Liu et al. (2025) Quantifying winter wheat phenology patterns in the North China Plain using Solar-Induced Chlorophyll Fluorescence

Identification

• Journal: Agricultural Water Management
• Year: 2025
• Date: 2025-10-13
• Authors: Y. Y. Liu, Hong Wan, Peng Guo, Wenhao Liu, Zhiwei Han, Yuge Jiao
• DOI: 10.1016/j.agwat.2025.109885

Research Groups

• College of Information Science and Engineering, Shandong Agricultural University, Tai’an, China

Short Summary

This study compared winter wheat phenology derived from Solar-Induced Chlorophyll Fluorescence (SIF) with other remote sensing indicators across the North China Plain from 2000 to 2023, finding that SIF-derived metrics offered superior stability and clearer delineation of growth stages, with phenological shifts primarily driven by rising temperatures.

Objective

• To compare the green-up date (GUD), heading date (HE), and maturity date (MA) phenology of winter wheat derived from different crop proxies (SIF, NDVI, EVI, EVI2, LAI, GPP).
• To quantify the interannual phenology variations using the best-performing proxy (SIF).
• To investigate the climatic influences (temperature and precipitation) on GUD, HE, and MA based on the high-performing proxy.

Study Configuration

• Spatial Scale: North China Plain (NCP), covering Beijing, Tianjin, Hebei, Henan, Shandong, Anhui, and Jiangsu provinces in China. Data spatial resolution was 0.05 degrees.
• Temporal Scale: 2000 to 2023 (24 years).

Methodology and Data

• Models used:
  • Double logistic (DL) function for time series reconstruction.
  • Combined threshold-based and change detection methods for phenology extraction.
  • Theil–Sen Median trend analysis.
  • Mann–Kendall test for trend significance.
  • Standard Deviation Ellipse (SDE) method for analyzing spatial clustering patterns.
  • Partial correlation analysis.
• Data sources:
  • Solar-Induced Chlorophyll Fluorescence (SIF): Global OCO-2-based SIF data set (GOSIF) (8-day, 0.05 degree).
  • Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), two-band Enhanced Vegetation Index (EVI2): MOD13C1 products (16-day, 0.05 degree).
  • Leaf Area Index (LAI) and Gross Primary Productivity (GPP): Global Land Surface Satellite (GLASS) products (8-day, 0.05 degree).
  • Meteorological data (2-meter air temperature, total precipitation): ERA5 monthly averaged data on single levels (resampled to 0.05 degree).
  • Validation data: ChinaCropPhen1 km (CCP1) dataset (1 kilometer resolution, LAI-derived), MCD12Q2 dataset (MODIS EVI-derived).
  • Ancillary data: ChinaCropArea 1 km dataset (winter wheat planting distribution), Digital Elevation Model (DEM).

Main Results

• SIF-derived phenological metrics exhibited superior stability (smallest standard deviations and narrowest ranges, all within 23 days) and clearer delineation of winter wheat growth stages compared to NDVI, EVI, EVI2, LAI, and GPP.
• The mean SIF-derived GUD, HE, and MA were 56, 113, and 151 days of the year (DOY), respectively.
• Spatially, GUD (93.6% between 52-56 DOY), HE (94.5% between 105-125 DOY), and MA (95.8% between 140-160 DOY) showed latitudinal and longitudinal gradients, with later events observed at higher latitudes and coastal regions. GUD exhibited the most significant spatial heterogeneity.
• Temporally, 52.4% of the North China Plain showed an advancing trend in GUD, while 58.7% showed a delaying trend in HE, and 58.6% showed a delaying trend in MA.
• Confidence ellipse analysis identified the most prevalent phenological variation pattern (23.67%) as advancements in all three growth stages (GUD, HE, MA), distributed along a southeast-to-northwest direction.
• Rising temperature was identified as the primary driver of phenological shifts, showing significant positive correlations with GUD (r = 0.72), HE (r = 0.59), and MA (r = 0.73). Precipitation had a limited influence (correlation coefficients of 0.03, 0.14, and 0.29 for GUD, HE, and MA, respectively) but an increasing effect as the growing season progressed.

Contributions

• Provided a systematic comparison of winter wheat phenology derived from SIF and other widely used remote sensing proxies (NDVI, EVI, EVI2, LAI, GPP) over a 24-year period in the North China Plain.
• Demonstrated the superior performance and robustness of SIF-derived metrics for accurately monitoring winter wheat phenology, offering guidance for proxy selection in crop phenology studies.
• Quantified the spatial and temporal patterns of GUD, HE, and MA, revealing advancing GUD and delaying HE/MA trends across the region.
• Identified the dominant climatic drivers (temperature and precipitation) for winter wheat phenological changes and highlighted their spatially heterogeneous influences.
• Contributed to a better understanding of crop responses to climate change and informed adaptive water management strategies in water-limited agricultural regions.

Funding

• Natural Science Foundation of Shandong Province (grant number: ZR2020MD017 and ZR2015DL003).

Citation

@article{Liu2025Quantifying,
  author = {Liu, Y. Y. and Wan, Hong and Guo, Peng and Liu, Wenhao and Han, Zhiwei and Jiao, Yuge},
  title = {Quantifying winter wheat phenology patterns in the North China Plain using Solar-Induced Chlorophyll Fluorescence},
  journal = {Agricultural Water Management},
  year = {2025},
  doi = {10.1016/j.agwat.2025.109885},
  url = {https://doi.org/10.1016/j.agwat.2025.109885}
}