Soil salinization monitoring method and system based on spaceflight hyperspectral remote sensing

Abstract

The invention relates to a soil salinization monitoring method and system based on spaceflight hyperspectral remote sensing, and belongs to the technical field of geographic condition monitoring, and the monitoring method comprises the steps: obtaining spaceflight hyperspectral remote sensing data of a target soil area; preprocessing the spaceflight hyperspectral remote sensing data to obtain surface reflectance data; performing pixel-by-pixel analysis on the surface reflectance data, and extracting spectral absorption data of each pixel at a preset wavelength; calculating a salinization index value of each pixel based on a preset algorithm according to the spectral absorption data of each pixel at the preset wavelength; based on the salinization index value of each pixel, salinization degree spatial analysis is carried out, and salinization degree spatial distribution information of the target soil area is obtained. The precision of soil salinization monitoring can be improved.

Description

Technical Field

[0001] The present application relates to the field of geographic national conditions monitoring technology, and in particular to a soil salinization monitoring method and system based on aerospace hyperspectral remote sensing. Background Art

[0002] Soil salinization is one of the widespread environmental problems in the world, especially in arid and semi-arid areas. Soil salinization not only restricts the growth and development of crops and reduces land productivity, but may also lead to ecological deterioration. Traditional soil salinization monitoring methods mainly rely on field sampling and analysis, including collecting soil samples and taking them back to the laboratory for chemical composition analysis. However, this method has significant disadvantages: first, due to the need for a lot of manual operation, the whole process takes a long time; second, the high manpower and economic costs make soil salinization monitoring over a large area very difficult; in addition, the limitations of point sampling make it difficult to fully reflect the actual situation of soil salinization in the region.

[0003] At present, with the development of remote sensing technology, especially the application of multispectral and hyperspectral remote sensing technology, people have begun to try to monitor and evaluate soil salinization through remote sensing. Compared with traditional methods, remote sensing technology can achieve large-area synchronous observation and is fast and efficient. However, most of the current monitoring and evaluation of saline soil based on remote sensing technology still mainly relies on the inversion of multispectral information index models or information extraction. Although these methods can reflect the overall situation of soil salinization to a certain extent, due to the low spectral resolution of multispectral remote sensing, it is impossible to effectively capture the diagnostic absorption characteristics of salt spectra and their influence on the spectra of clay minerals in the soil, so there are certain limitations in accuracy. Summary of the invention

[0004] In order to improve the accuracy of soil salinization monitoring, the present application provides a soil salinization monitoring method and system based on aerospace hyperspectral remote sensing.

[0005] In the first aspect, the present application provides a soil salinization monitoring method based on aerospace hyperspectral remote sensing, which adopts the following technical solutions: A soil salinization monitoring method based on aerospace hyperspectral remote sensing, the monitoring method comprising: Acquire spaceborne hyperspectral remote sensing data of target soil areas; Preprocessing the aerospace hyperspectral remote sensing data to obtain surface reflectance data; Analyzing the surface reflectance data pixel by pixel to extract spectral absorption data of each pixel at a preset wavelength; According to the spectral absorption data of each pixel at a preset wavelength, a salinization index value of each pixel is calculated based on a preset algorithm; Based on the salinization index value of each pixel, a spatial analysis of the salinization degree is performed to obtain the spatial distribution information of the salinization degree of the target soil area.

[0006] By adopting the above-mentioned technical scheme, based on the influence of carbonate diagnostic spectral absorption characteristics on the spectrum of clay minerals in the soil, this application provides a new soil salinization index monitoring method for hyperspectral remote sensing. Through scientific and reasonable data processing procedures and technical means, it not only improves the speed and efficiency of soil salinization monitoring, but also enhances the accuracy and reliability of the evaluation results. It can quickly evaluate the regional soil salinization distribution, and thus provide reference information for national space ecological restoration.

[0007] Optionally, extracting spectral absorption data of each pixel at a preset wavelength includes: Extracting the soil absorption depth of each pixel at a wavelength of 2200 nanometers; and extracting the soil absorption depth and absorption wavelength data of each pixel at a wavelength of 2350 nanometers.

[0008] By adopting the above technical solution, the spectral absorption data is extracted at wavelengths of 2200 nanometers and 2350 nanometers, which is conducive to accurately identifying the absorption characteristics of carbonates in the soil and providing key parameters for the subsequent calculation of the salinization index.

