Desertification evaluation method based on process

Abstract

The invention discloses a process-based desertification evaluation method, and relates to the technical field of cultivated land resource detection. According to the method, remote sensing image data of a desertification region, grassland resource sample plot survey data, surface soil sample salt content analysis and test data, monthly accumulated rainfall data, annual accumulated rainfall data, deep soil sample analysis and test texture data, deep soil organic carbon content data and digital elevation model data are integrated; according to the method, comprehensive evaluation of vegetation degradation, soil salinization, water and soil loss, land desertification and the like is carried out, delineation of the desertification area is further realized, the evaluation precision of the desertification area is improved, accurate data and technical method support is provided for proposing suggestions of land resource protection and utilization of the desertification area, and the method is suitable for popularization and application. And the contradictory problem of protection, prevention, development and utilization of the desertification region is solved.

Description

Technical Field

[0001] The present application relates to the technical field of cultivated land resource detection, and in particular to a process-based desertification evaluation method. Background Art

[0002] The current lack of research on the classification, distribution, and grading of desertification is an objective reality. In a narrow sense, desertification refers to the desertification of land caused by climate or human factors, resulting in a reduction in land productivity. In a broader sense, desertification refers to land degradation in arid, semi-arid, and sub-humid arid regions (climates with a Humidity Index between 0.05 and 0.65) caused by a variety of factors, including climate variability and human activities. This includes both vegetation degradation and soil degradation (such as desertification, soil salinization, and soil erosion). Currently, there is no unified standard for desertification classification. Most methods use a comprehensive evaluation method that combines natural and human factors or classify based on dominant factors. The thresholds for classification in relevant standards and specifications are primarily based on subjective perceptions, resulting in significant variation in thresholds, a large number of indicators, and difficulty obtaining original data sources. Current desertification assessments are mostly based on surface or shallow (less than 20 cm) two-dimensional spatial distribution, making the results less reliable.

[0003] Soil properties vary significantly across different types of desertification. Not all desertified areas are barren. For example, while the Songnen Plain in Northeast China is primarily affected by baking soda salinization, some areas still have slightly alkaline soils, high organic matter content, and relatively fertile soils suitable for arable land. The current amount of land available for arable land remains unclear, necessitating the urgent need for scientific assessment methods to clarify the availability of arable land in desertified areas and to implement scientific desertification prevention and control measures. Summary of the Invention

[0004] The purpose of this application is to provide a process-based desertification assessment method to improve the accuracy of desertification area assessment and resolve the contradiction between protection, prevention and development and utilization of desertified areas.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] This application provides a process-based desertification assessment method, including:

[0007] Obtain remote sensing image data of desertified areas, grassland resource sample survey data, surface soil sample salt content analysis and testing data, monthly cumulative precipitation data, annual cumulative precipitation data, deep soil sample analysis and testing texture data, deep soil organic carbon content data, and digital elevation model data;

[0008] Determining the distribution of vegetation degradation levels in the desertified area based on the remote sensing image data and grassland resource sample plot survey data;

[0009] Determining the distribution of soil salinization in the desertified area based on the remote sensing image data and the surface soil sample salt content analysis test data;

[0010] Determining the distribution of soil and water loss extent in the desertified area based on the remote sensing image data, the monthly cumulative precipitation data, the annual cumulative precipitation data, the texture data of the deep soil sample analysis test, the deep soil organic carbon content data, and the digital elevation model data;

[0011] Determining the distribution of land desertification in the desertified area based on the texture data of the deep soil sample analysis test;

[0012] Determine the desertification degree distribution in desertified areas based on the distribution of vegetation degradation degree, soil salinization degree, soil erosion degree and land sandification degree in desertified areas;

[0013] Based on the distribution of desertification levels in the desertified area, desertification prevention and control areas and reserve arable land resource areas in the desertified area are determined; the reserve arable land resource areas are black soil resource areas that can be used as reserve arable land. According to the specific embodiments provided in this application, this application has the following technical effects.

