Fine integrated inversion method of farmland soil moisture using collaborative unmanned aerial vehicle multi-spectral and thermal infrared imagery

Abstract

The present invention discloses a fine integrated inversion method for farmland soil moisture by coordinating UAV multi-spectral and thermal infrared images. The present invention relates to the technical field of integrated inversion of farmland soil moisture, and solves the problems of insufficient inversion accuracy and weak stability often faced by conventional machine learning inversion algorithms. The method includes the acquisition and preprocessing of UAV multi-spectral and thermal infrared image data, vegetation index calculation, construction of multi-modal data feature sets, collection of farmland soil moisture content sample points, construction of a farmland soil moisture integrated inversion model with residual optimization, farmland soil moisture inversion, and accuracy evaluation of the farmland soil moisture inversion results; the present invention effectively integrates the characteristics and advantages of optical and thermal infrared multi-modal remote sensing data, constructs a farmland soil moisture integrated inversion model with residual optimization, and improves the accuracy and stability of high-resolution soil moisture dynamic inversion.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated inversion of farmland soil moisture, and particularly relates to a fine integrated inversion method for farmland soil moisture that synergistically uses unmanned aerial vehicle (UAV) multi-spectral and thermal infrared images. Background Technique

[0002] Soil water content, as an important indicator reflecting farmland soil moisture, plays a key role in the material and energy cycle of the farmland ecosystem and is an important variable in the research fields of agricultural yield increase, hydrological cycle, etc. Therefore, constructing an efficient and accurate inversion method for soil water content with high spatial resolution at the field scale is of great significance for improving crop irrigation management level, agricultural water resource utilization efficiency, crop growth and drought monitoring ability, etc.

[0003] Satellite remote sensing receives electromagnetic wave signals reflected or emitted by the earth's surface through sensors and then calculates the information on the soil moisture status of the earth's surface in combination with algorithms. It has achieved large-area and rapid monitoring of soil moisture to a certain extent, but there are still problems such as insufficient timeliness, poor resolution, and low accuracy. UAV remote sensing has the advantages of high timeliness, high resolution, and the ability to carry multiple types of sensors, and can effectively realize the dynamic monitoring of soil moisture at the field block scale. Research shows that by comprehensively using UAV surface temperature, multi-spectral image data and their derived vegetation indices, soil moisture information can be better characterized, which is conducive to the fine-grained and accurate inversion of farmland soil moisture. The remote sensing inversion of soil moisture information is affected by various factors such as vegetation cover, surface roughness, and soil type, and the relationship between soil water content and data characteristic variables is complex. Machine learning algorithms have obvious advantages in solving non-linearity, heteroscedasticity, etc., and are currently effective solutions. However, when agricultural scenarios are complex and multi-modal data are used in combination, conventional machine learning inversion algorithms often face problems such as insufficient inversion accuracy and weak stability. Therefore, it is necessary to propose a fine integrated inversion method for farmland soil moisture that synergistically uses UAV multi-spectral and thermal infrared images to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide a fine integrated inversion method for farmland soil moisture that synergistically uses UAV multi-spectral and thermal infrared images to solve the problems that conventional machine learning inversion algorithms often face, such as insufficient inversion accuracy and weak stability.

[0005] The present invention provides a fine integrated inversion method for farmland soil moisture that synergistically uses UAV multi-spectral and thermal infrared images, including:

[0006] Step 1, collecting multi-spectral image data and thermal infrared image data of a target area by using a UAV; preprocessing the multi-spectral image data and thermal infrared image data;

[0007] Step 2: Based on the preprocessed multi-spectral image data, calculate the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), Leaf Chlorophyll Index (LCI), and Green-band Normalized Difference Vegetation Index (GNDVI) respectively;

[0008] Step 3: Based on the preprocessed multi-spectral image data and thermal infrared image data in Step 1, and the NDVI, NDRE, LCI, and GNDVI calculated in Step 2, use the data stacking method to construct a multi-modal data feature set for soil moisture status inversion;

[0009] Step 4: Based on the principle of spatial uniform distribution, use the soil moisture monitoring system to synchronously and evenly select ground farmland points in an air-ground collaborative manner and measure the soil moisture content of the farmland. The measurement time is consistent with the acquisition time of the multi-spectral image data and thermal infrared image data in Step 1; Combine the multi-modal data feature set in Step 3 to construct a farmland soil moisture content sample set. The multi-modal data features in the sample set are used as the independent variables input to the inversion model, and the corresponding farmland soil moisture content is used as the dependent variable input to the inversion model; Divide the sample set into a training set and a test set;

