CN117589093B - Hyperspectral remote sensing monitoring method, device, equipment and medium for crop leaf area index - Google Patents
Hyperspectral remote sensing monitoring method, device, equipment and medium for crop leaf area index Download PDFInfo
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Abstract
The invention provides a hyperspectral remote sensing monitoring method, device, equipment and medium for crop leaf area index, and relates to the technical field of remote sensing data processing, wherein the method comprises the following steps: the method comprises the steps of obtaining hyperspectral remote sensing data of target crops, wherein the hyperspectral remote sensing data are obtained by preprocessing original remote sensing data acquired by hyperspectral data acquisition equipment; based on hyperspectral remote sensing data, extracting reflectivity of crop canopy of a target crop in a plurality of preset wave bands as target reflectivity, wherein the preset wave bands at least comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands and 747nm wave bands; determining a corresponding target model based on a preset wave band, and inputting target reflectivity corresponding to the preset wave band into the target model to obtain leaf area index of the target crop, wherein the target model is a model for performing linear operation on the target reflectivity. The invention can realize the calculation of crop LAI under various conditions, and has high robustness.
Description
Technical Field
The invention relates to the technical field of remote sensing data processing, in particular to a hyperspectral remote sensing monitoring method, device, equipment and medium for crop leaf area index.
Background
Leaf Area Index (LAI) is a parameter of great concern in the fields of plant ecology and physiology and agricultural remote sensing, and generally refers to the sum of the single-sided leaf area of a crop leaf per unit soil area. LAI is closely related to the processes of photosynthesis, evaporation and the like of plants, plays an important role in modeling of climate, ecology and crops, and is also a key index for representing the size of a crop population, the growth situation, the development process of the climate and the prediction of the yield. The traditional method for measuring LAI by field sampling is time-consuming and labor-consuming, has poor timeliness and large damage to crop interference, and the remote sensing monitoring technology based on crop canopy spectral information in the prior art can overcome the defects of the traditional field sampling.
The LAI remote sensing monitoring method model in the prior art mainly comprises an empirical statistical relation model, an inversion method based on a radiation transmission model and a method combining the empirical statistical relation model and the radiation transmission model, wherein the inversion method based on the radiation transmission model relates to a plurality of parameters and priori knowledge, the uncertainty is large, the disease inversion problem is difficult to eliminate, the method based on the empirical statistical relation model mainly comprises the problems of low migration capacity and robustness, the application scene needs to be analyzed in a targeted mode, and when the application scene changes once and is inconsistent with the model training situation, the accuracy is reduced.
Disclosure of Invention
The invention provides a hyperspectral remote sensing monitoring method, device, equipment and medium for crop leaf area indexes, which are used for solving the defect of low robustness of a method for monitoring the crop leaf area indexes in the prior art and realizing stable monitoring of the crop leaf area indexes.
The invention provides a hyperspectral remote sensing monitoring method for a leaf area index of a crop, which comprises the following steps:
Acquiring hyperspectral remote sensing data of a target crop, wherein the hyperspectral remote sensing data is obtained by preprocessing original remote sensing data acquired by hyperspectral data acquisition equipment;
Based on the hyperspectral remote sensing data, extracting reflectivity of crop canopy of the target crop in a plurality of preset wave bands as target reflectivity, wherein the preset wave bands at least comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands and 747nm wave bands;
And determining a corresponding target model based on the preset wave band, and inputting the target reflectivity corresponding to the preset wave band into the target model to obtain a leaf area index of the target crop, wherein the target model is a model for performing linear operation on the target reflectivity.
According to the hyperspectral remote sensing monitoring method for the crop leaf area index, the preset wave bands comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands, 747nm wave bands, 739nm wave bands and 760nm wave bands.
According to the hyperspectral remote sensing monitoring method for the crop leaf area index, which is provided by the invention, the target model is as follows:
LAI=1.105+20.548×R920-31.646×R1095-330.815×R741+94.582×R747+216.473×R739+31.961×R760；
wherein, LAI is the leaf area index of the target crop, ra represents the emissivity of the crop canopy of the target crop in the a-band.
According to the hyperspectral remote sensing monitoring method for the crop leaf area index, the preset wave bands comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands, 747nm wave bands and 739nm wave bands.
According to the hyperspectral remote sensing monitoring method for the crop leaf area index, which is provided by the invention, the target model is as follows:
LAI=0.878+23.100×R920-27.894×R1095-260.733×R741+127.180×R747+139.045×R739；
wherein, LAI is the leaf area index of the target crop, ra represents the emissivity of the crop canopy of the target crop in the a-band.
