CN109033459B - A spatial grid construction method for soil bulk density data - Google Patents
A spatial grid construction method for soil bulk density data Download PDFInfo
- Publication number
- CN109033459B CN109033459B CN201811004198.5A CN201811004198A CN109033459B CN 109033459 B CN109033459 B CN 109033459B CN 201811004198 A CN201811004198 A CN 201811004198A CN 109033459 B CN109033459 B CN 109033459B
- Authority
- CN
- China
- Prior art keywords
- soil
- bulk density
- data
- grassland
- organic carbon
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000002689 soil Substances 0.000 title claims abstract description 349
- 238000010276 construction Methods 0.000 title claims abstract description 13
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims abstract description 63
- 229910052799 carbon Inorganic materials 0.000 claims abstract description 63
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000004458 analytical method Methods 0.000 claims abstract description 23
- 238000012360 testing method Methods 0.000 claims abstract description 11
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 238000000611 regression analysis Methods 0.000 claims abstract description 4
- 239000010410 layer Substances 0.000 claims description 67
- 230000008859 change Effects 0.000 claims description 24
- 239000002344 surface layer Substances 0.000 claims description 20
- 238000012546 transfer Methods 0.000 claims description 17
- 238000012417 linear regression Methods 0.000 claims description 10
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 8
- 238000005070 sampling Methods 0.000 claims description 8
- 238000011160 research Methods 0.000 claims description 7
- 230000009919 sequestration Effects 0.000 claims description 5
- 238000007619 statistical method Methods 0.000 claims description 5
- 229910052757 nitrogen Inorganic materials 0.000 claims description 4
- 238000002360 preparation method Methods 0.000 claims description 4
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 3
- 229910052698 phosphorus Inorganic materials 0.000 claims description 3
- 239000011574 phosphorus Substances 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 2
- 238000007689 inspection Methods 0.000 claims 1
- 238000011835 investigation Methods 0.000 abstract description 3
- 238000013517 stratification Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 5
- 241000196324 Embryophyta Species 0.000 description 3
- 230000015556 catabolic process Effects 0.000 description 3
- 238000006731 degradation reaction Methods 0.000 description 3
- 244000025254 Cannabis sativa Species 0.000 description 2
- 238000009792 diffusion process Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000008595 infiltration Effects 0.000 description 2
- 238000001764 infiltration Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 230000008635 plant growth Effects 0.000 description 2
- 238000012732 spatial analysis Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000004927 clay Substances 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 239000003337 fertilizer Substances 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000012010 growth Effects 0.000 description 1
- -1 heat Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 239000000843 powder Substances 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000010561 standard procedure Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Image Processing (AREA)
Abstract
本发明公开了一种土壤容重数据空间格网化构建方法，包括以下步骤：土壤剖面调查、样品采集及试验数据；土壤容重垂直剖面变率系数KSBD与土壤有机碳含量垂直剖面变率系数KSOC回归分析；分层土壤容重估算模型构建；不同土层的土壤容重估算与检验；草地土壤容重空间格网化计算方法；草地土壤容重空间格局特征分析。本发明的优点在于：实现了土壤容重的空间格网化估算，解决了土壤垂直剖面分层容重的数据重建的问题；利用有限的数据或指标，简单快捷地反演并获取草地土壤垂直分层和水平分布的土壤容重空间数据，方法简单，节约时间、精力和财力；具有较高的精度和可靠性，为土壤属性的数据构建提供有效方法和数据基础。
The invention discloses a spatial grid construction method for soil bulk density data, comprising the following steps: soil profile investigation, sample collection and test data; soil bulk density vertical profile variation coefficient K SBD and soil organic carbon content vertical profile variation coefficient K SOC regression analysis; construction of layered soil bulk density estimation model; soil bulk density estimation and testing of different soil layers; spatial grid calculation method of grassland soil bulk density; analysis of the spatial pattern characteristics of grassland soil bulk density. The invention has the advantages that: the spatial grid estimation of soil bulk density is realized, and the problem of data reconstruction of soil vertical profile stratified bulk density is solved; the limited data or indexes are used to invert and obtain the vertical stratification of grassland soil simply and quickly. and horizontally distributed soil bulk density spatial data, the method is simple, saves time, energy and financial resources; it has high accuracy and reliability, and provides an effective method and data basis for the construction of soil attribute data.
