Planting system diversity drawing method based on crop structure function theory framework

Abstract

Aiming at a series of challenges such as category imbalance, reference sample scarcity and weak space-time migration ability of a model algorithm, the invention jumps out of a data-driven model framework, and provides a planting system diversity remote sensing mapping method based on a crop structure function theory framework by exploring a crop growth mechanism and process. According to the method, a crop plant type-crop component-crop photosynthesis multi-level attribute framework is constructed, multi-dimensional time series data such as pigments, nitrogen, photosynthetic activity ratio, leaf area index and radar backscattering coefficient are fused, and high crown ratio, physiologically active substance ratio and unit leaf area photosynthetic efficiency index are designed in sequence; and establishing a space distribution integrated information extraction technology of different planting system types, further designing a planting system diversity index, and obtaining a planting system diversity space distribution diagram of the research area. The method has interpretability and cross-domain and cross-scale migration capability, and provides a key technology and a solution for obtaining agricultural condition information with time-space consistency for areas with complex and diversified planting types.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural remote sensing, in particular to a method for mapping the diversity of planting systems based on the theoretical framework of crop structure and function. Background Art

[0002] The information of crop planting systems, including crop maturity and crop types, etc., is an important support for the optimization of agricultural structure and macro management. The diversity of planting systems is an important direction for the development of modern agriculture. By scientifically and reasonably designing and implementing diverse planting systems, it can not only improve the stability and production efficiency of the agricultural ecosystem, but also promote the green transformation and sustainable development of agriculture. Timely and accurately obtaining the diversity of planting systems through remote sensing technology is of great significance for scientifically guiding the optimization and adjustment of agricultural structure and ensuring the realization of the food security strategic goal.

[0003] Remote sensing mapping of the diversity of planting systems faces a series of challenges such as class imbalance, scarce sample data, and weak spatio-temporal migration ability of model algorithms, especially in areas with complex and diverse planting types. Crop remote sensing classification is the basis for remote sensing mapping of the diversity of planting systems. Crop remote sensing classification methods mainly include data-driven methods and knowledge-based crop recognition methods. Machine learning or deep learning methods, as representative data-driven methods, usually face a series of challenges such as crop class imbalance and scarce crop sample data in areas with complex and diverse planting types, especially the extremely scarce sample data of minor crops. Due to a series of problems such as small samples, no samples, and sample imbalance, it is difficult for data-driven classification model algorithms to effectively identify minor crops, and at the same time, the spatio-temporal migration ability of the model algorithms is weak. Knowledge-based crop recognition methods mainly carry out crop thematic mapping by designing crop indices for a certain crop type, and currently still lack an integrated information extraction method for multiple crop types.

[0004] The present invention breaks out of the data-driven model framework. By exploring the growth mechanism and process of crops, a method for remote sensing mapping of the diversity of planting systems based on the theoretical framework of crop structure and function is proposed. By constructing a multi-level attribute framework of crop plant type - crop composition - crop photosynthesis, integrating multi-dimensional time-series data such as pigment, nitrogen, photosynthetic activity ratio, leaf area index, and radar backscattering coefficient, designing indices of high crown ratio, ratio of physiological active substances, and photosynthetic efficiency per unit leaf area, establishing an integrated information extraction technology for the spatial distribution of different planting system types, and further designing a diversity index of planting systems, a spatial distribution map of the diversity of planting systems in the study area is obtained. The mapping framework of the diversity of planting systems constructed by the present invention has interpretability and cross-domain and cross-scale migration ability, providing key technologies and solutions for obtaining spatio-temporally consistent agricultural situation information in areas with complex and diverse planting types. Summary of the Invention

[0005] The present invention proposes a method for mapping the diversity of cropping systems based on the theoretical framework of crop structure and function. Based on the theoretical framework of structure and function, a multi-level attribute framework of crop plant type - crop composition - crop photosynthesis is proposed to realize a remote sensing mapping method for the diversity of cropping systems. This method does not require a large number of training samples, can achieve multi-crop hierarchical classification and high-precision automatic mapping of different planting patterns, and obtain the diversity of cropping systems on this basis.

[0006] The present invention adopts the following technical solutions.

[0007] A method for mapping the diversity of cropping systems based on the theoretical framework of crop structure and function includes the following steps:

[0008] Step S01: Establish a time-series dataset of radar and optical images and obtain the crop growth period for each pixel;

[0009] Step S02: Design a crop high-crown ratio index based on the optical radar response characteristics of different plant types;

[0010] Step S03: Design a crop plant type discrimination index based on the crop high-crown ratio index;

[0011] Step S04: Establish a dataset of pigment content during the crop growth period;

[0012] Step S05: Design a ratio index of crop physiological active substances;

[0013] Step S06: Construct an index of photosynthetic efficiency per unit leaf area of crops;

[0014] Step S07: Design a crop photosynthetic capacity discrimination index based on the index of photosynthetic efficiency per unit leaf area;

[0015] Step S08: Construct a remote sensing mapping method for cropping systems based on the theoretical framework of crop structure and function;

[0016] Step S09: Design an evaluation index for the diversity of cropping systems in the study area;

[0017] Step S10: Create a spatial distribution map of the diversity of cropping systems in the study area.

[0018] Specifically, in step S01, on the remote sensing cloud platform, all Sentinel-1 SAR and Sentinel-2 MSI image data that meet the conditions are selected according to the scope of the study area during the study period; data preprocessing such as image mosaicking and speckle filtering is performed on the Sentinel-1 VV radar time-series data;

[0019] In the step S01, for the Sentinel-2 MSI original image data, image mosaicking, cloud removal, cropping, linear interpolation, and Whittaker Smoother temporal smoothing processing are performed to obtain a 10-day maximally synthesized Sentinel-2 MSI image dataset for the study area. Using the Sentinel-2 MSI image dataset, multi-dimensional spectral index time series datasets of vegetation, soil, pigment, and nitrogen are established, including the Enhanced Vegetation Index 2 (EVI2), Bare Soil Index (DBSI), Red Edge Position (REP), Anthocyanin Reflectance Index (ARI), Carotenoid Reflectance Index (CRI), Normalized Difference Nitrogen Index (NDNI), Green Vegetation Leaf Area Index (LAIgreen), and Photosynthetic Vitality Ratio Index (PVR). The calculation formulas are as follows:

[0020]

[0021] Among them, NIR represents the reflectance of the near-infrared band, Red represents the reflectance of the red band, Green represents the reflectance of the green band, Blue represents the reflectance of the blue band, SWIR1 represents the reflectance of the short-wave infrared band, VRE1, VRE2, and VRE3 respectively represent the reflectances of three red-edge bands, and log represents the logarithmic operation. In the step S01, the crop growth peak period is dynamically obtained pixel by pixel. Using the EVI2 time series curve of vegetation index, the date corresponding to the maximum value of the time series curve is obtained, and this date is determined as the crop growth peak period; 60 days before and 40 days after the crop growth peak period are respectively determined as the crop emergence period and the harvest period.

