CN113063739A - Rice canopy nitrogen content monitoring method based on airborne hyperspectral sensor - Google Patents
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Abstract
The invention discloses a rice canopy nitrogen content monitoring method based on an airborne hyperspectral sensor, which adopts the technical scheme that: the method comprises the following specific steps: s1 determining a sample prescription, S2 field sampling, S3 data acquisition, S4 hyperspectral remote sensing data acquisition, S5 regression model establishment, S6 inversion model establishment and S7 result practicability. In an actual application scene, an unmanned aerial vehicle is used for carrying a hyperspectral sensor to obtain near-low altitude and high remote sensing data, the nitrogen content of the rice canopy of the whole field area is gradually inverted, and finally, a kriging interpolation method is used for obtaining the distribution result of the nitrogen content of the rice canopy of the whole field area.
Description
Technical Field
The invention relates to the technical field of rice monitoring, in particular to a rice canopy nitrogen content monitoring method based on an airborne hyperspectral sensor.
Background
The monitoring of the nitrogen content of the rice canopy is an important method for obtaining the nutrition condition of the nitrogen of the rice canopy, and the monitoring result is closely related to the rice yield. At present, two methods are mainly used, wherein one method is a method of ground on-site sampling investigation to construct a sampling grid, canopy leaves of rice plants are uniformly obtained from a field block, and the nitrogen content percentage of the rice canopy is finally obtained through treatment such as laboratory drying and weighing. And the other type is based on multispectral remote sensing data, and combines a plurality of field survey data to establish an inversion model of the satellite remote sensing data and the field survey data to obtain a rice canopy nitrogen content distribution result of the whole field.
The prior art has the following defects: the field investigation method is time-consuming and labor-consuming in actual operation and use, consumes great financial and material resources, and cannot be popularized in a large area; the multispectral remote sensing data wave band is few, the difficulty of establishing an inversion model is large, and the method cannot be effectively and quickly implemented.
Therefore, the invention is necessary to provide a rice canopy nitrogen content monitoring method based on an airborne hyperspectral sensor.
Disclosure of Invention
Therefore, the invention provides a method for monitoring the nitrogen content of a rice canopy based on an airborne hyperspectral sensor, which comprises the steps of manually obtaining the true value of the nitrogen content of the rice canopy by field sampling, obtaining near-low altitude high-spectrum remote sensing data by using an unmanned aerial vehicle carrying the hyperspectral sensor, establishing a regression model between a normalized vegetation index with the maximum decision coefficient and the true value of field investigation based on an analysis method of the vegetation index, and inverting the nitrogen content of the rice canopy by using the hyperspectral remote sensing data of the unmanned aerial vehicle according to the established inversion model of the nitrogen content of the rice canopy so as to solve the problems that the field investigation method is time-consuming and labor-consuming in practical operation and use, the cost and material resources are greatly consumed, the popularization in a large area cannot be realized, the waveband of the multispectral remote sensing data is small, the difficulty in.
In order to achieve the above purpose, the invention provides the following technical scheme: a rice canopy nitrogen content monitoring method based on an airborne hyperspectral sensor specifically comprises the following steps;
s1 determines the sample: determining the size of a sample according to the ridge width of the selected experimental area crop planting;
s2 field sampling: acquiring rice canopy leaves in a sample prescription, and sending the rice canopy leaves to a laboratory to acquire a true value of the nitrogen content of the rice canopy;
s3 data acquisition: using an unmanned aerial vehicle carrying a hyperspectral sensor to automatically fly, and collecting corresponding ground hyperspectral remote sensing data in an S1 sample;
s4, acquiring hyperspectral remote sensing data: extracting the data of the sample by the recorded point location information of the sample, obtaining hyperspectral remote sensing data of the sample by calculation and analysis, and modeling a data set at the same time;
s5, establishing a regression model: acquiring a normalized vegetation index average value of hyperspectral remote sensing data, establishing a regression model between NDVI (normalized vegetation index) of a maximum decision coefficient and a field survey true value, and obtaining a regression coefficient;
s6, establishing an inversion model: according to satellite remote sensing data synchronized with remote sensing images of the unmanned aerial vehicle, after preprocessing, establishing an inversion model according to a modeling data set and NDVI of a large decision coefficient;
s7 results are practical: and (4) according to the established rice canopy nitrogen content inversion model, inverting the rice canopy nitrogen content by utilizing the hyperspectral remote sensing data of the unmanned aerial vehicle.
