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<p>Wheat productivity plays an important role in sustaining food security and agricultural policies, particularly in intensively cultivated regions such as the Chandauli district in eastern Uttar Pradesh, India. This study quantified Gross Primary Productivity (GPP) by employing both a Vegetation Photosynthesis Model (VPM) and GOSIF-derived GPP (based on satellite SIF observations) for the Rabi seasons (December–March) spanning from 2005 to 2021. Findings of wheat acreage mapping revealed that the wheat area expanded from 964 km<sup>2</sup> (2014–15) to 1359 km<sup>2</sup> (2024–25), reflecting a substantial increase in wheat cultivation (41%). Seasonal GPP derived from the VPM model ranged from 574 gC m⁻² in 2016 to 743 gC m⁻² in 2019, while GOSIF-derived estimates varied from 579 gC m⁻² in 2005 to 840 gC m⁻² in 2020, capturing pronounced interannual variability. Comparative assessment of GPP between the VPM and GOSIF-derived estimates showed the strongest agreement in 2005 (R² = 0.80, RMSE = 36.17 gC m⁻², MAE = 30.30 gC m⁻²), with weaker correspondence during 2014–15 (R² = 0.34, RMSE = 91.11 gC m⁻², MAE = 74.56 gC m⁻²). Furthermore, wheat yields predicted from the VPM peaked at 5,300&#xa0;kg ha⁻¹, whereas GOSIF-derived yields exhibited slightly higher fluctuations. Validation with Uttar Pradesh Directorate of Economics and Statistics (UPDES) data confirmed higher reliability of the VPM framework (<i>r</i> = 0.74, <i>p</i> &lt; 0.001) compared with GOSIF-derived estimates (<i>r</i> = 0.63, <i>p</i> &lt; 0.006). Collectively, these findings demonstrate that the VPM provides stable long-term estimates at finer spatial resolution, whereas the GOSIF-based approach reflects higher interannual variability in GPP estimates. The framework presented offers a scalable and transferable approach for monitoring wheat productivity at the district scale, with potential applicability to other regions within the Indo-Gangetic Plain, and supports regional food security planning under changing climatic conditions.</p>

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Modeling Wheat Yields Using Vegetation Photosynthesis Model Through Remote Sensing Satellite Data in Chandauli District, Eastern Uttar Pradesh (India)

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Bikash Ranjan Parida,
  • Jitesh Chandra,
  • Sagar Kumar Swain

摘要

Wheat productivity plays an important role in sustaining food security and agricultural policies, particularly in intensively cultivated regions such as the Chandauli district in eastern Uttar Pradesh, India. This study quantified Gross Primary Productivity (GPP) by employing both a Vegetation Photosynthesis Model (VPM) and GOSIF-derived GPP (based on satellite SIF observations) for the Rabi seasons (December–March) spanning from 2005 to 2021. Findings of wheat acreage mapping revealed that the wheat area expanded from 964 km2 (2014–15) to 1359 km2 (2024–25), reflecting a substantial increase in wheat cultivation (41%). Seasonal GPP derived from the VPM model ranged from 574 gC m⁻² in 2016 to 743 gC m⁻² in 2019, while GOSIF-derived estimates varied from 579 gC m⁻² in 2005 to 840 gC m⁻² in 2020, capturing pronounced interannual variability. Comparative assessment of GPP between the VPM and GOSIF-derived estimates showed the strongest agreement in 2005 (R² = 0.80, RMSE = 36.17 gC m⁻², MAE = 30.30 gC m⁻²), with weaker correspondence during 2014–15 (R² = 0.34, RMSE = 91.11 gC m⁻², MAE = 74.56 gC m⁻²). Furthermore, wheat yields predicted from the VPM peaked at 5,300 kg ha⁻¹, whereas GOSIF-derived yields exhibited slightly higher fluctuations. Validation with Uttar Pradesh Directorate of Economics and Statistics (UPDES) data confirmed higher reliability of the VPM framework (r = 0.74, p < 0.001) compared with GOSIF-derived estimates (r = 0.63, p < 0.006). Collectively, these findings demonstrate that the VPM provides stable long-term estimates at finer spatial resolution, whereas the GOSIF-based approach reflects higher interannual variability in GPP estimates. The framework presented offers a scalable and transferable approach for monitoring wheat productivity at the district scale, with potential applicability to other regions within the Indo-Gangetic Plain, and supports regional food security planning under changing climatic conditions.