Method for calculating segmented soil moisture stress coefficient based on multi-source remote sensing data

Abstract

The invention relates to a multi-source remote sensing data-based segmented soil moisture stress coefficient calculation method, which comprises the following steps of: extracting a crop planting structure by using an optical remote sensing image, collecting SAR (Synthetic Aperture Radar) image data and soil moisture content monitoring station data in the same crop planting area, and constructing an interval of a soil moisture content range; fitting data formed by an image remote sensing intensity value X and a soil water content value Y of each site in each interval, constructing a ratio comprehensive evaluation index RCEI to select an optimal algorithm, integrating the intervals to obtain an overall soil water content inversion formula, calculating soil water content inversion formulas of other crops, and calculating the soil water content of the other crops. And calculating a soil moisture stress correction coefficient by using the inverted soil moisture content data. According to the method, qualitative analysis and quantitative inversion are comprehensively utilized based on the multi-source remote sensing technology, different soil water content inversion algorithms are established according to different water content range intervals for different crop planting areas, and high-precision inversion of the soil water content is achieved.

Description

Technical Field

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

[0002] Water resources are an indispensable element in food production. With the continuous development of society, the contradiction between water supply and demand has become increasingly prominent. Improving agricultural production and ensuring food security through accurate allocation of water resources has become a key issue for the sustainable development of the agricultural economy. Accurate calculation of crop water requirements is the basis for accurate allocation of water resources. Soil moisture, crop growth, topography, temperature and climate are the main factors affecting crop water requirements. Therefore, obtaining accurate soil moisture data to calculate the soil moisture stress coefficient is of great significance for estimating crop water requirements.

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

[0004] The purpose of the present invention is to provide a method for calculating the segmented soil water stress coefficient based on multi-source remote sensing data. The method mainly calculates the soil water stress coefficient segmented based on multi-source remote sensing data using remote sensing technology.

[0005] The technical solution adopted by the present invention is: The calculation method of the segmented soil moisture stress coefficient based on multi-source remote sensing data includes the following steps: 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 crop planting structure, in the same crop planting area, collect SAR (synthetic aperture radar) 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 contentY 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 used as segmentation thresholds a and b respectively, and three intervals of soil moisture range are constructed with a and b as thresholds, and the following soil moisture inversion formula is determined for each interval; S5. Within each soil moisture content 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; 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 factor using the inverted soil water content data.

[0006] In the above method, 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.

[0007] The soil moisture information in step S3 includes the value of the soil moisture content and the acquisition time.

[0008] 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.

[0009] The calculation formula of the ratio comprehensive evaluation index RCEI in step S5 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.

[0010] The soil moisture stress correction coefficient formula described in step S8 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 is the maximum field water holding capacity. Wilting water content and maximum field water holding capacity are empirical parameters of agricultural water conservancy and are determined based on the actual conditions of the calculation area.

[0011] The beneficial effects of the present invention are: The present invention is based on the comprehensive use of qualitative analysis and quantitative inversion based on multi-source remote sensing technology. By extracting the types of crops planted, different crop planting areas are divided. For different crop planting areas, different soil moisture inversion algorithms are established according to different water content ranges, so as to achieve high-precision inversion of soil moisture content, provide accurate calculation parameters for dynamic estimation of water requirements of crops over a large range, help agricultural precision irrigation and optimal allocation of water resources, and have obvious exemplary significance for application in other regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Picture 1is a flow chart of the method of the present invention; Picture 2 A diagram of the crop planting structure division in the study area of ​​an embodiment of the present invention; Picture 3 This is a distribution map of monitoring stations in the corn planting area of ​​the study area of ​​the embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following is further described in conjunction with specific implementation methods and examples.

[0014] The calculation method of the segmented soil water stress coefficient based on multi-source remote sensing data includes the following steps (e.g. Picture 1 )as follows: S1. Obtain optical remote sensing images, extract crop planting structures, and obtain the regional division of different crops in the crop planting area: Optical remote sensing images are selected, and after radiation calibration, atmospheric correction and geometric correction are performed on the images, visual interpretation methods are used to extract crop planting areas. According to the actual planting conditions in the planting areas, existing methods are used in remote sensing images to divide the areas where different crops are planted.

[0015] S2. According to the extraction results of crop planting structure, collect SAR (synthetic aperture radar) image data in the same crop planting area, record the image data acquisition time, process the data, and obtain image remote sensing intensity data: According to the extraction results of crop planting structure, SAR (synthetic aperture radar) data are collected in the same crop planting area. The data needs to cover the crop planting area, record the image acquisition time, and perform multi-view processing, filtering, geocoding, radiation calibration and other processing on the data to obtain the corrected intensity data.

[0016] S3. Collect the latitude and longitude information and soil moisture information (including soil moisture value and acquisition time) of soil moisture monitoring stations in the crop planting area; select all station monitoring data at the same time (or near time) according to the image time, and obtain the remote sensing intensity value of the corresponding position on the 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 A set of data is formed, and multiple sites can obtain multiple sets of corresponding data.

[0017] S4. Take 20% and 80% of the total data of soil moisture data values ​​monitored in the same period as the segmentation thresholds a and b respectively, and construct three intervals of soil moisture range with a and b as thresholds. The following soil moisture inversion formula is determined for each interval: The soil moisture inversion algorithm is established in segments according to the soil moisture distribution. The segment thresholds a and b are usually 20% and 80% of the overall data of the soil moisture data values ​​monitored in the same period of history. They can be adjusted according to the actual situation. Three corresponding soil moisture ranges are established with a and b as thresholds.

