A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels
The bidirectional reflectance distribution function (BRDF) of the land surface contains information relating to its physical structure and composition. Accurate BRDF modeling for heterogeneous pixels is important for global ecosystem monitoring and radiation balance studies. However, the original ke...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2023-01-01
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| Series: | Journal of Remote Sensing |
| Online Access: | https://spj.science.org/doi/10.34133/remotesensing.0038 |
| _version_ | 1827963823693234176 |
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| author | Hanliang Li Kai Yan Si Gao Xuanlong Ma Yelu Zeng Wenjuan Li Gaofei Yin Xihan Mu Guangjian Yan Ranga B. Myneni |
| author_facet | Hanliang Li Kai Yan Si Gao Xuanlong Ma Yelu Zeng Wenjuan Li Gaofei Yin Xihan Mu Guangjian Yan Ranga B. Myneni |
| author_sort | Hanliang Li |
| collection | DOAJ |
| description | The bidirectional reflectance distribution function (BRDF) of the land surface contains information relating to its physical structure and composition. Accurate BRDF modeling for heterogeneous pixels is important for global ecosystem monitoring and radiation balance studies. However, the original kernel-driven models, which many operational BRDF/Albedo algorithms have adopted, do not explicitly consider the heterogeneity within heterogeneous pixels, which may result in large fitting residuals. In this paper, we attempted to improve the fitting ability of the kernel-driven models over heterogeneous pixels by changing the inversion approach and proposed a dynamic weighted least squares (DWLS) inversion approach. The performance of DWLS and the traditional ordinary least squares (OLS) inversion approach were compared using simulated data. We also evaluated its ability to reconstruct multiangle satellite observations and provide accurate BRDF using unmanned aerial vehicle observations. The results show that the developed DWLS approach improves the accuracy of modeled BRDF of heterogeneous pixels. The DWLS approach applied to satellite observations shows better performance than the OLS method in study regions and exhibits smaller mean fitting residuals both in the red and near-infrared bands. The DWLS approach also shows higher BRDF modeling accuracy than the OLS approach with unmanned aerial vehicle observations. These results indicate that the DWLS inversion approach can be a better choice when kernel-driven models are used for heterogeneous pixels. |
| first_indexed | 2024-04-09T17:05:14Z |
| format | Article |
| id | doaj.art-47a265c891af49c4a623c66625719aac |
| institution | Directory Open Access Journal |
| issn | 2694-1589 |
| language | English |
| last_indexed | 2024-04-09T17:05:14Z |
| publishDate | 2023-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Journal of Remote Sensing |
| spelling | doaj.art-47a265c891af49c4a623c66625719aac2023-04-20T16:42:00ZengAmerican Association for the Advancement of Science (AAAS)Journal of Remote Sensing2694-15892023-01-01310.34133/remotesensing.0038A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous PixelsHanliang Li0Kai Yan1Si Gao2Xuanlong Ma3Yelu Zeng4Wenjuan Li5Gaofei Yin6Xihan Mu7Guangjian Yan8Ranga B. Myneni9State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.School of Land Science and Technology, China University of Geosciences, Beijing 100083, China.College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730020, China.College of Land Science and Technology, China Agricultural University, Beijing 100083, China.State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China.Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China.State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.Department of Earth and Environment, Boston University, Boston, MA 02215, USA.The bidirectional reflectance distribution function (BRDF) of the land surface contains information relating to its physical structure and composition. Accurate BRDF modeling for heterogeneous pixels is important for global ecosystem monitoring and radiation balance studies. However, the original kernel-driven models, which many operational BRDF/Albedo algorithms have adopted, do not explicitly consider the heterogeneity within heterogeneous pixels, which may result in large fitting residuals. In this paper, we attempted to improve the fitting ability of the kernel-driven models over heterogeneous pixels by changing the inversion approach and proposed a dynamic weighted least squares (DWLS) inversion approach. The performance of DWLS and the traditional ordinary least squares (OLS) inversion approach were compared using simulated data. We also evaluated its ability to reconstruct multiangle satellite observations and provide accurate BRDF using unmanned aerial vehicle observations. The results show that the developed DWLS approach improves the accuracy of modeled BRDF of heterogeneous pixels. The DWLS approach applied to satellite observations shows better performance than the OLS method in study regions and exhibits smaller mean fitting residuals both in the red and near-infrared bands. The DWLS approach also shows higher BRDF modeling accuracy than the OLS approach with unmanned aerial vehicle observations. These results indicate that the DWLS inversion approach can be a better choice when kernel-driven models are used for heterogeneous pixels.https://spj.science.org/doi/10.34133/remotesensing.0038 |
| spellingShingle | Hanliang Li Kai Yan Si Gao Xuanlong Ma Yelu Zeng Wenjuan Li Gaofei Yin Xihan Mu Guangjian Yan Ranga B. Myneni A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels Journal of Remote Sensing |
| title | A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels |
| title_full | A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels |
| title_fullStr | A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels |
| title_full_unstemmed | A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels |
| title_short | A Novel Inversion Approach for the Kernel-Driven BRDF Model for Heterogeneous Pixels |
| title_sort | novel inversion approach for the kernel driven brdf model for heterogeneous pixels |
| url | https://spj.science.org/doi/10.34133/remotesensing.0038 |
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