Quantifying winter wheat phenology patterns in the North China Plain using Solar-Induced Chlorophyll Fluorescence

Accurate monitoring of winter wheat phenology is essential for irrigation optimization and food security in water-limited regions. However, discrepancies in how different phenological indicators respond to winter wheat phenology remain unclear. In this study, we compared winter wheat phenology derived from Solar-Induced Chlorophyll Fluorescence (SIF) and common remote sensing indicators from 2000 to 2023 across the North China Plain, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), two-band Enhanced Vegetation Index (EVI2), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). The results indicated that SIF-derived metrics exhibited superior stability in capturing phenological variations, with clearly defined stages and no overlapping phases. The SIF-derived results revealed that the green-up date (GUD) occurred mainly between 52 and 62 days of the year (DOY), with 52.4% of areas showing an advancing trend. The heading date (HE) clustered between 115 and 125 DOY, with 58.7% showing delays, while the maturity date (MA) clustered between 152 and 160 DOY, also dominated by delays (58.6%). Confidence ellipse analysis further exhibited the most widespread phenological variation pattern (23.67%), characterized by advances in all three growth stages and a southeast-to-northwest spatial direction. Rising temperature was the primary driver of earlier phenological patterns, while precipitation had limited influence but an increasing effect as the growing season progressed. These findings highlight the importance of proxy selection in crop phenology and guide adaptive water management strategies under climate variability. • SIF showed a superior monitoring effect for winter wheat phenology compared to VIs, LAI, and GPP. • Winter wheat is most sensitive to temperature, while its sensitivity to precipitation increases as the growth stages progress. • Winter wheat in the NCP mainly showed an advancing GUD, with delaying HE and MA. • Confidence ellipse revealed southeast-to-northwest pattern of phenology variation.

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Journal
Agricultural Water Management
Published
2025-10-13
DOI
https://doi.org/10.1016/j.agwat.2025.109885
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

Quantifying winter wheat phenology patterns in the North China Plain using Solar-Induced Chlorophyll Fluorescence

Zhiwei Han, Peng Guo, Yuge Jiao, Y. Y. Liu et al.
Agricultural Water Management
Remote Sensing in Agriculture
article

Quantifying winter wheat phenology patterns in the North China Plain using Solar-Induced Chlorophyll Fluorescence

Zhiwei Han, Peng Guo, Yuge Jiao, Y. Y. Liu, Hong Wan, Wenhao Liu
article en

Abstract

Accurate monitoring of winter wheat phenology is essential for irrigation optimization and food security in water-limited regions. However, discrepancies in how different phenological indicators respond to winter wheat phenology remain unclear. In this study, we compared winter wheat phenology derived from Solar-Induced Chlorophyll Fluorescence (SIF) and common remote sensing indicators from 2000 to 2023 across the North China Plain, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), two-band Enhanced Vegetation Index (EVI2), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). The results indicated that SIF-derived metrics exhibited superior stability in capturing phenological variations, with clearly defined stages and no overlapping phases. The SIF-derived results revealed that the green-up date (GUD) occurred mainly between 52 and 62 days of the year (DOY), with 52.4% of areas showing an advancing trend. The heading date (HE) clustered between 115 and 125 DOY, with 58.7% showing delays, while the maturity date (MA) clustered between 152 and 160 DOY, also dominated by delays (58.6%). Confidence ellipse analysis further exhibited the most widespread phenological variation pattern (23.67%), characterized by advances in all three growth stages and a southeast-to-northwest spatial direction. Rising temperature was the primary driver of earlier phenological patterns, while precipitation had limited influence but an increasing effect as the growing season progressed. These findings highlight the importance of proxy selection in crop phenology and guide adaptive water management strategies under climate variability. • SIF showed a superior monitoring effect for winter wheat phenology compared to VIs, LAI, and GPP. • Winter wheat is most sensitive to temperature, while its sensitivity to precipitation increases as the growth stages progress. • Winter wheat in the NCP mainly showed an advancing GUD, with delaying HE and MA. • Confidence ellipse revealed southeast-to-northwest pattern of phenology variation.

Agricultural Water ManagementVol. 320
Shandong Agricultural University (CN)
Openalex Percentile: Top 72%
Remote Sensing in Agriculture
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