Method for constructing soil ecosystem multifunctionality database based on sampling points

Abstract

The invention relates to a method for constructing a soil ecosystem multifunctionality database based on sampling points, comprising the following steps: collecting sampling point data, the sampling point data including sample name, longitude and latitude, sampling time, total nitrogen content, total phosphorus content and ammonium nitrogen content; downloading soil attribute data of the sampling point corresponding to the year; constructing a grid map, and reading the soil attributes in the grid map according to the longitude and latitude information of the sampling point, integrating the collected data, obtaining soil function indicators of each sampling point including total nitrogen content, total phosphorus content and ammonium nitrogen content, aboveground biomass, underground biomass, soil water content, soil organic carbon content and soil net primary productivity; obtaining a soil ecosystem multifunctional index based on the obtained data; performing inverse distance weighted difference on the collected global scattered data to obtain a global soil EMF simulation heat map, and obtaining global soil ecosystem multifunctional simulation grid data.

Description

Technical Field

[0001] The invention belongs to the field of database construction, and in particular relates to a method for constructing a soil ecosystem multifunctionality database based on sampling points. Background Art

[0002] Ecosystem multifunctionality refers to the multiple functions of an ecosystem in terms of biogeochemical cycles, energy flow, and biodiversity. These functions are interrelated and together support the health, stability, and sustainable development of the ecosystem. Ecosystem multifunctionality mainly includes the following aspects:

[0003] (1) Productivity: Ecosystems generate biomass and energy through biological production processes such as photosynthesis, primary production, and secondary production.

[0004] (2) Nutrient Cycle: Ecosystems transfer nutrients (such as carbon, nitrogen, phosphorus, etc.) between organisms, the atmosphere, water bodies, and soil through biogeochemical cycles. These cycles are crucial to the productivity and stability of ecosystems.

[0005] (3) Hydrological regulation: Ecosystems affect the circulation and distribution of water through vegetation interception and transpiration, thereby affecting the recharge and loss of surface and groundwater, and regulating hydrological processes such as river runoff and reservoir water storage.

[0006] The concept of ecosystem multifunctionality has enriched people's understanding of biodiversity and ecosystem functions. Compared with a single ecosystem function, an ecosystem needs a higher species richness to maintain multiple ecosystem functions at the same time. Research on a single ecosystem function often ignores the trade-off between different functions, and the concept of ecosystem multifunctionality has led researchers to explore this issue in a new way. The trade-off between different functions will lead to a reduction in the ability of an ecosystem to provide multiple functions at the same time, because it is difficult for multiple ecosystem functions to reach a high level at the same time.

[0007] The key to maintaining the multifunctionality of ecosystems is to protect biodiversity and maintain ecological processes. Through reasonable resource utilization and ecological protection measures, the realization of ecosystem multifunctionality can be promoted, thereby ensuring the sustainable development of humans and the natural environment. The realization of ecosystem multifunctionality is of great significance for maintaining global ecological balance, promoting ecological civilization construction and improving human well-being. Summary of the invention

[0008] The problem to be solved by the present invention is to provide a method for constructing a soil ecosystem multifunctionality database based on sampling points. The method collects soil data from sampling points around the world, obtains a soil ecosystem multifunctionality index through standardized addition, and simulates the point data into raster data through interpolation to obtain a global ecosystem multifunctionality database, which can be used for soil ecosystem research based on artificial intelligence. The technical solution of the present invention is as follows:

[0009] A method for constructing a soil ecosystem multifunctionality database based on sampling points comprises the following steps:

[0010] (1) Collect sampling point data through the Global Soil Mycobiome consortium database. The sampling point data include sample name, longitude and latitude, sampling time, total nitrogen content, total phosphorus content, and ammonium nitrogen content;

[0011] (2) Download soil property data of the sampling point corresponding to the year from the Google Earth Engine database;

[0012] (3) Construct a grid map, read the soil properties in the grid map according to the latitude and longitude information of the sampling points, integrate the data collected in steps (1) and (2), and obtain eight soil function indicators, including total nitrogen content, total phosphorus content and ammonium nitrogen content, aboveground biomass, underground biomass, soil water content, soil organic carbon content and soil net primary productivity at each sampling point.

