Simulation of Net Ecosystem Carbon Flux in Rice Planting Area of Yangtze River Delta based on Multi-layer Perceptron Model

Wenyang XI; Jianjun HE; Zhilin WANG; Lifeng GUO; Yarong LI

doi:10.7522/j.issn.1000-0534.2024.00056

2025 , Vol. 44 >Issue 1: 191 - 200

DOI: https://doi.org/10.7522/j.issn.1000-0534.2024.00056

Simulation of Net Ecosystem Carbon Flux in Rice Planting Area of Yangtze River Delta based on Multi-layer Perceptron Model

Wenyang XI ,
Jianjun HE ,
Zhilin WANG ,
Lifeng GUO ,
Yarong LI

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1. State Key Laboratory of Severe Weather，Chinese Academy of Meteorological Sciences/Key Laboratory of Atmospheric Chemistry，China Meteorological Administration，Beijing 200081，China
2. College of Marine Science and Engineering，Hainan University，Haikou 570228，Hainan，China
3. School of Computer Science and Software，Nanjing University of Information Science & Technology，Nanjing 210044，Jiangsu，China
4. School of Atmospheric Sciences，Lanzhou University，Lanzhou 730000，Gansu，China

Received date: 2024-03-19

Revised date: 2024-04-07

Online published: 2024-04-07

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Abstract

The Yangtze River Delta in China is a typical rice planting area， and its carbon source and sink have significant impacts on regional climate and environment.This study systematically examines the relationship between NEE and various meteorological factors in the Yangtze River Delta region and reveals that NEE exhibits the strongest correlation with solar short-wave radiation （R=-0.68）， followed by a robust linear association with humidity-related parameters （saturated water vapor pressure difference， relative humidity）.Additionally， diurnal variations are evident in the correlations between NEE and solar radiation， temperature， humidity factor， wind speed， and friction velocity.Based on these analyses， this paper constructed a multi-layer perceptron （MLP） model for simulating rice undersurface NEE in the Yangtze River Delta using observed NEE data alongside meteorological observations.The simulation performance and spatiotemporal stability of this model are evaluated.Results demonstrate that the constructed MLP model effectively captures NEE patterns； it achieves an R value of 0.88 with respect to observed values within the training set while maintaining an RMSE of 5.34 μmol·m^-2·s^-1.Moreover， this MLP model performs well when predicting NEE in the Yangtze River Delta region as evidenced by high correlation coefficients （>0.78） between simulated results and observations at Dongtai and Shouxian stations-indicating good spatiotemporal stability of the model's predictions.Notably， this MLP model demonstrates superior performance when capturing daily variations in daytime mean NEE compared to nighttime mean values.The research results reveal the main meteorological factors affecting rice carbon cycling， provide support for understanding the spatiotemporal distribution characteristics of carbon cycling in rice planting areas of the Yangtze River Delta， and have important significance for accurately evaluating global and regional carbon flux.

Key words： machine learning; multi-layer perceptron model; NEE; Yangtze River Delta; rice paddies

Cite this article

Wenyang XI , Jianjun HE , Zhilin WANG , Lifeng GUO , Yarong LI . Simulation of Net Ecosystem Carbon Flux in Rice Planting Area of Yangtze River Delta based on Multi-layer Perceptron Model[J]. Plateau Meteorology, 2025 , 44(1) : 191 -200 . DOI: 10.7522/j.issn.1000-0534.2024.00056

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