Description
Technical Field
The invention relates to the technical field of soil volume weight space gridding construction, in particular to a space construction method of soil volume weight vertical section hierarchical data.
Background
Soil is an important component of a land ecosystem, is an important foundation for vegetation to live, and the physicochemical properties of the soil not only influence the growth of plants, but also restrict the productivity level of the plants. The volume weight is an important basic physical property of soil, and has important influence on characteristics of the soil such as air permeability, infiltration property, water retention property, solute migration, soil corrosion resistance and the like. The volume weight of the soil can quantitatively represent ecological functions of water retention, infiltration, erosion resistance, air permeability and the like of the soil, and is one of important indexes for measuring the environment quality of the soil. The change of the volume weight of the soil is influenced by changing the hardness of the soil, so that the growth of plants is influenced; along with the increase of the volume weight, the soil hardness is increased, the root system elongation speed in the soil is reduced, and the root system is shortened and thickened. Through volume weight change, the arrangement of soil solid particles is different, so that different tortuous paths are formed, the diffusion of nutrients in soil is influenced, and the diffusion coefficient is further influenced by the change of charge density in unit soil body along with the change of volume weight. The conditions of water, heat, gas and fertilizer in the soil environment are adjusted through the change of the volume weight of the soil, and the growth condition of plants is further influenced. Through the change of the soil volume weight, the characteristic expression of the grassland degradation condition is also predicted while the soil quality degradation is indicated. In addition, on the regional scale, the soil volume weight is an essential parameter for estimating the soil reservoir volume and is also an important parameter for accurately estimating the carbon and nitrogen reserves of the soil. Therefore, the soil volume weight space-time data of the complete system is established, and the method has important practical significance for basic research of soil science, ecological environment evaluation and soil quality monitoring in China.
The volume weight of the soil is the mass (or weight) of the soil in unit volume under the state of an undisturbed soil column. At present, the international organization for standardization stipulates 3 soil volume weight sampling methods: the ring knife method, the dicing method and the clod method, the ring knife method being the most commonly used method. The industry standard method (NY/T1121.4-2006) released by Ministry of agriculture for measuring the volume weight of soil, and the research and investigation standard of carbon fixation current situation, speed and potential of the Chinese grassland ecosystem for formulating the concrete operation standard for soil volume weight sampling by the leader special project of the Chinese academy of sciences. However, all soil volume weight sampling methods are established on the premise of deeply digging a soil profile, and then original soil samples of soil can be collected to measure the volume weight of the soil. The operation is time-consuming, the problems of heavy work, high labor and material consumption and the like exist in the field large-scale practical application, and the sample point and the data volume of the soil volume weight determination are limited. Therefore, more studies are being conducted on the volume weight of the surface soil. More samples and data can be acquired over the area space. Meanwhile, in recent years, a transfer function prediction model is established by using limited soil volume weight measurement data, the soil volume weight is predicted by using other attributes of soil, and a good effect is obtained, for example, the transfer function prediction model is established based on the volume fraction of clay and powder, the content of organic carbon, the gradient and the conversion combination thereof, and the scale surface layer soil volume weight of the loess plateau area is simulated, and the soil volume weight prediction model is increasingly concerned and applied by scholars at home and abroad as a simple and rapid soil volume weight obtaining method. However, the research does not consider the influence of soil layer depth on soil volume weight simulation.