[0022] The step S02 is specifically as follows. The crop canopy and plant height change continuously with the growth and development of the crop. Under the same canopy width, the higher the plant, the stronger the VV vertical polarization radar backscattering coefficient; based on the change range of the soil index during the crop growth period, the change process of the crop canopy width is characterized; based on the change range of the VV vertical polarization radar backscattering coefficient during the crop growth period, the change range of the crop plant height is characterized; by fusing the optical and radar time series features and comprehensively considering the variability characteristics of the soil index and the VV vertical polarization radar backscattering coefficient during the crop growth period, a crop height-to-canopy ratio index HCR is designed, and its calculation formula is:

[0023]

[0024] Among them, VV t+1 and VV t respectively represent the radar backscattering coefficients on the (t + 1)-th day and the t-th day, DBSI t and DBSI t+1 respectively represent the bare soil indices on the (t + 1)-th day and the t-th day, a and b are adjustment coefficients, and Start and Heading respectively represent the crop emergence period and the growth peak period.

[0025] Specifically, step S03 is to discriminate the plant type of crops according to the crop height-to-crown ratio index HCR. Based on different numerical ranges of the crop height-to-crown ratio index, the plant types of crops are successively divided into prostrate type, dwarf type, and tall type. The discrimination formula is as follows:

[0026]

[0027] Among them, T 1 is a characteristic variable constructed based on the crop height-to-crown ratio index HCR, and f 1 represents the classification function under the feature of T 1 . In the function, 1, 2, and 3 represent prostrate type, dwarf type, and tall type crops respectively. θ 1 , θ 2 , θ 3 and θ 4 are adjusted according to different actual application areas.

[0028] Specifically, step S04 is to select the REP, ARI, and CRI indices to reflect the contents of chlorophyll, anthocyanin, and carotenoid in crops respectively. In order to eliminate the influence of dimensions, the REP, ARI, and CRI index datasets in the study area are normalized to obtain the normalized chlorophyll, anthocyanin, and carotenoid indices, namely CHL, ANTH, and CAR in sequence. During the study period, the REP, ARI, and CRI index datasets are normalized period by period, so as to obtain the normalized CHL, ANTH, and CAR time series datasets in the study area. The normalization calculation formulas of the REP, ARI, and CRI indices are as follows:

[0029]

[0030] Among them, REP min , ARI min , and CRI min represent the minimum values of REP, ARI, and CRI in the study area respectively, and REP max , ARI max , and CRI max represent the maximum values of REP, ARI, and CRI in the study area respectively.

[0031] Specifically, step S05 is that during the whole life cycle of crops from sowing and germination to mature harvesting, according to the main organ types and durations covered by the canopy, it can be divided into leaf canopy type, ear canopy type, and flower canopy type. For different types of crop canopies, there are significant differences in the contents of physiological active substances such as crop pigments and nitrogen. Based on the chlorophyll, anthocyanin, carotenoid, and nitrogen indices, the crop physiological active substance ratio PASR index is designed, and its calculation formula is:

[0032]

[0034] Among them, ANTH mean represents the mean value of ANTH during the growth period, CAR mean represents the mean value of CAR during the growth period, CHL mean represents the mean value of CHL during the growth period, |NDNI mean | represents the absolute value of the mean value of NDNI during the growth period.

[0035] According to the significant differences in the ratios of physiological active substances of different crops, the crops are divided into different categories, which is expressed by the formula:

[0036]

[0037] Among them, T 2 represents the characteristic variable constructed based on the physiological active substance ratio PASR of the crop, f 2 represents the classification function under the characteristic of T 2 In the function, 1, 2, and 3 represent leaf canopy type, spike canopy type, and flower canopy type crops respectively. θ 1 , θ 2 , θ 3 and θ 4 are adjusted according to different actual application areas.

[0038] The specific content of step S06 is as follows: The photosynthetic vitality ratio index PVR reflects the activity degree of leaf photosynthesis. For plants with a high PVR, the activity degree of their leaf photosynthesis is higher; the leaf area index LAIgreen reflects the leaf coverage ratio and the overlap degree of the canopy leaves. When the ground is completely covered by crops, an increase in LAIgreen reveals the overlap degree of the crop leaves; by combining the PVR and LAIgreen indices, the photosynthetic efficiency per unit leaf area index LABPE is designed to evaluate the photosynthetic efficiency capabilities of different crop types; the calculation formula of LABPE is as follows:

[0039] In the formula, PVR represents the photosynthetic vitality ratio index, and LAIgreen represents the leaf area index of green vegetation.

[0040] The specific content of step S07 is as follows: During the growth peak of crops with strong photosynthetic ability, the photosynthetic efficiency is high, and the leaf overlap degree is low, and the value of the leaf area index LAIgreen is low. Therefore, the photosynthetic efficiency per unit leaf area is high, and its photosynthetic efficiency per unit leaf area LABPE is high; compared with crops with strong photosynthetic ability, during the growth peak, the photosynthetic efficiency of crops with weak photosynthetic ability is relatively low, and the leaf overlap degree is high. Therefore, the photosynthetic efficiency per unit leaf area LABPE is low; based on the photosynthetic efficiency per unit leaf area index, a discriminant process for crop photosynthetic ability is designed, and its expression is:

[0041]

[0042] Among them, LABPE Heading represents the LABPE value during the vigorous growth period, and T 3 is a characteristic variable constructed based on the photosynthetic efficiency per unit leaf area of crops (LABPE), and f 3 represents the classification function under the characteristic of T 3 ; Heading represents the time point of the vigorous growth period of crops. In the function, 1 and 2 respectively represent crops with weak photosynthetic ability and crops with strong photosynthetic ability. θ 1 , θ 2 and θ 3 are adjusted according to different actual application areas.

