Soil Rated Cone Index Inversion Method for Mobile Multi-source Remote Sensing Data

Abstract

The present invention belongs to the field of remote sensing information processing technology, and specifically relates to a method for inverting the rated cone index of soil from multi-source remote sensing data. The method comprises the following steps: 1) acquiring different remote sensing images of the same area, and extracting vegetation index data (NDVI) and soil clay content (CLAY) from the different remote sensing images; 2) dividing regions according to NDVI, and calculating a comprehensive drought index (CDI) in different regions using different data and different calculation methods; 3) inputting the comprehensive drought index (CDI) of soil into a correlation regression model between the comprehensive drought index (CDI) of soil and soil moisture content to obtain soil moisture data; and 4) calculating the rated cone index (RCI) of soil based on the soil moisture data and the clay content data (CLAY). The method of the present invention does not require personnel to conduct on-site data measurement. Instead, it utilizes remote sensing images from different sources to perform data calculations to obtain the rated cone index of soil, which is more efficient and accurate, and solves the problem of high labor and time costs associated with field measurements.

Description

Technical Field

[0001] The invention belongs to the technical field of remote sensing information processing, and in particular relates to a soil rated cone index inversion method for mobile multi-source remote sensing data. Background Art

[0002] When vehicles complete certain practical tasks, numerous ground factors influence their maneuverability. A completely unfamiliar terrain presents a significant challenge to vehicle maneuverability, making it crucial to evaluate off-road performance using the Soil Information Index (SI). In the early 1940s, researchers combined the mechanical properties that comprise soil resistance and thrust characteristics (ignoring the influence of loading area) into a single value, called the Cone Index (CI), which comprehensively reflects the mechanical and physical properties of the soil. Research has shown that the CI reflects soil strength and plays a key role in vehicle drivability.

[0003] Currently, the most commonly used method for measuring soil cone index is field measurement using instruments. These instruments are called cone penetrometers, also known as cone penetrometers, soil compaction meters, soil static load bearing meters, and soil penetrometers. The development of cone penetrometers has continuously evolved, with a wide variety of instruments emerging, ranging from manual to motorized penetration, and from visual readings to electrical recording. Traditional cone penetrometers have evolved over time to mechanical electrical cone penetrometers that utilize microcomputers for control, recording, and data processing. These instruments ensure uniform penetration speeds and improve measurement accuracy, making them suitable for testing indoor soil trenches and field soils. However, these instruments still rely on point-by-point measurement, rather than continuous measurement, and are labor-intensive and time-consuming. Summary of the Invention

[0004] The purpose of the present invention is to provide a soil rated cone index inversion method for mobile multi-source remote sensing data to solve the problem of high manpower and time costs in field measurement of soil cone index.

[0005] To solve the above technical problems, the present invention provides a soil rated cone index inversion method for mobile multi-source remote sensing data, comprising the following steps:

[0006] 1) Obtain different remote sensing images of the same area and extract vegetation index data (NDVI) and soil clay content (CLAY) from these images;

[0007] 2) Comparing the vegetation index data NDVI with a set threshold a: when NDVI ≥ a, calculating the soil comprehensive drought index CDI based on the vegetation index data NDVI and the vegetation canopy temperature data LST in the remote sensing image obtained for the same area; when NDVI < a, calculating the soil comprehensive drought index CDI based on the surface temperature data T and the surface albedo data A in the remote sensing image obtained for the same area;

[0008] 3) Inputting the soil comprehensive drought index CDI into the correlation regression model between the soil comprehensive drought index CDI and soil moisture content to obtain soil moisture content data;

[0009] 4) Calculate the soil rated cone index RCI based on the soil moisture content data and the soil clay content data CLAY.

[0010] The beneficial effects are as follows: The present invention utilizes remote sensing images acquired from various sources for data processing to extract vegetation index data (NDVI) and soil clay content (CLAY). Based on the NVDI, the region is determined to be dense or sparse. Different data and calculation methods are used to determine the soil comprehensive drought index (CDI) for different situations. A correlation regression model is then used to calculate soil moisture data, which can then be used to calculate the rated soil cone index using this soil moisture data and the soil clay content data (CLAY). The entire process no longer requires on-site data measurement. Using different remote sensing images and performing corresponding calculations, the rated soil cone index can be calculated, resulting in faster, more efficient, and more accurate calculations, addressing the high labor and time costs associated with field measurements. Furthermore, calculating the comprehensive soil drought index (CDI) by region makes it more accurate. Overall, the method is simple, has excellent inversion results, and can achieve rapid, large-scale, and near-real-time acquisition of the soil cone index.