Disclosure of Invention
The invention provides a spatial gridding construction method of soil volume weight data, aiming at the defects of the prior art, which constructs a transfer function prediction model of a horizontal space and a vertical section, utilizes the existing easily-obtained limited data to reconstruct the layered volume weight data of the vertical section of the soil and estimate the soil volume weight data of a horizontal spatial gridding, inverts the soil volume weight data of the vertical section and the horizontal space of the soil volume weight of a grassland area in northern China, and provides services for basic research of the soil science and evaluation of the soil condition.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a soil volume-weight data space gridding construction method comprises the following steps:
step 1, soil profile investigation, sample collection and test data;
according to the carbon sequestration current situation, the carbon sequestration speed and the carbon sequestration speed of the Chinese grassland ecosystemPotential research survey Specification, according to the Specification, performing grassland sample plot survey and sampling in different regions, including grassland community survey and soil survey and sampling, and collecting soil volume weight and soil physicochemical analysis samples according to 0-5cm, 5-10cm, 10-20cm, 20-30cm and 30-50 cm. Analyzing the contents of all carbon, inorganic carbon, organic carbon, all nitrogen and all phosphorus in soil physicochemical analysis samples, and obtaining a vertical section variability coefficient K of the soil organic carbon content changing along with the soil layers through linear regression analysis according to the soil volume weight and the soil organic carbon content data of different soil layer test analysisSOCAnd the vertical section rate coefficient K of the soil volume weight changing with the soil layerSBD。
Step 2, soil volume weight vertical section variability coefficient KSBDCoefficient of variability K of vertical section of soil organic carbon contentSOCPerforming regression analysis;
according to the vertical section rate coefficient K of the organic carbon content of the soilSOCData and soil volume weight vertical section variability coefficient KSBDAnd (3) carrying out linear regression analysis on the data to obtain a transfer function equation of the soil layering volume weight change along with the soil organic carbon content layering change: y-0.056 x + 0.0254.
Step 3, constructing a layered soil volume weight estimation model;
and (3) constructing an estimation model of the soil volume weight of different soil layers changing along with the organic carbon content of the soil in a layering manner according to the transfer function equation of the soil volume weight vertical section change obtained in the step (2):
SBD(x)＝SBD(0)(1+KSBD*x)(x＝0,1,2,3)……………(1)
KSBD＝-0.056*KSOC+0.0254………………………(2)
SBD(x)the soil volume weight value of an x soil layer is 0-10cm of soil volume weight of the soil surface layer when x is 0, and the soil volume weight value of a 10-20cm soil layer when x is 1; when x is 2, the soil unit weight value of the soil layer of 20-30cm is obtained; when x is 3, the soil unit weight value of the soil layer of 30-50cm is obtained; SBD(0)The volume weight value of the soil surface layer is 0-10 cm; kSBDThe coefficient of vertical section variability of soil in a certain grassland plot, and the soilVertical profile rate coefficient K of organic carbon content in soilSOCIt is related.
Step 4, estimating and checking soil volume weights of different soil layers
And (3) according to the formula (1) and the formula (2), re-simulating and estimating the soil volume weight values of different soil layers of 0-10cm, 10-20cm, 20-30cm and 30-50cm of the grassland sample plot. According to average prediction error MPE, root mean square difference RMSPE and complex correlation coefficient R2And (5) checking the prediction accuracy of the transfer function of the change of the soil volume weight vertical section. The test result shows that: vertical section rate coefficient K utilizing organic carbon content of soilSOCThe method has high reliability and precision in predicting and estimating the soil unit weight values of different soil layers.
Step 5, the spatial grid calculation method for the unit weight of the grassland soil comprises the following steps:
5.1 soil surface layer of grassland (0-10cm) soil bulk density SBD(0)Preparing spatial grid data;
according to survey data of the surface soil volume weight of grassland survey, utilizing a multi-data-source inversion interpolation method to inversely interpolate the soil volume weight space grid data of the surface of grassland of 0-10 cm.
5.2 vertical cross-section variability coefficient K of organic carbon content of grassland soilSOCPreparing spatial grid data;
according to the soil layered organic carbon content data of grassland survey, obtaining the vertical section variability coefficient K of the organic carbon content of the corresponding sample plot along with the change of the soil layer depth by utilizing linear regression analysisSOCAnd applying a multi-data source inversion interpolation method to inversely interpolate the vertical section variability coefficient K of the organic carbon content of the soil of the grasslandSOCSpatial grid data.
