Remote sensing estimation method for SPAD value of rape leaves in different growth periods

Abstract

The invention discloses a remote sensing estimation method for the SPAD value of rape leaves in different growth periods. The method comprises the following steps: by taking commercial crop rapes in northwest regions as research objects, measuring the spectral reflectivity and SPAD value of rape leaves in each growth period; analyzing the relativity between 10 types of spectral indexes and the SPAD value of the rape leaves; constructing an SPAD random forest regression estimation model of the rape leaves based on the spectral indexes, and verifying the constructed model; and comparing the constructed model with a traditional simple linear regression model and a traditional multiple stepwise regression model. Modeling and verifying R2 respectively reach 0.91 and 0.74 or above, the verifying RMSE is between 1.571 and 5.004, RE is between 2.66% and 13.22%, and thus an optimal estimation model of the SPAD of the rape leaves can be obtained.

Description

technical field [0001] The invention belongs to the technical field of agriculture and relates to a remote sensing method for estimating SPAD values ​​of rapeseed leaves at different growth stages. Background technique [0002] Chlorophyll is an important substance for energy exchange between plants and the outside world. Since there is a good correlation between chlorophyll and leaf nitrogen content, it can characterize the nutritional status of plants. Hyperspectral technology has the advantages of wide spectral range, multiple bands, and large data volume. It can be used to monitor the biochemical components of crop leaves and canopies. In recent years, domestic and foreign scholars have made great efforts to use hyperspectral data to monitor plant chlorophyll content. A large number of studies, Dash.J et al. proposed to use MERID data sensitive to high chlorophyll density MTCI index to invert chlorophyll content; Gitelson chose corn and soybean as research objects, and e...

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Problems solved by technology

With the development of hyperspectral technology, some scholars have applied the random forest algorithm to vegetation hyperspectral remote sensing. However, taking rapeseed as the research object and using the random forest algorithm to construct a leaf chlorophyll hyperspectral inversion model has not been reported; at the same time, due to Different regions and different crop types will have significant differences in the sensitive bands of leaf chlorophyll, and the applicability of spectral indices will also vary.

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