长短期记忆网络在生态系统碳通量反演中的优势与应用

doi:10.12118/j.issn.1000-6060.2025.092

Abstract

Abstract:

This study proposes a carbon flux inversion model based on the long short-term memory (LSTM) network. A comprehensive Euclidean distance index is introduced by integrating FLUXNET flux tower observation data with corresponding remote-sensing biophysical parameter datasets to quantify data heterogeneity between training and testing sets. Furthermore, a fuzzy mathematics theory is incorporated to develop the inversion model. Models were developed using random forest, support vector machine, multiple linear regression, and LSTM algorithms through data preprocessing and model training. Results revealed that the LSTM network performed better than the other algorithms in carbon flux inversion. In addition, using the leave-one cross-validation strategy, many carbon flux machine learning models were developed to reflect the spatial heterogeneity of the surface, and the determination coefficient R² was used to evaluate the models. Results revealed that the comprehensive Euclidean distance was significantly negatively correlated with R². The constructed model was applied to the US flux station for verification, and the mean R² values of the total primary productivity and ecosystem respiration were both 0.72. Overall, this study proposed an effective carbon flux simulation method, which has good application potential.

Key words: machine learning, long short-term memory, remote sensing, carbon flux, fuzzy mathematics

GAO Ruixiang, LUO Geping, ZHANG Wenqiang, XIE Mingjuan, WANG Yuangang. Ecosystem carbon flux inversion method combining LSTM and fuzzy mathematics[J].Arid Land Geography, 2025, 48(12): 2210-2219.

Figures/Tables 9

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References 46

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干旱指数分区	站点数量
干旱区	15
半干旱区	44
半湿润区	20
湿润区	111

解释变量	描述	单位	时间/空间分辨率	来源
T_max	最高气温	℃	日	通量塔
T_min	最低气温	℃	日	通量塔
T_mean	平均气温	℃	日	通量塔
P	降水量	mm	日	通量塔
WS	风速	m·s^-1	日	通量塔
VPD	大气水气压差	hPa	日	通量塔
LAI	叶面积指数	m²	8 d/500 m	MODIS
DSR	向下短波辐射	W·m^-2	d/5 km	Seoul National
DEM	高程	m	静态变量	GLOBE from NOAA
Aspect	坡向	(°)	静态变量	GLOBE from NOAA
Slope	坡度	(°)	静态变量	GLOBE from NOAA
Clay	表土黏土占比	%	静态变量	HWSD from FAO
Sand	表土砂占比	%	静态变量	HWSD from FAO
Silt	表土粉砂占比	%	静态变量	HWSD from FAO

干旱指数分区	LSTM模型	RF模型	SVM模型	MLR模型
干旱区（GPP）	0.62（±0.37）	0.80（±0.20）	0.98（±0.55）	0.99（±0.32）
干旱区（ER）	0.78（±0.40）	0.99（±0.65）	1.01（±0.69）	1.17（±0.66）
半干旱区（GPP）	0.74（±0.50）	0.81（±0.52）	0.84（±0.33）	0.80（±0.51）
半干旱区（ER）	0.85（±1.13）	0.96（±1.07）	0.87（±0.76）	0.90（±0.91）
半湿润区（GPP）	1.06（±2.04）	1.31（±2.90）	1.29（±2.64）	2.99（±9.24）
半湿润区（ER）	0.80（±1.14）	0.84（±0.94）	0.90（±1.45）	1.95（±1.05）
湿润区（GPP）	0.61（±0.39）	0.63（±0.34）	0.78（±0.29）	0.70（±0.55）
湿润区（ER）	0.63（±0.45）	0.64（±0.51）	0.83（±0.46）	0.78（±0.70）

Ecosystem carbon flux inversion method combining LSTM and fuzzy mathematics

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