[0014] The present application provides a process-based desertification assessment method. The present application integrates remote sensing image data of desertified areas, grassland resource plot survey data, surface soil sample salt content analysis and test data, monthly cumulative precipitation data, annual cumulative precipitation data, deep soil sample analysis and test texture data, deep soil organic carbon content data and digital elevation model data, and conducts a comprehensive assessment of vegetation degradation, soil salinization, soil erosion, land desertification and other aspects, thereby achieving the delineation of desertified areas. The present application improves the accuracy of desertification area assessment, provides accurate data and technical method support for the proposal of land resource protection and utilization in desertified areas, and resolves the contradiction between protection, prevention and control and development and utilization of desertified areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of a process-based desertification assessment method provided in one embodiment of the present application.

[0017] Figure 2A schematic diagram of the desertification process provided in one embodiment of the present application.

[0018] Figure 3 An overall technical flow chart provided for one embodiment of the present application.

[0019] Figure 4 A schematic diagram of a three-dimensional spectral feature model provided in one embodiment of the present application.

[0020] Figure 5 This is a distribution diagram of the collection data source points provided in one embodiment of the present application.

[0021] Figure 6 A schematic diagram of determining the vegetation degradation threshold segmentation points provided in one embodiment of the present application.

[0022] Figure 7 A vegetation degradation distribution map provided in an embodiment of the present application.

[0023] Figure 8 A schematic diagram of determining the salinization threshold segmentation point provided in one embodiment of the present application.

[0024] Figure 9 This is a salinization distribution map provided in one embodiment of the present application.

[0025] Figure 10 A soil erosion distribution map provided for an embodiment of the present application.

[0026] Figure 11 This is a land desertification distribution map provided in one embodiment of the present application.

[0027] Figure 12 This is a desertification distribution map of Songyuan City provided in one embodiment of the present application.

[0028] Figure 13 This is a distribution map of reserve arable land resources in the desertified area of ​​Songyuan City provided in one embodiment of the present application.

[0029] Figure 14 This is a three-dimensional distribution map of deep soil organic carbon content in the black soil resource distribution area in the desertification area of ​​Songyuan City provided in one embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0031] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0032] In an exemplary embodiment, the present application provides a process-based desertification assessment method, comprising the following steps 101 to 107: Figure 1 shown.

[0033] Step 101: Acquire remote sensing image data of desertified areas, grassland resource sample survey data, surface soil sample salt content analysis and testing data, monthly cumulative precipitation data, annual cumulative precipitation data, deep soil sample analysis and testing texture data, deep soil organic carbon content data, land use vector data, and digital elevation model data;

[0034] Step 102: determining the distribution of vegetation degradation degree in the desertified area based on the remote sensing image data and grassland resource sample plot survey data;

[0035] Step 103: determining the distribution of soil salinization degree in the desertified area based on the remote sensing image data and the surface soil sample salt content analysis test data;

[0036] Step 104: determining the distribution of soil and water loss extent in the desertified area based on the remote sensing image data, the monthly cumulative precipitation data, the annual cumulative precipitation data, the texture data of the deep soil sample analysis test, the deep soil organic carbon content data, and the digital elevation model data;

[0037] Step 105: determining the distribution of the degree of land desertification in the desertified area based on the texture data of the deep soil sample analysis test;

[0038] Step 106, determining the desertification degree distribution of the desertified area based on the vegetation degradation degree distribution, soil salinization degree distribution, soil erosion degree distribution, and land sandification degree distribution of the desertified area;

[0039] Step 107: Determine desertification prevention and control areas and reserve arable land resource areas in the desertified area based on the desertification degree distribution in the desertified area; the reserve arable land resource area is a black soil resource area that can be used as reserve arable land.

[0040] The implementation of the above steps 101 to 107 can improve the accuracy of desertification area assessment and resolve the contradiction between protection and prevention and development and utilization of desertification areas.

[0041] The embodiments of the present application are based on a broad definition of desertification, which is a process of land degradation. It starts with the degradation of vegetation, and then gradually turns to soil degradation and the gradual collapse of the ecosystem due to the lack of vegetation on the surface to prevent wind and sand, and soil and water conservation. It is a multi-stage, nonlinear process (including land desertification, soil salinization, soil erosion, etc.). Figure 2 shown.

