Rapid mapping method for 16m-resolution soil organic matter distribution of Gaofen-1 satellite

Abstract

The invention belongs to the field of satellite remote sensing application, and particularly relates to a rapid mapping method for soil organic matter distribution, in particular to a rapid mapping method for 16m-resolution soil organic matter distribution of a Gaofen-1 satellite. The invention aims to solve the problems that the soil fertility condition of farmland cultivated land is reduced and the cultivated land quality is degraded. The method comprises the following steps: acquiring different wave band remote sensing reflectivity of a Gaofen-1 satellite WFV sensor in a target research area; remote sensing reflectivity and remote sensing reflectivity wave band combinations of different wave bands of WFV sensor images are used as input variables of a random forest algorithm, output variables are cultivated land surface soil organic matters (mg / g), and cultivated land surface soil pH mapping is achieved. Based on a domestic Gaofen-1 satellite WFV sensor, the method provided by the invention can realize dynamic monitoring of the cultivated land surface soil organic matters, and actual measurement sample point verification finds that the method provided by the invention can accurately estimate the cultivated land surface soil organic matters, and is beneficial to management in aspects of soil fertility condition monitoring, precision agriculture and land sustainable utilization.

Description

Technical Field

[0001] The invention belongs to the field of satellite remote sensing applications, and in particular relates to a method for quickly mapping soil organic matter distribution. Background Art

[0002] Soil organic matter is one of the important indicators for evaluating soil degradation, soil health and soil fertility, and is a key factor affecting crop yields. Therefore, in terms of smart agriculture and sustainable use of agricultural land, it is urgent to quickly and accurately estimate soil organic matter. Traditional spatial mapping methods represented by geostatistics (such as the Kriging method) require field sampling and testing, and often focus on spatial mapping of small-scale, fragmented cultivated land. They are often not applicable to large-scale regional remote sensing mapping. This is because the impact of soil organic matter in agricultural land is affected by multiple factors such as topography, parent material, climate, vegetation and agronomic measures, and the spatial heterogeneity is large. With the development of earth observation technology and the innovation of artificial intelligence algorithms, satellite remote sensing observation methods combined with big data analysis methods have become an important method for quantitative monitoring and mapping of surface environmental parameters in large-scale regions. In previous digital soil mapping studies, Landsat series satellites and Sentinel-2 series satellites were mostly used, while digital soil mapping based on the domestically produced Gaofen-1 satellite was less common.

[0003] Satellite remote sensing mapping of soil organic matter mainly relies on spectral technology to analyze the differences in the physical and chemical properties of different landforms, and the specific differences in spectral characteristics are mainly reflected in the differences in remote sensing reflectance in different spectral bands. Soil organic matter has reflectance spectral characteristics in the visible light and near-infrared wavelength range, and as the soil organic matter content increases, the soil reflectance decreases, causing the shape of the spectral curve to change. Therefore, in large-scale regional soil organic matter remote sensing mapping, it is necessary to construct an inversion algorithm that combines environmental variables and spectral indices based on the background of changing geographical environment, in order to improve the accuracy of large-scale regional soil organic matter remote sensing mapping. Summary of the invention

[0004] The purpose of the present invention is to solve the technical problem of declining soil fertility and deteriorating farmland quality, and to provide a method for rapid mapping of soil organic matter distribution with a 16m resolution using a Gaofen-1 satellite.

[0005] The method for rapid mapping of soil organic matter distribution with 16m resolution by Gaofen-1 satellite is as follows:

[0006] Step 1: Get the original data

[0007] Acquire WFV sensor image data from the Gaofen-1 satellite (GF-1) in the target study area;

[0008] Step 2: Image data preprocessing

[0009] The original WFV sensor image data of Gaofen-1 satellite was preprocessed to obtain the remote sensing reflectivity of different bands of Gaofen-1 satellite;

[0010] Step 3: Environmental covariate data processing

[0011] Download and process environmental data to obtain 30m terrain data and climate information. The terrain data is elevation, and the climate information is free grid data of annual average rainfall 1km and annual average temperature 1km. All data are resampled to 16m spatial resolution to be consistent with the spatial resolution data of WFV sensor images.

