Satellite remote sensing monitoring method for global cultivated land protection

Abstract

The invention relates to the technical field of remote sensing monitoring, in particular to a global cultivated land protection satellite remote sensing monitoring method, which comprises the following steps of: performing spectrum correction on each pixel point based on hyperspectral image data of a target cultivated land area, and adjusting the reflectivity of each wave band to a real level to obtain corrected spectrum data; according to the method, hyperspectral image data and a geostatistics method are integrated, the influence of atmospheric interference on data accuracy is reduced through step-by-step refining processing in the aspects of spectrum correction and radiometric calibration, and key spectral characteristics are accurately positioned by calculating the correlation coefficient of the soil organic matter content and the spectral reflectivity, so that the accuracy of the data is improved. According to the method, the change trend can be extracted from the time sequence data, the situation that cultivated land is occupied or degraded can be recognized in time through analysis of abnormal change indexes, instant and accurate data support is provided for decision makers, and visual information of cultivated land quality and production potential is provided through spatial interpolation processing and a constructed soil organic matter spatial distribution diagram.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing monitoring technology, and in particular to a satellite remote sensing monitoring method for global farmland protection. Background Art

[0002] Remote sensing monitoring technology involves the use of sensors carried by satellites, aircraft or drones to obtain data on the earth's surface from a distance. These sensors can capture signals in different bands, from visible light to infrared and microwaves. The core of this technology is the ability to monitor various physical and chemical properties of the earth's surface for a long time, over a large area and at a high frequency without direct contact with the monitored object. Remote sensing technology is widely used in agriculture, forestry, geological exploration, urban planning, environmental protection and other fields. It has the advantages of fast data collection, wide coverage and high cost-effectiveness. Through the analysis of remote sensing data, vegetation growth, land use changes, environmental pollution and natural disasters can be monitored, providing a basis for scientific research and policy making.

[0003] Among them, the satellite remote sensing monitoring method for global cultivated land protection is a method of using satellite remote sensing technology to continuously monitor cultivated land in the entire region. The purpose of this method is to ensure the rational use and protection of cultivated land and avoid illegal occupation or improper development. Through this monitoring, the occupation or degradation of cultivated land can be captured in real time, and agricultural policies can be adjusted or protective measures can be taken in a timely manner. In addition, this monitoring method can also be used to assess the quality and production potential of cultivated land and support policy making for food security and sustainable agricultural development. By integrating and analyzing data collected from satellite sensors, governments and agricultural management agencies can more effectively protect and utilize cultivated land, thereby ensuring national food security and agricultural sustainability.

[0004] Traditional monitoring methods face the problems of low data processing efficiency and insufficient monitoring accuracy in farmland monitoring operations. When processing large-scale data, traditional methods are difficult to extract specific trends and accurately identify illegal farming and land degradation areas due to the lack of effective data analysis tools. The lack of technology not only delays the response time to farmland encroachment or degradation, but also limits the scientific nature and foresight of agricultural policies. For example, the failure to identify and respond to farmland degradation in a timely manner has led to a continuous decline in soil quality, which in turn affects food production and ecological security. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a global cultivated land protection satellite remote sensing monitoring method.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a satellite remote sensing monitoring method for global cultivated land protection, comprising the following steps:

[0007] S1: Based on the hyperspectral image data of the target cultivated land area, spectral correction is performed on each pixel point, and the reflectance of each band is adjusted to the real level to obtain the corrected spectral data;

[0008] S2: Based on the corrected spectral data, the correlation coefficient between the reflectance of each band and the soil organic matter content is calculated, and the band with the highest correlation coefficient is selected to obtain the key spectral features;

[0009] S3: Based on the key spectral features, analyzing the change trend of cultivated land coverage, marking abnormal changes in the data in the time series, and obtaining abnormal change indicators;

[0010] S4: Based on the abnormal change index, compare the soil organic matter content data before and after the node, extract the soil organic matter change trend related to time, and obtain the historical change trend analysis result;

[0011] S5: Based on the historical change trend analysis results, the cultivated land coverage status is analyzed pixel by pixel, the degraded areas and the illegally cultivated areas are identified, and the characteristics of the healthy soil are compared to evaluate the cultivated land health status and obtain the cultivated land health status analysis results;

[0012] S6: Based on the analysis results of the cultivated land health status, combined with the correlation data of the surface and cultivated layer soil organic matter content, spatial interpolation processing is performed to construct a spatial distribution map of soil organic matter and obtain the spatial distribution information of cultivated layer soil organic matter.

[0013] As a further scheme of the present invention, the corrected spectral data includes reflectance corrected for the atmosphere, data without the influence of water vapor, and adjusted spectral curves; the key spectral features include highly correlated bands of soil organic matter, data after spectral smoothing, and selected sensitive bands; the abnormal change indicators include marked abnormal time points, abnormal degree scores, and change trends; the historical change trend analysis results include change trend lines, change rates, and key change nodes; the cultivated land health status analysis results include the location of degraded areas, the degree of degradation, and potential illegal farming activities; the spatial distribution information of cultivated layer soil organic matter includes the spatial distribution pattern of soil organic matter, key distribution areas, and soil type differences.

