Canopy Chlorophyll Content Inversion of Mountainous Heterogeneous Grasslands Based on the Synergy of Ground Hyperspectral and Sentinel-2 Data: A New Vegetation Index Approach
Canopy chlorophyll content (CCC) is a key indicator for assessing the carbon sequestration capacity and material cycling efficiency of ecosystems, and its accurate retrieval holds significant importance for analyzing ecosystem functioning. Although numerous destructive and remote sensing methods hav...
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2025-06-01
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| author | Yi Zheng Yao Wang Tayir Aziz Ali Mamtimin Yang Li Yan Liu |
| author_facet | Yi Zheng Yao Wang Tayir Aziz Ali Mamtimin Yang Li Yan Liu |
| author_sort | Yi Zheng |
| collection | DOAJ |
| description | Canopy chlorophyll content (CCC) is a key indicator for assessing the carbon sequestration capacity and material cycling efficiency of ecosystems, and its accurate retrieval holds significant importance for analyzing ecosystem functioning. Although numerous destructive and remote sensing methods have been developed to estimate CCC, the accurate estimation of CCC remains a significant challenge in mountainous regions with complex terrain and heterogeneous vegetation types. Through the synergistic analysis of ground hyperspectral and Sentinel-2 data, this study employed Pearson correlation analysis and spectral resampling techniques to identify Sentinel-2 blue band B1 (443 nm) and red band B4 (665 nm) as chlorophyll-sensitive bands through spectral matching with the hyperspectral reflectance of typical grassland vegetation. Based on this, we developed a new four-band vegetation index (VI), the Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI), for estimating the CCC of heterogeneous grasslands in the middle section of the Tianshan Mountains. DRECAVI incorporates red-edge anti-saturation modules (bands B4 and B7) and aerosol correction modules (bands B1 and B8). In order to test the performance of the new index, we compared it with eight commonly used indices and a hybrid model, the Sentinel-2 Biophysical Processor (S2BP). The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R<sup>2</sup> = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R<sup>2</sup> = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. In contrast, DRECAVI maintained similar precision while reducing algorithmic complexity, making it more suitable for regional-scale grassland dynamic monitoring. This study confirms that the synergistic use of multi-source data effectively overcomes the limitations of the spectral–spatial resolution of a single data source, providing a novel methodology for the precision monitoring of mountain ecosystems. |
| format | Article |
| id | doaj-art-e1f4bbfaff534d139bb082ffbf547ce5 |
| institution | Matheson Library |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-e1f4bbfaff534d139bb082ffbf547ce52025-07-11T14:42:16ZengMDPI AGRemote Sensing2072-42922025-06-011713214910.3390/rs17132149Canopy Chlorophyll Content Inversion of Mountainous Heterogeneous Grasslands Based on the Synergy of Ground Hyperspectral and Sentinel-2 Data: A New Vegetation Index ApproachYi Zheng0Yao Wang1Tayir Aziz2Ali Mamtimin3Yang Li4Yan Liu5Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, ChinaInstitute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, ChinaMeteorological Service of Huocheng County, Ili 835200, ChinaInstitute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, ChinaMeteorological and Technical Equipment Support Center of Xinjiang Uygur Autonomous Region, Urumqi 830002, ChinaInstitute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, ChinaCanopy chlorophyll content (CCC) is a key indicator for assessing the carbon sequestration capacity and material cycling efficiency of ecosystems, and its accurate retrieval holds significant importance for analyzing ecosystem functioning. Although numerous destructive and remote sensing methods have been developed to estimate CCC, the accurate estimation of CCC remains a significant challenge in mountainous regions with complex terrain and heterogeneous vegetation types. Through the synergistic analysis of ground hyperspectral and Sentinel-2 data, this study employed Pearson correlation analysis and spectral resampling techniques to identify Sentinel-2 blue band B1 (443 nm) and red band B4 (665 nm) as chlorophyll-sensitive bands through spectral matching with the hyperspectral reflectance of typical grassland vegetation. Based on this, we developed a new four-band vegetation index (VI), the Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI), for estimating the CCC of heterogeneous grasslands in the middle section of the Tianshan Mountains. DRECAVI incorporates red-edge anti-saturation modules (bands B4 and B7) and aerosol correction modules (bands B1 and B8). In order to test the performance of the new index, we compared it with eight commonly used indices and a hybrid model, the Sentinel-2 Biophysical Processor (S2BP). The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R<sup>2</sup> = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R<sup>2</sup> = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. In contrast, DRECAVI maintained similar precision while reducing algorithmic complexity, making it more suitable for regional-scale grassland dynamic monitoring. This study confirms that the synergistic use of multi-source data effectively overcomes the limitations of the spectral–spatial resolution of a single data source, providing a novel methodology for the precision monitoring of mountain ecosystems.https://www.mdpi.com/2072-4292/17/13/2149mountainous heterogeneous grasslandscanopy chlorophyll content (CCC)Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI)ground hyperspectral dataSentinel-2 data |
| spellingShingle | Yi Zheng Yao Wang Tayir Aziz Ali Mamtimin Yang Li Yan Liu Canopy Chlorophyll Content Inversion of Mountainous Heterogeneous Grasslands Based on the Synergy of Ground Hyperspectral and Sentinel-2 Data: A New Vegetation Index Approach Remote Sensing mountainous heterogeneous grasslands canopy chlorophyll content (CCC) Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI) ground hyperspectral data Sentinel-2 data |
| title | Canopy Chlorophyll Content Inversion of Mountainous Heterogeneous Grasslands Based on the Synergy of Ground Hyperspectral and Sentinel-2 Data: A New Vegetation Index Approach |
| title_full | Canopy Chlorophyll Content Inversion of Mountainous Heterogeneous Grasslands Based on the Synergy of Ground Hyperspectral and Sentinel-2 Data: A New Vegetation Index Approach |
| title_fullStr | Canopy Chlorophyll Content Inversion of Mountainous Heterogeneous Grasslands Based on the Synergy of Ground Hyperspectral and Sentinel-2 Data: A New Vegetation Index Approach |
| title_full_unstemmed | Canopy Chlorophyll Content Inversion of Mountainous Heterogeneous Grasslands Based on the Synergy of Ground Hyperspectral and Sentinel-2 Data: A New Vegetation Index Approach |
| title_short | Canopy Chlorophyll Content Inversion of Mountainous Heterogeneous Grasslands Based on the Synergy of Ground Hyperspectral and Sentinel-2 Data: A New Vegetation Index Approach |
| title_sort | canopy chlorophyll content inversion of mountainous heterogeneous grasslands based on the synergy of ground hyperspectral and sentinel 2 data a new vegetation index approach |
| topic | mountainous heterogeneous grasslands canopy chlorophyll content (CCC) Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI) ground hyperspectral data Sentinel-2 data |
| url | https://www.mdpi.com/2072-4292/17/13/2149 |
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