CN110222475B - Method for inverting moisture content of winter wheat plants based on multispectral remote sensing of unmanned aerial vehicle - Google Patents
Method for inverting moisture content of winter wheat plants based on multispectral remote sensing of unmanned aerial vehicle Download PDFInfo
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Abstract
The invention provides a method for inverting the moisture content of winter wheat plants based on multi-spectral remote sensing of an unmanned aerial vehicle2) And the Relative Error (RE) and the Root Mean Square Error (RMSE) are tested to determine an optimal plant moisture content estimation model based on the spectral information, so that a theoretical basis is provided for realizing accurate crop monitoring, and the applicability of the multispectral remote sensing monitoring of the unmanned aerial vehicle is further enhanced. Through the design, the real-time regional monitoring for simply, conveniently and accurately predicting the moisture content of the winter wheat plant can be realized.
Description
Technical Field
The invention belongs to the field of plant growth monitoring, and particularly relates to a method for inverting the moisture content of winter wheat plants based on multispectral remote sensing of an unmanned aerial vehicle.
Background
The plant water content is one of indexes reflecting the crop water condition, is also an important basis for developing crop water shortage diagnosis, timely and accurately acquires the plant water content information, and has important significance for accurate agricultural development and efficient utilization of agricultural water resources. The traditional method for estimating the water content of the plant mainly comprises destructive sampling and measurement, is complex to operate, consumes a large amount of manpower and material resources, and is not enough to meet the requirement of implementing rapid monitoring, so that the research on the estimation method of the water content of the plant, which is simple and has high precision, becomes a current hotspot. At present, research on plant water content based on satellite remote sensing or ground remote sensing at home and abroad has made a certain progress, and the unmanned aerial vehicle remote sensing technology plays an increasingly important role in the fields of agriculture, water quality monitoring, surveying and mapping and the like due to the advantages of strong mobility and good applicability of a platform, high image acquisition resolution and short operation period, and provides a new solution for agricultural condition monitoring research. The existing research shows the potential of the unmanned aerial vehicle remote sensing technology in acquiring vegetation canopy information, and the research method for inverting specific indexes by acquiring remote sensing images through carrying sensors is more and more popular, but the research for directly inverting the moisture content of crop plants by using the unmanned aerial vehicle multispectral remote sensing is rarely reported.
According to the method, the multispectral camera is carried by the unmanned aerial vehicle to obtain the spectral reflectivity of the winter wheat, a plant water content and spectral reflectivity and a spectral vegetation index model are constructed, and a plant water content estimation model is preferably constructed from the spectral reflectivity and the spectral vegetation index model, so that the real-time regional monitoring method for simply, conveniently and accurately predicting the water content of the winter wheat plant is obtained.
Disclosure of Invention
Aiming at the defects in the prior art, the method for inverting the moisture content of the winter wheat plant based on the multispectral remote sensing of the unmanned aerial vehicle can realize real-time area monitoring for simply, conveniently and accurately predicting the moisture content of the winter wheat plant.
In order to achieve the above purpose, the invention adopts the technical scheme that:
the scheme provides a method for inverting the moisture content of winter wheat plants based on multispectral remote sensing of an unmanned aerial vehicle, which comprises the following steps:
s1, acquiring a five-waveband spectrum image of the winter wheat plant in the research area by using an unmanned aerial vehicle multispectral remote sensing technology;
s2, calculating the water content of the winter wheat plants in the research area by using a wet basis water content method;
s3, calculating according to the spectrum images of the five wave bands to obtain the spectrum reflectivity of winter wheat in the research area;
s4, cutting the image of the spectral reflectivity of the winter wheat by utilizing ENVI software to extract 6 spectral vegetation indexes;
s5, analyzing the correlation between the water content of the winter wheat plant and the spectral reflectivity and the correlation between the water content of the winter wheat plant and the 6 spectral vegetation indexes, and establishing a plurality of spectral reflectivity inversion models and a plurality of spectral vegetation index inversion models by respectively utilizing a forced entry method and a stepwise regression method;
s6, analyzing parameters in the plurality of spectral reflectivity inversion models and the plurality of spectral vegetation index inversion models respectively to obtain an optimal spectral reflectivity inversion model and an optimal spectral vegetation index inversion model;
and S7, verifying the simulation value and the predicted measured value of the plant water content in the optimal spectral reflectivity inversion model and the optimal spectral vegetation index inversion model respectively to obtain an optimal inversion model, thereby completing inversion of the plant water content of the winter wheat.
