A novel restoration approach for vegetation reflectance spectra at noisy bands using the principal component analysis method. Issue 6 (18th March 2020)

• Record Type: Journal Article
• Authors: Song, Bowen; Liu, Liangyun; Zhang, Bing
• Is Part Of: International journal of remote sensing. Volume 41:Issue 6(2020)
• Page Start: 2303
• Page End: 2325
• Publication Date: 2020-03-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1688415
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Due to the signal-to-noise ratio (SNR) of sensors, as well as atmospheric absorption and illumination conditions, etc., hyperspectral data at some bands are of poor quality. Data restoration for noisy bands is important for many remote sensing applications. In this paper, we present a novel data-driven Principal Component Analysis (PCA) approach for restoring leaf reflectance spectra at noisy bands using the spectra at effective bands. The technique decomposes the leaf reflectance spectra into their principal components (PCs), selects the leading PCs that describe the most variance in the data, and restores the data from these components. First, the first 10 PCs were determined from a training dataset simulated by the leaf optical properties model (PROSPECT-5) that contained 99.998% of the total information in the 3636 training samples. Then, the performance of the PCA method for restoration of the reflectance at noisy bands was investigated using the ANGERS leaf optical properties dataset; the results showed the spectral root mean squared error (RMSE) is in the range 6.46 × 10 −4 to 6.44 × 10 −2, which is about 3 − 34 times more accurate than the stepwise regression method and partial least squares method (PLSR) for the ANGERS dataset. The results also showed that if the noisy bands are far away from the effective bands, the accuracy of the restored leaf reflectance spectra will decrease. Thirdly, the reliability of the restored reflectance spectra for retrieving leaf biochemical contents was assessed using the ANGERS dataset and leaf optical properties dataset established by the Beijing Academy of Agriculture and Forestry Sciences (BAAFS). Three water-sensitive vegetation indices − normalized difference water index (NDWI), normalized difference infrared index (NDII) and Datt water index (DWI), derived from the restored leaf spectra − were employed to retrieve the equivalent water thickness (EWT). The results showed that the leaf water content can be accurately retrieved from the restored leaf reflectance spectra. In addition, the PCA method to restore vegetation spectral reflectance can be applied on canopy level as well. The results showed that the spectral root mean squared error (RMSE) is in the range 8.22 × 10 −4 to 1.87 × 10 −2 . The performance of the restored canopy spectra was investigated according to the results of retrieving canopy equivalent water thickness (CEWT) using the five spectral indices NDWI, NDWI1370, NDWI1890, NDII and DWI. The results indicated that the restored canopy spectra can be used for retrieving CEWT reliably and improve the accuracy of retrieval compared to the results using original canopy reflectance spectra.