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<p>Analyzing agricultural production systems by identifying the prominent factors affecting yield variations across regions can support new strategies for problem resolving in agroecosystems. Assessing the physiological status of crop fields offers valuable insights into the efficiency of these systems in terms of resource acquisition and utilization. This study investigates temporal changes in Crop Growth Rate (CGR), Leaf Area Index (LAI), and Net Assimilation Rate (NAR), and generates corresponding spatial maps using satellite images and mathematical models. It also compares these indicators across wheat fields in different watersheds to illustrate the physiological conditions of the crops. Additionally, the research aims to propose a reliable model for large-scale estimation of wheat leaf area. Our findings showed that empirical NDVI-based models for estimating LAI were less accurate than models relying on satellite-derived indices. Additionally, CGR did not reach its maximum potential in the study area, and the watershed basins exhibited diverse time-series patterns in terms of CGR and LAI. In contrast, NAR remained relatively stable across different basins. These results suggest that agronomic practices and breeding strategies aimed at enhancing leaf area development could assist farmers in reducing yield variability between watersheds and contribute to overall yield enhancement.</p>

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Temporal Mapping of Wheat Growth Traits Using Sentinel-2 and Empirical Models in Northern Iran

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Roohollah Akbari,
  • Behnam Kamkar,
  • Parisa Alizadeh Dehkordi,
  • Hossein Kazemi

摘要

Analyzing agricultural production systems by identifying the prominent factors affecting yield variations across regions can support new strategies for problem resolving in agroecosystems. Assessing the physiological status of crop fields offers valuable insights into the efficiency of these systems in terms of resource acquisition and utilization. This study investigates temporal changes in Crop Growth Rate (CGR), Leaf Area Index (LAI), and Net Assimilation Rate (NAR), and generates corresponding spatial maps using satellite images and mathematical models. It also compares these indicators across wheat fields in different watersheds to illustrate the physiological conditions of the crops. Additionally, the research aims to propose a reliable model for large-scale estimation of wheat leaf area. Our findings showed that empirical NDVI-based models for estimating LAI were less accurate than models relying on satellite-derived indices. Additionally, CGR did not reach its maximum potential in the study area, and the watershed basins exhibited diverse time-series patterns in terms of CGR and LAI. In contrast, NAR remained relatively stable across different basins. These results suggest that agronomic practices and breeding strategies aimed at enhancing leaf area development could assist farmers in reducing yield variability between watersheds and contribute to overall yield enhancement.