[0012] The free raster data of annual average rainfall at 1 km and annual average temperature at 1 km were downloaded from the National Meteorological Center (http: / / data.cma.cn / ), and the 30 m terrain data were obtained from USGS (https: / / gdex.cr.usgs.gov / gdex / ), where the elevation was extracted in ArcGIS software;

[0013] Step 4: Random Forest Algorithm Inversion

[0014] Input variables of the random forest algorithm: average annual rainfall, average annual temperature, elevation, spectral index RI2, spectral index RI3, spectral index DI2, spectral index DI3, spectral index NDVI, spectral index RVI, spectral index DVI, spectral index NDI1, spectral index NDI2, spectral index NDI3, spectral index RDVI1, spectral index RDVI2, RDVI3, spectral index GDVI1 and spectral index GDVI3;

[0015] Random forest algorithm output variables: organic matter in the topsoil of cultivated land, unit: mg / kg;

[0016] The spectral index:

[0017] RI2=Band3 / Band2

[0018] RI3=Band4 / Band1

[0019] DI2=Band3-Band2

[0020] DI3=Band4-Band1

[0021] NDVI=(Band4-Band3) / (Band4+Band3)

[0022] RVI=Band4 / Band3

[0023] DVI=Band4-Band3

[0024] NDI1=(Band3-Band2) / (Band3+Band2)

[0025] NDI2=(Band4-Band1) / (Band4+Band1)

[0026] NDI3=(Band4-Band2) / (Band4+Band2)

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] Band1 represents the remote sensing reflectance of Band 1 of the WFV sensor of the Gaofen-1 satellite;

[0033] Band2 represents the remote sensing reflectivity of Band 2 of the WFV sensor of the Gaofen-1 satellite;

[0034] Band3 represents the remote sensing reflectivity of Band 3 of the WFV sensor of the Gaofen-1 satellite;

[0035] Band4 represents the remote sensing reflectivity of Band 4 of the WFV sensor of the Gaofen-1 satellite;

[0036] Step 5: Soil organic matter remote sensing mapping: Substitute the prediction model into the WFV sensor image of the Gaofen-1 satellite (GF-1) to achieve rapid mapping of regional soil organic matter distribution.

[0037] The preprocessing described in step 2 includes radiation calibration, orthorectification and atmospheric correction.

[0038] The input variables of the random forest algorithm described in step 4 are constructed based on the measured soil organic matter inversion model. The construction process of the soil organic matter random forest inversion model is as follows:

[0039] a. During the bare soil period in Northeast China (late April), a total of 482 soil samples with a surface depth of 0-20 cm were collected from different terrain conditions and soil types, and the GPS locations of the samples were recorded. The collected soil samples were air-dried, ground and sieved, and the soil organic matter content was obtained using the potassium dichromate heating method;

[0040] b. Obtain the WFV sensor image of the Gaofen-1 satellite (GF-1), and after the image data preprocessing process in step 2, extract the remote sensing reflectivity corresponding to the measured soil sample according to the GPS position, and obtain the remote sensing reflectivity of different bands of the Gaofen-1 satellite;

[0041] c. After step 3, obtain environmental covariate data:

[0042] d. Based on the measured soil organic matter data and remote sensing reflectance of different bands of Gaofen-1 satellite, a random forest inversion model of soil organic matter was established:

[0043] Randomly select measured soil organic matter data and the corresponding remote sensing reflectance of different bands of Gaofen-1 satellite as the correction data set of the model, analyze the linear correlation between remote sensing reflectance of different bands, spectral index calculated by remote sensing reflectance of different bands, environmental covariate data and measured soil organic matter data, and screen out variables related to measured soil organic matter through the significance level p<0.05 and the confidence level of 0.01 on both sides, namely, annual average rainfall, annual average temperature, elevation, spectral index RI2, spectral index RI3, spectral index DI2, spectral index DI3, spectral index NDVI, spectral index RVI, spectral index DVI, spectral index NDI1, spectral index NDI2, spectral index NDI3, spectral index RDVI1, spectral index RDVI2, RDVI3, spectral index GDVI1 and spectral index GDVI3; use the screened variables as input variables of random forest algorithm to establish soil organic matter remote sensing inversion model;

