Crop yield remote sensing inversion method based on chlorophyll fluorescence mechanism

Abstract

The invention provides a crop yield remote sensing inversion method based on a chlorophyll fluorescence mechanism, and belongs to the technical field of agricultural crop growth monitoring, and the method comprises the steps: obtaining sunlight-induced chlorophyll fluorescence remote sensing data, photosynthetically active radiation data and crop type distribution data in the whole growth period of crops; and distinguishing the photosynthetic path according to the crop type distribution data, calculating by adopting a corresponding photoreaction mechanism model based on the obtained data to obtain the total primary productivity of the crop, obtaining the net primary productivity by combining the historical net primary productivity and the total primary productivity ratio, and obtaining the crop yield based on carbon content inversion. By utilizing coupling association between sunlight-induced chlorophyll fluorescence and a photosynthesis process, the problem that photosynthetic physiology in vegetation is difficult to effectively capture due to dependence on a static vegetation index in an existing crop technology is solved; the problem of how to accurately associate instantaneous sunlight-induced chlorophyll fluorescence remote sensing data with photosynthesis of crops in the whole growth period is solved.

Description

Technical Field

[0001] This invention belongs to the field of agricultural crop growth monitoring technology, and in particular relates to a remote sensing inversion method for crop yield based on chlorophyll fluorescence mechanism. Background Technology

[0002] Food security is a major strategic issue concerning people's livelihood. Accurate and rapid forecasting of grain production is crucial for the formulation of grain regulation policies. Traditional crop yield estimation methods mainly rely on manual field surveys, which have inherent defects such as low efficiency, high cost, and difficulty in achieving large-scale synchronous monitoring.

[0003] Although remote sensing technology has been widely used in yield prediction due to its macroscopic and rapid advantages, existing remote sensing-based solutions have significant limitations: First, they rely on traditional vegetation indices such as normalized difference vegetation index (NDVI) and leaf area index (LAI). These indices mainly reflect static structural information such as vegetation "greenness" and are slow to respond to the core physiological processes of crop photosynthesis, failing to effectively capture the intrinsic changes in photosynthetic function caused by environmental stress, resulting in unstable prediction accuracy during critical growth periods and under stress conditions. Second, while the emerging solar-induced chlorophyll fluorescence (SIF) can directly detect vegetation photosynthesis and is hailed as a "probe for photosynthesis," its application in yield prediction still faces key technical bottlenecks: SIF signals are transient, while crop yield is the result of the accumulation and distribution of photosynthetic products throughout the entire growth period. How to effectively correlate transient SIF signals with the cumulative yield over the entire growth period is a problem that existing methods have not solved well. Furthermore, the relationship between SIF and photosynthesis and final yield differs significantly among crops with different photosynthetic pathways (C3 and C4). Existing single models generally lack specific characterization of these physiological differences, resulting in poor model universality and limited inversion accuracy. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention provides a remote sensing inversion method for crop yield based on chlorophyll fluorescence mechanism. This method solves the problems of unstable crop yield inversion results caused by the reliance on traditional static vegetation indices, which make it difficult to accurately capture crop photosynthesis, and how to effectively and accurately correlate instantaneous sunlight-induced chlorophyll fluorescence remote sensing data with crop photosynthesis throughout its entire growth period.

[0005] To achieve the above objectives, the technical solution adopted in this invention is as follows: a remote sensing inversion method for crop yield based on chlorophyll fluorescence mechanism, comprising the following steps: Acquire remote sensing data on sunlight-induced chlorophyll fluorescence, photosynthetically active radiation, and crop type distribution in the target area throughout the entire crop growth period; Based on crop type distribution data, the photosynthetic pathway types of crops are distinguished, and the total primary productivity of crops is calculated using the corresponding light reaction mechanism model based on sunlight-induced chlorophyll fluorescence remote sensing data and photosynthetically active radiation data. Based on total primary productivity, the net primary productivity of crops is obtained by using the ratio of historical net primary productivity to total primary productivity. Crop yield is calculated by inversion based on the crop's net primary productivity and carbon content.