[0010] Step 5: Construct a residual-optimized integrated inversion model for farmland soil moisture; Among them, in the first stage, based on three inversion algorithms of random forest, extreme gradient boosting, and support vector regression, use the training set in Step 4 as the input, train the three inversion algorithms respectively, evaluate the trained inversion algorithms on the test set, and calculate the coefficient of determination; Design the weight coefficient with the coefficient of determination as the measurement index, and perform weighted summation integration on the inversion results of the three inversion algorithms to obtain the basic integrated inversion result of the soil moisture content; In the second stage, calculate the residual between the basic integrated inversion result in the training set and the true value of the soil moisture content, and use the extreme gradient boosting regression algorithm as the residual correction model to fit the independent variable and the residual in the training set; Finally, sum the basic integrated inversion result in the first stage and the residual generated by the residual correction model in the second stage to obtain the final farmland soil moisture content, reflecting the soil moisture status;

[0011] Step 6: Use the residual-optimized integrated inversion model for farmland soil moisture constructed in Step 5, with the multi-modal data features of the target area as the input, to perform farmland soil moisture inversion.

[0012] Furthermore, in Step 1, the preprocessing of the multi-spectral image data includes band registration, image mosaicking, radiometric calibration, and standard deviation normalization; the preprocessing of the thermal infrared image data includes image mosaicking, bad value interpolation, and standard deviation normalization; After preprocessing the multi-spectral image data and thermal infrared image data, through image registration and image cropping operations, achieve spatial alignment between the two, and finally obtain the available image data covering the target area.

[0013] Further, in step one, the spatial resolutions of the multispectral image data and the thermal infrared image data are both 0.2 m. Among them, the bands of the multispectral image data include a blue light band, a green light band, a red light band, a red edge band, a first near-infrared band, and a second near-infrared band, a total of six bands.

[0014] Further, the wavelengths of the six bands are 450 nm, 550 nm, 660 nm, 680 nm, 840 nm, and 900 nm in sequence.

[0015] Further, in step two, the calculation formula is as follows:

[0016]

[0017]

[0018]

[0019]

[0020] In the formula, is the normalized difference vegetation index, is the normalized difference red edge index, is the leaf area chlorophyll index, is the green band normalized difference vegetation index; , , respectively represent the reflectance values of the red light band, the green light band, the red edge band, and the first near-infrared band of the UAV multispectral image data.

[0021] Further, in step five, the calculation method of the weight coefficient is as follows:

[0022]

[0023] In the formula, represents the determination coefficient of the i th inversion algorithm, is the weight coefficient of the i th inversion algorithm during weighted summation integration.

[0024] Further, the method further includes:

[0025] Step seven, based on the farmland soil moisture content result obtained by the farmland soil moisture integrated inversion model optimized by residuals, calculate evaluation indexes such as determination coefficient, root mean square error, mean relative error, and mean absolute error, and evaluate the farmland soil moisture inversion ability of the farmland soil moisture fine integrated inversion method that synergistically uses UAV multispectral and thermal infrared images.

[0026] Furthermore, in Step 7, the calculation formulas for the evaluation indicators of coefficient of determination, root mean square error, mean relative error, and mean absolute error are as follows:

[0027]

[0028]

[0029]

[0030]

[0031] In the formula, is the coefficient of determination of the evaluation indicator, is the root mean square error, is the mean relative error, is the mean absolute error; , , respectively represent the inversion prediction value, true value, and average value of the soil moisture content in farmland, and n is the number of samples; the closer the coefficient of determination of the evaluation indicator is to 1, and the closer the root mean square error, mean relative error, and mean absolute error are to 0, the higher the inversion accuracy of the model and the better the prediction performance.

[0032] The present invention has the following beneficial effects: The fine integrated inversion method for farmland soil moisture conditions by synergistically using unmanned aerial vehicle multi-spectral and thermal infrared images of the present invention constructs a residual-optimized integrated inversion model for farmland soil moisture conditions based on multi-spectral and thermal infrared image data, breaks through the limitations of weak dynamics and low spatial resolution in satellite remote sensing monitoring, and improves the accuracy and stability of soil moisture condition inversion. This method effectively integrates the characteristics and advantages of optical and thermal infrared multi-modal remote sensing data, and can realize low-cost, fast, and accurate inversion of soil moisture conditions at the field block scale with high spatial resolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0034] Figure 1 is the flow chart of the fine integrated inversion method for farmland soil moisture conditions by synergistically using unmanned aerial vehicle multi-spectral and thermal infrared images of the present invention;

[0035] Figure 2 is the result diagram of the inversion of soil moisture conditions in the field;

[0036] Figure 3 is the diagram of the true value and inversion result of the soil moisture content in the test set of farmland;

[0037] Figure 4 It is a scatter plot of the true value of soil moisture content in farmland in the test set and the inversion result. Specific implementation manners

[0038] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below with reference to the drawings.