[0013] The method of constructing a raster map and reading the soil properties in the raster map according to the latitude and longitude information of the sampling points is as follows:

[0014] To ensure the uniformity of the data, the resolution is determined based on the lowest resolution map among all the used raster maps to reconstruct the raster map; reconstruction and downsampling are performed based on stereo convolution technology; the reconstructed raster map is then divided into two groups: one group without year distinction and the other group with year sequence; for the data with year sequence, it is further subdivided according to the sampling time to obtain the raster file for each year; combined with the latitude and longitude information of the sampling points, the year information is added to the raster file with year, the information of the raster file is read, and matched with the name and year of the sampling point, and the data is integrated and put into a unified table file; for the map without year distinction, the latitude and longitude information of all sampling points is used to read the data, and the data is integrated and put into a unified table file;

[0015] (4) The eight soil function indicators obtained were standardized to obtain the soil ecosystem multifunctional index;

[0016] (5) Perform inverse distance weighted difference on the collected global scattered data to obtain the global soil EMF simulation heat map and obtain the global soil ecosystem multifunctional simulation raster data.

[0017] Before step (2), the initially collected sampling point data is filtered, and the filtering conditions are:

[0018] (i) Remove samples without longitude and latitude;

[0019] (ii) remove samples with incorrect sampling dates;

[0020] (iii) Filter samples from non-standard sampling;

[0021] (iv) Filtering samples that are at risk of contamination;

[0022] (v) Eliminate samples that do not contain measured 15N, total nitrogen, or total phosphorus;

[0023] (vi) Remove samples with obviously wrong longitude and latitude.

[0024] The method of step (3) is as follows:

[0025] Aboveground biomass and belowground biomass were taken from the ‘Global Aboveground and Belowground Biomass Carbon Density Maps’ database, using bands named ‘agb’ and ‘bgb’, representing the carbon stock density of aboveground biomass in woody and herbaceous cover and the carbon stock density of belowground biomass in woody and herbaceous cover, respectively;

[0026] The soil water content SW is from the ERA5-Land Hourly database, using the band named ‘volumetric_soil_water_layer_1’, which represents the water volume in soil layer 1, i.e., 0–7 cm depth, with an hourly time scale, downloaded as the annual average of daily water content values;

[0027] Soil organic carbon (SOC) comes from the 'OpenLandMap Soil Organic Carbon Content' database, using bands named 'b0' and 'b10', representing the soil organic carbon content at 0 cm depth and 10 cm depth, respectively, and calculating the average of the soil organic carbon content at these two depths;

[0028] Net primary production (NPP) is from the MOD17A3HGF.061 database, using the band named 'NPP', representing net primary productivity on an annual time scale, downloaded according to the sampling site year.

[0029] Compared with the prior art, the present invention constructs a soil ecosystem multifunctionality database, which can provide users with soil multifunctionality data and simulated raster data of global sampling points, which can be used for artificial intelligence-based soil ecosystem research in the future. The database construction method proposed in the present invention can have the advantages of large data volume, global coverage and timeliness. In addition, it also uses the functions of ArcGIS Pro itself to provide additional services such as update, visualization, and data analysis, attracting users to expand the database and apply the database. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a block diagram of the method for constructing a soil ecosystem multifunctionality database based on sampling points provided by the present invention. DETAILED DESCRIPTION

[0031] To make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation methods described herein are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0032] Based on the background technology, the present invention collects soil database data and downloads soil grid data, obtains the global sampling point soil ecosystem multifunctionality (EMF) through processing and calculation, and uses ArcGis Pro to obtain simulated global grid data.

