Rice high temperature stress measurement method based on remote sensing image

Abstract

The invention provides a rice high-temperature stress measurement method based on a remote sensing image, which relates to the technical field of rice high-temperature stress measurement, and comprises the following five steps: obtaining remote sensing index data, obtaining field monitoring data, establishing an estimation measurement model, setting a reference value and judging rice high-temperature stress. Based on remote sensing image data and in combination with on-site monitoring data of the ground, the accuracy and reliability of measurement are improved, meanwhile, large-range monitoring is achieved by introducing the remote sensing image data, micro-scale analysis is conducted in combination with the ground monitoring data, multi-scale integration is achieved, and the measurement accuracy and reliability are improved. And remote sensing image data is carried out by adopting an unmanned aerial vehicle remote sensing mode, so that real-time monitoring on high-temperature stress of rice can be realized, measures can be taken in time, the influence of high temperature on rice growth is reduced, and the rice yield and quality are improved.

Description

Technical Field

[0001] The invention relates to the technical field of rice high temperature stress measurement, and in particular to a rice high temperature stress measurement method based on remote sensing images. Background Art

[0002] Rice belongs to C3 plants, and its optimal growth temperature is 25℃~30℃. Studies have shown that climate change has both positive and negative effects on rice photosynthesis, and the effects vary greatly in different places. Therefore, studying the dependence of rice photosynthesis on temperature is crucial to accurately estimate rice productivity and understand the carbon sink function of farmland ecosystems.

[0003] High temperature stress refers to the phenomenon that when the environmental temperature exceeds the upper limit that the plant can tolerate for normal growth and development, a series of physiological and biochemical processes in the plant are disturbed or destroyed, resulting in stunted growth and development, decreased yield and quality, and even death. For example, high temperature can cause the activity of key enzymes in plant photosynthesis to decrease, the photosynthetic rate to decrease, and seriously affect the carbon fixation and growth of the plant.

[0004] High temperature stress is one of the important factors affecting rice growth and yield, so measuring rice high temperature stress is of great significance for ensuring food security, but the traditional ground monitoring method is limited by the number and distribution of monitoring points, and it is difficult to fully cover the entire rice field, resulting in insufficient data representativeness. In addition, manual measurement may have subjective errors, affecting the accuracy of the data, so the present invention proposes a rice high temperature stress measurement method based on remote sensing images to solve the problems existing in the prior art. Summary of the invention

[0005] In view of the above problems, the purpose of the present invention is to propose a method for measuring rice high temperature stress based on remote sensing images, which has the advantages of convenient and accurate measurement and can solve the problems existing in the prior art.

[0006] To achieve the purpose of the present invention, the present invention is implemented by the following technical scheme: a method for measuring high temperature stress of rice based on remote sensing images, comprising the following steps:

[0007] Step 1: Acquisition of remote sensing indicator data

[0008] Confirm the rice research area, then obtain the multispectral remote sensing data of the research area, perform preprocessing, and then calculate the normalized difference vegetation index of each pixel range based on the processed multispectral remote sensing data;

[0009] Step 2: Acquisition of field monitoring data

[0010] Using manual actual measurement methods, within the confirmed rice research area, with the help of measuring instruments, the rice in the rice research area is measured, the field monitoring data is collected, and then pre-processed;

[0011] Step 3: Establishment of estimation measurement model

[0012] Using the field monitoring data obtained in step 2, one-to-one correspondence is made with the remote sensing index data obtained in step 1, and a chlorophyll content estimation model is constructed based on a linear regression model, and then the chlorophyll content estimation model is trained;

[0013] Step 4: Establishment of reference values

[0014] Under normal temperature conditions, random sampling was used to obtain N groups of field monitoring data in the confirmed rice research area, and then the data were processed to obtain reference values;

[0015] Step 5: Determination of high temperature stress in rice

[0016] Re-obtain the multispectral remote sensing data of the rice-growing area to be judged, process it, and input it into the chlorophyll content estimation model constructed in step three to obtain the chlorophyll content estimation result, compare the result with the reference value, and judge whether the rice is under high temperature stress based on the comparison result.

[0017] A further improvement is that in step 1, the preprocessing method is to perform radiation correction, atmospheric correction and geometric correction on the multispectral remote sensing data.