[0014] As a further solution of the present invention, based on the hyperspectral image data of the target cultivated land area, spectral correction is performed on each pixel point, and the reflectance of each band is adjusted to the real level. The steps of obtaining the corrected spectral data are specifically as follows:

[0015] S101: Based on the hyperspectral image data of the target cultivated land area, spectral correction is performed on each pixel in the data set, and the deviation caused by the atmosphere is corrected by calculating the atmospheric scattering model to obtain basic correction data;

[0016] S102: Based on the basic correction data, radiometric calibration is performed, by adjusting the ratio between the gray value of the image and the known ground reflectivity, adjusting the reflectivity data to be close to the ground truth, and obtaining the spectral data after radiometric calibration;

[0017] S103: Based on the spectral data after the radiation calibration, perform spectral reflectance normalization processing to standardize all band data to a unified scale, eliminate the influence of equipment differences and condition changes, and obtain corrected spectral data.

[0018] As a further solution of the present invention, based on the corrected spectral data, the correlation coefficient between the reflectance of each band and the soil organic matter content is calculated, and the band with the highest correlation coefficient is selected to obtain the key spectral characteristics. Specifically, the steps are as follows:

[0019] S201: Based on the corrected spectral data, the correlation between the reflectance of each band and the organic matter content in the known soil sample is calculated, and the correlation strength between the differentiated band and the soil organic matter is determined by statistical analysis to obtain the band correlation analysis result;

[0020] S202: Based on the band correlation analysis result, select the band with the highest correlation coefficient as the target band to obtain target spectral band data;

[0021] S203: Based on the target spectral band data, a high-pass filter is applied to smooth the data, thereby reducing random errors caused by equipment and environmental variables, enhancing signal contrast in the data, optimizing data quality, and obtaining key spectral feature data.

[0022] As a further solution of the present invention, based on the key spectral features, the change trend of cultivated land coverage is analyzed, abnormal changes of data are marked in the time series, and the steps of obtaining abnormal change indicators are specifically as follows:

[0023] S301: extracting and sorting the spectral data at each time point in the sequence based on the key spectral features, arranging the data in chronological order to form a continuous time series, and obtaining time-series spectral data;

[0024] S302: Based on the time-series spectral data, calculate the spectral difference between every two consecutive time points, and determine whether the difference is significant, analyze the data difference between consecutive time points, identify the change trend of cultivated land coverage, and obtain change trend data;

[0025] S303: Based on the change trend data, a threshold method is used to determine whether a data point is abnormal. The determination is based on the standard deviation and mean in the statistical analysis, and the time point when the data change exceeds the normal fluctuation is marked to obtain an abnormal change indicator.

[0026] As a further solution of the present invention, based on the abnormal change index, the soil organic matter content data before and after the node is compared, the soil organic matter change trend related to time is extracted, and the steps of obtaining the historical change trend analysis result are specifically as follows:

[0027] S401: Based on the abnormal change index, identify the time nodes marked as abnormal, collect and organize the soil organic matter content data before and after each marked abnormal time node, and obtain time node comparison data;

[0028] S402: Based on the time node comparison data, compare the soil organic matter content before and after the abnormal node, use difference analysis to calculate the organic matter change amount of each node, evaluate the soil organic matter change at each time point, and obtain the node change analysis result;

[0029] S403: Based on the node change analysis results, by performing time series analysis on the changes in all marked time points, extracting long-term change trends, evaluating the soil organic matter change pattern during the entire observation period, and obtaining historical change trend analysis results.

[0030] As a further solution of the present invention, the formula for extracting the long-term change trend is:

[0031]

[0032] Among them, TI represents the trend index, ΔF k represents the change in soil organic matter at the kth time point, W k is the weight of the kth time point, and K is the number of time points.

[0033] As a further solution of the present invention, based on the historical change trend analysis results, the cultivated land coverage status is analyzed pixel by pixel, the degraded areas and the illegally cultivated areas are identified, and the characteristics of the healthy soil are compared to evaluate the cultivated land health status. The specific steps of obtaining the cultivated land health status analysis results are:

[0034] S501: Based on the historical change trend analysis result, the cultivated land coverage status of each pixel is analyzed pixel by pixel, and the coverage change is identified by comparing the time series data of the pixel to obtain the pixel-by-pixel coverage change data;

[0035] S502: Based on the pixel-by-pixel coverage change data, compare the change data with the normal farming pattern, identify the area showing signs of degradation and the area of ​​illegal farming, and obtain data of identifying the degradation and illegal farming areas;

[0036] S503: Based on the data of the marked degraded and illegally cultivated areas, the data are compared with known healthy soil characteristics, the health status of the soil in the area is analyzed, the health status of the entire cultivated land is evaluated, and the analysis results of the cultivated land health status are obtained.

[0037] As a further solution of the present invention, the formula for evaluating the health status of the entire cultivated land is:

[0038]

[0039] Among them, HS is the farmland health score, F ci Represents the i-th feature in the spectral feature vector of the current region, F hi The i-th feature in the spectral feature vector representing healthy soil, W i is the weight of the i-th feature, and n is the dimension of the feature vector.