5.3, performing space grid calculation on the soil volume weights of different soil layers of the grassland;
according to the transfer function estimation model of the soil volume weight of different soil layers constructed in the step 3, the soil surface layer soil volume weight SBD in the step 5.1 is loaded in the ArcGIS platform(0)Spatial grid data and vertical cross-section variability coefficient K of soil organic carbon content in step 5.2SOCSpatial grid data. Open in ArctolBox "A 'map algebra' -grid calculator 'tool of Spatial analysis' substitutes a formula (1) and a formula (2) into a grid calculator to respectively perform interpolation and inversion to soil volume weight space grid data of different soil layers.
When x is 0, the soil surface layer is the soil volume weight data of 0-10 cm;
when x is 1, the soil volume weight data of a soil layer of 10-20cm is obtained;
when x is 2, the soil volume weight data of a soil layer of 20-30cm is obtained;
and when x is 3, the soil unit weight data of a soil layer of 30-50cm is obtained.
Step 6, analyzing the volume weight spatial pattern characteristics of the grassland soil;
and 5, according to the spatial grid data of the soil volume weights of the soil layers of 0-10cm, 10-20cm, 20-30cm and 30-50cm of the grassland obtained by the inversion interpolation in the step 5, statistically analyzing the spatial distribution pattern characteristics of the soil volume weights in different geographic areas and the heterogeneous variation characteristics of the vertical sections of the soil volume weights by using a statistical analysis tool in ArcGIS.
Compared with the prior art, the invention has the advantages that: the spatial grid estimation of the volume weight of the soil is realized, and the data reconstruction of the layered volume weight of the vertical section of the soil is realized; meanwhile, a transfer function equation is constructed by using data or indexes which are relatively easy to obtain, and grassland layered soil volume weight data are estimated and inverted, so that the method is simple, and time, energy and financial resources are saved; the method has high precision and reliability, and provides an effective method and a data base for the data construction of the soil property.
Drawings
FIG. 1 shows the soil volume-weight vertical section variability coefficient (K) of the embodiment of the present inventionSBD) Coefficient of vertical section change (K) with soil total carbon contentSOC) A correlation regression trend graph of (1);
FIG. 2 is a schematic diagram comparing the predicted value and the measured value of the soil volume weight of different soil layers in the embodiment of the invention;
FIG. 3 is a graph showing a spatial distribution of the volume weight of soil on the surface (0-10cm) of grassland in a northern temperate zone grassland area in accordance with an embodiment of the present invention;
FIG. 4 shows the northern temperate zone grass of the embodiment of the inventionVertical cross section variability coefficient (K) of organic carbon content of grassland soil in original areaSOC) A spatial distribution map;
FIG. 5 is a soil volume weight spatial distribution diagram of 0-10cm soil layers of grassland areas in northern temperate zones according to an embodiment of the present invention;
FIG. 6 is a soil volume weight spatial distribution diagram of a 10-20cm soil layer of grassland in a northern temperate zone grassland area according to an embodiment of the invention;
FIG. 7 is a soil volume weight spatial distribution diagram of 20-30cm soil layers of grassland areas in northern temperate zones according to an embodiment of the invention;
FIG. 8 is a soil volume weight spatial distribution diagram of a 30-50cm soil layer of grassland in a northern temperate zone grassland area in the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail by referring to the following examples.
A soil volume-weight data space gridding construction method comprises the following steps:
step 1, soil profile survey, sample collection and test data
The subject group undertakes the sample plot survey of the special subject of Guangxi-Hainan-Jiangxi-Anhui of the Chinese academy of sciences. According to research and survey specifications of carbon sequestration current situation, rate and potential of the Chinese grassland ecosystem, grassland community survey, soil survey and sampling of 143 grassland sample plots are carried out in four provinces of Guangxi, Hainan, Jiangxi and Anhui. Wherein 117 sample plots with complete cross sections are obtained, each sample plot is 5 times repeated (corresponding to the sample prescription), and soil volume weight and soil physicochemical analysis samples are collected according to 0-5cm, 5-10cm, 10-20cm, 20-30cm and 30-50cm in a layering manner. The soil physical and chemical analysis sample is sent to a plant institute analysis center of Chinese academy for analyzing the content of total carbon, inorganic carbon, organic carbon, total nitrogen and total phosphorus. The analysis data of the organic carbon content of the soil layering are shown in a table 1, and the experimental data of the soil layering volume weight are shown in a table 2.