[0018] A further improvement is that: the expression formula for calculating the normalized difference vegetation index is:

[0019]

[0020] In the formula, NIR is the reflectivity of the near-infrared band, R is the reflectivity of the red light band, and the NDVI value range is -1 to 1. The larger the value, the better the coverage.

[0021] A further improvement is that in step 2, the specific method of preprocessing is to perform data cleaning and data standardization on the field monitoring data in sequence.

[0022] A further improvement is that in step 2, the field monitoring data and the remote sensing index data are in the same time window.

[0023] A further improvement is that in step 3, the chlorophyll content estimation model expression is:

[0024] T 叶绿素含量 =β0+β1×NDVI+∈

[0025] Where β0 is the intercept term, β1 is the slope term, and ∈ is the error term.

[0026] A further improvement is that in step 4, the processing method is: remove the lowest value and the highest value in N groups of field monitoring data, and then take the average value of the remaining field monitoring data, and the average value is the reference value.

[0027] A further improvement is that in step 5, when the result is 80-90% lower than the reference value, it is regarded as being under high temperature stress.

[0028] The beneficial effects of the present invention are as follows: the present invention provides a rice high temperature stress measurement method based on remote sensing images, which is based on remote sensing image data and combined with ground field monitoring data to improve the accuracy and reliability of measurement. At the same time, by introducing remote sensing image data, large-scale monitoring is achieved, and micro-scale analysis is performed in combination with ground monitoring data to achieve multi-scale integration. The remote sensing image data is obtained by unmanned aerial vehicle remote sensing, thereby enabling real-time monitoring of rice high temperature stress, so that measures can be taken in time to reduce the impact of high temperature on rice growth and improve rice yield and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0030] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0031] Embodiment 1

[0032] according to Figure 1 As shown, this embodiment proposes a method for measuring high temperature stress of rice based on remote sensing images, comprising the following steps:

[0033] Step 1: Acquisition of remote sensing indicator data

[0034] Confirm the rice research area, then obtain the multispectral remote sensing data of the research area, and then preprocess it. Then, according to the processed multispectral remote sensing data, calculate the normalized difference vegetation index of each pixel range. In step 1, the preprocessing method is to perform radiation correction, atmospheric correction and geometric correction on the multispectral remote sensing data. The expression formula of the normalized difference vegetation index is calculated as follows:

[0035]

[0036] In the formula, NIR is the reflectance of the near-infrared band, R is the reflectance of the red light band, and the NDVI value range is -1 to 1. The larger the value, the better the coverage.

[0037] Step 2: Acquisition of field monitoring data

[0038] By adopting the method of manual actual measurement, within the confirmed rice research area, the rice in the rice research area is measured with the method of measuring instruments, the field monitoring data is collected, and then pre-processed. In step 2, the specific method of pre-processing is to perform data cleaning and data standardization on the field monitoring data in turn. In step 2, the field monitoring data and the remote sensing index data are in the same time window;

[0039] Step 3: Establishment of estimation measurement model

[0040] The field monitoring data obtained in step 2 are used to correspond one-to-one with the remote sensing index data obtained in step 1. A chlorophyll content estimation model is constructed based on a linear regression model. The chlorophyll content estimation model is then trained. In step 3, the chlorophyll content estimation model expression is:

[0041] T 叶绿素含量 =β0+β1×NDVI+∈

[0042] In the formula, β0 is the intercept term, β1 is the slope term, and ∈ is the error term;

[0043] Step 4: Establishment of reference values

[0044] Under normal temperature conditions, in the confirmed rice research area, N groups of field monitoring data are obtained by random sampling, and then processed to obtain reference values. In step 4, the processing method is: remove the lowest value and the highest value in the N groups of field monitoring data, and then take the average value of the remaining field monitoring data, and the average value is the reference value;

[0045] Step 5: Determination of high temperature stress in rice

[0046] Re-obtain the multispectral remote sensing data of the rice-growing area to be judged, process it, and input it into the chlorophyll content estimation model constructed in step three to obtain the chlorophyll content estimation result, compare the result with the reference value, and judge whether the rice is under high temperature stress based on the comparison result. In step five, when the result is lower than 90% of the reference value, it is regarded as under high temperature stress.

