Improved Estimation of Leaf Nitrogen Content in Ginkgo Saplings and Trees Using Deep Gaussian Processes Models with Feature Selection Strategies.
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| Title: | Improved Estimation of Leaf Nitrogen Content in Ginkgo Saplings and Trees Using Deep Gaussian Processes Models with Feature Selection Strategies. |
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| Authors: | Zhu, Xingzhou1 (AUTHOR), Liu, Jingyuan1 (AUTHOR), Pan, Jinru (AUTHOR), Zhou, Kai (AUTHOR) kaizhou@njfu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1935. 24p. |
| Subjects: | Ginkgo, Gaussian processes, Regression analysis, Hyperspectral imaging systems, Feature selection, Nitrogen content of plants, Wavelength measurement |
| Abstract: | Highlights: What are the main findings? MSC preprocessing and sequential CARS-SPA wavelength screening gave the lowest measured-test error within this Ginkgo leaf hyperspectral dataset. DGP combined with CARS-SPA selected bands yielded the lowest measured-only test error within the main evaluation design (R2 = 0.82; RMSE = 2.07 mg g−1) What are the implications of the main findings?Ginkgo LNC estimation depends strongly on the combined choice of spectral preprocessing, wavelength selection order, and regression model structure. PROSPECT-PRO assisted spectra can support training set augmentation, but the present results should still be interpreted as a method comparison and candidate band reference rather than an operational monitoring model. Leaf nitrogen concentration (LNC) is an important indicator of Ginkgo nutritional status, but its hyperspectral estimation remains challenging because leaf spectra are high dimensional, strongly collinear, and affected by overlapping structural and biochemical signals. This study examined how spectral preprocessing, wavelength selection sequence, and regression model choice influence leaf scale Ginkgo LNC estimation, while separating simulation-assisted model development from measured sample-based prediction assessment. We assembled 717 field measured Ginkgo leaf spectra with corresponding laboratory measured LNC values and used PROSPECT-PRO simulated spectra only for wavelength screening or calibration augmentation, not as independent validation data. Three evaluation schemes were compared: measured-only analysis, simulated spectra-assisted wavelength selection followed by measured data calibration and testing, and simulated spectra-assisted wavelength selection and calibration followed by measured-only testing. The third scheme was used as the main inference framework because it retained an independent measured sample test boundary. Within this framework, multiple preprocessing methods, two wavelength selection sequences, and four regression models (PLSR, GPR, 1D-CNN, and DGP) were evaluated. MSC showed comparatively low error in the preprocessing comparison, and CARS-SPA identified a compact set of informative wavelengths concentrated mainly in the shortwave infrared region. Under the simulation-assisted calibration framework, the combination of MSC preprocessing, CARS-SPA wavelength selection, and DGP regression produced the lowest test error on the measured sample set (R2 = 0.82; RMSE = 2.07 mg g−1). These results indicate that Ginkgo LNC estimation depends on the combined choice of preprocessing method, wavelength selection strategy, and regression model, and provide a methodological reference for simulation-assisted hyperspectral modeling. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? MSC preprocessing and sequential CARS-SPA wavelength screening gave the lowest measured-test error within this Ginkgo leaf hyperspectral dataset. DGP combined with CARS-SPA selected bands yielded the lowest measured-only test error within the main evaluation design (R2 = 0.82; RMSE = 2.07 mg g−1) What are the implications of the main findings?Ginkgo LNC estimation depends strongly on the combined choice of spectral preprocessing, wavelength selection order, and regression model structure. PROSPECT-PRO assisted spectra can support training set augmentation, but the present results should still be interpreted as a method comparison and candidate band reference rather than an operational monitoring model. Leaf nitrogen concentration (LNC) is an important indicator of Ginkgo nutritional status, but its hyperspectral estimation remains challenging because leaf spectra are high dimensional, strongly collinear, and affected by overlapping structural and biochemical signals. This study examined how spectral preprocessing, wavelength selection sequence, and regression model choice influence leaf scale Ginkgo LNC estimation, while separating simulation-assisted model development from measured sample-based prediction assessment. We assembled 717 field measured Ginkgo leaf spectra with corresponding laboratory measured LNC values and used PROSPECT-PRO simulated spectra only for wavelength screening or calibration augmentation, not as independent validation data. Three evaluation schemes were compared: measured-only analysis, simulated spectra-assisted wavelength selection followed by measured data calibration and testing, and simulated spectra-assisted wavelength selection and calibration followed by measured-only testing. The third scheme was used as the main inference framework because it retained an independent measured sample test boundary. Within this framework, multiple preprocessing methods, two wavelength selection sequences, and four regression models (PLSR, GPR, 1D-CNN, and DGP) were evaluated. MSC showed comparatively low error in the preprocessing comparison, and CARS-SPA identified a compact set of informative wavelengths concentrated mainly in the shortwave infrared region. Under the simulation-assisted calibration framework, the combination of MSC preprocessing, CARS-SPA wavelength selection, and DGP regression produced the lowest test error on the measured sample set (R2 = 0.82; RMSE = 2.07 mg g−1). These results indicate that Ginkgo LNC estimation depends on the combined choice of preprocessing method, wavelength selection strategy, and regression model, and provide a methodological reference for simulation-assisted hyperspectral modeling. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121935 |