[0042] The present embodiment uses the latest classification standards to classify the degree of vegetation degradation, land desertification, soil salinization, and soil erosion. As shown in Table 1, the combined score range of the four is 1 to 16. Therefore, the final desertification degree classification method is: the presence of vegetation degradation and the overall score range of 1 to 4, 4 to 8, 8 to 12, and 12 to 16 correspond to mild, moderate, strong, and severe desertification, respectively, as shown in Table 2.

[0043] Table 1 Desertification types and classifications

[0044]

[0045]

[0046] In Table 1, the thickness of sand and gravel is classified according to the soil texture classification method of the United States Department of Agriculture Natural Resources Conservation Service (USDA-NRCS), with weight percentages of 0.02-2 g / kg and >2 g / kg representing sand and gravel, respectively; GB / T20483-2019 is the national standard of the People's Republic of China for land desertification monitoring methods; HJ 1173-2021 is the national ecological environment standard of the People's Republic of China for national ecological status survey and assessment technical specifications - ecosystem service function assessment; HJ 1174-2021 is the national ecological environment standard of the People's Republic of China for national ecological status survey and assessment technical specifications - ecological problem assessment.

[0047] Table 2 Desertification degree classification

[0048]

[0049] Based on Table 1 and Table 2 above, the technical process of a process-based desertification assessment method of this application is as follows: Figure 3 shown.

[0050] In an exemplary embodiment, the above step 101 is a process of data collection, including commonly used multi-spectral high-resolution, remote sensing image data of vegetation growth period, grassland resource sample survey data of the same period, surface soil (within 20 cm) sample salt content analysis test data collected in salinized areas, monthly and annual cumulative precipitation data, deep (within 5 m) soil sample analysis test texture data and soil organic carbon SOC content data, land use vector data, digital elevation model DEM data, etc.

[0051] In an exemplary embodiment, the above steps 102 to 105 are the process of processing various types of data and extracting the distribution areas of vegetation degradation, soil salinization, soil erosion, and land desertification.

[0052] (1) Vegetation degradation: Studies have shown that there is a positive linear relationship between vegetation index and vegetation coverage. Therefore, the vegetation coverage data of grassland resource plots surveyed during the period of vigorous vegetation growth were fitted with the standardized vegetation index NDVI (Formula 1) calculated from multispectral images of the same period. The vegetation coverage threshold segmentation point was determined according to the vegetation coverage classification method in Table 1 to characterize the current status of vegetation degradation. In Formula (1), B NIR 、B R These are the near-infrared band data and the visible red band data in the remote sensing image data. This step is calculated using the MATLAB 2022b compiler and the calculation results are converted into vectors in ENVI 5.3.

[0053] NDVI=(B NIR -B R ) / (B NIR +B R ) (1)

[0054]

[0055] Table 3 Commonly used soil salinity spectral index SI

[0056]

[0057]

[0058] (2) Soil salinization: The salinization index SAI is calculated using a method based on the optimal three-dimensional spectral feature model, as shown in formula (2), Figure 4As shown in the figure, the model is based on the mutual constraints among the soil salinity index (SI), vegetation index (VI), and soil moisture index (WI) (i.e., soil salinity inhibits vegetation growth, while soil moisture promotes vegetation growth and helps reduce soil salinity). The SAI was calculated using a MATLAB 2022b compiler program that iterates through all commonly used SI (Table 3), VI (Table 4), and WI (Table 5). The band numbers in Tables 3-5 represent the parameters of each band in the Sentinel-2 imagery, as shown in Table 6. These bands were then linearly regressed with salinity data collected from field salinized soil samples and analyses during the same period. The regression model with the smallest fitting error was selected as the optimal SAI model. Threshold segmentation points were determined according to the classification method in Table 1 to characterize soil salinization. Finally, the threshold segmentation results were converted into vectors in ENVI 5.3.

