Causal Research on Soil Temperature and Moisture Content at Different Depths

Abstract

The soil system is complex and dynamic, making it difficult to understand using traditional statistical approaches. In this paper, we analyze the causal relationship of soil temperature and moisture content at different depths in summer and winter based on dynamic empirical modelling. Specifically, we describe the complexity of soil temperature and moisture content system through mathematical methods. Moreover, we demonstrate the direction and magnitude of causal relationship between soil moisture content and temperature at different depths by equation-free methods. Besides, we describe the difference of soil system properties in summer and winter through causal research. The experiments show that results obtained are consistent with the actual soil environment. The causality is described by dynamic empirical modelling rather than prior soil knowledge. The paper may provide a new idea for soil dynamics research.

Keywords

• Machine learning
• soil science
• causal research
• dynamic empirical modelling