[0040] As a further solution of the present invention, based on the analysis results of the cultivated land health status, combined with the correlation data of the surface and plough layer soil organic matter content, spatial interpolation processing is performed to construct a spatial distribution map of soil organic matter, and the steps of obtaining the spatial distribution information of plough layer soil organic matter are specifically as follows:

[0041] S601: Based on the analysis result of the farmland health status, collect the related surface and plough layer soil organic matter content data, and obtain the soil organic matter content statistical data by counting the sample data of each area;

[0042] S602: Based on the soil organic matter content statistical data, a spatial interpolation analysis is performed to analyze the soil organic matter distribution in the entire region, and the missing area is estimated by the Kriging method to optimize the data continuity and obtain a spatial interpolation result;

[0043] S603: Based on the spatial interpolation result, adjust the interpolation parameters, check the accuracy of the interpolation result, optimize the spatial model, construct a soil organic matter distribution map, and obtain the spatial distribution information of soil organic matter in the cultivated layer.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by integrating hyperspectral image data and geostatistical methods, the influence of atmospheric interference on data accuracy is reduced through step-by-step refinement in spectral correction and radiation calibration, and by calculating the correlation coefficient between soil organic matter content and spectral reflectance, key spectral features are accurately located, which provides a basis for analyzing the trend of changes in cultivated land coverage, can extract change trends from time series data, and can timely identify the situation of cultivated land being occupied or degraded through analysis of abnormal change indicators, providing decision makers with immediate and accurate data support. Through spatial interpolation processing, a spatial distribution map of soil organic matter is constructed, which provides intuitive information on cultivated land quality and production potential, improves the efficiency and coverage of data processing, and enhances the adaptability and scientific nature of agricultural policies. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 It is a schematic diagram of the main steps of the present invention.

[0048] Figure 2 This is a schematic diagram of the refinement of S1 of the present invention.

[0049] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention.

[0050] Figure 4 This is a schematic diagram of the refinement of S3 of the present invention.

[0051] Figure 5 It is a schematic diagram of the refinement of S4 of the present invention.

[0052] Figure 6 It is a schematic diagram of the refinement of S5 of the present invention.

[0053] Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0056] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0057] In the embodiments of the present invention, sometimes the subscripts such as W 1 It may be written in non-subscript form such as W1. When the difference is not emphasized, the meaning is the same.

[0058] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0059] See also Figure 1 The present invention provides a technical solution: a satellite remote sensing monitoring method for global cultivated land protection, comprising the following steps:

[0060] S1: Based on the hyperspectral image data of the target cultivated land area, spectral correction is performed on each pixel point, the influence of atmospheric scattering and absorption is gradually corrected, and radiation calibration processing is applied to adjust the reflectance of each band to the real level to obtain the corrected spectral data;

[0061] S2: Based on the corrected spectral data, the correlation coefficient between the reflectance of each band and the soil organic matter content is calculated, the band with the highest correlation coefficient is selected, the target band is smoothed to reduce the random error of the data and obtain the key spectral features;

[0062] S3: Based on key spectral features, analyze the changing trend of cultivated land coverage, track the changes in soil status at each time point through time series data, analyze the degree of isolation of data points, mark abnormal changes in data in the time series, and obtain abnormal change indicators;

[0063] S4: Based on the abnormal change index, the marked abnormal time nodes are analyzed, the soil organic matter content data before and after the nodes are compared, the soil organic matter change trend related to time is extracted, and the historical change trend analysis results are obtained;

[0064] S5: Based on the results of historical change trend analysis, the cultivated land coverage status is analyzed pixel by pixel, the degraded areas and illegal cultivation areas are identified, the image characteristics of the areas are analyzed, and compared with the characteristics of healthy soil to evaluate the health status of cultivated land and obtain the analysis results of the health status of cultivated land;

[0065] S6: Based on the results of the farmland health status analysis, combined with the correlation data of the surface and cultivated layer soil organic matter content, geostatistical methods are used to perform spatial interpolation processing, construct a spatial distribution map of soil organic matter, and obtain the spatial distribution information of cultivated layer soil organic matter.

[0066] The corrected spectral data include reflectance corrected for the atmosphere, data after removing the influence of water vapor, and adjusted spectral curves. The key spectral features include soil organic matter high correlation bands, spectral smoothing data, and selected sensitive bands. Abnormal change indicators include marked abnormal time points, abnormal degree scores, and change trends. The historical change trend analysis results include change trend lines, change rates, and key change nodes. The results of the cultivated land health status analysis include the location of degraded areas, the degree of degradation, and potential illegal farming activities. The spatial distribution information of soil organic matter in the cultivated layer includes the spatial distribution pattern of soil organic matter, key distribution areas, and soil type differences.

[0067] See also Figure 2 Based on the hyperspectral image data of the target cultivated land area, spectral correction is performed on each pixel point, the influence of atmospheric scattering and absorption is gradually corrected, and radiation calibration processing is applied to adjust the reflectance of each band to the real level. The specific steps to obtain the corrected spectral data are as follows:

[0068] S101: Based on the hyperspectral image data of the target cultivated land area, spectral correction is performed on each pixel in the data set, and the deviation caused by the atmosphere is corrected by calculating the atmospheric scattering model to obtain basic correction data;

[0069] Based on the hyperspectral image data of the target cultivated land area, the spectral response of the image data is measured, and the scattering effects of the atmosphere, including Rayleigh scattering and Mie scattering, are calculated. The spectral data of each pixel is predicted and modeled using the radiation transfer equation, and the data of each band is compensated to correct the deviations caused by atmospheric absorption and scattering. At the same time, the ground reflectance data and the irradiance data at the top of the atmosphere are used to calibrate the sensor readings to ensure the consistency of the data in spectral response. The consistency of the entire data set is verified to confirm the accuracy of the correction and obtain the basic correction data.

[0070] S102: Based on the basic correction data, radiometric calibration is performed, by adjusting the ratio between the gray value of the image and the known ground reflectivity, adjusting the reflectivity data to be close to the ground truth, and obtaining the spectral data after radiometric calibration;

[0071] Based on the basic correction data, by accurately measuring the targets with known reflectivity on the ground, the ground truth data is used to calibrate the sensor, and a linear regression analysis is performed on the reflectivity data of each pixel to find the proportional relationship between the grayscale value of the image and the ground reflectivity. The image data is adjusted according to the proportional relationship to ensure that the reflectivity data of each band can accurately reflect the actual situation of the ground. The ratio is optimized through multiple iterative adjustments, so that the spectral data has good consistency and repeatability under different conditions, and the spectral data after radiation calibration is obtained.

[0072] S103: Based on the spectral data after radiation calibration, perform spectral reflectance normalization processing to standardize all band data to a unified scale, eliminate the impact of equipment differences and condition changes, and obtain corrected spectral data.

[0073] Based on the spectral data after radiation calibration, the spectral reflectance is normalized to determine the extreme points in the data of all bands. The extreme points are used as references to linearly scale the data of each band to ensure that all data are on the same scale. The processed data can reduce fluctuations caused by different equipment sensitivities or environmental changes. Statistical analysis methods such as standard deviation and mean are used to evaluate the effect of normalization to ensure that the normalized spectral data can accurately reflect the actual spectral characteristics of different bands, thereby obtaining corrected spectral data.

[0074] See also Figure 3 , based on the corrected spectral data, calculate the correlation coefficient between the reflectance of each band and the soil organic matter content, select the band with the highest correlation coefficient, smooth the target band, reduce the random error of the data, and obtain the key spectral features in the following steps:

[0075] S201: Based on the corrected spectral data, the correlation between the reflectance of each band and the organic matter content in the known soil sample is calculated, and the correlation strength between the differentiated band and the soil organic matter is determined by statistical analysis to obtain the band correlation analysis result;

[0076] Based on the corrected spectral data, the reflectance data of each band was screened to eliminate outliers and noise data. Statistical methods such as correlation analysis and regression analysis were used to calculate the correlation coefficient between the reflectance of each band and the organic matter content in the known soil samples. Multivariate statistical analysis techniques were used to analyze the data. By continuously adjusting and verifying the model, the band with the strongest correlation was found. This process ensured the accuracy and reliability of the analysis results and obtained the band correlation analysis results.

[0077] S202: Based on the band correlation analysis result, the band with the highest correlation coefficient is selected as the target band to obtain the target spectral band data;

[0078] Based on the results of band correlation analysis, the band with the highest correlation coefficient is selected. This selection process is based on the sorting and screening functions in the statistical analysis software, and is based on the numerical value of the correlation coefficient. By comparing the correlation coefficients of each band, the band with the highest correlation is selected. This band shows the strongest linear correlation with the soil organic matter content. This process ensures that the selected target band will provide the most critical data support for subsequent applications such as soil quality monitoring. After selection, the target spectral band data is obtained.

[0079] S203: Based on the target spectral band data, a high-pass filter is applied to smooth the data, reduce the random errors caused by equipment and environmental variables, enhance the signal contrast in the data, optimize the data quality, and obtain key spectral feature data;

[0080] Based on the target spectral band data, the high-pass filter technology is used to pre-process the data to filter out low-frequency noise and irrelevant variables. Then, the high-pass filter parameters are optimized through multiple iterations to ensure that the useful signals in the data are enhanced. At the same time, the quality of the filtered data is evaluated to ensure that the filtering process does not introduce additional deviations or distortions, so that the data quality is optimized and the key spectral feature data is obtained.

[0081] See also Figure 4 ,Based on the key spectral features, the changing trend of cultivated land coverage is analyzed, the soil state changes at each time point are tracked through time series data, the degree of isolation of data points is analyzed, and abnormal changes of data are marked in the time series. The specific steps to obtain abnormal change indicators are as follows:

[0082] S301: extracting and sorting the spectral data at each time point in the sequence based on the key spectral features, arranging the data in chronological order to form a continuous time series, and obtaining time-series spectral data;

[0083] Based on key spectral features, spectral data at each time point in the sequence is collected, and the data is quality checked and preprocessed to eliminate possible errors or outliers. Data at each time point is extracted using data sorting technology, and sorted and aligned in chronological order. The process includes verification and adjustment of time tags to ensure the timing accuracy of the data. After the operation is completed, the data is rearranged to form a continuous time series, which allows subsequent analysis to be performed in the correct time context to obtain time-serialized spectral data.