Further, in the step S2, the moisture content C of the winter wheat plants in the research area is calculated by using a wet basis moisture content methodwThe expression of (a) is as follows:
wherein L iswIndicating the fresh weight of the winter wheat plant, LdRepresents the dry weight of the winter wheat plant.
Still further, the step S3 includes the following steps:
a1, synthesizing the five-waveband spectral images into a tif-format five-waveband spectral image;
a2, constructing an ROI (region of interest) by utilizing a mask method according to the synthesized five-waveband spectral image;
a3, respectively calculating the spectral reflectivity of five wave bands in the ROI area by using a wave band operation tool Bandmath;
and A4, calculating the average spectral reflectivity of the spectral reflectivities of the five wave bands in the ROI area to obtain the spectral reflectivity of winter wheat in the research area.
Still further, the 6 spectral vegetation indexes in step S4 include: normalized spectral vegetation index NDVI, soil conditioning spectral vegetation index SAVI, enhanced spectral vegetation index EVI, ratio spectral vegetation index SR, greenness normalized spectral vegetation index GNDVI, and atmospheric resistance index VARI.
Still further, the expression of the normalized spectral vegetation index NDVI is as follows:
the expression of the soil conditioning spectral vegetation index SAVI is as follows:
the expression of the enhanced spectral vegetation index EVI is as follows:
the expression of the ratio spectral vegetation index SR is as follows:
the greenness normalized spectral vegetation index GNDVI has the following expression:
the expression of the anti-atmospheric index VARI is as follows:
in the above formulae, Rblue、Rgreen、Rred、RnirThe average reflectivity of the gray plate to the blue, green, red and near infrared bands of the RedEdge camera is respectively.
Still further, the constructing of the plurality of spectral reflectance abnormal models in step S5 includes the following steps:
b1, analyzing the correlation degree of the moisture content of the winter wheat plant and the spectral reflectance of the winter wheat by using a multivariate regression method of SPSS software;
b2, establishing a spectrum reflectivity inversion model containing the reflectivity of five wave bands by using a forced entry method;
and B3, sequentially introducing the moisture content of the winter wheat plants into the spectral reflectance inversion models one by utilizing a stepwise regression method according to the correlation degree to obtain a plurality of spectral reflectance inversion models.
Still further, the constructing a plurality of spectral vegetation index inversion models in step S5 includes the following steps:
c1, analyzing the correlation degree of the moisture content of the winter wheat plant and the spectral vegetation index of the winter wheat by using a multivariate regression method of SPSS software;
c2, establishing a spectrum vegetation index inversion model containing 6 spectrum vegetation indexes by using a forced entry method;
and C3, sequentially introducing the moisture content of the winter wheat plants into the spectral vegetation index inversion models one by utilizing a stepwise regression method according to the correlation degree to obtain a plurality of spectral vegetation index inversion models.
Still further, step S6 is specifically:
d1, analyzing the determining coefficient R of each model in the plurality of spectral reflectivity inversion models2Root mean square error RMSE and relative error RE parameters;
d2 obtaining a determination coefficient R according to the analysis result2The maximum model with the minimum root mean square error RMSE and relative error RE is used as an optimal spectrum reflectivity inversion model;
d3, analyzing the determining coefficient R of each model in the plurality of spectrum vegetation index inversion models2Root mean square error RMSE and relative error RE parameters;
d4 obtaining a determination coefficient R according to the analysis result2And taking the model with the maximum root mean square error RMSE and the minimum relative error RE as an optimal spectrum vegetation index inversion model.
Still further, the decision coefficient R2The expression of (a) is as follows:
the root mean square error RMSE is expressed as follows:
the expression for the relative error RE is as follows:
in the above formulas, n represents the number of verification samples, i represents the sample number,
the predicted value y representing the water content of the winter wheat plantiThe measured value of the water content of the winter wheat plant is shown,
the average value of the water content of the winter wheat plants is shown.
