CN113673366A - Crop growth monitoring method based on remote sensing inversion - Google Patents

Crop growth monitoring method based on remote sensing inversion Download PDF

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CN113673366A
CN113673366A CN202110863047.0A CN202110863047A CN113673366A CN 113673366 A CN113673366 A CN 113673366A CN 202110863047 A CN202110863047 A CN 202110863047A CN 113673366 A CN113673366 A CN 113673366A
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杜一博
朱瑞飞
巩加龙
马经宇
李竺强
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Abstract

The invention discloses a crop growth monitoring method based on remote sensing inversion, which comprises the following steps: carrying out satellite planning shooting on crops to be evaluated to obtain remote sensing images, and preprocessing the remote sensing images to obtain a reflectivity curve; on the basis of a PROSAIL model, a lookup table is constructed by inputting different values of different parameters, and an effective wavelength range is determined at the same time to construct a characteristic value matching algorithm; matching the lookup table with the reflectivity curve by using a characteristic value matching algorithm, and inverting the leaf area index value and the chlorophyll concentration value of the remote sensing image; monitoring the growth vigor of the crops to be evaluated based on a time angle and a space angle by using the leaf area index value and the chlorophyll concentration value, and grading the growth vigor according to an industrial standard and a local standard. The method monitors the growth vigor of the crops from two angles of time and space, and solves the problem that the physical significance of the growth vigor of the crops cannot be clearly monitored in multiple angles and high reliability.

Description

Crop growth monitoring method based on remote sensing inversion
Technical Field
The invention relates to the technical field of agricultural remote sensing, in particular to a crop growth monitoring method based on remote sensing inversion.
Background
The growth of crops refers to the growth condition and the variation trend of crops, and a reasonable population area formed by individuals with strong development is a good growth crop area. The method has the advantages that the growth condition and the change trend of the crops are scientifically, quickly and quantitatively monitored, scientific basis can be provided for formulation and dynamic adjustment of field appropriate management measures, and accurate cultivation is realized to guarantee normal growth of the crops. At present, domestic and foreign methods are mostly based on remote sensing images such as NDVI, EVI and the like, differential value grading is carried out by taking historical data as a standard, the growth vigor of crops is monitored from a time angle, but remote sensing indexes such as NDVI, EVI and the like are only sensitive to vegetation, vegetation and non-vegetation can be well distinguished, the growth vigor monitoring of vegetation has no definite physical significance and has a saturation phenomenon, meanwhile, the growth vigor of crops is monitored only from a time angle and is not comprehensive, the growth vigor condition on the spatial distribution of crops cannot be known, namely, the existing methods and angles are single, and the physical significance is not definite enough.
Therefore, a method for monitoring the growth of crops in a multi-angle and high-reliability manner with clear physical significance is urgently needed.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the invention aims to provide a crop growth monitoring method based on remote sensing inversion, which solves the problem that the physical significance of the crop growth cannot be monitored in multiple angles and high reliability.
In order to achieve the purpose, the embodiment of the invention provides a crop growth monitoring method based on remote sensing inversion, which comprises the following steps: step S1, carrying out satellite planning shooting on crops to be evaluated to obtain remote sensing images, and preprocessing the remote sensing images to obtain a reflectivity curve; step S2, constructing a lookup table by inputting different values of different parameters based on a PROSAIL model, and simultaneously determining an effective wavelength range to construct a characteristic value matching algorithm; step S3, matching the lookup table with the reflectivity curve by using the characteristic value matching algorithm, and inverting the leaf area index value and the chlorophyll concentration value of the remote sensing image; and S4, monitoring the growth vigor of the crops to be evaluated based on a time angle and a space angle by using the leaf area index value and the chlorophyll concentration value, and grading the growth vigor according to an industry standard and a local standard.
According to the crop growth monitoring method based on remote sensing inversion, the physical model is used for inverting vegetation parameters directly representing the vegetation growth state, the obtained vegetation parameters are utilized to monitor the crop growth from two angles of time and space, the result is divided into three grades of good, normal and poor, and the problem that the physical significance of the crop growth cannot be monitored in multiple angles and high reliability is solved.
In addition, the crop growth monitoring method based on remote sensing inversion according to the above embodiment of the present invention may further have the following additional technical features:
further, in an embodiment of the present invention, in step S1, the remote sensing image is subjected to radiation calibration and surface reflectance calculation by using a radiation calibration coefficient and a 6S (second calibration of the Satellite Signal in the Solar spectrum) model, and the remote sensing image is subjected to spatial correspondence by using a geographic automatic registration algorithm, so as to obtain the reflectance curve.
Further, in an embodiment of the present invention, the step S2 specifically includes: step S201, inputting different values of different parameters into the PROSAIL model to obtain a vegetation canopy reflectivity curve set of 400nm-2500nm at intervals of 1nm, namely the lookup table, and setting the range of 400nm-1000nm as the effective wavelength range; step S202, in the effective wavelength range, the lookup table and the characteristic value of the reflectivity curve are respectively constructed, and the characteristic value of the lookup table is matched with the characteristic value of the reflectivity curve, namely, the characteristic value matching algorithm.
Further, in an embodiment of the present invention, in the eigenvalue matching algorithm, the central wavelength of the eigenvalue does not have to completely correspond to the central wavelength of each waveband in the remote sensing image, and only needs to be nearby.
Further, in an embodiment of the present invention, the step S3 specifically includes: step S301, in the effective wavelength range, utilizing a spectral response function of a satellite to carry out normalization processing on the lookup table; and S302, respectively calculating the characteristic values of the normalized lookup table and the reflectivity curve of each pixel, and matching the obtained characteristic values.
Further, in an embodiment of the present invention, the specific matching method in step S302 is: and calculating the sum of the absolute values of the difference values of the characteristic values of the normalized lookup table and the characteristic value of the reflectivity curve of each pixel, wherein the leaf area index and the chlorophyll concentration value corresponding to the minimum lookup table are the vegetation parameter values of the pixel.
Further, in an embodiment of the present invention, the step S4 specifically includes: step S401, carrying out time angle monitoring on the crops to be evaluated by using a relative growth monitoring method, wherein the relative growth monitoring method comprises the following steps: carrying out difference and grading on the monitoring indexes of the crops to be evaluated in a certain period and the average value of the crops to be evaluated in a certain period or a certain period so as to evaluate the growth vigor of the crops to be evaluated; step S402, carrying out space angle monitoring on the crops to be evaluated by using an absolute growth monitoring method, wherein the absolute growth monitoring method comprises the following steps: and grading the monitoring indexes of the crops to be evaluated according to a preset standard, so as to evaluate the growth vigor of the crops to be evaluated from the spatial distribution.
Further, in an embodiment of the present invention, the specific calculation process in step S401 is as follows:
respectively calculating growth monitoring indexes CCDL of remote sensing images of the current crop area and the past crop area, wherein the calculation formula is as follows:
CCDL＝CCD×LAI
in the formula, CCDL is the chlorophyll content of unit land area, and CCD and LAI are the chlorophyll concentration and leaf area index obtained by PROSAIL model inversion;
and (4) carrying out difference on the growth monitoring index CCDL of the remote sensing image of the current crop area and the growth monitoring index CCDL of the remote sensing image of the past crop area, and carrying out grading according to the difference.
Further, in an embodiment of the present invention, the ranking specifically includes: if the difference is greater than 10, the growth vigor is better; the difference value is more than or equal to-10 and less than or equal to 10, the growth vigor is normal; if the difference is less than-10, the growth vigor is poorer.