[0084] S302: Based on the time-series spectral data, the spectral difference between every two consecutive time points is calculated, and whether the difference is significant is determined, the data difference between consecutive time points is analyzed, the change trend of cultivated land coverage is identified, and the change trend data is obtained;

[0085] Based on time-series spectral data, data processing technology is used to calculate the spectral differences between consecutive time points, including the use of difference and normalization methods to quantify the changes between each pair of consecutive data points in the time series. The differences are statistically analyzed to determine whether they are significant. The significance test includes the use of methods such as t-test or ANOVA. The analysis helps identify and explain patterns and changing trends in time series, such as seasonal changes in soil cover or abnormal events. Through this meticulous analysis, trend data characterizing changes in cultivated land are obtained.

[0086] S303: Based on the change trend data, a threshold method is used to determine whether the data point is abnormal. The determination is based on the standard deviation and mean in the statistical analysis, and the time point when the data change exceeds the normal fluctuation is marked to obtain an abnormal change index;

[0087] Based on the change trend data, the threshold analysis method is used to evaluate each data point in the time series, including calculating the statistical standard deviation and mean of the data, and then setting the threshold to identify data points that exceed the normal fluctuation range. The threshold is set based on historical data and statistical standards to ensure the accuracy and reliability of anomaly detection. The data points that exceed the threshold are marked. The marked data points indicate that abnormal changes have occurred in the spectral characteristics of cultivated land, and the abnormal change index is obtained.

[0088] See also Figure 5 ,Based on the abnormal change index, the marked abnormal time nodes are analyzed, the soil organic matter content data before and after the nodes are compared, and the time-related soil organic matter change trend is extracted. The specific steps to obtain the historical change trend analysis results are as follows:

[0089] S401: Based on the abnormal change index, identify the time nodes marked as abnormal, collect and organize the soil organic matter content data before and after each marked abnormal time node, and obtain time node comparison data;

[0090] Based on the abnormal change index, the specific time nodes marked as abnormal in the time series data are identified. The soil spectral data before and after the node are collected by accessing the database and calling the data extraction function. The data includes quantitative information on the soil organic matter content. The collected data are sorted and organized to ensure the consistency of the timeline and the integrity of the data. The organized data will be rearranged in chronological order, so that analysts can easily compare the changes in soil characteristics at different time points and obtain time node comparison data.

[0091] S402: Based on the time node comparison data, the soil organic matter content before and after the abnormal node is compared, and the organic matter change amount of each node is calculated by difference analysis, and the soil organic matter change at each time point is evaluated to obtain the node change analysis result;

[0092] Based on the time node comparison data, the difference analysis method is used to calculate the specific difference in soil organic matter content before and after each marked abnormal time node to determine the amount of organic matter change at each time node. The obtained change is statistically analyzed, including calculating the mean and variance, to evaluate the change in soil organic matter at each time point and obtain the node change analysis results.

[0093] S403: Based on the node change analysis results, by performing time series analysis on the change amounts of all marked time points, extracting the long-term change trend, evaluating the soil organic matter change pattern during the entire observation period, and obtaining the historical change trend analysis results;

[0094] The formula for extracting long-term change trends is:

[0095]

[0096] Among them, TI represents the trend index, ΔF k represents the change in soil organic matter at the kth time point, W k is the weight of the kth time point, and K is the number of time points.

[0097] formula:

[0098]

[0099] Parameter details and how to obtain them:

[0100] ΔF k : represents the change in soil organic matter at the kth time point relative to the previous time point. These changes are usually obtained from the analysis results of soil samples, and each sample corresponds to the soil organic matter content at a specific time point.

[0101] W k : represents the weight of the kth time point. These weights are set based on the recency of the time point, i.e. the most recent time point has a higher weight. The weight is usually set based on the inverse function of the time span from the time point to the current analysis point.

[0102] K: is the number of time points. This value is determined by the length of the time period being analyzed and the availability of data.

[0103] Calculation example:

[0104] The following parameters are set: There are four time points of soil organic matter changes, the specific values ​​are: ΔF 1 =0.2, ΔF 2 =-0.1, ΔF 3 =0.3, ΔF 4 =-0.2.W 1 =1.0,W2 =0.8,W 3 =0.6,W 4 =0.4, the number of time points K=4.

[0105] Calculate the weighted change:

[0106] W 1 ΔF 1 =1.0·0.2=0.2;

[0107] W 2 ΔF 2 =0.8·-0.1=-0.08;

[0108] W 3 ΔF 3 =0.6·0.3=0.18;

[0109] W 4 ΔF 4 =0.4·-0.2=-0.08;

[0110] Calculate the trend index TI:

[0111]

[0112] The trend index TI is 0.055, which reflects the overall trend of soil organic matter content during the observation period. A positive value indicates that the overall trend is increasing, although the growth rate is slow. This result can help decision makers understand the changing trend of soil quality and take corresponding land management or improvement measures.