[0011] Furthermore, in step 1), the soil clay content data CLAY, the vegetation canopy temperature data LST, the surface temperature data T, and the surface albedo data A have the same resolution as the vegetation index data NDVI.

[0012] The beneficial effect is that the obtained data have the same resolution, so that the data can be calculated at a same precision, thereby making the obtained calculation results more accurate.

[0013] Furthermore, in step 1), the surface albedo data A is:

[0014] A=0.16B1+0.291B2+0.243B3+0.116B4+0.112B5+0.081B7-0.0015

[0015] Among them, B1-B5 and B7 correspond to the data of the coastal band, blue light band, green light band, red light band, near-infrared band, and short-wave infrared band respectively.

[0016] Its beneficial effects are: calculating the surface albedo data based on the data of each band, providing data support for the calculation of subsequent data, making the calculation faster and more accurate.

[0017] Furthermore, in step 1), the vegetation index data NDVI is:

[0018]

[0019] Among them, red is the data of the red light band, and nir is the data of the near infrared band.

[0020] Its beneficial effect is that NDVI data can be calculated through red light band data and near-infrared band data, providing a data basis for regional division.

[0021] Furthermore, in step 2), when NDVI ≥ a, the VSWI model is used to calculate the soil comprehensive drought index CDI:

[0022] VSWI=NDVI / LST

[0023]

[0024] Among them, VSWI is the vegetation water supply index, LST is the vegetation canopy temperature; CDI i is the comprehensive drought index at any pixel point i, VSWI i is the vegetation water supply index of any pixel point i, VSWI max and VSWI min are the maximum and minimum values ​​of the vegetation water supply index, respectively.

[0025] Its beneficial effect is: for areas with NDVI ≥ a, the VSWI model is used to calculate the comprehensive soil drought index of the area, making the calculation of the comprehensive drought index in the area more accurate.

[0026] Furthermore, in step 2), when NDVI < a, the ATI model is used to calculate the soil comprehensive drought index CDI:

[0027] ATI=(1-A) / (T max -T min )

[0028]

[0029] Among them, ATI is the apparent thermal inertia, A is the full-band albedo, T max and T minare the maximum and minimum temperatures of the day; CDI i is the comprehensive drought index at any pixel point i, ATI i is the apparent thermal inertia of any pixel point i, ATI max and ATI min are the maximum and minimum values ​​of the apparent thermal inertia, respectively.

[0030] The beneficial effect is that for areas where NDVI is less than a, the ATI model is used to calculate the comprehensive soil drought index of the area, making the calculation of the comprehensive drought index in the area more accurate.

[0031] Furthermore, the threshold a is set to 0.33.

[0032] Furthermore, in step 3), the correlation regression model between the comprehensive soil drought index CDI and the soil water content is a correlation regression equation, and the fitting method of the correlation regression equation is: extracting the real soil water content site data WS and the calculated comprehensive soil drought index CDI, making a scatter plot and fitting the correlation regression equation.

[0033] Its beneficial effects are as follows: the correlation regression model is a correlation regression equation. By extracting the real soil water content site data, combining it with the calculated soil comprehensive drought index, making a scatter plot, and performing data fitting, the correlation regression equation can be obtained. When calculating the soil cone index, there is no need to measure the soil water content data on the spot. The comprehensive drought index is input into the regression equation to calculate the soil moisture data, which solves the problem of high time and labor costs caused by field measurement of soil moisture content.

[0034] Furthermore, in step 4), the formula for calculating the soil rated cone index is:

[0035]

[0036] Where RCI stands for Soil Rated Cone Index, C stands for soil clay content, and M stands for soil moisture content.