[0018] S5. Within each soil moisture content 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: In each soil moisture content range, the collected data were divided into a test data set and a modeling data set in a ratio of 3:7. The inversion algorithm was constructed using linear fitting (Formula 1), polynomial fitting (Formula 2), power fitting (Formula 3), exponential fitting (Formula 4) and logarithmic fitting (Formula 5), ​​and then the inversion algorithms were calculated respectively. X and Y The Pearson correlation coefficient (r) between them (Formula 6) (, and then use the test data set to verify the inversion algorithm. The verification is verified by calculating the mean absolute error (MAE) (Formula 7), comprehensively comparing and MAE, constructing the ratio comprehensive evaluation index RCEI, and selecting the algorithm with the largest comprehensive evaluation index RCEI value as the optimal algorithm, and selecting the optimal algorithm within the soil moisture content range. The formula calculation method is as follows: (Formula 1), (Formula 2), (Formula 3), (Formula 4), (Formula 5), (Formula 6), (Formula 7), (Formula 8), In the formula: a, b, c are parameters to be determined; 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 the algorithm calculates it; n is the number of samples; 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.

[0019] 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 content corresponding to the intensity value based on the remote sensing intensity value, and then obtain the soil moisture inversion results of each area in the crop planting area.

[0020] S9. Calculate the soil moisture stress correction coefficient using the inverted soil moisture data: The formula nine for converting soil moisture content data into soil moisture stress correction coefficient is as follows: (Formula 9), 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.

[0021] Example 1: In a certain planting area, a variety of crops are planted. The sample area is selected by visual interpretation, and the crop planting structure is extracted in the planting area by supervised classification. The extraction results are as follows: Picture 2 As shown, According to the extraction results of crop planting structure, soil water stress correction coefficients were calculated in corn and soybean areas respectively. Taking the corn area as an example, in the corn planting area, synthetic aperture radar (SAR) data on XX / XX / XXXX was collected, and the data was processed by multi-view processing, filtering, geocoding, radiation calibration, etc. to obtain intensity data; the latitude and longitude information of soil moisture monitoring stations in the crop planting area was collected, and the distribution of monitoring stations in the planting area was as follows: Picture 3As shown in Figure 1, the intensity data of each point corresponding to the point on the SAR image is obtained according to the location of the monitoring station. The point intensity data and the measured soil moisture data constitute a set of data. After eliminating the missing data of the monitoring station data, there are 30 sets of valid data in the corn planting area (see Table 1).

[0022] Table 1: Monitoring station intensity and soil moisture content in corn growing areas .

[0023] The soil moisture inversion algorithm is established in segments according to the soil moisture distribution. The segment thresholds a and b are usually 20% and 80% of the overall soil moisture data values ​​monitored in the same period of history. They can be adjusted according to the actual situation. According to 20% and 80% of the historical data, a and b are taken as 10 and 17.8 respectively. Therefore, the inversion algorithm is constructed and selected according to the soil moisture content of 0-10, 10-17.8 and above 17.8 (the range of values ​​does not include the front-end value, but includes the back-end value).

[0024] In each soil moisture range, the collected data are divided into a test data set and a modeling data set in a ratio of 3:7. The inversion algorithm is constructed using polynomial fitting, power fitting, exponential fitting and logarithmic fitting. The Pearson correlation coefficient (r) between X and Y is calculated respectively. The inversion algorithm is verified using the test data set. After verification, the mean absolute error (MAE) is calculated. The Pearson correlation coefficient r and the mean absolute error (MAE) are comprehensively compared. According to the evaluation comprehensive index AECI, the optimal algorithm within the soil moisture range is selected. The soil moisture inversion algorithms of the three soil moisture ranges are integrated to obtain the overall soil moisture inversion formula. The corresponding remote sensing image intensity data is calculated according to the inversion formula as a basis for judging whether different inversion formulas are used for remote sensing images.

[0025] Taking the data of soil moisture content 0-10 as an example, according to the ratio of 3:7, 2 groups of data are randomly selected as test data and 4 groups of data are selected as modeling data. The data are as follows: Table 2: Soil moisture content 0-8.8% segment data division .

[0026] Table 3: Algorithm fitting results .

[0027] Table 4: Calculated Pearson correlation coefficient r .

[0028] Table 5: Mean absolute error .

[0029] Table 6: AECI calculation results .

[0030] According to the AECI index, in the range of soil moisture content 0-10, the exponential fitting y = 0.001e is selected 99.764x As the fitting formula; similarly, the optimal fitting formula for calculating soil moisture content 10-17.8 is: y = 3.332x 0.319 , the optimal fitting formula for soil moisture content above 17.8 is: y = 4.912e 0.903x , since the remote sensing data is intensity data, it is necessary to convert the segmentation points of the piecewise function into remote sensing intensity. The converted formula is as follows: , Similarly, the segmented inversion formula for soil moisture content in soybean planting areas was calculated.

[0031] After obtaining the SAR image of the area in a certain period, the image is preprocessed to obtain the corrected intensity value. The planting area is first divided into corn planting area and soybean planting area. After the division, the corresponding soil moisture inversion formula is used respectively. According to the threshold range of the intensity value, the corresponding formula is selected for calculation to obtain the soil moisture inversion result of the area. The soil moisture content data is converted into the soil moisture stress correction coefficient formula 9 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 is the maximum field water holding capacity. Wilting water content and maximum field water holding capacity are empirical parameters of agricultural water conservancy and are determined based on the actual conditions 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. Based on the extraction results of 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; 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 factor using the inverted soil water content data.

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.

5. 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 calculation formula of the ratio comprehensive evaluation index RCEI in step S5 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.

6. 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 stress correction coefficient formula described in step S8 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.

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