[0043] The specific content of step S08 is to construct a remote sensing mapping method for cropping systems based on the theoretical framework of crop structure and function. Based on the theoretical framework of structure and function, a multi-level attribute framework of crop plant type - crop composition - crop photosynthesis is constructed, and a mapping method for the diversity of cropping systems based on the theoretical framework of crop structure and function is designed. The hierarchical structure of the method and its classification process are described as follows:

[0044] First, regarding the crop plant type, different crop plant types are divided based on the ratio of the change range of plant height and canopy width during the crop growth process; according to the designed crop height - canopy ratio index HCR, the crop plant types are successively divided into prostrate type, dwarf type, and tall type. The discrimination basis is as follows: for prostrate type crops, the canopy structure is close to the ground and low - lying, and the HCR value is small; for dwarf type crops, the plant height is relatively low and the canopy width is relatively small, and the HCR value is medium; for tall type crops, the stem is tall and the canopy width is relatively small, and the HCR value is large. The expression for using HCR to discriminate plant type is:

[0045]

[0046] Among them, represents different crop types, and T 1 is a characteristic variable constructed based on the crop height - canopy ratio index HCR, and f 1It is a classification function. 1, 2, and 3 represent creeping, dwarf, and tall crops respectively. On this basis, different crop types are further distinguished according to crop components. According to the designed crop physiological active substance ratio (PASR), crops are divided into leaf canopy type, ear canopy type, and flower canopy type; the discrimination basis is as follows: for leaf canopy type crops, the PASR value is small because the chlorophyll and nitrogen content are high during their growth period, while the carotenoid and anthocyanin content are low; for flower canopy type crops, the PASR value is large because the chlorophyll and nitrogen content are low during their growth period, while the carotenoid and anthocyanin content are high; the PASR value of ear canopy type crops is between that of leaf canopy type and flower canopy type; therefore, the crop physiological active substance ratio (PASR) can be used to discriminate crop types, and its expression is:

[0047]

[0048] Among them, represents different crop types, T 2 represents the feature constructed based on the crop physiological active substance ratio (PASR), f 2 is the classification function. 1, 2, and 3 represent leaf canopy type, ear canopy type, and flower canopy type crops respectively.

[0049] Finally, crop types are discriminated according to the crop photosynthesis efficiency; according to the designed leaf area-based photosynthetic efficiency index (LABPE) of crops, crop types are further divided into weak photosynthetic capacity crops and strong photosynthetic capacity crops; the discrimination basis is as follows: compared with weak photosynthetic capacity crops, strong photosynthetic capacity crops have higher photosynthesis efficiency and higher leaf area-based photosynthetic efficiency (LABPE) of crops; the expression for crop classification and discrimination using LABPE is:

[0050]

[0051] Among them, represents different crop types, T 3 represents the characteristic variable constructed based on the leaf area-based photosynthetic efficiency (LABPE) of crops, f 3 is the classification function. 1 and 2 represent weak photosynthetic capacity crops and strong photosynthetic capacity crops respectively.

[0052] In step S08, for multiple cropping systems, crop discrimination is carried out according to the cropping system information for each growth period, so as to obtain the crop planting system information of the pixel in the study year.

[0053] Specifically, step S09 is to comprehensively count the number of cropping system types in the study area and their corresponding areas, and then design an evaluation index for the diversity of cropping systems to evaluate the diversity degree of cropping systems in the study area. The calculation basis of the evaluation index for the diversity of cropping systems is as follows: the more cropping system types in the study area and the more balanced the proportion of the corresponding distribution areas, the higher the diversity degree of cropping systems in the study area; the larger the value of the PDI of the evaluation index for the diversity of cropping systems, the higher the diversity degree of cropping systems in the study area. The calculation formula is as follows:

[0054]

[0055] where m is the number of cropping system types in the entire study area, n is the number of cropping system types in the study unit, and P i represents the area proportion corresponding to the cropping system type i in the study unit; the study area is divided into several study units. When the study unit is the entire study area, the corresponding PDI reveals the diversity degree of cropping systems in the entire study area.

[0056] When the value of the PDI is < 0.5, 0.5 - 1, or > 1, it indicates that the diversity degree of cropping systems in the study area is at a low, medium, or high level respectively. By quantifying the study unit using the PDI of the evaluation index for the diversity of cropping systems, technical methods and data support are provided for the analysis, evaluation, and optimization of the diversity of cropping systems.

[0057] The present invention constructs a multi - level attribute framework of crop plant type - crop composition - crop photosynthesis, integrates multi - dimensional time - series data such as pigments, nitrogen, photosynthetic activity ratio, leaf area index, and radar backscattering coefficient, designs high crown ratio, physiological active substance ratio, and photosynthetic efficiency per unit leaf area indexes in sequence, establishes an integrated information extraction technology for the spatial distribution of different cropping system types, further designs a cropping system diversity index, obtains a spatial distribution map of the cropping system diversity in the study area, provides methods and data support for the adjustment of agricultural planting structure and the realization of local - condition - based agricultural production layout, and promotes the sustainable development of agriculture in China.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] (1) In response to a series of challenges such as class imbalance, scarce reference samples, and weak spatio - temporal migration ability of model algorithms, the present invention breaks out of the data - driven model framework. By exploring the crop growth mechanism and process, a remote sensing mapping method for the diversity of cropping systems based on the theoretical framework of crop structure and function is proposed. The present invention has strong interpretability, good spatio - temporal migration ability, and can be popularized and applied across regions and years without samples.

[0060] (2) The present invention is ingeniously designed and highly extensible. It depicts the crop structure hierarchy from two aspects: the physical space structure and the material composition structure, further explores the crop functional characteristics under different plant type component structures, and constructs a remote sensing monitoring method for the diversity of planting systems by designing a multi-level framework of crop structure and function. Compared with thematic mapping, it can effectively reduce the uncertainty brought by cumulative classification errors and provide key technologies and solutions for obtaining spatio-temporally consistent agricultural information in areas with complex and diverse planting types. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:

[0062] Attached Figure 1 is a schematic diagram of the implementation process of the embodiment of the present invention;

[0063] Attached Figure 2 is a time series signal diagram of the bare soil index DBSI and the radar backscattering coefficient VV for peanuts, sweet potatoes, corn, and soybeans;

[0064] Attached Figure 3 is a time series signal diagram of the photosynthetic vitality ratio index PVR and the green vegetation leaf area index LAIgreen for corn, sorghum, rice, soybeans, peanuts, and sweet potatoes;

[0065] Attached Figure 4 is a hierarchical diagram of the remote sensing mapping method for planting systems based on the crop structure and function theoretical framework. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0067] It should be noted that the following detailed descriptions are all exemplary and are intended to provide further explanations for the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0068] The present invention provides a method for mapping the diversity of planting systems based on the crop structure and function theoretical framework, including the following steps (see Figure 1 ):

[0069] Step S01, establish a time series dataset of radar and optical images and obtain the crop growth period for each pixel;

[0070] Step S02: Design the crop height-to-canopy ratio index based on the lidar response characteristics of different plant types;

[0071] Step S03: Design the crop plant type discrimination index based on the crop height-to-canopy ratio index;

[0072] Step S04: Establish the dataset of pigment content during the crop growth period;

[0073] Step S05: Design the ratio index of crop physiological active substances;

[0074] Step S06: Construct the photosynthetic efficiency index per unit leaf area of crops;

[0075] Step S07: Design the crop photosynthetic capacity discrimination index based on the photosynthetic efficiency index per unit leaf area;

[0076] Step S08: Construct a remote sensing mapping method for cropping systems based on the theoretical framework of crop structure and function;

[0077] Step S09: Design the evaluation index for the diversity of cropping systems in the study area;

[0078] Step S10: Create the spatial distribution map of the diversity of cropping systems in the study area.

