Calculation Method of Segmented Soil Moisture Stress Coefficient Based on Multi-Source Remote Sensing Data

Abstract

The present invention relates to a method for calculating a segmented soil moisture stress coefficient based on multi-source remote sensing data. The method extracts crop planting structures using optical remote sensing images, collects SAR image data and soil moisture content monitoring station data within the same crop planting area, constructs intervals of soil moisture content ranges, and for each station within each interval, the remote sensing intensity value X and the soil moisture content value Y of the data formed are fitted, a ratio comprehensive evaluation index RCEI is constructed to select the optimal algorithm, the overall soil moisture content inversion formula is obtained by integrating each interval, the soil moisture content inversion formula for other crops is calculated, and the soil moisture stress correction coefficient is calculated using the inverted soil moisture content data. The present invention comprehensively utilizes qualitative analysis and quantitative inversion based on multi-source remote sensing technology, respectively establishes different soil moisture content inversion algorithms according to different ranges of water content for different crop planting areas, and realizes high-precision inversion of soil moisture content.

Description

Technical Field

[0001] The present invention relates to a method for calculating a segmented soil moisture stress coefficient based on multi-source remote sensing data, belonging to the technical fields of remote sensing technology and smart agriculture. Background Art

[0002] Water resources are essential elements for food production. With the continuous development of society, the contradiction between the supply and demand of water resources has become increasingly prominent. Improving agricultural production through precise water resource allocation and ensuring food security have become key issues for the sustainable development of the agricultural economy. The accurate calculation of crop water requirements is the basis for precise water resource allocation. Conditions such as soil moisture, crop growth, topography, and temperature and climate are the main factors affecting crop water requirements. Therefore, obtaining accurate soil moisture data for calculating the soil moisture stress coefficient is of great significance for estimating crop water requirements.

[0003] The soil moisture stress coefficient is mainly calculated by obtaining soil water content and empirical parameters. Traditional soil water content depends on soil moisture monitoring equipment, and the obtained data exists in the form of point data, making it difficult to obtain soil water content information for the entire plot and accurately reflect the soil moisture stress coefficient. Therefore, some people use data obtained from remote sensing satellites to invert soil water content and then obtain soil moisture stress, but do not consider the influence of different soil water content ranges and different crop planting types on the soil water content inversion algorithm. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for calculating a segmented soil moisture stress coefficient based on multi-source remote sensing data. This method mainly uses remote sensing technology to calculate the soil moisture stress coefficient in segments based on multi-source remote sensing data.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A method for calculating a segmented soil moisture stress coefficient based on multi-source remote sensing data, comprising the following steps:

[0007] S1. Obtain an optical remote sensing image, extract the crop planting structure, and obtain the regional division of different planted crops in the crop planting area;

[0008] S2. According to the extraction result of the crop planting structure, within the area of the same planted crop, collect SAR (Synthetic Aperture Radar) image data, record the image data acquisition time, process the data, and obtain the image remote sensing intensity data;

[0009] S3. Collect the longitude and latitude information and soil water content information of the soil water content monitoring stations in the crop planting area. According to the image acquisition time, select the soil water content monitoring data of all stations at the same moment or adjacent moments, and obtain the remote sensing intensity values at the corresponding positions on the SAR image according to the longitude and latitude information of each station. The image remote sensing intensity value of each station X and the soil water content value Y constitute a set of data, and multiple sets of corresponding data can be obtained for multiple stations;

[0010] S4. Respectively use 20% and 80% of the total data of the soil water content data monitored in the same historical period as the segmentation thresholds a and b, and construct three intervals of soil water content ranges with a and b as the thresholds, and determine the following soil water content inversion formulas for each interval respectively;

[0011] S5. In each soil water content range, divide the data collected in step S3 into data sets, use polynomial fitting, power fitting, exponential fitting and logarithmic fitting to construct an inversion algorithm, and then calculate each fitting formula X and Y The Pearson correlation coefficient r between them, use the test data set to verify the inversion algorithm, calculate the mean absolute error MAE after passing the verification, construct the ratio comprehensive evaluation index RCEI, and select the optimal algorithm formula in this soil water content range;