The invention has the beneficial effects that:
(1) the invention establishes a spectral reflectivity model and a spectral vegetation index model by two regression analysis methods through the correlation analysis of the water content of plants with the spectral reflectivity and the spectral vegetation index respectively, and the correlation (R) of the models is determined2) The Relative Error (RE) and the Root Mean Square Error (RMSE) are checked to determine an optimal plant moisture content estimation model based on the spectral information, so that a theoretical basis is provided for realizing accurate crop monitoring, and the applicability of the multispectral remote sensing monitoring of the unmanned aerial vehicle is further enhanced;
(2) the spectral reflectivity model is modeled by adopting a plurality of wave bands, and the spectral reflectivity model established by the near infrared wave band, the red wave band and the blue wave band has high precision, is simple and stable;
(3) the invention adopts a plurality of spectral index comprehensive analysis models, thereby effectively improving the precision of the models.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of the water content of the plants treated with different water contents in different growth periods of winter wheat in this example.
Fig. 3 is a schematic diagram illustrating comparison between the simulation value and the measured value of the plant water content under different modeling methods in this embodiment.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Examples
As shown in fig. 1, the invention provides a method for inverting the moisture content of winter wheat plants based on multispectral remote sensing of an unmanned aerial vehicle, which is implemented as follows:
s1, acquiring a five-waveband spectrum image of the winter wheat plant in the research area by using an unmanned aerial vehicle multispectral remote sensing technology;
s2, calculating the moisture content of the winter wheat plants in the research area by using a wet basis moisture content method, wherein the expression is as follows:
wherein, CwIndicating the moisture content, L, of the winter wheat plantwIndicating the fresh weight of the winter wheat plant, LdRepresents the dry weight of the winter wheat plant;
s3, calculating the spectral reflectivity of winter wheat in the research area according to the five-waveband spectral image, wherein the method comprises the following steps:
a1, synthesizing the five-waveband spectral images into a tif-format five-waveband spectral image;
a2, constructing an ROI (region of interest) by utilizing a mask method according to the synthesized five-waveband spectral image;
a3, respectively calculating the spectral reflectivity of five wave bands in the ROI area by using a wave band operation tool Bandmath;
a4, calculating the average spectral reflectivity of the spectral reflectivities of five wave bands in the ROI area to obtain the spectral reflectivity of winter wheat in the research area;
s4, cutting the image of the spectral reflectivity of the winter wheat by utilizing ENVI software to extract 6 spectral vegetation indexes, wherein the 6 spectral vegetation indexes comprise: normalized spectral vegetation index NDVI, soil conditioning spectral vegetation index SAVI, enhanced spectral vegetation index EVI, ratio spectral vegetation index SR, greenness normalized spectral vegetation index GNDVI and atmospheric resistance index VARI;
s5, analyzing the correlation between the water content of the winter wheat plant and the spectral reflectivity and the correlation between the water content of the winter wheat plant and the 6 spectral vegetation indexes, respectively, and establishing a plurality of spectral reflectivity inversion models and a plurality of spectral vegetation index inversion models by respectively utilizing a forced entry method and a stepwise regression method, wherein,
the construction of a plurality of spectral reflectivity anomaly models comprises the following steps:
b1, analyzing the correlation degree of the moisture content of the winter wheat plant and the spectral reflectance of the winter wheat by using a multivariate regression method of SPSS software;
b2, establishing a spectrum reflectivity inversion model containing the reflectivity of five wave bands by using a forced entry method;
b3, sequentially introducing the moisture content of the winter wheat plants into a spectral reflectance inversion model one by utilizing a stepwise regression method according to the correlation degree to obtain a plurality of spectral reflectance inversion models;
the method for constructing the multiple spectrum vegetation index inversion models comprises the following steps:
c1, analyzing the correlation degree of the moisture content of the winter wheat plant and the spectral vegetation index of the winter wheat by using a multivariate regression method of SPSS software;
c2, establishing a spectrum vegetation index inversion model containing 6 spectrum vegetation indexes by using a forced entry method;
c3, sequentially introducing the moisture content of the winter wheat plants into the spectral vegetation index inversion models one by utilizing a stepwise regression method according to the correlation degree to obtain a plurality of spectral vegetation index inversion models;