[0079] The following are the specific embodiments of the present invention.

[0080] Step S01: Establish the time-series dataset of radar and optical images and obtain the crop growth period for each pixel;

[0081] On the remote sensing cloud platform, all Sentinel-1 SAR (Synthetic Aperture Radar) and Sentinel-2 MSI (Multi-Spectral Imaging) image data that meet the conditions are screened out according to the research area range within the research time period; for the Sentinel-1 VV radar time series data, data preprocessing such as image mosaicking and speckle filtering is carried out; in step S01, for the Sentinel-2 MSI original image data, image mosaicking, cloud removal, cropping, linear interpolation, and Whittaker Smoother time series smoothing processing are carried out to obtain a 10-day maximized synthetic Sentinel-2 MSI image dataset for the research area. Using the Sentinel-2 MSI image dataset, multi-dimensional spectral index time series datasets of vegetation, soil, pigments, and nitrogen are established, including the vegetation index EVI2 (2-bands enhanced vegetation index), the dry bare-soil index DBSI (Dry Bare-Soil Index), the chlorophyll REP (Red-Edge Position), the anthocyanin reflectance index ARI (Anthocyanin Reflectance Index), the carotenoid reflectance index CRI (Carotenoid Reflectance Index), the normalized nitrogen index NDNI (Normalized Difference Nitrogen Index), the leaf area index of green vegetation LAIgreen (Leaf Area Index of green vegetation), and the photosynthetic vigour ratio index PVR (PhotosyntheticVigour Ratio). The calculation formulas are as follows:

[0082]

[0083] Among them, NIR represents the reflectance of the near-infrared band, Red represents the reflectance of the red band, Green represents the reflectance of the green band, Blue represents the reflectance of the blue band, SWIR1 represents the reflectance of the short-wave infrared band, VRE1, VRE2, and VRE3 respectively represent the reflectances of three red-edge bands, and log represents the logarithmic operation.

[0084] In step S01, the full growth stage of crops is dynamically obtained pixel by pixel. By using the time series curve of the vegetation index EVI2, the date corresponding to the maximum value of the time series curve is obtained, and this date is determined as the full growth stage of the crops; 60 days before and 40 days after the full growth stage of the crops are respectively determined as the emergence stage and the harvest stage of the crops.

[0085] Step S02: Design the crop high canopy ratio index based on the lidar response characteristics of different plant types;

[0086] The plant type of a crop refers to the overall morphological and structural characteristics formed during the growth process of the crop; the characteristics of different crop plant types are described in terms of plant height and canopy width: plant height refers to the vertical distance from the base to the top of the crop, and there are differences in the heights of different species and varieties of crops; crop width refers to the number, distribution, and structural characteristics of lateral branches formed during the growth process of the crop. The crop canopy and plant height change continuously during the growth and development of the crop. Under the same canopy width, the higher the plant, the stronger the VV vertical polarization radar backscattering coefficient; based on the change range of the soil index during the crop growth period, the change process of the crop canopy width is characterized; based on the change range of the VV vertical polarization radar backscattering coefficient during the crop growth period, the change range of the crop plant height is characterized (see Figure 2 ). The optical remote sensing index DBSI represents the bare soil index and can depict the change of the crop canopy width during the growth process. The smaller the value of DBSI, the less dry bare soil can be observed on the surface, and thus it reflects a larger crop canopy width. There are differences in the change of the crop canopy width during the growth process of different plant types. For example, the relative change range of the maize canopy width is relatively small, while the relative change range of the canopy width of prostrate-growing crops such as peanut and sweet potato is relatively large, and the change range of the soybean canopy width is between the two; while the radar backscattering coefficient VV can reflect the change of the crop height. Since the change of the crop height of peanut and sweet potato is relatively small, the change of VV from the beginning of growth to the growth peak period is relatively gentle, while for tall-stemmed crops such as maize, the change of VV from the beginning of growth to the growth peak period is relatively large. By fusing the optical and radar temporal characteristics and comprehensively considering the variability characteristics of the soil index and the VV vertical polarization radar backscattering coefficient during the crop growth period, the crop high canopy ratio index HCR (High Canopy Ratio) is designed, and its calculation formula is:

[0087]

[0088] where VV t+1 and VV t represent the radar backscattering coefficients on the (t + 1)-th day and the t-th day respectively, DBSI t and DBSI t+1 represent the bare soil indices on the (t + 1)-th day and the t-th day respectively, and a and b are adjustment coefficients, taking values of 1.2 and 0.5 respectively, and Start and Heading represent the crop emergence stage and the growth peak stage respectively.

[0089] Step S03: Design the crop plant type discrimination index based on the crop high canopy ratio index;

[0090] During the growth period of crops, with the growth and development of the crops, the canopy structures of crops with different plant types show significant differences. For tall-stemmed crops, the canopy height varies greatly with growth and development. For dwarf-stemmed crops, the change range of the canopy height with growth and development is relatively small. For prostrate crops, the growth is manifested as the horizontal expansion of the leaves, and the canopy width continuously increases. According to the crop height-to-canopy ratio index HCR, the plant types of crops are discriminated. Based on different numerical ranges of the crop height-to-canopy ratio index, the plant types of crops are successively divided into prostrate type, dwarf-stemmed type and tall-stemmed type. The discrimination formula is:

[0091]

[0092] where T 1 is the characteristic variable constructed based on the crop height-to-canopy ratio index HCR, and f 1 represents the classification function under the characteristic of T 1 . In the function, 1, 2, and 3 represent prostrate type, dwarf-stemmed type and tall-stemmed type crops respectively. θ 1 , θ 2 , θ 3 and θ 4 are respectively recommended to take the values of 0.05, 0.2, 0.4 and 0.7.