TABLE 1 organic carbon content data of Guangxi-Hainan-Anhui-Jiangxi grassland sample plot
*KSOCThe coefficient of variability of soil organic carbon content with soil depth is obtained by linear regression.
TABLE 2 soil volume weight data of Guangxi-Hainan-Anhui-Jiangxi grassland sample plot
*KSBDThe coefficient is the variability coefficient of the volume weight of the soil changing with the depth of the soil layer and is obtained by linear regression.
Step 2. vertical section variability coefficient of soil volume weight (K)SBD) Coefficient of vertical section rate (K) of change with organic carbon content of soilSOC) Regression analysis, as shown in fig. 1;
vertical Cross-section variability coefficient of organic carbon content (K) of soil according to Table 1SOC) Data and soil volume-weight vertical section variability coefficient (K) of Table 2SBD) And (3) carrying out linear regression analysis on the data to obtain a transfer function equation of the soil layering volume weight change along with the soil organic carbon content layering change:
y＝-0.056x+0.0254。
step 3, constructing a soil volume weight estimation model
According to the transfer function equation of the change of the vertical section of the soil volume weight obtained in the step 2, an estimation model of the soil volume weight of different soil layers changing along with the layering of the organic carbon content of the soil can be constructed:
SBD(x)＝SBD(0)(1+KSBD*x)(x＝0,1,2,3)…………(1)
KSBD＝-0.056*KSOC+0.0254……………………(2)
SBD(x)the soil volume weight value of an x soil layer is the soil volume weight of a soil surface layer (0-10cm) when x is 0, and the soil volume weight value of a soil layer of 10-20cm when x is 1; when x is 2, the soil unit weight value of the soil layer of 20-30cm is obtained; when x is 3, the soil unit weight value of the soil layer of 30-50cm is obtained; SBD(0)The volume weight value of the soil surface layer; kSBDThe vertical coefficient of variation of soil from the organic carbon content of a certain grassland plot (K)SOC) (equation 2).
Step 4, estimating and checking soil bulk densities of different soil layers
According to the formula (1) and the formula (2), soil volume weight values of different soil layers of 0-10cm, 10-20cm, 20-30cm, 30-50cm and the like of 117 grassland areas such as Guangxi-Hainan-Jiangxi-Anhui and the like can be simulated and estimated again. The comparison result between the simulated estimated soil volume weight value and the measured value is shown in fig. 2. Based on Mean Prediction Error (MPE), root mean square error (RMSPE) and complex correlation coefficient (R)2) The prediction accuracy of the transfer function of the change of the soil volume-weight vertical section is checked, and the result shows that: utilizing the vertical section rate coefficient (K) of organic carbon content in soilSOC) The method has high reliability and precision for predicting and estimating the soil unit weight values of different soil layers.
TABLE 3 soil volume weight prediction value and measured value prediction accuracy test
| Soil stratification | MPE | RMSPE | R2 |
| 0-10cm | 0.00 | 0.00 | 1.00 |
| 10-20cm | 0.022 | 0.104 | 0.617 |
| 20-30cm | 0.001 | 0.118 | 0.513 |
| 30-50cm | -0.027 | 0.124 | 0.469 |
Step 5, gridding calculation of volume-weight spatial layering data of grassland soil in northern temperate zone grassland area
The examination and analysis in the step 4 show that a transfer function model for estimating the volume weight of the layered soil is established by using the vertical section variability coefficient of the organic carbon content of the soil, and the volume weight data of the soil at different soil depths is estimated, so that the method is a reliable estimation method. According to the invention, the survey data of the northern temperate zone grassland area is utilized by the subject group, and spatial grid inversion and estimation are carried out on the volume weight of different soil layers of the northern temperate zone grassland area in China. The method comprises the following steps:
5.1 surface soil volume weight SBD of grassland soil in northern temperate zone grassland area(0)Preparation of spatial grid data
According to survey data of surface soil volume weights of 576 grassland survey plots for grassland degradation survey in northern temperate zone grassland area, soil volume weight space grid data (1km multiplied by 1km as shown in figure 3) of the surface (0-10cm) of the grassland in northern temperate zone grassland area in China are obtained by utilizing a multi-data source inversion interpolation method.
