Winter wheat dynamic harvest index remote sensing estimation method obtained based on D-fG parameter remote sensing

Abstract

The invention discloses a D-fG parameter remote sensing acquisition-based winter wheat dynamic harvest index remote sensing estimation method, which is based on a winter wheat field control experiment and provides a dynamic parameter D-fG of a ratio between overground biomass accumulated in different periods from a flowering period to a mature period and overground biomass in a corresponding period. A sensitive wave band center and an optimal wave band combination for D-fG estimation are screened out according to a correlation between a narrow wave band spectral index NDSI constructed by crop canopy hyperspectral data and D-fG, so that accurate estimation of D-fG by using the canopy hyperspectrum is realized. Finally, based on D-fG parameter remote sensing information and a quantitative relation between actually-measured D-fG and an actually-measured dynamic crop harvest index (D-HI), optimal estimation of the winter wheat crop harvest index based on canopy hyperspectral remote sensing is realized, so that the estimation precision and level of the winter wheat harvest index are further improved, and the method is suitable for popularization and application. And a theoretical basis and a technical reference are provided for high-precision acquisition of the crop harvest index by utilizing a remote sensing satellite technology in a large range.

Description

technical field [0001] What the present invention relates to is a kind of based on canopy hyperspectral sensitive band D-f G Remote sensing estimation method of dynamic harvest index of winter wheat obtained from parameter remote sensing. Background technique [0002] Harvest Index (HI), also known as economic coefficient, refers to the ratio of economic yield (grain, fruit) to biological yield when a crop is harvested, and the index reflects the distribution ratio of assimilated products between grain and vegetative organs. For food crops (such as wheat, corn, etc.), the harvest index is the percentage of grain yield to aboveground biological yield, where aboveground biological yield refers to the total dry matter above ground (Donald, 1962; Donald and Hamblin, 1976; Pan Xiaohua et al., 2007). Since the crop harvest index plays an important role in crop yield simulation and estimation (Fan et al., 2017; Hu et al., 2019; Lorenz et al., 2010), crop variety selection (Hay, 1...

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Problems solved by technology

The above methods are simple and easy to implement, and the required time series of remote sensing data is relatively short and easy to obtain, which is conducive to the practical application of the method. However, the above methods are only aimed at the estimation of the harvest index at the mature stage, and none of them realize the dynamic change process index information of the harvest index change. acquisition, which needs to be further strengthened

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