[0047] Embodiment 2

[0048] according to Figure 1 As shown, this embodiment proposes a method for measuring high temperature stress of rice based on remote sensing images, comprising the following steps:

[0049] Step 1: Acquisition of remote sensing indicator data

[0050] In this embodiment, relevant designs are carried out for rice, thereby determining the specific geographical scope of rice research, and then obtaining multispectral remote sensing data of the research area, which is obtained by using drone remote sensing technology, and drone remote sensing technology can obtain multispectral data. Multispectral sensors are installed on drones and can capture reflectivity information of the surface in multiple bands. These bands usually include visible light, near infrared and other specific electromagnetic bands, and drone remote sensing technology can monitor and update in real time, and then pre-processing is performed. The pre-processing method is to perform radiation correction, atmospheric correction and geometric correction, wherein radiation correction is to convert the data of remote sensing images into surface reflectivity to eliminate the influence of sensors and atmosphere, and atmospheric correction is to further remove the influence of atmospheric scattering and absorption on remote sensing signals, and obtain more accurate surface reflectivity, and geometric correction is to correct the geometric deformation of the image to ensure the accurate correspondence between the image and the scope of the research area. Then, based on the processed multispectral remote sensing data, the normalized difference vegetation index of each pixel (multispectral remote sensing data is usually an image composed of multiple bands, each band captures the reflectance information of the surface within a specific wavelength range. These data are spatially divided into pixels, and each pixel represents the information of a certain area on the surface) is calculated, and the expression formula for the normalized difference vegetation index is obtained as follows:

[0051]

[0052] In the formula, NIR is the reflectance of the near-infrared band, R is the reflectance of the red light band, and the NDVI value range is -1 to 1. The larger the value, the better the coverage.

[0053] Step 2: Acquisition of field monitoring data

[0054] The rice in the rice research area is measured manually with a chlorophyll meter. Specifically, the field monitoring data and the remote sensing index data are in the same time window. Therefore, within the same time range, the field measurement data can correspond to the multispectral remote sensing data. The field monitoring data corresponds to the measured chlorophyll content, and then preprocessing is performed. The specific method of preprocessing is to perform data cleaning and data standardization on the field monitoring data in turn. Data cleaning is to check whether there are outliers, errors or missing values ​​in the data, and perform corresponding processing. Data standardization is achieved by subtracting the mean and dividing by the standard deviation, so that the data has the same mean and variance. That is, the actual manual measurement method is used. In the confirmed rice research area, the rice in the rice research area is measured with the help of measuring instruments, and the field monitoring data is collected, and then preprocessing is performed;

[0055] Step 3: Establishment of estimation measurement model

[0056] After step one and step two, the field monitoring data obtained in step two are used to correspond one-to-one with the remote sensing index data obtained in step one to ensure that each set of chlorophyll content data has a corresponding NDVI value. Specifically, the area covered by the multispectral remote sensing data corresponds to the rice research area, so the pixels involved in the multispectral remote sensing data correspond to a small range, and the field monitoring data are measured within the corresponding range. Therefore, each set of NDVI values ​​will correspond to a set of chlorophyll content data. On this basis, a linear regression model is used to construct a chlorophyll content estimation model. The chlorophyll content estimation model expression is:

[0057] T 叶绿素含量 =β0+β1×NDVI+∈

[0058] In the formula, β0 is the intercept term, β1 is the slope term, and ∈ is the error term. Based on the collected data, the statistical software SPSS is used for linear regression analysis, and then the intercept term and slope term are obtained. In the regression analysis, the error term can be estimated by calculating the residual. The residual is the difference between the observed value and the model predicted value. Once the model parameters (β0 and β1) are estimated, the error term can be calculated in the following way:

[0059] ∈=T 观测叶绿素 -(β0+β1×NDVI)

[0060] Where, T 观测叶绿素 is the detected chlorophyll content.

