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Article

The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval

1
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
2
School of Geography, Geomatics & Planning, Jiangsu Normal University, Xuzhou 221116, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(14), 2510; https://doi.org/10.3390/rs17142510
Submission received: 24 May 2025 / Revised: 10 July 2025 / Accepted: 17 July 2025 / Published: 18 July 2025
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

Abstract

Hyperspectral technology has been widely applied to the retrieval of soil properties, such as soil organic matter (SOM) and particle size distribution (PSD). However, most previous studies have focused on hyperspectral data acquired from the nadir direction, and the influence of viewing geometry on hyperspectral-based soil property retrieval remains unclear. In this study, bidirectional reflectance factors (BRFs) were collected at 48 different viewing angles for 154 soil samples with varying SOM contents and PSDs. SOM and PSD were then retrieved using combinations of ten spectral preprocessing methods (raw reflectance, Savitzky–Golay filter (SG), first derivative (D1), second derivative (D2), standard normal variate (SNV), multiplicative scatter correction (MSC), SG + D1, SG + D2, SG + SNV, and SG + MSC), one sensitive wavelength selection method, and three retrieval algorithms (partial least squares regression (PLSR), support vector machine (SVM), and convolutional neural networks (CNNs)). The influence of viewing geometry on the selection of spectral preprocessing methods, retrieval algorithms, sensitive wavelengths, and retrieval accuracy was systematically analyzed. The results showed that soil BRFs are influenced by both soil properties and viewing angles. The viewing geometry had limited effects on the choice of preprocessing method and retrieval algorithm. Among the preprocessing methods, D1, SG + D1, and SG + D2 outperformed the others, while PLSR achieved a higher accuracy than SVM and CNN when retrieving soil properties. The selected sensitive wavelengths for both SOM and PSD varied slightly with viewing angle and were mainly located in the near-infrared region when using BRFs from multiple viewing angles. Compared with single-angle data, multi-angle BRFs significantly improved retrieval performance, with the R2 increasing by 11% and 15%, and RMSE decreasing by 16% and 30% for SOM and PSD, respectively. The optimal viewing zenith angle ranged from 10° to 20° for SOM and around 40° for PSD. Additionally, backward viewing directions were more favorable than forward directions, with the optimal viewing azimuth angles being 0° for SOM and 90° for PSD. These findings provide useful insights for improving the accuracy of soil property retrieval using multi-angle hyperspectral observations.
Keywords: viewing geometry; multi-angle hyperspectral; soil organic matter; particle size distribution viewing geometry; multi-angle hyperspectral; soil organic matter; particle size distribution

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MDPI and ACS Style

Gao, Y.; Ma, L.; Zhang, Z.; Pan, X.; Yuan, Z.; Wang, C.; Yu, D. The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval. Remote Sens. 2025, 17, 2510. https://doi.org/10.3390/rs17142510

AMA Style

Gao Y, Ma L, Zhang Z, Pan X, Yuan Z, Wang C, Yu D. The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval. Remote Sensing. 2025; 17(14):2510. https://doi.org/10.3390/rs17142510

Chicago/Turabian Style

Gao, Yucheng, Lixia Ma, Zhongqi Zhang, Xianzhang Pan, Ziran Yuan, Changkun Wang, and Dongsheng Yu. 2025. "The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval" Remote Sensing 17, no. 14: 2510. https://doi.org/10.3390/rs17142510

APA Style

Gao, Y., Ma, L., Zhang, Z., Pan, X., Yuan, Z., Wang, C., & Yu, D. (2025). The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval. Remote Sensing, 17(14), 2510. https://doi.org/10.3390/rs17142510

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