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Related Concept Videos

Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
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  5. Agricultural, Veterinary And Food Sciences
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  9. Agricultural Production Systems Simulation
  11. Improving The Simulation Accuracy Of Summer Maize Growth And Yield By Pixel-based Parameterization Based On Assimilating Upscaled Modis Lai

Improving the simulation accuracy of summer maize growth and yield by pixel-based parameterization based on assimilating upscaled MODIS LAI

Dianchen Han1, Peijuan Wang2, Yihui Ding3

  • 1Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu Province, China; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China.

The Science of the Total Environment
|October 3, 2024

Related Experiment Videos

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
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Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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View abstract on PubMed

Summary
This summary is machine-generated.

Improving maize yield simulation for food security is crucial. This study optimized crop model parameters using upscaled satellite leaf area data, enhancing regional yield prediction accuracy.

Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Crop Modeling

Background:

  • Accurate regional maize yield simulation is vital for food security and policy.
  • Process-based crop models require precise parameterization for reliable predictions.

Purpose of the Study:

  • To improve the simulation accuracy of summer maize growth and yield at the regional scale.
  • To optimize sensitive parameters of the Chinese Agricultural Meteorological Model (CAMM) using assimilated data.

Main Methods:

  • Sobol sensitivity analysis to identify key crop model parameters.
  • Upscaling of MODIS Leaf Area Index (LAI) data to reduce intra-pixel heterogeneity.
  • 4DVar data assimilation algorithm to integrate upscaled LAI into the CAMM model.
  • Pixel-based parameterization of specific leaf area parameters (SLATB_0, SLATB_1).
Keywords:
Data assimilationPixel-based parameterizationSummer maizeUpscaled remotely sensed data

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Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
00:09

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

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Main Results:

  • Specific leaf area parameters (SLATB_0, SLATB_1) were identified as most impactful on maize yield.
  • Upscaling LAI and assimilating it into CAMM significantly improved yield simulation accuracy compared to non-assimilated schemes.
  • The dual-parameter optimization scheme (DA2) demonstrated superior accuracy (r: 0.64-0.93) over single-parameter optimization (DA1).

Conclusions:

  • Upscaling remotely sensed LAI effectively reduces data uncertainties at larger pixel scales.
  • Assimilating upscaled LAI data into crop models substantially enhances regional crop growth and yield simulation.
  • Optimized crop models provide more reliable tools for agricultural management and food security assessments.