[0093] Step S04: Establish a dataset of pigment content during the crop growth period;

[0094] Pigments mainly include chlorophyll, carotenoids and anthocyanins, etc. Chlorophyll is the main pigment in leaves, with a relatively high content in leaves, giving leaves a green color and playing a key role in photosynthesis. Carotenoids and anthocyanins have a relatively high content in the flowers and fruits of crops, can reflect the morphological characteristics of canopy leaves or organs, make the flowers and fruits show rich colors, and have important physiological functions. When the crops enter the senescence stage, the chlorophyll in the leaves begins to degrade, the green gradually fades, and the leaves turn yellow or red. At the same time, the content of carotenoids and anthocyanins increases accordingly. In the present invention, the REP, ARI, and CRI indexes are respectively selected to reflect the content levels of chlorophyll, anthocyanin and carotenoid in crops. In order to eliminate the influence of dimensions, the REP, ARI, and CRI index datasets in the study area are normalized to obtain the normalized chlorophyll, anthocyanin and carotenoid indexes, which are CHL (Chlorophyll), ANTH (Anthocyanin) and CAR (Carotenoid) in sequence; during the study period, the REP, ARI, and CRI index datasets are normalized period by period, so as to obtain the CHL, ANTH and CAR time series datasets of the normalized study area. The normalization calculation formulas of the REP, ARI, and CRI indexes are respectively:

[0095]

[0096] Among them, REP min , ARI min and CRI min respectively represent the minimum values of REP, ARI, and CRI in the study area. REP max , ARI max and CRI max respectively represent the maximum values of REP, ARI, and CRI in the study area.

[0097] Step S05: Design an index for the ratio of crop physiologically active substances;

[0098] Within the scope of the study area, during the entire life cycle of crops from sowing, germination to mature harvesting, according to the main organ types covered by the canopy and the duration, it can be divided into ear-canopy type, leaf-canopy type, and flower-canopy type; for different types of crop canopies, there are significant differences in the contents of physiological active substances such as crop pigments and nitrogen. There is a complementary relationship between the pigment contents of vegetation. When the chlorophyll content is relatively high, the contents of anthocyanin and carotenoid are usually relatively low. Among ear-canopy type crops, the chlorophyll content of cereal crops is particularly prominent. While in leaf-canopy type crops such as potato crops, a large amount of organic matter produced by photosynthesis is transported to tubers, and the chlorophyll level in its canopy is lower than that of cereal crops, and the levels of anthocyanin and carotenoid are higher than those of cereal crops. In addition, for some flower-canopy type crops such as rape and sunflower, the canopy is covered by yellow flowers during specific phenological periods, and at this time, the contents of anthocyanin and carotenoid are relatively high. For leaf-canopy type crops such as soybean and peanut, which require a large amount of nitrogen to synthesize proteins during the growth period, the nitrogen content in their leaves is much higher than that of ear-canopy type crops. Based on the above characteristics, design an index for the ratio of crop physiologically active substances PASR (Physiologically Active Substance Ratio), and its calculation formula is:

[0099]

[0100] Among them, ANTH mean represents the mean value of ANTH during the growth period, CAR mean represents the mean value of CAR during the growth period, CHL mean represents the mean value of CHL during the growth period, |NDNI mean | represents the absolute value of the mean value of NDNI during the growth period.

[0101] According to the significant differences in the ratios of physiological active substances of different crops, classify the crops into different categories, and express it with a formula as:

[0102]

[0103] Among them, T 2Denoted as the characteristic variable f constructed based on the ratio of crop physiological active substances PASR 2 Denotes at T 2 The classification function under the characteristics, where 1, 2, and 3 in the function represent leaf canopy type, ear canopy type, and flower canopy type crops respectively. θ 1 、θ 2 、θ 3 and θ 4 Are respectively recommended to take the values of 0.1, 0.55, 1.2, and 4.7.

[0104] Step S06: Construct the photosynthetic efficiency index per unit leaf area of crops;

[0105] Photosynthetic efficiency refers to the ability of plants to convert light energy into chemical energy through photosynthesis. This process is crucial for the growth and development of crops, and photosynthesis runs through the entire growth process of crops. The photosynthetic activity ratio index PVR reflects the activity degree of leaf photosynthesis. For plants with a high PVR, the activity degree of leaf photosynthesis is higher; the leaf area index LAIgreen reflects the leaf coverage ratio and the overlap degree of canopy leaves. When the ground is completely covered by crops, an increase in LAIgreen reveals the overlap degree of crop leaves; based on this, using the time series dataset of the green vegetation leaf area index LAIgreen and the photosynthetic activity ratio PVR (see Figure 3 ), design the photosynthetic efficiency index per unit leaf area of crops LABPE (Leaf Area - Based Photosynthetic Efficiency) by comprehensively considering the ratio of PVR and LAIgreen to evaluate the photosynthetic efficiency of different crop types. Calculate LABPE period by period during the research period to obtain the time series dataset of LABPE in the study area. The calculation formula of LABPE is as follows:

[0106]

[0107] In the formula, PVR represents the photosynthetic activity ratio index, and LAIgreen represents the green vegetation leaf area index.

[0108] Step S07: Design a discriminant index for crop photosynthetic ability based on the photosynthetic efficiency index per unit leaf area;

[0109] Crops with strong photosynthetic ability have high photosynthetic efficiency during the growth peak period, and low leaf overlap degree and low value of the leaf area index LAIgreen. Therefore, the photosynthetic efficiency per unit leaf area is high, and its LABPE is high; compared with crops with strong photosynthetic ability, weak photosynthetic ability crops have relatively low photosynthetic efficiency during the growth peak period and high leaf overlap degree. Therefore, the photosynthetic efficiency per unit leaf area LABPE is low; based on the photosynthetic efficiency index per unit leaf area, design the discriminant process for crop photosynthetic ability, and its expression is:

[0110]

[0111] Among them, LABPE Heading represents the LABPE value during the vigorous growth period, and T 3 is a characteristic variable constructed based on the photosynthetic efficiency per unit leaf area of crops LABPE, and f 3 represents the classification function under the characteristic of T 3 . Heading represents the time point of the vigorous growth period of crops. In the function, 1 and 2 represent crops with weak photosynthetic ability and strong photosynthetic ability respectively. θ 1 , θ 2 and θ 3 are respectively recommended to take the values of 0.02, 0.06, and 0.12.