[0059] Table 4 Commonly used vegetation index VI

[0060]

[0061] Table 5 Commonly used soil moisture index WI

[0062]

[0063]

[0064] Table 6 Parameters of each band of Sentinel-2 image

[0065]

[0066] (3) Soil and water loss: The average soil erosion modulus Q is calculated according to the modified soil loss equation (RUSLE) proposed in the technical specifications HJ 1173-2021 and HJ 1174-2021, as shown in formula (3), where R is the rainfall erosivity factor, in MJ mm (hm2). 2 ·h·a); K is the soil erodibility factor, unit is t·hm 2 ·h / (hm 2 ·MJ·mm); L and S are slope length and slope factor, respectively, dimensionless; C is vegetation cover factor, dimensionless, ranging from 0 to 1. i The rainfall erosivity factor R was calculated based on the empirical formula (Formula (4)) proposed by Wischmeier in 1969 and the annual cumulative precipitation P data; the surface soil texture (sand grains m s 、Powder particles m silt 、Clay m c), soil organic carbon (SOC) data (in percentage), and the soil erodibility factor K was calculated based on the EPIC model of soil erosion proposed by Williams in 1990 (Formula (5)). The slope factor S (Formula (6)) and slope length factor L (Formula (7)) were calculated using digital elevation model (DEM) data, where θ is the slope and λ is the slope length. The standardized vegetation index (NDVI) was calculated using multispectral image data and fitted with a regression equation based on vegetation coverage data from field plot surveys. The NDVI was converted to vegetation coverage c, and the vegetation coverage factor C was calculated (Formula (8)). All the above calculations were performed using ArcGIS software combined with the MATLAB 2022b compiler.

[0067] Q=R×K×L×S×C (3)

[0068]

[0069] C=0.221-0.595log(c) (8)

[0070] (4) Land desertification: According to the commonly used soil texture classification method of the United States Department of Agriculture Natural Resources Conservation Service (USDA-NRCS), soil texture data within a depth of 5 m were used, combined with field surveys of land desertification soil texture types, and the MATLAB 2022b compiler was used to screen out land desertification points and calculate the thickness of the sand cover. Then, ordinary kriging interpolation was used to obtain the distribution of the sand cover thickness in the study area, and the degree of land desertification was graded according to the classification method in Table 1.

[0071] In another exemplary embodiment, the above step 106 is a process of grading and distributing the degree of desertification.

[0072] The program was compiled in MATLAB 2022b. According to the classification methods of vegetation degradation, soil salinization, soil erosion, and land desertification in Table 1 and the process-based desertification degree classification method in Table 2, the scores were calculated and saved as raster files. Then, the desertification raster files were density-segmented and converted into vector files in ENVI 5.3 software according to the classification method in Table 2 to obtain the desertification distribution results.

[0073] In another exemplary embodiment, the above step 107 is the screening of desertification prevention areas and black soil resource areas that can be used as reserve arable land in the desertification area.

[0074] In ArcGIS 10.7, the land use vector patch file and the desertification distribution vector were spatially overlaid and analyzed to screen out the distribution of unused land in the desertification area; then, using the soil organic carbon (SOC) data within a depth of 5m, according to the definition of black soil of the FAO World Reference Base for Soil Resources (i.e., the total thickness of the surface black soil is greater than 25cm and the SOC content is greater than 2.5%), the black soil distribution areas in the desertification area with the land use type of other unused land were screened out as reserve arable land resource areas, and the remaining areas are desertification prevention and control areas that need to be protected and carried out.

[0075] In another exemplary embodiment, the above steps 101 to 107 are all implemented using ENVI 5.3, ArcGIS 10.7 combined with MATLAB 2022b compiler.

[0076] To illustrate the specific implementation of this process-based desertification assessment method, a specific example is provided. This example uses Songyuan City in the Songnen Plain in Northeast China as an example to demonstrate the technical effectiveness. This region, located on the eastern edge of my country's northern sand-control belt, is characterized by significant soil salinization, desertification, and soil erosion. It is a typical desertification region in China and a concentrated area of ​​black soil resources.

[0077] The first step is to collect data sources.