[0033] See also Figure 1 The method for constructing a soil ecosystem multifunctionality database based on sampling points provided in this embodiment collects soil data from 2808 sampling points around the world, and specifically includes the following steps:

[0034] (1) Download the database file from the website provided by the Global Soil Mycobiome consortium database, the website is https: / / doi.plutof.ut.ee / doi / 10.15156 / BIO / 2263453, and download the file named 'GSMcdata set02-09-2021.7z'. After decompressing the file, a file named 'Fungi_GSMc_sample_metadata.txt' is obtained. The file contains data information of 3200 sampling points. In order to make the data source of the constructed database unified and the numerical values ​​reliable, the information of these sampling points is filtered. The filtering conditions are:

[0035] (i) Remove samples without longitude and latitude;

[0036] (ii) remove samples with incorrect sampling dates;

[0037] (iii) Filter samples from non-standard sampling;

[0038] (iv) Filtering samples that are at risk of contamination;

[0039] (v) Eliminate samples that do not contain measured 15N, total nitrogen, or total phosphorus;

[0040] (vi) Remove samples with obviously incorrect longitude and latitude: S706 (longitude and latitude are 0, which is inconsistent with being in Thailand).

[0041] Finally, we got 2808 samples, and extracted the sample name column, longitude and latitude column, sampling time column, total nitrogen content column, total phosphorus content column and ammonium nitrogen content column from this table.

[0042] (2) Soil property raster data were downloaded from Google Earth Engine (GEE) at https: / / earthengine.google.com / . Aboveground biomass and belowground biomass were taken from the ‘Global Aboveground and Belowground Biomass Carbon Density Maps’ database with a resolution of 300 m. Bands named ‘agb’ and ‘bgb’ were used to represent the carbon stock density of aboveground biomass in woody and herbaceous cover and the carbon stock density of belowground biomass in woody and herbaceous cover, respectively, in Mg C / ha. Soil water content (SW) was obtained from the ERA5-Land Hourly database with a resolution of 11132 m using a band named ‘volumetric_soil_water_layer_1’. This band represents the water content in soil layer 1 (0–7 cm) from the ECMWF Integrated Forecast System with an hourly time scale. Data were downloaded as annual averages of daily water content values ​​in units of 1 (volume fraction). Soil Organic Carbon (SOC) comes from the 'OpenLandMap Soil Organic Carbon Content' database with a resolution of 250 meters, using bands named 'b0' and 'b10'. These bands represent the soil organic carbon content at a depth of 0 cm and 10 cm, respectively, and the average of the two values ​​is calculated in g / kg. Net Primary Production (NPP) comes from the MOD17A3HGF.061 database, which has a resolution of 500 meters, using a band named 'NPP'. This band represents the net primary productivity on an annual time scale, and the data are downloaded according to the year of the sampling site, in kg C / m2.

[0043] (3) The creation and downloading of raster maps were performed on the code editor platform on GEE, using JavaScript as the programming language. The main functions were ee.Image(), ee.ImageCollection(), and Export.image.to.Drive(). After the raster maps were created, they were saved to Google Drive and then downloaded locally. To ensure the uniformity of the data, the resolution of all downloaded Tif format files was uniformly adjusted to 27,830 meters, which is the lowest resolution value among all raster maps used. Adjustment was performed using ArcGIS Pro, using the "resampling" method and "cubic convolution" technology. Cubic convolution technology is an interpolation method in resampling. It takes into account the 16 pixels around the target pixel and uses their values ​​to calculate the value of the new pixel. Compared with other interpolation methods (such as nearest neighbor or bilinear interpolation), cubic convolution can provide smoother and more accurate results.

[0044] The raster maps are divided into two groups: one without year distinction and the other with year sequences, the latter of which is further subdivided according to the sampling time from 2009 to 2020, resulting in one raster file per year. Using the "raster" package in R 4.3, combined with the latitude and longitude information of the sampling points (if the raster file has a year sequence, the year information is added), the information on the raster file is read and matched with the name and year of the sampling point, and the data is integrated and put into a unified table file. So far, data have been collected from the Global Soil Mycobiome consortium database, three functional indicators of total nitrogen content, total phosphorus content and ammonium nitrogen content. Data were collected from GEE, five functional indicators of aboveground biomass, belowground biomass, soil water content, soil organic carbon content and soil net primary productivity. A total of 8 soil functional indicators.

[0045] (4) Use the custom function in R to standardize the eight indicators. The standardization formula is as follows:

[0046]

[0047] The standardized formula is added to obtain the soil ecosystem multifunctional index. The inverse distance difference method (IDW) in ArcGis Pro 3.0 is used to make a global soil EMF simulation heat map. IDW is an interpolation method used to predict the data value of an unknown location based on the data of a known point. The IDW interpolation method in ArcGIS Pro calculates the value of an unknown point based on distance weights, assuming that closer known points have a greater impact on the predicted value of an unknown point, while farther known points have a smaller impact. The basic idea of ​​the IDW interpolation method is to first calculate the distance between the unknown point and all known points, then calculate the weight based on these distances, and finally perform a weighted average of the values ​​of the known points according to the weights to obtain the predicted value of the unknown point.

[0048] In ArcGis Pro, add the point layer of latitude, longitude and EMF to the global map, open the 'Geoprocessing' pane in the toolbox, select the IDW tool, use the point layer as the input point feature, set the Z value field to the "value" column, and execute the settings. Adjust the color of the generated map from yellow to red with the EMF gradually increasing, add a legend, and export the map.

Claims

1. A method for constructing a soil ecosystem multifunctionality database based on sampling points, comprising the following steps: (1) Collect sampling point data through the GlobalSoilMycobiomeconsortium database. The sampling point data include sample name, longitude and latitude, sampling time, total nitrogen content, total phosphorus content and ammonium nitrogen content; (2) Download soil property data of the sampling point corresponding to the year from the Google Earth Engine database; (3) constructing a grid map, and reading the soil properties in the grid map according to the latitude and longitude information of the sampling points, integrating the data collected in steps (1) and (2), and obtaining the soil function indicators of each sampling point, including total nitrogen content, total phosphorus content and ammonium nitrogen content, aboveground biomass, underground biomass, soil water content, soil organic carbon content and soil net primary productivity, wherein: The method of constructing a raster map and reading the soil properties in the raster map according to the latitude and longitude information of the sampling points is as follows: To ensure the uniformity of the data, the resolution is determined based on the lowest resolution map among all the used raster maps to reconstruct the raster map; reconstruction and downsampling are performed based on stereo convolution technology; the reconstructed raster map is then divided into two groups: one group without year distinction and the other group with year sequence; for the data with year sequence, it is further subdivided according to the sampling time to obtain the raster file for each year; combined with the latitude and longitude information of the sampling points, the year information is added to the raster file with year, the information of the raster file is read, and matched with the name and year of the sampling point, and the data is integrated and put into a unified table file; for the map without year distinction, the latitude and longitude information of all sampling points is used to read the data, and the data is integrated and put into a unified table file; (4) Standardizing the obtained soil functional indicators to obtain the soil ecosystem multifunctional index; (5) Perform inverse distance weighted difference on the collected global scattered data to obtain the global soil EMF simulation heat map and obtain the global soil ecosystem multifunctional simulation raster data.

2. The method for constructing a soil ecosystem multifunctional database according to claim 1, characterized in that: Before step (2), the initially collected sampling point data is filtered, and the filtering conditions are: (i) Remove samples without longitude and latitude; (ii) remove samples with incorrect sampling dates; (iii) Filter samples from non-standard sampling; (iv) Filtering samples that are at risk of contamination; (v) retain samples containing measurements of 15N, total nitrogen or total phosphorus; (vi) Remove samples with obviously wrong longitude and latitude.

3. The method for constructing a soil ecosystem multifunctional database according to claim 1, characterized in that: The method of step (3) is as follows: The aboveground biomass and belowground biomass were taken from the Global Aboveground Database and Belowground Biomass Carbon Density Maps Database, respectively, using bands named "agb" and "bgb" to represent the carbon stock density of aboveground biomass covered by woody and herbaceous plants and the carbon stock density of belowground biomass covered by woody and herbaceous plants, respectively; The soil water content SW comes from the ERA5-Land Hourly Database, using the band named "volumetric_soil_water_layer_1", which represents the water volume in soil layer 1, i.e., 0-7 cm depth, and the time scale is hourly, downloaded as the annual average of daily water content values; Soil organic carbon (SOC) comes from the OpenLandMap Soil Organic Carbon Content database. The bands named "b0" and "b10" represent the soil organic carbon content at a depth of 0 cm and 10 cm, respectively. The average soil organic carbon content at these two depths is calculated. Net primary production (NPP) comes from the MOD17A3HGF.061 database, using a band named "NPP", representing net primary productivity on an annual time scale, and is downloaded based on the sampling point year.

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