According to the hyperspectral remote sensing monitoring method for the crop leaf area index, the preset wave bands comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands and 747nm wave bands; the target model is as follows:
LAI=1.563+29.258×R920-42.057×R1095-83.136×R741+95.009×R747；
wherein, LAI is the leaf area index of the target crop, ra represents the emissivity of the crop canopy of the target crop in the a-band.
According to the hyperspectral remote sensing monitoring method for the leaf area index of the crop, the target crop is wheat.
The invention also provides a hyperspectral remote sensing monitoring device for the leaf area index of the crop, which comprises the following components:
The data acquisition module is used for acquiring hyperspectral remote sensing data of the target crops, wherein the hyperspectral remote sensing data are obtained by preprocessing original remote sensing data acquired by hyperspectral data acquisition equipment;
the reflectivity extraction module is used for extracting the reflectivity of the crop canopy of the target crop in a plurality of preset wave bands as target reflectivity based on the hyperspectral remote sensing data, wherein the preset wave bands at least comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands and 747nm wave bands;
The LAI determining module is used for determining a corresponding target model based on the preset wave band, inputting the target reflectivity corresponding to the preset wave band into the target model to obtain the leaf area index of the target crop, and the target model is a model for carrying out linear operation on the target reflectivity.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the hyperspectral remote sensing monitoring method of the crop leaf area index according to any one of the above when executing the program.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of hyperspectral remote sensing monitoring of crop leaf area index as described in any one of the above.
According to the hyperspectral remote sensing monitoring method, device, equipment and medium for the leaf area index of the crop, the target reflectivity of the crop canopy in the preset wave band (comprising 920nm wave band, 1095nm wave band, 741nm wave band and 747nm wave band) is extracted from hyperspectral remote sensing data of the crop, the leaf area index of the target crop is obtained after linear operation is carried out on the target reflectivity, the leaf area index can be accurately monitored without analyzing the data by combining scenes or involving a large amount of parameters and priori knowledge, and experimental verification shows that the calculation of the LAI of the crop under various conditions can be realized, and the robustness is high.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a hyperspectral remote sensing monitoring method for crop leaf area index provided by the invention;
FIG. 2 is a second schematic flow chart of a hyperspectral remote sensing monitoring method for leaf area index of crops according to the present invention;
FIG. 3 is a schematic diagram of the accuracy of the LAI monitoring result of the hyperspectral remote sensing method for crop leaf area index provided by the invention;
FIG. 4 is a second schematic diagram of the accuracy of the LAI monitoring result of the hyperspectral remote sensing method for crop leaf area index provided by the invention;
FIG. 5 is a third schematic diagram of the accuracy of the LAI monitoring result of the hyperspectral remote sensing method for crop leaf area index provided by the invention;
FIG. 6 is a graph showing the results of prior art LAI monitoring;
FIG. 7 is a second diagram of the LAI monitoring result of the prior art;
FIG. 8 is a third diagram of the LAI monitoring result in the prior art;
FIG. 9 is a diagram showing the results of LAI monitoring in the prior art;
FIG. 10 is a fifth schematic diagram of the LAI monitoring result of the prior art;
FIG. 11 is a diagram showing the results of LAI monitoring in the prior art;
FIG. 12 is a graph of the LAI monitoring results of the prior art;
FIG. 13 is a diagram of a prior art LAI monitoring result eighth;
FIG. 14 is a diagram of a prior art LAI monitoring result;
FIG. 15 is a schematic diagram of LAI monitoring results of the prior art;
FIG. 16 is a schematic structural diagram of a hyperspectral remote sensing monitoring device for crop leaf area index provided by the invention;
Fig. 17 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the prior art, the method for monitoring the LAI by adopting remote sensing data has the main problems of weak generalization capability and robustness, and once scene conditions are changed, the precision is difficult to maintain, and the main reasons are that the spectrum of the crop canopy can reflect the LAI, and is also influenced by various factors such as soil background, observation geometry (the geometrical positional relationship between an observation target, an observation sensor and the sun), other optical characteristics of the crop canopy (such as leaf angle, biochemical components and vertical heterogeneity thereof, the growth and development process and the influence of non-leaf organs) and the like. In order to solve the defect of low robustness of the method for performing LAI monitoring on remote sensing data in the prior art, the invention provides a crop leaf area index hyperspectral remote sensing monitoring method, device, equipment and medium.