[0039] Please refer to Figure 1 , an embodiment of the present invention provides a fine integrated inversion method for farmland soil moisture conditions by synergistically using UAV multi-spectral and thermal infrared images, including:

[0040] Step 1, collecting multi-spectral image data and thermal infrared image data of a target area by using a UAV; preprocessing the multi-spectral image data and the thermal infrared image data.

[0041] Specifically, during the period from 11 am to 3 pm on a certain day, a DJI M350RTK UAV equipped with an MS600PRO multi-spectral and a Zenmuse H20T thermal infrared imager is used to collect multi-spectral image data and thermal infrared image data of the target area respectively. The preprocessing of the multi-spectral image data includes band registration, image stitching, radiometric calibration and standard deviation normalization; the preprocessing of the thermal infrared image data includes image stitching, bad value interpolation and standard deviation normalization; after preprocessing the multi-spectral image data and the thermal infrared image data, through image registration and image cropping operations, spatial alignment between the two is achieved, and finally available image data covering the target area is obtained.

[0042] The spatial resolution of both the multi-spectral image data and the thermal infrared image data is 0.2 meters. Among them, the bands of the multi-spectral image data include a blue light band, a green light band, a red light band, a red edge band, a first near-infrared band and a second near-infrared band, a total of six bands. Specifically, the wavelengths of the six bands are 450nm, 550nm, 660nm, 680nm, 840nm and 900nm in sequence.

[0043] Step 2: Based on the preprocessed multi-spectral image data, calculate the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge (NDRE), Leaf Chlorophyll Index (LCI), and Green-band Normalized Vegetation Index (GNDVI) respectively.

[0044] Specifically, the calculation formulas are as follows:

[0045]

[0046]

[0047]

[0048]

[0049] In the formulas, is the Normalized Difference Vegetation Index, is the Normalized Difference Red Edge, is the Leaf Chlorophyll Index, is the Green-band Normalized Vegetation Index; and and respectively represent the reflectance values of the red light band, green light band, red edge band, and first near-infrared band of the UAV multi-spectral image data.

[0050] Step 3: Based on the preprocessed multi-spectral image data and thermal infrared image data in Step 1, and the Normalized Difference Vegetation Index, Normalized Difference Red Edge, Leaf Chlorophyll Index, and Green-band Normalized Vegetation Index calculated in Step 2, use the data stacking method to construct a multi-modal data feature set for soil moisture status inversion.

[0051] Step 4: Based on the principle of spatial uniform distribution, use the soil moisture monitoring system to synchronously and evenly select ground farmland points in an air-ground collaborative manner and measure the soil moisture content of the farmland. The measurement time is the same as the acquisition time of the multi-spectral image data and thermal infrared image data in Step 1; combined with the multi-modal data feature set in Step 3, construct a farmland soil moisture content sample set. The multi-modal data features in the sample set are used as independent variables for input to the inversion model, and the corresponding farmland soil moisture content is used as the dependent variable for input to the inversion model; divide the sample set into a training set and a test set.

[0052] In this embodiment, the MiniTrase soil moisture monitoring system is used to measure the soil water content value. After data processing, a total of 93 sample points of farmland soil water content are obtained. The sample set is divided into a training set and a test set according to the ratio of 7:3, which are used for the training and accuracy evaluation of the farmland soil moisture inversion model respectively. MiniTrase is a portable measuring device for soil moisture status based on the principle of Time Domain Reflectometry (TDR); the soil water content refers to the volumetric water content of the soil, and the unit is %.

[0053] Step Five, construct an integrated inversion model of farmland soil moisture with residual optimization;

[0054] Among them, in the first stage, based on three inversion algorithms: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR), using the training set in Step Four as the input, train the three inversion algorithms respectively, evaluate the trained inversion algorithms on the test set, and calculate the coefficient of determination (R 2 ); design the weight coefficient with R 2 as the measurement index, perform weighted summation integration on the inversion results of the three inversion algorithms, and obtain the basic integrated inversion result of the soil water content;

[0055] In the second stage, calculate the residual between the basic integrated inversion result and the true value of the soil water content in the training set, and use the XGBoost regression algorithm as the residual correction model to fit the independent variable and the residual in the training set; finally, add the basic integrated inversion result in the first stage and the residual generated by the residual correction model in the second stage to obtain the final farmland soil water content, which reflects the soil moisture status.

