A wheat scab monitoring method based on satellite remote sensing

Abstract

The application discloses a kind of wheat scab monitoring methods based on satellite remote sensing, belong to remote sensing monitoring technical field, first obtain the remote sensing data and meteorological precipitation data of wheat planting area in predicted year and N years before predicted year, the remote sensing data include multispectral data and thermal infrared remote sensing data;Then the relative vigor index is calculated using the multispectral data;Using the multispectral data and thermal infrared remote sensing data obtains normalized surface temperature index;Using the meteorological precipitation data obtains normalized precipitation index;Finally, the relative vigor index, normalized surface temperature index and normalized precipitation index are used to calculate the wheat scab incidence index of predicted year;The application can better reduce the interference caused by the difference of wheat vigor in different regions in large-scale monitoring on scab monitoring model, improve the objectivity and practicability of scab remote sensing monitoring.

Description

Technical Field

[0001] This invention relates to the field of remote sensing monitoring technology, specifically to a method for monitoring wheat scab based on satellite remote sensing. Background Technology

[0002] Current research on monitoring wheat scab using remote sensing technology mostly focuses on changes in the spectral characteristics of wheat leaves when the disease occurs. This involves analyzing the spectral characteristics of the visible to near-shortwave infrared bands and the specific relationships between different bands to identify sensitive indicators for scab occurrence and establishing relevant estimation models. However, the analysis of these sensitive indicators often requires a large amount of leaf spectral data, and wheat growth status, agro-ecological conditions affecting wheat growth, and disease- and non-disease-related factors vary across different regions. Therefore, some remote sensing monitoring models for wheat scab often suffer from problems such as large-area sample collection and laboratory workload, low timeliness, and limited practical application in production.

[0003] To address the aforementioned issues, in recent years, some scholars have studied the pathogenic conditions affecting wheat scab disease, introducing major agro-atmospheric influencing factors (such as air temperature) and index characteristics reflecting wheat growth and leaf spectral changes (such as RDVI and NDVI) to establish regression models related to wheat scab in monitoring areas. Compared with traditional methods, this simplifies the workload of experiments and sample collection, and improves the timeliness and specificity of disease monitoring. However, due to significant differences in the index characteristics and non-disease factors affecting wheat scab at different regional scales, and because the meteorological factors that significantly affect scab mainly consider indicators reflecting near-surface atmospheric conditions rather than surface temperature, especially precipitation which has a significant impact on scab, previous models still have certain shortcomings in describing large-scale monitoring and agro-atmospheric conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a method for monitoring wheat scab based on satellite remote sensing in order to solve the aforementioned technical problems.

[0005] The technical solution adopted in this invention is as follows:

[0006] A method for monitoring wheat scab based on satellite remote sensing includes the following steps:

[0007] Step 1: Obtain remote sensing data and meteorological precipitation data of the wheat-growing area for the predicted year and the N years prior to the predicted year.

[0008] The remote sensing data includes multispectral data and thermal infrared remote sensing data;

[0009] Step 2: Calculate the relative growth index using the multispectral data;

[0010] Step 3: Obtain the normalized surface temperature index using the multispectral data and thermal infrared remote sensing data;

[0011] Step 4: Obtain the normalized precipitation index using the meteorological precipitation data;

[0012] Step 5: Using the relative growth index, normalized surface temperature index, and normalized precipitation index, calculate the predicted...

[0013] Yearly wheat scab incidence index.

[0014] Furthermore, in step 2, the relative growth index includes the relative NDVI index and the relative RDVI index.

[0015] Furthermore, the calculation steps for the relative growth index are as follows:

[0016] Step 201: Calculate the NDVI and RDVI indices for the predicted year and the same period within N years prior to the predicted year using the multispectral data. The formula used is as follows:

[0017] NDVI Index:

[0018] RDVI Index: DVI q =NIR q -R q (2),

[0019] Where q represents the year sequence number, NIR q R represents the near-infrared band of the multispectral data in year q. q The red band represents the multispectral data for year q, NDVI. q RDVI represents the NDVI index in year q. q This represents the RDVI index in year q.

[0020] Step 202: Stack the NDVI indices of the N years before the predicted year to obtain a multi-layer NDVI index, and stack the RDVI indices of the N years before the predicted year to obtain a multi-layer RDVI index. The NDVI index of the predicted year is a single-layer NDVI index, and the RDVI index of the predicted year is a single-layer RDVI index.