The method for hyperspectral remote sensing monitoring of the leaf area index of the crop provided by the invention is described below with reference to fig. 1 to 15, and as shown in fig. 1, the method comprises the following steps:
s110, acquiring hyperspectral remote sensing data of a target crop, wherein the hyperspectral remote sensing data are obtained by preprocessing original remote sensing data acquired by hyperspectral data acquisition equipment;
S120, extracting reflectivity of crop canopy of a target crop in a plurality of preset wave bands as target reflectivity based on hyperspectral remote sensing data, wherein the preset wave bands at least comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands and 747nm wave bands;
s130, determining a corresponding target model based on a preset wave band, and inputting target reflectivity corresponding to the preset wave band into the target model to obtain leaf area indexes of target crops, wherein the target model is a model for performing linear operation on the target reflectivity.
The method provided by the invention can realize rapid and accurate extraction of the canopy LAI under different observation scenes, and model parameters do not need to be corrected again. The invention provides a canopy LAI remote sensing estimation model which is applicable to different conditions and has reliable precision and stable performance based on canopy hyperspectral information through data experiment verification of various influencing factors such as different soil background influence degrees, different observation zenith angles, different solar zenith angles, different LAI vertical distribution, different leaf angle vertical distribution, different chlorophyll vertical distribution, different leaf water vertical distribution, different growth period, different water and fertilizer conditions, different years, different varieties and the like.
The method provided by the invention can be suitable for various crops, in particular, in the method provided by the invention, the target crop is wheat, and the wheat is taken as an investigation object to establish the target model provided by the invention, so that the robust remote sensing monitoring of the wheat LAI (laser light induced interference) capable of minimizing the interference of various influencing factors is realized.
Specifically, according to the method provided by the invention, the original remote sensing data is firstly collected by the hyperspectral data collection equipment, and then the hyperspectral remote sensing data is obtained by preprocessing. The hyperspectral data acquisition equipment can be a hyperspectral sensor or a near-earth hyperspectral instrument carried by a hyperspectral satellite or an unmanned aerial vehicle. As shown in fig. 2, the position, the range and the space-time resolution requirements of the crops with leaf area indexes are monitored according to the requirements, and a proper hyperspectral data acquisition device is determined to acquire original remote sensing data. And after the original remote sensing data are obtained, preprocessing the original remote sensing data to obtain hyperspectral remote sensing data. And (3) carrying out pretreatment such as radiometric calibration, atmospheric correction, orthographic correction and the like on the original remote sensing data acquired by the high-resolution satellite after checking that the data are not abnormal. And (3) carrying out preprocessing such as image splicing, radiometric calibration, geographic correction and the like on the original remote sensing data acquired by the hyperspectral sensor carried by the unmanned aerial vehicle after checking that the data are abnormal. The original remote sensing data acquired by the hyperspectral sensors carried by the high-resolution satellites and the unmanned aerial vehicle can be finished by Envi and Arcgis image processing software. For original remote sensing data acquired by a near-earth hyperspectral instrument (such as an ASD portable hyperspectral instrument), after checking that the data is abnormal, the canopy reflectivity data can be directly derived by using matched data processing software to serve as hyperspectral remote sensing data.
The reflectivity data of the crop canopy of the target crop is extracted from hyperspectral remote sensing data, specifically, the reflectivity of the crop canopy of the target crop in a plurality of preset wave bands is extracted as the target reflectivity. In the method provided by the invention, the reflectivity of the crop canopy of the target crop at the wave bands 920nm, 1095nm, 741nm, 747nm, 739nm and 760nm is extracted and is marked as R920, R1095, R741, R747, R739 and R760, wherein R represents the reflectivity, and the numbers represent the wave bands and nm. The reflectivities of all of these six bands are preferably obtained, and if not, only the reflectivities of the first four bands (920 nm, 1095nm, 741nm, 747 nm) or the first 5 bands (920 nm, 1095nm, 741nm, 747nm, 739 nm) may be obtained.