5.2 vertical cross-section coefficient of variability (K) of organic carbon content in grassland soil in northern temperate zone grassland areaSOC) Preparation of spatial grid data
Similarly, according to survey data of the organic carbon content of soil layering of 576 grassland survey plots of the grassland deterioration survey in the northern temperate zone, a slope coefficient of change (K) of the organic carbon content of the corresponding plot along with the change of the soil depth is obtained by utilizing linear regression analysisSOC) And applying a plurality of data source inversion interpolation methods to invert and interpolate the vertical section slope rate (K) of the organic carbon content of the grassland soil in the grassland area of the northern temperate zone of ChinaSOC) Spatial raster data (1km × 1km as shown in fig. 4).
5.3 spatial gridding calculation of soil volume weights of different soil layers of grassland in northern temperate zone grassland area
According to the transfer function estimation model of the soil volume weight of different soil layers constructed in the step 3, which is changed along with the soil organic carbon content in a layered mode, loading the surface soil volume weight SBD of the grassland soil in the northern temperate zone grassland area in the ArcGIS platform(0)Spatial grid data (data from step 5.1) and vertical cross-sectional coefficient of variability (K) of organic carbon content of soil in grassland in northern temperate zone grasslandSOC) Spatial grid data (data of step 5.2). Open "map generation" - "of" Spatial analysis "in ArctolBox"The grid calculator' tool substitutes the formula (1) and the formula (2) into the grid calculator, and carries out interpolation inversion respectively to obtain soil volume weight spatial distribution data (1km multiplied by 1km) of different soil layers.
When x is 0, the soil surface layer soil volume weight data (and soil surface layer soil volume weight SBD) is 0-10cm(0)Spatial grid data is the same) (fig. 5);
when x is 1, the soil volume weight data of a soil layer of 10-20cm is obtained (figure 6);
when x is 2, the soil volume weight data of a soil layer of 20-30cm is obtained (figure 7);
when x is 3, the soil volume weight data of a soil layer of 30-50cm is shown (figure 8).
Step 6, carrying out volume weight spatial pattern characteristic analysis on grassland soil in northern temperate zone grassland area
According to the spatial grid data (figure 5-8) of the soil volume weight of soil layers of 0-10cm, 10-20cm, 20-30cm, 30-50cm and the like of grassland areas in northern temperate zones of China, which are obtained by inversion interpolation in the step 5, the spatial distribution pattern characteristics of the soil volume weight in different geographical areas and the heterogeneous variation characteristics of vertical sections of the soil volume weight can be statistically analyzed by using a statistical analysis tool in ArcGIS.
According to the spatial grid data of soil volume weights of soil layers of 0-10cm, 10-20cm, 20-30cm, 30-50cm and the like of grasslands in northern temperate zones of China in the figures 5-8, statistical data analysis shows that: the total average soil volume weight of grassland vegetation in temperate zone grassland areas in north China is 1.47g/cm3Wherein the average soil volume weight of 0-10cm, 10-20cm, 20-30cm and 30-50cm soil layers is 1.39g/cm3、1.44g/cm3、1.50g/cm3、1.55g/cm3。
TABLE 4 proportion of the distribution area of grasslands with different soil bulk weights
According to the statistical analysis in Table 4, the soil volume weight of grassland in northern temperate zone grassland of China is mainly distributed at 1.5g/cm3More than 60 percent of the total grassland area, and the soil volume weight is 0.8g/cm3The grassland accounts for less than 5 percent, and the volume weight of the soil is 0.8-1.5g/cm3The grass is about 35 percent. And with the increase of the depth of the soil layer, the volume weight is high (1.5 g/cm)3Above) has an increasing trend of the specific weight of grassland, and the soil volume weight of 30-50cm soil layer is 1.5g/cm3The grassland occupies about 70 percent of the total area; and a low volume weight (0.8 g/cm)3Below) has a tendency to decrease from about 6% to about 3%.
