Abstract
Soil temperature (ST) plays an important role in agriculture and other fields, and has a close relationship with plant growth and development. Therefore, accurate ST prediction methods are widely needed. Deep learning (DL) models have been widely applied for ST prediction. However, the traditional DL models may fail to capture the spatiotemporal relationship due to its complex dependency under different related hydrologic variables. Hence, the DL models with Ensemble Empirical Mode Decomposition (EEMD) are proposed in this study. The proposed models can capture more complex spatiotemporal relationship after decomposing the ST into different intrinsic mode functions. Therefore, the performance of models is further improved. The results show that the performance of DL models with EEMD are better than that of corresponding DL models without EEMD. Moreover, EEMD-Conv3d has the best performance among all the experimental models. It has the highest R2 ranging from 0.9826 to 0.9893, the lowest RMSE ranging from 1.3096 to 1.6497 and the lowest MAE ranging from 0.9656 to 1.2056 in predicting ST at the lead time from one to five days. In addition, the lines between predicted ST and observed ST are closer to the ideal line (y = x) than other DL models. The results show that our EEMD-Conv3D can better capture spatiotemporal correlation and is an applicable method for predicting spatiotemporal ST.
Highlights
Soil temperature (ST) is one of the most important factors affecting the processes of soil properties involved in plant growth [1,2,3,4]
The results show that our Ensemble Empirical Mode Decomposition (EEMD)-Conv3D can better capture spatiotemporal correlation and is an applicable method for predicting spatiotemporal ST
The results show that the performance of the Deep learning (DL) models are significantly improved after combining with EEMD, and EEMD-Conv3D has the best prediction performance
Summary
Soil temperature (ST) is one of the most important factors affecting the processes of soil properties involved in plant growth [1,2,3,4]. It controls physical, chemical, and biological processes in soil and influences plant growth, development, and soil formation. ST plays an important role because it is one of the factors that contribute to seed germination, and most soil organisms need to function at optimal soil temperature. Various biochemical processes in soil, such as those caused by microbial activity and non-living chemical processes, are influenced by ST. Prediction methods are mainly focus on the timebased temperature series of a site, but spatiotemporal ST prediction has been little studied
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