Mechanism, Modeling and Methodology Research of Inversion of Crop Physical and Chemical Parameters Through Quantitative Remote Sensing

To meet the urgent need for precision agricultural management, the study integrated 'area information' from remote sensing with the 'spot information' of the ground. A forecast study of agricultural precision variables, including fertigation and agricultural yield was conducted on scales of county, farm and paddy field. Based on spectral data and crop growth model assimilation, a method for precision variable-rate fertilization and benefit evaluation of crop was proposed. A winter wheat growth monitoring and yield forecast model based on hyperspectral data remote monitoring and crop growth model was established. The study established a remote monitoring model of winter wheat grain protein based on crop nitrogen translocation and water stress, providing a scientific basis for wheat classification and acquisition, processing and value-adding. Through measures such as providing thematic maps and remote sensing monitoring reports, the research has enhanced the application of spatial information technology in crop production layout, precise fertilization and decision-making of variable irrigation. The practice of the research has been promoted in provinces like Heilongjiang, Jilin, Hebei, Henan, Shandong, Jiangsu, Hunan and other major grain-producing areas in China, as well as in Beijing.

(1) The physiochemical parameters response mechanism of crops under the stress of different nutrient, water, pest and disease conditions was illustrated, and the inversion model of crops' physiochemical parameters such as nitrogen, water, chlorophyll and leaf area index was established.

Under the current research, the photosynthetically active radiation vertical distribution model in crop canopy was improved. The physiochemical parameters response mechanism of crops under the stress of different nutrient, water, pest and disease conditions was illustrated, and the inversion model of crops' physiochemical parameters such as nitrogen, water, chlorophyll and leaf area index was established. It proposed a new method and a diagnostic model for monitoring crop nutrient deficiency with the ratio of carotenoids to chlorophyll, which has improved the precision of remote sensing inversion of biochemical parameters, making it an achievement with guiding significance for crop fertilizer and water diagnosis research.

(2) Remote sensing inversion of crop structure parameters was carried out. A multi-angle and multi-phase remote sensing method for plant type identification was proposed to improve the precision of remote sensing inversion of leaf area index.

To deal with the influence of crop structure parameters on the inversion of light distribution and physical and chemical parameters vertical distribution in crop canopy, a mechanism model for kernel-driven crop geometry identification based on kernel drive was established. A calculation model for FPAR vertical distribution in canopy was constructed to improve the precision of leaf area index inversion.

(3) A remote sensing inversion method and model for crop chlorophyll and nitrogen nutrients vertical distribution were proposed, which preliminarily achieved early diagnosis of crop nutrition stress.

The symptom of early nitrogen deficiency in crops usually appears first in the lower leaves, while the traditional remote sensing method can only obtain the information of the upper canopy of crops. In the face of this problem, the research proposed a remote sensing multi-angle inversion for crop chlorophyll and nitrogen nutrients vertical distribution, which preliminarily achieved early diagnosis of crop nutrition stress. The research findings can provide a theoretical basis for designing the sensitive bands and best viewing angles of the new generation satellite.