[0056] The calculation method of the weight coefficient is as follows:

[0057]

[0058] In the formula, represents the coefficient of determination of the i th inversion algorithm, and is the weight coefficient of the i th inversion algorithm during weighted summation integration.

[0059] Step Six, use the integrated inversion model of farmland soil moisture with residual optimization constructed in Step Five, take the multi-modal data features of the target area as the input, and perform farmland soil moisture inversion. The field moisture inversion result is as Figure 2 shown.

[0060] Step 7: Based on the farmland soil moisture content results obtained from the integrated inversion model of farmland soil moisture based on residual optimization, calculate the evaluation indices coefficient of determination (R 2 ), root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) to evaluate the farmland soil moisture inversion ability of the fine integrated inversion method for farmland soil moisture by synergistic unmanned aerial vehicle multispectral and thermal infrared images.

[0061] In this embodiment, the R 2 , RMSE, MRE, and MAE of the farmland soil moisture inversion results are 0.80, 2.41, 0.05, and 1.46 respectively. The comparison with the true value of soil moisture is shown in Figure 3 and Figure 4 .

[0062] Specifically, the calculation formulas for the evaluation indices coefficient of determination, root mean square error, mean relative error, and mean absolute error are as follows:

[0063]

[0064]

[0065]

[0066]

[0067] Wherein, is the coefficient of determination of the evaluation index, is the root mean square error, is the mean relative error, is the mean absolute error; , , respectively represent the inversion prediction value, true value, and average value of the farmland soil moisture content, and n is the number of samples; the closer the coefficient of determination of the evaluation index is to 1, and the closer the root mean square error, mean relative error, and mean absolute error are to 0, the higher the inversion accuracy of the model and the better the prediction performance.

[0068] In summary, the present invention makes use of the characteristics of UAV remote sensing, such as high timeliness, high resolution, and the ability to carry multiple types of sensors, effectively overcoming the deficiencies of satellite remote sensing in soil moisture monitoring, including weak dynamics and low spatial resolution. By comprehensively utilizing UAV surface temperature, multispectral image data, and the derived vegetation indices, the present invention enhances the representation of soil moisture information and constructs an integrated inversion model for farmland soil moisture with residual optimization, improving the accuracy and stability of high-resolution dynamic inversion of soil moisture. Aiming at the problems of insufficient inversion accuracy and weak stability often faced by conventional machine learning inversion algorithms, the present invention proposes a fine integrated inversion method for farmland soil moisture by synergistically using UAV multispectral and thermal infrared images. By comprehensively utilizing multimodal data such as UAV surface temperature, multispectral images, and the derived vegetation indices, and based on algorithms such as RF, SVR, and XGBoost, an integrated inversion model for farmland soil moisture with residual optimization is constructed to improve the accuracy and stability of high-resolution dynamic inversion of soil moisture.

[0069] The above-described embodiments of the present invention do not constitute a limitation on the protection scope of the present invention.