[0009] Optionally, the step of extracting the soil absorption depth of each pixel at a wavelength of 2200 nanometers includes: Extract the spectral subset S of each pixel in the range of 2120 nm to 2290 nm 2120-2290 ; Taking the spectral subset S 2120-2290 The two ends of the continuum are the endpoints, and the continuum removal spectrum is obtained by normalization based on the linear continuum. Calculate the continuum removal spectrum Minimum value of And the corresponding wavelength W 2200 ; Based on the minimum and the corresponding wavelength W 2200 , calculate the corresponding soil absorption depth D 2200 for:

[0010] By adopting the above technical solution, the spectral data in the range of 2120 nanometers to 2290 nanometers are continuously normalized, and the absorption characteristics of carbonate ions near 2200 nanometers are accurately extracted, providing a numerical absorption depth index that can quantitatively reflect the presence of carbonate ions in the soil and further evaluate the degree of salinization.

[0011] Optionally, the step of extracting soil absorption depth and absorption wavelength data of each pixel at a wavelength of 2350 nanometers includes: Cut off the spectral subset S of each pixel in the range of 2290 nm to 2390 nm 2290-2390 ; Taking the spectral subset S 2290-2390 The two ends of the continuum are the endpoints, and the continuum removal spectrum is obtained by normalization based on the linear continuum. Calculate the continuum removal spectrum Minimum value of And the corresponding wavelength W 2350 ; Based on the minimum and the corresponding wavelength W 2350 , calculate the corresponding soil absorption depth D 2350 for:

[0012] By adopting the above technical solution, the spectral data in the range of 2290 nanometers to 2390 nanometers are continuously normalized, and the absorption characteristics of specific minerals or compounds near 2350 nanometers are accurately extracted, providing a numerical absorption depth index that can quantitatively reflect the presence of specific minerals or compounds in the soil, and then evaluate the degree of salinization.

[0013] Optionally, the calculation formula of the preset algorithm is: In the above formula, D 2200 The soil absorption depth at 2200 nm for each pixel, D 2350 is the soil absorption depth at 2350 nm for each pixel, W 2350 is the absorption wavelength of each pixel at 2350 nm, k is the proportionality constant, and σ is the standard deviation.

[0014] By adopting the above technical solution, the spectral absorption data at wavelengths of 2200 nanometers and 2350 nanometers were extracted and analyzed, and combined with a specific preset algorithm (including ratios, proportional constants and Gaussian function terms), a rapid and accurate assessment of the degree of soil salinization was ultimately achieved.

[0015] Optionally, the step of obtaining the k value in the calculation formula of the preset algorithm includes: Extract the spectral subset S of each pixel in the range of 2120 nm to 2290 nm 2120-2290 ; According to the spectrum subset S of each pixel 2120-2290 , calculate the corresponding average spectral reflectance value M 2120-2290 ; According to the average spectral reflectance value M of all pixels 2120-2290 , calculate the average value TM 2120-2290 ; According to the average spectral reflectance value M of each pixel 2120-2290 and the average value TM 2120-2290 The K value corresponding to each pixel is calculated as:

[0016] By adopting the above technical solution, the average spectral reflectance of each pixel is calculated, and the proportional constant k is determined in combination with the average value of the overall image, thereby providing a normalization factor for each pixel relative to the overall image, making the calculation of the salinization index I more accurate and better able to reflect local changes.

[0017] Optionally, the step of performing spatial analysis of the degree of salinization based on the salinization index value of each pixel includes: determining the salinization level corresponding to each pixel according to the salinization index value of each pixel; Each pixel is represented by a different color according to the salinization level corresponding to each pixel, generating a visual information map of the spatial distribution of salinization degree.

[0018] By adopting the above technical solutions, the generated visualization map can not only intuitively display the degree of salinization in different areas, but also facilitate decision makers to quickly understand the overall situation and formulate targeted restoration measures.

[0019] Optionally, the step of performing spatial analysis of the degree of salinization based on the salinization index value of each pixel includes: drawing contour lines according to the salinization index value of each pixel to generate a corresponding contour line map.

[0020] By adopting the above technical solution, the spacing between contour lines reflects the rate of change of salinization degree. The smaller the spacing, the faster the change, and vice versa. The contour map can intuitively display the spatial distribution of salinization degree, allowing users to see at a glance which areas are severely salinized and which areas are relatively better, thereby helping decision makers identify key areas of salinization problems and formulate corresponding land management and restoration measures.

[0021] In the second aspect, the present application provides a soil salinization monitoring system based on aerospace hyperspectral remote sensing, which adopts the following technical solutions: A soil salinization monitoring system based on aerospace hyperspectral remote sensing, the monitoring system comprising: An acquisition module is used to acquire aerospace hyperspectral remote sensing data of the target soil area; A preprocessing module, used for preprocessing the aerospace hyperspectral remote sensing data to obtain surface reflectance data; An extraction module, used to perform pixel-by-pixel analysis on the surface reflectance data and extract spectral absorption data of each pixel at a preset wavelength; A salinization index calculation module, used to calculate the salinization index value of each pixel based on a preset algorithm according to the spectral absorption data of each pixel at a preset wavelength; The spatial distribution information generation module is used to perform spatial analysis of the degree of salinization based on the salinization index value of each pixel to obtain the spatial distribution information of the degree of salinization in the target soil area.