And selecting a corresponding target model to calculate crop canopy LAI according to the target emissivity acquisition conditions of different preset wave bands. Specifically, when the preset wavelength band includes 920nm wavelength band, 1095nm wavelength band, 741nm wavelength band, 747nm wavelength band, 739nm wavelength band, and 760nm wavelength band, the target model for calculating the LAI is: lai=1.105+20.548×r920-31.646 ×r1095-330.815 ×r741+94.582 ×r747+216.473 ×r739+31.961 ×r760. When the preset wave bands comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands, 747nm wave bands and 739nm wave bands, the target model for calculating the LAI is: lai=0.878+23.100×r920-27.894 ×r1095-260.733 ×r741+127.180 ×r747+139.045 ×r739. When the preset wave band comprises a 920nm wave band, a 1095nm wave band, a 741nm wave band and a 747nm wave band, the target model for calculating the LAI is as follows: lai=1.563+29.258×r920-42.057 ×r1095-83.136 ×r741+95.009 ×r747.
Through experiments, the calculation accuracy of the target model is highest when the preset wave bands include 920nm wave band, 1095nm wave band, 741nm wave band, 747nm wave band, 739nm wave band and 760nm wave band, and the calculation accuracy is slightly lowered but the amplitude is not large when the preset wave bands include 920nm wave band, 1095nm wave band, 741nm wave band, 747nm wave band and 739nm wave band or include 920nm wave band, 1095nm wave band, 741nm wave band and 747nm wave band.
Wheat is taken as an investigation object, and based on 474 conditions (shown in table 1) covering various influence factors (LAI change range, soil background, observation zenith angle, solar zenith angle, LAI vertical distribution, leaf angle vertical distribution, chlorophyll vertical distribution, leaf water vertical distribution, canopy structure development and organ development, water and fertilizer conditions, varieties and the like) possibly related to wheat LAI remote sensing monitoring, the LAI monitoring is carried out by adopting the method provided by the invention, and the monitoring results and the accuracy are shown in figures 3, 4 and 5. As can be seen from fig. 3 to fig. 5, in the method provided by the invention, the accuracy of the LAI result obtained by calculating three target models (the target models corresponding to six wave bands, five wave bands and four wave bands respectively) is very high, and the three models have very high robustness.
TABLE 1
Meanwhile, based on the situation in table 1, the existing LAI monitoring method is adopted for monitoring, and the obtained monitoring result and accuracy are shown in fig. 6, fig. 7, fig. 8, fig. 9, fig. 10, fig. 11, fig. 12, fig. 13, fig. 14 and fig. 15, wherein fig. 6-fig. 15 are respectively LAIs monitored by 10 methods in 9 existing documents (the document publication years of document authors in brackets of titles in each figure), and as can be seen from fig. 6-fig. 15, compared with the existing LAI calculation model, the method provided by the invention has obvious advantages in terms of accuracy and robustness. That is, compared with the existing model method, the method provided by the invention can weaken the interference of various influencing factors to the greatest extent, is suitable for hyperspectral remote sensing estimation of the LAI information of the wheat crops under various conditions, has the advantages of reliable precision, strong robustness and universality, does not need to recalibrate model parameters, and has a simple structure, convenience and practicability.
The hyperspectral remote sensing monitoring device for the crop leaf area index provided by the invention is described below, and the hyperspectral remote sensing monitoring device for the crop leaf area index described below and the hyperspectral remote sensing monitoring method for the crop leaf area index described above can be correspondingly referred to each other. As shown in fig. 16, the hyperspectral remote sensing monitoring device for crop leaf area index provided by the invention comprises:
The data acquisition module 1610 is configured to acquire hyperspectral remote sensing data of the target crop, where the hyperspectral remote sensing data is obtained by preprocessing original remote sensing data acquired by the hyperspectral data acquisition device;
The reflectivity extraction module 1620 is configured to extract reflectivity of a crop canopy of a target crop in a plurality of preset bands as a target reflectivity based on hyperspectral remote sensing data, where the preset bands at least include a 920nm band, a 1095nm band, a 741nm band, and a 747nm band;
The LAI determining module 1630 is configured to determine a corresponding target model based on a preset band, input a target reflectivity corresponding to the preset band into the target model, and obtain a leaf area index of the target crop, where the target model is a model that performs a linear operation on the target reflectivity.
Fig. 17 illustrates a physical structure diagram of an electronic device, which may include: processor 1710, communication interface (Communications Interface) 1720, memory 1730, and communication bus 1740, wherein processor 1710, communication interface 1720, memory 1730 complete communication with each other through communication bus 1740. Processor 1710 may invoke logic instructions in memory 1730 to perform a crop leaf area index hyperspectral telemetry method comprising: the method comprises the steps of obtaining hyperspectral remote sensing data of target crops, wherein the hyperspectral remote sensing data are obtained by preprocessing original remote sensing data acquired by hyperspectral data acquisition equipment; based on hyperspectral remote sensing data, extracting reflectivity of crop canopy of a target crop in a plurality of preset wave bands as target reflectivity, wherein the preset wave bands at least comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands and 747nm wave bands; determining a corresponding target model based on a preset wave band, and inputting target reflectivity corresponding to the preset wave band into the target model to obtain leaf area index of the target crop, wherein the target model is a model for performing linear operation on the target reflectivity.