Claims

1. A fine integrated inversion method for farmland soil moisture content by synergistically using UAV multi-spectral and thermal infrared images, characterized in that, Including: Step 1: Collect multispectral image data and thermal infrared image data of the target area by using a drone; Preprocess the multispectral image data and the thermal infrared image data; Step 2: Based on the preprocessed multispectral image data, calculate the normalized difference vegetation index, normalized difference red edge index, leaf chlorophyll index, and green band normalized difference vegetation index respectively; Step 3: Based on the preprocessed multispectral image data, thermal infrared image data in Step 1, and the normalized difference vegetation index, normalized difference red edge index, leaf chlorophyll index, and green band normalized difference vegetation index calculated in Step 2, adopt the data stacking method to construct a multi-modal data feature set for soil moisture condition inversion; Step 4: Based on the principle of spatial uniform distribution, use the soil moisture monitoring system to synchronously and evenly select ground farmland points in an air-ground collaborative manner and measure the soil moisture content of the farmland. The measurement time is consistent with the acquisition time of the multispectral image data and the thermal infrared image data in Step 1; Combine the multi-modal data feature set in Step 3 to construct a farmland soil moisture content sample set. The multi-modal data features in the sample set are used as the independent variables input to the inversion model, and the corresponding farmland soil moisture content is used as the dependent variable input to the inversion model; Divide the sample set into a training set and a test set; Step 5: Construct a residual-optimized integrated inversion model for farmland soil moisture; Among them, in the first stage, based on three inversion algorithms of random forest, extreme gradient boosting, and support vector regression, use the training set in Step 4 as the input, train the three inversion algorithms respectively, evaluate the trained inversion algorithms on the test set, and calculate the coefficient of determination; Design the weight coefficient with the coefficient of determination as the measurement index, and perform weighted summation integration on the inversion results of the three inversion algorithms to obtain the basic integrated inversion result of the soil moisture content; In the second stage, calculate the residual between the basic integrated inversion result in the training set and the true value of the soil moisture content, and use the extreme gradient boosting regression algorithm as the residual correction model to fit the independent variable and the residual in the training set; Finally, add the basic integrated inversion result in the first stage and the residual generated by the residual correction model in the second stage to obtain the final farmland soil moisture content, reflecting the soil moisture condition; Step 6: Use the residual-optimized integrated inversion model for farmland soil moisture constructed in Step 5, with the multi-modal data features of the target area as the input, to perform farmland soil moisture inversion.

2. The fine integrated inversion method of farmland soil moisture content from collaborative UAV multi-spectral and thermal infrared images according to claim 1, characterized in that In Step 1, the preprocessing of the multispectral image data includes band registration, image mosaicking, radiometric calibration, and standard deviation normalization; The preprocessing of the thermal infrared image data includes image mosaicking, bad value interpolation, and standard deviation normalization; After preprocessing the multispectral image data and the thermal infrared image data, through image registration and image cropping operations, achieve spatial alignment between the two, and finally obtain the available image data covering the target area.

3. The fine integrated inversion method for farmland soil moisture using collaborative UAV multi-spectral and thermal infrared images as claimed in claim 1, wherein, In Step 1, the spatial resolutions of the multispectral image data and the thermal infrared image data are both 0.2 m. Among them, the bands of the multispectral image data include a blue light band, a green light band, a red light band, a red edge band, a first near-infrared band, and a second near-infrared band, a total of six bands.

4. The fine integrated inversion method of farmland soil moisture content from cooperative UAV multi-spectral and thermal infrared images according to claim 3, wherein, The wavelengths of the six bands are 450 nm, 550 nm, 660 nm, 680 nm, 840 nm, and 900 nm in sequence.

5. The fine integrated inversion method for farmland soil moisture using collaborative UAV multi-spectral and thermal infrared images according to claim 4, characterized in that, In Step 2, the calculation formula is as follows: In the formula, is the normalized difference vegetation index, is the normalized difference red-edge index, is the leaf area chlorophyll index, is the green-band normalized difference vegetation index; , , respectively represent the reflectance values of the red band, green band, red-edge band and first near-infrared band of the UAV multispectral image data.

6. The fine integrated inversion method of farmland soil moisture content from collaborative UAV multi-spectral and thermal infrared images according to claim 1, characterized in that In Step 5, the calculation method of the weight coefficient is as follows: In the formula, represents the coefficient of determination of the i th inversion algorithm, is the weight coefficient of the i th inversion algorithm in the weighted summation integration.

7. The fine integrated inversion method for farmland soil moisture content from collaborative UAV multi-spectral and thermal infrared images according to claim 1, characterized in that It also includes: In Step 7, based on the results of the farmland soil moisture content obtained by the integrated inversion model of farmland soil moisture based on residual optimization, calculate the evaluation indexes of the coefficient of determination, root mean square error, average relative error, and average absolute error to evaluate the farmland soil moisture inversion ability of the fine integrated inversion method of farmland soil moisture by synergistic unmanned aerial vehicle multispectral and thermal infrared images.

8. The fine integrated inversion method for farmland soil moisture content from collaborative UAV multi-spectral and thermal infrared images according to claim 7, characterized in that In Step 7, the calculation formulas of the evaluation indexes of the coefficient of determination, root mean square error, average relative error, and average absolute error are as follows: In the formula, is the coefficient of determination of the evaluation index, is the root mean square error, is the average relative error, is the average absolute error; , , respectively represent the inversion prediction value, the true value and the average value of the soil moisture content in farmland, and n is the number of samples; the closer the coefficient of determination of the evaluation index is to 1, and the closer the root mean square error, the average relative error and the average absolute error are to 0, the higher the inversion accuracy of the model and the better the prediction performance.

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