[0022] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the methods in the first aspect.

[0023] To summarize, the present application includes at least one of the following beneficial technical effects: the technical solution of the present application not only overcomes the shortcomings of long cycle and high cost of traditional sampling and analysis methods, but also improves the accuracy and reliability of monitoring results, enhances the accuracy and reliability of evaluation results, and provides strong technical support for national land space ecological restoration work, and is particularly suitable for monitoring carbonate salinization and bare soil or sparse vegetation coverage areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a first flow chart of a soil salinization monitoring method according to one of the embodiments of the present application.

[0025] Figure 2 It is a second flow chart of the soil salinization monitoring method according to one of the embodiments of the present application.

[0026] Figure 3 It is a third flow chart of the soil salinization monitoring method of one of the embodiments of the present application.

[0027] Figure 4 It is a fourth flow chart of the soil salinization monitoring method of one of the embodiments of the present application.

[0028] Figure 5It is a fifth flow chart of the soil salinization monitoring method of one of the embodiments of the present application. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-5 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0030] At present, traditional soil salinization monitoring is mainly carried out through field sampling and analysis, which not only takes a long time, but also requires high manpower and economic costs. Remote sensing has the characteristics of large-scale synchronous observation, rapid and efficient, and macro-integrated, and has been widely used in the monitoring and evaluation of salinization. However, the current use of remote sensing to monitor and evaluate saline soil is mainly based on the index model inversion and information extraction of multispectral information. Due to the limitations of the spectral resolution of multispectral remote sensing, these salt index models based on multispectral remote sensing mainly use the statistical characteristics of salinized soil spectra and cannot effectively capture the impact of the spectral diagnostic absorption of salt on the spectra of soil clay minerals.

[0031] Based on this, an embodiment of the present application discloses a soil salinization monitoring method based on aerospace hyperspectral remote sensing.

[0032] Reference Figure 1 , a soil salinization monitoring method based on aerospace hyperspectral remote sensing, the monitoring method includes: Step S101, obtaining aerospace hyperspectral remote sensing data of a target soil area; Among them, hyperspectral remote sensing technology can provide continuous spectral information from visible light to short-wave infrared, which is convenient for capturing the reflective characteristics of surface materials (such as soil) at different wavelengths. Hyperspectral sensors carried by platforms such as satellites or aircraft can image specific areas and obtain data sets containing hundreds of narrow-band spectral channels.

[0033] Exemplarily, the study area can be photographed by a hyperspectral camera equipped with VNIR (visible near infrared) and SWIR (short wave infrared) sensors to obtain hyperspectral image data of the target soil area.

[0034] Step S102, preprocessing the aerospace hyperspectral remote sensing data to obtain surface reflectance data; Among them, the original remote sensing images may contain defects such as noise and stripes, and are affected by atmospheric scattering. Therefore, the preprocessing steps include bad line correction, radiation calibration, and atmospheric correction to ensure that the final surface reflectance data is accurate and reliable.

[0035] For example, the MODTRAN model can be applied to perform atmospheric correction to remove atmospheric absorption and scattering effects, and radiation calibration can be used to convert DN values ​​into surface reflectivity to improve data quality and ensure the accuracy of subsequent analysis results.

[0036] Step S103, performing pixel-by-pixel analysis on the surface reflectance data to extract spectral absorption data of each pixel at a preset wavelength; Among them, different minerals or compounds will show obvious spectral absorption characteristics within a specific wavelength range; by selecting key bands (such as 2200nm, 2350nm) and calculating the absorption depth and absorption wavelength in these bands, the salt content in the soil can be quantitatively evaluated.

[0037] Step S104, calculating the salinization index value of each pixel based on a preset algorithm according to the spectral absorption data of each pixel at a preset wavelength; For example, combined with the key parameters extracted in the previous step, a specific mathematical model (taking into account the σ balance factor) can be used to quantify the degree of salinization of each pixel. This algorithm design aims to comprehensively consider the influence of multiple factors and improve the representativeness and robustness of the index.

[0038] Step S105, performing a spatial analysis of the degree of salinization based on the salinization index value of each pixel to obtain the spatial distribution information of the degree of salinization in the target soil area.

[0039] Among them, by statistically analyzing and visualizing the salinization index of all pixels in the entire image, the spatial variation pattern of the salinization degree in the region can be clearly displayed. Commonly used methods include color grading display and contour map drawing.