Further, in an embodiment of the present invention, the specific calculation process in step S402 is as follows: solving a normal distribution probability density function of the leaf area index value and the chlorophyll concentration value, determining an interval (mu-sigma, mu + sigma) according to the normal distribution probability density function, wherein mu is a mean value, sigma is a standard deviation, the interval (mu-sigma, mu + sigma) is used as a normal standard, and grading is carried out, when mu + sigma is less than CCD, and LAI is less than mu-sigma, the growth vigor of the crops to be evaluated is normal; when mu + sigma is smaller than CCD and LAI is smaller than or equal to mu-sigma, the growth vigor of the crops to be evaluated is better; when mu + sigma is less than CCD and mu + sigma is less than LAI, the growth vigor of the crops to be evaluated is better; when the CCD is more than or equal to mu-sigma and less than or equal to mu + sigma and LAI is less than mu-sigma, the growth vigor of the crops to be evaluated is poorer; when the CCD is more than or equal to mu-sigma and less than or equal to mu + sigma and the LAI is more than or equal to mu-sigma and less than or equal to mu-sigma, the growth vigor of the crops to be evaluated is normal; when the CCD is more than or equal to mu-sigma and less than or equal to mu + sigma and the mu + sigma is less than LAI, the growth vigor of the crops to be evaluated is better; when CCD is less than mu-sigma and LAI is less than mu-sigma, the growth vigor of the crops to be evaluated is poor; when the CCD is less than mu-sigma, and the LAI is less than or equal to mu-sigma and less than or equal to mu + sigma, the growth vigor of the crops to be evaluated is poorer; when CCD < mu-sigma and mu + sigma < LAI, the growth vigor of the crops to be evaluated is normal.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a crop growth monitoring method based on remote sensing inversion according to an embodiment of the invention;
FIG. 2 is a flow chart of a specific implementation of a crop growth monitoring method based on remote sensing inversion according to an embodiment of the invention;
FIG. 3 is a graph of the relative growth monitoring results of Wanchang town and fork river town of 23 days 8.2020 in accordance with one embodiment of the present invention;
fig. 4 is a graph showing the results of monitoring the absolute growth of wangchang town and branch river town in 8/23/2020 in accordance with an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The crop growth monitoring method based on remote sensing inversion provided by the embodiment of the invention is described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of a crop growth monitoring method based on remote sensing inversion according to an embodiment of the invention.
Fig. 2 is a specific execution flow chart of the crop growth monitoring method based on remote sensing inversion according to an embodiment of the invention.
As shown in fig. 1 and 2, the crop growth monitoring method based on remote sensing inversion comprises the following steps:
in step S1, the crop to be evaluated is subjected to satellite planning shooting to obtain a remote sensing image, and the remote sensing image is preprocessed to obtain a reflectivity curve.
Further, in an embodiment of the present invention, in step S1, the radiometric calibration and the surface reflectance calculation are performed on the remote sensing image by using the radiometric calibration coefficient and the 6S model, and the remote sensing image is spatially corresponding by using the geographic automatic registration algorithm, so as to obtain a reflectance curve.
Specifically, the crop in the time phase of the growing season is subjected to satellite planning shooting, the shot remote sensing images are subjected to radiometric calibration, atmospheric correction and geographic registration preprocessing to obtain images after atmospheric correction, the same pixel has respective reflectivity at different wave band images, and the reflectivity is sequenced according to the central wavelengths of the different wave band images from small to large, so that a reflectivity curve can be obtained.
In step S2, a lookup table is constructed by inputting different values of different parameters based on the PROSAIL model, and an effective wavelength range is determined at the same time, so as to construct a feature value matching algorithm.
Further, in an embodiment of the present invention, step S2 specifically includes:
step S201, inputting different values of different parameters into a PROSAIL model to obtain a vegetation canopy reflectivity curve set (namely a lookup table) of 400nm-2500nm with 1nm as an interval, and setting 400nm-1000nm as an effective wavelength range;
step S202, in the effective wavelength range, respectively constructing a lookup table and a characteristic value of a reflectivity curve, and matching the characteristic value of the lookup table with the characteristic value of the reflectivity curve, namely a characteristic value matching algorithm.
That is, the PROSAIL model can obtain a set of vegetation canopy reflectance curves of 400nm to 2500nm at 1nm intervals, called a look-up table (LUT), by inputting different values of different parameters, which are shown in table 1.
TABLE 1PROSAIL model input parameters
Figure BDA0003186406960000041
Figure BDA0003186406960000051
By matching with the pixel reflectivity curve of the step S1, the leaf area index and the chlorophyll concentration value contained in the most matched LUT curve are the leaf area index and the chlorophyll concentration value of the pixel, and the leaf area index and the chlorophyll concentration value are obtained by the other pixels through the same method.
The number of spectral curves in the lookup table determines the matching precision and speed, and in order to improve the matching precision as much as possible and reduce the matching times, according to the characteristics of crops, two vegetation biochemical parameter databases of Lopex1993 and Angers2003 are comprehensively referred to, and the parameter range and the parameter step length suitable for the crops are provided. The parameter ranges and parameter step lengths of the inversion of the crop leaf area index and the chlorophyll concentration are shown in tables 2 and 3.
TABLE 2LAI inversion parameter ranges and step sizes
Figure BDA0003186406960000052
TABLE 3CCD inversion parameter Range and step Length
Figure BDA0003186406960000053
Figure BDA0003186406960000061
Wherein tts, tto and psi can be obtained through a remote sensing image header file;
Figure BDA0003186406960000062
psoil is a dry soil proportion coefficient and can be obtained through actual investigation or priori knowledge; LIDF is the mean value of the included angle between the leaf surface and the horizontal ground; car 0.2224 × Cab + 1.1287. Through the set parameter range and parameter step size, a lookup table (LUT) containing 33696 spectral curves is established.
Furthermore, a vegetation canopy reflectivity curve with the interval of 1nm from 400nm to 2500nm can be obtained by operating the PROSAIL model, and when the vegetation canopy reflectivity curve is matched with the pixel reflectivity curve, a redundant wavelength range exists. Sensitivity analysis was performed on leaf area index and chlorophyll concentration in order to remove redundant wavelength ranges and improve matching efficiency. Analysis shows that effective curve matching can be completed for inversion of the leaf area index and the chlorophyll concentration in the wavelength range of 400nm-1000nm, so that the wavelength range of 400nm-1000nm is set as the effective wavelength range.
It can be understood that the most accurate curve matching is point-to-point matching, and the curve with the minimum sum of absolute values of errors is the most matched curve, but the method is large in calculation amount, so that the curve matching algorithm needs to be improved.
According to the embodiment of the invention, from the continuous iterative analysis and the practical effect, in the effective wavelength range, the method for constructing the characteristic value aiming at the inversion of the leaf area index and the chlorophyll concentration is most effective, the calculation efficiency can be greatly improved, and the characteristic value calculation method is as follows:
T1＝(Band842-Band560)/(Band842+Band560) (1)
T2＝(Band865-Band665)/(Band865+Band665) (2)
T3＝(Band783-Band705)/(Band783+Band705) (3)
T4＝(Band945-Band740)/(Band945+Band740) (4)
wherein, Band560, Band665, Band705, Band740, Band783, Band842, Band865 and Band945 are respectively the reflectivities at the central wavelengths of 560nm, 665nm, 705nm, 740nm, 783nm, 842nm, 865nm and 943 nm.
By matching the characteristic value of the lookup table with the characteristic value of each wave band in the reflectivity curve, the obtained result is almost indistinguishable from point-to-point matching, but the calculation efficiency is greatly improved. The central wavelength of each wave band of the remote sensing image does not need to completely correspond to the central wavelength of the characteristic value, and only needs to be near the central wavelength.
In step S3, matching the lookup table with the reflectance curve by using a characteristic value matching algorithm, and inverting the leaf area index value and the chlorophyll concentration value of the remote sensing image.
Further, in an embodiment of the present invention, step S3 specifically includes:
s301, normalizing the lookup table by using a spectral response function of the satellite in an effective wavelength range;
and step S302, respectively calculating the characteristic values of the normalized lookup table and the reflectivity curve of each pixel, and matching the obtained characteristic values.
Specifically, the method includes the steps of operating a PROSAIL model by using the parameter range and the parameter step length determined in step S2, performing normalization processing on the obtained lookup table by using a spectral response function of the satellite, wherein the processed center wavelength corresponds to the center wavelength of each band of the remote sensing image one by one, then performing characteristic value calculation on the reflectivity of each pixel of the processed lookup table (LUT) and the remote sensing image respectively, and matching the obtained characteristic values, wherein the specific method is to calculate the sum of absolute values of the difference values of the characteristic values, and the leaf area index and the chlorophyll concentration value corresponding to the minimum LUT curve are the vegetation parameter value of the pixel, and the matching formula is as follows:
Figure BDA0003186406960000071
wherein Sum is the Sum of absolute values of the difference values of the characteristic values,
Figure BDA0003186406960000072
and
Figure BDA0003186406960000073
respectively, the image pixel and the LUT.
In step S4, the growth vigor of the crop to be evaluated is monitored based on the time angle and the space angle using the leaf area index value and the chlorophyll concentration value, and the growth vigor is graded according to the industry standard and the local standard.
The growth vigor is divided into three levels, namely a better level, a normal level and a poorer level according to an industry standard 'manufacturing standard of satellite remote sensing winter wheat growth monitoring graphic products' and a local standard 'technical regulation of polar orbit satellite monitoring crop growth'.
Further, in an embodiment of the present invention, step S4 specifically includes:
step S401, carrying out time angle monitoring on crops to be evaluated by using a relative growth monitoring method, wherein the relative growth monitoring method comprises the following steps: the monitoring index of a certain expected crop to be evaluated is subjected to difference and grading with the average value of a certain period or a certain period in the past, so that the growth vigor of the crop to be evaluated is evaluated;
step S402, carrying out space angle monitoring on crops to be evaluated by using an absolute growth monitoring method, wherein the absolute growth monitoring method comprises the following steps: and grading the monitoring indexes of the crops to be evaluated according to a preset standard, so as to evaluate the growth vigor of the crops to be evaluated from the spatial distribution.
Specifically, the relative growth monitoring method refers to: the growth of crops is evaluated by differentiating and grading the crop monitoring index expected to be evaluated at a certain time with the average value of the past certain period or a certain period of time, and the growth is monitored from the aspect of time. The chlorophyll concentration obtained by the PROSAIL model inversion is the chlorophyll content on the unit leaf area, the leaf area index is the leaf area on the unit land area, and the product of the chlorophyll concentration and the leaf area index is the chlorophyll content on the unit land area. The chlorophyll content represents the photosynthesis capacity of the vegetation, the higher the chlorophyll content is, the stronger the productivity of the vegetation is, and the chlorophyll content is a key parameter for directly representing the growth state of the vegetation, and the calculation method of the relative growth monitoring index is as follows:
CCDL＝CCD×LAI (6)
wherein CCDL is chlorophyll content in unit land area, and the unit is mu g/cm2CCD and LAI are chlorophyll concentration and leaf area index obtained by PROSAIL model inversion, and the unit is respectively microgram/cm2And cm2/cm2。
Respectively calculating the growth monitoring indexes CCDL of the remote sensing images of the current crop area and the past crop area, and performing difference and classification, wherein the classification method is as follows:
if the difference is greater than 10, the growth vigor is better;
the difference value is more than or equal to-10 and less than or equal to 10, the growth vigor is normal;
if the difference is less than-10, the growth vigor is poorer.
Further, the absolute growth monitoring method is to grade the crop monitoring indexes according to a certain standard, so as to evaluate the growth of crops in terms of spatial distribution, and is to monitor the growth in terms of space. The leaf area index and the chlorophyll concentration obtained by the PROSAIL model inversion respectively represent the density degree and the productivity of the vegetation, and the two are key parameters directly representing the growth state of the vegetation. Therefore, the comprehensive growth monitoring on the spatial distribution of the crops is carried out from the two directions of the dense vegetation degree and the strong and weak productivity.
For a crop population, the range represented by the mode of the crop population characterizes the normal grade of the crop population, the range greater than the normal grade characterizes a better grade, and the range less than the normal grade characterizes a poorer grade. The chlorophyll concentration and leaf area index images obtained by crop remote sensing image inversion have histogram distribution conforming to normal distribution, and 68% of numerical values are located in an interval (mu-sigma, mu + sigma) (mu is a mean value, sigma is a standard deviation) according to a normal distribution probability density function, so that the interval is used as a normal standard for grading, and the absolute growth grading standard of crops is shown in table 4:
TABLE 4 Absolute growth monitoring grading Standard
Figure BDA0003186406960000081
The following example is an experimental result of the crop growth monitoring method based on remote sensing inversion provided by the embodiment of the invention to further explain the embodiment of the invention.
For example, the growth monitoring of rice in marchan town and bifurcation town 8, 19 days in 2019 and 23 days in 8, 2020 is performed, and the image is Jilin number one spectral star image with the resolution of 5 meters. Fig. 3 and 4 show the results of monitoring the growth of rice by the method proposed in the present patent, which are the results of monitoring the relative growth and the results of monitoring the absolute growth, respectively.
From the aspect of time, the growth vigor of Wanchang town and Fulu Zhengpaddy rice in 8 and 23 days in 2020 is better for most of the growth vigors, poorer for a small part and normal for a small part compared with 19 days in 8 and 19 days in 2019; from the space, the growth vigor of Wanchang town and Fugu river is mostly normal, the growth vigor of a small part is poor and the growth vigor of a small part is good in 8-23 days in 2020. The relative growth monitoring result and the absolute growth monitoring result are consistent with the field investigation result, and the result shows that the method provided by the embodiment of the invention can be used for monitoring the growth of crops in a multi-angle and high-reliability manner with clear physical significance under the condition of relatively less manpower and material resources.
To sum up, the crop growth monitoring method based on remote sensing inversion provided by the embodiment of the invention can be used for carrying out physical and clear monitoring on crop growth in multiple angles and with high reliability under the condition of relatively less input of manpower and material resources, is also basic data relied on by accurate agriculture, and meanwhile, along with the continuous development of agriculture, the requirement for monitoring the crop growth can be continuously increased, the physical and clear monitoring on the crop growth in multiple angles and with high reliability can be carried out, so that the growth condition of crops can be known, corresponding management measures can be taken to ensure the normal growth of the crops, and the method has important significance for providing scientific basis for national macro regulation and control of grain markets and establishment of international grain trade policies and guaranteeing national grain safety.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (10)

1. A crop growth monitoring method based on remote sensing inversion is characterized by comprising the following steps:
step S1, carrying out satellite planning shooting on crops to be evaluated to obtain remote sensing images, and preprocessing the remote sensing images to obtain a reflectivity curve;
step S2, constructing a lookup table by inputting different values of different parameters based on a PROSAIL model, and simultaneously determining an effective wavelength range to construct a characteristic value matching algorithm;
step S3, matching the lookup table with the reflectivity curve by using the characteristic value matching algorithm, and inverting the leaf area index value and the chlorophyll concentration value of the remote sensing image;
and S4, monitoring the growth vigor of the crops to be evaluated based on a time angle and a space angle by using the leaf area index value and the chlorophyll concentration value, and grading the growth vigor according to an industry standard and a local standard.
2. The crop growth monitoring method based on remote sensing inversion according to claim 1, wherein in the step S1, radiometric calibration and surface reflectivity calculation are performed on the remote sensing images by using radiometric calibration coefficients and a 6S model, and the reflectivity curve is obtained by spatially corresponding the remote sensing images by using a geographic automatic registration algorithm.
3. The remote sensing inversion-based crop growth monitoring method according to claim 1, wherein the step S2 specifically comprises:
step S201, inputting different values of different parameters into the PROSAIL model to obtain a vegetation canopy reflectivity curve set of 400nm-2500nm at intervals of 1nm, namely the lookup table, and setting the range of 400nm-1000nm as the effective wavelength range;
step S202, in the effective wavelength range, the lookup table and the characteristic value of the reflectivity curve are respectively constructed, and the characteristic value of the lookup table is matched with the characteristic value of the reflectivity curve, namely, the characteristic value matching algorithm.
4. The crop growth monitoring method based on remote sensing inversion according to claim 3, wherein in the eigenvalue matching algorithm, the central wavelength of the eigenvalue does not necessarily completely correspond to the central wavelength of each waveband in the remote sensing image, and only needs to be nearby.
5. The remote sensing inversion-based crop growth monitoring method according to claim 1, wherein the step S3 specifically comprises:
step S301, in the effective wavelength range, utilizing a spectral response function of a satellite to carry out normalization processing on the lookup table;
and S302, respectively calculating the characteristic values of the normalized lookup table and the reflectivity curve of each pixel, and matching the obtained characteristic values.
6. The remote sensing inversion-based crop growth monitoring method according to claim 5, wherein the concrete matching method in the step S302 is as follows:
and calculating the sum of the absolute values of the difference values of the characteristic values of the normalized lookup table and the characteristic value of the reflectivity curve of each pixel, wherein the leaf area index and the chlorophyll concentration value corresponding to the minimum lookup table are the vegetation parameter values of the pixel.
7. The remote sensing inversion-based crop growth monitoring method according to claim 1, wherein the step S4 specifically comprises:
step S401, carrying out time angle monitoring on the crops to be evaluated by using a relative growth monitoring method, wherein the relative growth monitoring method comprises the following steps: carrying out difference and grading on the monitoring indexes of the crops to be evaluated in a certain period and the average value of the crops to be evaluated in a certain period or a certain period so as to evaluate the growth vigor of the crops to be evaluated;
step S402, carrying out space angle monitoring on the crops to be evaluated by using an absolute growth monitoring method, wherein the absolute growth monitoring method comprises the following steps: and grading the monitoring indexes of the crops to be evaluated according to a preset standard, so as to evaluate the growth vigor of the crops to be evaluated from the spatial distribution.
8. The remote sensing inversion-based crop growth monitoring method according to claim 7, wherein the concrete calculation process in the step S401 is as follows:
respectively calculating growth monitoring indexes CCDL of remote sensing images of the current crop area and the past crop area, wherein the calculation formula is as follows:
CCDL＝CCD×LAI
in the formula, CCDL is the chlorophyll content of unit land area, and CCD and LAI are the chlorophyll concentration and leaf area index obtained by PROSAIL model inversion;
and (4) carrying out difference on the growth monitoring index CCDL of the remote sensing image of the current crop area and the growth monitoring index CCDL of the remote sensing image of the past crop area, and carrying out grading according to the difference.
9. The crop growth monitoring method based on remote sensing inversion according to claim 8, wherein the grading specifically comprises:
if the difference is greater than 10, the growth vigor is better;
the difference value is more than or equal to-10 and less than or equal to 10, the growth vigor is normal;
if the difference is less than-10, the growth vigor is poorer.
10. The remote sensing inversion-based crop growth monitoring method according to claim 7, wherein the concrete calculation process of the step S402 is as follows:
solving a normal distribution probability density function of the leaf area index value and the chlorophyll concentration value, determining an interval (mu-sigma, mu + sigma) according to the normal distribution probability density function, wherein mu is a mean value, sigma is a standard deviation, and carrying out grade division by taking the interval (mu-sigma, mu + sigma) as a normal standard,
when mu + sigma is less than CCD and LAI is less than mu-sigma, the growth vigor of the crops to be evaluated is normal;
when mu + sigma is smaller than CCD and LAI is smaller than or equal to mu-sigma, the growth vigor of the crops to be evaluated is better;
when mu + sigma is less than CCD and mu + sigma is less than LAI, the growth vigor of the crops to be evaluated is better;
when the CCD is more than or equal to mu-sigma and less than or equal to mu + sigma and LAI is less than mu-sigma, the growth vigor of the crops to be evaluated is poorer;
when the CCD is more than or equal to mu-sigma and less than or equal to mu + sigma and the LAI is more than or equal to mu-sigma and less than or equal to mu-sigma, the growth vigor of the crops to be evaluated is normal;
when the CCD is more than or equal to mu-sigma and less than or equal to mu + sigma and the mu + sigma is less than LAI, the growth vigor of the crops to be evaluated is better;
when CCD is less than mu-sigma and LAI is less than mu-sigma, the growth vigor of the crops to be evaluated is poor;
when the CCD is less than mu-sigma, and the LAI is less than or equal to mu-sigma and less than or equal to mu + sigma, the growth vigor of the crops to be evaluated is poorer;
when CCD < mu-sigma and mu + sigma < LAI, the growth vigor of the crops to be evaluated is normal.
CN202110863047.0A 2021-07-29 2021-07-29 Crop growth monitoring method based on remote sensing inversion Pending CN113673366A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115983503A (en) * 2023-03-18 2023-04-18 杭州领见数字农业科技有限公司 Crop maturity prediction method, equipment and storage medium
CN117859549A (en) * 2024-03-11 2024-04-12 中化现代农业有限公司 Cotton variable topping method and device, electronic equipment and storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115983503A (en) * 2023-03-18 2023-04-18 杭州领见数字农业科技有限公司 Crop maturity prediction method, equipment and storage medium
CN117859549A (en) * 2024-03-11 2024-04-12 中化现代农业有限公司 Cotton variable topping method and device, electronic equipment and storage medium

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