Cultivated land quality grading and updating method based on hyperspectrum and soil indexes

Abstract

The invention relates to the technical field of cultivated land evaluation, in particular to a cultivated land quality grading and updating method based on hyperspectrum and soil indexes, which comprises the following steps of: respectively and directly responding to water content and roughness by defining and calculating moisture absorption depth and shadow proportion with definite physical significance; the moisture-roughness coupling imbalance index is innovatively constructed, the abnormal degree of the coupling effect of the moisture-roughness coupling imbalance index and the moisture-roughness coupling imbalance index is directly quantified, and coupling interference which is difficult to catch is converted into a computable and analyzable scalar quantity; a partition threshold is set based on the moisture-roughness coupling imbalance index statistical values of all the pixels, and a region corresponding to each pixel is divided into three physical state regions; different correction strategies are adopted for different physical state areas, the influence of moisture-roughness coupling interference on organic matter content inversion is effectively eliminated, index inversion precision is remarkably improved, scientificity and practicability of the method are considered, and accurate and reliable core index support is provided for follow-up cultivated land quality grading.

Description

Technical Field

[0001] This invention relates to the field of arable land assessment technology, and in particular to a method for classifying and updating arable land quality grades based on hyperspectral and soil indicators. Background Technology

[0002] In the field of farmland quality monitoring and evaluation, hyperspectral remote sensing technology has shown great potential due to its ability to rapidly, non-destructively, and over large areas acquire continuous spectral information of the land surface. By establishing quantitative relationships between spectral characteristics and key soil physicochemical indicators (such as organic matter and water content), spatial continuous mapping of soil properties can be achieved, thereby supporting the scientific classification of farmland quality grades.

[0003] In practical hyperspectral soil parameter inversion, there is a long-standing technical bottleneck that has not been effectively resolved: soil moisture content and soil surface roughness have a strong, nonlinear coupling interference on the surface reflectance spectral signal. Increased soil surface roughness leads to more shadows and complex multiple scattering, thereby increasing the effective path length of light in the soil and amplifying the water absorption characteristics. That is, at the same moisture content, the spectrum of a rough surface will show a deeper water absorption valley. Increased soil moisture content will significantly reduce the overall reflectance of the soil in all bands (especially visible light to shortwave infrared). This strong attenuation effect will mask or distort the subtle changes in reflectance caused by surface roughness.

[0004] Existing approaches typically involve directly performing statistical regression or machine learning modeling (e.g., PLSR, random forests) between the full-band spectrum or several spectral indices and target soil parameters (such as organic matter). This method treats coupling interference as random noise; its drawback is that the model is calibrated under specific moisture and roughness conditions, exhibiting extremely poor generalization ability and robustness. Alternatively, it attempts to perform simple corrections for either moisture or roughness, such as adding a moisture index as an additional independent variable to the model, or qualitatively classifying different tillage conditions. However, this approach fails to fundamentally decouple the factors. The fundamental flaw in existing technologies lies in their failure to quantify the intrinsic physical coupling mechanism between soil moisture content and roughness, instead treating it as an independent or simply correlated interference factor.

[0005] Therefore, there is an urgent need to propose a method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators. Summary of the Invention

[0006] The main objective of this invention is to provide a method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators. By defining and calculating water absorption depth and shading ratio, which have clear physical meanings, directly responding to water content and roughness respectively, a novel water-roughness coupling imbalance index is constructed. This directly quantifies the degree of abnormality in the coupling effect between the two, transforming the elusive coupling interference into a calculable and analyzable scalar. Based on the statistical values ​​of the water-roughness coupling imbalance index for all pixels, a partitioning threshold is set, dividing the area corresponding to each pixel into three physical state zones. This partitioning method is adaptive to the data itself and applicable to images from different regions and time phases. Differentiated correction strategies are adopted for different physical state zones, effectively eliminating the impact of water-roughness coupling interference on organic matter content inversion, significantly improving the accuracy of indicator inversion, and balancing the scientific validity and practicality of the method. This provides accurate and reliable core indicator support for subsequent arable land quality grade classification.