s6, parameters in the plurality of spectral reflectivity inversion models and the plurality of spectral vegetation index inversion models are respectively analyzed to obtain an optimal spectral reflectivity inversion model and an optimal spectral vegetation index inversion model, and the implementation method comprises the following steps:
d1, analyzing the determining coefficient R of each model in the plurality of spectral reflectivity inversion models2Root mean square error RMSE and relative error RE parameters;
d2 obtaining a determination coefficient R according to the analysis result2The maximum model with the minimum root mean square error RMSE and relative error RE is used as an optimal spectrum reflectivity inversion model;
d3, analyzing the determining coefficient R of each model in the plurality of spectrum vegetation index inversion models2Root mean square error RMSE and relative error RE parameters;
d4 obtaining a determination coefficient R according to the analysis result2The maximum model with the minimum root mean square error RMSE and relative error RE is used as an optimal spectrum vegetation index inversion model;
the determination coefficient R2The expression of (a) is as follows:
the root mean square error RMSE is expressed as follows:
the expression for the relative error RE is as follows:
in the formula, n represents the number of verification samples, i represents the number of the samples, namely the number of the winter wheat cell in the research area of the invention,
the predicted value y representing the water content of the winter wheat plantiThe measured value of the water content of the winter wheat plant is shown,
mean value representing the moisture content of winter wheat plants
And S7, verifying the simulation value and the predicted measured value of the plant water content in the optimal spectral reflectivity inversion model and the optimal spectral vegetation index inversion model respectively to obtain an optimal inversion model, thereby completing inversion of the plant water content of the winter wheat.
The invention is developed in great-rise test bases (39 degrees 37.25'N and 116 degrees 25.51' E39 degrees) of the Chinese institute of water conservancy and hydropower science, has an altitude of about 30m, belongs to temperate and semi-arid continental season climate, has an average temperature of 12.1 ℃ for many years, an average rainfall of 540 mm for many years, is concentrated in 6 and 9 months, and accounts for more than 80% of the rainfall of the whole year, so that the climate characteristics of winter, spring, summer and rainy season are formed, the average wind speed of the years is 1.2m/s, the wind speed of the winter and spring is higher, and the wind speed of the autumn is the smallest. The annual average relative humidity is 52%, the annual sunshine hours is 2502h, the annual effective accumulated temperature of more than 10 ℃ is 4730 ℃, and the daily average solar net radiation is 171W/m2The photo-thermal condition is rich, the frost-free period of the whole year is 185 days averagely, the evaporation capacity of the soil is 1021mm all year round, the soil type is mainly sandy loam, and the method is suitable for the growth of various grain crops such as wheat.
The wheat variety in the test area is medium wheat 175, the wheat is sown in 2017 in 10-month and 13-day, the wheat is ripened in 2018 in 6-month and 6-day, the test area is 10 cells, 3 repeated cells and 30 cells in total, and the area of each cell is 58m2Test with waterThe treatment is mainly, the irrigation level is W0(0mm)、W1(60mm)、W2(120mm)、W3(180mm)、W4(240mm)、W5(300 mm). The drone test was performed in 2018 at 5 months 5 days (heading period), 5 months 24 days (filling period), and 6 months 3 days (maturity period). The heading stage, the filling stage and the mature stage are key stages related to the yield and the quality of winter wheat, the plant water content can show obvious difference, the defect of specificity of a crop water model in the growth stage is avoided in the construction of a plant water content model, the larger the plant water content change range is, the more universal the model is, and the higher the fault tolerance rate is.
In this embodiment, the platform used for the control is a longitude and latitude M600 type six-rotor unmanned aerial vehicle system controlled by an open source flight control Pixhawk, and the remote sensing sensor mounted on the platform is RedEdge. The focal length of the camera is 5.5mm, the image resolution is 1280 pixel x 960 pixel, the camera is provided with five spectrum channels, the central wavelengths are 475nm (blue), 560nm (green), 668nm (red), 840nm (near infrared) and 717nm (red edge), and the camera is simultaneously provided with a 30cm x 30cm gray plate and a light intensity sensor. The unmanned aerial vehicle is set to a certain height, the lens is vertically downward, the reflectivity is corrected by shooting a ground gray board, then the unmanned aerial vehicle flies to an area according to a preset air route to shoot multispectral images, and the shooting time is 10 in the morning each time: 00-12: between 00, the flying height of the unmanned aerial vehicle is set to be 60m, the fixed route is in flight, the course and the side direction overlapping degree are both 80%, the flying speed is 3m/s, and the pixel resolution is 4.09 cm.
