Abstract

Soil temperature (T s ) is a vital meteorological parameter for ecological, physical and biological research. T s estimation is very important for a variety of fields and presents great challenges because the relevant areas have complex characteristics, human activities, and a nonlinear nature between T s and its environmental factors. Hence, a novel model based on long short-term memory (LSTM), is proposed here as an alternative data-intelligence tool. The proposed model designs a novel function that combines LSTM loss with an adversarial term for enhancing the correlation between T s and its environmental factors. For this purpose, we designed a novel function that combines LSTM loss with an adversarial term. The adversarial term encourages the LSTM model to estimate T s , which cannot be distinguished from the observed T s by an adversarial model. In this study, the proposed model is trained using meteorological information from two stations in China (Changbai Mountain and Haibei). The model is constructed using hourly input variables, including air temperature (T a ), wind speed (W), relative humidity (RH), solar radiation (SR), and vapor pressure deficit(VPD), while the objective variable is the T s measurement at a 5 cm depth for the period 2003-2005. Different statistical evaluation criteria, including the root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NS), Willmott index of agreement(WI) and the Legates and McCabe index (LMI), have been employed to assess the model performance. Through experimentation, the proposed model generally performs superior to the other seven state-of-the-art estimation models (autoregressive integrated moving average model, linear regression, backpropagation neural networks, support vector regression, extreme learning machine, eXtreme gradient boosting and long short-term memory) in T s estimation at a 5 cm depth over the Changbai Mountain and Haibei stations. For this case, the most accurate performance is attained for T s estimation at a 5 cm depth, with the highest values of NS = 0.915, WI = 0.978, and LMI = 0.686, and the lowest values of the relative RMSE and MAE being 2.276 and 1.796, respectively. In accordance with the present results, it is concluded that the proposed model can serve as an alternative approach for estimating T s , while ensuring that an appropriate combination of meteorological inputs is applied to yield an optimal model.

Highlights

  • Soil temperature (Ts) is a vital meteorological parameter for ecological, physical and biological research [1], and it is aThe associate editor coordinating the review of this manuscript and approving it for publication was Yassine Maleh .crucial variable for balancing the interaction of heat energy between the atmosphere and the land surface [2]

  • the outputs (Ts) is one crucial aspect to the variables that balance the interaction of heat energy between the atmosphere and the land surface, and Ts estimation is very important for a variety of fields; for example, it can be applied to investigate the soil characteristics that are required for better crop yield

  • We research the ability of linear regression (LR), artificial neural networks (ANNs), support vector regression (SVR), extreme learning machine (ELM), eXtreme gradient boosting (XGBoost), long short-term memory (LSTM) network and our GENERATIVE ADVERSARIAL NETWORKS (GANs)-LSTM model for estimating the hourly Ts at a 5 cm depth at the China Changbai Mountain and Haibei stations

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Summary

Introduction

Soil temperature (Ts) is a vital meteorological parameter for ecological, physical and biological research [1], and it is aThe associate editor coordinating the review of this manuscript and approving it for publication was Yassine Maleh .crucial variable for balancing the interaction of heat energy between the atmosphere and the land surface [2]. Soil temperature (Ts) is a vital meteorological parameter for ecological, physical and biological research [1], and it is a. Ts estimation at different depths has different. Q. Li et al.: GANs-LSTM Model for Soil Temperature Estimation From Meteorological contributions. Ts estimation depends on different environmental factors, such as air temperature, solar radiation, relative humidity, atmospheric pressure, wind speed and other surface characteristics [7], [8]. A number of studies about Ts estimation at different depths and depending on different environmental factors have been conducted [9]–[11]. Kisi et al [7] modeled the monthly Ts at four soil depths (5,10,50 and 100cm) according to Ta, Rs, RH, and wind speed(W ). Feng et al [13] modeled half-hourly Ts at four soil depths (2,5,10 and 20cm) from meteorological data, including Ta, W , RH, Rs, and vapor pressure deficit(VPD)

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