[0007] The technical solution of the present invention is as follows: Firstly, a method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators is proposed. This method includes the following steps: S1. Acquire hyperspectral images of the target area. For each pixel in the hyperspectral image, extract the surface reflectance data of the four characteristic bands of the area corresponding to each pixel, and simultaneously acquire the solar zenith angle and the observed zenith angle. S2. For the region corresponding to each pixel in the hyperspectral image, the surface reflectance data is converted into a characteristic index reflecting soil moisture content to obtain the water absorption depth. The shadow ratio of each pixel is calculated to quantify the soil surface roughness, and the moisture-roughness coupling mismatch index is further calculated. S3. Based on the statistical value of the moisture-roughness coupling mismatch index of all pixels, set the partition threshold, and according to the comparison between the moisture-roughness coupling mismatch index of each pixel and the partition threshold, divide the area corresponding to each pixel into three physical state areas, namely the normal coupling area, the moisture-dominated mismatch area and the roughness-dominated mismatch area. S4. For the region corresponding to each pixel in the hyperspectral image, calculate the band ratio to obtain the basic soil organic matter content, and correct the basic soil organic matter content according to the divided physical state zone to obtain the corrected soil organic matter content. S5. Based on the corrected soil organic matter content of the region corresponding to each pixel, a spatial distribution map of soil organic matter content is obtained, and the regional arable land quality grade is classified based on the spatial distribution of soil organic matter content.

[0008] A further improvement of the present invention is that the specific content of S1 is as follows: acquiring a hyperspectral image of the target area, and for each pixel in the hyperspectral image, extracting the surface reflectance data of four characteristic bands of the area corresponding to each pixel, including the average surface reflectance of a predetermined wavelength range near the center wavelength of 660nm, the average surface reflectance of a predetermined wavelength range near the center wavelength of 800nm, the average surface reflectance of a predetermined wavelength range near the center wavelength of 970nm, the average surface reflectance of a predetermined wavelength range near the center wavelength of 1450nm, and the average surface reflectance of a predetermined wavelength range near the center wavelength of 1650nm; simultaneously acquiring the solar zenith angle and the observation zenith angle of the area corresponding to each pixel, wherein the solar zenith angle is the angle between the solar incident direction and the zenith direction of the area corresponding to the pixel, and the observation zenith angle is the angle between the sensor observation direction and the zenith direction of the area corresponding to the pixel.

[0009] A further improvement of the present invention is that step S2 includes the following specific steps: S21. For the region corresponding to each pixel in the hyperspectral image, based on the average surface reflectance in a predetermined wavelength range near the center wavelength of 970 nm and the average surface reflectance in a predetermined wavelength range near the center wavelength of 1650 nm, the continuum reflectance at a wavelength of 1450 nm is calculated by linear interpolation, using the following formula: ; in, Represents a cell The corresponding region's continuum reflectance at a wavelength of 1450 nm Represents a cell The average surface reflectance of the corresponding region within a predetermined wavelength range near the center wavelength of 970nm. Represents a cell The average surface reflectance of the corresponding region in the predetermined wavelength range near the center wavelength of 1650nm. This is the cell row index, with values ​​ranging from 1 to M. This is a cell column index with values ​​from 1 to N; S22. For the region corresponding to each pixel in the hyperspectral image, calculate the water absorption depth using the following formula: ; in, Represents a cell The water absorption depth of the corresponding area. Represents a cell The average surface reflectance of the corresponding region within a predetermined wavelength range near the center wavelength of 1450nm.

[0010] A further improvement of the present invention is that S2 further includes: S23. Extract the set of pixels S corresponding to the smooth surface regions in the hyperspectral image, and calculate the global scaling factor using the following formula: ; in, This is the global scaling factor. Let S be the total number of pixels in the pixel set S. Represents a cell The average surface reflectance of the corresponding region within a predetermined wavelength range near the center wavelength of 660nm. Represents a cell The solar zenith angle of the corresponding region; S24. Calculate the shadow ratio of each pixel to quantify soil surface roughness. The formula is: ; in, For pixels The proportion of shadows; S25. Calculate the moisture-roughness coupling mismatch index, wherein the formula for calculating the moisture-roughness coupling mismatch index is: ; in, Represents a cell The corresponding moisture-roughness coupling mismatch index of the region. Represents a cell The theoretical water absorption depth of the corresponding area.

