The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval
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...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/14/2510 |
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| author | Yucheng Gao Lixia Ma Zhongqi Zhang Xianzhang Pan Ziran Yuan Changkun Wang Dongsheng Yu |
| author_facet | Yucheng Gao Lixia Ma Zhongqi Zhang Xianzhang Pan Ziran Yuan Changkun Wang Dongsheng Yu |
| author_sort | Yucheng Gao |
| collection | DOAJ |
| description | 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 R<sup>2</sup> 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. |
| format | Article |
| id | doaj-art-496009fbbd314bed97b216f0296a73cb |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Remote Sensing |
| spelling | doaj-art-496009fbbd314bed97b216f0296a73cb2025-08-20T02:47:14ZengMDPI AGRemote Sensing2072-42922025-07-011714251010.3390/rs17142510The Influence of Viewing Geometry on Hyperspectral-Based Soil Property RetrievalYucheng Gao0Lixia Ma1Zhongqi Zhang2Xianzhang Pan3Ziran Yuan4Changkun Wang5Dongsheng Yu6State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, ChinaState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, ChinaSchool of Geography, Geomatics & Planning, Jiangsu Normal University, Xuzhou 221116, ChinaState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, ChinaState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, ChinaState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, ChinaState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, ChinaHyperspectral 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 R<sup>2</sup> 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.https://www.mdpi.com/2072-4292/17/14/2510viewing geometrymulti-angle hyperspectralsoil organic matterparticle size distribution |
| spellingShingle | Yucheng Gao Lixia Ma Zhongqi Zhang Xianzhang Pan Ziran Yuan Changkun Wang Dongsheng Yu The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval Remote Sensing viewing geometry multi-angle hyperspectral soil organic matter particle size distribution |
| title | The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval |
| title_full | The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval |
| title_fullStr | The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval |
| title_full_unstemmed | The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval |
| title_short | The Influence of Viewing Geometry on Hyperspectral-Based Soil Property Retrieval |
| title_sort | influence of viewing geometry on hyperspectral based soil property retrieval |
| topic | viewing geometry multi-angle hyperspectral soil organic matter particle size distribution |
| url | https://www.mdpi.com/2072-4292/17/14/2510 |
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