Forest green-up in the Pearl River Basin: trends, driving factors, and relationships with net primary productivity
The spring green-up date (GUD), a critical indicator of ecosystem response to climate change, profoundly influences terrestrial productivity. However, the complex drivers of GUD remain elusive, while the contentious relationship between GUD and net primary productivity (NPP) challenges the universal validity of the hypothesis that earlier green-up enhances productivity. Using MODIS NDVI data (2000-2020), we quantified forest GUD dynamics in the Pearl River Basin and combined random forest, SHAP, and structural equation modelling to identify drivers and assess GUD-NPP relationships. GUD was advanced by approximately 5 days, with 81.8% of forestland pixels advancing and 18.2% delaying. Only 5.39% of advances showed significant trends, whereas 30.45% of delays were concentrated near croplands and developed areas. The random forest model explained 58.2% of GUD variation. Elevation, precipitation, and soil potassium and phosphorus were dominant; elevation-induced thermal gradients outweighed ambient temperature effects, with elevations above 1,200 m exhibiting markedly delayed GUD. Spatially, GUD had a positive correlation with NPP, indicating that later GUD was associated with higher forest NPP, possibly because of early-spring cold snaps and frost. Temporally, areas with stronger GUD delays tended to show greater NPP increases. This study identified key controls on subtropical forest phenology and revealed a GUD-NPP trade-off.
Authors
- Xueming Zhao
- Zhaoju Zheng
- Shijie Yang (ORCID: https://orcid.org/0009-0001-7573-5509)
- Zhipeng Zhang
- Yuan Zeng
- Xiaohui Cui
Institutions
- Chinese Academy of Sciences (CN)
- Beijing Forestry University (CN)
- Aerospace Information Research Institute (CN)
- Institute of Geographic Sciences and Natural Resources Research (CN)
- University of Chinese Academy of Sciences (CN)
- State Key Laboratory of Resources and Environmental Information System
Publication Details
- Journal
- International Journal of Digital Earth
- Published
- 2026-10-15
- DOI
- https://doi.org/10.1080/17538947.2026.2738487
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation