CN115758232A - Wheat seedling condition classification method and system based on fitting model - Google Patents
Wheat seedling condition classification method and system based on fitting model Download PDFInfo
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Abstract
The invention provides a wheat seedling condition classification method and system based on a fitting model, wherein MODIS data of winter wheat in a period before overwintering in a preset time period are received through a satellite remote sensing receiving system; carrying out calibration, geometric correction, projection and coordinate system conversion processing on the MODIS data; collecting and analyzing ground data; respectively calculating the ratio of the area of each seedling in the seedling condition investigation data to the preset sowing area to obtain the percentage of each seedling in each planting area in a preset time period; selecting a normalized vegetation index NDVI as a remote sensing evaluation index, and constructing a remote sensing monitoring model; the MODIS data are analyzed in a multivariate regression analysis mode; and outputting the fitting result after analysis. The invention realizes the dynamic and quantitative determination of the classification threshold value of the seedling condition of winter wheat monitored by satellite remote sensing. The wheat seedling condition classification based on the fitting model is more accurate, and the accuracy of seedling condition classification satellite remote sensing monitoring is improved.
Description
Technical Field
The invention relates to the technical field of wheat seedling condition analysis, in particular to a wheat seedling condition classification method and system based on a fitting model.
Background
Wheat is used as crops which are widely distributed in the world and have large planting area and trade total amount, and has high influence on the supply of grains. For agricultural production and management departments, the method can timely, accurately and objectively obtain the growth information of the winter wheat, and has very important significance for agricultural decision and management. The satellite remote sensing data has the advantages of wide coverage, large information amount, quick updating period, strong situational property and the like, and is an important means for agricultural informatization construction at present. The winter wheat seedling condition classification in the wintering period and the green turning period is an important parameter index for agricultural production departments to measure the growth vigor of winter wheat in the current year, is an important basis for guiding wheat production activities, and has important significance for strengthening crop production management and formulating scientific and reasonable agricultural management.
At present, the transverse and longitudinal quantitative comparison of the wheat seedling condition is always a difficult problem to be solved no matter in scientific research or agricultural management and decision. The quantitative calculation problem related to the diagnosis and analysis of the wheat seedling condition is lack of a unified and effective calculation method due to the fact that research is not deep enough. Moreover, in the process of wheat seedling condition classification evaluation, certain qualitative analysis or subjective judgment components are doped, and the obtained result has certain deviation from the actual condition. In the prior art, when agricultural management is performed, experts are organized to perform classified evaluation on wheat seedling conditions through field sampling investigation at the beginning stage of the winter-crossing period every year, although the investigation result can give the percentages of vigorous seedlings, first-class seedlings, second-class seedlings and third-class seedlings in cities in all regions, the spatial distribution information of the wheat seedlings cannot be provided, and agricultural production units are difficult to perform farmland management measures in a targeted manner.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a wheat seedling condition classification method based on a fitting model, which utilizes long-time sequence satellite data and agricultural department seedling condition survey data in a corresponding period, and uses vegetation indexes inverted by satellite remote sensing to be correspondingly matched with the agricultural survey data, so that a seedling condition classification statistical inversion model with strong correlation with the agricultural survey data is obtained, and the accuracy of the seedling condition classification satellite remote sensing monitoring is improved.
The wheat seedling condition evaluation method based on the fitting model comprises the following steps:
firstly, receiving MODIS data of winter wheat in a period before overwintering in a preset time period through a satellite remote sensing receiving system;
step two, carrying out calibration, geometric correction, projection and coordinate system conversion processing on MODIS data;
step three, collecting and analyzing the ground data;
calculating the ratio of the areas of the vigorous seedlings, the first class seedlings, the second class seedlings and the third class seedlings in the seedling condition investigation data to the preset sowing area respectively to obtain the percentage of the vigorous seedlings, the first class seedlings, the second class seedlings and the third class seedlings in each planting area in a preset time period;
selecting the normalized vegetation index NDVI as a remote sensing evaluation index, and constructing a remote sensing monitoring model;
analyzing the MODIS data in a multiple regression analysis mode;
and step six, outputting the fitting result after analysis.
It should be further noted that, in the step one, the received MODIS data are updated data for a minimum of 2 times of day and 2 times of night each day.