[0061] Step 4: Establishment of reference values

[0062] Under normal temperature conditions, in the confirmed rice research area, a random sampling method is used to obtain N groups of field monitoring data, and then processed to obtain reference values. The specific processing method is: remove the lowest and highest values ​​in the N groups of field monitoring data, and then take the average value of the remaining field monitoring data, and the average value is the reference value. Rice varieties with higher chlorophyll content have advantages in photosynthesis efficiency, because chlorophyll is the key pigment for photosynthesis in plants, it can absorb light energy and convert it into chemical energy for plant growth and development. Therefore, in this embodiment, a reference value is established for subsequent result comparison;

[0063] Step 5: Determination of high temperature stress in rice

[0064] Re-obtain the multi-spectral remote sensing data of the rice planting area to be judged, and the acquisition method is the same as step one, but the area becomes the rice planting area to be judged, and the rice research area is the data source for the model establishment in this embodiment, and then the multi-spectral remote sensing data for judging the rice planting area is processed, and the processing method is also the same as step one, and the chlorophyll content estimation model constructed in step three is input after processing, that is, the chlorophyll content estimation result is obtained, and the result is compared with the reference value, and whether the rice is subjected to high temperature stress is judged according to the comparison result. In the step 5, when the result is less than 80% of the reference value, it is regarded as subjected to high temperature stress. In this embodiment, the relative change of chlorophyll content is used as the comparison object. When the chlorophyll content has a significant decrease during or after the high temperature event occurs, it means that the rice is very likely to be subjected to high temperature stress, so a range lower than the reference value is used as a benchmark. With the passage of time and the accumulation of data, the reference value of chlorophyll content is regularly updated.

[0065] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the framework and scope of application of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for measuring high temperature stress of rice based on remote sensing images, characterized in that: The following steps are involved: Step 1: Acquisition of remote sensing indicator data Confirm the rice research area, then obtain the multispectral remote sensing data of the research area, perform preprocessing, and then calculate the normalized difference vegetation index of each pixel range based on the processed multispectral remote sensing data; Step 2: Acquisition of field monitoring data Using manual actual measurement methods, within the confirmed rice research area, with the help of measuring instruments, the rice in the rice research area is measured, the field monitoring data is collected, and then pre-processed; Step 3: Establishment of estimation measurement model Using the field monitoring data obtained in step 2, one-to-one correspondence is made with the remote sensing index data obtained in step 1, and a chlorophyll content estimation model is constructed based on a linear regression model, and then the chlorophyll content estimation model is trained; Step 4: Establishment of reference values Under normal temperature conditions, random sampling was used to obtain N groups of field monitoring data in the confirmed rice research area, and then the data were processed to obtain reference values; Step 5: Determination of high temperature stress in rice Re-obtain the multispectral remote sensing data of the rice-growing area to be judged, process it, and input it into the chlorophyll content estimation model constructed in step three to obtain the chlorophyll content estimation result, compare the result with the reference value, and judge whether the rice is under high temperature stress based on the comparison result.

2. The method for measuring rice high temperature stress based on remote sensing images according to claim 1, characterized in that: In the step 1, the preprocessing method is to perform radiation correction, atmospheric correction and geometric correction on the multispectral remote sensing data.

3. The method for measuring high temperature stress of rice based on remote sensing images according to claim 1, characterized in that: The expression formula for calculating the normalized difference vegetation index is: In the formula, NIR is the reflectivity of the near-infrared band, R is the reflectivity of the red light band, and the NDVI value range is -1 to 1. The larger the value, the better the coverage.

4. The method for measuring high temperature stress of rice based on remote sensing images according to claim 1, characterized in that: In the step 2, the specific method of preprocessing is to perform data cleaning and data standardization on the field monitoring data in sequence.

5. The method for measuring rice high temperature stress based on remote sensing images according to claim 1, characterized in that: In the step 2, the field monitoring data and the remote sensing index data are in the same time window.

6. The method for measuring high temperature stress of rice based on remote sensing images according to claim 1, characterized in that: In step 3, the chlorophyll content estimation model expression is: T 叶绿素含量 =β0+β1×NDVI+∈ Where β0 is the intercept term, β1 is the slope term, and ∈ is the error term.

7. The method for measuring high temperature stress of rice based on remote sensing images according to claim 1, characterized in that: In the step 4, the processing method is: remove the lowest value and the highest value in the N groups of field monitoring data, and then take the average value of the remaining field monitoring data, and the average value is the reference value.

8. The method for measuring high temperature stress of rice based on remote sensing images according to claim 1, characterized in that: In the step 5, when the result is 80-90% lower than the reference value, it is regarded as being under high temperature stress.

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