[0006] To address the shortcomings of existing remote sensing methods for crop yield estimation, which rely excessively on traditional vegetation indices reflecting vegetation structure, are slow to respond to crop photosynthetic physiological processes, and fail to effectively capture intrinsic changes in photosynthetic function caused by environmental stress, as well as the difficulties in correlating instantaneous signals with cumulative yield over the entire growth period and the lack of differentiation between different photosynthetic physiological mechanisms in crops with C3 and C4 photosynthetic pathways, this invention proposes a remote sensing inversion method for crop yield based on the chlorophyll fluorescence mechanism. By coupling satellite remote sensing data of sunlight-induced chlorophyll fluorescence with a mechanistic photoresponse model, this method uses remote sensing data of sunlight-induced chlorophyll fluorescence over the entire growth period as the main input, and adds photosynthetically active radiation data and crop type distribution data for calculation. This constructs a complete inversion process from sunlight-induced chlorophyll fluorescence remote sensing data to the final crop yield estimate. This method solves the problems of insufficient mechanistic rationality and poor universality in traditional crop yield estimation methods, achieving high-precision inversion of crop yield and providing reliable technical support for agricultural management and food security decision-making.

[0007] Furthermore: the expression for the total primary productivity (GPP) is as follows:

[0008]

[0009]

[0010]

[0011]

[0012]

[0013] in, For total primary productivity, It is the intercellular carbon dioxide concentration. This is the carbon dioxide concentration compensation point. The proportion allocated to electron transport between the mesophyll and bundle sheath. This represents the actual electron transfer rate from optical system II to optical system I. and All of these are photosynthetic pathways. For maximum photochemical quantum yield, The constitutive heat dissipation rate constant is The fluorescence rate constant is This is the ratio of the constitutive heat dissipation rate constant to the fluorescence rate constant. The degree of openness of the reaction center in the photosystem II process. To induce chlorophyll fluorescence under sunlight, The proportion of fluorescence quantum escape. and All are empirical fitting coefficients. For effective photosynthetic radiation, It is the shortwave solar radiation on the Earth's surface. Near-infrared reflectance of crop vegetation, The proportion of photosynthetically active radiation absorbed. Normalized Difference Vegetation Index (NDVI) This refers to the surface reflectance in the near-infrared band.

[0014] Furthermore, the expression for the net primary productivity (NPP) is as follows:

[0015] in, For net primary productivity, For total primary productivity, Net primary productivity in historical datasets, This represents the total primary productivity in the historical dataset.

[0016] Furthermore, the expression for the crop yield is as follows:

[0017] in, This represents the cumulative net photosynthetic productivity during the crop's growing season. The yield per unit area of ​​crop. For crop type, For carbon content, For the harvest index, It refers to the proportion of aboveground biomass to total biomass.

[0018] The beneficial effects of this invention are as follows: It overcomes the limitations of traditional vegetation indices, which only reflect structural information and ignore the lag in response to photosynthetic physiological processes. By directly linking the cumulative sunlight-induced chlorophyll fluorescence remote sensing data throughout the entire growth period to the photosynthetic electron transport process, it realizes dynamic monitoring of crop photosynthetic function and provides a more accurate basis for yield prediction. At the same time, this invention constructs a complete set of steps for estimating crop yield from sunlight-induced chlorophyll fluorescence remote sensing data, and designs differentiated light reaction mechanism models for different photosynthetic pathways of C3 and C4 crops, improving the model's practical crop rationality and applicability to different crop types. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the process for remote sensing inversion of crop yield based on chlorophyll fluorescence mechanism. Detailed Implementation

