Abstract
The adoption of climate-smart agriculture requires the comprehensive development of environmental monitoring tools, including online observation of climate and soil settings. They are often designed to measure soil properties automatically at different depths at hour or minute intervals. It is essential to have a complete dataset to use statistical models for the prediction of soil properties and to make short-term decisions regarding soil tillage operations and irrigation during a vegetation period. This is also important in applied hydrological studies. Nevertheless, the time series of soil hydrological measurements often have data gaps for different reasons. The study focused on solving a problem of gap-filling in hourly time series of soil temperature and moisture, measured at the 30 cm depth using a weighted gravitation lysimeter station while meteorological data were recorded simultaneously by a weather station. The equipment was installed in the Kulunda Steppe in the Altai Krai, Russia. Considering that climate conditions affect soil temperature and moisture content directly, we did a comparative analysis of the gap-filling performance using the three imputation methods—linear interpolation, multiple linear regression, and extended ARMA (p,q) models with exogenous climatic variables. The results showed that, according to the minimum of the mean absolute error, ARMA (p,q) models with optimally selected order parameters, and an adaptive window, had some advantages compared to other single-imputation methods. The ARMA (p,q) model produced a good quality of gap-filling in time series with the mean absolute error of 0.19 °C and 0.08 Vol. % for soil temperature and moisture content, respectively. The findings supplemented the methodology of hydrological data processing and the development of digital tools for the online monitoring of climate and soil properties in agriculture.
Highlights
The monitoring of soil temperature and moisture at different depths is vital in agriculture to plan seasonal soil tillage operations, soil water conservation, and evaluation of crop growing conditions
We compared various gapfilling methods in the time series of topsoil properties measured by the lysimeter station for two cases–arable land and natural steppe vegetation during vegetation periods of 2013
Despite the relatively low mean absolute error (MAE) of gap-filling and the simplicity of the method, linear interpolation cannot describe non-linear hourly dynamics of soil temperature and moisture, especially if a gap length is longer than 6 h
Summary
The monitoring of soil temperature and moisture at different depths is vital in agriculture to plan seasonal soil tillage operations, soil water conservation, and evaluation of crop growing conditions. Extreme soil temperature and low soil moisture negatively affect crop growth [1]. These soil properties significantly influence crop yield [2,3,4] and can cause physical damage and yield loss. The data on soil temperature and soil moisture help in estimating the soil heat flux, which can provide data for the analysis of water balance and actual evapotranspiration (ETa) [6,7,8], with water content being the primary indicator to assess crop water stress [6,9,10]. Soil temperature and moisture monitoring are critical because climate data do not provide complete information about soil properties affecting plant growth
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