Wheat drought disaster remote sensing monitoring evaluation method in flowering period

Abstract

The invention provides a wheat drought disaster remote sensing monitoring and evaluation method in a flowering period, and relates to the technical field of agricultural disaster early warning. Remote sensing image data is collected based on an unmanned aerial vehicle, and a normalized vegetation index, an enhanced vegetation index and a soil moisture index are generated by extracting multispectral reflectivity in the wheat flowering period; generating a comprehensive remote sensing index, establishing a time sequence set, setting time windows in the time sequence set, calculating a change rate of each window, setting a threshold value, identifying a potential drought region, and generating a drought influence index by analyzing drought duration and an image range; finally, a drought influence threshold value is generated in combination with historical data to judge the drought condition of the current monitoring area.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural disaster early warning, and in particular to a remote sensing monitoring and evaluation method for drought disaster of wheat in flowering period. Background Art

[0002] In agricultural production, especially during the growth cycle of important crops such as wheat, drought disasters have a significant impact on yield and quality. Traditional drought monitoring methods usually rely on ground observations, which have problems such as poor timeliness and insufficient spatial coverage, making it impossible to obtain effective monitoring and evaluation in time when crops suffer from drought, thus affecting farmers' decision-making and response measures. In addition, although existing remote sensing technology can provide some vegetation and soil moisture information, it often lacks comprehensive analysis and dynamic monitoring of multiple indicators. When generating drought evaluation indices, there are fewer evaluation dimensions and insufficient comprehensiveness, which makes it impossible to fully evaluate the scope and severity of drought.

[0003] In the prior art, publication number CN110231246A discloses a winter wheat drought early warning system, which monitors the moisture content of winter wheat and soil moisture content through a moisture content monitoring device, integrates the moisture content data, establishes a database corresponding to the winter wheat drought level and moisture content, compares the real-time collected moisture content data with the database to obtain the current winter wheat drought level, and issues an alarm when the current winter wheat drought level reaches a preset threshold.

[0004] The main problems with the above method are: the data source is single, and it mainly relies on the data obtained by the moisture content monitoring device. However, the impact of drought is not only determined by the moisture content, but also affected by other environmental factors, such as temperature, wind speed, light intensity, etc. Moisture content monitoring alone may not be able to fully reflect the actual drought situation of crops; and the system relies on comparing the moisture content data collected in real time with the preset database. This method has a certain lag, especially in the early stages of drought, when the change in moisture content is not obvious, resulting in the system failing to issue a timely warning when drought occurs, affecting the subsequent formulation of response measures.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a remote sensing monitoring and evaluation method for drought disaster of wheat in flowering period, so as to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A remote sensing monitoring and evaluation method for drought disaster of wheat in flowering period, comprising the following specific steps: Step 1: Use a drone to collect remote sensing image data of the target area at a set time interval during the wheat flowering period, and perform atmospheric correction and geometric correction on the collected image data. The set time interval is: 12~24 hours; Step 2: Extract the reflectance of the near-infrared band and the red band from the remote sensing image to generate the normalized vegetation index of the target area, additionally extract the reflectance of the blue band to generate the enhanced vegetation index of the target area, and additionally extract the reflectance of the short-wave infrared band to generate the soil moisture index; Step 3: Weighted average of the NDVI, EVI and SMI to generate a comprehensive remote sensing index; Step 4: Arrange the comprehensive remote sensing indexes of all time periods in chronological order to generate a time series set, set a time window in the time series set, calculate the change rate in each window in turn, and arrange them in order according to the time window to generate a change rate sequence. The width of the time window ranges from 24 to 36 hours; Step 5: Set the change rate threshold by analyzing historical data, select the time windows and corresponding areas below the threshold in the change rate sequence, mark them as potential drought areas, determine the duration of drought based on the time windows of all potential drought areas, count all potential drought areas, determine the area of ​​drought areas, and generate the drought impact index based on the absolute value of the change rate, drought duration and drought area; Step 6: Generate an impact threshold based on the mean and standard deviation of the drought impact index in historical data, and judge the drought situation by comparing the drought impact index and the impact threshold.

[0008] Furthermore, the principles for generating the normalized difference vegetation index, enhanced vegetation index, and soil moisture index are as follows: ; in, represents the normalized difference vegetation index, represents the reflectivity in the near-infrared band, Indicates the reflectivity of red light band; The formula used to generate the Enhanced Vegetation Index is: ; in, represents the enhanced vegetation index, represents the gain factor, and , represents the atmospheric correction factor, and , , Represents the reflectivity of the blue light band, represents the correction for soil background effects, and ; The formula used to generate the Soil Moisture Index is: ; in, represents the soil moisture index, Represents the reflectivity in the shortwave infrared band.

[0009] Furthermore, the formula for generating the comprehensive remote sensing index is: ; in, represents the comprehensive impact index, represents the normalized difference vegetation index, represents the weight coefficient of the normalized difference vegetation index, represents the enhanced vegetation index, represents the weight coefficient of the enhanced vegetation index, represents the soil moisture index, represents the weight coefficient of soil moisture index, ,and .

[0010] Furthermore, the formula for generating the rate of change of each window is: ; in, Indicates The rate of change of a time window, represents the index of the time window, and , represents the total number of time windows, Indicates The comprehensive remote sensing index corresponding to the time window is: Indicates The comprehensive remote sensing index corresponding to the time window.

[0011] Furthermore, the principle for generating the change rate threshold is: ; ; in, Represents the average value of the rate of change in historical data, An index representing the rate of change in historical data, Indicates the historical data The rate of change, Represents the total number of change rates in historical data, Represents the standard deviation of the rate of change in historical data; The formula used to generate the rate-of-change threshold is: ; in, represents the change rate threshold, is a constant determined by the actual precision, and .

[0012] Furthermore, the principle on which the drought impact index is generated is: ; in, represents the drought impact index, represents the index of the time window, represents the total number of time windows, Indicates The rate of change of a time window, represents the change rate threshold, Indicates The result of logical judgment on the drought situation in each time window is When there is a drought area within a time window, ,otherwise , Indicates The size of the drought area in each time window.

[0013] Furthermore, the principle for comparing the drought impact index and the impact threshold to judge the drought situation is: The formula used to generate the impact threshold is: ; ; in, represents the average value of the drought impact index in historical data, An index representing the drought impact index, represents the total number of drought impact indices in the historical data, Indicates Drought Impact Index, represents the standard deviation of the drought impact index in historical data; The high drought impact thresholds are: ; in, indicates a high drought impact threshold; The low drought impact thresholds are: ; in, indicates a low drought impact threshold; The principles for judging drought conditions are: when When the drought is severe, when When the drought is moderate, when When the drought is mild or non-drought.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention establishes a systematic wheat drought monitoring and evaluation system based on high-resolution image data collected by unmanned aerial vehicles and advanced remote sensing technology. During the flowering period of wheat, the normalized vegetation index, enhanced vegetation index and soil moisture index are generated respectively through the reflectivity of the infrared band, near-infrared band, blue light band and short-wave infrared band. After weighted averaging, a comprehensive remote sensing index is generated to evaluate the remote sensing monitoring results. The system can effectively integrate the advantages of different indices, reduce the deviation of a single index under specific circumstances, and can more comprehensively reflect the growth status of wheat under drought conditions, providing more accurate monitoring results.

[0015] The present invention also directly observes and analyzes the dynamic changes of the wheat growth stage by arranging the comprehensive remote sensing index in chronological order to generate a time series set, and can capture the rapid changes of vegetation and soil moisture under drought conditions. By subdividing the time window, high-frequency data update and analysis can be achieved. The calculation of the change rate can not only quantify the impact of drought, but also reflect the trend and intensity of the drought condition. Through the change rate sequence, the aggravation or relief of drought can be more clearly identified, which helps to better understand the dynamic characteristics of the change in drought intensity. And set a dynamic change rate threshold, accurately screen out the areas affected by drought, and dynamically adjust the threshold according to the actual situation, which is more flexible and adaptable. The drought impact index is generated based on the absolute value of the change rate, the duration of drought and the image range, which can comprehensively reflect the severity of drought and provide a quantitative basis for subsequent decision-making. By analyzing the historical data of the drought impact index, the average value and standard deviation are calculated to generate the impact threshold. Compared with the judgment that only relies on current data, the combination of historical data makes the monitoring results more reliable and more in line with the actual changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a schematic diagram of the method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Example: See also Figure 1 , the present invention provides a technical solution: A remote sensing monitoring and evaluation method for drought disaster of wheat in flowering period, comprising the following specific steps: Step 1: Use a drone to collect remote sensing image data of the target area at a set time interval during the wheat flowering period, and perform atmospheric correction and geometric correction on the collected image data. The set time interval is: 12~24 hours; The purpose of atmospheric correction in this embodiment is to reduce the influence of the atmosphere on the remote sensing image so that the radiation value in the image more truly reflects the spectral characteristics of the surface. According to the specific image situation, a suitable model is selected from the atmospheric radiation transmission models such as 6S, MODTRAN, and RTTOV, and the atmospheric correction calculation is performed in combination with the atmospheric model and meteorological parameters to output the surface radiation brightness, and the radiation brightness after atmospheric correction is normalized to a reflectivity value between 0 and 1; The purpose of geometric correction is to correct the geometric distortion caused by factors such as sensor viewing angle, earth curvature and camera position in remote sensing images, so that the image can accurately reflect the position and shape of surface features. When performing geometric correction, a set of control points with known positions that are evenly distributed in the image and cover the entire image area are selected, and the pixel coordinates of the control points are matched with their corresponding geographic coordinates. According to the distribution of control points and the nature of image distortion, a suitable geometric transformation model is selected from linear transformation, quadratic polynomial transformation, high-order polynomial transformation and affine transformation. The transformation parameters are calculated based on the control points through an optimization algorithm to describe the relationship between image coordinates and geographic coordinates. The calculated transformation parameters are used to perform a geometric transformation on the entire image, convert pixel positions into geographic coordinates, and compare the corrected image with other known accurate images to verify the effectiveness of the geometric correction.

[0020] Step 2: Extract the reflectance of the near-infrared band and the red band from the remote sensing image to generate the normalized vegetation index of the target area, additionally extract the reflectance of the blue band to generate the enhanced vegetation index of the target area, and additionally extract the reflectance of the short-wave infrared band to generate the soil moisture index; In this embodiment, the principles for generating the normalized vegetation index, the enhanced vegetation index, and the soil moisture index are as follows: ; in, represents the normalized difference vegetation index, represents the reflectivity in the near-infrared band, Indicates the reflectivity of red light band; The Normalized Difference Vegetation Index reflects the health and growth of plants. The higher the value, the healthier the vegetation and the more vigorous its growth. It is proportional to the reflectivity in the near-infrared band and inversely proportional to the reflectivity in the red band; The formula used to generate the Enhanced Vegetation Index is: ; in, represents the enhanced vegetation index, Represents the gain factor, usually , represents the atmospheric correction factor, usually , , Represents the reflectivity of the blue light band, Indicates the correction value for soil background effect, usually ; The enhanced vegetation index reflects the growth status of plants, and the gain factor is used to adjust dynamic range, making it more sensitive, It is a gain factor value that can provide more accurate results under various vegetation types and coverage based on empirical data analysis. The atmospheric correction factor is to reduce the effect of red and blue light on the The impact of It can better separate vegetation signals from background signals, ensuring consistent results in a variety of situations. The correction value for soil background effect is designed to correct for soil background in situations where vegetation is sparse or soil is exposed, which helps improve Sensitivity to vegetation; the enhanced vegetation index is proportional to the reflectance of the near-infrared band, inversely proportional to the reflectance of the red band, and inversely proportional to the reflectance of the blue band.

[0021] The formula used to generate the Soil Moisture Index is: ; in, represents the soil moisture index, Represents the reflectivity in the shortwave infrared band.

[0022] The near-infrared band is more sensitive to the wetness of vegetation and soil. Dry soil and vegetation have higher reflectivity in the near-infrared band, while soil and vegetation with higher water content have lower reflectivity in this band, that is, as the water content increases, the reflectivity of the near-infrared band decreases; the short-wave infrared band is very sensitive to the absorption of water, and water has a strong absorption effect on the light in the short-wave infrared band. Therefore, soil and vegetation with high water content have lower reflectivity in the short-wave infrared band, and dry soil and vegetation have higher reflectivity in the short-wave infrared band. The soil moisture index reflects the moisture content of the soil through the different reflectivity of soils with different water contents to the band. When the water content increases, the reflectivity of the near-infrared band decreases, the reflectivity of the short-wave infrared band decreases, and the soil moisture index increases.

[0023] Step 3: Weighted average of the NDVI, EVI and SMI to generate a comprehensive remote sensing index; In this embodiment, the formula for generating the comprehensive remote sensing index is: ; in, represents the comprehensive impact index, represents the normalized difference vegetation index, represents the weight coefficient of the normalized difference vegetation index, represents the enhanced vegetation index, represents the weight coefficient of the enhanced vegetation index, represents the soil moisture index, represents the weight coefficient of soil moisture index, ,and .

[0024] The comprehensive remote sensing index reflects the soil moisture content and wheat health of the target area through remote sensing data. The comprehensive remote sensing index is proportional to the normalized vegetation index, enhanced vegetation index and soil moisture index. The normalized vegetation index has been a standard index for vegetation detection for a long time. In most cases, it can better reflect the health of vegetation, so it has a higher weight. The enhanced vegetation index is similar to the normalized vegetation index in some aspects, so its weight is slightly lower than that of the normalized vegetation index. The soil moisture index has a direct impact on drought monitoring, so its weight is set to the same as the enhanced vegetation index. .

[0025] Step 4: Arrange the comprehensive remote sensing indexes of all time periods in chronological order to generate a time series set, set a time window in the time series set, calculate the change rate in each window in turn, and arrange them in order according to the time window to generate a change rate sequence. The width of the time window ranges from 24 to 36 hours; In this embodiment, the formula for generating the change rate of each window is: ; in, Indicates The rate of change of a time window, represents the index of the time window, and , represents the total number of time windows, Indicates The comprehensive remote sensing index corresponding to the time window is: Indicates The comprehensive remote sensing index corresponding to the time window.

[0026] Step 5: Set the change rate threshold by analyzing historical data, select the time windows and corresponding areas below the threshold in the change rate sequence, mark them as potential drought areas, determine the duration of drought based on the time windows of all potential drought areas, count all potential drought areas, determine the area of ​​drought areas, and generate the drought impact index based on the absolute value of the change rate, drought duration and drought area; In this embodiment, the principle for generating the change rate threshold is: ; ; in, Represents the average value of the rate of change in historical data, An index representing the rate of change in historical data, Indicates the historical data The rate of change, Represents the total number of change rates in historical data, Represents the standard deviation of the rate of change in historical data; The formula used to generate the rate-of-change threshold is: ; in, represents the change rate threshold, is a constant determined by the actual precision, and .

[0027] The change rate threshold reflects whether the change degree of the comprehensive remote sensing index is significantly lower than a preset standard within a certain time window, so as to judge whether there is potential drought in the area. The value of can change the change rate threshold to adjust the accuracy. The larger the value, the larger the change rate threshold, which means that the criteria for determining potential drought areas are looser.

[0028] The principles underlying the generation of the Drought Impact Index are: ; in, represents the drought impact index, represents the index of the time window, represents the total number of time windows, Indicates The rate of change of a time window, represents the change rate threshold, Indicates The result of logical judgment on the drought situation in each time window is When there is a drought area within a time window, ,otherwise , Indicates The size of the drought area in each time window.

[0029] The drought impact index reflects the intensity of drought under the combined influence of the window's change rate, drought duration, and drought space. For each window, the drought impact index is calculated based on the drought time within the window and the monitoring area of ​​the corresponding window. The drought impact index of all windows is added together to generate the drought impact index of the entire monitoring area. The drought impact index is proportional to the window's change rate and to the drought impact time and space.

[0030] Step 6: Generate an impact threshold based on the mean and standard deviation of the drought impact index in historical data, and judge the drought situation by comparing the drought impact index and the impact threshold.

[0031] In this embodiment, the principle for comparing the drought impact index and the impact threshold to determine the drought situation is: The formula used to generate the impact threshold is: ; ; in, represents the average value of the drought impact index in historical data, An index representing the drought impact index, represents the total number of drought impact indices in the historical data, Indicates Drought Impact Index, represents the standard deviation of the drought impact index in historical data; The high drought impact thresholds are: ; in, indicates a high drought impact threshold; The low drought impact thresholds are: ; in, Indicates a low drought impact threshold.

[0032] According to the principle of statistical distribution, the high drought impact threshold and the low drought impact threshold are set as the average value of the drought impact index plus and minus the standard deviation, respectively. As the data is updated, the drought judgment criteria can be adjusted in real time to reflect the latest wheat drought situation.

[0033] The principles for judging drought conditions are: when When the drought is severe, when When the drought is moderate, when When the drought is mild or non-drought.

[0034] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0035] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0036] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0037] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A remote sensing monitoring and evaluation method for drought disaster of wheat during flowering period, characterized in that: The specific steps include: Step 1: Use a drone to collect remote sensing image data of the target area at a set time interval during the wheat flowering period, and perform atmospheric correction and geometric correction on the collected image data. The set time interval is: 12~24 hours; Step 2: Extract the reflectance of the near-infrared band and the red band from the remote sensing image to generate the normalized vegetation index of the target area, additionally extract the reflectance of the blue band to generate the enhanced vegetation index of the target area, and additionally extract the reflectance of the short-wave infrared band to generate the soil moisture index; Step 3: Weighted average of the NDVI, EVI and SMI to generate a comprehensive remote sensing index; Step 4: Arrange the comprehensive remote sensing indices of all time periods in chronological order to generate a time series set, set a time window in the time series set, calculate the change rate in each window in turn, and arrange them in order according to the time window to generate a change rate sequence. The width of the time window ranges from 24 to 36 hours; Step 5: Set the change rate threshold by analyzing historical data, select the time windows and corresponding areas below the threshold in the change rate sequence, mark them as potential drought areas, determine the duration of drought based on the time windows of all potential drought areas, count all potential drought areas, determine the area of ​​drought areas, and generate the drought impact index based on the absolute value of the change rate, drought duration and drought area; Step 6: Generate an impact threshold based on the mean and standard deviation of the drought impact index in historical data, and judge the drought situation by comparing the drought impact index and the impact threshold.

2. The remote sensing monitoring and evaluation method for drought disaster of wheat in flowering period according to claim 1 is characterized in that: The principle for generating the normalized vegetation index, enhanced vegetation index and soil moisture index in step 2 is: ； in, represents the normalized difference vegetation index, represents the reflectivity in the near-infrared band, Indicates the reflectivity of red light band; The formula used to generate the Enhanced Vegetation Index is: ； in, represents the enhanced vegetation index, represents the gain factor, and , represents the atmospheric correction factor, and , , Represents the reflectivity of the blue light band, represents the correction for soil background effects, and ; The formula used to generate the Soil Moisture Index is: ； in, represents the soil moisture index, Represents the reflectivity in the shortwave infrared band.

3. The remote sensing monitoring and evaluation method for drought disaster of wheat in flowering period according to claim 1 is characterized in that: The formula for generating the comprehensive remote sensing index in step 3 is: ； in, represents the comprehensive impact index, represents the normalized difference vegetation index, represents the weight coefficient of the normalized difference vegetation index, represents the enhanced vegetation index, represents the weight coefficient of the enhanced vegetation index, represents the soil moisture index, represents the weight coefficient of soil moisture index, ,and .

4. The remote sensing monitoring and evaluation method for drought disaster of wheat in flowering period according to claim 1 is characterized by: The formula for generating the change rate of each window in step 4 is: ； in, Indicates The rate of change of a time window, represents the index of the time window, and , represents the total number of time windows, Indicates The comprehensive remote sensing index corresponding to the time window is: Indicates The comprehensive remote sensing index corresponding to the time window.

5. The remote sensing monitoring and evaluation method for drought disaster of wheat in flowering period according to claim 1 is characterized by: The principle for generating the change rate threshold in step 5 is: ； ； in, Represents the average value of the rate of change in historical data, An index representing the rate of change in historical data, Indicates the historical data The rate of change, Represents the total number of change rates in historical data, Represents the standard deviation of the rate of change in historical data; The formula used to generate the rate-of-change threshold is: ； in, represents the change rate threshold, is a constant determined by the actual precision, and .

6. The remote sensing monitoring and evaluation method for drought disaster of wheat in flowering period according to claim 1, characterized in that: The principle for generating the drought impact index in step 5 is: ； in, represents the drought impact index, represents the index of the time window, represents the total number of time windows, Indicates The rate of change of a time window, represents the change rate threshold, Indicates The result of logical judgment on the drought situation in each time window is When there is a drought area within a time window, ,otherwise , Indicates The size of the drought area in each time window.

7. The remote sensing monitoring and evaluation method for drought disaster of wheat in flowering period according to claim 1 is characterized by: The principle for comparing the drought impact index and the impact threshold in step 6 to determine the drought situation is: The formula used to generate the impact threshold is: ； ； in, represents the average value of the drought impact index in historical data, An index representing the drought impact index, represents the total number of drought impact indices in the historical data, Indicates Drought Impact Index, represents the standard deviation of the drought impact index in historical data; The high drought impact thresholds are: ； in, indicates a high drought impact threshold; The low drought impact thresholds are: ； in, indicates a low drought impact threshold; The principles for judging drought conditions are: when When the drought is severe, when When the drought is moderate, when When the drought is mild or non-drought.

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