Abstract
While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China’s grasslands. The four models were trained with two strategies: training for all of northern China’s grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China’s grasslands fairly well, while the SAE model performed best (R2 = 0.858, RMSE = 0.472 gC m−2 d−1, MAE = 0.304 gC m−2 d−1). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.
Highlights
Ecosystem respiration (RE) is a major flux in the global carbon cycle
We systematically evaluated and compared three traditional machine learning (ML) models and a deep learning (DL) model in terms of estimating ecosystem respiration (RE) in northern China’s grasslands
Our results show that all four ML models estimated RE in the grasslands of northern China fairly well, while the stacked autoencoders model performed best (R2 = 0.858, root mean squared error (RMSE) = 0.472 gC m−2 d−1, mean absolute error (MAE) = 0.304 gC m−2 d−1)
Summary
Ecosystem respiration (RE) is a major flux in the global carbon cycle. Small changes in RE can have a significant impact on the atmospheric CO2 concentration and be a potentially positive feedback mechanism to the warming climate [1,2]. Jian et al [11] obtained global Rs using different timescales of Rs and climate data with the RF model. These ML models have been successful in estimating RE at different temporal and spatial scales, some uncertainties still exist. These ML models are usually constructed based on different learning principles; few attempts have been made to compare the predictive performance of these models in estimating RE [12]. RE under different environmental conditions may have different responses to climate change [13,14], and whether separately training ML models for each ecosystem type can improve performance is unclear. Systematic evaluation and comparison of the performance of these ML models in estimating RE are essential
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