[0044] The linear correlation analysis was implemented in SPSS 16.0 software platform, and the Pearson correlation coefficient r was expressed as follows:

[0045]

[0046] Among them, x i is the measured value of soil organic matter, y i is the remote sensing reflectance of different bands of Gaofen-1 satellite corresponding to the measured value of soil organic matter, and N is the number of samples;

[0047] e. Perform error verification on the soil organic matter remote sensing inversion model. The error verification expression is as follows:

[0048]

[0049] Where RMSE is the root mean square error, N is the number of samples, and y i is the measured value of soil organic matter, y i ' is the estimated soil organic matter;

[0050] f. Algorithm test data set linear fit R 2=0.92, RMSE is 4.24 g / kg.

[0051] The random forest algorithm described in step 4 uses the RandomForest package for the random forest inversion algorithm. The number of decision trees ntree is 500, and the number of random variables for splitting nodes mtry is 1 / 3 of the input. At this time, the value is small and the error within the model is basically stable.

[0052] Based on the 16m resolution soil organic matter distribution rapid mapping method of the Gaofen-1 satellite of the present invention, large-scale regional cultivated land surface soil organic matter estimation and spatial mapping can be performed, and the spatial distribution of interannual changes in soil organic matter content can be accurately obtained. In general, the present invention constructs a soil organic matter random forest algorithm using 482 soil organic matter measured values ​​covering the Northeast region to train the random forest algorithm, determine the input variables of the algorithm, and can better represent the changes in different geographical environment backgrounds, soil types and elevations. The soil organic matter inversion model developed by the algorithm of the present invention has good applicability, reduces the time and cost of on-site sampling of traditional methods, and effectively evaluates soil fertility and the performance of improving cultivated land productivity.

[0053] Based on the WFV sensor of the domestically produced GF-1 satellite, the method of the present invention can realize dynamic monitoring of organic matter in the surface soil of cultivated land. It is found through verification of actual sample points that the method of the present invention can accurately estimate the organic matter in the surface soil of cultivated land, which is helpful for soil fertility monitoring, precision agriculture and sustainable land use management, ensuring high-precision monitoring of soil organic matter content and expanding the application of domestically produced GF series satellites in agricultural management. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is the calibration diagram of the soil organic matter inversion model in the Northeast region in Experiment 1;

[0055] Figure 2 This is the validation diagram of the soil organic matter inversion model in Northeast China in Experiment 1;

[0056] Figure 3 The present invention is a schematic flow chart of the steps of a method for rapidly mapping soil organic matter distribution using the Gaofen-1 satellite with a resolution of 16 m. DETAILED DESCRIPTION

[0057] The technical solution of the present invention is not limited to the specific implementation modes listed below, but also includes any combination of the specific implementation modes.

[0058] Specific implementation method 1: In this implementation method, the method for quickly mapping soil organic matter distribution with a 16m resolution using the Gaofen-1 satellite is as follows:

[0059] Step 1: Get the original data

[0060] Acquire WFV sensor image data from the Gaofen-1 satellite (GF-1) in the target study area;

[0061] Step 2: Image data preprocessing

[0062] The original WFV sensor image data of Gaofen-1 satellite was preprocessed to obtain the remote sensing reflectivity of different bands of Gaofen-1 satellite;

[0063] Step 3: Environmental covariate data processing

[0064] Download and process environmental data to obtain 30m terrain data and climate information. The terrain data is elevation, and the climate information is free grid data of annual average rainfall 1km and annual average temperature 1km. All data are resampled to 16m spatial resolution to be consistent with the spatial resolution data of WFV sensor images.

[0065] The free raster data of annual average rainfall at 1 km and annual average temperature at 1 km were downloaded from the National Meteorological Center (http: / / data.cma.cn / ), and the 30 m terrain data were obtained from USGS (https: / / gdex.cr.usgs.gov / gdex / ), where the elevation was extracted in ArcGIS software;

[0066] Step 4: Random Forest Algorithm Inversion

[0067] Input variables of the random forest algorithm: average annual rainfall, average annual temperature, elevation, spectral index RI2, spectral index RI3, spectral index DI2, spectral index DI3, spectral index NDVI, spectral index RVI, spectral index DVI, spectral index NDI1, spectral index NDI2, spectral index NDI3, spectral index RDVI1, spectral index RDVI2, RDVI3, spectral index GDVI1 and spectral index GDVI3;