Preferably, in the step S1, the portable terrestrial GPS receiving station is used to record the 20cm by 20cm frame position of the sample, and determine to prepare a plurality of samples.
Preferably, in the step S2, the canopy leaves of the rice in the sample are obtained by manual collection, and sent to a laboratory, and the leaves are put into an oven for enzyme deactivation, and the temperature is set to 105 ℃ for 30min, so as to obtain the true value of the nitrogen content of the canopy of the rice, and simultaneously model a data set.
Preferably, in the step S2, the adopted measuring method is a half-micro kjeldahl method, and the percentage of the nitrogen content of the leaf blade in the total weight of the leaf blade is finally obtained and used as the true value.
Preferably, in the data collected in step S3, a clear sky is selected, the unmanned aerial vehicle flies at low altitude, the height is 4-6 m, and the shooting direction of the camera is vertically downward.
Preferably, in the step S4, the method for obtaining the normalized vegetation index average value of the hyperspectral remote sensing data after preprocessing such as filtering after obtaining the data includes: the method comprises the steps of firstly obtaining dark current data of a hyperspectral sensor, then obtaining reference white board data, and then utilizing the dark current data and the reference white board data to conduct radiation correction on hyperspectral remote sensing data collected in a sample.
Preferably, in the step S5, in the step of establishing a regression model, based on the calculated NDVI for each band combination, 70% of the total samples of the real values of the ground survey are extracted as a modeling set, and the remaining 30% are used as a verification set, so as to calculate the determination coefficients of the NDVI for each band and the real values of the nitrogen content of the rice canopy.
Preferably, in the step S5, in the establishing of the regression model, any two wave bands within the hyperspectral wave band range are selected, combined to construct the NDVI (normalized vegetation index), and the determination coefficients between all the NDVI and the field investigation true values are obtained, and by selecting the wave band combination of the NDVI corresponding to the largest determination coefficient, the linear regression model between the NDVI and the field investigation true values is established, so as to obtain the regression coefficient.
Preferably, in the step S6, obtaining satellite remote sensing data synchronized with the unmanned aerial vehicle remote sensing image in the inversion model, preprocessing the satellite remote sensing data, obtaining a rice canopy nitrogen content inversion result according to the modeling data set, and correcting the satellite inversion result based on the rice canopy nitrogen content result obtained by unmanned aerial vehicle remote sensing inversion to obtain a final multisource remote sensing data rice canopy nitrogen content inversion model.
Preferably, in practical results of the step S7, the NDVI band combination with the largest decision coefficient is selected from the NDVI band combinations, a linear regression estimation model is established, the NDVI is substituted into the regression model to obtain an estimated value of the nitrogen content of the rice canopy, the nitrogen content of the rice canopy in the whole field area is gradually inverted, and finally, a kriging interpolation method is used to obtain a distribution result of the nitrogen content of the rice canopy in the whole field.
The invention has the beneficial effects that:
in the invention, by using an unmanned aerial vehicle, rice canopy hyperspectral data are obtained, decision coefficients between all NDVI and field investigation real values are obtained, a linear regression model between the NDVI and the field investigation real values is established by selecting the wave band combination of the NDVI corresponding to the maximum decision coefficient, a regression coefficient is obtained, a rice canopy nitrogen content inversion model based on the hyperspectral remote sensing data is obtained, the rice canopy nitrogen content is inverted by using the hyperspectral remote sensing data of the unmanned aerial vehicle according to the established rice canopy nitrogen content inversion model, a method for estimating the rice canopy nitrogen content based on the NDVI of the maximum decision coefficient is proved to be feasible and reliable through a large amount of experimental data, the rice canopy nitrogen content is monitored by using near-low altitude unmanned aerial vehicle hyperspectral remote sensing and other modes, long-time sequence monitoring can be carried out on a monitored area, short boards of time, space and efficiency are compensated, and a more accurate long-time sequence distribution change result of the rice canopy nitrogen content is obtained, is the basis of precision agriculture and intelligent agriculture.