[0078] This includes field surveys from June to October 2022 and 2023, with 3,259 soil texture and soil organic carbon analysis test data within a sampling depth of 5m; 80 grassland resource plot survey data; and 60 surface salinized soil sampling and analysis test salt content (total salinity) data. Figure 5 The Sentinel-2 multispectral remote sensing imagery for the same period, with band parameters shown in Table 6, was downloaded from the ESA official website. The 2022 monthly and annual cumulative precipitation ERA5 data were downloaded from the ESA Climate Data Center. The Digital Elevation Model (DEM) data were downloaded from the USGS official website, with a resolution of 12.5 meters. Land use type vector patch data was also included.

[0079] The second step is to extract the distribution areas of vegetation degradation, soil salinization, soil erosion, and land desertification.

[0080] (1) Vegetation degradation area

[0081] The vegetation coverage data obtained from the grassland resource plot survey and the standardized vegetation index NDVI calculated from the multispectral remote sensing image of the same period were fitted using the weighted least squares method in MATLAB 2022b, as shown in the following example: Figure 6As shown in Table 1, the threshold segmentation point is determined according to the vegetation coverage classification method, and converted into vegetation degradation area vector in ENVI 5.3, as shown in Figure 7 shown.

[0082] (2) Soil salinization area

[0083] The soil salinization index (SAI) calculated from Sentinel-2 multispectral remote sensing images was combined with the total soil salinity data of surface soil (within 20 cm) collected from salinized areas of varying degrees, and the weighted least squares method was used to fit the data in MATLAB 2022b. Figure 8 As shown in Figure 1, the regression model with the smallest fitting error is the optimal SAI. The threshold segmentation point is determined according to the soil salinization classification method in Table 1. The threshold segmentation result is converted into the salinization area vector in ENVI 5.3, as shown in Figure 1. Figure 9 shown.

[0084] (3) Soil and water erosion areas

[0085] Using the 2022 ERA5 monthly and annual cumulative precipitation data, the rainfall erosivity factor R was calculated according to formula (4); using the surface soil texture and soil organic carbon SOC analysis test data, the soil erodibility factor K was calculated according to formula (5); using the 12.5-meter resolution digital elevation model DEM data, the slope factor S and slope length factor L were calculated according to formulas (6) and (7); using multispectral image data to calculate the standardized vegetation index NDVI and field plot survey vegetation coverage data, the weighted least squares method was used to fit the one-variable linear regression equation in MATLAB2022b, and the NDVI was converted into vegetation coverage c, and then the vegetation coverage factor C was calculated according to formula (8). Then, the soil erosion modulus Q was calculated according to formula (3), and the soil and water loss distribution area was delineated according to the classification method in Table 1, as shown in the following figure: Figure 10 shown.

[0086] (4) Land desertification areas

[0087] Based on field surveys and the internationally commonly used soil texture classification method of the United States Department of Agriculture Natural Resources Conservation Service (USDA-NRCS), the desertified soils in the study area are mainly sandy soil, loamy sandy soil, and sandy loam. Field survey sampling (within 5 m of depth) was used to analyze and test soil texture data. A program compiled in MATLAB 2022b was used to screen the distribution of land desertification points and calculate their sand cover thickness. Kriging interpolation was used to obtain the distribution of sand cover thickness in the study area. The sand cover thickness was classified according to the classification method in Table 1 to delineate areas of land desertification to varying degrees, such as: Figure 11 shown.

[0088] The third step is to classify the degree of desertification and delineate desertified areas.

[0089] The program was compiled in MATLAB 2022b. According to the classification methods of vegetation degradation, soil salinization, soil erosion, and land desertification in Table 1 and the process-based desertification degree classification method in Table 2, the scores were calculated and saved as raster files. Then, the desertification raster files were density segmented and converted into vector files in ENVI 5.3 software according to the classification method in Table 2 to obtain the desertification distribution results, as shown in Figure 2. Figure 12 shown.

[0090] The fourth step is to screen desertification prevention and control areas and black soil resource areas that can be used as reserve arable land.