[0021] Step 203: Using a scrolling small window region algorithm, calculate the relative NDVI index using multi-layer NDVI index and single-layer NDVI index, and calculate the relative RDVI index using multi-layer RDVI index and single-layer RDVI index.

[0022] Furthermore, step 203 specifically includes:

[0023] Step 2031: Construct a window and initialize the cell values ​​within the window;

[0024] Step 2032: Place the window at the initial position of the multi-layer NDVI index or multi-layer RDVI index;

[0025] Step 2033: Calculate the maximum and minimum values ​​of each cell in the multi-layer NDVI index or multi-layer RDVI index covered by the current window, and store the maximum and minimum values ​​in the corresponding cell positions in the current window to update the cell values ​​in the current window;

[0026] Step 2034: After the current window is updated, calculate the mean of the minimum value and the mean of the maximum value of all pixels in the current window;

[0027] Step 2035: Locate the position of the current window within the single-layer NDVI index or single-layer RDVI index. Update the cell values ​​within the single-layer NDVI index or single-layer RDVI index using the cell values ​​covered by the current window, along with the minimum and maximum mean values. The formula used is as follows:

[0028]

[0029] Where C(i,j) represents the cell value in the single-layer NDVI index or single-layer RDVI index covered by the current window, C'(i,j) represents the updated cell value in the single-layer NDVI index or single-layer RDVI index, min_Value represents the mean of the minimum value, max_Value represents the mean of the maximum value, and i and j represent the subscripts of the cell positions within the window.

[0030] Step 2036: Move the window and repeat steps 2033-2035 until all pixel values ​​in the single-layer NDVI index or single-layer RDVI index are updated. The updated single-layer NDVI index is the relative NDVI index, and the updated single-layer RDVI index is the relative RDVI index.

[0031] Furthermore, step 3 specifically includes:

[0032] Step 301: Use the surface temperature inversion model based on the radiative transfer equation to invert the surface temperature, and normalize the surface temperature.

[0033] Step 302: Filter the normalized surface temperature index;

[0034] Step 303: Resample the filtered surface temperature to obtain the normalized surface temperature index.

[0035] Furthermore, step 4 specifically involves:

[0036] Step 401: Normalize the meteorological precipitation data using the following formula:

[0037]

[0038] Where p represents the sequence number of the meteorological station, P(xp) represents the precipitation value of the p-th station, and min(P(xp)) = min(xp ... p )) represents the minimum precipitation value at the p-th station, max(P(x) p )) represents the maximum precipitation value at the p-th station;

[0039] Step 402: Perform inverse distance interpolation on the normalized precipitation data and station locations;

[0040] Step 403: Perform geographic projection transformation and resolution resampling on the interpolation results to obtain the normalized precipitation index.

[0041] Furthermore, in step 5, the wheat scab incidence index is directly proportional to the risk of scab disease, and the formula for calculating the wheat scab incidence index is:

[0042]

[0043] Where α1 represents the relative NDVI index, α2 represents the relative RDVI index, α3 represents the normalized surface temperature index, α4 represents the normalized precipitation index, k1 and k2 are the coefficients of the two groups of influencing factors, and the sum of the coefficients of each group of influencing factors is 1.

[0044] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0045] 1. Introduce a relative growth index of wheat in a small area compared to previous years to address the interference caused by differences in ecophysiological factors in different regions during large-scale regional monitoring.

[0046] 2. Since wheat crops are relatively low in height, the impact of air temperature on wheat scab is mainly reflected in the influence of surface temperature. Modifying conventional atmospheric air temperature data to remote sensing inverted near-surface temperature data (LST) can better describe the influence of air temperature factors on wheat scab.

[0047] 3. Since the factors affecting the incidence of wheat scab are mainly related to the temperature and humidity of the growing environment, the model incorporates normalized precipitation data related to humidity and weights it together with the temperature factor. This makes the mathematical expression of scab more objective. Because the model has few weight coefficients and the sum of the weight coefficients of the two factors in each multiplication term is 1, the values ​​can be determined by analyzing the degree of influence of each factor. This has certain advantages in reducing the amount of sample experiments and improving the efficiency of monitoring in larger areas.

[0048] 4. This invention employs a small-scale window rolling algorithm, which introduces historical growth data to obtain the minimum and maximum average index values ​​within the window. Compared with the method of finding the maximum and minimum values ​​only within a single-layer data window, this method can effectively avoid the influence of low-quality pixels that may exist within the window on the calculation results. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0052] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0053] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0054] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0055] A method for monitoring wheat scab based on satellite remote sensing includes the following steps:

[0056] Step 1: Obtain remote sensing data and meteorological precipitation data of the wheat-growing area for the predicted year and the N years prior to the predicted year.

[0057] The remote sensing data includes multispectral data and thermal infrared remote sensing data;

[0058] Step 2: Calculate the relative growth index using the multispectral data;

[0059] Step 3: Obtain the normalized surface temperature index using the multispectral data and thermal infrared remote sensing data;

[0060] Step 4: Obtain the normalized precipitation index using the meteorological precipitation data;

[0061] Step 5: Using the relative growth index, normalized surface temperature index, and normalized precipitation index, calculate the predicted...

[0062] Yearly wheat scab incidence index.

[0063] Furthermore, in step 2, the relative growth index includes the relative NDVI index and the relative RDVI index.

[0064] Furthermore, the calculation steps for the relative growth index are as follows:

[0065] Step 201: Calculate the NDVI and RDVI indices for the predicted year and the same period within N years prior to the predicted year using the multispectral data. The formula used is as follows:

[0066] NDVI Index:

[0067] RDVI Index: DVI q =NIR q -R q (7),

[0068] Where q represents the year sequence number, NIR q R represents the near-infrared band of the multispectral data in year q. q The red band represents the multispectral data for year q, NDVI. q RDVI represents the NDVI index in year q. q This represents the RDVI index in year q.

[0069] Step 202: Stack the NDVI indices of the N years before the predicted year to obtain a multi-layer NDVI index, and stack the RDVI indices of the N years before the predicted year to obtain a multi-layer RDVI index. The NDVI index of the predicted year is a single-layer NDVI index, and the RDVI index of the predicted year is a single-layer RDVI index.

[0070] Step 203: Using a scrolling small window region algorithm, calculate the relative NDVI index using multi-layer NDVI index and single-layer NDVI index, and calculate the relative RDVI index using multi-layer RDVI index and single-layer RDVI index.

[0071] Furthermore, step 203 specifically includes:

[0072] Step 2031: Construct a window and initialize the cell values ​​within the window;

[0073] Step 2032: Place the window at the initial position of the multi-layer NDVI index or multi-layer RDVI index;

[0074] Step 2033: Calculate the maximum and minimum values ​​of each cell in the multi-layer NDVI index or multi-layer RDVI index covered by the current window, and store the maximum and minimum values ​​in the corresponding cell positions in the current window to update the cell values ​​in the current window;

[0075] Step 2034: After the current window is updated, calculate the mean of the minimum value and the mean of the maximum value of all pixels in the current window;

[0076] Step 2035: Locate the position of the current window within the single-layer NDVI index or single-layer RDVI index. Update the cell values ​​within the single-layer NDVI index or single-layer RDVI index using the cell values ​​covered by the current window, along with the minimum and maximum mean values. The formula used is as follows:

[0077]

[0078] Where C(i,j) represents the cell value in the single-layer NDVI index or single-layer RDVI index covered by the current window, C'(i,j) represents the updated cell value in the single-layer NDVI index or single-layer RDVI index, min_Value represents the mean of the minimum value, max_Value represents the mean of the maximum value, and i and j represent the subscripts of the cell positions within the window.

[0079] Step 2036: Move the window and repeat steps 2033-2035 until all pixel values ​​in the single-layer NDVI index or single-layer RDVI index are updated. The updated single-layer NDVI index is the relative NDVI index, and the updated single-layer RDVI index is the relative RDVI index.

[0080] Furthermore, step 3 specifically includes:

[0081] Step 301: Use the surface temperature inversion model based on the radiative transfer equation to invert the surface temperature, and normalize the surface temperature.

[0082] Step 302: Filter the normalized surface temperature index;

[0083] Step 303: Resample the filtered surface temperature to obtain the normalized surface temperature index.

[0084] Furthermore, step 4 specifically involves:

[0085] Step 401: Normalize the meteorological precipitation data using the following formula:

[0086]

[0087] Where p represents the sequence number of the meteorological station, P(x p ) represents the precipitation value at the p-th station, min(P(x) p )) represents the minimum precipitation value at the p-th station, max(P(x) p )) represents the maximum precipitation value at the p-th station;

[0088] Step 402: Perform inverse distance interpolation on the normalized precipitation data and station locations;

[0089] Step 403: Perform geographic projection transformation and resolution resampling on the interpolation results to obtain the normalized precipitation index.

[0090] Furthermore, in step 5, the wheat scab incidence index is directly proportional to the risk of scab disease, and the formula for calculating the wheat scab incidence index is:

[0091]

[0092] Where α1 represents the relative NDVI index, α2 represents the relative RDVI index, α3 represents the normalized surface temperature index, α4 represents the normalized precipitation index, k1 and k2 are the coefficients of the two groups of influencing factors, and the sum of the coefficients of each group of influencing factors is 1.

[0093] Example

[0094] This embodiment is used to illustrate the present invention.

[0095] Step 1: Obtain remote sensing data and meteorological precipitation data of the wheat-growing area for the predicted year and the N years prior to the predicted year.

[0096] The remote sensing data includes multispectral data and thermal infrared remote sensing data;

[0097] In this embodiment, the detection area is winter wheat planting in Henan Province, which covers a large area. To reduce the amount of data and the data processing time, the remote sensing data used is MODIS 09Q1 data with a resolution of 250m and a time period of mid-to-late April from 2009 to 2019. The ground temperature data used is MODIS 11A2 data with a resolution of 1000*1000m from late March to mid-to-late April. The meteorological precipitation data is the daily average precipitation data collected from meteorological stations throughout the province from late March to mid-to-late April. The remaining data are the distribution mask and boundary vector files of winter wheat in Henan Province.

[0098] Step 2: Calculate the relative growth index using the multispectral data;

[0099] MODIS 09Q1 was preprocessed by mosaicking and projection to extract red and near-infrared band data. The RDVI (Renormalized Difference Vegetation Index) and NDVI (Normalized Difference Vegetation Index) indices were obtained according to the RDVI and NDVI calculation formulas. The RDVI and NDVI indices for each year from 2009 to 2018 were overlaid, and the overlaid data and the 2019 data were cropped using boundary vectors.

[0100] Step 201: Calculate the NDVI and RDVI indices for 2019 and the same period from 2009 to 2018 using the multispectral data, using the following formula:

[0101] NDVI Index:

[0102] RDVI Index: DVI q =NIR q -R q (12),

[0103] Where q represents the year sequence number, NIR q R represents the near-infrared band of the multispectral data in year q. q The red band represents the multispectral data for year q, NDVI. q RDVI represents the NDVI index in year q. q This represents the RDVI index in year q.

[0104] Step 202: Stack the NDVI indices from 2009 to 2018 to obtain a multi-layer NDVI index, and stack the RDVI indices from 2009 to 2018 to obtain a multi-layer RDVI index. Each pixel in the multi-layer NDVI index and the multi-layer RDVI index is a vector of length 10. The NDVI index for 2019 is a single-layer NDVI index, and the RDVI index for 2019 is a single-layer RDVI index.

[0105] Step 203: Using a scrolling small window region algorithm, calculate the relative NDVI index using multi-layer NDVI index and single-layer NDVI index, and calculate the relative RDVI index using multi-layer RDVI index and single-layer RDVI index.

[0106] Step 2031: Construct a window. The window size can be 3*3, 5*5, or 7*7. In this embodiment, it is a 5*5 window. Initialize the pixel values ​​in the window, i.e., the pixel value is 0, i.e., W'(i',j')=0;

[0107] Step 2032: Place the window at the initial position of the multi-layer NDVI index or multi-layer RDVI index. Here, the initial position is set at the upper left corner, that is, W'(i',j') = B(i,j), where W'(i',j') represents the cell in the window, B(i,j) represents the cell in the multi-layer NDVI index or multi-layer RDVI index, and i = i' = {0,1,2,3,4,5}, j = j' = {0,1,2,3,4,5}.