[0037] Its beneficial effect is that the rated cone index of soil can be calculated by using the clay content and water content of soil. The method is simple, the calculation speed is fast and the effect is good. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a technical flow chart of the method of the present invention;

[0039] Figure 2 is a flow chart of the method of the present invention;

[0040] Figure 3 It is the NDVI data map of the present invention;

[0041] Figure 4 It is the vegetation canopy temperature data graph of the present invention;

[0042] Figure 5 is a surface temperature data graph of the present invention;

[0043] Figure 6 is a surface albedo data map of the present invention;

[0044] Figure 7 It is a soil clay content data graph of the present invention;

[0045] Figure 8 It is a soil moisture content data graph of the present invention;

[0046] Figure 9 It is the RCI Soil Rated Cone Index chart of the present invention. DETAILED DESCRIPTION

[0047] The key point of this invention is that the entire process no longer requires on-site data measurement. Instead, the soil rated cone index can be calculated using various remote sensing images and corresponding calculations. This calculation is faster, more efficient and more accurate, eliminating the labor-intensive and time-consuming problem of field measurements. Furthermore, the calculation of the soil comprehensive drought index (CDI) is performed on a regional basis, making it more accurate.

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Example of a soil rated cone index inversion method for mobile multi-source remote sensing data:

[0050] The present invention is a method embodiment for inverting soil rated cone index from multi-source remote sensing data for mobile applications. The overall method flow is as follows: Figure 1 and Figure 2 shown.

[0051] 1. Obtain different remote sensing images of the same area and extract vegetation index data NDVI, vegetation canopy temperature data LST, surface temperature data T, surface albedo data A, and soil clay content CLAY from different remote sensing images.

[0052] (1) Obtaining original data. Vegetation index is a factor that characterizes the growth status and spatial distribution density of vegetation. Common vegetation indices include NDVI, which is the normalized vegetation index. The full name is Normalized Difference Vegetation Index. The vegetation index data NDVI comes from the MOD09A1 dataset, such as Figure 3As shown; the vegetation canopy temperature data LST and the surface temperature data T are derived from the MOD11A1 dataset, where the vegetation canopy temperature LST is derived from the band Land-surfacetemperature, as shown Figure 4 As shown in Figure 2, the surface temperature T comes from the emissivity of Band 31, as Figure 5 As shown; the surface albedo data is obtained based on MCD43A3 data. The ability of the planetary surface to reflect solar radiation is also different. The reflectivity is usually measured by albedo, such as Figure 6 As shown in the figure, the soil clay content data CLAY comes from the Harmonized World Soil Database (HWSD), which contains data on various physical and chemical properties of the soil. The clay content in the soil is an important indicator of the degree of soil adhesion and provides data support for obtaining the water content in the soil, such as Figure 7 shown.

[0053] (2) Perform data calculations.

[0054] ①Calculate albedo A:

[0055] A=0.16B1+0.291B2+0.243B3+0.116B4+0.112B5+0.081B7-0.0015

[0056] Among them, B1-B5 and B7 correspond to the data of the coastal band, blue light band, green light band, red light band, near-infrared band, and short-wave infrared band respectively.

[0057] ②Calculate NDVI:

[0058]

[0059] Among them, red is the data of the red light band, and nir is the data of the near infrared band.

[0060] 2. Determine the resolution of NDVI and resample the multi-source remote sensing images. Resample the vegetation canopy temperature data (LST), surface temperature data (T), surface albedo data (A), and soil clay content data (CLAY) to ensure that the resampled data has the same resolution as the vegetation index data (NDVI).

[0061] 3. Compare the vegetation index data NDVI with the set threshold a: when NDVI ≥ a, calculate the soil comprehensive drought index CDI based on the vegetation index data NDVI and the vegetation canopy temperature data LST in the remote sensing image obtained for the same area; when NDVI < a, calculate the soil comprehensive drought index CDI based on the surface temperature data T and the surface albedo data A in the remote sensing image obtained for the same area.

[0062] To calculate the comprehensive soil drought index (CDI), it is necessary to classify the vegetation cover. NDVI reflects the vegetation cover. Setting the NDVI threshold is used to distinguish vegetation cover types so that the appropriate index for different cover types can be used to invert soil moisture, thereby improving the accuracy of soil moisture inversion for the entire region. In this embodiment, the threshold a is set to 0.33.

[0063] (1) The area corresponding to the value of NDVI ≥ 0.33 is regarded as the dense vegetation area. Based on the vegetation index data NDVI and the vegetation canopy temperature data LST, the soil comprehensive drought index CDI is calculated using the VSWI model:

[0064] VSWI=NDVI / LST

[0065]

[0066] Among them, VSWI is the vegetation water supply index, LST is the vegetation canopy temperature; CDI i is the comprehensive drought index at any pixel point i, VSWI i is the vegetation water supply index of any pixel point i, VSWI max and VSWI min are the maximum and minimum values ​​of the vegetation water supply index, respectively.