The moisture content of wheat plants is measured by a drying method, wheat plants in the area with the width of 20cm are picked from each cell, the plants are respectively put into freshness protection bags, the fresh weight of the wheat plants is weighed and recorded, then the wheat plants are put into an oven at 105 ℃ for fixation for half an hour, and then the wheat plants are dried to constant weight after being adjusted to 70 ℃ and the dry weight of the plants is recorded. The invention adopts a wet basis water content formula to calculate the water content of the winter wheat plant.
In the embodiment, a five-band spectrum image in a tif format is synthesized from five-band spectrum images acquired by multispectral remote sensing of an unmanned aerial vehicle, a region ROI is first constructed in a reflectivity image by using a mask method, spectral reflectivities of five bands are respectively calculated by using a band operation tool Bandmath, an average spectrum of wheat in the ROI region range is used as the wheat spectral reflectivity of the cell, and spectral reflectivity data of 30 cells in each day are obtained by calculation.
In this example, the dependent variable (plant moisture content y) and five independent variables [ R ] are revealed by stepwise regression and forced entry in the multivariate linear regression analysis methodnir(x1),Rred(x2),Rgreen(x3),Rblue(x4),Rrededge(x5)]A linear model of (a) in (b), wherein Rblue、Rgreen、Rred、RnirThe average reflectivity of the gray plate to the blue, green, red and near infrared bands of the RedEdge camera is respectively. Firstly, the effect or the obvious degree of the spectral reflectivity of different wave bands on the plant water content is not considered, a forced entry method is adopted to establish a spectral reflectivity inversion model of the plant water content by taking the average reflectivity of five wave bands as independent variables, wherein the spectral reflectivity inversion model is respectively a primary linear model, a quadratic curve model and a logarithmic curve model, and secondly, introducing spectral reflectivity inversion models of five considered bands one by one from large to small according to the effect or the significant degree of the spectral reflectivity on the water content of the plants by adopting a stepwise regression method, wherein variables with insignificant effects may not be introduced into the spectral reflectivity inversion models all the time, the introduced variables may lose importance after new variables are introduced, the introduced variables need to be removed from the spectral reflectivity inversion models, the introduced or removed variables are called stepwise regression, and F inspection is carried out in each step to ensure that the regression models only contain variables with significant effects before the new variables are introduced.
In the common spectral vegetation indexes, the normalized spectral vegetation index NDVI can represent the change height of the water content of a plant, the vegetation leaf area index and the like, the soil regulation spectral vegetation index SAVI is considered as a key relation for establishing a simple 'plant', the influence of a soil background can be eliminated, and a soil adjustment factor in the enhanced spectral vegetation index EVI is more sensitive to the terrain condition than the NDVI and plays a great role in monitoring the vegetation change; the specific value spectrum vegetation index SR is a sensitive indicating parameter of green plants, vegetation information is effectively enhanced, non-vegetation information is reduced, the greenness normalized spectrum vegetation index GNDVI is more sensitive to the reaction degree of leaf area indexes in a wide range, and the research on the influence of the atmospheric resistance index VARI on the atmosphere and soil is more sufficient.
In this embodiment, an estimation model of vegetation water content is established by using the 6 spectral vegetation indexes. Firstly, establishing a unitary linear relation between the plant moisture content and 6 spectral vegetation indexes respectively to obtain 6 unitary linear models, and secondly, establishing linear models between a dependent variable (plant moisture content y) and six independent variables (normalized spectral vegetation index NDVI, soil regulation spectral vegetation index SAVI, enhanced spectral vegetation index EVI, ratio spectral vegetation index SR, greenness normalized spectral vegetation index GNDVI and atmospheric resistance index VARI) by adopting a stepwise regression method and a forced entry method in a multivariate linear regression analysis method. Firstly, the action or the significance degree of different spectral indexes on the water content of plants is not considered, a forced entry method is adopted to establish a spectral vegetation index inversion model by taking 6 spectral indexes as independent variables, the model is a primary linear model, secondly, the 6 spectral indexes are introduced into the spectral vegetation index inversion model one by one from large to small according to the action or the significance degree of the 6 spectral indexes on the water content of plants, and variables with insignificant action may not be introduced into the spectral vegetation index inversion model all the time.
