A Phenologically Simplified Two-Stage Clumping Index Product Derived from the 8-Day Global MODIS-CI Product Suite
The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. Ho...
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2025-01-01
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| author | Ge Gao Ziti Jiao Zhilong Li Chenxia Wang Jing Guo Xiaoning Zhang Anxin Ding Zheyou Tan Sizhe Chen Fangwen Yang Xin Dong |
| author_facet | Ge Gao Ziti Jiao Zhilong Li Chenxia Wang Jing Guo Xiaoning Zhang Anxin Ding Zheyou Tan Sizhe Chen Fangwen Yang Xin Dong |
| author_sort | Ge Gao |
| collection | DOAJ |
| description | The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001–2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CI<sub>LOS</sub> is smaller than the CI<sub>LFS</sub> across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI. |
| format | Article |
| id | doaj-art-eb94655c11c0430f9f508f1f78ee546e |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-eb94655c11c0430f9f508f1f78ee546e2025-01-24T13:47:49ZengMDPI AGRemote Sensing2072-42922025-01-0117223310.3390/rs17020233A Phenologically Simplified Two-Stage Clumping Index Product Derived from the 8-Day Global MODIS-CI Product SuiteGe Gao0Ziti Jiao1Zhilong Li2Chenxia Wang3Jing Guo4Xiaoning Zhang5Anxin Ding6Zheyou Tan7Sizhe Chen8Fangwen Yang9Xin Dong10State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaThe clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001–2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CI<sub>LOS</sub> is smaller than the CI<sub>LFS</sub> across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI.https://www.mdpi.com/2072-4292/17/2/233clumping indexseasonal variationMODISspatiotemporal variationremote sensing productssurface BRDF |
| spellingShingle | Ge Gao Ziti Jiao Zhilong Li Chenxia Wang Jing Guo Xiaoning Zhang Anxin Ding Zheyou Tan Sizhe Chen Fangwen Yang Xin Dong A Phenologically Simplified Two-Stage Clumping Index Product Derived from the 8-Day Global MODIS-CI Product Suite Remote Sensing clumping index seasonal variation MODIS spatiotemporal variation remote sensing products surface BRDF |
| title | A Phenologically Simplified Two-Stage Clumping Index Product Derived from the 8-Day Global MODIS-CI Product Suite |
| title_full | A Phenologically Simplified Two-Stage Clumping Index Product Derived from the 8-Day Global MODIS-CI Product Suite |
| title_fullStr | A Phenologically Simplified Two-Stage Clumping Index Product Derived from the 8-Day Global MODIS-CI Product Suite |
| title_full_unstemmed | A Phenologically Simplified Two-Stage Clumping Index Product Derived from the 8-Day Global MODIS-CI Product Suite |
| title_short | A Phenologically Simplified Two-Stage Clumping Index Product Derived from the 8-Day Global MODIS-CI Product Suite |
| title_sort | phenologically simplified two stage clumping index product derived from the 8 day global modis ci product suite |
| topic | clumping index seasonal variation MODIS spatiotemporal variation remote sensing products surface BRDF |
| url | https://www.mdpi.com/2072-4292/17/2/233 |
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