Soil Moisture Monitoring and Evaluation in Agricultural Fields Based on NDVI Long Time Series and CEEMDAN

Abstract

The North China Plain is an important area for agricultural economic development in China. But water shortages, severe groundwater over-exploitation and drought problems make it difficult to exercise the topographic resource advantages of the plain. Therefore, the precise monitoring of soil moisture is of great significance for the rational use of water resources. Soil characteristics vary in natural farmland ecosystems, crops are constrained by multiple compound stresses and the precise extraction of soil moisture stress is a difficult and critical problem. The long time series was decomposed via complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain different intrinsic mode function (IMF) components, and the statistical descriptors of each component were calculated to realize the precise discrimination of soil moisture stress. A quantitative evaluation model of soil moisture was established, and the different noise addition ratios and modeling types were set respectively to investigate the optimal inversion model. The results showed that: (1) The reconstruction error of the CEEMDAN was small and almost 0; it had a high reconstruction accuracy and was more suitable for the decomposition of the long time series. The first two components, IMF1 and IMF2, were soil moisture stress subsequences, and it could effectively reflect the moisture stress situation. (2) The inversion model performed well when ε was 0.05 and the model type was quadratic, with a coefficient of determination R2 of 0.98, which gave a better fit and less error. (3) The overall soil moisture content in the study area was low, basically in the range of 6.9% to 15.7%, with the central part, especially the south-central part, being the most affected by soil moisture stress, and the overall impact of soil moisture stress showed a decreasing trend from February to May. The utilization of CEEMDAN further enhances the accuracy of soil moisture inversion in agricultural fields, realizing the effective application of remote sensing observation technology and time-frequency analysis technology in the field of soil moisture research.