[0067] (2) The area corresponding to the NDVI value < 0.33 is regarded as the sparse vegetation area. Based on the surface temperature data T and the surface albedo data A, the soil comprehensive drought index CDI is calculated using the ATI model (without considering factors such as solar altitude angle and latitude):

[0068] ATI=(1-A) / (T max -T min )

[0069]

[0070] Where ATI is the apparent thermal inertia, A is the full-band albedo, which is obtained from the reflectivity of channels 1 and 2 of MODIS data; T max and T min The maximum and minimum temperatures of the day can be obtained from the surface temperature of channel 31 of MODIS data; CDI i is the comprehensive drought index at any pixel point i, ATI i is the apparent thermal inertia of any pixel point i, ATI i is the apparent thermal inertia of any pixel point i, ATI max and ATI min are the maximum and minimum values ​​of the apparent thermal inertia, respectively.

[0071] 4. Extract real soil moisture data from the station and perform regression analysis using the calculated comprehensive drought index (CDI) to obtain a correlation regression model. This model is a correlation regression equation. The specific fitting process is to extract the real soil moisture data (WS) from the station and the calculated comprehensive drought index (CDI), create a scatter plot, and then fit the correlation regression equation.

[0072] 5. Obtain different remote sensing images of the predicted area, extract vegetation index data NDVI, vegetation canopy temperature data LST, surface temperature data T, and surface albedo data A from different remote sensing images, calculate the comprehensive drought index CDI according to steps 1 to 3, and substitute it into the regression equation in step 4 to obtain soil moisture data, such as Figure 8 As shown; obtain the soil clay content data CLAY, combine it with the calculated soil moisture data, and calculate the soil rated cone index RCI.

[0073] The ratio of the rated cone index (RCI) to the initially measured cone index (CI) is called the remodeling index (RI), and the relationship is:

[0074] RCI=CI×RI

[0075] If the initial cone index is 80 and the remodeling index is 1, the rated cone index is 80.

[0076] It can be seen that the initially measured cone index indicates the bearing capacity of the soil layer, while the rated cone index indicates the minimum bearing capacity of the soil after reshaping. The two are comprehensive parameters of the soil for judging the soft soil passability of vehicles. Collins calculated the rated cone index (RCI) of soil in 1971 and outlined the complete test process. The rated cone index of soil is calculated according to the Collins formula (e.g. Figure 9 shown):

[0077]

[0078] Where RCI stands for Soil Rated Cone Index, C stands for soil clay content, and M stands for soil moisture content.

[0079] The present invention's method for inverting the soil nominal cone index from multi-source remote sensing data for mobile applications can be implemented using computer program code. This computer program code is implemented based on an apparatus for implementing the method for inverting the soil nominal cone index from multi-source remote sensing data for mobile applications. The apparatus includes a memory, a processor, and an internal bus. The processor and memory communicate and exchange data with each other via the internal bus. The processor is configured to execute program instructions stored in the memory to implement the method for inverting the soil nominal cone index from multi-source remote sensing data for mobile applications described in the method embodiments of the present invention.

[0080] The processor may be a microprocessor MCU, a programmable logic device FPGA or other processing devices.

[0081] The memory can be various types of memories that use electrical energy to store information, such as RAM, ROM, etc.; it can also be various types of memories that use magnetic energy to store information, such as hard disks, floppy disks, magnetic tapes, magnetic core memories, bubble memories, USB flash drives, etc.; it can also be various types of memories that use optical methods to store information, such as CDs, DVDs, etc.; of course, it can also be other types of memories, such as quantum memories, graphene memories, etc.

[0082] In summary, the present invention has the following characteristics:

[0083] (1) The present invention uses remote sensing images acquired from different sources to perform data processing to extract vegetation index data NDVI and soil clay content CLAY. Based on the NVDI, it is determined whether the area is dense or sparse. Different data and different calculation methods are used to determine the soil comprehensive drought index CDI in different situations. Then, the soil moisture data can be obtained by calculation using the correlation regression model. Then, the soil rated cone index can be calculated using the soil moisture data and the soil clay content data CLAY. The entire process no longer requires people to go to the site to measure data. Using different remote sensing images, the soil rated cone index can be calculated by performing corresponding calculations. The calculation is faster, more efficient and more accurate, solving the problem of high manpower and time costs caused by field measurements. Moreover, the calculation of the soil comprehensive drought index CDI is carried out in different regions, making the calculation of the comprehensive drought index CDI more accurate. Overall, the method is simple, has good inversion effect, and can achieve rapid, large-scale, and near-real-time acquisition of the soil cone index.