[0040] For example, the salinization index I can be divided into multiple levels, each level represented by a different color, and made into a map form; at the same time, contour maps can be drawn to more finely depict the salinization trend, so as to achieve a rapid and comprehensive assessment of the soil salinization status, providing an important reference for decision makers to formulate targeted measures.

[0041] In the above implementation, based on the influence of carbonate diagnostic spectral absorption characteristics on the spectrum of clay minerals in the soil, the present application provides a new soil salinization index monitoring method for hyperspectral remote sensing. Through scientific and reasonable data processing procedures and technical means, it not only improves the speed and efficiency of soil salinization monitoring, but also enhances the accuracy and reliability of the evaluation results. It can quickly evaluate the regional soil salinization distribution, and thus provide reference information for the ecological restoration of the national land space.

[0042] In one embodiment of the present application, extracting spectral absorption data of each pixel at a preset wavelength includes: extracting soil absorption depth of each pixel at a wavelength of 2200 nanometers; and extracting soil absorption depth and absorption wavelength data of each pixel at a wavelength of 2350 nanometers.

[0043] Among them, absorption depth data refers to the intensity or degree of light absorption by a substance at a specific wavelength, which can reflect the concentration or content of the substance and is usually related to the chemical composition and structure of the substance; absorption wavelength data refers to the absorption characteristics of a substance to light under specific conditions, which is usually manifested as absorption peaks or absorption valleys in the spectrum, reflecting the substance's ability to absorb light of a specific wavelength.

[0044] It is understandable that since the carbonate ions in the soil have an obvious absorption peak near the wavelength of 2200 nanometers, this absorption feature can be used to evaluate the carbonate content in the soil. By calculating the absorption depth in this band, the presence of carbonates in the soil can be quantified. By accurately identifying the absorption characteristics of carbonates in the soil, key parameters can be provided for the subsequent calculation of the salinization index.

[0045] In addition, the absorption features near the 2350 nanometer wavelength are usually related to other minerals or compounds in the soil, such as iron oxides and clay minerals. Therefore, by calculating the absorption depth and absorption wavelength in this band, we can further understand the changes in soil composition. By providing additional spectral information, it helps to more comprehensively evaluate the degree of soil salinization and its influencing factors.

[0046] In the above implementation, extracting spectral absorption data at wavelengths of 2200 nanometers and 2350 nanometers is conducive to accurately identifying the absorption characteristics of carbonates in the soil, and providing key parameters for the subsequent calculation of the salinization index.

[0047] Reference Figure 2 In some embodiments, the step of extracting the soil absorption depth of each pixel at a wavelength of 2200 nanometers includes: Step S201, intercepting a spectrum subset S of each pixel in the range of 2120 nanometers to 2290 nanometers 2120-2290 ; The selection of this interval is based on the fact that carbonate ions in the soil have obvious spectral absorption characteristics near this band. By intercepting the spectral data in this specific range, we can focus on information that is indicative of salinization. By narrowing the analysis range and focusing on spectral regions that are sensitive to salinization, we can reduce interference from irrelevant information.

[0048] For example, for a certain pixel point, the reflectance value in the wavelength range of 2120 nm to 2290 nm is extracted from its complete hyperspectral data to obtain a subset S 2120-2290 .

[0049] Step S202, using the spectrum subset S 2120-2290 The two ends of the continuum are the endpoints of the continuum. The continuum removal spectrum is obtained by normalization based on the linear continuum. Specifically, the continuum refers to the background spectrum without specific absorption features. By taking the two ends of the spectrum subset (i.e., 2120 nanometers and 2290 nanometers) as reference points and assuming that there is no specific absorption feature between these two points, a straight line can be constructed to represent the continuum. This straight line is then used to remove the background components in the original spectrum, thereby highlighting the absorption features, eliminating background noise, and making the carbonate ion absorption peak in the soil more obvious, which is convenient for subsequent analysis.

[0050] Exemplarily, a straight line is determined using the reflectance values ​​at 2120 nm and 2290 nm, and then from S 2120-2290 Subtract this straight line from the

[0051] Step S203, calculating the continuum removal spectrum Minimum value of And the corresponding wavelength W 2200 ; Among them, in the spectrum after the continuum is removed, the lowest point and its corresponding wavelength are found. This lowest point reflects the maximum absorption position of carbonate ions in this band, and the corresponding wavelength provides information about the position of the absorption feature. By accurately locating the absorption feature of carbonate ions, a key parameter is provided for calculating the absorption depth. For example, by scanning The minimum reflectivity value can be found and its corresponding wavelength W 2200 .

[0052] Step S204, based on the minimum value and the corresponding wavelength W 2200 , calculate the corresponding soil absorption depth D 2200 for:

[0053] Among them, absorption depth is a quantitative indicator used to measure the intensity of spectral absorption; the above formula takes into account the relationship between absorption depth and reflectivity, and introduces the sign function sgn() to ensure that only absorption within a specific wavelength range (i.e., between 2195 nanometers and 2225 nanometers) is counted.

[0054] For example, if And W 2200 =2210 nanometers, then: D 2200=(1-0.8)sgn(2210-2195)sgn(2225-2210)=0.211.

[0055] In the above implementation, the spectral data in the range of 2120 nanometers to 2290 nanometers are continuously normalized to accurately extract the absorption characteristics of carbonate ions near 2200 nanometers, providing a numerical absorption depth index that can quantitatively reflect the presence of carbonate ions in the soil and further evaluate the degree of salinization.

[0056] Reference Figure 3 In some embodiments, the step of extracting soil absorption depth and absorption wavelength data for each pixel at a wavelength of 2350 nanometers includes: Step S301, intercepting the spectrum subset S of each pixel in the range of 2290 nanometers to 2390 nanometers 2290-2390 ; The selection of this interval is based on the fact that specific minerals or compounds in the soil (such as iron oxides and clay minerals) have obvious spectral absorption characteristics near this band. By intercepting the spectral data in this specific range, we can focus on information that is indicative of salinization. By narrowing the analysis scope and focusing on spectral regions that are sensitive to salinization, we can reduce interference from irrelevant information.

[0057] For example, for a certain pixel point, the reflectance value in the wavelength range of 2290 nm to 2390 nm is extracted from its complete hyperspectral data to form a subset S 2290-2390 .

[0058] Step S302: using the spectrum subset S 2290-2390 The two ends of the continuum are the endpoints of the continuum. The continuum removal spectrum is obtained by normalization based on the linear continuum. Among them, the continuum refers to the background spectrum without specific absorption features. By taking the two ends of the spectral subset (i.e., 2290 nanometers and 2390 nanometers) as reference points and assuming that there is no specific absorption feature between these two points, a straight line can be constructed to represent the continuum. Then, this straight line is used to remove the background components in the original spectrum, thereby highlighting the absorption features and eliminating background noise, making the absorption peaks of specific minerals or compounds in the soil more obvious, which is convenient for subsequent analysis.

[0059] Exemplarily, a straight line is determined using the reflectance values ​​at 2290 nm and 2390 nm, and then Subtract this straight line from the original spectrum to obtain a new spectrum.

[0060] Step S303, calculating the continuum removal spectrum Minimum value of And the corresponding wavelength W 2350 ; In the spectrum after the continuum is removed, look for the lowest point and its corresponding wavelength. This lowest point reflects the maximum absorption position of a specific mineral or compound in this band, while the corresponding wavelength provides information about the position of the absorption feature. By accurately locating the absorption features of a specific mineral or compound, it provides key parameters for calculating the absorption depth.

[0061] For example, by scanning The minimum reflectivity value can be found and its corresponding wavelength W 2350 .

[0062] Step S304, based on the minimum value and the corresponding wavelength W 2350 , calculate the corresponding soil absorption depth D 2350 for:

[0063] Among them, absorption depth is a quantitative indicator used to measure the intensity of spectral absorption; the above formula takes into account the relationship between absorption depth and reflectivity, and introduces the sign function sgn() to ensure that only absorption within a specific wavelength range (i.e., between 2330 nanometers and 2350 nanometers) is counted.

[0064] For example, if And W 2350 =2340 nanometers, then: D_ 2350 =(1-0.7)*sgn(2340-2330)*sgn(2350-2340)=0.3.

[0065] In the above implementation, the spectral data in the range of 2290 nanometers to 2390 nanometers are continuously normalized to accurately extract the absorption characteristics of specific minerals or compounds near 2350 nanometers, providing a numerical absorption depth index that can quantitatively reflect the presence of specific minerals or compounds in the soil, and further evaluate the degree of salinization.

[0066] As an implementation of the preset algorithm, the calculation formula of the preset algorithm is: In the above formula, D 2200 The soil absorption depth at 2200 nm for each pixel, D 2350 is the soil absorption depth at 2350 nm for each pixel, W 2350is the absorption wavelength of each pixel at 2350 nanometers, k is the proportional constant, σ is the standard deviation, which is used to balance the influence of the absorption caused by carbonate on the absorption depth and absorption wavelength of the spectrum of clay minerals in the soil, and is generally between 5 and 10.