[0068] Random forest algorithm output variables: organic matter in the topsoil of cultivated land, unit: mg / kg;

[0069] The spectral index:

[0070] RI2=Band3 / Band2

[0071] RI3=Band4 / Band1

[0072] DI2=Band3-Band2

[0073] DI3=Band4-Band1

[0074] NDVI=(Band4-Band3) / (Band4+Band3)

[0075] RVI=Band4 / Band3

[0076] DVI=Band4-Band3

[0077] NDI1=(Band3-Band2) / (Band3+Band2)

[0078] NDI2=(Band4-Band1) / (Band4+Band1)

[0079] NDI3=(Band4-Band2) / (Band4+Band2)

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] Band1 represents the remote sensing reflectance of Band 1 of the WFV sensor of the Gaofen-1 satellite;

[0086] Band2 represents the remote sensing reflectivity of Band 2 of the WFV sensor of the Gaofen-1 satellite;

[0087] Band3 represents the remote sensing reflectivity of Band 3 of the WFV sensor of the Gaofen-1 satellite;

[0088] Band4 represents the remote sensing reflectivity of Band 4 of the WFV sensor of the Gaofen-1 satellite;

[0089] Step 5: Soil organic matter remote sensing mapping: Substitute the prediction model into the WFV sensor image of the Gaofen-1 satellite (GF-1) to achieve rapid mapping of regional soil organic matter distribution.

[0090] Specific implementation method 2: This implementation method is different from specific implementation method 1 in that the preprocessing described in step 2 includes radiation calibration, orthorectification and atmospheric correction. The rest is the same as specific implementation method 1.

[0091] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that the input variables of the random forest algorithm in step four are constructed based on the measured soil organic matter inversion model. The soil organic matter random forest inversion model construction process is as follows:

[0092] a. During the bare soil period in Northeast China (late April), a total of 482 soil samples with a surface depth of 0-20 cm were collected from different terrain conditions and soil types, and the GPS locations of the samples were recorded. The collected soil samples were air-dried, ground and sieved, and the soil organic matter content was obtained using the potassium dichromate heating method;

[0093] b. Obtain the WFV sensor image of the Gaofen-1 satellite (GF-1), and after the image data preprocessing process in step 2, extract the remote sensing reflectivity corresponding to the measured soil sample according to the GPS position, and obtain the remote sensing reflectivity of different bands of the Gaofen-1 satellite;

[0094] c. After step 3, obtain environmental covariate data:

[0095] d. Based on the measured soil organic matter data and remote sensing reflectance of different bands of Gaofen-1 satellite, a random forest inversion model of soil organic matter was established:

[0096] Randomly select measured soil organic matter data and the corresponding remote sensing reflectance of different bands of Gaofen-1 satellite as the correction data set of the model, analyze the linear correlation between remote sensing reflectance of different bands, spectral index calculated by remote sensing reflectance of different bands, environmental covariate data and measured soil organic matter data, and screen out variables related to measured soil organic matter through the significance level p<0.05 and the confidence level of 0.01 on both sides, namely, annual average rainfall, annual average temperature, elevation, spectral index RI2, spectral index RI3, spectral index DI2, spectral index DI3, spectral index NDVI, spectral index RVI, spectral index DVI, spectral index NDI1, spectral index NDI2, spectral index NDI3, spectral index RDVI1, spectral index RDVI2, RDVI3, spectral index GDVI1 and spectral index GDVI3; use the screened variables as input variables of random forest algorithm to establish soil organic matter remote sensing inversion model;

[0097] The linear correlation analysis was implemented in SPSS 16.0 software platform, and the Pearson correlation coefficient r was expressed as follows:

[0098]

[0099] Among them, x i is the measured value of soil organic matter, y i is the remote sensing reflectance of different bands of Gaofen-1 satellite corresponding to the measured value of soil organic matter, and N is the number of samples;

[0100] e. Perform error verification on the soil organic matter remote sensing inversion model. The error verification expression is as follows:

[0101]

[0102] Where RMSE is the root mean square error, N is the number of samples, and y i is the measured value of soil organic matter, y i ' is the estimated soil organic matter;

[0103] f. Algorithm test data set linear fit R 2 =0.92, RMSE is 4.24 g / kg. Other aspects are the same as those of the first or second specific implementation.