[0112] Step S08: Construct a remote sensing mapping method for cropping systems based on the theoretical framework of crop structure and function;

[0113] Construct a remote sensing mapping method for cropping systems based on the theoretical framework of crop structure and function. Based on the theoretical framework of structure and function, a multi-level attribute framework of crop plant type - crop composition - crop photosynthesis is constructed, and a cropping system diversity mapping method based on the theoretical framework of crop structure and function is designed. The hierarchical structure of the method and its classification process are described as follows:

[0114] First, for crop plant types, different crop plant types are divided based on the ratio of the change range of plant height and canopy width during the crop growth process; according to the designed crop height-to-canopy ratio index HCR, crop plant types are successively divided into prostrate type, dwarf type, and tall type. The discrimination basis is as follows: For prostrate type crops, the canopy structure is close to the ground and low-lying, and the HCR value is small; for dwarf type crops, the plant height is relatively low and the canopy width is relatively small, and the HCR value is medium; for tall type crops, the stem is tall and the canopy width is relatively small, and the HCR value is large. The expression for using HCR to discriminate plant types (see the crop plant type hierarchy in Figure 4 ) is:

[0115]

[0116] Among them, represents different crop types, and T 1 is a characteristic variable constructed based on the crop height-to-canopy ratio index HCR, and f 1 is the classification function. 1, 2, and 3 represent prostrate type, dwarf type, and tall type crops respectively. In this hierarchical classification, peanuts, sweet potatoes, alfalfa, etc. belong to prostrate type crops; rice, wheat, millet, soybeans, tobacco leaves, rapeseed, and cotton, etc. belong to dwarf type crops; corn, sorghum, sugarcane, and sunflowers, etc. belong to tall type crops.

[0117] On this basis, different crop types are further distinguished according to crop components. According to the designed physiological active substance ratio of crops (PASR), crops are divided into leaf-crown type, ear-crown type and flower-crown type; the discrimination basis is as follows: for leaf-crown type crops, the PASR value is small because the chlorophyll and nitrogen content are high during their growth period, while the carotenoid and anthocyanin content are low; for flower-crown type crops, the PASR value is large because the chlorophyll and nitrogen content are low during their growth period, while the carotenoid and anthocyanin content are high; the PASR value of ear-crown type crops is between that of leaf-crown type and flower-crown type; therefore, the crop physiological active substance ratio (PASR) can be used to discriminate crop types (see Figure 4 the crop component level in

[0118]

[0119] where represents different crop types, T 2 represents the feature constructed based on the physiological active substance ratio of crops (PASR), f 2 is the classification function. 1, 2, and 3 represent leaf-crown type, ear-crown type and flower-crown type crops respectively. In this hierarchical classification, leaf-crown type crops mainly include peanuts, sweet potatoes, soybeans, sugarcane and tobacco leaves, etc., ear-crown type crops mainly include rice, wheat, millet, corn and sorghum, etc., and flower-crown type crops mainly include alfalfa, rapeseed, cotton and sunflowers, etc.

[0120] Finally, the crop types are discriminated according to the photosynthetic efficiency of crops; according to the designed photosynthetic efficiency index per unit leaf area of crops (LABPE), the crop types are further divided into crops with weak photosynthetic ability and crops with strong photosynthetic ability; the discrimination basis is as follows: compared with crops with weak photosynthetic ability, crops with strong photosynthetic ability have higher photosynthetic efficiency and higher photosynthetic efficiency per unit leaf area (LABPE) of crops; LABPE is used for crop classification discrimination (see Figure 4 the crop photosynthesis level in

[0121]

[0122] where represents different crop types, T 3 represents the characteristic variable constructed based on the photosynthetic efficiency per unit leaf area of crops (LABPE), f 3 is the classification function. 1 and 2 represent crops with weak photosynthetic ability and crops with strong photosynthetic ability respectively. Crops with weak photosynthetic ability mainly include peanuts, tobacco leaves, rice, wheat, alfalfa, cotton and sunflowers, etc., and crops with strong photosynthetic ability mainly include sugarcane, millet, corn, sorghum, soybeans, rapeseed and sweet potatoes, etc.

[0123] In a certain city within the selected research area, after the above steps, the extracted crop types are: winter wheat, rapeseed, rice, tobacco leaves, corn, millet, soybeans, peanuts, sweet potatoes, sugarcane, sunflowers, sorghum, alfalfa, cotton, etc. The research area mainly adopts a double-cropping system, namely winter wheat-corn, winter wheat-rice, winter wheat-soybeans, winter wheat-peanuts, winter wheat-sweet potatoes, winter wheat-sorghum, winter wheat-sugarcane, rapeseed-rice.

[0124] Step S09: Design evaluation indicators for the diversity of the planting system in the research area;

[0125] Affected by factors such as geographical diversity, climate differences, and a large population, the crop planting systems in China are complex and diverse. Compared with a single planting system, a diverse planting system can maintain high yields with fewer external inputs (such as chemical fertilizers and pesticides) and lower environmental externalities (such as reducing soil degradation and water pollution), thereby improving the utilization efficiency of resources; the planting combinations of different crops help to enhance the ecological balance of the system, improve soil fertility and biodiversity, and effectively mitigate the risk of pests and diseases. By comprehensively counting the number of planting system types in the research area and the corresponding areas, evaluation indicators for the diversity of the planting system are designed to evaluate the degree of diversity of the planting system in the research area; the calculation basis for the evaluation indicators of the diversity of the planting system is: the more types of planting systems in the research area and the more balanced the proportion of the corresponding distribution areas, the higher the degree of diversity of the planting system in the research area; the larger the value of the Planting Diversity Index (PDI) of the evaluation indicators of the diversity of the planting system, the higher the degree of diversity of the planting system in the research area; its calculation formula is:

[0126]

[0127] where m is the number of planting system types in the entire research area, n is the number of planting system types in the research unit, and P i represents the area proportion corresponding to the planting system type i in the research unit; the research area is divided into several research units. When the research unit is the entire research area, the corresponding PDI reveals the degree of diversity of the planting system in the entire research area.

[0128] When the value of the PDI is <0.5, 0.5 - 1, or >1, it indicates that the degree of diversity of the planting system in the research area is at a low, medium, or high level respectively. By quantifying the research unit using the Planting Diversity Index (PDI) of the evaluation indicators of the diversity of the planting system, technical methods and data support are provided for the analysis, evaluation, and optimization of the diversity of the planting system.

[0129] Step S10: Create a spatial distribution map of the diversity of the planting system in the research area;

[0130] Taking a certain city as the study area, using the national standard administrative division vector map as the base map, taking winter wheat, corn, rice, soybeans and peanuts as examples for remote sensing mapping, and creating the spatial distribution of the planting system in the study area according to steps S01 to S10. In order to evaluate the planting system diversity in a certain city in the study area, first calculate the number m of planting system types in the study area. In this example, m is 14. Then, successively calculate the number n of planting system types in each county under the jurisdiction of the city and the area proportion corresponding to different planting systems. Based on the designed planting system diversity evaluation index, obtain the spatial distribution of the planting system diversity in the city.