[0067] Specifically, the ratio By comparing the absorption depth at 2350 nm and 2200 nm, the relative intensity of spectral absorption by different minerals or compounds can be reflected. This ratio can provide information about changes in soil composition. The proportional constant k is used to adjust the magnitude of the entire formula to ensure that the calculated salinization index I is within a reasonable range. The specific value of k can be calibrated according to actual data. Gaussian function term Considering the absorption wavelength W 2350 The deviation between W and 2350 nm is used to control the width of the function by introducing a standard deviation σ. 2350 When it is close to 2350 nanometers, the function value is close to 1; when W 2350 When moving away from 2350 nm, the function value decreases rapidly, which reflects the influence of the absorption wavelength position on the degree of salinization. 2200 When I is zero, I takes the value of zero.

[0068] In the above implementation, the spectral absorption data at wavelengths of 2200 nanometers and 2350 nanometers are extracted and analyzed, and combined with a specific preset algorithm (including ratios, proportional constants and Gaussian function terms), ultimately achieving a rapid and accurate assessment of the degree of soil salinization.

[0069] Reference Figure 4 In one embodiment of the present application, the step of obtaining the k value in the calculation formula of the preset algorithm includes: Step S401, intercepting a spectrum subset S of each pixel in the range of 2120 nanometers to 2290 nanometers 2120-2290 ; Among them, the wavelength range of 2120 nanometers to 2290 nanometers contains the absorption characteristics of carbonate ions. By extracting the spectral data in this range, it is possible to better reflect the presence of carbonates in the soil, focus on the spectral region sensitive to salinization, and reduce interference from irrelevant information.

[0070] Step S402: according to the spectrum subset S of each pixel 2120-2290 , calculate the corresponding average spectral reflectance value M 2120- 2290 ; Specifically, by calculating the average reflectivity of each pixel in the range of 2120 nanometers to 2290 nanometers, the overall reflectivity characteristics of the pixel in this specific band can be obtained, which helps to eliminate the influence of local noise and provide more stable spectral information.

[0071] For example, assuming that the reflectivity values ​​of a certain pixel in the range of 2120 nanometers to 2290 nanometers are 0.3, 0.4, 0.5, ..., respectively, then the average spectral reflectivity value M of the pixel is 2120-2290 is the arithmetic mean of these values.

[0072] Step S403: based on the average spectral reflectance value M of all pixels 2120-2290 , calculate the average value TM 2120-2290 Among them, by statistically analyzing the average spectral reflectance values ​​of all pixels in the entire image, the overall reflectance characteristics of the entire image in the wavelength range of 2120 nanometers to 2290 nanometers can be obtained. This average value represents the overall reflectance level of the soil in the study area.

[0073] Step S404: based on the average spectral reflectance value M of each pixel 2120-2290 and the mean value TM 2120-2290 The K value corresponding to each pixel is calculated as:

[0074] The ratio reflects the difference in reflectivity of a single pixel relative to the overall image. If the reflectivity of a pixel is higher than the overall average level, the k value is greater than 1; conversely, if it is lower than the average level, the k value is less than 1.

[0075] In the above embodiment, the average spectral reflectance of each pixel is calculated, and the proportional constant k is determined in combination with the average value of the overall image, thereby providing a normalization factor for each pixel relative to the overall image, making the calculation of the salinization index I more accurate and better able to reflect local changes.

[0076] Reference Figure 5 As an implementation of step S105, the step of performing spatial analysis of the degree of salinization based on the salinization index value of each pixel includes: Step S501, determining the salinization level corresponding to each pixel according to the salinization index value of each pixel; Among them, the salinization index I is a quantitative indicator that reflects the level of salt content in the soil. By setting different threshold ranges, the I value can be divided into multiple levels, each level representing a different degree of salinization. This grading method helps to intuitively display and understand the salinization status of different regions.

[0077] For example, assuming that the salinization index I ranges from 0 to 1, it can be divided into the following levels: I<0.2: mild salinization, 0.2≤I<0.4: moderate salinization, 0.4≤I<0.6: severe salinization; I≥0.6: extremely severe salinization; Through classification, areas with different degrees of salinization can be clearly distinguished, which is convenient for subsequent spatial analysis and decision-making.

[0078] Step S502: Different colors are used to represent each pixel according to the salinization level corresponding to each pixel, so as to generate a visualized spatial distribution information map of the salinization degree.

[0079] Among them, by assigning different colors to each salinization level, the spatial distribution of salinization degree can be intuitively displayed on the map. Color coding not only improves the readability of the data, but also helps users quickly identify areas with severe salinization.

[0080] Exemplarily, the configurable level colors are: mild salinization (I<0.2) is represented by green; moderate salinization (0.2≤I<0.4) is represented by yellow; severe salinization (0.4≤I<0.6) is represented by orange; and extremely severe salinization (I≥0.6) is represented by red.

[0081] In the above implementation, the generated visualization map can not only intuitively display the degree of salinization in different areas, but also facilitate decision makers to quickly understand the overall situation and formulate targeted restoration measures.

