Intelligent soil moisture content monitoring method based on remote sensing technology

Abstract

The present invention relates to the technical field of soil monitoring, and disclosed is an intelligent soil moisture content monitoring method based on remote sensing technology. The method comprises: a processing terminal dividing a monitoring area into multiple sub-areas, wherein the division of the sub-areas may be by means of equal parts by area, acquiring image data of each sub-area on the basis of a remote sensing satellite, acquiring, on the basis of a sensor device, multi-source data related to the monitoring area and soil moisture content, and performing pre-processing on the image data and multi-source data; comprehensively analyzing the image data and the multi-source data, and evaluating the soil moisture content conditions of each sub-area; and, by means of the soil moisture content conditions of each sub-area, comprehensively analyzing the overall soil moisture content conditions of the monitoring area. The present monitoring method comprehensively analyzes the multi-source data to then evaluate the soil moisture of the monitoring area, such that analysis is more comprehensive, and accuracy is higher.

Description

An intelligent soil moisture monitoring method based on remote sensing technology Technical Field

[0001] The present invention relates to the technical field of soil monitoring, and in particular to an intelligent soil moisture monitoring method based on remote sensing technology. Background Art

[0002] Soil moisture is a key parameter for measuring soil moisture and water content. It is of great significance to agriculture, water resource management, environmental protection and other fields. Appropriate soil moisture helps decision-makers better implement farmland irrigation, drought monitoring, flood control and management.

[0003] The intelligent soil moisture monitoring system based on remote sensing technology is a system that combines remote sensing technology, sensor technology and data analysis technology. It aims to monitor and evaluate soil moisture conditions, that is, soil water content and humidity, in real time.

[0004] The existing technology has the following deficiencies:

[0005] Existing monitoring systems typically use remote sensing satellites to collect image data of the monitored area. They then perform color segmentation on the soil based on the image data. Color grids representing good soil moisture are compared with those representing poor soil moisture to determine the overall soil moisture condition in the monitored area. However, when influencing factors (such as rainy or foggy weather) exist, this analysis method can be inaccurate, affecting administrators' judgments.

[0006] Therefore, this application proposes an intelligent soil moisture monitoring method based on remote sensing technology, which comprehensively analyzes multi-source parameters to evaluate the soil moisture in the monitoring area with higher accuracy.

[0007] Summary of the Invention

[0008] The purpose of the present invention is to provide a soil moisture intelligent monitoring method based on remote sensing technology to solve the shortcomings of the background technology.

[0009] In order to achieve the above object, the present invention provides the following technical solution: a soil moisture intelligent monitoring method based on remote sensing technology, the monitoring method comprising the following steps:

[0010] S1: The processing end divides the monitoring area into several sub-areas, obtains image data of each sub-area based on remote sensing satellites, obtains multi-source data related to the monitoring area and soil moisture conditions based on sensor equipment, and pre-processes the image data and multi-source data;

[0011] S2: Comprehensively analyze image data and multi-source data to assess the soil moisture conditions in each sub-region;

[0012] S3: Comprehensively analyze the overall soil moisture conditions in the monitoring area through the soil moisture conditions of each sub-area;

[0013] S4: Visualize the overall soil moisture conditions in the monitoring area;

[0014] S5: Send the visualization report to the administrator.

[0015] Preferably, in step S2, the image data and multi-source data are comprehensively analyzed, the image data includes the color segmentation index, and the multi-source data includes the humidity coefficient and the moisture content floating coefficient.

[0016] Preferably, in step S2, evaluating the soil moisture condition of each sub-region includes the following steps:

[0017] S2.1: The color segmentation index, humidity coefficient, and moisture content floating coefficient are calculated to obtain the soil moisture coefficient. The calculation expression is:

[0018] In the formula, sd x is the humidity coefficient, ys f is the color segmentation index, hs f is the floating coefficient of moisture content, α, β, and γ are the proportional coefficients of humidity coefficient, color segmentation index, and floating coefficient of moisture content, respectively, and α, β, and γ are all greater than 0;

[0019] S2.2: If the soil moisture coefficient sq x If the value is greater than or equal to the soil moisture threshold, the soil moisture in the sub-region is evaluated to be good. x If the value is less than the soil moisture threshold, the soil moisture in the sub-area is assessed to be poor.

[0020] Preferably, in step S3, comprehensively analyzing the overall soil moisture condition of the monitoring area through the soil moisture condition of each sub-area includes the following steps:

[0021] S3.1: Get the number of sub-regions and the soil moisture coefficient sq of each sub-region x value;

[0022] S3.2: Calculate the average value of the soil moisture coefficient of all sub-areas;

[0023] S3.3: Soil moisture coefficient sq based on sub-region x The dispersion degree of soil moisture coefficient in the monitoring area is calculated by using the average value of soil moisture coefficient.

[0024] S3.4: Analyze the soil moisture conditions and soil moisture change trends in the monitoring area through the dispersion degree of the soil moisture coefficient in the monitoring area.

[0025] Preferably, in step S3.4, the calculation expression for the dispersion degree of the soil moisture coefficient of the monitoring area is:

[0026] Where b = {1, 2, 3, ..., c}, c represents the number of sub-regions, c is a positive integer, R b Represents the moisture content coefficient sq of the b-th sub-region x value, represents the average value of soil moisture coefficient;

[0027] If the average value of the soil moisture coefficient is ≥ the soil moisture threshold, and the dispersion of the soil moisture coefficient is ≤ the dispersion threshold, the overall soil moisture condition in the monitoring area is good, and the soil moisture condition will develop in a positive trend in the future;

[0028] If the average value of the soil moisture coefficient is greater than or equal to the soil moisture threshold, and the dispersion of the soil moisture coefficient is greater than the dispersion threshold, the overall soil moisture condition in the monitoring area is considered good, and there are unstable factors in the future development trend of soil moisture.

[0029] If the average value of the soil moisture coefficient is less than the soil moisture threshold, and the dispersion of the soil moisture coefficient is greater than the dispersion threshold, the overall soil moisture condition in the monitoring area is poor, and there are unstable factors in the future development trend of soil moisture;

[0030] If the average value of the soil moisture coefficient is less than the soil moisture threshold, and the dispersion degree of the soil moisture coefficient is less than or equal to the dispersion threshold, the overall soil moisture condition in the monitoring area is extremely poor, and the soil moisture condition will develop in a deteriorating trend in the future.

[0031] Preferably, the calculation expression of the color segmentation index is:

[0032] Where ys f is the color segmentation index, sh t Indicates the number of grids occupied by areas with good soil moisture in the exposed soil area, sc t Indicates the number of grids occupied by the soil moisture poor area in the soil exposed area, sh b Indicates the number of grids occupied by areas with good soil moisture in the vegetation area, sc b Indicates the number of grids occupied by areas with poor soil moisture in the vegetation area.

[0033] Preferably, the calculation expression of the humidity coefficient is:

[0034] In the formula, sd x is the humidity coefficient, i={1, 2, ..., n}, n is a positive integer, n represents the number of monitoring points at different soil depths in the sub-region, represents the soil humidity at the i-th monitoring point, sd i represents the weight of soil moisture at the i-th monitoring point, and a iGreater than 0.

[0035] Preferably, the calculation expression of the moisture content floating coefficient is:

[0036] Where H(t) is the real-time soil moisture content in the sub-region, [t x , t y ] is the period of early warning of environmental evaporation in the sub-region, [t i , t j ] is the period of groundwater level warning in the sub-region.

[0037] Preferably, the logic for obtaining the sub-region environmental evaporation warning period is: calculating the sub-region environmental evaporation through an evaporating dish, and the period when the evaporation is greater than the evaporation threshold is the sub-region environmental evaporation warning period.

[0038] Preferably, the logic for obtaining the period of the sub-region groundwater level warning is: obtaining the groundwater level of the sub-region by radar, and the period when the groundwater level is lower than the water level threshold is the period of the sub-region groundwater level warning.

[0039] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0040] The present invention divides a monitoring area into several sub-areas through a processing end. The sub-areas can be divided into equal parts by area. Image data of each sub-area is acquired based on a remote sensing satellite. Multi-source data related to the monitoring area and soil moisture conditions is acquired based on sensor equipment. The image data and multi-source data are pre-processed, and the image data and multi-source data are comprehensively analyzed. After the soil moisture conditions of each sub-area are evaluated, the overall soil moisture conditions of the monitoring area are comprehensively analyzed based on the soil moisture conditions of each sub-area. This monitoring method comprehensively analyzes the multi-source data and then evaluates the soil moisture conditions of the monitoring area, resulting in a more comprehensive analysis and higher accuracy.

[0041] The present invention obtains the soil moisture coefficient by comprehensively calculating the color segmentation index, humidity coefficient and moisture content floating coefficient, which makes the analysis more comprehensive and accurate. Moreover, after analyzing the soil moisture conditions of multiple sub-areas, the overall soil moisture conditions and soil moisture development trend of the monitored area are judged by analyzing the soil moisture conditions of multiple sub-areas, thereby facilitating the administrator to formulate management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0043] FIG1 is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] Example 1: Referring to FIG1 , this example describes an intelligent soil moisture monitoring method based on remote sensing technology. The monitoring method includes the following steps:

[0046] The processing end divides the monitoring area into several sub-areas. The division of the sub-areas can be divided into equal parts by area, including the following steps:

[0047] Determine the monitoring area: First, identify the entire area that needs to be monitored; this area can be a farmland, watershed, ecosystem, or other geographical area;

[0048] Select the factors for the sub-region division: Determine the factors for the sub-region division; usually, area is a common division factor, but other factors such as land use type, topography, precipitation distribution, etc. can also be considered;

[0049] Data preparation: Collect or obtain data related to the selected partitioning factor. For example, if area is selected as the partitioning factor, the area data of each small area needs to be obtained.

[0050] Determine the division criteria: Based on the selected division factors and data, determine the division criteria. For example, if area is used as the division factor, you can set minimum and maximum area limits for each sub-area, or divide the area according to a certain interval.

[0051] Sub-region division: using computer programs or GIS (Geographic Information System) tools, the monitoring area is divided into a number of sub-regions according to the division criteria; this can be an automated process, using predetermined rules, or by manually drawing boundary lines;

[0052] Verification and optimization: Verify whether the division results are as expected and whether they meet the needs of monitoring and analysis; if necessary, adjust and optimize the sub-areas to ensure the rationality and practicality of the division;

[0053] Sub-area identification and management: Assign a unique identifier or code to each sub-area to facilitate identification and management during subsequent monitoring and data analysis.

[0054] Acquiring image data of each sub-region based on remote sensing satellites includes the following steps:

[0055] Satellite selection and orbit planning: Select appropriate remote sensing satellites based on the location and characteristics of the monitoring area, and plan the satellite orbit and observation time to ensure that the satellite can capture images of the required sub-area;

[0056] Remote sensing data source selection: Select appropriate remote sensing data sources, which can include public satellite data (such as Landsat, MODIS, Sentinel, etc.) or commercial remote sensing data (such as WorldView, QuickBird, etc.);

[0057] Data acquisition and download: Obtain remote sensing image data of the required sub-area through the satellite data provider's platform or data archiving center; ensure that the selected data matches the monitoring area and time period;

[0058] Data preprocessing: Downloaded remote sensing data usually requires some preprocessing, including decompression, atmospheric correction, radiometric calibration, etc., to ensure data quality and consistency;

[0059] Sub-region image cropping: Using the boundary information of the sub-region, use GIS or image processing software to crop the remote sensing image, retaining only the image portion within the sub-region;

[0060] Data calibration: If you are using multiple satellite images or sensor data at the same time, you need to ensure that they are in the same coordinate and projection system for subsequent data analysis;

[0061] Image stitching (optional): If the sub-region spans multiple satellite images or shooting dates, image stitching may be required to generate a complete sub-region image;

[0062] Image quality assessment: Perform quality assessment on sub-region images to check for cloud cover, shadows, or other issues that may affect subsequent data analysis and monitoring;

[0063] Data storage and management: Store and manage the processed sub-region image data to ensure data security and accessibility;

[0064] Periodic data acquisition (optional): If you need to monitor changes in the sub-area regularly, you can set up an automated data acquisition program to obtain the latest remote sensing image data.

[0065] Acquiring multi-source data related to the monitoring area and soil moisture conditions based on sensor equipment includes the following steps:

[0066] Sensor selection: Select appropriate sensor equipment based on monitoring needs and soil moisture parameters; these sensors may include soil moisture sensors, soil temperature sensors, precipitation sensors, weather stations, etc.

[0067] Positioning: Arrange sensor equipment within the monitoring area, ensuring that the location and layout of the sensors are representative of the soil conditions in the entire area. The density and location of the sensors should take into account factors such as soil type, topography, and vegetation cover.

[0068] Sensor calibration: Before installing the sensor, the sensor is calibrated to ensure the accuracy and consistency of the sensor measurement data; calibration usually involves comparison and adjustment using standard soil samples;

[0069] Data collection settings: Set the data collection parameters of the sensor equipment, including sampling frequency, data storage method, transmission method, etc.; ensure that the sensor can collect soil moisture data as needed;

[0070] Sensor data collection: Start sensor devices to collect data; the sensors regularly measure soil moisture parameters and store the data internally or send it wirelessly to a data center or cloud.

[0071] Data quality control: Regularly check the quality of sensor data to ensure there is no drift or anomaly; perform data correction and repair as needed;

[0072] Data integration: Integrate soil moisture data acquired by sensors with other relevant data sources (such as meteorological data and remote sensing data) to obtain more comprehensive information and ensure the temporal and spatial consistency of data;

[0073] Data storage and management: Establish a data storage and management system to ensure secure storage and easy access to sensor data; back up data to prevent data loss.

[0074] After pre-processing image data and multi-source data, ensuring data consistency and quality includes the following steps:

[0075] Data cleaning: First, clean the data to remove any obvious outliers, errors, or missing data; this can be done by writing scripts or using data processing software;

[0076] Correction: Data correction is performed, including radiation correction, atmospheric correction, geometric correction, etc., based on the data type and sensor characteristics to ensure data accuracy and comparability. The specific correction method depends on the data type and sensor.

[0077] Data registration: If you are using multiple data sources, ensure that the data is in the same coordinate system so that you can overlay and compare the data correctly; this may require image registration or georeferencing.

[0078] Cloud and shadow processing: Detecting and masking cloud cover and shadows in remote sensing images to reduce their impact on data quality; this typically requires the use of cloud detection algorithms and image processing techniques.

[0079] Denoising: For sensor data, apply appropriate denoising techniques to reduce the impact of noise on the data; this includes using filters or signal processing methods to smooth the data.

[0080] Data interpolation: If there are missing data points, interpolation methods can be used to estimate the values ​​of these points; methods such as linear interpolation and Kriging interpolation can be used to fill in missing data;

[0081] Data normalization: Normalize data from different data sources to ensure that they have the same scale and range for comparison and integration;

[0082] Outlier detection and processing: Detect and process outliers in the data, which may be caused by sensor failure or other reasons. Statistical methods can be used to identify and process outliers.

[0083] Time synchronization: If you are processing time series data, ensure that timestamps across different data sources are synchronized so that the data can be analyzed and compared correctly.

[0084] After comprehensively analyzing image data and multi-source data and evaluating the soil moisture conditions in each sub-area, the overall soil moisture conditions in the monitored area are comprehensively analyzed through the soil moisture conditions in each sub-area.

[0085] Visualizing the overall soil moisture conditions in the monitored area, such as creating a soil moisture map, chart, or report, helps users better understand the spatial distribution and changing trends of soil moisture. Sending the report to the administrator involves the following steps:

[0086] Visualization tool selection: Choose appropriate visualization tools, such as maps, charts, or reports, based on analysis needs and user preferences;

[0087] Create a soil moisture map: If you choose to create a soil moisture map, you can use GIS software or an online mapping platform to create the map. Use color coding or symbols on the map to represent soil moisture levels in different sub-regions so that users can understand the spatial distribution at a glance.

[0088] Create charts: If you choose to create a chart, you can create a line chart, bar chart, or heat map to show the trends and changes in soil moisture data. Data from different sub-regions or time points can be compared in the chart.

[0089] Generate reports: If reports are required, integrate the visualization results and analysis conclusions into reports; reports should include key soil moisture data, trend analysis, important observations, and recommendations;

[0090] Report Design: Design the layout and style of the report to ensure it is easy to read and understand; may include graphics, tables, headings, and explanatory text;

[0091] Report delivery: Send the generated report to the administrator or relevant stakeholders; this can be done via email, online sharing platform or printed report;

[0092] Regular updates: If soil moisture monitoring is a regular activity, ensure that reports are updated regularly to reflect the latest data and trends;

[0093] User training (optional): If needed, provide training to administrators or other users to ensure they can correctly understand and use the information in the reports.

[0094] This application divides the monitoring area into several sub-areas through the processing end. The sub-areas can be divided into equal parts by area. Image data of each sub-area is obtained based on remote sensing satellites. Multi-source data related to the monitoring area and soil moisture conditions are obtained based on sensor equipment. The image data and multi-source data are pre-processed, and the image data and multi-source data are comprehensively analyzed. After the soil moisture conditions of each sub-area are evaluated, the overall soil moisture conditions of the monitoring area are comprehensively analyzed through the soil moisture conditions of each sub-area. This monitoring method comprehensively analyzes multi-source data and then evaluates the soil moisture conditions of the monitoring area. The analysis is more comprehensive and more accurate.

[0095] Example 2: After comprehensively analyzing image data and multi-source data and evaluating the soil moisture condition of each sub-region, the overall soil moisture condition of the monitored area is comprehensively analyzed based on the soil moisture condition of each sub-region.

[0096] Image data includes color segmentation index, and multi-source data includes humidity coefficient and water content floating coefficient;

[0097] The logic for obtaining the color segmentation index is:

[0098] Use remote sensing satellites to take pictures of the sub-areas, and determine the boundaries of the sub-areas in the pictures after scaling the pictures proportionally;

[0099] The image is segmented for the first time based on color segmentation technology to separate the exposed soil area from the vegetation area in the image. Then, the image is segmented for the second time based on color segmentation technology to separate the areas with good soil moisture from the areas with poor soil moisture in the exposed soil area, and the areas with good soil moisture from the areas with poor soil moisture in the vegetation area.

[0100] Generally speaking, within a monitoring area, the color depth of soil with good soil moisture is greater than that of soil with poor soil moisture. Moreover, in the vegetation area, the gloss and color of vegetation with good soil moisture are better than those with poor soil moisture. Therefore, color segmentation thresholds are set for the bare soil area and the vegetation area respectively, so as to segment the areas with good soil moisture from the areas with poor soil moisture in the bare soil area, and segment the areas with good soil moisture from the areas with poor soil moisture in the vegetation area.

[0101] Based on the grid method, the sub-region image is divided into several small square grids (the number of grids is set according to actual needs. The more grid divisions there are, the greater the calculation amount but the higher the accuracy). The number of grids occupied by the areas with good soil moisture in the exposed soil area, the number of grids occupied by the areas with poor soil moisture in the exposed soil area, the number of grids occupied by the areas with good soil moisture in the vegetated area, and the number of grids occupied by the areas with poor soil moisture in the vegetated area are obtained. The color segmentation index is calculated and expressed as:

[0102] Where ys f is the color segmentation index, sh t Indicates the number of grids occupied by areas with good soil moisture in the exposed soil area, sc t Indicates the number of grids occupied by the soil moisture poor area in the soil exposed area, sh b Indicates the number of grids occupied by areas with good soil moisture in the vegetation area, sc b It indicates the number of grids occupied by the area with poor soil moisture in the vegetation area. The larger the color segmentation index, the worse the soil moisture in the sub-area.

[0103] The calculation expression of humidity coefficient is:

[0104] In the formula, sd x is the humidity coefficient, i={1, 2, ..., n}, n is a positive integer, indicating the number of monitoring points at different soil depths in the sub-region, sd i represents the soil moisture at the i-th monitoring point, a i represents the weight of soil moisture at the i-th monitoring point, and a i If it is greater than 0, the larger the moisture coefficient is, the greater the overall soil moisture value of the sub-region is after weighted calculation, that is, the better the soil moisture condition is;

[0105] In the prior art, a humidity sensor is usually only set at one soil humidity point to monitor soil humidity, which lacks comprehensive analysis of soil humidity at different depths and has poor accuracy.

[0106] The calculation expression of the moisture content floating coefficient is:

[0107] Where H(t) is the real-time soil moisture content in the sub-region, [t x , t y ] is the period of early warning of environmental evaporation in the sub-region, [t i , t j ] is the period of groundwater level warning in the sub-region;

[0108] The logic for obtaining the time period for sub-region environmental evaporation warning is as follows: the environmental evaporation of the sub-region is calculated using an evaporation dish. An evaporation dish is a field instrument commonly used to monitor environmental evaporation. They are shallow, tray-shaped containers filled with water and placed outdoors. Evaporation is estimated by measuring the amount of water level drop over a certain period of time. An evaporimeter is an automated instrument used to record environmental evaporation. The greater the evaporation, the faster the moisture content of the soil in the sub-region decreases over a period of time. Therefore, the time period when the evaporation is greater than the evaporation threshold is the time period for sub-region environmental evaporation warning.

[0109] The logic for obtaining the sub-region groundwater level warning period is as follows: the groundwater level in the sub-region is obtained through radar. The groundwater level has a significant impact on the soil moisture in the sub-region. A higher groundwater level can improve the soil's moisture retention capacity. Therefore, the period when the groundwater level is below the water level threshold is the sub-region groundwater level warning period. At this time, the soil's moisture retention capacity decreases, resulting in poor soil moisture.

[0110] According to the inverse relationship between image data and multi-source data, the color segmentation index, humidity coefficient and moisture content floating coefficient are comprehensively calculated to obtain the soil moisture coefficient. The calculation expression is:

[0111] In the formula, sd x is the humidity coefficient, ys f is the color segmentation index, hs f is the moisture content floating coefficient, α, β, and γ are the proportional coefficients of humidity coefficient, color segmentation index, and moisture content floating coefficient, respectively, and α, β, and γ are all greater than 0.

[0112] By the soil moisture coefficient sq x The calculation formula shows that the soil moisture coefficient sq x The larger the value, the better the soil moisture in the sub-region. Therefore, the soil moisture coefficient sq x The value is compared with the preset soil moisture threshold. If the soil moisture coefficient sq xIf the value is greater than or equal to the soil moisture threshold, the soil moisture in the sub-region is evaluated to be good. x If the value is less than the soil moisture threshold, the soil moisture in the sub-area is assessed to be poor.

[0113] Comprehensively analyze the overall soil moisture conditions in the monitoring area through the soil moisture conditions in each sub-area;

[0114] After evaluating the soil moisture conditions in each sub-area of ​​the monitoring area, the overall soil moisture conditions in the monitoring area are analyzed through the soil moisture conditions in each sub-area, including the following steps:

[0115] Get the number of sub-regions and the soil moisture coefficient sq of each sub-region x value;

[0116] Calculate the average soil moisture coefficient of all sub-areas;

[0117] The dispersion degree of the soil moisture coefficient of the monitoring area is calculated based on the soil moisture coefficient values ​​of the sub-areas and the average soil moisture coefficient value;

[0118] The soil moisture conditions and soil moisture change trends in the monitoring area are analyzed by the dispersion degree of the soil moisture coefficient in the monitoring area;

[0119] in:

[0120] The calculation expression of the discrete degree of soil moisture coefficient in the monitoring area is:

[0121] Where b = {1, 2, 3, ..., c}, c represents the number of sub-regions, c is a positive integer, R b Represents the moisture content coefficient sq of the b-th sub-region x value, Represents the average value of soil moisture coefficient.

[0122] If the average value of the soil moisture coefficient is ≥ the soil moisture threshold, and the dispersion of the soil moisture coefficient is ≤ the dispersion threshold, the overall soil moisture condition in the monitoring area is good, and the soil moisture condition will develop in a positive direction in the future (i.e., the soil moisture coefficients sq of multiple sub-areas in the monitoring area are ≤ the dispersion threshold). x value ≥ soil moisture threshold);

[0123] If the average value of the soil moisture coefficient is greater than or equal to the soil moisture threshold, and the dispersion of the soil moisture coefficient is greater than the dispersion threshold, the overall soil moisture condition in the monitoring area is good, and there are unstable factors in the future development trend of soil moisture (i.e., the soil moisture coefficient sq in some sub-areas of the monitoring area is greater than or equal to the soil moisture threshold). x value < soil moisture threshold);

[0124] If the average value of the soil moisture coefficient is less than the soil moisture threshold, and the dispersion of the soil moisture coefficient is greater than the dispersion threshold, the overall soil moisture condition in the monitoring area is poor, and there are unstable factors in the future development trend of soil moisture (that is, there are some sub-areas in the monitoring area with soil moisture coefficients sq x value ≥ soil moisture threshold);

[0125] If the average value of the soil moisture coefficient is less than the soil moisture threshold, and the dispersion of the soil moisture coefficient is less than or equal to the dispersion threshold, the overall soil moisture condition in the monitoring area is extremely poor, and the soil moisture condition will develop in a bad trend in the future (i.e., the soil moisture coefficients of multiple sub-areas in the monitoring area are sq x value < soil moisture threshold).

[0126] This application obtains the soil moisture coefficient by comprehensively calculating the color segmentation index, humidity coefficient and moisture content floating coefficient, which makes the analysis more comprehensive and accurate. In addition, after analyzing the soil moisture conditions of multiple sub-areas, the overall soil moisture conditions and soil moisture development trends of the monitored area are judged by analyzing the soil moisture conditions of multiple sub-areas, thereby facilitating administrators to formulate management strategies.

[0127] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0128] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0129] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0130] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0131] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0132] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0133] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0135] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0136] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0137] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random-access memory (RAM), a magnetic disk or an optical disk.