[0131] The above is the preferred embodiment of the present invention. All changes made according to the technical solution of the present invention and whose functional effects do not exceed the scope of the technical solution of the present invention belong to the protection scope of the present invention.

Claims

1. A method for mapping cropping system diversity based on the crop structure-function theory framework, characterized by: The steps include: Step S01, establishing a radar and optical image time series data set and obtaining the crop growth period pixel by pixel; Step S02, designing a crop height-to-crown ratio index based on optical radar response characteristics of different plant types; Step S03, designing a crop plant type discrimination index based on the crop height-crown ratio index; Step S04, establishing a data set of pigment content during the crop growth period; Step S05, designing crop physiologically active substance ratio index; Step S06, constructing a crop photosynthetic efficiency per unit leaf area index; Step S07, designing a crop photosynthetic capacity discrimination index based on the photosynthetic efficiency per unit leaf area index; Step S08, constructing a cropping system remote sensing mapping method based on the crop structure function theory framework; Step S09, designing evaluation indicators for the diversity of cropping systems in the study area; Step S10: Create a spatial distribution map of the diversity of cropping systems in the study area.

2. The method for mapping the diversity of cropping systems based on the crop structure-function theory framework according to claim 1 is characterized by: Specifically, step S01 includes, on the remote sensing cloud platform, screening out all the Sentinel-1SAR and Sentinel-2MSI image data that meet the conditions according to the scope of the study area within the study period; performing image mosaicking, speckle filtering and other data preprocessing on the Sentinel-1VV radar time series data; In the step S01, the Sentinel-2MSI original image data is subjected to image mosaicking, cloud removal, cropping, linear interpolation and Whittaker Smoother time series smoothing to obtain a 10-day maximum synthetic Sentinel-2MSI image dataset of the study area; using the Sentinel-2MSI image dataset, a multi-dimensional spectral index time series dataset of vegetation, soil, pigment and nitrogen is established, including vegetation index EVI2, bare soil index DBSI, chlorophyll REP, anthocyanin reflectance index ARI, carotenoid reflectance index CRI, normalized nitrogen index NDNI, green vegetation leaf area index LAIgreen and photosynthetic activity ratio index PVR; the calculation formulas are: Among them, NIR represents the reflectivity of the near infrared band, Red represents the reflectivity of the red light band, Green represents the reflectivity of the green light band, Blue represents the reflectivity of the blue light band, SWIR1 represents the reflectivity of the short-wave infrared band, VRE1, VRE2 and VRE3 represent the reflectivity of the three red edge bands respectively, and log represents logarithmic operation; In step S01, the peak growth period of crops is dynamically obtained pixel by pixel, and the date of the maximum value of the time series curve is obtained by using the vegetation index EVI2 time series curve, and the date is determined as the peak growth period of crops; the 60 days before and 40 days after the peak growth period of crops are respectively determined as the crop emergence period and harvest period.

3. The method for mapping the diversity of cropping systems based on the crop structure-function theory framework according to claim 1 is characterized by: The step S02 is specifically as follows: the crop canopy and plant height change continuously with the growth and development of the crop. Under the same canopy width, the taller the plant is, the stronger its VV vertical polarization radar backscatter coefficient is; the change process of the crop canopy width is characterized according to the change range of the soil index during the crop growth period; the change range of the crop plant height is characterized according to the change range of the VV vertical polarization radar backscatter coefficient during the crop growth period; the crop height-to-crop ratio index HCR is designed by integrating the optical and radar time series characteristics and the variability characteristics of the soil index and the VV vertical polarization radar backscatter coefficient during the crop growth period. The calculation formula is: Among them, VV t+1 、VV t Denote the radar backscatter coefficients on day t+1 and day t, respectively, DBSI t 、DBSI t+1 They represent the bare soil index on the t+1th day and the tth day respectively, a and b are adjustment coefficients, and Start and Heading represent the crop emergence stage and peak growth stage respectively.

4. The method for mapping the diversity of cropping systems based on the crop structure-function theory framework according to claim 1, characterized in that: Specifically, step S03 comprises: distinguishing the plant types of crops according to the crop height-to-crown ratio index HCR, and classifying the plant types of crops into creeping type, short stem type and tall stem type based on the numerical ranges of different crop height-to-crown ratio indexes; The discriminant formula is: Among them, T1 is the characteristic variable constructed based on the crop height-crown ratio index HCR, f1 represents the classification function under the T1 feature, and 1, 2 and 3 in the function represent creeping, short-stem and tall-stem crops, respectively; θ1, θ2, θ3 and θ4 are adjusted according to different actual application areas.

5. The method for mapping the diversity of cropping systems based on the crop structure-function theory framework according to claim 1 is characterized by: The step S04 specifically includes selecting REP, ARI, and CRI indices to reflect the chlorophyll, anthocyanin, and carotenoid content levels of crops, respectively. In order to eliminate the influence of the dimension, the REP, ARI, and CRI index data sets of the study area are normalized to obtain the normalized chlorophyll, anthocyanin, and carotenoid indices, which are CHL, ANTH, and CAR, respectively; the REP, ARI, and CRI index data sets are normalized period by period during the study period to obtain the normalized CHL, ANTH, and CAR time series data sets of the study area; wherein the normalized calculation formulas of the REP, ARI, and CRI indices are: Among them, REP min 、ARI min and CRI min Respectively represent the minimum values ​​of REP, ARI and CRI in the study area, REP max 、ARI max and CRI max They represent the maximum values ​​of REP, ARI and CRI in the study area, respectively.

6. The method for mapping the diversity of cropping systems based on the crop structure-function theory framework according to claim 1, characterized in that: Specifically, step S05 is that crops can be divided into leaf crown type, ear crown type and corolla type according to the main organ type and duration of canopy coverage during the entire life cycle from sowing and germination to maturity and harvesting; different types of crop canopies have significant differences in the content of physiologically active substances such as crop pigments and nitrogen. Based on the chlorophyll, anthocyanin, carotenoid and nitrogen index, the crop physiologically active substance ratio PASR index is designed, and its calculation formula is: ANTH mean Represents the mean value of ANTH during the growth period, CAR mean represents the mean value of CAR during the growth period, CHL mean represents the mean value of CHL during the growth period, |NDNI mean | represents the absolute value of the mean NDNI during the growth period; According to the significant differences in the ratios of physiologically active substances in different crops, crops are divided into different categories, which can be expressed as follows: Among them, T2 represents the characteristic variable constructed based on the PASR ratio of crop physiologically active substances, f2 represents the classification function under the T2 feature, and 1, 2 and 3 in the function represent leaf crown type, ear crown type and corolla type crops respectively; θ1, θ2, θ3 and θ4 are adjusted according to different actual application areas.