[0082] As another implementation of step S105, the step of performing spatial analysis of the degree of salinization based on the salinization index value of each pixel includes: performing contour drawing according to the salinization index value of each pixel to generate a corresponding contour map.

[0083] Among them, contour lines (or isovalue lines) are a commonly used method for visualizing geographic information, which are used to represent the changes of a continuous variable on a two-dimensional plane. In this application scenario, the salinization index I is a continuous variable. By drawing contour lines, the spatial distribution and gradient changes of the degree of salinization can be intuitively displayed. Contour maps can clearly show the degree of salinization in different regions and their changing trends.

[0084] For example, assuming that the salinization index I of each pixel in the entire study area has been calculated, the appropriate contour interval can be set according to actual needs and data distribution; for example, if the salinization index I ranges from 0 to 1, you can choose 0.1 as a contour interval; then, use GIS software (such as ArcGIS, QGIS, etc.) or programming tools (such as the Matplotlib library in Python) to draw contour lines, and by inputting the location coordinates (latitude and longitude) of each pixel and the corresponding salinization index I, a contour map can be generated.

[0085] In the above implementation, the spacing between the contour lines reflects the rate of change of the salinization degree. The smaller the spacing, the faster the change, and vice versa. The contour map can intuitively display the spatial distribution of the salinization degree, so that users can see at a glance which areas are severely salinized and which areas are relatively better, thereby helping decision makers identify key areas of salinization problems and formulate corresponding land management and restoration measures.

[0086] In practical applications, this application is particularly suitable for monitoring carbonate salinization areas, which usually have high soil pH and are rich in cations such as calcium and magnesium. By using advanced hyperspectral remote sensing technology, it is possible to accurately capture subtle changes in soil surface reflectivity, which are closely related to the salt content in the soil, especially the carbonate content, thus providing strong support for the assessment of this special type of salinization.

[0087] In addition, this application is particularly suitable for areas with bare soil or only sparse vegetation cover, because under these conditions, the ground signal is less disturbed by vegetation and can more directly and accurately reflect the properties of the soil itself. This highly targeted and efficient technical means has shown great potential in the protection and restoration of the ecological environment in arid and semi-arid areas and other areas prone to salinization, and helps to achieve rapid diagnosis and effective management of the affected land status.

[0088] The embodiment of the present application also discloses a soil salinization monitoring system based on aerospace hyperspectral remote sensing.

[0089] A soil salinization monitoring system based on aerospace hyperspectral remote sensing, the monitoring system includes: An acquisition module is used to acquire aerospace hyperspectral remote sensing data of the target soil area; The preprocessing module is used to preprocess the aerospace hyperspectral remote sensing data to obtain the surface reflectance data; An extraction module is used to analyze the surface reflectance data pixel by pixel and extract the spectral absorption data of each pixel at a preset wavelength; A salinization index calculation module, used to calculate the salinization index value of each pixel based on a preset algorithm according to the spectral absorption data of each pixel at a preset wavelength; The spatial distribution information generation module is used to perform spatial analysis of the degree of salinization based on the salinization index value of each pixel to obtain the spatial distribution information of the degree of salinization in the target soil area.

[0090] In the above implementation, the rapid, accurate and comprehensive monitoring of the salinization degree of the target soil area is achieved through the coordinated work of the acquisition module, the preprocessing module, the extraction module, the salinization index calculation module and the spatial distribution information generation module. The system can efficiently process hyperspectral remote sensing data, accurately extract the spectral absorption characteristics at key wavelengths, and calculate the salinization index value of each pixel in combination with a preset algorithm, and finally generate an intuitive salinization degree spatial distribution information map. This technical solution not only overcomes the shortcomings of the traditional sampling and analysis method, which is long cycle and high cost, but also improves the accuracy and reliability of the monitoring results. It is particularly suitable for the monitoring of carbonate salinization and bare soil or sparse vegetation coverage areas.

[0091] A soil salinization monitoring system based on aerospace hyperspectral remote sensing in an embodiment of the present application can implement any of the above-mentioned soil salinization monitoring methods, and the specific working process of each module in the soil salinization monitoring system can refer to the corresponding process in the above-mentioned method embodiment.

[0092] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are only illustrative; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0093] The embodiment of the present application also discloses a computer-readable storage medium.

[0094] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the above-mentioned soil salinization monitoring methods based on aerospace hyperspectral remote sensing.