[0138] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A soil moisture intelligent monitoring method based on remote sensing technology, characterized in that: The monitoring method comprises the following steps: S1: The processing end divides the monitoring area into several sub-areas, obtains image data of each sub-area based on remote sensing satellites, obtains multi-source data related to the monitoring area and soil moisture conditions based on sensor equipment, and pre-processes the image data and multi-source data; S2: Comprehensively analyze image data and multi-source data to evaluate the soil moisture conditions in each sub-area; S3: Comprehensively analyze the overall soil moisture conditions in the monitoring area through the soil moisture conditions in each sub-area; S4: Visualize the overall soil moisture status of the monitoring area; S5: Send the visualization report to the administrator; In step S2, the image data and multi-source data are comprehensively analyzed, the image data includes the color segmentation index, and the multi-source data includes the humidity coefficient and the water content floating coefficient; In step S2, evaluating the soil moisture condition of each sub-region includes the following steps: S2.1: Calculate the color segmentation index, humidity coefficient and water content floating coefficient to obtain the moisture content coefficient sq x , the calculation expression is: In the formula, sd x is the humidity coefficient, ys f is the color segmentation index, hs f is the floating coefficient of moisture content, α, β, and γ are the proportional coefficients of humidity coefficient, color segmentation index, and floating coefficient of moisture content, respectively, and α, β, and γ are all greater than 0; S2.2: If the soil moisture coefficient sq x If the value is ≥ the soil moisture threshold, the soil moisture in the sub-area is evaluated to be good. x When the value is less than the soil moisture threshold, the soil moisture in the sub-area is evaluated as poor; In step S3, comprehensively analyzing the overall soil moisture condition of the monitoring area through the soil moisture condition of each sub-area includes the following steps: S3.1: Get the number of sub-regions and the soil moisture coefficient sq of each sub-region x value; S3.2: Calculate the average value of soil moisture coefficient of all sub-areas; S3.3: Soil moisture coefficient sq based on sub-area x The dispersion degree of soil moisture coefficient in the monitoring area is calculated by using the soil moisture coefficient average value. S3.4: Analyze the soil moisture status and soil moisture change trend of the monitoring area through the dispersion degree of the soil moisture coefficient of the monitoring area; In step S3.4, the dispersion degree of soil moisture coefficient in the monitoring area is s The calculation expression is: Where b = {1, 2, 3, ..., c}, c represents the number of sub-regions, c is a positive integer, R b represents the moisture content coefficient sq of the bth sub-region x value, It represents the average value of soil moisture coefficient; If the average value of the soil moisture coefficient is ≥ the soil moisture threshold, and the dispersion degree of the soil moisture coefficient is ≤ the dispersion threshold, the overall soil moisture condition in the monitoring area is good, and the soil moisture condition will develop in a positive trend in the future; If the average value of the soil moisture coefficient is ≥ the soil moisture threshold, and the dispersion degree of the soil moisture coefficient is greater than the dispersion threshold, the overall soil moisture condition in the monitoring area is good, and there are unstable factors in the future development trend of soil moisture; If the average value of the soil moisture coefficient is less than the soil moisture threshold, and the dispersion degree of the soil moisture coefficient is greater than the dispersion threshold, the overall soil moisture condition in the monitoring area is poor, and there are unstable factors in the future development trend of soil moisture; If the average value of the soil moisture coefficient is less than the soil moisture threshold, and the dispersion degree of the soil moisture coefficient is less than or equal to the discrete threshold, the overall soil moisture condition in the monitored area is extremely poor, and the soil moisture condition will develop in a deteriorating trend in the future.

2. The method for intelligent monitoring of soil moisture based on remote sensing technology according to claim 1, characterized in that: The calculation expression of the color segmentation index is: In the formula, ys f is the color segmentation index sh t Indicates the number of grids occupied by areas with good soil moisture in the soil exposed area, sc t Indicates the number of grids occupied by the area with poor soil moisture in the exposed soil area, sh b Indicates the number of grids occupied by areas with good soil moisture in the vegetation area, sc b Indicates the number of grids occupied by areas with poor soil moisture in the vegetation area.

3. The method for intelligent monitoring of soil moisture based on remote sensing technology according to claim 2, characterized in that: The calculation expression of the humidity coefficient is: In the formula, sd x is the humidity coefficient, i={1, 2, ..., n}, n is a positive integer, n represents the number of monitoring points at different soil depths in the sub-area, sd i represents the soil moisture at the i-th monitoring point, a i represents the weight of soil moisture at the i-th monitoring point, and a i Greater than 0.

4. The method for intelligent monitoring of soil moisture based on remote sensing technology according to claim 3 is characterized in that: The calculation expression of the moisture content floating coefficient is: Where H(t) is the real-time soil moisture content in the sub-region, [t x , t y ] is the period of sub-region environmental evaporation warning, [t i , t j ] is the period of groundwater level warning in the sub-region.

5. The method for intelligent monitoring of soil moisture based on remote sensing technology according to claim 4 is characterized in that: The logic for obtaining the time period for the sub-region environmental evaporation warning is as follows: the environmental evaporation of the sub-region is calculated by an evaporation dish, and the time period when the evaporation is greater than the evaporation threshold is the time period for the sub-region environmental evaporation warning.

6. The method for intelligent monitoring of soil moisture based on remote sensing technology according to claim 5, characterized in that: The logic for obtaining the time period for the sub-region groundwater level warning is as follows: the groundwater level of the sub-region is obtained by radar, and the time period when the groundwater level is lower than the water level threshold is the time period for the sub-region groundwater level warning.

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