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<p>Hill agroecosystems represent critical yet understudied carbon sinks, requiring precise gross primary production (GPP) quantification to resolve their climate feedbacks. Rice and wheat, India’s most vital staple crops, significantly influence regional carbon fluxes, yet field-scale GPP assessments in the Himalayan agroecosystem remain lacking. To our knowledge, this study provides the first high-resolution GPP assessment for Himalayan croplands, leveraging advances in remote sensing and modeling to bridge a key gap in regional carbon budgets. Using the Vegetation Photosynthesis Model (VPM), we estimated GPP for rice and wheat across Kangra, Palampur, and Dharamshala <i>tehsils</i> (Himachal Pradesh) during the 2020–2023 cropping seasons. Crop phenology-guided masks were generated via Random Forest classification on Google Earth Engine, while eddy covariance (EC) data from the Palampur flux station calibrated INSAT-3D-derived photosynthetically active radiation (PAR). The fraction of absorbed PAR (fAPAR) was derived from leaf area index (LAI) maps using Beer-Lambert’s Law, with multi-stage light-use efficiency (ε<sub>max</sub>) parameterized via EC observations. Environmental stressors on ε<sub>max</sub> was evaluated through ERA-5 temperature records and LSWI computed from Sentinel-2 imagery. Results revealed significantly higher carbon assimilation in rice (seasonal GPP: 997.89&#xa0;g C m⁻²; peak fortnightly GPP: 11.98&#xa0;g C m⁻² day⁻¹) compared to wheat (822.17&#xa0;g C m⁻²; 9.56&#xa0;g C m⁻² day⁻¹). The VPM-derived GPP demonstrated strong agreement with EC-based estimates (agreement index: 0.995; root mean square error: 0.95&#xa0;g C m⁻² day⁻¹; mean absolute percentage error: 16.88%). Given the Himalayan region’s acute sensitivity to climate change, these findings provide a critical baseline for evaluating cropland carbon dynamics and informing adaptive management in vulnerable food systems.</p>

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Satellite-derived gross primary productivity estimation in hill agriculture over rice–wheat cropping systems using the vegetation photosynthesis model

  • 期刊论文

  • 发表日期：

    2025年9月13日

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  • Akarsh Shivhalli Gopal,
  • Shweta Pokhariyal,
  • N. R. Patel,
  • R. S. Rana,
  • R. P. Singh,
  • Prakash Chauhan

摘要

Hill agroecosystems represent critical yet understudied carbon sinks, requiring precise gross primary production (GPP) quantification to resolve their climate feedbacks. Rice and wheat, India’s most vital staple crops, significantly influence regional carbon fluxes, yet field-scale GPP assessments in the Himalayan agroecosystem remain lacking. To our knowledge, this study provides the first high-resolution GPP assessment for Himalayan croplands, leveraging advances in remote sensing and modeling to bridge a key gap in regional carbon budgets. Using the Vegetation Photosynthesis Model (VPM), we estimated GPP for rice and wheat across Kangra, Palampur, and Dharamshala tehsils (Himachal Pradesh) during the 2020–2023 cropping seasons. Crop phenology-guided masks were generated via Random Forest classification on Google Earth Engine, while eddy covariance (EC) data from the Palampur flux station calibrated INSAT-3D-derived photosynthetically active radiation (PAR). The fraction of absorbed PAR (fAPAR) was derived from leaf area index (LAI) maps using Beer-Lambert’s Law, with multi-stage light-use efficiency (εmax) parameterized via EC observations. Environmental stressors on εmax was evaluated through ERA-5 temperature records and LSWI computed from Sentinel-2 imagery. Results revealed significantly higher carbon assimilation in rice (seasonal GPP: 997.89 g C m⁻²; peak fortnightly GPP: 11.98 g C m⁻² day⁻¹) compared to wheat (822.17 g C m⁻²; 9.56 g C m⁻² day⁻¹). The VPM-derived GPP demonstrated strong agreement with EC-based estimates (agreement index: 0.995; root mean square error: 0.95 g C m⁻² day⁻¹; mean absolute percentage error: 16.88%). Given the Himalayan region’s acute sensitivity to climate change, these findings provide a critical baseline for evaluating cropland carbon dynamics and informing adaptive management in vulnerable food systems.