| 摘要 |
The entire process of agricultural production is directly constrained by meteorological factors, and conditions such as temperature, precipitation, light, and humidity determine crop growth rhythms, final yields, and agricultural product quality. Traditional meteorological observation stations are sparsely deployed, and data have broad spatial representativeness, which can only reflect the average meteorological conditions of the region and cannot capture the microclimate differences between field plots, making it difficult to support the development needs of precision agriculture. Refined meteorological observation relies on automatic monitoring stations in farmlands, remote sensing inversion, grid numerical simulation and other methods to achieve small-scale, high-frequency, and multi-factor continuous monitoring, which can accurately depict the dynamic changes of micro meteorology in the field. In this paper, the core characteristics of refined meteorological observation data are summarized. Combines practical production scenarios such as agricultural sowing, field management, disaster prevention, irrigation and plant protection, this paper systematically elaborates on its application path, analyzes the current practical obstacles to its implementation, and proposes feasible optimization strategies, in order to provide practical reference for meteorological data to empower modern agriculture and ensure stable grain production. |
