Abstract:
Soil fertility is one of the most important elements required for crop growth. The specific level of soil fertility directly affects the growth and even the yield of crops. However, the development of crop growth depends mainly on a variety of stress factors. Therefore, it is very necessary to avoid the interference of stress factors in the monitoring of soil fertility. The purpose of this study was to accurately identify and monitor the soil fertility stress using Ensemble Empirical Mode Decomposition (EEMD). The research area was taken as the winter wheat cultivated land in the central latitude and longitude of 116.98°E and 39.88°N of the Dachang Hui Autonomous County, Langfang City, Hebei Province, China. The total area was 176.29 km2, among which the cultivated land accounted for 11 581.54 hm2. The landform was mainly plain with mostly tidal brown or tidal soil. The decomposed Intrinsic Mode Function (IMF) components were synthesized using EEMD, according to the annual, inter-annual and intra-annual scales. Different time scales and stress characteristics were combined to eliminate the stress of soil water, pest, and heavy metal, in order to obtain the effective screening and extraction of soil fertility stress. The organic matter, total nitrogen, available phosphorus, and available potassium were also collected from the field soil samples in each planting area of winter wheat. A principal component analysis was performed on the four nutrient indexes to convert them into the three principal components. A simple model was preliminarily obtained for the comprehensive evaluation of soil fertility. Finally, a quantitative evaluation model was established for the comprehensive level of soil fertility after fitting with the decomposition. The results showed that: 1) The components were mainly divided into three groups of fluctuation components after EEMD. Among them, the inter-annual fluctuation component better reflects the effect of soil fertility stress on crop growth in the study area. 2) The principal component analysis of the four indicators showed that the accuracy of the overall expression with the first three principal components fully met the needs, where the cumulative contribution rate reached 99.16%. The Least Square Method (LSM) was used to fit the soil nutrient index data. The correlation coefficient between the fitting and the original data reached 0.857, indicating the better representative for the change level of the original data. 3) The average error between the evaluation and the measurement was 11.82%, indicating that the predicted performance of the model was in better agreement with the actual situation. Therefore, the inversion of the model can be expected to better reflect the soil fertility level in the study area. The effective screening of soil fertility stress was achieved to quantitatively evaluate the soil fertility level using remote sensing image data.