[0091] In ArcGIS 10.7, the vector patches of land use type "other unused land" were spatially overlaid with the desertification distribution vectors to screen out the distribution of unused land in the desertification area. Then, using the soil organic carbon (SOC) data within a depth of 5 m, according to the definition of black soil in the FAO World Reference Base for Soil Resources (i.e., the total thickness of the surface black soil is greater than 25 cm and the SOC content is greater than 2.5%), the black soil distribution areas in the desertification area with land use type "other unused land" were screened as black soil resource areas that can be used as reserve arable land, such as Figure 13 As shown. The final screening area is 11.81 hectares, including 6.81 hectares of bare land and 4.85 hectares of saline-alkali land, which are located in the mild desertification area; 0.15 hectares of sandy land are located in the severe desertification area. The corresponding three-dimensional distribution map of deep soil organic carbon (SOC) content is shown in Figure 14 In the desertification area, except for the black soil resource area that can be used as reserve arable land, the remaining area is the desertification prevention and control area that needs to be protected and implemented.

[0092] The process-based desertification evaluation method proposed in the embodiment of the present application is more scientific, has better visualization effect of processing results, higher accuracy, and better meets the actual production application needs.

[0093] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A process-based desertification assessment method, characterized in that: include: Obtain remote sensing image data of desertified areas, grassland resource sample survey data, surface soil sample salt content analysis and testing data, monthly cumulative precipitation data, annual cumulative precipitation data, deep soil sample analysis and testing texture data, deep soil organic carbon content data, and digital elevation model data; Determining the distribution of vegetation degradation levels in the desertified area based on the remote sensing image data and grassland resource sample plot survey data; Determining the distribution of soil salinization in the desertified area based on the remote sensing image data and the surface soil sample salt content analysis test data; Determining the distribution of soil and water loss extent in the desertified area based on the remote sensing image data, the monthly cumulative precipitation data, the annual cumulative precipitation data, the texture data of the deep soil sample analysis test, the deep soil organic carbon content data, and the digital elevation model data; Determining the distribution of land desertification in the desertified area based on the texture data of the deep soil sample analysis test; Determine the desertification degree distribution in desertified areas based on the distribution of vegetation degradation, soil salinization, soil erosion, and land desertification in the desertified areas; Based on the distribution of desertification degrees in the desertified area, desertification prevention and control areas and reserve arable land resource areas in the desertified area are determined; the reserve arable land resource areas are black soil resource areas that can be used as reserve arable land.

2. The process-based desertification assessment method according to claim 1, characterized in that: Based on the distribution of desertification degrees in the desertified areas, the desertification prevention and control areas and reserve arable land resource areas in the desertified areas are determined, specifically including: Conduct spatial overlay analysis on the vector map of land use types and the vector map of desertification degree distribution in the desertified area to determine the distribution of unused land in the desertified area; Based on the deep soil organic carbon content data and the distribution of unused land in the desertification area, the unused black soil resource distribution area in the desertification area is determined as the reserve arable land resource area, and the area other than the reserve arable land resource area in the desertification area is determined as the desertification prevention and control area.

3. The process-based desertification assessment method according to claim 1, characterized in that: Determine the distribution of vegetation degradation in the desertified area based on the remote sensing image data and grassland resource sample plot survey data, specifically including: Calculating the standardized vegetation index at different locations within the desertification area based on the remote sensing image data; Calculating vegetation coverage at different locations within the desertified area using a fitting formula based on standardized vegetation indices at different locations within the desertified area; the fitting formula is obtained by fitting the standardized vegetation indices at the same location during the same period with surveyed vegetation coverage, the surveyed vegetation coverage being obtained from grassland resource plot survey data; The degree of vegetation degradation at different locations in the desertification area is determined based on the vegetation coverage threshold segmentation points and the vegetation coverage at different locations in the desertification area.

4. The process-based desertification assessment method according to claim 3, characterized in that: The formula for calculating the normalized vegetation index is: NDVI=(B NIR -B R ) / (B NIR +B R ); Among them, NDVI is the normalized vegetation index, B NIR is the near-infrared band data in the remote sensing image data, B R It is the visible light red band data in remote sensing image data.

5. The process-based desertification assessment method according to claim 1, characterized in that: Determining the distribution of soil salinization in the desertified area based on the remote sensing image data and the surface soil sample salt content analysis test data specifically includes: Calculating salinization indexes at different locations within the desertification area based on the remote sensing image data; Determining the salt content at different locations within the desertified area using a linear regression model based on the salinization index at different locations within the desertified area; the linear regression model characterizes the relationship between the salinization index and the salt content at the same location during the same period, and the linear regression model is obtained by fitting the salinization index at the same location during the same period with a surveyed salt content, where the surveyed salt content is obtained from salt content analysis test data of the surface soil samples; The degree of soil salinization at different locations in the desertification area is determined based on the salt content segmentation threshold and the salt content at different locations in the desertification area.

6. The process-based desertification assessment method according to claim 5, characterized in that: The calculation formula of salinization index is: Among them, SAI is the salinization index, SI is the soil salinity index, WI is the soil moisture index, and VI is the vegetation index.

7. The process-based desertification assessment method according to claim 1, characterized in that: Determining the distribution of soil and water loss in the desertified area based on the remote sensing image data, the monthly cumulative precipitation data, the annual cumulative precipitation data, the texture data of the deep soil sample analysis test, the deep soil organic carbon content data, and the digital elevation model data, specifically includes: Calculating the average erosion modulus at different locations within the desert area based on the remote sensing image data, the monthly cumulative precipitation data, the annual cumulative precipitation data, the deep soil sample analysis and testing texture data, the deep soil organic carbon content data, and the digital elevation model data; The degree of soil and water loss at different locations in the desert area is determined based on the average erosion modulus segmentation threshold and the average erosion modulus at different locations in the desert area.

8. The process-based desertification assessment method according to claim 1, characterized in that: Calculating the average erosion modulus at different locations in the desert area based on the remote sensing image data, the monthly cumulative precipitation data, the annual cumulative precipitation data, the deep soil sample analysis and testing texture data, the deep soil organic carbon content data, and the digital elevation model data, specifically including: Calculating vegetation coverage factors at different locations in the desert area based on the remote sensing image data; Calculate the slope factor and slope length factor at different locations in the desert area based on the digital elevation model data; Calculating rainfall erosivity factors at different locations in the desert area based on the monthly cumulative precipitation data and the annual cumulative precipitation data; Calculate the soil erodibility factor at different locations in the desert area based on the texture data and deep soil organic carbon content data from deep soil sample analysis; The average erosion modulus at different locations in the desert area is calculated based on the vegetation coverage factor, slope factor, slope length factor, rainfall erosion factor and soil erodibility factor at different locations in the desert area.

9. The process-based desertification assessment method according to claim 8, characterized in that: The calculation formula of the vegetation coverage factor is: C = 0.221-0.595log(c); Among them, C is the vegetation coverage factor, c is the vegetation coverage; The slope factor is calculated as: Where S is the slope factor and θ is the slope; The calculation formula of slope length factor is: Where L is the slope length factor, λ is the slope length; The calculation formula of rainfall erosivity factor is: Among them, R is the rainfall erosivity factor, P is the annual cumulative precipitation, and P i is the monthly cumulative precipitation in month i; The calculation formula of soil erodibility factor is: Where K is the soil erodibility factor, m s is the sand content, m silt is the particle content, m c is the clay content, SOC is the organic carbon content; The calculation formula of the average erosion modulus is: Q = R × K × L × S × C; Where Q is the average erosion modulus.

10. The process-based desertification assessment method according to claim 1, characterized in that: Determining the distribution of land desertification in the desertified area based on the texture data of the deep soil sample analysis test specifically includes: Determine the thickness of sand cover at different locations within the desertification area based on deep soil sample analysis and testing texture data; Based on the sand cover thickness at different land desertification points in the desertification area, the ordinary kriging interpolation method is used to determine the sand cover thickness at different locations in the desertification area; The degree of land desertification at different locations in the desertification area is determined based on the sand cover thickness segmentation threshold and the sand cover thickness at different locations in the desertification area.

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