From the distribution analysis of the geographical regions (Table 5), the average soil volume weight of the grassland in the grassland area of the northern temperate zone of China is 1.47g/cm3Wherein the volume weight of the soil in the Tianshan mountain area is the lowest, and the average volume weight is 1.07g/cm3The surface layer volume weight is 0.99g/cm on average3(ii) a The second is an Aletan mountain area with an average volume weight of 1.11g/cm3The surface layer volume weight is 1.04g/cm on average3(ii) a The third is a pamil-Kunlun mountain-Aljinshan plateau area with an average volume weight of 1.12g/cm3The surface layer volume weight is 1.04g/cm on average3. The grassland soil of the southern Xinjiang basin has the highest volume weight, and the average volume weight is 1.91g/cm3The surface layer volume weight is 1.85g/cm on average3(ii) a Secondly, the grasslands of Alxa plateau, river sleeve and terranean plain have the average volume weight of 1.84g/cm3The surface layer bulk density is 1.76g/cm3(ii) a The third is a northern Xinjiang pseudo-Pascal basin grassland with the average volume weight of 1.71g/cm3The surface layer bulk density is 1.63g/cm3。
TABLE 5 statistical analysis of soil volume weight in different geographical areas
It will be appreciated by those of ordinary skill in the art that the examples described herein are intended to assist the reader in understanding the manner in which the invention is practiced, and it is to be understood that the scope of the invention is not limited to such specific statements and examples. Those skilled in the art can make various other specific modifications and combinations based on the teachings of the present invention without departing from the spirit of the invention, and such modifications and combinations are within the scope of the invention.
Claims (4)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811004198.5A CN109033459B (en) | 2018-08-30 | 2018-08-30 | A spatial grid construction method for soil bulk density data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811004198.5A CN109033459B (en) | 2018-08-30 | 2018-08-30 | A spatial grid construction method for soil bulk density data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109033459A CN109033459A (en) | 2018-12-18 |
| CN109033459B true CN109033459B (en) | 2021-06-15 |
Family
ID=64625864
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811004198.5A Active CN109033459B (en) | 2018-08-30 | 2018-08-30 | A spatial grid construction method for soil bulk density data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109033459B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109856368B (en) * | 2019-03-28 | 2021-06-25 | 中国科学院地球环境研究所 | A method for calculating soil erosion carbon pool loss in historical periods |
| CN112986058A (en) * | 2021-02-02 | 2021-06-18 | 贵州省烟草科学研究院 | Visual analysis method for migration rule of fertilizer nutrients in soil |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102590007A (en) * | 2012-02-24 | 2012-07-18 | 中国科学院亚热带农业生态研究所 | Method for estimating net carbon reserve in surface soil of rice field |
| CN103235103A (en) * | 2013-04-09 | 2013-08-07 | 中国科学院亚热带农业生态研究所 | karst area soil nutrient reserve sampling and calculating method |
| CN105137043A (en) * | 2015-09-29 | 2015-12-09 | 河南科技大学 | Fast metering method for any depth organic carbon reserve of forest soil |
| CN108332719A (en) * | 2018-01-06 | 2018-07-27 | 中国科学院、水利部成都山地灾害与环境研究所 | Sloping upland soil cultivation erosion rate Method of fast estimating |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9945764B2 (en) * | 2012-10-05 | 2018-04-17 | Allan James Yeomans | Method of and apparatus for determining the carbon content of soils |
-
2018
- 2018-08-30 CN CN201811004198.5A patent/CN109033459B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102590007A (en) * | 2012-02-24 | 2012-07-18 | 中国科学院亚热带农业生态研究所 | Method for estimating net carbon reserve in surface soil of rice field |
