Modeling Soil Temperature for Different Days Using Novel Quadruplet Loss-Guided LSTM.

Xuezhi Wang,Xiaoning Li,Qingliang Li,Wenhui Li

doi:10.1155/2022/9016823

Xuezhi Wang, Xiaoning Li + Show 2 more

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https://doi.org/10.1155/2022/9016823

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Abstract

Soil temperature (Ts), a key variable in geosciences study, has generated growing interest among researchers. There are many factors affecting the spatiotemporal variation of Ts, which poses immense challenges for the Ts estimation. To enrich processing information on loss function and achieve better performance in estimation, the paper designed a new long short-term memory model using quadruplet loss function as an intelligence tool for data processing (QL-LSTM). The model in this paper combined the traditional squared-error loss function with distance metric learning between the sample features. It can zoom analyze the samples accurately to optimize the estimation accuracy. We applied the meteorological data from Laegern and Fluehli stations at 5, 10, and 15 cm depth on the 1st, 5th, and 15th day separately to verify the performance of the proposed soil temperature estimation model. Meanwhile, this paper inputs the variables into the proposed model including radiation, air temperature, vapor pressure deficit, wind speed, air pressure, and past Ts data. The performance of the model was tested by several error evaluation indices, including root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NS), Willmott Index of Agreement (WI), and Legates and McCabe index (LMI). As the test results at different soil depths show, our model generally outperformed the four existing advanced estimation models, namely, backpropagation neural networks, extreme learning machines, support vector regression, and LSTM. Furthermore, as experiments show, the proposed model achieved the best performance at the 15 cm depth of soil on the 1st day at Laegern station, which achieved higher WI (0.998), NS (0.995), and LMI (0.938) values, and got lower RMSE (0.312) and MAE (0.239) values. Consequently, the QL-LSTM model is recommended to estimate daily Ts profiles estimation on the 1st, 5th, and 15th days.

Highlights

Soil temperature (Ts) is a main physical variable of the land surface, which has a direct influence on the atmosphere [1]
Several other advanced models based on data-driven technology (SVR, BackPropagation Neural Networks (BPNN), Extreme Learning Machine (ELM), and Long Short-Term Memory (LSTM)) were considered in Ts estimation
For the BPNN model, the square error is used as the loss function, and the optimization is Adam. e number of samples selected for the model is 400, the iteration is set to 500, the learning rate is set to 5.0e-4, and the size of the nodes is set to 128. e elm function was used to model the ELM model, the sigmoid served to activate the function in the hidden layer, and we set the same size of the nodes to BPNN

Summary

Introduction

Soil temperature (Ts) is a main physical variable of the land surface, which has a direct influence on the atmosphere [1]. Relevant fields including geoscience and forestry application aspects have drawn attention from researchers [2, 3]. All the interactions in terrestrial ecosystems are companied by Ts variations since they involve energy exchanges. The death of animals and plants produces plenty of carbon substrates and a high volume of greenhouse gases in the soil. It results in an increase in Ts, expediting carbon dioxide emission to the atmosphere [5]. Erefore, accurate Ts monitoring is crucial for agricultural management and atmosphere environment forecast. Ts data in most areas is still measured by using traditional sensors, and the Ts data cannot be collected at different depths [6]

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