Drawings
FIG. 1 is a block diagram of a regression model process provided by the present invention;
FIG. 2 is a block diagram of a hyperspectral remote sensing data model process provided by the invention;
FIG. 3 is a R2 contour plot of the combined NDVI and canopy nitrogen content for each band provided by the present invention;
FIG. 4 is a line graph of inversion results of nitrogen content of rice canopy in modeling set (left) and verification set (right) provided by the invention;
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
In the embodiment, referring to the attached figures 1-2, the method for monitoring the nitrogen content of the rice canopy based on the airborne hyperspectral sensor specifically comprises the following steps;
s1 determines the sample: determining the size of a sample according to the ridge width of the selected experimental area crop planting;
s2 field sampling: acquiring rice canopy leaves in a sample prescription, and sending the rice canopy leaves to a laboratory to acquire a true value of the nitrogen content of the rice canopy;
s3 data acquisition: using an unmanned aerial vehicle carrying a hyperspectral sensor to automatically fly, and collecting corresponding ground hyperspectral remote sensing data in an S1 sample;
s4, acquiring hyperspectral remote sensing data: extracting the data of the sample by the recorded point location information of the sample, obtaining hyperspectral remote sensing data of the sample by calculation and analysis, and modeling a data set at the same time;
s5, establishing a regression model: acquiring a normalized vegetation index average value of hyperspectral remote sensing data, establishing a regression model between NDVI (normalized vegetation index) of a maximum decision coefficient and a field survey true value, and obtaining a regression coefficient;
s6, establishing an inversion model: according to satellite remote sensing data synchronized with remote sensing images of the unmanned aerial vehicle, after preprocessing, establishing an inversion model according to a modeling data set and NDVI of a large decision coefficient;
s7 results are practical: and (4) according to the established rice canopy nitrogen content inversion model, inverting the rice canopy nitrogen content by utilizing the hyperspectral remote sensing data of the unmanned aerial vehicle.
In the invention, by using an unmanned aerial vehicle, rice canopy hyperspectral data are obtained, decision coefficients between all NDVI and field investigation real values are obtained, a linear regression model between the NDVI and the field investigation real values is established by selecting the wave band combination of the NDVI corresponding to the maximum decision coefficient, a regression coefficient is obtained, a rice canopy nitrogen content inversion model based on the hyperspectral remote sensing data is obtained, the rice canopy nitrogen content is inverted by using the hyperspectral remote sensing data of the unmanned aerial vehicle according to the established rice canopy nitrogen content inversion model, a method for estimating the rice canopy nitrogen content based on the NDVI of the maximum decision coefficient is proved to be feasible and reliable through a large amount of experimental data, the rice canopy nitrogen content is monitored by using near-low altitude unmanned aerial vehicle hyperspectral remote sensing and other modes, long-time sequence monitoring can be carried out on a monitored area, short boards of time, space and efficiency are compensated, and a more accurate long-time sequence distribution change result of the rice canopy nitrogen content is obtained, is the basis of precision agriculture and intelligent agriculture
Further, in the step S1, determining a position of a sample frame of 20cm by 20cm in the sample, and determining to prepare a plurality of samples;
further, in the step S2, rice canopy leaves in the sample are obtained in a manual collection manner, sent to a laboratory, and put into an oven for enzyme deactivation, and at the same time, the temperature is set to 105 ℃, the time is 30min, the true value of the nitrogen content of the rice canopy is obtained, and a data set is modeled at the same time;
further, in the step S2, the adopted determination method is a semi-micro kjeldahl method, and the percentage of the nitrogen content of the leaf blade to the total weight of the leaf blade is finally obtained and used as a real value, and the size of the sample is determined according to the ridge width of the selected experimental area crop;
the method comprises the steps of recording the position of a sample square frame of 20cm x 20cm by a portable ground GPS receiving station, acquiring the leaves of the rice canopy in the sample square in a manual acquisition mode, sending the leaves to a laboratory, putting the leaves into an oven for fixation, setting the temperature to be 105 ℃, setting the time to be 30min, and then measuring the total nitrogen of the leaves in a chemical laboratory, wherein the measuring method adopted in the experiment is a semi-micro Kjeldahl method, and finally obtaining the percentage of the nitrogen content of the leaves to the total weight of the leaves, and taking the percentage as a true value
Furthermore, in the data collected in step S3, clear weather is selected, the unmanned aerial vehicle flies at low altitude, the height is 4-6 m, the shooting direction of the camera is vertically downward, the specific performance model of the unmanned aerial vehicle using the high spectrum sensor is that the wheat flying probe is a light-weight COMS-based high spectrum sensor, the measurement waveband range is 337.854-823.295nm, the spectral resolution is 1.5nm, and 1024 wavebands are counted;
further, in the step S4, the method for obtaining the normalized vegetation index average value of the hyperspectral remote sensing data after preprocessing such as filtering after obtaining the data includes: firstly, dark current data of a hyperspectral sensor are obtained, then reference whiteboard data are obtained, then radiation correction is carried out on hyperspectral remote sensing data collected in a sample by utilizing the dark current data and the reference whiteboard data, wherein the normalized vegetation index of the maximum decision coefficient is the prior mature technology and is not repeated herein, a plurality of groups of normalized vegetation indexes are constructed by combining any hyperspectral wave bands, a linear regression model of each vegetation index and a corresponding field investigation true value is established, the model precision is evaluated by using the root mean square error and the regression coefficient, and a method for obtaining the normalized vegetation index average value of the hyperspectral remote sensing data is obtained: firstly, dark current data of a hyperspectral sensor are obtained, then reference white board data are obtained, then radiation correction is carried out on hyperspectral remote sensing data collected in a sample by using the dark current data and the reference white board data, and a formula is calculated: the method comprises the following steps that (sample hyperspectral-dark current)/(white board-dark current) is 0.73, the corrected sample reflectivity value is 0-1, NDVI (normalized vegetation index) is constructed by combining any two wave bands (lambda 1 and lambda 2), and the calculation formula of the normalized vegetation index is as follows;
referring to fig. 3, in the step S5, in establishing a regression model, based on the NDVI of each band combination, 70% of the total samples of the real values of the ground survey are extracted as a modeling set, the remaining 30% are used as verification sets, and the determination coefficients of the NDVI of each band and the real values of the nitrogen content of the rice canopy are determined, in the present invention, based on the NDVI of each band combination, 62 of the total samples (89) of the real values of the ground survey are extracted as modeling sets, the remaining 27 are used as verification sets, the determination coefficients of the NDVI of each band and the real values of the nitrogen content of the rice canopy are determined, the NDVI band combination (638nm and 640nm) with the largest determination coefficient is selected, and a linear regression estimation model is established,
further, it is characterized byStep S5, in the step of establishing a regression model, selecting any two wave bands within the hyperspectral wave band range, combining and constructing NDVI (normalized vegetation index), obtaining decision coefficients between all NDVI and field survey true values, establishing a linear regression model between NDVI and field survey true values by selecting a wave band combination of NDVI corresponding to the largest decision coefficient, and obtaining a regression coefficient, a linear regression formula: and y is ax + b. In the formula, y is a regression value of the nitrogen content of the rice canopy; x is NDVI; a. b is regression coefficient, the regression result is shown in figure 4, in the figure, y is 0.835x +0.023 is regression model, RMSE is root mean square error, R is root mean square error2Substituting NDVI into regression model to obtain estimated value of nitrogen content in rice canopy, which is shown in FIG. 3 as modeling set R2Reach significance level of 0.835, simultaneously the RMSE is smaller, and a verification set R2The method reaches 0.935, the RMSE is only 2.186, and the method for estimating the nitrogen content of the rice canopy based on the NDVI of the maximum decision coefficient is feasible and reliable;
referring to fig. 4, in the step S6, acquiring satellite remote sensing data synchronized with the remote sensing image of the unmanned aerial vehicle in the inversion model, preprocessing the satellite remote sensing data, obtaining a rice canopy nitrogen content inversion result according to the modeling data set, correcting the satellite inversion result based on the rice canopy nitrogen content result obtained by the remote sensing inversion of the unmanned aerial vehicle to obtain a final multisource remote sensing data rice canopy nitrogen content inversion model, counting the modeling data set and the verification data set, and making a line graph;
further, in practical results of the step S7, an NDVI band combination with the largest decision coefficient is selected, a linear regression estimation model is established, NDVI is substituted into the regression model to obtain an estimated value of the nitrogen content of the rice canopy, the nitrogen content of the rice canopy in the whole field area is gradually inverted, and finally, a kriging interpolation method is used to obtain a distribution result of the nitrogen content of the rice canopy in the whole field.
The using process of the invention is as follows: the invention acquires rice canopy hyperspectral data by acquiring field survey true values of 89 sample data and hovering and flying a plant protection unmanned aerial vehicle carrying a hyperspectral sensor, the flying height is 5m, after preprocessing such as filtering, any two wave bands in a hyperspectral wave band range are selected, NDVI (normalized vegetation index) is combined and constructed, decision coefficients between all NDVI and the field survey true values are acquired, a linear regression model between the NDVI and the field survey true values is established by selecting the wave band combination of the NDVI corresponding to the maximum decision coefficient, a regression coefficient is obtained, a rice canopy nitrogen content inversion model based on the hyperspectral remote sensing data is obtained, in the model application process, satellite remote sensing data synchronous with unmanned aerial vehicle remote sensing images is firstly acquired, after preprocessing, a rice canopy nitrogen content inversion result is obtained according to an empirical model, and then the rice canopy nitrogen content result obtained by the unmanned aerial vehicle remote sensing inversion is taken as the basis, and correcting the satellite inversion result to obtain a final multisource remote sensing data rice canopy nitrogen content inversion model, and inverting the rice canopy nitrogen content by utilizing the hyperspectral remote sensing data of the unmanned aerial vehicle according to the established rice canopy nitrogen content inversion model.
The above description is only a preferred embodiment of the present invention, and any person skilled in the art may modify the present invention or modify it into an equivalent technical solution by using the technical solution described above. Therefore, any simple modifications or equivalent substitutions made in accordance with the technical solution of the present invention are within the scope of the claims of the present invention.
Claims (10)
1. A rice canopy nitrogen content monitoring method based on an airborne hyperspectral sensor is characterized by comprising the following steps: the method specifically comprises the following steps;
s1 determines the sample: determining the size of a sample according to the ridge width of the selected experimental area crop planting;
s2 field sampling: acquiring rice canopy leaves in a sample prescription, and sending the rice canopy leaves to a laboratory to acquire a true value of the nitrogen content of the rice canopy;
s3 data acquisition: using an unmanned aerial vehicle carrying a hyperspectral sensor to automatically fly, and collecting corresponding ground hyperspectral remote sensing data in an S1 sample;
s4, acquiring hyperspectral remote sensing data: extracting the data of the sample by the recorded point location information of the sample, obtaining hyperspectral remote sensing data of the sample by calculation and analysis, and modeling a data set at the same time;
s5, establishing a regression model: acquiring a normalized vegetation index average value of hyperspectral remote sensing data, establishing a regression model between NDVI (normalized vegetation index) of a maximum decision coefficient and a field survey true value, and obtaining a regression coefficient;
s6, establishing an inversion model: according to satellite remote sensing data synchronized with remote sensing images of the unmanned aerial vehicle, after preprocessing, establishing an inversion model according to a modeling data set and NDVI of a large decision coefficient;
s7 results are practical: and (4) according to the established rice canopy nitrogen content inversion model, inverting the rice canopy nitrogen content by utilizing the hyperspectral remote sensing data of the unmanned aerial vehicle.
2. The rice canopy nitrogen content monitoring method based on the airborne hyperspectral sensor as claimed in claim 1, wherein: in the step S1, a portable ground GPS receiving station is used to record the position of a 20cm by 20cm sample frame in the sample, and a plurality of samples are prepared.
3. The rice canopy nitrogen content monitoring method based on the airborne hyperspectral sensor as claimed in claim 1, wherein: and S2, acquiring the leaves of the canopy of the rice in the sample by a manual acquisition mode, sending the leaves to a laboratory, putting the leaves into an oven for fixation, setting the temperature to be 105 ℃ and the time to be 30min, acquiring the true value of the nitrogen content of the canopy of the rice, and modeling a data set.
4. The rice canopy nitrogen content monitoring method based on the airborne hyperspectral sensor as claimed in claim 1, wherein: in the step S2, the adopted determination method is a half-micro kjeldahl method, and the percentage of the nitrogen content of the blade in the total weight of the blade is finally obtained and used as a true value.
5. The rice canopy nitrogen content monitoring method based on the airborne hyperspectral sensor as claimed in claim 1, wherein: in the data collected in step S3, clear weather is selected, the unmanned aerial vehicle flies at low altitude, the height is 4-6 m, and the shooting direction of the camera is vertically downward.
6. The rice canopy nitrogen content monitoring method based on the airborne hyperspectral sensor as claimed in claim 1, wherein: in the step S4, the method for obtaining the normalized vegetation index average value of the hyperspectral remote sensing data after preprocessing such as filtering after obtaining the data includes: the method comprises the steps of firstly obtaining dark current data of a hyperspectral sensor, then obtaining reference white board data, and then utilizing the dark current data and the reference white board data to conduct radiation correction on hyperspectral remote sensing data collected in a sample.
7. The rice canopy nitrogen content monitoring method based on the airborne hyperspectral sensor as claimed in claim 1, wherein: in the step S5, in establishing a regression model, based on the calculated combined NDVI of each band, 70% of the total samples of the corresponding ground survey true values are extracted as a modeling set, and the remaining 30% are used as a verification set, so as to calculate the determination coefficient of the NDVI of each band and the true value of the nitrogen content of the rice canopy.
8. The rice canopy nitrogen content monitoring method based on the airborne hyperspectral sensor as claimed in claim 1, wherein: in the step S5, in the regression model, any two wave bands within the hyperspectral wave band range are selected, combined to construct NDVI (normalized vegetation index), decision coefficients between all NDVI and field investigation true values are obtained, and a linear regression model between NDVI and field investigation true values is established by selecting the wave band combination of NDVI corresponding to the largest decision coefficient, so as to obtain a regression coefficient.
9. The rice canopy nitrogen content monitoring method based on the airborne hyperspectral sensor as claimed in claim 1, wherein: in the step S6, satellite remote sensing data synchronous with the unmanned aerial vehicle remote sensing image is obtained in the inversion model, after preprocessing, the inversion result of the nitrogen content of the rice canopy is obtained according to the modeling data set, and then the inversion result of the satellite is corrected on the basis of the result of the nitrogen content of the rice canopy obtained by unmanned aerial vehicle remote sensing inversion, so that the final multisource remote sensing data rice canopy nitrogen content inversion model is obtained.
10. The rice canopy nitrogen content monitoring method based on the airborne hyperspectral sensor as claimed in claim 1, wherein: in practical results of the step S7, selecting the NDVI wave band combination with the largest decision coefficient, establishing a linear regression estimation model, substituting the NDVI into the regression model to obtain the estimated value of the nitrogen content of the rice canopy, gradually inverting the nitrogen content of the rice canopy in the whole field area, and finally obtaining the distribution result of the nitrogen content of the rice canopy in the whole field by using a Krigin interpolation method.
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| CN113670913A (en) * | 2021-08-18 | 2021-11-19 | 沈阳农业大学 | Construction method for inverting hyperspectral vegetation index by using nitrogen content of rice |
| CN114112906A (en) * | 2021-10-12 | 2022-03-01 | 中通服咨询设计研究院有限公司 | Water body feature extraction system based on unmanned aerial vehicle low-altitude remote sensing and local topography |
| CN114460015A (en) * | 2021-10-29 | 2022-05-10 | 东北农业大学 | Method and model for predicting chlorophyll content of canopy of rice in cold region |
| TWI838787B (en) * | 2022-07-07 | 2024-04-11 | 國立屏東科技大學 | Rice leaf nitrogen content detection method and system thereof using rice leaf images |
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