A Retrieval Method of Leaf Potassium Concentration Based on Litchi Canopy Spectra

Abstract

The invention provides a leaf potassium concentration reversing method based on a litchi canopy spectrum. The leaf potassium concentration reversing method comprises the following steps: S1, selecting litchi leaf samples at the same or different growth stages for serving as training samples and evaluation samples, and actually measuring leaf potassium concentration values of the samples; S2, constructing a potassium concentration reversing spectrum index set by using a current potassium concentration reversing spectrum index and a combination form spectrum index; S3, screening data in the potassium concentration reversing spectrum index set through a remarkable correlation coefficient and a Pearson correlation coefficient; S4, constructing a potassium concentration reversing model by using a multiple regression method; S5, evaluating the potassium concentration reversing model through a model determining coefficient and a root-mean-square error. By adopting the leaf potassium concentration reversing method, on the basis of angle analysis of litchi canopy spectrum reflectivity variation, and the correlation between reflectivity variation of an inner canopy and the potassium concentration variation in different growth periods, a simple linear potassium concentration reversing model is provided, and the model has a high determining coefficient, is simple, and is easy to transplant.

Description

technical field [0001] The patent of the present invention relates to a leaf potassium concentration inversion method based on litchi canopy spectrum, and relates to the field of inversion technology. Background technique [0002] In recent years, the development of spectral and remote sensing technology has made many achievements in the retrieval of crop biochemical parameters such as moisture, LAI (Leaf Area Index), chlorophyll, biomass, etc., and based on spectral and remote sensing technology, the retrieval of crop nitrogen The method and mechanism were studied. However, there are relatively few studies on the spectrum and remote sensing of potassium. Some scholars conducted experiments by artificially applying different amounts of potassium fertilizers, and found that the spectral changes of leaves are related to the amount of potassium fertilizers, and pointed out some bands and models for potassium detection. However, these studies are mainly based on leaf reflectanc...

AI Technical Summary

Problems solved by technology

However, there are relatively few studies on the spectrum and remote sensing of potassium. Some scholars conducted experiments through the artificial application of different amounts of potassium fertilizers, and found that the spectral changes of leaves are related to the amount of potassium fertilizer application, and pointed out some bands and models for potassium detection.

However, these studies are mainly based on leaf reflectance spectra, and the potassium estimation method is mainly based on the partial least squares regression method, which has a complex model and poor portability. Some researchers pointed out that the growth period and variety are important factors affecting the remote sensing of crops; different growth periods, changes in crop canopy structure and biochemical parameters have an important impact on the retrieval of nutrient elements

These reasons make it inconvenient to retrieve potassium at the image scale, so it is necessary to explore a simple and portable potassium retrieval model based on canopy litchi spectra

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