[0104] Specific implementation method 4: This implementation method is different from specific implementation methods 1 to 3 in that the random forest algorithm described in step 4 uses the RandomForest package, the number of decision trees ntree is 500, and the number of random variables of the split nodes mtry is 1 / 3 of the input amount, at which time the value is small and the error within the model is basically stable. Others are the same as specific implementation methods 1 to 3.

[0105] The following experiments were used to verify the effect of the present invention:

[0106] Experiment 1:

[0107] The method for rapid mapping of soil organic matter distribution with 16m resolution by Gaofen-1 satellite is as follows:

[0108] Step 1: Get the original data

[0109] Acquire WFV sensor image data from the Gaofen-1 satellite (GF-1) in the target study area;

[0110] Step 2: Image data preprocessing

[0111] The original WFV sensor image data of Gaofen-1 satellite was preprocessed to obtain the remote sensing reflectivity of different bands of Gaofen-1 satellite;

[0112] Step 3: Environmental covariate data processing

[0113] Download and process environmental data to obtain 30m terrain data and climate information. The terrain data is elevation, and the climate information is free grid data of annual average rainfall 1km and annual average temperature 1km. All data are resampled to 16m spatial resolution to be consistent with the spatial resolution of WFV sensor image data.

[0114] The free raster data of annual average rainfall at 1 km and annual average temperature at 1 km were downloaded from the National Meteorological Center (http: / / data.cma.cn / ), and the 30 m terrain data were obtained from USGS (https: / / gdex.cr.usgs.gov / gdex / ), where the elevation was extracted in ArcGIS software;

[0115] Step 4: Random Forest Algorithm Inversion

[0116] Input variables of the random forest algorithm: average annual rainfall, average annual temperature, elevation, spectral index RI2, spectral index RI3, spectral index DI2, spectral index DI3, spectral index NDVI, spectral index RVI, spectral index DVI, spectral index NDI1, spectral index NDI2, spectral index NDI3, spectral index RDVI1, spectral index RDVI2, RDVI3, spectral index GDVI1 and spectral index GDVI3;

[0117] Random forest algorithm output variables: organic matter in the topsoil of cultivated land, unit: mg / kg;

[0118] The spectral index:

[0119] RI2=Band3 / Band2

[0120] RI3=Band4 / Band1

[0121] DI2=Band3-Band2

[0122] DI3=Band4-Band1

[0123] NDVI=(Band4-Band3) / (Band4+Band3)

[0124] RVI=Band4 / Band3

[0125] DVI=Band4-Band3

[0126] NDI1=(Band3-Band2) / (Band3+Band2)

[0127] NDI2=(Band4-Band1) / (Band4+Band1)

[0128] NDI3=(Band4-Band2) / (Band4+Band2)

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] Band1 represents the remote sensing reflectance of Band 1 of the WFV sensor of the Gaofen-1 satellite;

[0135] Band2 represents the remote sensing reflectivity of Band 2 of the WFV sensor of the Gaofen-1 satellite;

[0136] Band3 represents the remote sensing reflectivity of Band 3 of the WFV sensor of the Gaofen-1 satellite;

[0137] Band4 represents the remote sensing reflectivity of Band 4 of the WFV sensor of the Gaofen-1 satellite;

[0138] Step 5: Soil organic matter remote sensing mapping: Substitute the prediction model into the WFV sensor image of the Gaofen-1 satellite (GF-1) to achieve rapid mapping of regional soil organic matter distribution.

[0139] The preprocessing described in step 2 includes radiation calibration, orthorectification and atmospheric correction.

[0140] The input variables of the random forest algorithm described in step 4 are constructed based on the measured soil organic matter inversion model. The construction process of the soil organic matter random forest inversion model is as follows:

[0141] a. During the bare soil period in Northeast China (late April), a total of 482 soil samples with a surface depth of 0-20 cm were collected from different terrain conditions and soil types, and the GPS locations of the samples were recorded. The collected soil samples were air-dried, ground and sieved, and the soil organic matter content was obtained using the potassium dichromate heating method;

[0142] b. Obtain the WFV sensor image of the Gaofen-1 satellite (GF-1), and after the image data preprocessing process in step 2, extract the remote sensing reflectivity corresponding to the measured soil sample according to the GPS position, and obtain the remote sensing reflectivity of different bands of the Gaofen-1 satellite;

[0143] c. After step 3, obtain environmental covariate data:

[0144] d. Based on the measured soil organic matter data and remote sensing reflectance of different bands of Gaofen-1 satellite, a random forest inversion model of soil organic matter was established:

[0145] Randomly select measured soil organic matter data and the corresponding remote sensing reflectance of different bands of Gaofen-1 satellite as the correction data set of the model, analyze the linear correlation between remote sensing reflectance of different bands, spectral index calculated by remote sensing reflectance of different bands, environmental covariate data and measured soil organic matter data, and screen out variables related to measured soil organic matter through the significance level p<0.05 and the confidence level of 0.01 on both sides, namely, annual average rainfall, annual average temperature, elevation, spectral index RI2, spectral index RI3, spectral index DI2, spectral index DI3, spectral index NDVI, spectral index RVI, spectral index DVI, spectral index NDI1, spectral index NDI2, spectral index NDI3, spectral index RDVI1, spectral index RDVI2, RDVI3, spectral index GDVI1 and spectral index GDVI3; use the screened variables as input variables of random forest algorithm to establish soil organic matter remote sensing inversion model;

[0146] The linear correlation analysis was implemented in SPSS 16.0 software platform, and the Pearson correlation coefficient r was expressed as follows:

[0147]

[0148] Among them, x i is the measured value of soil organic matter, y i is the remote sensing reflectance of different bands of Gaofen-1 satellite corresponding to the measured value of soil organic matter, and N is the number of samples;

[0149] e. Perform error verification on the soil organic matter remote sensing inversion model. The error verification expression is as follows:

[0150]

[0151] Where RMSE is the root mean square error, N is the number of samples, and y i is the measured value of soil organic matter, y i ' is the estimated soil organic matter;

[0152] f. Algorithm test data set linear fit R 2 =0.81, RMSE is 4.96g / kg, which meets the internationally accepted quantitative remote sensing inversion accuracy requirements (>45%). Figure 2 .

[0153] The random forest algorithm described in step 4 uses the RandomForest package for the random forest inversion algorithm. The number of decision trees ntree is 500, and the number of random variables for splitting nodes mtry is 1 / 3 of the input. At this time, the value is small and the error within the model is basically stable.

Claims

1. A method for rapid mapping soil organic matter distribution using 16m resolution satellite Gaofen-1, characterized in that The method for rapid mapping soil organic matter distribution with 16m resolution using the Gaofen-1 satellite is as follows: Step 1: Get the original data Acquire WFV sensor image data from the Gaofen-1 satellite in the target study area; Step 2: Image data preprocessing The original WFV sensor image data of Gaofen-1 satellite was preprocessed to obtain the remote sensing reflectivity of different bands of Gaofen-1 satellite; Step 3: Environmental covariate data processing Download and process environmental data to obtain 30m terrain data and climate information. The terrain data is elevation, and the climate information is free grid data of annual average rainfall 1km and annual average temperature 1km. All data are resampled to 16m spatial resolution to be consistent with the spatial resolution data of WFV sensor images. The free raster data of annual average rainfall at 1 km and annual average temperature at 1 km were downloaded from the National Meteorological Center, and the 30 m terrain data were obtained from USGS, of which the elevation was extracted in ArcGIS software; Step 4: Random Forest Algorithm Inversion Input variables of the random forest algorithm: average annual rainfall, average annual temperature, elevation, spectral index RI2, spectral index RI3, spectral index DI2, spectral index DI3, spectral index NDVI, spectral index RVI, spectral index DVI, spectral index NDI1, spectral index NDI2, spectral index NDI3, spectral index RDVI1, spectral index RDVI2, RDVI3, spectral index GDVI1 and spectral index GDVI3; Random forest algorithm output variables: organic matter in the topsoil of cultivated land, unit: mg / kg; The spectral index: RI2=Band3 / Band2 RI3=Band4 / Band1 DI2=Band3-Band2 DI3=Band4-Band1 NDVI=(Band4-Band3) / (Band4+Band3) RVI=Band4 / Band3 DVI=Band4-Band3 NDI1=(Band3-Band2) / (Band3+Band2) NDI2=(Band4-Band1) / (Band4+Band1) NDI3=(Band4-Band2) / (Band4+Band2) Band1 represents the remote sensing reflectance of Band 1 of the WFV sensor of the Gaofen-1 satellite; Band2 represents the remote sensing reflectivity of Band 2 of the WFV sensor of the Gaofen-1 satellite; Band3 represents the remote sensing reflectivity of Band 3 of the WFV sensor of the Gaofen-1 satellite; Band4 represents the remote sensing reflectivity of Band 4 of the WFV sensor of the Gaofen-1 satellite; Step 5: Soil organic matter remote sensing mapping: Substitute the prediction model into the WFV sensor image of the Gaofen-1 satellite to achieve rapid mapping of regional soil organic matter distribution.

2. The method for rapid mapping soil organic matter distribution using the Gaofen-1 satellite at a resolution of 16 m according to claim 1, characterized in that The preprocessing described in step 2 includes radiation calibration, orthorectification and atmospheric correction.

3. The method for rapid mapping soil organic matter distribution using the Gaofen-1 satellite at a resolution of 16 m according to claim 1, characterized in that The input variables of the random forest algorithm described in step 4 are constructed based on the measured soil organic matter inversion model. The construction process of the soil organic matter random forest inversion model is as follows: a. During the bare soil period in Northeast China, a total of 482 soil samples with a surface depth of 0-20 cm were collected from different terrain conditions and soil types, and the GPS locations of the samples were recorded. The collected soil samples were air-dried, ground and sieved, and the soil organic matter content was obtained using the potassium dichromate heating method; b. Obtain the WFV sensor image of the Gaofen-1 satellite, and after the image data preprocessing process in step 2, extract the remote sensing reflectivity corresponding to the measured soil sample according to the GPS position, and obtain the remote sensing reflectivity of different bands of the Gaofen-1 satellite; c. After step 3, obtain environmental covariate data: d. Based on the measured soil organic matter data and remote sensing reflectance of different bands of Gaofen-1 satellite, a random forest inversion model of soil organic matter was established: Randomly select measured soil organic matter data and the corresponding remote sensing reflectance of different bands of Gaofen-1 satellite as the correction data set of the model, analyze the linear correlation between remote sensing reflectance of different bands, spectral index calculated by remote sensing reflectance of different bands, environmental covariate data and measured soil organic matter data, and screen out variables related to measured soil organic matter through the significance level p<0.05 and the confidence level of 0.01 on both sides, namely, annual average rainfall, annual average temperature, elevation, spectral index RI2, spectral index RI3, spectral index DI2, spectral index DI3, spectral index NDVI, spectral index RVI, spectral index DVI, spectral index NDI1, spectral index NDI2, spectral index NDI3, spectral index RDVI1, spectral index RDVI2, RDVI3, spectral index GDVI1 and spectral index GDVI3; use the screened variables as input variables of random forest algorithm to establish soil organic matter remote sensing inversion model; The linear correlation analysis was implemented in SPSS 16.0 software platform, and the Pearson correlation coefficient r was expressed as follows: Among them, x i is the measured value of soil organic matter, y i is the remote sensing reflectance of different bands of Gaofen-1 satellite corresponding to the measured value of soil organic matter, and N is the number of samples; e. Perform error verification on the soil organic matter remote sensing inversion model. The error verification expression is as follows: Where RMSE is the root mean square error, N is the number of samples, and y i is the measured value of soil organic matter, y i ' is the estimated soil organic matter; f. Algorithm test data set linear fit R 2 =0.92, RMSE is 4.24 g / kg.

4. The method for rapid mapping soil organic matter distribution using the Gaofen-1 satellite at a resolution of 16 m according to claim 1, characterized in that The random forest algorithm described in step 4 uses the RandomForest package for the random forest inversion algorithm. The number of decision trees ntree is 500, and the number of random variables for splitting nodes mtry is 1 / 3 of the input. At this time, the value is small and the error within the model is basically stable.

Images