[0020] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0021] Example 1 like Figure 1 The diagram shows a flowchart of a remote sensing inversion method for crop yield based on chlorophyll fluorescence mechanism. This invention provides a remote sensing inversion method for crop yield based on chlorophyll fluorescence mechanism, comprising the following steps: Acquire remote sensing data on sunlight-induced chlorophyll fluorescence, photosynthetically active radiation, and crop type distribution in the target area throughout the entire crop growth period; Based on crop type distribution data, the photosynthetic pathway types of crops are distinguished, and the total primary productivity of crops is calculated using the corresponding light reaction mechanism model based on sunlight-induced chlorophyll fluorescence remote sensing data and photosynthetically active radiation data. Based on total primary productivity, the net primary productivity of crops is obtained by using the ratio of historical net primary productivity to total primary productivity. Crop yield is calculated by inversion based on the crop's net primary productivity and carbon content.

[0022] In one embodiment of the present invention, sunlight-induced chlorophyll fluorescence remote sensing data, i.e., SIF, is the spectral signal (650-800 nm) emitted by the photosynthetic center of a plant under sunlight conditions. It has two peaks: red light (approximately 690 nm) and near-infrared light (approximately 740 nm), which can directly reflect the dynamic changes of actual photosynthesis in plants and can be used for remote sensing observation of current vegetation. The present invention distinguishes crop photosynthetic pathway types, including C3 and C4, based on crop type distribution data. Plants with different photosynthetic pathways (C3 and C4) differ in environmental requirements and global change responses, and are generally considered two important plant functional types. C4 photosynthesis evolved from the C3 pathway as an adaptation to high light intensity, high temperature, and dryness. C4 plants have higher resource utilization efficiency and potential productivity. Therefore, there are differences in photosynthetic pathways and photosynthetic capacity between C3 and C4 crops, requiring the construction of different light reaction mechanism models for calculation.

[0023] In a specific embodiment of the present invention, the expression for constructing the light response mechanism model of C3 crops is as follows:

[0024] in, GPP For total primary productivity, It is the intercellular carbon dioxide concentration. This is the carbon dioxide concentration compensation point; The expression for constructing the light response mechanism model of C4 crops is as follows:

[0025] in, GPP For total primary productivity, The proportion allocated to electron transport between the mesophyll and bundle sheath. The actual electron transfer rate from optical system II to optical system I; The expression is as follows:

[0026]

[0027]

[0028]

[0029] in, For maximum photochemical quantum yield, The constitutive heat dissipation rate constant is The fluorescence rate constant is This is the ratio of the constitutive heat dissipation rate constant to the fluorescence rate constant. The degree of openness of the reaction center in the photosystem II process. To induce chlorophyll fluorescence under sunlight, The proportion of fluorescence quantum escape. and All are empirical fitting coefficients. For effective photosynthetic radiation, Shortwave solar radiation at Earth's surface (J / m 2 ), where 0.46 is an empirical coefficient, an energy conversion coefficient that represents the approximate proportion of the energy carried by the photosynthetically active radiation band in the solar spectrum to the total energy of the entire solar shortwave radiation (SSRD), which can be adjusted according to different regions and times.

[0030] Due to the reabsorption and scattering effects of crop leaves and canopy, the sunlight-induced chlorophyll fluorescence remote sensing data signal acquired by satellite sensors represents only a small portion of the total emission from the vegetation canopy. Therefore, it is necessary to calculate the fluorescence escape probability to retrieve the total canopy sunlight-induced chlorophyll fluorescence remote sensing data, i.e., the fluorescence quantum escape ratio. Its expression is as follows:

[0031] in, Near-infrared reflectance of crop vegetation, The proportion of photosynthetically active radiation absorbed. Normalized Difference Vegetation Index (NDVI) This refers to the surface reflectance in the near-infrared band.