[0012] S6. Integrate the optimal soil water content inversion algorithms for the three soil water content intervals to obtain the overall soil water content inversion formula, and reverse determine the corresponding SAR data remote sensing intensity threshold range according to the inversion formula to obtain the soil water content segmented inversion formula corresponding to different SAR data remote sensing intensities of the crop;

[0013] S7. Calculate the corresponding soil water content segmented inversion formulas for other crops in the planting area according to steps S2 - S6 respectively;

[0014] S8. Obtain the SAR image of a certain period in the crop planting area. First, classify the area according to different crops. After classification, select the corresponding soil water content inversion formula according to the type of the planted crop, and then calculate the soil water content corresponding to the intensity value according to the remote sensing intensity value, so as to obtain the soil water content inversion results of each area in the crop planting area;

[0015] S9. Calculate the soil water stress correction coefficient using the inverted soil water content data.

[0016] In the above method,

[0017] In step S2, the SAR image data needs to cover the crop planting area. Processing the data means performing multi-look processing, filtering, geocoding, and radiometric calibration on the data to obtain the corrected remote sensing intensity data.

[0018] The soil moisture content information described in step S3 includes the value of the soil moisture content and the acquisition time.

[0019] The polynomial fitting, power fitting, exponential fitting, and logarithmic fitting formulas described in step S5 are as follows:

[0020] ,

[0021] ,

[0022] ,

[0023] ,

[0024] ,

[0025] a, b, and c are undetermined parameters;

[0026] The calculation formula for the Pearson correlation coefficient r is:

[0027] ,

[0028] The calculation formula for the mean absolute error MAE is:

[0029] ,

[0030] x i is the predicted value of the hydrological monitoring data, y i represents the true value of the i-th hydrological monitoring data, represents the predicted value obtained after the i-th sample is calculated by the algorithm; n is the number of samples.

[0031] The calculation formula for the ratio comprehensive evaluation index RCEI in step S5 is:

[0032] ,

[0033] p is the weight adjustment factor, and its value is the same as the proportion of the test data and the modeling data; r is the Pearson correlation coefficient; MAE is the mean absolute error.

[0034] The formula for the soil moisture stress correction coefficient described in step S8 is as follows:

[0035] ,

[0036] In the formula, K S is the soil moisture stress correction coefficient; θ is the soil water content retrieved by remote sensing; θ wp is the wilting water content; θ j is the field capacity. The wilting water content and the field capacity are empirical parameters in agricultural hydraulics and are determined according to the actual situation of the calculation area.

[0037] The beneficial effects of the present invention are as follows:

[0038] Based on the comprehensive utilization of qualitative analysis and quantitative inversion of multi-source remote sensing technology, the present invention extracts the types of planted crops, divides different crop planting areas, and for different crop planting areas, different soil water content inversion algorithms are established respectively according to different ranges of water content, realizing high-precision inversion of soil water content, providing accurate calculation parameters for the dynamic estimation of crop water requirements in a large area, facilitating precise agricultural irrigation and optimal allocation of water resources, and having obvious demonstration for applications in other regions. Description of the Drawings

[0039] Figure 1 is the method flow chart of the present invention;

[0040] Figure 2 is the crop planting structure division map of the research area in the embodiment of the present invention;

[0041] Figure 3 is the distribution map of monitoring stations in the corn planting area of the research area in the embodiment of the present invention. Detailed Embodiments

[0042] The following further explains in conjunction with the detailed embodiments and examples.

[0043] The calculation method of the segmented soil moisture stress coefficient based on multi-source remote sensing data includes the following steps (as Figure 1 ):

[0044] S1. Obtain optical remote sensing images, extract the crop planting structure, and obtain the regional division of different planted crops in the crop planting area:

[0045] Select optical remote sensing images. After radiometric calibration, atmospheric correction, and geometric correction of the images, the method of visual interpretation is used to extract the crop planting area, and according to the actual planting situation in the planting area, the existing method is used in the remote sensing image to conduct the regional division of different planted crops in the crop planting area.

[0046] S2. According to the extraction results of the crop planting structure, within the area of the same planted crop, collect SAR (Synthetic Aperture Radar) image data, record the acquisition time of the image data, process the data, and obtain the image remote sensing intensity data:

[0047] According to the extraction results of the crop planting structure, within the same crop planting area, collect SAR (Synthetic Aperture Radar) data. The data needs to cover the crop planting area. Record the image acquisition time, and perform multi-look processing, filtering, geocoding, radiometric calibration, etc. on the data to obtain the corrected intensity data.

[0048] S3. Collect the longitude and latitude information and soil moisture content information (including the value of soil moisture content and the acquisition time) of the soil moisture monitoring stations within the crop planting area; according to the image time, select the monitoring data of all stations at the same moment (or adjacent moments), and respectively obtain the remote sensing intensity values at the corresponding positions on the image according to the longitude and latitude information of each station. The image remote sensing intensity value of each station X and the soil moisture content value Y constitute a set of data, and multiple stations can obtain multiple sets of corresponding data.

[0049] S4. Respectively use 20% and 80% of the overall data of the historically monitored soil moisture content data values as the segmentation thresholds a and b, and construct three intervals of soil moisture content ranges with a and b as the thresholds, and then determine the following soil moisture content inversion formulas for each interval respectively:

[0050] Establish a soil moisture content inversion algorithm by segmenting according to the soil moisture content distribution. The segmentation thresholds a and b are usually 20% and 80% of the overall data of the historically monitored soil moisture content data values, and can be adjusted according to the actual situation. Establish corresponding three intervals of soil moisture content ranges with a and b as the thresholds.

[0051] S5. Within each soil moisture content range, divide the data collected in step S3 into data sets, use polynomial fitting, power fitting, exponential fitting, and logarithmic fitting to construct an inversion algorithm, and then calculate the Pearson correlation coefficient r between each fitting formula X and Y respectively, use the test data set to verify the inversion algorithm, calculate the mean absolute error MAE after passing the verification, construct the ratio comprehensive evaluation index RCEI, and select the optimal algorithm formula within this soil moisture content range:

[0052] Within each soil moisture content range, divide the collected data into a test data set and a modeling data set according to a ratio of 3:7, use linear fitting (Equation 1), polynomial fitting (Equation 2), power fitting (Equation 3), exponential fitting (Equation 4), and logarithmic fitting (Equation 5) to construct an inversion algorithm, and then calculate X andY The Pearson correlation coefficient (r) between them (Equation VI) (, and then using the test data set to verify the inversion algorithm. The verification is carried out by calculating the mean absolute error (MAE) (Equation VII). By comprehensively comparing and MAE, a ratio comprehensive evaluation index RCEI is constructed. The algorithm with a larger RCEI value is selected as the optimal algorithm, and the optimal algorithm within this soil water content range is selected. The formula calculation method is as follows:

[0053] (Equation I),

[0054] (Equation II),

[0055] (Equation III),

[0056] (Equation IV),

[0057] (Equation V),

[0058] (Equation VI),

[0059] (Equation VII),

[0060] (Equation VIII),

[0061] In the formula: a, b, c are undetermined parameters; x i is the predicted value of hydrological monitoring data, y i represents the true value of the i-th hydrological monitoring data, represents the predicted value obtained after the i-th sample is calculated by the algorithm; n is the number of samples; p is the weight adjustment factor, and its value is the same as the proportion of test data and modeling data; r is the Pearson correlation coefficient; MAE is the mean absolute error.

[0062] S6. Integrate the optimal soil water content inversion algorithms for the three soil water content intervals to obtain the overall soil water content inversion formula. According to the inversion formula, reverse-determine the corresponding SAR data remote sensing intensity threshold range, and obtain the soil water content segmented inversion formula corresponding to different SAR data remote sensing intensities of the crop;

[0063] S7. For other crops in the planting area, calculate their corresponding soil water content segmented inversion formulas respectively according to steps S2 - S6;

[0064] S8. Obtain the SAR images of a certain period in the crop planting area. First, classify the area according to different crops. After classification, select the corresponding soil moisture inversion formula according to the types of planted crops. Then, calculate the soil moisture content corresponding to the remote sensing intensity value based on the remote sensing intensity value, and thus obtain the soil moisture inversion results of each area in the crop planting area.

[0065] S9. Calculate the soil moisture stress correction coefficient by using the inverted soil moisture content data:

[0066] The formula for converting the soil moisture content data into the soil moisture stress correction coefficient is as follows:

[0067] (Formula IX),

[0068] In the formula, K S is the soil moisture stress correction coefficient; θ is the soil moisture content retrieved by remote sensing; θ wp is the wilting moisture content; θ j is the field capacity.

[0069] Example 1: In a certain planting area, there are multiple crops. Select the sample area through visual interpretation, and use supervised classification to extract the crop planting structure in the planting area. The extraction results are as Figure 2 shown,

[0070] According to the extraction results of the crop planting structure, calculate the soil moisture stress correction coefficient in the corn and soybean areas respectively. Taking the corn area as an example, in the corn planting area, collect the synthetic aperture radar (SAR) data on XX month XX day, XXXX year, and perform multi-look processing, filtering, geocoding, radiometric calibration, etc. on the data to obtain the intensity data; collect the longitude and latitude information of the soil moisture monitoring stations in the crop planting area. The distribution of the monitoring stations in the planting area is as Figure 3 shown. Obtain the intensity data of the corresponding points on the SAR image for each point according to the location of the monitoring stations. The intensity data of this point and the measured soil moisture content data form a set of data. Exclude the data with missing monitoring station data. There are 30 sets of valid data in the corn planting area, as shown in Table 1.

[0071] Table 1: Intensity and soil moisture content of monitoring stations in the corn planting area

[0072] .

[0073] Establish a soil moisture content inversion algorithm in segments according to the distribution of soil moisture content. The threshold values a and b for segmentation are usually 20% and 80% of the overall data of the soil moisture content data monitored during the same historical period, and can be adjusted according to the actual situation. According to 20% and 80% of the historical data, a and b are respectively set to 10 and 17.8. Therefore, the construction and selection of the inversion algorithm are carried out according to the soil moisture content ranges of 0 - 10, 10 - 17.8, and above 17.8 (the value range does not include the front-end value but includes the back-end value).

[0074] Within each soil moisture content range, the collected data is divided into a test data set and a modeling data set in a ratio of 3:7. Use polynomial fitting, power fitting, exponential fitting, and logarithmic fitting to establish the inversion algorithm, and then calculate the Pearson correlation coefficient (r) between X and Y respectively. Use the test data set to verify the inversion algorithm. After passing the verification, calculate the mean absolute error (MAE). Comprehensively compare the Pearson correlation coefficient r and the mean absolute error (MAE), and select the optimal algorithm within this soil moisture content range according to the evaluation comprehensive index AECI. Integrate the soil moisture content inversion algorithms for the three soil moisture content intervals to obtain the overall soil moisture content inversion formula, and then calculate the corresponding remote sensing image intensity data according to the inversion formula as the judgment basis for different inversion formulas of the remote sensing image.

[0075] Taking the data of soil moisture content in the range of 0 - 10 as an example, randomly select 2 groups of data as test data and 4 groups of data as modeling data in a ratio of 3:7. The data is as follows:

[0076] Table 2: Data division for soil moisture content in the range of 0 - 8.8% in segments

[0077] 。

[0078] Table 3: Algorithm fitting results

[0079] 。

[0080] Table 4: Calculated Pearson correlation coefficient r

[0081] 。

[0082] Table 5: Mean absolute error

[0083] 。

[0084] Table 6: AECI calculation results

[0085] 。

[0086] Judged by the AECI index, within the range of soil moisture content of 0 - 10, select the exponential fitting y = 0.001e99.764x As the fitting formula; similarly, the optimal fitting formula for soil water content of 10 - 17.8 is: y = 3.332x 0.319 For soil water content above 17.8, the optimal fitting formula is: y = 4.912e 0.903x Since the remote sensing data obtains intensity data, it is necessary to convert the segmentation points of the piecewise function into remote sensing intensity. The converted formula is as follows:

[0087] ,

[0088] Similarly, the piecewise inversion formula for soil water content in the soybean planting area is calculated.

[0089] After obtaining the SAR image of this area in a certain period, preprocess the image to obtain the corrected intensity value. First, divide the planting area into the corn planting area and the soybean planting area. After division, use the corresponding soil water content inversion formula respectively, and select the corresponding formula for calculation according to the threshold range where the intensity value is located to obtain the soil water content inversion result of this area. The formula for converting soil water content data to the soil water stress correction coefficient is as follows:

[0090] ,

[0091] In the formula, K S is the soil water stress correction coefficient; θ is the soil water content retrieved by remote sensing; θ wp is the wilting water content; θ j is the field capacity. The wilting water content and the field capacity are empirical parameters in agricultural hydraulics and are determined according to the actual situation of the calculation area.

