Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

Schlerf, Martin; Atzberger, Clement; Hill, Joachim; Buddenbaum, Henning; Werner, Willy; Schüler, Gebhard

doi:10.1016/j.jag.2009.08.006

Cited by 130 publications

(112 citation statements)

References 52 publications

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“…Moreover, the results obtained for ENF tree species are surprising as previous studies investigating the relationship between foliar N[%] and in situ measured spectra 305 reported higher r 2 values, r 2 = 0.59 and r 2 = 0.81 in spruce and pine forest, respectively (Stein et al, 2014;Schlerf et al, 2010). …”

Section: Canopy N Concentration Detection 285mentioning

confidence: 77%

“…Imaging spectrometry from either airborne or spaceborne sensors coupled with different analysis methods, including partial least squares regression (PLS), continuum removal, spectral unmixing or vegetation indices, has proven efficient in improving N 50 sensing capabilities at the local scale (Smith et al, 2003;Ollinger et al, 2008;Huber et al, 2008;Martin et al, 2008;Schlerf et al, 2010;Wang et al, 2016). However, it can also be obtained from other sensors' reflectance data and a similar product will be available from the ESA Sentinel-2 satellite mission (Drusch et al, 2012).…”

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confidence: 99%

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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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Section: Canopy N Concentration Detection 285mentioning

confidence: 77%

mentioning

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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“…Band depth (BD) was calculated on the full spectral range (350-2500 nm) to identify the absorption features related to SOM content. For R, log 1/R, the first derivative of reflectance (DR) and the first derivative of logarithm of reciprocal of reflectance (D log 1/R) at each waveband were computed to remove the baseline effects (Ben-Dor et al, 1997;Duckworth, 1998) and strengthen the spectral features (Lu et al, 2007;Schlerf et al, 2010) (Fig. 2).…”

Section: Measurement and Preprocessing Of Soil Diffuse Reflectance Spmentioning

confidence: 99%

“…The CR reflectance at a certain wavelength was calculated by dividing the original reflectance value by the values of the continuum line at the corresponding wavelength. Then, BD at each wavelength was computed by subtracting the CR reflectance from l (Schlerf et al, 2010) as explained by Eqs. (1), (2) and (3).…”

Section: Measurement and Preprocessing Of Soil Diffuse Reflectance Spmentioning

confidence: 99%

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Chen

Peng

et al. 2012

Soil and Tillage Research

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“…Gao and Goetz [41] extracted reflectance spectra of dry leaves from those of fresh leaves using a nonlinear least squares spectral matching technique. An improved version of this algorithm was proposed by Schlerf et al [42]. Ramoelo et al [43] increased the accuracy of nitrogen and phosphorus estimation using this water-removal method.…”

Section: Introductionmentioning

confidence: 99%

Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation

Wang

Chen

et al. 2017

Remote Sensing

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Abstract:Nitrogen is an essential nutrient in many terrestrial ecosystems because it affects vegetation's primary production. Due to the variety of nitrogen-containing substances and the differences in their composition across species, statistical approaches are now dominant in remote sensing retrieval of leaf nitrogen content. Many studies remove spectral regions characterized by strong water absorptions before retrieving nitrogen content, because water is believed to mask the absorption features of nitrogen. The objectives of this study are to discuss the necessity of this practice and to explore how water absorption affects leaf nitrogen estimation. Spectral measurements and chemical analyses for Maize, Sawtooth Oak, and Sweetgum leaves were carried out in 2014. The leaf optical properties model PROSPECT5 was used to eliminate the influences of water on the measured reflectance spectra. The inversion accuracy of PROPECT5 for chlorophyll, carotenoid, water, and dry matter of Maize was also discussed. Measured, simulated, and water-removed spectra were used to: (1) find the optimal nitrogen-related spectral index; and (2) regress with the area-based leaf nitrogen concentration (LNC) using the partial least square regression technique (PLSR). Two types of spectral indices were selected in this study: Normalized Difference Spectral Index (NDSI) and Ratio Spectral Index (RSI). Additionally, first-order derivative forms of measured, simulated, and water-removed spectra were devised to search for the optimal spectral indices. Finally, species-specific optimal indices and cross-species optimal indices, as well as their root mean square errors (RMSE) and coefficients of determination (R 2 ), were obtained. The Ending Top Percentile (ETP), an indicator of the performance of cross-species optimal indices, was also calculated. PLSR was combined with leave-one-out cross validation (LOOCV) for each species. The predicted root mean square errors (RMSEP) and predicted R 2 were finally calculated. The results showed that chlorophyll, carotenoid, and water contents could be estimated with R 2 of 0.75, 0.59, and 0.69, respectively, which were acceptable for fresh leaves. The dry matter was retrieved with a relatively lower accuracy because of the fixed absorption coefficients adopted by PROSPECT5. The performances of species-specific optimal indices using water-free spectra were comparable to or worse than the corresponding indices derived with measured or simulated spectra. Compared with measured spectra, ETP did not change much after the effects of water were removed, and the R 2 between cross-species optimal spectral indices and area-based LNC for Sawtooth Oak and Sweetgum decreased while it remained almost the same for Maize, suggesting that the water-removed cross-species optimal indices were inferior to the corresponding optimal indices found without water removal. ETP was larger than 30% for all spectra, demonstrating the non-existence of common optimal NDSI or RSI for the three species. After water removal, the accuracy...

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Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

Cited by 130 publications

References 52 publications

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

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

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Limited Effects of Water Absorption on Reducing the Accuracy of Leaf Nitrogen Estimation

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