| CN103235103A (en) * | 2013-04-09 | 2013-08-07 | 中国科学院亚热带农业生态研究所 | karst area soil nutrient reserve sampling and calculating method |
| CN105137043A (en) * | 2015-09-29 | 2015-12-09 | 河南科技大学 | Fast metering method for any depth organic carbon reserve of forest soil |
| CN108332719A (en) * | 2018-01-06 | 2018-07-27 | 中国科学院、水利部成都山地灾害与环境研究所 | Sloping upland soil cultivation erosion rate Method of fast estimating |
Non-Patent Citations (3)
| Title |
|---|
| 内蒙古地区草地表层土壤容重空间格局分析;乔宇鑫;《草地学报》;20160715;全文 * |
| 新疆伊犁地区草地土壤全碳含量空间格局分析;周李磊;《草业科学》;20161103;全文 * |
| 新疆伊犁地区草地土壤容重空间格局分析;周李磊;《草业学报》;20160120;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109033459A (en) | 2018-12-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Santra et al. | Spatial variability of soil properties and its application in predicting surface map of hydraulic parameters in an agricultural farm | |
| Wu et al. | Simulation of soil loss processes based on rainfall runoff and the time factor of governance in the Jialing River Watershed, China | |
| Farid et al. | Estimation of infiltration models parameters and their comparison to simulate the onsite soil infiltration characteristics | |
| CN106018238B (en) | The assay method of soil profile pore structure | |
| Yan et al. | Spatial variability of soil physical properties based on GIS and geo-statistical methods in the red beds of the Nanxiong Basin, China | |
| Ma et al. | Spatial distribution and driving factors of soil organic carbon in the Northeast China Plain: Insights from latest monitoring data | |
| CN104678452A (en) | Method for quantitatively evaluating ore-forming contribution degree of uranium resource body for sandstone type uranium ore | |
| Le Vine et al. | Diagnosing hydrological limitations of a land surface model: application of JULES to a deep-groundwater chalk basin | |
| CN109033459B (en) | A spatial grid construction method for soil bulk density data | |
| Hu et al. | Using 137 Cs and 210 Pb ex to investigate the soil erosion and accumulation moduli on the southern margin of the Hunshandake Sandy Land in Inner Mongolia | |
| CN105891442B (en) | A kind of soil texture granule content Forecasting Methodology | |
| CN118395870A (en) | Unsaturated soil hydraulic parameter prediction method for improving BP neural network model based on krill swarm algorithm | |
| Schneider et al. | Inverse modelling with a genetic algorithm to derive hydraulic properties of a multi-layered forest soil | |
| CN110205909A (en) | A kind of pavement structure flexure based on bitumen layer equivalent temperature means target temperature correction | |
| CN117556695A (en) | A deep learning-based simulation method for crop root soil moisture content | |
| Vather et al. | Cosmic ray neutrons provide an innovative technique for estimating intermediate scale soil moisture | |
| CN102903132A (en) | Digital mapping method for salinized soil | |
| Duan et al. | Field capacity in black soil region, northeast China | |
| CN107145644B (en) | Soil salinization interpretation method based on multi-source data coupling | |
| Sun et al. | Three-dimensional modelling of soil organic carbon density and carbon sequestration potential estimation in a dryland farming region of China | |
| CN116878400A (en) | Inversion mountain soil layer thickness obtaining method and system | |
| Jun-Jun et al. | Estimates of soil organic carbon stocks in Zhejiang Province of China based on 1: 50000 soil database using the PKB method | |
| CN116842717A (en) | Sequential deviation-based frozen soil roadbed freezing and thawing damage degree evaluation method | |
| Mishra et al. | Horizontal and vertical profiling of soil organic carbon stock in Nagaland, North East India | |
| Hu et al. | Determination of 137 Cs reference inventories in a large-scale region: A case study in the central-eastern Inner Mongolia Plateau |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |