Abstract:

Soil moisture plays a crucial role in the study of agricultural drought monitoring, yield prediction, and soil erosion, which are of great significance for agriculture, drought, and climate studies. Automatic soil moisture observation instruments have become an important component of the automatic soil moisture observation stations run by the meteorological department in China given their high precision, high spatial and temporal resolution, and nondestructive capabilities. The accuracy of data obtained through the process of observing soil moisture is seriously affected by the calibration methods used, the stability of the equipment, and the texture of the soil. Thus, it is especially important to establish a quality control method for automatic soil moisture observation data that are free from these error sources that affect the observation quality. In attempting to solve the outstanding quality problems in automatic soil moisture observation data, this paper studies the inherent variation characteristics of soil moisture on the basis of the data obtained by the automatic soil moisture observation stations in China between 2013 and 2015. Combined with the instrument observation principle and the data characteristics and error sources of abnormal data, this study classifies and statistically analyzes the form of the abnormal data, and given the threshold, it puts forward a preliminary practical set of quality control methods for the hourly soil moisture observation data, which includes frequency detection (FD), threshold detection for volume moisture content and relative humidity, break drop detection, sudden change detection, and constant detection. The application effect of this quality control method has been verified using data obtained in China in 2019. The results show that (1) the four main categories of abnormal data are gross errors, mutations, abnormally high values, and stiffness; these are mainly caused by instrument failure, abnormal soil hydrological constants, and unreasonable calibration of sensors. (2) Based on the FD method, which analyzes the change characteristics through the frequency values measured by the sensors in the air and water, this quality control method can effectively detect the abnormal data caused by instrument failure, with the accuracy rate reaching 100%. (3) The evaluation results from the hourly data of the national automatic soil moisture stations from June to September 2019 show that the accuracy rate of data is 93.8%, the abnormal rate is 1.7%, and the missing rate is 4.5%. The abnormal data are mainly distributed in the Qinghai, Sichuan, Shandong, Hebei, and Guangdong Provinces. The soil surface layer (0-20 cm) has a higher proportion of anomalous data than that of other layers, and only a few stations have an anomaly within the whole observation layer. Although the abnormal phenomena can be observed at most sites across the country, the primary problem is that a small number of sites have long-term abnormalities, which have been caused either by the calibration of the sensor or the inaccurate determination of the soil’s hydrological and physical constants. (4) This quality control method can effectively detect abnormal data in China. Presently, it has been applied to the Integrated Meteorology Observation Data Quality Control System.

Key words: automatic soil moisture observation, quality control, abnormal data, instrument observation principle