Further, the logic instructions in the memory 1730 described above may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of performing the method for hyperspectral remote sensing monitoring of crop leaf area index provided by the methods described above, the method comprising: the method comprises the steps of obtaining hyperspectral remote sensing data of target crops, wherein the hyperspectral remote sensing data are obtained by preprocessing original remote sensing data acquired by hyperspectral data acquisition equipment; based on hyperspectral remote sensing data, extracting reflectivity of crop canopy of a target crop in a plurality of preset wave bands as target reflectivity, wherein the preset wave bands at least comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands and 747nm wave bands; determining a corresponding target model based on a preset wave band, and inputting target reflectivity corresponding to the preset wave band into the target model to obtain leaf area index of the target crop, wherein the target model is a model for performing linear operation on the target reflectivity.
In yet another aspect, the present invention provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the method for hyperspectral remote sensing monitoring of crop leaf area index provided by the methods described above, the method comprising: the method comprises the steps of obtaining hyperspectral remote sensing data of target crops, wherein the hyperspectral remote sensing data are obtained by preprocessing original remote sensing data acquired by hyperspectral data acquisition equipment; based on hyperspectral remote sensing data, extracting reflectivity of crop canopy of a target crop in a plurality of preset wave bands as target reflectivity, wherein the preset wave bands at least comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands and 747nm wave bands; determining a corresponding target model based on a preset wave band, and inputting target reflectivity corresponding to the preset wave band into the target model to obtain leaf area index of the target crop, wherein the target model is a model for performing linear operation on the target reflectivity.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. The hyperspectral remote sensing monitoring method for the leaf area index of the crop is characterized by comprising the following steps of:
Acquiring hyperspectral remote sensing data of a target crop, wherein the hyperspectral remote sensing data is obtained by preprocessing original remote sensing data acquired by hyperspectral data acquisition equipment, and the target crop is wheat;
Based on the hyperspectral remote sensing data, extracting reflectivity of crop canopy of the target crop in a plurality of preset wave bands as target reflectivity, wherein the preset wave bands comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands, 747nm wave bands, 739nm wave bands and 760nm wave bands;
Determining a corresponding target model based on the preset wave band, and inputting the target reflectivity corresponding to the preset wave band into the target model to obtain a leaf area index of the target crop, wherein the target model is a model for performing linear operation on the target reflectivity;
The target model is as follows:
LAI=1.105+20.548×R920-31.646×R1095-330.815×R741+94.582×R747+216.473×R739+31.961×R760；
wherein, LAI is the leaf area index of the target crop, ra represents the emissivity of the crop canopy of the target crop in the a-band.
2. A hyperspectral remote sensing monitoring device for a leaf area index of a crop, comprising:
The data acquisition module is used for acquiring hyperspectral remote sensing data of target crops, wherein the hyperspectral remote sensing data are obtained by preprocessing original remote sensing data acquired by hyperspectral data acquisition equipment, and the target crops are wheat;
The reflectivity extraction module is used for extracting the reflectivity of the crop canopy of the target crop in a plurality of preset wave bands as target reflectivity based on the hyperspectral remote sensing data, wherein the preset wave bands comprise 920nm wave bands, 1095nm wave bands, 741nm wave bands, 747nm wave bands, 739nm wave bands and 760nm wave bands;
The LAI determining module is used for determining a corresponding target model based on the preset wave band, inputting the target reflectivity corresponding to the preset wave band into the target model to obtain a leaf area index of the target crop, wherein the target model is a model for carrying out linear operation on the target reflectivity;
The target model is as follows:
LAI=1.105+20.548×R920-31.646×R1095-330.815×R741+94.582×R747+216.473×R739+31.961×R760；
wherein, LAI is the leaf area index of the target crop, ra represents the emissivity of the crop canopy of the target crop in the a-band.
3. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the crop leaf area index hyperspectral remote sensing method of claim 1 when the program is executed by the processor.
4. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the crop leaf area index hyperspectral remote sensing monitoring method of claim 1.
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