[0095] Among them, computer-readable storage media can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus or device; the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0096] It should be noted that in the above embodiments, the description of each embodiment has different emphases, and for the parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A soil salinization monitoring method based on aerospace hyperspectral remote sensing, characterized in that: The monitoring method comprises: Acquire spaceborne hyperspectral remote sensing data of target soil areas; Preprocessing the aerospace hyperspectral remote sensing data to obtain surface reflectance data; Analyzing the surface reflectance data pixel by pixel to extract spectral absorption data of each pixel at a preset wavelength; According to the spectral absorption data of each pixel at a preset wavelength, a salinization index value of each pixel is calculated based on a preset algorithm; Based on the salinization index value of each pixel, a spatial analysis of the salinization degree is performed to obtain the spatial distribution information of the salinization degree of the target soil area.

2. The soil salinization monitoring method based on aerospace hyperspectral remote sensing according to claim 1 is characterized in that: Extracting the spectral absorption data of each pixel at a preset wavelength includes: Extracting the soil absorption depth of each pixel at a wavelength of 2200 nanometers; and extracting the soil absorption depth and absorption wavelength data of each pixel at a wavelength of 2350 nanometers.

3. The soil salinization monitoring method based on aerospace hyperspectral remote sensing according to claim 2 is characterized in that: The step of extracting the soil absorption depth of each pixel at a wavelength of 2200 nanometers comprises: Extract the spectral subset S of each pixel in the range of 2120 nm to 2290 nm 2120-2290 ; Taking the spectral subset S 2120-2290 The two ends of the continuum are the endpoints, and the continuum removal spectrum is obtained by normalization based on the linear continuum. Calculate the continuum removal spectrum Minimum value of And the corresponding wavelength W 2200 ; Based on the minimum and the corresponding wavelength W 2200 , calculate the corresponding soil absorption depth D 2200 for:

4. The soil salinization monitoring method based on aerospace hyperspectral remote sensing according to claim 2 is characterized in that: The step of extracting soil absorption depth and absorption wavelength data of each pixel at a wavelength of 2350 nanometers comprises: Cut off the spectral subset S of each pixel in the range of 2290 nm to 2390 nm 2290-2390 ; Taking the spectral subset S 2290-2390 The two ends of the continuum are the endpoints, and the continuum removal spectrum is obtained by normalization based on the linear continuum. Calculate the continuum removal spectrum Minimum value of And the corresponding wavelength W 2350 ; Based on the minimum and the corresponding wavelength W 2350 , calculate the corresponding soil absorption depth D 2350 for:

5. The soil salinization monitoring method based on aerospace hyperspectral remote sensing according to claim 1 is characterized in that: The calculation formula of the preset algorithm is: In the above formula, D 2200 The soil absorption depth at 2200 nm for each pixel, D 2350 is the soil absorption depth at 2350 nm for each pixel, W 2350 is the absorption wavelength of each pixel at 2350 nm, k is the proportionality constant, and σ is the standard deviation.

6. The soil salinization monitoring method based on aerospace hyperspectral remote sensing according to claim 5 is characterized in that: The step of obtaining the k value in the calculation formula of the preset algorithm includes: Extract the spectral subset S of each pixel in the range of 2120 nm to 2290 nm 2120-2290 ; According to the spectrum subset S of each pixel 2120-2290 , calculate the corresponding average spectral reflectance value M 2120-2290 ; According to the average spectral reflectance value M of all pixels 2120-2290 , calculate the average value TM 2120-2290 ; According to the average spectral reflectance value M of each pixel 2120-2290 and the average value TM 2120-2290 The K value corresponding to each pixel is calculated as:

7. The soil salinization monitoring method based on aerospace hyperspectral remote sensing according to claim 1 is characterized in that: The steps of performing spatial analysis of the degree of salinization based on the salinization index value of each pixel include: Determining the salinization level corresponding to each pixel according to the salinization index value of each pixel; Each pixel is represented by a different color according to the salinization level corresponding to each pixel, generating a visual information map of the spatial distribution of salinization degree.

8. The soil salinization monitoring method based on aerospace hyperspectral remote sensing according to claim 1 is characterized in that: The steps of performing spatial analysis of the degree of salinization based on the salinization index value of each pixel include: Contour lines are drawn according to the salinization index value of each pixel to generate a corresponding contour map.

9. A soil salinization monitoring system based on aerospace hyperspectral remote sensing, characterized in that: The monitoring system comprises: An acquisition module is used to acquire aerospace hyperspectral remote sensing data of the target soil area; A preprocessing module, used for preprocessing the aerospace hyperspectral remote sensing data to obtain surface reflectance data; An extraction module, used to perform pixel-by-pixel analysis on the surface reflectance data and extract spectral absorption data of each pixel at a preset wavelength; A salinization index calculation module, used to calculate the salinization index value of each pixel based on a preset algorithm according to the spectral absorption data of each pixel at a preset wavelength; The spatial distribution information generation module is used to perform spatial analysis of the degree of salinization based on the salinization index value of each pixel to obtain the spatial distribution information of the degree of salinization in the target soil area.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 8.

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