[0113] See also Figure 6 Based on the historical trend analysis results, the cultivated land coverage status is analyzed pixel by pixel, the degraded areas and illegal cultivated areas are identified, the image characteristics of the analysis area are compared with the characteristics of healthy soil, and the health status of the cultivated land is evaluated. The specific steps to obtain the cultivated land health status analysis results are as follows:

[0114] S501: Based on the historical change trend analysis results, the cultivated land coverage status of each pixel is analyzed pixel by pixel, and the coverage change is identified by comparing the time series data of the pixel to obtain the pixel-by-pixel coverage change data;

[0115] Based on the results of historical change trend analysis, the historical cultivated land coverage status records of each pixel are accessed through the data query system. The spectral data of each pixel is compared point by point using time series analysis technology, including tracking and recording changes in continuous time series. By comparing the changes between data points, the change pattern of cultivated land coverage is identified. Image processing software and scripts are used to automate this process, thereby reducing human errors and speeding up data processing to obtain pixel-by-pixel coverage change data.

[0116] S502: Based on the pixel-by-pixel coverage change data, the change data is compared with the difference of the normal farming pattern, and the area showing signs of degradation and the area of ​​illegal farming are marked to obtain the marked degradation and illegal farming area data;

[0117] Based on the pixel-by-pixel coverage change data, geographic information systems and analysis software are used to compare the change data of each pixel point with the normal farming pattern. By setting thresholds and parameters, data points that are significantly different from the normal pattern are identified, and areas showing signs of soil degradation and illegal farming are marked. This process includes visual analysis of the data and the application of automatic recognition technology, which ensures the accuracy and operability of the analysis and obtains data on identified degradation and illegal farming areas.

[0118] S503: Based on the data of the marked degraded and illegally cultivated areas, the data are compared with the known healthy soil characteristics, the health status of the soil in the area is analyzed, the health status of the entire cultivated land is evaluated, and the analysis results of the cultivated land health status are obtained;

[0119] The formula for evaluating the health status of the entire cultivated land is:

[0120]

[0121] Among them, HS is the farmland health score, F ci Represents the i-th feature in the spectral feature vector of the current region, F hi The i-th feature in the spectral feature vector representing healthy soil, W i is the weight of the i-th feature, and n is the dimension of the feature vector.

[0122] formula:

[0123]

[0124] Parameter details and how to obtain them:

[0125] F ci : represents the i-th feature in the spectral feature vector of the current area, the reflectance of a specific band of the spectral data of the current area. These data are usually obtained through remote sensing technology, and the reflectance is obtained by measuring the ratio of the reflected light intensity to the incident light intensity in a specific band.

[0126] F hi : The i-th feature in the spectral feature vector representing healthy soil, the reflectance of healthy soil in the same band. The data can be obtained from the soil health database or through field measurements as a health standard control.

[0127] W i : The weight of the ith feature. The weight is determined based on the sensitivity and coefficient of variation of the feature, which can be obtained through historical data analysis.

[0128] n: The dimension of the feature vector, i.e. the number of bands included in the analysis. This number is determined based on the actual remote sensing equipment and data availability.

[0129] Calculation example:

[0130] The following data and parameter settings are set: n = 3, W 1 =0.5,W 2 =1.0,W 3 =0.8 (spectral weight), F c1 =0.3, F c2 =0.45, F c3 =0.5 (spectral reflectance of current soil), F h1 =0.2, F h2 =0.5, F h3 =0.55 (spectral reflectance of healthy soil).

[0131] Compute the product of the difference and the weight:

[0132] W 1 ·|F c1 -F h1 |=0.5·|0.3-0.2|=0.05;

[0133] W 2 ·|F c2 -F h2 |=1.0·|0.45-0.5|=0.05;

[0134] W 3 ·|F c3 -F h3 |=0.8·|0.5-0.55|=0.04;

[0135] Compute the square root of the sum of the squares of the weights:

[0136]

[0137] Calculate the farmland health score HS:

[0138]

[0139] The calculation results show that the farmland health score obtained through quantitative analysis is 0.104, which can be used to compare with the scores of other regions or time points to assess changes in soil health. In practical applications, this score can help agricultural scientists and policymakers identify land areas that need to be restored or focused on monitoring.

[0140] See also Figure 7 Based on the results of the farmland health status analysis, combined with the correlation data of the surface and plough layer soil organic matter content, the geostatistical method is used to perform spatial interpolation processing to construct the spatial distribution map of soil organic matter. The specific steps for obtaining the spatial distribution information of plough layer soil organic matter are as follows:

[0141] S601: Based on the results of the farmland health status analysis, collect the associated surface and plough layer soil organic matter content data, and obtain the soil organic matter content statistical data by counting the sample data of each area;

[0142] Based on the results of the farmland health status analysis, the surface and tillage soil organic matter content data of different regions were obtained through database query and field sample collection methods. The data were sorted and verified to ensure data quality. Statistical software was used to conduct a detailed statistical analysis of the soil organic matter content of samples in each region, including the calculation of descriptive statistical data such as the mean, median, and standard deviation. The process helped determine the baseline and variation range of the soil organic matter content, making the data more valuable for reference and obtaining statistical data on the soil organic matter content.