[0108] Step 2033: Calculate the maximum and minimum values ​​of each cell in the multi-layer NDVI index or multi-layer RDVI index covered by the current window, and store the maximum and minimum values ​​in the corresponding cell positions in the current window to update the cell values ​​in the current window;

[0109] W'(i',j')=Vector(min(B(i,j),max(B(i,j))) (13),

[0110] i=i'+step_i*N, j=j'+step_j*N (14),

[0111] min(B(i,j))=min({b1(i,j),b2(i,j),b3(i,j)...b 10 (i,j)}) (15),

[0112] max(B(i,j))=max({b1(i,j),b2(i,j),b3(i,j)...b 10 (i,j)}) (16),

[0113] Where Step_i represents the horizontal scrolling step of the window, Step_j represents the vertical scrolling step of the window, and N represents the current number of times the window has scrolled;

[0114] Step 2034: After the current window is updated, calculate the mean of the minimum value and the mean of the maximum value of all pixels in the current window;

[0115]

[0116]

[0117] Where W'(i',j')[0] represents the first element in the calculated Vector, W'(i',j')[1] represents the second element in the calculated Vector, and M = 5*5;

[0118] Step 2035: Locate the position of the current window within the single-layer NDVI index or single-layer RDVI index. Update the cell values ​​within the single-layer NDVI index or single-layer RDVI index using the cell values ​​covered by the current window, along with the minimum and maximum mean values. The formula used is as follows:

[0119]

[0120] Where C(i,j) represents the cell value in the single-layer NDVI index or single-layer RDVI index covered by the current window, C'(i,j) represents the updated cell value in the single-layer NDVI index or single-layer RDVI index, min_Value represents the mean of the minimum value, max_Value represents the mean of the maximum value, and i and j represent the subscripts of the cell positions within the window.

[0121] Step 2036: Move the window and repeat steps 2033-2035 until all cell values ​​in the single-layer NDVI index or single-layer RDVI index are updated (for boundaries less than 5, fill with 0 and no longer participate in the calculation). The updated single-layer NDVI index is the relative NDVI index, and the updated single-layer RDVI index is the relative RDVI index.

[0122] Step 3: Obtain the normalized surface temperature index using the multispectral data and thermal infrared remote sensing data;

[0123] Step 301: Use the surface temperature inversion model based on the radiative transfer equation to invert the surface temperature, and normalize the surface temperature.

[0124] Step 302: Filter the normalized surface temperature index using a 3*3 window Gaussian kernel convolution filter;

[0125] Step 303: Input the filtered surface temperature into ENVI for resolution resampling to obtain the normalized surface temperature index.

[0126] Step 4: Obtain the normalized precipitation index using the meteorological precipitation data;

[0127] Step 401: Normalize the meteorological precipitation data using the following formula:

[0128]

[0129] Where p represents the sequence number of the meteorological station, P(x p ) represents the precipitation value at the p-th station, min(P(x) p )) represents the minimum precipitation value at the p-th station, max(P(x) p )) represents the maximum precipitation value at the p-th station;

[0130] Step 402: Input the normalized precipitation data and station locations into ArcGIS for inverse distance interpolation;

[0131] Step 403: Input the interpolation results into ENVI for geographic projection transformation and resolution resampling to obtain the normalized precipitation index.

[0132] Step 5: Using the relative growth index, normalized surface temperature index, and normalized precipitation index, calculate the predicted...

[0133] Yearly wheat scab incidence index;

[0134] The incidence index of wheat Fusarium head blight is directly proportional to the risk of Fusarium head blight. The formula for calculating the incidence index of wheat Fusarium head blight is:

[0135]

[0136] Where α1 represents the relative NDVI index, α2 represents the relative RDVI index, α3 represents the normalized surface temperature index, α4 represents the normalized precipitation index, k1 and k2 are the coefficients of the two groups of influencing factors, and the sum of the coefficients of each group of influencing factors is 1; the wheat scab disease incidence index is calculated between 0 and 1. The closer the index is to 1, the greater the risk of scab disease, and the closer it is to 0, the smaller the risk.

[0137] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring wheat scab based on satellite remote sensing, characterized in that: Includes the following steps: Step 1: Obtain remote sensing data and meteorological precipitation data of the wheat planting area for the predicted year and N years prior to the predicted year. The remote sensing data includes multispectral data and thermal infrared remote sensing data. Step 2: Calculate the relative growth index using the multispectral data; Step 3: Obtain the normalized surface temperature index using the multispectral data and thermal infrared remote sensing data; Step 4: Obtain the normalized precipitation index using the meteorological precipitation data; Step 5: Calculate the wheat scab incidence index for the predicted year using the relative growth index, normalized surface temperature index, and normalized precipitation index. In step 2, the relative growth index includes the relative NDVI index and the relative RDVI index; The calculation steps for the relative growth index are as follows: Step 201: Calculate the NDVI index and RDVI index for the predicted year and the same period within N years prior to the predicted year using the multispectral data, using the following formula: NDVI Index: NDVI q (1) RDVI Index: DVI q = NIR q - R q (2) Where q represents the year number NIR q R represents the near-infrared band of the multispectral data for year q. q The red band represents the multispectral data for year q. NDVI q This represents the NDVI index in year q. This represents the RDVI index in year q. Step 202: Stack the NDVI indices of the N years preceding the predicted year to obtain a multi-layer NDVI index, and stack the RDVI indices of the N years preceding the predicted year to obtain a multi-layer RDVI index. The NDVI index of the predicted year is a single-layer NDVI index, and the RDVI index of the predicted year is a single-layer RDVI index. Step 203: Using a scrolling small window region-based algorithm, calculate the relative NDVI index using multi-layer NDVI index and single-layer NDVI index. The NDVI index is calculated using multi-layer RDVI indices and single-layer RDVI indices to determine the relative RDVI index; step 203 specifically involves: Step 2031: Construct a window and initialize the cell values ​​within the window; Step 2032: Place the window at the initial position of the multi-layer NDVI index or multi-layer RDVI index; Step 2033: Calculate the maximum and minimum values ​​of each cell in the multi-layer NDVI index or multi-layer RDVI index covered by the current window, and store the maximum and minimum values ​​in the corresponding cell positions in the current window to update the cell values ​​in the current window; Step 2034: After the current window is updated, calculate the mean of the minimum value and the mean of the maximum value of all pixels in the current window; Step 2035: Locate the position of the current window within the single-layer NDVI index or single-layer RDVI index, and utilize the current window... The pixel values ​​in the covered single-layer NDVI index or single-layer RDVI index, along with the mean of the minimum and maximum values, are used to update the pixel values ​​in the single-layer NDVI index or single-layer RDVI index, using the following formula: C’’(i,j)= (3) Where C(i,j) represents the cell value in a single layer of NDVI or RDVI index covered by the current window. C'(i, j) represents the updated cell value in a single-layer NDVI index or a single-layer RDVI index, and min_Value represents the most... The minimum mean value, max_Value represents the maximum mean value, and i and j represent the subscripts of the cell positions within the window; Step 2036: Move the window and repeat steps 2033-2035 until all cell values ​​in the single-layer NDVI index or single-layer RDVI index are updated. The updated single-layer NDVI index is the relative NDVI index, and the updated single-layer RDVI index is the relative RDVI index. Step 3 specifically involves: Step 301: Use the surface temperature inversion model based on the radiative transfer equation to invert the surface temperature, and normalize the surface temperature. Step 302: Filter the normalized surface temperature index; Step 303: Resample the filtered surface temperature to obtain the normalized surface temperature index; Step 4 specifically involves: Step 401: Normalizing the meteorological precipitation data using the following formula: (4) Where p represents the sequence number of the meteorological station; This represents the precipitation value at the p-th station; This represents the minimum precipitation value at the p-th station; This represents the maximum precipitation value at the p-th station; Step 402: Perform inverse distance interpolation on the normalized precipitation data and station locations; Step 403: Perform geographic projection transformation and resolution resampling on the interpolation results to obtain the normalized precipitation index; In step 5, the wheat scab incidence index is directly proportional to the risk of scab disease. The formula for calculating the wheat scab incidence index is: （5） in Indicates the relative NDVI index. Indicates the relative RDVI index. Represents the normalized surface temperature index. The normalized precipitation index is represented by k1 and k2, which are the coefficients of two groups of influencing factors, and the sum of the coefficients of each group of influencing factors is 1.

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