[0011] A further improvement of this invention is that the formula for calculating the theoretical moisture absorption depth in step S25 is as follows: ; in, For coupling enhancement coefficient, This is the saturation adjustment coefficient.

[0012] A further improvement of the present invention is that step S3 includes the following specific steps: S31. Calculate the standard deviation of the moisture-roughness coupling mismatch index for all pixels in the hyperspectral image. The formula is: ; in, This represents the standard deviation of the moisture-roughness coupling mismatch index for all pixels in the hyperspectral image. The mean value of the moisture-roughness coupling mismatch index for all pixels in the hyperspectral image; S32, Set partition threshold for Based on the comparison between the moisture-roughness coupling mismatch index of each pixel and the partitioning threshold, it is divided into three physical state regions: when At that time, the pixel The corresponding region is marked as the normal coupling region; when At that time, the pixel The corresponding area is marked as the water-dominant imbalance zone; when At that time, the pixel The corresponding region is marked as the roughness-dominated imbalance region.

[0013] A further improvement of the present invention is that step S4 includes the following specific steps: S41. For the region corresponding to each pixel in the hyperspectral image, extract the average surface reflectance of the predetermined wavelength range near the center wavelength of 660nm and the average surface reflectance of the predetermined wavelength range near the center wavelength of 800nm ​​to calculate the band ratio. The formula is as follows: ; in, Represents a cell The band ratio of the corresponding region; S42. Calculate the organic matter content of the basic soil using the following formula: ; in, Represents a cell The organic matter content of the basic soil in the corresponding area. The first regression coefficient, The second regression coefficient; S43. Correct the organic matter content of the basic soil according to the divided physical state zones. For the normal coupling zone, the correction formula is: ; For the water-dominated imbalance region, the correction formula is: ; For the roughness-dominated mismatch region, the correction formula is: ; in, Represents a cell The corrected soil organic matter content for the corresponding region. This is the moisture compensation coefficient. This is the roughness correction coefficient.

[0014] Secondly, a computer-readable storage medium is proposed, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned method for classifying and updating arable land quality grades based on hyperspectral and soil indicators.

[0015] Thirdly, an electronic device is proposed, including a memory for storing instructions and a processor for executing the instructions, causing the device to perform the above-mentioned method for classifying and updating arable land quality grades based on hyperspectral and soil indicators.

[0016] The technical effects of this invention are as follows: A method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators was developed. By defining and calculating water absorption depth and shading ratio, which have clear physical meanings, directly responding to water content and roughness respectively, a novel water-roughness coupling imbalance index was constructed. This index directly quantifies the degree of anomalousness in the coupling effect between the two, transforming the elusive coupling interference into a calculable and analyzable scalar. Based on the statistical values ​​of the water-roughness coupling imbalance index of all pixels, a partitioning threshold was set, dividing the area corresponding to each pixel into three physical state zones. This partitioning method is adaptive to the data itself and applicable to images from different regions and time phases. Differentiated correction strategies were adopted for different physical state zones, effectively eliminating the impact of water-roughness coupling interference on organic matter content inversion, significantly improving the accuracy of indicator inversion, and balancing the scientific validity and practicality of the method. This provides accurate and reliable core indicator support for subsequent arable land quality grade classification. Attached Figure Description

[0017] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators according to Embodiment 1 of the present invention. Detailed Implementation

[0018] Example 1: This example proposes a method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators. By defining and calculating water absorption depth and shading ratio, which have clear physical meanings, directly responding to water content and roughness respectively, a water-roughness coupling imbalance index is innovatively constructed. This directly quantifies the degree of abnormality in the coupling effect between the two, transforming the elusive coupling interference into a calculable and analyzable scalar. Based on the statistical values ​​of the water-roughness coupling imbalance index of all pixels, a partitioning threshold is set, dividing the area corresponding to each pixel into three physical state zones. This partitioning method is adaptive to the data itself and applicable to images from different regions and time phases. Differentiated correction strategies are adopted for different physical state zones, effectively eliminating the impact of water-roughness coupling interference on organic matter content inversion, significantly improving the accuracy of indicator inversion, and balancing the scientific validity and practicality of the method. This provides accurate and reliable core indicator support for subsequent arable land quality grade classification. Specifically, as shown... Figure 1As shown in the figure, the method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators proposed in this embodiment includes the following specific steps: S1. Acquire hyperspectral images of the target area. For each pixel in the hyperspectral image, extract the surface reflectance data of the four characteristic bands of the area corresponding to each pixel, and simultaneously acquire the solar zenith angle and the observed zenith angle.

[0019] In this embodiment, S1 specifically involves: acquiring a hyperspectral image of the target area; for each pixel in the hyperspectral image, extracting the surface reflectance data of four characteristic bands of the area corresponding to each pixel, including the average surface reflectance of a predetermined wavelength range near the center wavelength of 660nm, the average surface reflectance of a predetermined wavelength range near the center wavelength of 800nm, the average surface reflectance of a predetermined wavelength range near the center wavelength of 970nm, the average surface reflectance of a predetermined wavelength range near the center wavelength of 1450nm, and the average surface reflectance of a predetermined wavelength range near the center wavelength of 1650nm; and simultaneously acquiring the solar zenith angle and the observation zenith angle of the area corresponding to each pixel, wherein the solar zenith angle is the angle between the solar incident direction and the zenith direction of the area corresponding to the pixel, and the observation zenith angle is the angle between the sensor observation direction and the zenith direction of the area corresponding to the pixel.

[0020] In this embodiment, the average surface reflectance is extracted within a predetermined wavelength range near the center wavelengths of 660nm, 800nm, 970nm, 1450nm, and 1650nm. The length of the predetermined wavelength range is 20nm. 660nm and 800nm ​​are sensitive to soil organic matter, 970nm and 1450nm are characteristic absorption bands of water, 970nm is a weak absorption band of water, 1450nm is a strong absorption band of water, and 1650nm is often used as a reference band for water absorption characteristics.

[0021] S2. For the region corresponding to each pixel in the hyperspectral image, the surface reflectance data is converted into a characteristic index reflecting soil moisture content to obtain the moisture absorption depth. The shadow ratio of each pixel is calculated to quantify the soil surface roughness, and the moisture-roughness coupling mismatch index is further calculated.

[0022] In this embodiment, S2 includes the following specific steps: S21. For the region corresponding to each pixel in the hyperspectral image, based on the average surface reflectance in a predetermined wavelength range near the center wavelength of 970 nm and the average surface reflectance in a predetermined wavelength range near the center wavelength of 1650 nm, the continuum reflectance at a wavelength of 1450 nm is calculated by linear interpolation, using the following formula: ; in, Represents a cell The corresponding region's continuum reflectance at a wavelength of 1450 nm Represents a cell The average surface reflectance of the corresponding region within a predetermined wavelength range near the center wavelength of 970nm. Represents a cell The average surface reflectance of the corresponding region in the predetermined wavelength range near the center wavelength of 1650nm. This is the cell row index, with values ​​ranging from 1 to M. This is a cell column index with values ​​from 1 to N.

[0023] S22. For the region corresponding to each pixel in the hyperspectral image, calculate the water absorption depth using the following formula: ; in, Represents a cell The water absorption depth of the corresponding area. Represents a cell The average surface reflectance of the corresponding region within a predetermined wavelength range near the center wavelength of 1450nm.

[0024] In this embodiment, the continuum reflectance is the theoretical reflectance after removing the interference of 1450nm water absorption. It only reflects the inherent characteristics of soil roughness and texture, and its function is to separate the coupling effect of water and roughness. The water absorption depth is set to 0-1. The water absorption intensity is quantified by the ratio of actual reflectance to theoretical reflectance. The larger the value, the higher the soil moisture content of the area corresponding to that pixel.

[0025] S23. Extract the set of pixels S corresponding to the smooth surface regions in the hyperspectral image, and calculate the global scaling factor using the following formula: ; in, This is the global scaling factor. Let S be the total number of pixels in the pixel set S. Represents a cell The average surface reflectance of the corresponding region within a predetermined wavelength range near the center wavelength of 660nm. Represents a cell The solar zenith angle of the corresponding region.

[0026] S24. Calculate the shadow ratio of each pixel to quantify soil surface roughness. The formula is: ; in, For pixels The proportion of shadows.

[0027] In this embodiment, the global scaling factor is first determined using the set S of known smooth surface (such as water bodies and road surfaces) pixels in the hyperspectral image. , The theoretical reflectance of an ideal smooth surface at 660 nm is represented by the shadow ratio, which is used to quantify the roughness of the soil surface. The shadow ratio ranges from 0 to 1. The larger the value, the stronger the shadow effect caused by the roughness of the soil surface, which indirectly reflects the higher the roughness of the soil surface.

[0028] S25. Calculate the moisture-roughness coupling mismatch index, wherein the formula for calculating the moisture-roughness coupling mismatch index is: ; in, Represents a cell The corresponding moisture-roughness coupling mismatch index of the region. Represents a cell The theoretical water absorption depth for the corresponding area. The formula for calculating the theoretical water absorption depth is: ;in, For coupling enhancement coefficient, This is the saturation adjustment coefficient.

[0029] In this embodiment, since increased soil surface roughness enhances the water absorption path through multiple scattering, thereby increasing the water absorption depth, but this enhancement effect will saturate, a theoretical formula between the theoretical water absorption depth and soil surface roughness is first established, and the coupling enhancement coefficient is obtained through field sample experiments combined with least squares fitting. With saturation adjustment coefficient The moisture-roughness coupling mismatch index is a dimensionless index that quantifies the synergistic state between moisture and roughness.

[0030] S3. Based on the statistical value of the moisture-roughness coupling mismatch index of all pixels, set the partition threshold, and according to the comparison between the moisture-roughness coupling mismatch index of each pixel and the partition threshold, divide the region corresponding to each pixel into three physical state regions, namely the normal coupling region, the moisture-dominated mismatch region, and the roughness-dominated mismatch region.

[0031] In this embodiment, S3 includes the following specific steps: S31. Calculate the standard deviation of the moisture-roughness coupling mismatch index for all pixels in the hyperspectral image. The formula is: ; in, This represents the standard deviation of the moisture-roughness coupling mismatch index for all pixels in the hyperspectral image. The mean value of the moisture-roughness coupling mismatch index for all pixels in the hyperspectral image; S32, Set partition threshold for Based on the comparison between the moisture-roughness coupling mismatch index of each pixel and the partitioning threshold, it is divided into three physical state regions: when At that time, the pixel The corresponding region is marked as the normal coupling region; when At that time, the pixel The corresponding area is marked as the water-dominant imbalance zone; when At that time, the pixel The corresponding region is marked as the roughness-dominated imbalance region.

[0032] In this embodiment, the normal coupling region means that the spectral signal is reliable and can be directly used for inversion; the moisture-dominated imbalance region and the roughness-dominated imbalance region respectively identify the areas where the spectral signal is severely interfered with by specific factors.

[0033] S4. For the region corresponding to each pixel in the hyperspectral image, calculate the band ratio to obtain the basic soil organic matter content, and correct the basic soil organic matter content according to the divided physical state zone to obtain the corrected soil organic matter content.

[0034] In this embodiment, S4 includes the following specific steps: S41. For the region corresponding to each pixel in the hyperspectral image, extract the average surface reflectance of the predetermined wavelength range near the center wavelength of 660nm and the average surface reflectance of the predetermined wavelength range near the center wavelength of 800nm ​​to calculate the band ratio. The formula is as follows: ; in, Represents a cell The band ratio of the corresponding region; In this embodiment, the band ratio is a dimensionless number. 660nm (red light) has strong absorption of organic matter, while 800nm ​​(near infrared) has weak absorption of organic matter. The ratio between the two is positively correlated with the content of organic matter.

[0035] S42. Calculate the organic matter content of the basic soil using the following formula: ; in, Represents a cell The organic matter content of the basic soil in the corresponding area. The first regression coefficient, The second regression coefficient; the basic soil organic matter content is a preliminary inversion value that does not consider the interference of moisture-roughness coupling, and the first regression coefficient. With the second regression coefficient The results were obtained through field sample experiments combined with least squares fitting.

[0036] S43. Correct the organic matter content of the basic soil according to the divided physical state zones. For the normal coupling zone, the correction formula is: ; For the water-dominated imbalance region, the correction formula is: ; For the roughness-dominated mismatch region, the correction formula is: ; in, Represents a cell The corrected soil organic matter content for the corresponding region. This is the moisture compensation coefficient. This is the roughness correction coefficient.

[0037] In this embodiment, for the water-dominated imbalance zone, due to abnormally active water, water absorption characteristics distort the spectral information related to organic matter, leading to an overestimation of the organic matter content in the basic soil. Therefore, it is necessary to combine the water-roughness coupling imbalance index proportionally. To reduce the surface condition, for roughness-dominated imbalance regions, a large absolute value of the moisture-roughness coupling imbalance index indicates an abnormal surface condition, making the spectral signal unreliable. Therefore, a downward correction to the organic matter content of the base soil is also necessary. The correction intensity is proportional to the severity of the imbalance (the absolute value of the moisture-roughness coupling imbalance index), with a proportionality coefficient of [value missing]. .

[0038] S5. Based on the corrected soil organic matter content of the region corresponding to each pixel, a spatial distribution map of soil organic matter content is obtained, and the regional arable land quality grade is classified based on the spatial distribution of soil organic matter content.

[0039] Example 2: This example provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned method for classifying and updating arable land quality grades based on hyperspectral and soil indicators by calling the computer program stored in the memory.

[0040] The electronic device can vary considerably depending on its configuration and performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the method for classifying and updating arable land quality grades based on hyperspectral and soil indicators provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0041] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0042] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0043] This invention is described with reference to flowchart illustrations and block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and block diagrams, as well as combinations of blocks in the flowchart illustrations and block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0044] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and boxes Figure 1 The steps of the function specified in one or more boxes.

[0045] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators, characterized in that: The specific steps include the following: S1. Acquire hyperspectral images of the target area. For each pixel in the hyperspectral image, extract the surface reflectance data of the four characteristic bands of the area corresponding to each pixel, and simultaneously acquire the solar zenith angle and the observed zenith angle. S2. For the region corresponding to each pixel in the hyperspectral image, the surface reflectance data is converted into a characteristic index reflecting soil moisture content to obtain the water absorption depth. The shadow ratio of each pixel is calculated to quantify the soil surface roughness, and the moisture-roughness coupling mismatch index is further calculated. S3. Based on the statistical value of the moisture-roughness coupling mismatch index of all pixels, set the partition threshold, and divide the area corresponding to each pixel into three physical state areas according to the comparison between the moisture-roughness coupling mismatch index of each pixel and the partition threshold: normal coupling area, moisture-dominated mismatch area and roughness-dominated mismatch area. S4. For the region corresponding to each pixel in the hyperspectral image, calculate the band ratio to obtain the basic soil organic matter content, and correct the basic soil organic matter content according to the divided physical state zone to obtain the corrected soil organic matter content. S5. Based on the corrected soil organic matter content of the region corresponding to each pixel, a spatial distribution map of soil organic matter content is obtained, and the regional arable land quality grade is classified based on the spatial distribution of soil organic matter content.

2. The method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators according to claim 1, characterized in that: The specific content of S1 is as follows: acquire hyperspectral images of the target area, and for each pixel in the hyperspectral image, extract the surface reflectance data of four characteristic bands of the area corresponding to each pixel, including the average surface reflectance of the predetermined wavelength range near the center wavelength of 660nm, the average surface reflectance of the predetermined wavelength range near the center wavelength of 800nm, the average surface reflectance of the predetermined wavelength range near the center wavelength of 970nm, the average surface reflectance of the predetermined wavelength range near the center wavelength of 1450nm, and the average surface reflectance of the predetermined wavelength range near the center wavelength of 1650nm. Simultaneously acquire the solar zenith angle and the observation zenith angle of the area corresponding to each pixel. The solar zenith angle is the angle between the solar incident direction and the zenith direction of the area corresponding to the pixel, and the observation zenith angle is the angle between the sensor observation direction and the zenith direction of the area corresponding to the pixel.

3. The method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators according to claim 2, characterized in that: S2 includes the following specific steps: S21. For the region corresponding to each pixel in the hyperspectral image, based on the average surface reflectance in a predetermined wavelength range near the center wavelength of 970 nm and the average surface reflectance in a predetermined wavelength range near the center wavelength of 1650 nm, the continuum reflectance at a wavelength of 1450 nm is calculated by linear interpolation, using the following formula: ； in, Represents a cell The corresponding region's continuum reflectance at a wavelength of 1450 nm Represents a cell The average surface reflectance of the corresponding region within a predetermined wavelength range near the center wavelength of 970nm. Represents a cell The average surface reflectance of the corresponding region in the predetermined wavelength range near the center wavelength of 1650nm. This is the cell row index, with values ​​ranging from 1 to M. This is a cell column index with values ​​from 1 to N; S22. For the region corresponding to each pixel in the hyperspectral image, calculate the water absorption depth using the following formula: ； in, Represents a cell The water absorption depth of the corresponding area. Represents a cell The average surface reflectance of the corresponding region within a predetermined wavelength range near the center wavelength of 1450nm.

4. The method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators according to claim 3, characterized in that: S2 further includes: S23. Extract the set of pixels S corresponding to the smooth surface regions in the hyperspectral image, and calculate the global scaling factor using the following formula: ； in, This is the global scaling factor. Let S be the total number of pixels in the pixel set S. Represents a cell The average surface reflectance of the corresponding region within a predetermined wavelength range near the center wavelength of 660nm. Represents a cell The solar zenith angle of the corresponding region; S24. Calculate the shadow ratio of each pixel to quantify soil surface roughness. The formula is: ； in, For pixels The proportion of shadows; S25. Calculate the moisture-roughness coupling mismatch index, wherein the formula for calculating the moisture-roughness coupling mismatch index is: ； in, Represents a cell The corresponding moisture-roughness coupling mismatch index of the region. Represents a cell The theoretical water absorption depth of the corresponding area.

5. The method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators according to claim 4, characterized in that: The formula for calculating the theoretical water absorption depth in S25 is as follows: ； in, For coupling enhancement coefficient, This is the saturation adjustment coefficient.

6. The method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators according to claim 5, characterized in that: S3 includes the following specific steps: S31. Calculate the standard deviation of the moisture-roughness coupling mismatch index for all pixels in the hyperspectral image. The formula is: ； in, This represents the standard deviation of the moisture-roughness coupling mismatch index for all pixels in the hyperspectral image. The mean value of the moisture-roughness coupling mismatch index for all pixels in the hyperspectral image; S32, Set partition threshold for Based on the comparison between the moisture-roughness coupling mismatch index of each pixel and the partitioning threshold, it is divided into three physical state regions: when At that time, the pixel The corresponding region is marked as the normal coupling region; when At that time, the pixel The corresponding area is marked as the water-dominant imbalance zone; when At that time, the pixel The corresponding region is marked as the roughness-dominated imbalance region.

7. The method for classifying and updating arable land quality grades based on hyperspectral data and soil indicators according to claim 6, characterized in that: S4 includes the following specific steps: S41. For the region corresponding to each pixel in the hyperspectral image, extract the average surface reflectance of the predetermined wavelength range near the center wavelength of 660nm and the average surface reflectance of the predetermined wavelength range near the center wavelength of 800nm ​​to calculate the band ratio. The formula is as follows: ； in, Represents a cell The band ratio of the corresponding region; S42. Calculate the organic matter content of the basic soil using the following formula: ； in, Represents a cell The organic matter content of the basic soil in the corresponding area. The first regression coefficient, The second regression coefficient; S43. Correct the organic matter content of the basic soil according to the divided physical state zones. For the normal coupling zone, the correction formula is: ； For the water-dominated imbalance region, the correction formula is: ； For the roughness-dominated mismatch region, the correction formula is: ； in, Represents a cell The corrected soil organic matter content for the corresponding region. This is the moisture compensation coefficient. This is the roughness correction coefficient.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for classifying and updating arable land quality grades based on hyperspectral and soil indicators as described in any one of claims 1-7.

9. An electronic device, characterized in that, It includes a memory for storing instructions; and a processor for executing the instructions, causing the device to perform the method for classifying and updating arable land quality grades based on hyperspectral and soil indicators as described in any one of claims 1 to 7.

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