[0143] S602: Based on the statistical data of soil organic matter content, perform spatial interpolation analysis to analyze the distribution of soil organic matter in the entire region, and estimate the missing areas through the Kriging method to optimize data continuity and obtain spatial interpolation results;

[0144] Based on the statistical data of soil organic matter content, geostatistical methods are used for spatial interpolation analysis, especially the Kriging method, to model the soil organic matter distribution in the entire region. The analysis includes the calculation of semivariogram and model fitting to ensure that the model can accurately reflect the actual spatial distribution of soil organic matter. This method is used to estimate the data blank areas and optimize the continuity and integrity of the data in the entire region. The model parameters are adjusted through multiple iterations to ensure the accuracy of the final result and obtain the spatial interpolation result.

[0145] S603: Based on the spatial interpolation results, adjust the interpolation parameters, verify the accuracy of the interpolation results, optimize the spatial model, construct a soil organic matter distribution map, and obtain the spatial distribution information of soil organic matter in the cultivated layer.

[0146] Based on the spatial interpolation results, the parameters of the Kriging interpolation model were adjusted to improve the accuracy and applicability of the interpolation. The accuracy of the interpolation results was verified using methods such as cross-validation and error analysis to ensure that the model could effectively reflect the true distribution of soil organic matter. Through adjustment and optimization, a soil organic matter distribution map that was consistent with the field verification data was constructed. The distribution map provided key information for land management and decision-making, and the spatial distribution information of soil organic matter in the plow layer was obtained.

[0147] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0148] In the present invention, "at least one" means one or more, and "more than one" 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 be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0149] It should be understood that in various embodiments of the present invention, 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 invention.

[0150] 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 to be beyond the scope of the present invention.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, 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.

[0153] 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, that is, they 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.

[0154] In addition, each functional unit in each embodiment of the present invention 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.

[0155] 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 invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. 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 perform all or part of the steps of the methods described in various embodiments of the present invention. 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.

[0156] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A satellite remote sensing monitoring method for global cultivated land protection, characterized in that: The following steps are involved: Based on the hyperspectral image data of the target cultivated land area, spectral correction is performed on each pixel point, and the reflectance of each band is adjusted to the real level to obtain the corrected spectral data; Based on the corrected spectral data, the correlation coefficient between the reflectance of each band and the soil organic matter content is calculated, and the band with the highest correlation coefficient is selected to obtain the key spectral features; Based on the key spectral features, the changing trend of cultivated land coverage is analyzed, abnormal changes of data are marked in the time series, and abnormal change indicators are obtained; Based on the abnormal change index, the soil organic matter content data before and after the node are compared, the soil organic matter change trend related to time is extracted, and the historical change trend analysis result is obtained; Based on the historical change trend analysis results, the cultivated land coverage status is analyzed pixel by pixel, the degraded areas and illegal cultivated areas are identified, and the characteristics of healthy soil are compared to evaluate the cultivated land health status and obtain the cultivated land health status analysis results; Based on the analysis results of the cultivated land health status, combined with the correlation data of the surface and cultivated layer soil organic matter content, spatial interpolation processing is performed to construct a spatial distribution map of soil organic matter and obtain the spatial distribution information of cultivated layer soil organic matter.

2. The satellite remote sensing monitoring method for global cultivated land protection according to claim 1 is characterized in that: The corrected spectral data include reflectance corrected for the atmosphere, data after removing the influence of water vapor, and adjusted spectral curves; the key spectral features include soil organic matter high correlation bands, data after spectral smoothing, and selected sensitive bands; the abnormal change indicators include marked abnormal time points, abnormal degree scores, and change trends; the historical change trend analysis results include change trend lines, change rates, and key change nodes; the cultivated land health status analysis results include the location of degraded areas, the degree of degradation, and potential illegal farming activities; the spatial distribution information of soil organic matter in the cultivated layer includes the spatial distribution pattern of soil organic matter, key distribution areas, and soil type differences.

3. The satellite remote sensing monitoring method for global cultivated land protection according to claim 1 is characterized in that: Based on the hyperspectral image data of the target cultivated land area, spectral correction is performed on each pixel point to adjust the reflectance of each band to the real level. The specific steps to obtain the corrected spectral data are as follows: Based on the hyperspectral image data of the target cultivated land area, spectral correction is performed on each pixel in the data set. By calculating the atmospheric scattering model, the deviation caused by the atmosphere is corrected to obtain basic correction data. Based on the basic correction data, radiometric calibration is performed, and the reflectivity data is adjusted to be close to the ground truth by adjusting the ratio between the gray value of the image and the known ground reflectivity, so as to obtain the spectral data after radiometric calibration; Based on the spectral data after the radiation calibration, the spectral reflectance is normalized, all band data are standardized to a uniform scale, the influence of equipment differences and condition changes is eliminated, and the corrected spectral data is obtained.