[0032] In one embodiment of the present invention, the historical net primary productivity (NPP) to total primary productivity (TPP) ratio is obtained using historical NPP and TPP datasets. This ratio can be obtained from satellite remote sensing products such as MODIS (e.g., MOD17A2H, MOD17A3HGF). Based on this historical NPP to TPP ratio, the crop's NPP is derived. The expression for NPP is as follows:

[0033] in, For net primary productivity, For total primary productivity, Net primary productivity in historical datasets, This represents the total primary productivity in the historical dataset.

[0034] In one embodiment of the present invention, the expression for crop yield is as follows:

[0035] in, This represents the cumulative net photosynthetic productivity during the crop's growing season, i.e., the crop's yield. The yield per unit area of ​​crop. For crop type, For carbon content, For the harvest index, It refers to the proportion of aboveground biomass to total biomass.

[0036] The beneficial effects of this invention are as follows: It overcomes the limitations of traditional vegetation indices, which only reflect structural information and ignore the lag in response to photosynthetic physiological processes. By directly linking the cumulative sunlight-induced chlorophyll fluorescence remote sensing data throughout the entire growth period to the photosynthetic electron transport process, it realizes dynamic monitoring of crop photosynthetic function and provides a more accurate basis for yield prediction. At the same time, this invention constructs a complete set of steps for estimating crop yield from sunlight-induced chlorophyll fluorescence remote sensing data, and designs differentiated light reaction mechanism models for different photosynthetic pathways of C3 and C4 crops, improving the model's practical crop rationality and applicability to different crop types.

Claims

1. A remote sensing inversion method for crop yield based on chlorophyll fluorescence mechanism, characterized in that, Includes the following steps: Acquire remote sensing data on sunlight-induced chlorophyll fluorescence, photosynthetically active radiation, and crop type distribution in the target area throughout the entire crop growth period; Based on crop type distribution data, the photosynthetic pathway types of crops are distinguished, and the total primary productivity of crops is calculated using the corresponding light reaction mechanism model based on sunlight-induced chlorophyll fluorescence remote sensing data and photosynthetically active radiation data. Based on total primary productivity, the net primary productivity of crops is obtained by using the ratio of historical net primary productivity to total primary productivity. Crop yield is calculated by inversion based on the crop's net primary productivity and carbon content.

2. The crop yield remote sensing inversion method based on chlorophyll fluorescence mechanism according to claim 1, characterized in that, The expression for the Total Primary Productivity (GPP) is as follows: in, For total primary productivity, It is the intercellular carbon dioxide concentration. This is the carbon dioxide concentration compensation point. The proportion allocated to electron transport between the mesophyll and bundle sheath. The actual electron transfer rate from optical system II to optical system I. and All of these are photosynthetic pathways. For maximum photochemical quantum yield, The constitutive heat dissipation rate constant is The fluorescence rate constant is... This is the ratio of the constitutive heat dissipation rate constant to the fluorescence rate constant. The degree of openness of the reaction center in the photosystem II process. To induce chlorophyll fluorescence under sunlight, The proportion of fluorescence quantum escape. and All are empirical fitting coefficients. For effective photosynthetic radiation, It is the shortwave solar radiation on the Earth's surface. Near-infrared reflectance of crop vegetation, The proportion of photosynthetically active radiation absorbed. Normalized Difference Vegetation Index (NDVI) This refers to the surface reflectance in the near-infrared band.

3. The crop yield remote sensing inversion method based on chlorophyll fluorescence mechanism according to claim 1, characterized in that, The expression for the net primary productivity (NPP) is as follows: in, For net primary productivity, For total primary productivity, Net primary productivity in historical datasets, This represents the total primary productivity in the historical dataset.

4. The crop yield remote sensing inversion method based on chlorophyll fluorescence mechanism according to claim 1, characterized in that, The expression for crop yield is as follows: in, This represents the cumulative net photosynthetic productivity during the crop's growing season. The yield per unit area of ​​crop. For crop type, For carbon content, For the harvest index, It refers to the proportion of aboveground biomass to total biomass.

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