[0084] (2) The correlation regression model is a correlation regression equation. By extracting the actual soil water content site data, combining it with the calculated soil comprehensive drought index, making a scatter plot, and performing data fitting, the correlation regression equation can be obtained. When calculating the soil cone index, there is no need to measure the soil water content data on the spot. The comprehensive drought index is input into the regression equation to calculate the soil water content data, which solves the problem of high time and labor costs caused by field measurement of soil water content.

Claims

1. A soil rated cone index inversion method for multi-source remote sensing data for mobile applications, characterized in that: The following steps are involved: 1) Obtain different remote sensing images of the same area and extract vegetation index data (NDVI) and soil clay content (CLAY) from the different remote sensing images; 2) Comparing the vegetation index data NDVI with a set threshold a: when NDVI ≥ a, calculating the soil comprehensive drought index CDI based on the vegetation index data NDVI and the vegetation canopy temperature data LST in the remote sensing image obtained for the same area; when NDVI < a, calculating the soil comprehensive drought index CDI based on the surface temperature data T and the surface albedo data A in the remote sensing image obtained for the same area; 3) Inputting the soil comprehensive drought index CDI into the correlation regression model between the soil comprehensive drought index CDI and soil moisture content to obtain soil moisture content data; 4) Calculate the soil rated cone index RCI based on the soil moisture content data and the soil clay content data CLAY.

2. The soil rated cone index inversion method for mobile multi-source remote sensing data according to claim 1 is characterized in that: In step 1), the soil clay content data CLAY, the vegetation canopy temperature data LST, the surface temperature data T, and the surface albedo data A have the same resolution as the vegetation index data NDVI.

3. The soil rated cone index inversion method for mobile multi-source remote sensing data according to claim 1 is characterized in that: In step 1), the surface albedo data A is: A=0.16B1+0.291B2+0.243B3+0.116B4+0.112B5+0.081B7-0.0015 Among them, B1-B5 and B7 correspond to the data of the coastal band, blue light band, green light band, red light band, near-infrared band, and short-wave infrared band respectively.

4. The soil rated cone index inversion method for mobile multi-source remote sensing data according to claim 1 is characterized in that: In step 1), the vegetation index data NDVI is: Among them, red is the data of the red light band, and nir is the data of the near infrared band.

5. The soil rated cone index inversion method for mobile multi-source remote sensing data according to claim 1 is characterized in that: In step 2), when NDVI ≥ a, the VSWI model is used to calculate the soil comprehensive drought index CDI: VSWI=NDVI / LST Among them, VSWI is the vegetation water supply index, LST is the vegetation canopy temperature; CDI i is the comprehensive drought index at any pixel point i, VSWI i is the vegetation water supply index of any pixel point i, VSWI max and VSWI min are the maximum and minimum values ​​of the vegetation water supply index, respectively.

6. The soil rated cone index inversion method for mobile multi-source remote sensing data according to claim 1 is characterized in that: In step 2), when NDVI < a, the ATI model is used to calculate the soil comprehensive drought index CDI: AND=(1-A) / (T max -T min ) Among them, ATI is the apparent thermal inertia, A is the full-band albedo, T max and T min are the maximum and minimum temperatures of the day; CDI i is the comprehensive drought index at any pixel point i, ATI i is the apparent thermal inertia of any pixel point i, ATI max and ATI min are the maximum and minimum values ​​of the apparent thermal inertia, respectively.

7. The soil rated cone index inversion method for mobile multi-source remote sensing data according to claim 1, 5 or 6, characterized in that: Set the threshold a=0.

33.

8. The soil rated cone index inversion method for mobile multi-source remote sensing data according to claim 1 is characterized in that: In step 3), the correlation regression model between the comprehensive soil drought index CDI and the soil water content is a correlation regression equation, and the fitting method of the correlation regression equation is: extracting the real soil water content site data WS and the calculated comprehensive soil drought index CDI, making a scatter plot and fitting the correlation regression equation.

9. The soil rated cone index inversion method for mobile multi-source remote sensing data according to claim 1, characterized in that: In step 4), the formula for calculating the soil rated cone index is: Where RCI stands for Soil Rated Cone Index, C stands for soil clay content, and M stands for soil moisture content.

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