4. The satellite remote sensing monitoring method for global cultivated land protection according to claim 1 is characterized in that: Based on the corrected spectral data, the correlation coefficient between the reflectance of each band and the soil organic matter content is calculated, and the band with the highest correlation coefficient is selected to obtain the key spectral characteristics. Specifically, the steps are as follows: Based on the corrected spectral data, the correlation between the reflectance of each band and the organic matter content in the known soil sample is calculated, and the correlation strength between the differentiated band and the soil organic matter is determined by statistical analysis to obtain the band correlation analysis result; Based on the band correlation analysis result, the band with the highest correlation coefficient is selected as the target band to obtain the target spectral band data; Based on the target spectral band data, a high-pass filter is applied to smooth the data, reduce the random errors caused by equipment and environmental variables, enhance the signal contrast in the data, optimize the data quality, and obtain key spectral feature data.

5. The satellite remote sensing monitoring method for global cultivated land protection according to claim 1 is characterized in that: Based on the key spectral features, the changing trend of cultivated land coverage is analyzed, abnormal changes of data are marked in the time series, and the steps of obtaining abnormal change indicators are as follows: Based on the key spectral features, the spectral data of each time point in the sequence is extracted and sorted, and the data is arranged in chronological order to form a continuous time series to obtain time-series spectral data; Based on the time-series spectral data, the spectral difference between every two consecutive time points is calculated, and whether the difference is significant is determined, the data difference between consecutive time points is analyzed, the change trend of cultivated land coverage is identified, and the change trend data is obtained; Based on the change trend data, a threshold method is used to determine whether a data point is abnormal. The determination is based on the standard deviation and mean in statistical analysis, marking the time point when the data change exceeds the normal fluctuation to obtain an abnormal change indicator.

6. The satellite remote sensing monitoring method for global farmland protection according to claim 1 is characterized in that: Based on the abnormal change index, the soil organic matter content data before and after the node are compared, the soil organic matter change trend related to time is extracted, and the steps of obtaining the historical change trend analysis result are specifically as follows: Based on the abnormal change index, identify the time nodes marked as abnormal, collect and organize the soil organic matter content data before and after each marked abnormal time node, and obtain time node comparison data; Based on the time node comparison data, the soil organic matter content before and after the abnormal node is compared, the difference analysis is used to calculate the organic matter change of each node, the soil organic matter change at each time point is evaluated, and the node change analysis result is obtained; Based on the node change analysis results, by performing time series analysis on the changes in all marked time points, long-term change trends are extracted, and the soil organic matter change pattern during the entire observation period is evaluated to obtain historical change trend analysis results.

7. The satellite remote sensing monitoring method for global farmland protection according to claim 1 is characterized in that: The formula for extracting the long-term trend is: Among them, TI represents the trend index, ΔF k represents the change in soil organic matter at the kth time point, W k is the weight of the kth time point, and K is the number of time points.

8. The satellite remote sensing monitoring method for global farmland protection according to claim 1 is characterized in that: Based on the historical trend analysis results, the cultivated land coverage status is analyzed pixel by pixel, the degraded areas and illegal cultivated areas are identified, and the characteristics of healthy soil are compared to evaluate the cultivated land health status. The specific steps for obtaining the cultivated land health status analysis results are as follows: Based on the historical change trend analysis results, the cultivated land coverage status of each pixel is analyzed pixel by pixel, and the coverage change is identified by comparing the time series data of the pixel points to obtain the pixel by pixel coverage change data; Based on the pixel-by-pixel coverage change data, comparing the change data with the normal farming pattern, identifying areas showing signs of degradation and areas of illegal farming, and obtaining data identifying degradation and illegal farming areas; Based on the data of the marked degraded and illegally cultivated areas, the data are compared with known healthy soil characteristics, the health status of the soil in the area is analyzed, the health status of the entire cultivated land is evaluated, and the results of the cultivated land health status analysis are obtained.

9. The satellite remote sensing monitoring method for global farmland protection according to claim 1 is characterized in that: The formula for evaluating the health status of the entire cultivated land is: Among them, HS is the farmland health score, F ci Represents the i-th feature in the spectral feature vector of the current region, F hi The i-th feature in the spectral feature vector representing healthy soil, W i is the weight of the i-th feature, and n is the dimension of the feature vector.

10. The satellite remote sensing monitoring method for global farmland protection according to claim 1, characterized in that: Based on the analysis results of the cultivated land health status, combined with the correlation data of the organic matter content of the surface and cultivated layers of soil, spatial interpolation processing is performed to construct a spatial distribution map of soil organic matter. The steps for obtaining the spatial distribution information of the organic matter in the cultivated layer of soil are specifically as follows: Based on the analysis results of the farmland health status, the associated surface and plough layer soil organic matter content data are collected, and the soil organic matter content statistical data is obtained by counting the sample data of each area; Based on the soil organic matter content statistical data, a spatial interpolation analysis is performed to analyze the soil organic matter distribution in the entire region, and the missing areas are estimated by the Kriging method to optimize the data continuity and obtain the spatial interpolation results; Based on the spatial interpolation results, the interpolation parameters are adjusted, the accuracy of the interpolation results is tested, the spatial model is optimized, the soil organic matter distribution map is constructed, and the spatial distribution information of the soil organic matter in the cultivated layer is obtained.

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