Claims

1. A method for calculating the segmented soil moisture stress coefficient based on multi-source remote sensing data, characterized in that: The steps include: S1. Obtain optical remote sensing images, extract crop planting structures, and obtain regional divisions of different crops in crop planting areas; S2. According to the extraction results of the crop planting structure, within the same crop planting area, collect SAR image data, record the image data acquisition time, process the data, and obtain image remote sensing intensity data; S3. Collect the latitude and longitude information and soil moisture information of the soil moisture monitoring stations in the crop planting area. According to the image acquisition time, select the soil moisture monitoring data of all stations at the same time or near the same time, and obtain the remote sensing intensity value of the corresponding position on the SAR image according to the longitude and latitude information of each station. The image remote sensing intensity value of each station X Soil moisture content Y To form a set of data, multiple sites can obtain multiple sets of corresponding data; S4. 20% and 80% of the total data of soil moisture data values ​​monitored in the same period of history are respectively used as segmentation thresholds a and b, and three intervals of soil moisture range are constructed with a and b as thresholds, and the following soil moisture inversion formula is determined in each interval; S5. In each soil moisture range, the data collected in step S3 are divided into data sets, and an inversion algorithm is constructed using polynomial fitting, power fitting, exponential fitting, and logarithmic fitting, and then the fitting formulas are calculated respectively. X and Y The Pearson correlation coefficient r between them is used to verify the inversion algorithm using the test data set. After verification, the mean absolute error MAE is calculated, the ratio comprehensive evaluation index RCEI is constructed, and the optimal algorithm formula within the soil moisture content range is selected; The calculation formula of the ratio comprehensive evaluation index RCEI is: ， p is the weight adjustment factor, and its value is the same as the proportion of test data and modeling data; r is the Pearson correlation coefficient; MAE is the mean absolute error; S6. Integrate the optimal inversion algorithm of soil moisture content in three soil moisture content intervals to obtain the overall soil moisture content inversion formula, reversely determine the corresponding SAR data remote sensing intensity threshold range based on the inversion formula, and obtain the segmented inversion formula of soil moisture content corresponding to different SAR data remote sensing intensities of crops; S7. Calculate the corresponding soil moisture content segmented inversion formula for other crops in the planting area according to steps S2-S6; S8. Obtain SAR images of the crop planting area at a certain period, first classify the regions according to different crops, select the corresponding soil moisture inversion formula according to the type of crops planted, and then calculate the soil moisture corresponding to the intensity value according to the remote sensing intensity value, and then obtain the soil moisture inversion results of each area of ​​the crop planting area; S9. Calculate the soil water stress correction coefficient using the inverted soil water content data. ， The soil moisture stress correction coefficient formula is as follows: In the formula, K S is the soil water stress correction factor; θ is the soil moisture content inverted by remote sensing; θ wp is the wilting water content; θ j It is the maximum water holding capacity in the field.

2. The method for calculating the segmented soil moisture stress coefficient based on multi-source remote sensing data according to claim 1, characterized in that: In step S2, the SAR image data needs to cover the crop planting area, and the data is processed by multi-view processing, filtering, geocoding, and radiation calibration to obtain corrected remote sensing intensity data.

3. The method for calculating the segmented soil moisture stress coefficient based on multi-source remote sensing data according to claim 1, characterized in that: The soil moisture information in step S3 includes the value of the soil moisture content and the acquisition time.

4. The method for calculating the segmented soil moisture stress coefficient based on multi-source remote sensing data according to claim 1, characterized in that: The polynomial fitting, power fitting, exponential fitting and logarithmic fitting formulas described in step S5 are as follows: ， ， ， ， ， a, b, c are parameters to be determined; The calculation formula of Pearson correlation coefficient r is: ， The calculation formula for the mean absolute error MAE is: ， x i is the predicted value of the hydrological monitoring data, y i represents the true value of the i-th hydrological monitoring data, It represents the predicted value of the i-th sample after being calculated by the algorithm; n is the number of samples.

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