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Primary Production

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The total amount of energy acquired by primary producers in an ecosystem is called gross primary production (GPP). However, of this energy, producers use some for metabolic processes, and some is lost as heat, decreasing the amount of energy available to the next trophic level. The remaining usable amount of energy is called the net primary productivity (NPP). In terrestrial ecosystems, NPP is driven by climate, while light penetration and nutrient availability drive NPP in aquatic ecosystems.

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Net production efficiency (NPE) is the efficiency at which organisms assimilate energy into biomass for the next trophic level. Due to low metabolic rates and less energy spent on thermoregulatory processes, the NPE of ectotherms (cold-blooded animals) is 10 times higher than endotherms (warm-blooded animals).

Trophic Efficiency

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Related Experiment Video

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Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

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Net Primary Productivity Retrieval Based on ESTARFM Fusion and an Improved CASA Model.

Yuanji Cai¹, Chunling Chen^1,2,3, Wanning Li¹

¹College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China.

Plants (Basel, Switzerland)

|May 27, 2026

Summary

This summary is machine-generated.

This study improves crop Net Primary Productivity (NPP) estimation using fused NDVI data and an optimized CASA model. The enhanced method boosts accuracy and reliability for agricultural management and carbon cycle research.

Keywords:

improved CASA model maize net primary productivity remote sensing rice spatiotemporal fusion

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Area of Science:

Ecology
Remote Sensing
Agricultural Science

Background:

Net Primary Productivity (NPP) is crucial for assessing ecosystem carbon sequestration and vegetation potential.
Accurate NPP estimation is vital for agricultural management and understanding regional carbon cycles.
Cloud cover often disrupts the temporal continuity of single-source optical remote sensing data.

Purpose of the Study:

To develop a robust method for estimating crop NPP with high spatial and temporal resolution.
To overcome limitations of cloud-induced data gaps in remote sensing.
To enhance the accuracy and stability of NPP estimation for agricultural crops.

Main Methods:

Utilized Sentinel-2 imagery and MODIS NDVI products.
Applied the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to create a continuous NDVI time series.
Integrated the Simple Ratio (SR) index and optimized the Carnegie-Ames-Stanford Approach (CASA) model with crop-specific parameters.

Main Results:

The fused NDVI effectively filled data gaps caused by clouds, aligning with crop growth patterns.
Improved Fraction of Photosynthetically Active Radiation (FPAR) using fused NDVI accurately reflected crop phenology.
The optimized CASA model significantly enhanced NPP estimation accuracy for maize and rice, with notable reductions in error metrics (e.g., R2 increased to 0.88 for maize, MAPE decreased to 23.43% for rice).

Conclusions:

Spatiotemporal fusion of remote sensing data creates highly continuous vegetation index time series.
Optimizing the CASA model with SR index and crop-specific parameters improves NPP estimation stability and accuracy.
The developed approach offers a reliable tool for agricultural NPP monitoring and carbon cycle studies.