7. The method for mapping the diversity of cropping systems based on the crop structure-function theory framework according to claim 1 is characterized by: Specifically, step S06 includes: photosynthetic activity ratio index PVR reflects the photosynthetic activity of leaves. Plants with high PVR have higher photosynthetic activity of leaves; leaf area index LAIgreen reflects the leaf coverage ratio and the overlap of canopy leaves. When the ground is completely covered by crops, the increase of LAIgreen reveals the overlap of crop leaves; combining PVR and LAIgreen index, designing the photosynthetic efficiency per unit leaf area LABPE index to evaluate the photosynthetic efficiency capacity of different crop types; the calculation formula of LABPE is as follows: Where PVR represents the photosynthetic activity ratio index, and LAIgreen represents the green vegetation leaf area index.

8. The method for mapping the diversity of cropping systems based on the crop structure-function theory framework according to claim 1 is characterized by: In the step S07, the photosynthetic efficiency of the crop with strong photosynthetic ability is high during the peak growth period, and the leaf overlap is low, and the leaf area index LAIgreen value is low, so the photosynthetic efficiency per unit leaf area is high, and the photosynthetic efficiency per unit leaf area LABPE is high; Compared with crops with strong photosynthetic capacity, crops with weak photosynthetic capacity have relatively low photosynthetic efficiency during the peak growth period, and the leaf overlap is high, so the photosynthetic efficiency per unit leaf area LABPE is low; Based on the photosynthetic efficiency per unit leaf area index, a crop photosynthetic capacity identification process is designed, and its expression is: Among them, LABPE Heading It represents the LABPE value at the peak growth period, T3 is the characteristic variable constructed based on the LABPE of photosynthetic efficiency per unit leaf area of ​​crops, f3 represents the classification function under the T3 feature, Heading represents the time point of the peak growth period of crops, 1 and 2 in the function represent crops with weak photosynthetic capacity and crops with strong photosynthetic capacity respectively; θ1, θ2 and θ3 are adjusted according to different actual application areas.

9. The method for mapping the diversity of cropping systems based on the crop structure-function theory framework according to claim 1, characterized in that: In the step S08, a crop system remote sensing mapping method based on the crop structure and function theory framework is constructed; based on the structure and function theory framework, a crop plant type-crop component-crop photosynthesis multi-level attribute framework is constructed, and a crop system diversity mapping method based on the crop structure and function theory framework is designed; the hierarchical structure of the method and its classification process are described as follows: First, for crop plant types, different crop plant types are divided based on the ratio of plant height and canopy width changes during crop growth; according to the designed crop height-to-crown ratio index HCR, crop plant types are divided into creeping type, short-stem type and tall-stem type in turn. The discrimination basis is: the canopy structure of creeping crops is close to the ground and low, and the HCR value is small; the short-stem crops have a low plant height, a small canopy width, and a medium HCR value; the tall-stem crops have tall stems, a relatively small canopy width, and a large HCR value; the expression for plant type discrimination using HCR is: in, represents different crop types, T1 represents the characteristic variable constructed based on the crop height-crown ratio index HCR, and f1 is the classification function; 1, 2 and 3 represent creeping, short-stem and tall-stem crops respectively; on this basis, different crop types are further distinguished according to crop components; according to the designed crop physiologically active substance ratio PASR, crops are divided into leaf crown type, spike crown type and corolla type; the basis for discrimination is: the PASR value of leaf crown type crops is small, the reason is that the chlorophyll and nitrogen content is high during its growth period, while the carotenoid and anthocyanin content is low; for corolla type crops, the PASR value is large, the reason is that the chlorophyll and nitrogen content is low during its growth period, while the carotenoid and anthocyanin content is high; the PASR value of spike crown type crops is between the leaf crown type and the corolla type; therefore, the crop physiologically active substance ratio PASR can be used to discriminate crop types, and its expression is: in, represents different crop types, T2 represents the features constructed based on the PASR ratio of crop physiologically active substances, and f2 is the classification function; 1, 2, and 3 represent leaf crown type, ear crown type, and corolla type crops, respectively; Finally, the crop types are identified based on the crop photosynthetic efficiency; based on the designed crop unit leaf area photosynthetic efficiency index LABPE, the crop types are subdivided into weak photosynthetic capacity crops and strong photosynthetic capacity crops; the basis for the identification is: compared with weak photosynthetic capacity crops, strong photosynthetic capacity crops have higher photosynthetic efficiency, and the crop unit leaf area photosynthetic efficiency LABPE is higher; the crop classification identification expression using LABPE is: in, represents different crop types, T3 represents the characteristic variable constructed based on the photosynthetic efficiency per unit leaf area of ​​crops LABPE, f3 is the classification function; 1 and 2 represent crops with weak photosynthetic capacity and crops with strong photosynthetic capacity, respectively; In step S08, for multiple cropping systems, crop identification is performed for each growing period based on the cropping system information, thereby obtaining the crop planting system information for the pixel in the research year.

10. The method for mapping the diversity of cropping systems based on the crop structure-function theory framework according to claim 1, characterized in that: In step S09, the number of planting system types and the corresponding areas in the study area are comprehensively counted, and then the planting system diversity evaluation index is designed to evaluate the degree of planting system diversity in the study area; the calculation basis of the planting system diversity evaluation index is: the more planting system types there are in the study area, and the more balanced the corresponding distribution area ratio is, the higher the degree of planting system diversity in the study area is; the larger the value of the planting system diversity evaluation index PDI is, the higher the degree of planting system diversity in the study area is; its calculation formula is: Where m is the number of cropping system types in the entire study area, n is the number of cropping system types in the study unit, and P i It represents the area proportion corresponding to the cropping system type i in the study unit. The study area is divided into several study units. When the study unit is the entire study area, the corresponding PDI reveals the diversity of the cropping system in the entire study area. When the PDI value is <0.5, 0.5-1 or >1, it means that the diversity of planting systems in the study area is at a low, medium or high level respectively. By using the planting system diversity assessment index PDI to quantify the research units, technical methods and data support are provided for the analysis, evaluation and optimization of planting system diversity.

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