Spatiotemporal Divergence in SIF- and NDVI-Derived Vegetation Phenology and Its Impact on Water Use Efficiency on the Qinghai-Tibetan Plateau.
Saved in:
| Title: | Spatiotemporal Divergence in SIF- and NDVI-Derived Vegetation Phenology and Its Impact on Water Use Efficiency on the Qinghai-Tibetan Plateau. |
|---|---|
| Authors: | Feng, Zihao1 (AUTHOR), Liu, Haoxiang1,2 (AUTHOR), Chen, Jianjun1,2 (AUTHOR), Chen, Changjun1,2 (AUTHOR) chencj@whu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p2033. 22p. |
| Subjects: | Plant phenology, Normalized difference vegetation index, Remote sensing, Growing season, Water use |
| Geographic Terms: | Tibetan Plateau |
| Abstract: | Highlights: What are the main findings? WUE on the Qinghai-Tibetan Plateau showed an overall increasing trend of approxi-mately 0.15 g C m−2 mm−1 decade−1 during 2001–2018. SIF and NDVI captured consistent SOG advances but divergent EOG trends, indicating a mismatch between canopy greenness and photosynthetic activity. What are the implications of the main findings? SIF-derived SOG provided more information on early-season physiological activity, whereas NDVI-derived EOG better reflected late-season canopy greenness persistence. Joint use of SIF and NDVI phenology helps distinguish physiological activity and canopy greenness when interpreting phenology–WUE associations. Changes in vegetation phenology affect ecosystem carbon uptake and water use, thereby regulating water use efficiency (WUE). However, in alpine ecosystems of the Qinghai-Tibetan Plateau (QTP), uncertainty remains regarding the phenological information characterized by different remote-sensing data sources and its associations with WUE. Using solar-induced chlorophyll fluorescence (SIF) and MODIS normalized difference vegetation index (NDVI) data from 2001 to 2018, we derived the start of growth (SOG) and end of growth (EOG) using multiple phenology extraction methods. WUE was calculated using gross primary productivity (GPP) and evapotranspiration (ET) data. We then employed trend analysis, statistical modeling, and a machine learning interpretive framework to systematically evaluate spatiotemporal differences in phenology derived from SIF and NDVI and their associations with WUE. The results showed that: (1) WUE generally increased across the QTP at approximately 0.15 g C m−2 mm−1 decade−1, with significant increases mainly in the central-eastern and southeastern regions. Both NDVI- and SIF-derived SOG advanced at rates of −1.08 and −1.14 doy decade−1, respectively. In contrast, EOG showed clear data source divergence: EOGNDVI was delayed by 0.62 doy decade−1, whereas EOGSIF advanced by −0.48 doy decade−1. SOGSIF occurred on average 6.6 days later than SOGNDVI, EOG differences were larger, with EOGSIF occurring 17.2 days earlier than EOGNDVI on average. Trend consistency was also higher for SOG than for EOG, whereas opposite EOG trends accounted for 25.3%. (2) After accounting for climatic covariates, SIF- and NDVI-derived phenological indicators showed distinct model-based associations with WUE, but their explanatory contributions were generally weaker than those of key climatic variables. (3) GAM results further showed that SOG was generally negatively associated with standardized WUE in both phenological datasets, whereas the EOG–WUE partial association differed between SIF and NDVI, with positive associations for EOGSIF and negative associations for EOGNDVI. This study highlights the differences between SIF- and NDVI-derived phenological indicators and their model-based associations with WUE, providing complementary remote-sensing information for interpreting vegetation phenology and WUE dynamics on the QTP. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? WUE on the Qinghai-Tibetan Plateau showed an overall increasing trend of approxi-mately 0.15 g C m−2 mm−1 decade−1 during 2001–2018. SIF and NDVI captured consistent SOG advances but divergent EOG trends, indicating a mismatch between canopy greenness and photosynthetic activity. What are the implications of the main findings? SIF-derived SOG provided more information on early-season physiological activity, whereas NDVI-derived EOG better reflected late-season canopy greenness persistence. Joint use of SIF and NDVI phenology helps distinguish physiological activity and canopy greenness when interpreting phenology–WUE associations. Changes in vegetation phenology affect ecosystem carbon uptake and water use, thereby regulating water use efficiency (WUE). However, in alpine ecosystems of the Qinghai-Tibetan Plateau (QTP), uncertainty remains regarding the phenological information characterized by different remote-sensing data sources and its associations with WUE. Using solar-induced chlorophyll fluorescence (SIF) and MODIS normalized difference vegetation index (NDVI) data from 2001 to 2018, we derived the start of growth (SOG) and end of growth (EOG) using multiple phenology extraction methods. WUE was calculated using gross primary productivity (GPP) and evapotranspiration (ET) data. We then employed trend analysis, statistical modeling, and a machine learning interpretive framework to systematically evaluate spatiotemporal differences in phenology derived from SIF and NDVI and their associations with WUE. The results showed that: (1) WUE generally increased across the QTP at approximately 0.15 g C m−2 mm−1 decade−1, with significant increases mainly in the central-eastern and southeastern regions. Both NDVI- and SIF-derived SOG advanced at rates of −1.08 and −1.14 doy decade−1, respectively. In contrast, EOG showed clear data source divergence: EOGNDVI was delayed by 0.62 doy decade−1, whereas EOGSIF advanced by −0.48 doy decade−1. SOGSIF occurred on average 6.6 days later than SOGNDVI, EOG differences were larger, with EOGSIF occurring 17.2 days earlier than EOGNDVI on average. Trend consistency was also higher for SOG than for EOG, whereas opposite EOG trends accounted for 25.3%. (2) After accounting for climatic covariates, SIF- and NDVI-derived phenological indicators showed distinct model-based associations with WUE, but their explanatory contributions were generally weaker than those of key climatic variables. (3) GAM results further showed that SOG was generally negatively associated with standardized WUE in both phenological datasets, whereas the EOG–WUE partial association differed between SIF and NDVI, with positive associations for EOGSIF and negative associations for EOGNDVI. This study highlights the differences between SIF- and NDVI-derived phenological indicators and their model-based associations with WUE, providing complementary remote-sensing information for interpreting vegetation phenology and WUE dynamics on the QTP. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18122033 |