It should be further noted that the second step further includes:
(1) Unpacking MODIS data, reading CCSDS format of the data, extracting time, scanning lines, various view field data frames, lost packets, telemetering data, engineering data, satellite attitude information, ephemeris information and detection data with various resolutions, and storing the data according to HDF format in a layered and classified manner;
(2) Calculating the geographical longitude and latitude of each detection data according to the MODIS data, and storing the geographical longitude and latitude in an HDF format;
(3) And calculating the reflectivity or radiation value of the detection data of each channel according to the satellite-borne calibration coefficient issued with the data, and storing the reflectivity or radiation value in an HDF format.
It should be further noted that, step four further includes: calculating a normalized vegetation index NDVI based on single-time satellite data,
in the formula:
I NDVI monitoring the NDVI of the wheat pixels for a certain satellite for a single time;
R NIR is the reflectivity of the pixel near-infrared band;
R RED the reflectivity of a red light wave band of the pixel;
based on each single normalized vegetation index in the determined time period, selecting the maximum value of the vegetation index of the same pixel as the value of the pixel after multiple time synthesis for calculation, wherein the calculation mode is as the formula (2)
I NDVI (i)＝max(I NDVI (i,1),I NDVI (i,2),…,I NDVI (i,t)) (2)
In the formula:
I NDVI (i) NDVI after the ith winter wheat pixel is synthesized;
i is the sequence number of the winter wheat pixel in the region;
I NDVI (i, t) is NDVI of the ith winter wheat pixel at the tth time;
the total observation time of the pixel in a given observation time period is set;
calculating an NDVI mean value which is an average value of the NDVI maximums of all winter wheat pixels in the region after synthesis in a calculation mode shown as a formula (3);
in the formula:
m represents the total number of pixels of winter wheat in the region;
i-the sequence number of the winter wheat pixels in the region;
I i -NDVI after synthesis of the ith winter wheat pixel in the region;
r-region code.
It should be further noted that the multiple regression analysis method in step five includes:
setting the standard deviation of NDVI data of all pixels in a certain area in a linear regression relation model as a variable, and performing multivariate fitting;
the multiple regression relation model is formula (5);
in the formula:
t is a critical value of classified NDVI of wheat seedling conditions in a certain area; respectively calculating the classification critical values of vigorous seedling and first class seedling, first class seedling and second class seedling and third class seedling, and recording as T 0/1 ,T 1/2 And T 2/3 ；
sigma is NDVI standard deviation of a certain region;
and a, b and c are regression coefficients.
It is further noted that in the method, vigorous seedling and first seedling critical values, first seedling and second seedling critical values and second seedling and third seedling critical values are respectively set;
the critical value of the vigorous seedling and the first class seedling is as follows:
R 2 ：0.7839；
critical values of first-class seedlings and second-class seedlings:
R 2 ：0.8543；
critical values of second-class seedlings and third-class seedlings:
R 2 ：0.7976。
the invention also provides a wheat seedling condition evaluation system based on the fitting model, which comprises the following steps: the system comprises a winter wheat pre-winter data acquisition module, an MODIS data preprocessing module, a ground data analysis module, a remote sensing monitoring module, a data analysis module and a result output module;
the winter wheat pre-wintering data acquisition module is used for receiving MODIS data of the winter wheat pre-wintering period in a preset time period through a satellite remote sensing receiving system;
the MODIS data preprocessing module is used for carrying out calibration, geometric correction, projection and coordinate system conversion processing on the MODIS data;
the ground data analysis module is used for collecting and analyzing ground data; calculating the ratio of the areas of the vigorous seedlings, the first class seedlings, the second class seedlings and the third class seedlings in the seedling condition investigation data to the preset sowing area respectively to obtain the percentage of the vigorous seedlings, the first class seedlings, the second class seedlings and the third class seedlings in each planting area in a preset time period;
the remote sensing monitoring module is used for selecting the normalized vegetation index NDVI as a remote sensing evaluation index and constructing a remote sensing monitoring model;
the data analysis module is used for analyzing the MODIS data in a multiple regression analysis mode;
and the result output module is used for outputting the fitting result after analysis.
According to the technical scheme, the invention has the following advantages:
the system utilizes a long-time sequence set of years of MODIS satellite data and winter wheat seedling condition field survey data of agricultural departments corresponding to the years in the winter period, utilizes statistics such as mean value, standard deviation and the like of vegetation indexes of winter wheat pixels to carry out linear or nonlinear fitting with the vegetation indexes corresponding to the seedling condition classification field survey data, establishes a threshold algorithm model of satellite remote sensing seedling condition classification monitoring, calculates and analyzes multi-field seedling condition classification thresholds, provides an objective and quantitative method for winter wheat seedling condition classification remote sensing monitoring, and provides objective basis and technical support for refined management of winter wheat production.
According to the method, long-time sequence satellite data and seedling condition survey data of corresponding periods are utilized, and a seedling condition classification statistics inversion model with strong data correlation is obtained based on the corresponding relation between vegetation indexes and the survey data which are inverted by satellite remote sensing, so that the accuracy of remote sensing monitoring of seedling condition classification satellites is improved.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of a wheat seedling condition classification method based on a fitting model;
FIG. 2 is a synthetic diagram of the maximum value of the winter wheat pixel vegetation index (NDVI) before overwintering in Shandong province;
FIG. 3 is a graph of a normal distribution;
FIG. 4 is a schematic diagram of a wheat seedling classification system based on a fitting model.
Detailed Description
The wheat seedling condition classification system based on the fitting model is used for facilitating the agricultural production management department to know the production conditions of wheat in various regions in the actual production of winter wheat, dividing the winter wheat into first-class seedlings, second-class seedlings, third-class seedlings and vigorous seedlings according to the growth vigor and seedling classification indexes of the local winter wheat, and estimating the area and the proportion of each seedling. In the prior art, wheat seedling condition classification information is obtained mainly by ground sampling investigation, but under the conventional condition, the method needs a large amount of manpower and material resources for support, the information aging is slow, only seedling condition classification data of each region can be obtained, and the specific spatial distribution conditions of first-class seedlings, second-class seedlings, third-class seedlings and vigorous seedlings cannot be known. The method has the advantages of fast and objective timeliness, large monitoring range and the like through satellite remote sensing data, can invert quantitative information of the growth vigor of the wheat in the whole province by utilizing a satellite remote sensing technology, can obtain classification results and spatial distribution conditions of the wheat seedling conditions in various regions by combining with classification index parameters of the seedling conditions of the satellite remote sensing technology, and can provide a remote sensing monitoring result which can objectively reflect the actual growth condition of the wheat by combining with sampling ground survey verification, thereby providing technical reference for production management departments and decision-making departments. And remote sensing monitoring indexes such as vegetation indexes can better reflect the growth condition of the winter wheat and can be used for evaluating the growth vigor of the winter wheat.
The invention provides a wheat seedling condition evaluation system based on a fitting model, which comprises: the system comprises a data acquisition module before winter wheat overwintering, an MODIS data preprocessing module, a ground data analysis module, a remote sensing monitoring module, a data analysis module and a result output module;
the winter wheat pre-wintering data acquisition module is used for receiving MODIS data of the winter wheat pre-wintering period in a preset time period through a satellite remote sensing receiving system;
the MODIS data preprocessing module is used for carrying out calibration, geometric correction, projection and coordinate system conversion processing on the MODIS data;
the ground data analysis module is used for collecting and analyzing ground data; calculating the ratio of the areas of the vigorous seedlings, the first class seedlings, the second class seedlings and the third class seedlings in the seedling condition investigation data to the preset sowing area respectively to obtain the percentage of the vigorous seedlings, the first class seedlings, the second class seedlings and the third class seedlings in each planting area in a preset time period;
the remote sensing monitoring module is used for selecting the normalized vegetation index NDVI as a remote sensing evaluation index and constructing a remote sensing monitoring model;
the data analysis module is used for analyzing the MODIS data in a multiple regression analysis mode; and the result output module is used for outputting the fitting result after analysis.
Therefore, the system utilizes the MODIS satellite data long-time sequence set for many years and the winter wheat seedling condition field investigation data of the agricultural department corresponding to the years in the winter period, utilizes the statistics of the average value, the standard deviation and the like of the vegetation index of the winter wheat pixel to carry out linear or nonlinear fitting with the vegetation index corresponding to the seedling condition classification field investigation data, establishes a threshold algorithm model for satellite remote sensing seedling condition classification monitoring, calculates and analyzes the multi-field seedling condition classification threshold, provides an objective and quantitative method for the winter wheat seedling condition classification remote sensing monitoring, and provides objective basis and technical support for the refined management of winter wheat production.
According to the method, long-time sequence satellite data and seedling condition survey data of corresponding periods are utilized, and a seedling condition classification statistics inversion model with strong data correlation is obtained based on the corresponding relation between vegetation indexes and the survey data which are inverted by satellite remote sensing, so that the accuracy of remote sensing monitoring of seedling condition classification satellites is improved.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a wheat seedling condition evaluation method based on a fitting model, which comprises the following steps: as shown in fig. 1, S101, receiving MODIS data of winter wheat in a period before winter in a preset time period through a satellite remote sensing receiving system;
specifically, the satellite data used by the invention is based on MODIS data received by a satellite remote sensing receiving system. The MODIS data has a wide range of bands including 36 bands, and the data spatial resolution includes three dimensions of 250 meters, 500 meters and 1000 meters. (table 1. The data have higher practical value for the comprehensive research of the earth science and the research of classification of land, atmosphere and ocean; furthermore, the TERRA and AQUA satellites are both sun-synchronous polar orbiting satellites, TERRA passing in the morning and AQUA passing in the afternoon of the local time.
TERRA and MODIS data on AQUA are matched on time updating frequency, and by adding night crossing data, 2 times of day and 2 times of night updating data can be obtained for receiving MODIS data at least. Such data update frequency is of great practical value for real-time earth observation and research of earth systems of frequency in the day.
TABLE 1 MODIS band distribution characteristics
S102, carrying out calibration, geometric correction, projection and coordinate system conversion processing on MODIS data;
in the embodiment of the invention, MODIS data of winter wheat overwintering periods of preset years are collected and sorted, and preprocessing such as calibration, geometric correction, projection, coordinate system conversion, cloud detection and the like is carried out on satellite data, and all grid data and vector data are unified into a geographic coordinate system and a projection method.
For the EOS satellite MODIS data, IMAPP (International MODIS/aires Preprocessing Package), or International MODIS/aires Preprocessing software Package, is adopted, and is provided for any user with MODIS direct broadcast data. The basic computing process of the IMAPP software package is as follows:
PDS (i.e. MODIS 0 grade) data = > (upnp pack unpacking program)
= > MOD01 (namely MODIS 1A) data = > (geocoate location procedure)
= > MOD03 data = > (Calibrate scaling program)
Data of = > MOD02 (i.e., MODIS 1B)
The corresponding components include:
(1) Unpack unpacking program: the method comprises the steps of reading a CCSDS format of a PDS file, extracting time, scanning lines, various field data frames, lost packets, telemetering data, engineering data, satellite attitude information, ephemeris information, detection data with various resolutions and the like, and storing the information in a layered and classified manner according to an HDF format.
(2) Geolocate location program: and calculating the geographical latitude and longitude of each detection data according to the data (such as time, telemetering data, attitude, ephemeris and other information and some auxiliary input data) of the MOD01, and storing the information in an HDF format.
(3) Calibrate scaling program: and calculating the reflectivity or radiation value of the detection data of each channel according to the satellite-borne calibration coefficient and some auxiliary input data issued with the data, and storing the reflectivity or radiation value in an HDF format.
S103, collecting and analyzing ground data;
in the embodiment of the invention, the standard for dividing the seedling condition of winter wheat before overwintering always refers to each index in the table 2. And taking the grade market as a unit, and respectively calculating the ratio of the areas of the vigorous seedlings, the first class seedlings, the second class seedlings and the third class seedlings in the seedling condition investigation result to the sowing area of the whole market to obtain the percentage of the vigorous seedlings, the first class seedlings, the second class seedlings and the third class seedlings in each grade market in 2008-2019.
TABLE 2 Standard of division of winter wheat seedling conditions before overwintering
S104, selecting the normalized vegetation index NDVI as a remote sensing evaluation index, and constructing a remote sensing monitoring model;
the selection mode of the remote sensing index comprises the following steps: the normalized vegetation index (NDVI) is the most common index for evaluating the growth vigor of the vegetation, compared with other vegetation indexes, the NDVI can better reflect the growth vigor of crops, the NDVI is sensitive to the change of soil background, and when the vegetation coverage is less than 15%, the NDVI of the vegetation is slightly larger than bare soil; when the vegetation coverage is 25-80%, the NDVI is approximately linearly increased along with the increase of the vegetation coverage; and the NDVI can eliminate most of the changes of irradiance related to instrument calibration, solar angles, terrain, cloud shadow and atmospheric conditions, enhances the response capability to vegetation, and is one of dozens of existing vegetation indexes which are most widely applied at present. Therefore, the normalized vegetation index NDVI is selected as a remote sensing evaluation index.
(1) Single time normalized difference vegetation index
Normalized vegetation index (NDVI) is calculated for single time satellite data, see equation (1).
In the formula:
I NDVI -NDVI of a single hour of wheat pels monitored by a certain satellite;
R NIR -the reflectivity of the near infrared band of the pixel;
R RED -the reflectivity of the red band of the picture element.
(2) NDVI Synthesis
And aiming at each single normalized vegetation index in the determined time period, selecting the maximum value of the vegetation index of the same pixel as the value of the pixel after multiple time synthesis, and obtaining a formula (2).
I NDVI (i)＝max(I NDVI (i,1),I NDVI (i,2),…,I NDVI (i,t))............(2)
In the formula:
I NDVI (i) -NDVI after synthesis of the ith winter wheat pel;
i-the sequence number of the winter wheat pixels in the region;
I NDVI (i, t) -the nth NDVI of the ith winter wheat pel time t;
t- - -the total number of observations of the pel in a given observation period.
(3) Average value of NDVI
The NDVI mean value is the mean value of the synthesized maximum NDVI values of all winter wheat pixels in the region, and is shown in a formula (3).
In the formula:
m represents the total number of pixels of winter wheat in the region;
i-the sequence number of the winter wheat pixels in the region;
I i -NDVI after synthesis of the ith winter wheat pixel in the region;
r-region code.
Taking the Shandong province before overwintering in 2008-2019 as an example, the NDVI maximum value synthesis of the land surface of the Shandong province before overwintering in 2008-2019 is respectively calculated, the winter wheat pixels are extracted by using the distribution vector data of the winter wheat in the corresponding year, and the NDVI data of the Shandong province before overwintering in 2008-2019 are established. The average NDVI values of winter wheat pixels in Shandong province in 2008-2019 are respectively calculated, as can be seen from a curve shown in FIG. 2, the NDVI values are between 219.74 and 277.25, the NDVI fluctuation is large year by year in 2008-2012, and the average NDVI value in this period is 250.35; the curve fluctuation then becomes smaller, with an average value of 271.35 in 2013-2019.
Constructing a remote sensing monitoring model: in biology, certain characteristics of the same population, such as plant height, spike length, etc., of a wheat population generally conform to a normal distribution. Because the NDVI is widely accepted in the application of inversion of vegetation growth, and the NDVI sequence of the satellite remote sensing wheat pixel in a certain area is approximately in normal distribution.
Normal distribution (also called "Normal distribution"), also known as Gaussian distribution (Gaussian distribution), is a bell-shaped curve with low ends and high middle, and is symmetrical left and right, so it is often called a bell-shaped curve, as shown in fig. 3. If the random variable X obeys a mathematical expectation of mu and the variance of sigma 2 Normal distribution of (d) is expressed as N (μ, σ) 2 ). The expected value μ of a normal distribution determines its position and the standard deviation σ determines the amplitude of the distribution. When μ =0, σ =1Is a standard normal distribution.
The normal distribution probability density function is:
through counting the NDVI critical value corresponding to the classification percentage of the wheat seedling condition in ground field investigation for 10 years (2008-2017 years), a certain corresponding relation exists between the NDVI critical value of a certain area and the average value and the standard deviation of the NDVI of the wheat pixels in the area in the current year. Therefore, a parameter regression equation of the NDVI critical value of a certain region and the corresponding NDVI average value can be established, and the winter wheat seedling condition classification critical value can be dynamically determined.
S105, analyzing MODIS data in a multivariate regression analysis mode;
and S106, outputting the fitting result after analysis.
The multiple regression analysis mode in the fifth step of the invention comprises the following steps: from the expression of positive distribution, the standard deviation is a parameter expressing positive distribution, in addition to the average value. Therefore, the standard deviation of the NDVI data of all pixels in a certain area can be added in the regression model to be used as another variable for carrying out multivariate fitting.
The multiple regression relation model is formula (5);
in the formula: t is a critical value of classified NDVI of the wheat seedling conditions in a certain area; calculating classification critical values of vigorous seedlings and first class seedlings, first class seedlings and second class seedlings and third class seedlings respectively, and recording the classification critical values as T 0/1 ,T 1/2 And T 2/3 ；
sigma is NDVI standard deviation of a certain region;
and a, b and c are regression coefficients.
A multiple regression model is established, and parameters, goodness of fit and F test conditions of the model are shown in Table 3.
TABLE 3 multiple regression fitting model parameters, goodness of fit, and Significance F
The invention establishes a unified regression model by taking the data of the whole province range as a whole. The expression is as follows:
the critical value of vigorous seedling and first class seedling is as follows:
R 2 ：0.7839；
critical values of first-class seedlings and second-class seedlings:
R 2 ：0.8543；
critical values of second-class seedlings and third-class seedlings:
R 2 ：0.7976。
in order to verify the effect of the regression model in estimating the wheat seedling classification, the corresponding data of 2018, 2019 and 2020 are used for verifying the regression model. In general, the difference between the estimated value of the seedling classification model of 16 cities and the ground survey value is in a reasonable range.
In order to quantitatively describe the validation effect of the fitted model, analysis can be performed from the perspective of the error between the fitted estimated value and the actual value. Firstly, calculating the percentages of first class seedlings, second class seedlings, third class seedlings and vigorous seedlings corresponding to the fitting values of the regression model, and then respectively calculating the absolute value | E of the absolute errors of the numerical values and the ground investigation values ai Considering that a certain difference exists between the planting areas of winter wheat in each city of Shandong province, the contribution of the seedling condition classification percentage of each city of Shandong province to the seedling condition classification percentage of the whole province is different. Therefore, when describing model verification errors, multiplying the fitting absolute errors of various seedlings in each city by the area coefficient S of the corresponding year i (S i The planting area of a certain city divided by the planting area of the whole province) as a weighted error | E ai |S i When the error average value of various seedlings in the whole province is analyzed, the sum of the weighted errors of 16 cities is required: sigma | E ai |S i 。
The average error value of each seedling in the whole province analyzed by binary regression in 2018 is 3.31%, which is smaller than the result (5.08%) analyzed by unitary regression. In 2019, the mean absolute errors of the univariate and dyadic regression analyses were 6.64% and 4.52%, respectively, and in 2020, the two values were 5.06% and 4.54%, respectively. The model established by the unary regression method and the binary regression method has a good effect on the fitting of the seedling condition classification, but the fitting result of the binary regression is better than that of the unary regression in general.
From the perspective of four classifications of first-class seedlings, second-class seedlings, third-class seedlings and vigorous seedlings, in 2018, the average values of 16-terrain-market errors of the classification fitting results of the unitary regression analysis are respectively 0.34 of first-class seedlings, 0.36 of second-class seedlings, 0.13 of third-class seedlings and 0.23 of vigorous seedlings, and correspondingly, the median of the fitting results of the binary regression analysis is respectively 0.33 of first-class seedlings, 0.29 of second-class seedlings, 0.11 of third-class seedlings and 0.12 of vigorous seedlings. The fitting error of the binary regression model is smaller than that of the univariate regression model for the first-class seedlings, the second-class seedlings, the third-class seedlings and the vigorous seedlings.
The wheat seedling condition evaluation method based on the fitting model provided by the invention is combined with the units and algorithm steps of each example described in the embodiment disclosed in the text, and can be realized by electronic hardware, computer software or the combination of the two. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
As will be appreciated by one skilled in the art, various aspects of the fit model based wheat seedling condition assessment methods provided herein can be embodied as a system, method, or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. A wheat seedling condition evaluation method based on a fitting model is characterized by comprising the following steps:
firstly, receiving MODIS data of winter wheat in a period before winter in a preset time period through a satellite remote sensing receiving system;
secondly, calibrating, geometrically correcting, projecting and converting a coordinate system on the MODIS data;
step three, collecting and analyzing ground data;
respectively calculating the ratio of the areas of the vigorous seedling, the first class seedling, the second class seedling and the third class seedling in the seedling condition investigation data to the preset sowing area to obtain the percentage of the vigorous seedling, the first class seedling, the second class seedling and the third class seedling in each planting area in a preset time period;
selecting the normalized vegetation index NDVI as a remote sensing evaluation index, and constructing a remote sensing monitoring model;
analyzing MODIS data in a multivariate regression analysis mode;
and step six, outputting the fitting result after analysis.
2. The wheat seedling condition evaluation method based on the fitting model according to claim 1,
in step one, the received MODIS data are updated data for a minimum of 2 days and 2 nights per day.
3. The wheat seedling condition evaluation method based on the fitting model according to claim 1,
the second step further comprises:
(1) Unpacking MODIS data, reading CCSDS format of the data, extracting time, scanning lines, various view field data frames, lost packets, telemetering data, engineering data, satellite attitude information, ephemeris information and detection data with various resolutions, and storing the data according to HDF format in a layered and classified manner;
(2) Calculating the geographical longitude and latitude of each detection data according to the MODIS data, and storing the geographical longitude and latitude in an HDF format;
(3) And calculating the reflectivity or radiation value of the detection data of each channel according to the satellite-borne calibration coefficient issued with the data, and storing the reflectivity or radiation value in an HDF format.
4. The wheat seedling condition evaluation method based on the fitting model according to claim 1,
the fourth step also comprises: calculating a normalized vegetation index NDVI based on single-time satellite data,
in the formula:
I NDVI monitoring the NDVI of the wheat pixels for a certain satellite for a single time;
R NIR is the reflectivity of the pixel near-infrared band;
R RED the reflectivity of a red light wave band of the pixel;
based on each single normalized vegetation index in the determined time period, selecting the maximum value of the vegetation index of the same pixel as the value of the pixel after multiple time synthesis for calculation, wherein the calculation mode is as the formula (2)
I NDVI (i)＝max(I NDVI (i,1),I NDVI (i,2),…,I NDVI (i,t)) (2)
In the formula:
I NDVI (i) NDVI after the ith winter wheat pixel is synthesized;
i is the sequence number of the winter wheat pixels in the region;
I NDVI (i, t) is the NDVI of the ith winter wheat pixel time t;
the total observation time of the pixel in a given observation time period is set;
calculating an NDVI mean value which is an average value of the NDVI maximums of all winter wheat pixels in the region after synthesis in a calculation mode shown as a formula (3);
in the formula:
m represents the total number of pixels of winter wheat in the region;
i-the sequence number of the winter wheat pixels in the region;
I i -NDVI after synthesis of the ith winter wheat pixel in the region;
r-region code.
5. The wheat seedling condition evaluation method based on the fitting model according to claim 4,
the multivariate regression analysis mode in the fifth step comprises the following steps:
setting the standard deviation of the NDVI data of all pixels in a certain area in a linear regression relation model as a variable, and performing multivariate fitting;
the multiple regression relation model is formula (5);
in the formula:
t is a critical value of classified NDVI of wheat seedling conditions in a certain area; respectively calculating the classification critical values of vigorous seedling and first class seedling, first class seedling and second class seedling and third class seedling, and recording as T 0/1 ,T 1/2 And T 2/3 ；
sigma is NDVI standard deviation of a certain region;
and a, b and c are regression coefficients.
6. The wheat seedling condition evaluation method based on the fitting model of claim 5, wherein in the method, a vigorous seedling and first seedling critical value, a first seedling and second seedling critical value and a second seedling and third seedling critical value are respectively set;
the critical value of the vigorous seedling and the first class seedling is as follows:
R 2 ：0.7839；
critical values of first-class seedlings and second-class seedlings:
R 2 ：0.8543；
critical values of second-class seedlings and third-class seedlings:
R 2 ：0.7976。
7. a wheat seedling condition evaluation system based on a fitting model is characterized in that the system adopts the wheat seedling condition evaluation method based on the fitting model according to any one of claims 1 to 6;
the system comprises: the system comprises a data acquisition module before winter wheat overwintering, an MODIS data preprocessing module, a ground data analysis module, a remote sensing monitoring module, a data analysis module and a result output module;
the winter wheat pre-wintering data acquisition module is used for receiving MODIS data of the winter wheat pre-wintering period in a preset time period through a satellite remote sensing receiving system;
the MODIS data preprocessing module is used for carrying out calibration, geometric correction, projection and coordinate system conversion processing on the MODIS data;
the ground data analysis module is used for collecting and analyzing ground data; respectively calculating the ratio of the areas of the vigorous seedling, the first class seedling, the second class seedling and the third class seedling in the seedling condition investigation data to the preset sowing area to obtain the percentage of the vigorous seedling, the first class seedling, the second class seedling and the third class seedling in each planting area in a preset time period;
the remote sensing monitoring module is used for selecting the normalized vegetation index NDVI as a remote sensing evaluation index to construct a remote sensing monitoring model;
the data analysis module is used for analyzing MODIS data in a multivariate regression analysis mode;
and the result output module is used for outputting the fitting result after analysis.
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