shukla et al (2004) and Alam et al (2006) used leaf color chart (LCC) to monitor N status of rice and wheat to improve N fertilizer management.…”

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confidence: 99%

A Knowledge-Based Model for Nitrogen Management in Rice and Wheat

Cao

Jing

Zhu

et al. 2009

Plant Production Science

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: Excessive nitrogen fertilization results in low nitrogen-use effi ciency. To improve nitrogen management for high yield and high nitrogen efficiency in rice and wheat, we developed a knowledge-based nitrogen fertilization model by integrating the quantitative relationship between N fertilization and yield target with respect to N supply and demand balance. The total amount of nitrogen and ratio of basal to top dressing nitrogen could be determined by this nitrogen fertilization model, and the desirable nitrogen fertilizer strategies could be made under the conditions of different climates, soil types and managements. Furthermore, the function of dynamic regulation of pre-designed N dressing rate could be determined by using the nitrogen fertilization model on the basis of actual growth status under a specific production system. The nitrogen fertilization model is evaluated using the data from field experiments of rice and wheat at Nanjing, and the results on crop growth pattern and N use showed that the grain yield and N recovery were markedly improved by the N fertilization plan given by the model. The nitrogen fertilization model can be used as guidance for quantifying N fertilization strategy in cereal crop production.

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Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales

Cited by 578 publications

References 17 publications

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

Net Primary Production and Canopy Nitrogen in a Temperate Forest Landscape: An Analysis Using Imaging Spectroscopy, Modeling and Field Data

A Knowledge-Based Model for Nitrogen Management in Rice and Wheat

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