In this embodiment, the collected data of 20 cells in the test area is selected for regression modeling, the data of the remaining 10 cells is used for verifying the model accuracy, and the decision coefficient R is used2The root mean square error RMSE and the relative error RE are used as index decision coefficients R for judging the accuracy of the model2The more close to 1, the smaller the root mean square error RMSE is, the smaller the relative error RE is, which shows that the model has better precision and better effect of inverting the plant moisture content.
In this embodiment, the external conditions such as water stress and nutrition condition and the growth vigor of the plant can cause the plant characteristics of the same plant to be different, so that the spectral reflection characteristics are different. As shown in fig. 2, fig. 2 shows the plant water content of winter wheat under different moisture treatments in different growth periods, compared with the plant water content observed by the unmanned aerial vehicle flying through the LSD method in the one-factor variance analysis method, the plant water content in the heading period differs from the average value of the plant water content in the filling period by 13.2%, differs from the average value of the plant water content in the maturity period by 31.2%, and the plant water content in the filling period differs from the average value of the plant water content in the maturity period by 18%, which are significant at a significance level of 0.05.
In this embodiment, correlation analysis is performed on the average spectral reflectance of each band and the plant water content, a determination coefficient of a model established by the average spectral reflectance of a single band is low, only the determination coefficient of the near-infrared band is 0.602, the model accuracy is low, and the plant water content cannot be accurately predicted, so that a model is established by multivariate linear regression analysis.
In this example, table 1 shows a spectral reflectance model, and the determination coefficients R of 9 models2The moisture contents are all larger than 0.65, which shows the feasibility of inverting the plant moisture content based on the multispectral remote sensing information. Model 2 and model 7 determine the coefficient R2The larger, the smaller the root mean square error RMSE and the relative error RE, are preferred over other models, but the model 7 is simpler and more convenient than the model 2, so the model 7 is selected as the preferred model of the spectral reflectivity.
TABLE 1
In the above formulae, y represents the moisture content of the winter wheat plant, and x1Denotes the reflectivity, x, of the near infrared band2Representing the reflectivity, x, of the red wavelength band3Representing the reflectance of the green band, x4Representing the reflectivity, x, of the blue band5Indicating the red-side band reflectivity and ln indicates the logarithm of the natural logarithm to the base constant e.
TABLE 2 index number of spectral vegetationType, six plant water ratio models established by single spectrum vegetation index, determining coefficient R2The method is small, the root mean square error RMSE and the relative error RE are large, and the model fitting precision is low, so that accurate prediction cannot be realized. Therefore, a plant moisture content model constructed by combining 6 spectral vegetation indexes is researched by adopting multivariate linear regression analysis, as the soil adjustment spectral vegetation index SAVI, the normalized spectral vegetation index NDVI and the enhanced spectral vegetation index EVI exist, the spectral vegetation index GNDVI and the atmospheric resistance index VARI have the problem of collinearity, and in the modeling process of the stepwise regression method, variables such as the enhanced spectral vegetation index EVI, the ratio spectral vegetation index SR and the atmospheric resistance index VARI are sequentially selected for modeling in order to avoid the influence of the collinearity problem on the accuracy of the linear regression analysis. Coefficient of determination R of model 17 and model 182All of which are more than 0.75, the root mean square error RMSE is less than 7.00%, and the relative error RE is less than 10.00%, both of which can be used as preferred models, but the model 17 is more suitable as a preferred model than the model 18, taking into account the co-linearity between the variables during the regression analysis.
TABLE 2
In this example, as shown in FIG. 3, FIG. 3 shows the comparison between the simulated value and the measured value of the plant moisture content under different modeling methods, and Table 3 shows the comparison between the simulated value and the measured value of the plant moisture content under the preferred model, and the determination coefficients R of two regression models2The root mean square errors RMSE are less than 6%, the relative errors RE are less than 7%, and the simulation precision of the spectral reflectivity model 7 and the spectral index model 17 is better, so that the moisture content of the plants can be monitored by the unmanned aerial vehicle multispectral remote sensing. However, compared with the spectral reflectance model, the spectral vegetation index model has the advantage of considering the internal parameters of the plant, can reduce the interference of the environment and the self factors when predicting the water content of the plant, and has stronger practicability, so the model 17 is selected for modeling, wherein the sensitive waveband of the water content of the plant of the crop is a near infrared region.
TABLE 3
In this embodiment, the determination coefficient of the average spectral reflectance of a single band and the plant moisture content is low, and the modeling R of the spectral reflectance of multiple bands is2The spectral reflectance model built by the near infrared band, the red band and the blue band has high precision, is simple and stable. In this embodiment, an enhanced spectral vegetation index (EVI) constructed based on a near-infrared band and a red band determines a coefficient R in spectral vegetation index correlation analysis2The highest model building precision is lower by a single spectrum vegetation index, and the precision is higher by adopting a plurality of spectrum index comprehensive analysis models. The simulation effect of the preferred spectral vegetation index model in the embodiment is superior to that of the spectral reflectivity model, the internal parameters of the plant are considered, and the model is adopted to predict the water content of the plant in future application.
According to the invention, through the design, the correlation analysis of the spectral reflectivity and the plant water content is carried out, a spectral reflectivity model and a spectral vegetation index model are established by utilizing two regression analysis methods, and the correlation (R) of the models is carried out2) And the Relative Error (RE) and the Root Mean Square Error (RMSE) are tested to determine an optimal plant moisture content estimation model based on the spectral information, so that a theoretical basis is provided for realizing accurate crop monitoring, and the applicability of the multispectral remote sensing monitoring of the unmanned aerial vehicle is further enhanced.
Claims (8)
1. A method for inverting the moisture content of winter wheat plants based on multispectral remote sensing of an unmanned aerial vehicle is characterized by comprising the following steps:
s1, acquiring a five-waveband spectrum image of the winter wheat plant in the research area by using an unmanned aerial vehicle multispectral remote sensing technology;
s2, calculating the water content of the winter wheat plants in the research area by using a wet basis water content method;
s3, calculating according to the spectrum images of the five wave bands to obtain the spectrum reflectivity of winter wheat in the research area;
s4, cutting the image of the spectral reflectivity of the winter wheat by utilizing ENVI software to extract 6 spectral vegetation indexes;
s5, analyzing the correlation between the water content of the winter wheat plant and the spectral reflectivity and the correlation between the water content of the winter wheat plant and the 6 spectral vegetation indexes, and establishing a plurality of spectral reflectivity inversion models and a plurality of spectral vegetation index inversion models by respectively utilizing a forced entry method and a stepwise regression method;
the expression of the optimal spectral reflectivity inversion model is as follows:
the expression of the optimal spectrum vegetation index inversion model is as follows:
y＝1.579EVI-0.035SR+0.448VARI+0.136
wherein y represents the water content of the winter wheat plant, and x1Denotes the reflectivity, x, of the near infrared band2Representing the reflectivity, x, of the red wavelength band3Representing the reflectance of the green band, x4Representing the reflectivity, x, of the blue band5The reflectivity of a red edge wave band is represented, ln represents the logarithm of natural logarithm with a constant e as a base number, EVI represents an enhanced spectral vegetation index, SR represents a ratio spectral vegetation index, and VARI represents an atmospheric resistance index;
s6, analyzing parameters in the plurality of spectral reflectivity inversion models and the plurality of spectral vegetation index inversion models respectively to obtain an optimal spectral reflectivity inversion model and an optimal spectral vegetation index inversion model;
the step S6 specifically includes:
d1, analyzing the determining coefficient R of each model in the plurality of spectral reflectivity inversion models2Root mean square error RMSE and relative error RE parameters;
d2 obtaining a determination coefficient R according to the analysis result2The maximum model with the minimum root mean square error RMSE and relative error RE is used as an optimal spectrum reflectivity inversion model;
d3, analyzing the determining coefficient R of each model in the plurality of spectrum vegetation index inversion models2Root mean square error RMSE and relative error RE parameters;
d4 obtaining a determination coefficient R according to the analysis result2The maximum model with the minimum root mean square error RMSE and relative error RE is used as an optimal spectrum vegetation index inversion model;
and S7, selecting an optimal model from the optimal spectral reflectivity inversion model and the optimal spectral vegetation index inversion model according to the parameters in the winter wheat plant, the environment and interference factors when the water content of the winter wheat plant is predicted and the practicability, so that the inversion of the water content of the winter wheat plant is completed.
2. The method for inverting the moisture content of the winter wheat plant based on the unmanned aerial vehicle multispectral remote sensing as claimed in claim 1, wherein the moisture content C of the winter wheat plant in the research area is calculated by using a wet basis moisture content method in the step S2wThe expression of (a) is as follows:
wherein L iswIndicating the fresh weight of the winter wheat plant, LdRepresents the dry weight of the winter wheat plant.
3. The method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing according to claim 1, wherein the step S3 comprises the following steps:
a1, synthesizing the five-waveband spectral images into a tif-format five-waveband spectral image;
a2, constructing an ROI (region of interest) by utilizing a mask method according to the synthesized five-waveband spectral image;
a3, respectively calculating the spectral reflectivity of five wave bands in the ROI area by using a wave band operation tool Bandmath;
and A4, calculating the average spectral reflectivity of the spectral reflectivities of the five wave bands in the ROI area to obtain the spectral reflectivity of winter wheat in the research area.
4. The method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing according to claim 1, wherein the 6 spectral vegetation indexes in the step S4 comprise: normalized spectral vegetation index NDVI, soil conditioning spectral vegetation index SAVI, enhanced spectral vegetation index EVI, ratio spectral vegetation index SR, greenness normalized spectral vegetation index GNDVI, and atmospheric resistance index VARI.
5. The method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing according to claim 4, wherein the expression of the normalized spectral vegetation index NDVI is as follows:
the expression of the soil conditioning spectral vegetation index SAVI is as follows:
the expression of the enhanced spectral vegetation index EVI is as follows:
the expression of the ratio spectral vegetation index SR is as follows:
the greenness normalized spectral vegetation index GNDVI has the following expression:
the expression of the anti-atmospheric index VARI is as follows:
in the above formulae, Rblue、Rgreen、Rred、RnirThe average reflectivity of the gray plate to the blue, green, red and near infrared bands of the RedEdge camera is respectively.
6. The method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing according to claim 1, wherein the step S5 of constructing a plurality of spectral reflectivity abnormal models comprises the following steps:
b1, analyzing the correlation degree of the moisture content of the winter wheat plant and the spectral reflectance of the winter wheat by using a multivariate regression method of SPSS software;
b2, establishing a spectrum reflectivity inversion model containing the reflectivity of five wave bands by using a forced entry method;
and B3, sequentially introducing the moisture content of the winter wheat plants into the spectral reflectance inversion models one by utilizing a stepwise regression method according to the correlation degree to obtain a plurality of spectral reflectance inversion models.
7. The method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing according to claim 1, wherein the step S5 of constructing a plurality of spectral vegetation index inversion models comprises the following steps:
c1, analyzing the correlation degree of the moisture content of the winter wheat plant and the spectral vegetation index of the winter wheat by using a multivariate regression method of SPSS software;
c2, establishing a spectrum vegetation index inversion model containing 6 spectrum vegetation indexes by using a forced entry method;
and C3, sequentially introducing the moisture content of the winter wheat plants into the spectral vegetation index inversion models one by utilizing a stepwise regression method according to the correlation degree to obtain a plurality of spectral vegetation index inversion models.
8. The method for inverting the moisture content of winter wheat plants based on unmanned aerial vehicle multispectral remote sensing according to claim 1, wherein the decision coefficient R is2The expression of (a) is as follows:
the root mean square error RMSE is expressed as follows:
the expression for the relative error RE is as follows:
in the above formulas, n represents the number of verification samples, i represents the sample number,
the predicted value y representing the water content of the winter wheat plantiThe measured value of the water content of the winter wheat plant is shown,
the average value of the water content of the winter wheat plants is shown.
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