Farmland low-lying waterlogging rapid extraction method based on spectrum-process-terrain cooperation

Abstract

The invention discloses a spectrum-process-terrain cooperation-based farmland low-lying waterlogging rapid extraction method. The method comprises the following steps of 1, obtaining and preprocessing a satellite remote sensing image; step 2, grading and marking the waterlogging area; step 3, process quantity extraction; 4, calculating a red edge area index; step 5, terrain factor processing and feature combination; step 6, model training and optimal feature subset screening; by constructing the soil moisture change process quantity in the bare soil period and the crop growth change process quantity in the growth period, the evolution trajectory of waterlogging along with the growth stage is accurately described, and the limitation that a single time phase cannot reflect the process difference is broken through; by constructing a red edge area index, the waterlogging spectrum difference is enhanced, easily-confused land types in the bare soil period are distinguished, and vegetation canopy shielding interference in the growth period is avoided; the influence of the terrain on the ponding depth and the water retention duration is reflected through the terrain factors, and the waterlogging spatial heterogeneity recognition precision is improved; the accuracy of waterlogging grading is improved by adopting a random forest algorithm.

Description

Technical Field

[0001] This invention relates to the field of remote sensing technology, specifically to a rapid extraction method for low-lying and waterlogged areas in farmland based on spectral-process-topographic synergy. Background Technology

[0002] Low-lying and waterlogged farmland is the primary limiting factor restricting stable and high crop yields. Currently, monitoring technologies for this are mainly divided into two categories. One category is manual monitoring technology, which relies on on-site measurements with water gauges, manual delineation of the waterlogged area, or small-scale aerial photography by drones to obtain information on farmland waterlogging through human intervention. Although this type of technology can ensure the measurement accuracy of local points, it can only obtain sample data from a small number of fields and cannot achieve synchronous monitoring of large-scale farmland. In addition, it requires manual on-site operation, which is time-consuming, labor-intensive, and has high manpower and time costs, as well as poor timeliness, making it difficult to meet the needs of rapid monitoring of large-scale farmland waterlogging. Another approach utilizes satellite remote sensing technology to rapidly acquire information on waterlogging in large areas of farmland. This method employs a single-phase NDVI / NDWI thresholding method, setting a fixed index threshold to identify waterlogged areas. While this method boasts a high degree of automation, it still suffers from the following drawbacks: 1. The spectral characteristics of dry topsoil during the bare soil stage are similar to those of thin-layered waterlogged areas, making it difficult for the single-phase NDVI / NDWI thresholding method to effectively distinguish between the two, leading to misidentification or missed detection of waterlogged areas; 2. During the crop growing season, the vegetation canopy obscures the water surface below, making it difficult for the NDWI index to accurately capture water surface information, significantly reducing the accuracy of waterlogging identification; 3. Existing technologies do not consider the continuous time series of waterlogging evolution from the bare soil stage to the crop growing season, relying solely on single-phase data. This fails to leverage the differences in waterlogging characteristics at different growth stages to improve monitoring accuracy, resulting in limited overall identification effectiveness. Summary of the Invention

[0003] The purpose of this invention is to provide a rapid extraction method for low-lying waterlogged areas in farmland based on spectral-process-topography synergy, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a rapid extraction method for low-lying and waterlogged farmland based on spectral-process-topographic synergy, comprising the following steps: Step 1, satellite remote sensing image acquisition and preprocessing; Step 2, hierarchical labeling of waterlogged areas; Step 3, process quantity extraction; Step 4, red edge area index calculation; Step 5, topographic factor processing and feature combination; Step 6, model training and optimal feature subset selection.

[0005] In step one above, cloudless Sentinel-2A satellite remote sensing images of the bare soil period and crop growth period in the target area are selected. After image preprocessing, the NDWI and NDVI of each period of image are calculated.

[0006] In step two above, the preprocessed remote sensing images are cropped according to the vector range of farmland plots in the target area, images after rainfall during the crop growing season are selected, and visual interpretation is performed using a dual-window linkage mode. Five-level labels are added to low-lying and waterlogged areas of farmland, and a plot-level label vector library is generated.

[0007] In step three above, time series curves of NDWI during the bare soil period and NDVI during the crop growing period are plotted, and the soil moisture change process ΔNDWI during the bare soil period and the crop vegetation change process ΔNDVI during the growing period are obtained based on the time series curves.

[0008] In step four above, the red edge area index is calculated using the trapezoidal integral principle based on the red edge bands B5, B6 and B7 of Sentinel-2A satellite remote sensing imagery.

[0009] In step five above, topographic factors are extracted using DEM as the topographic source. The topographic factors are then resampled to a resolution consistent with the spectral characteristics of Sentinel-2A satellite remote sensing images. They are then concatenated with image bands, NDWI, NDVI, and the ΔNDWI, ΔNDVI obtained in step three and the red edge area index obtained in step four at the pixel level to form a multidimensional feature vector.

[0010] In step six above, the multidimensional feature vectors obtained in step five are divided into training set and independent validation set. Multiple feature combination schemes are constructed to drive the training of random forest model and the evaluation of out-of-bag error. OA, Kappa coefficients and feature importance sequences are output simultaneously. By comparing the recognition performance under different combinations, the optimal feature subset with the smallest out-of-bag error and significant improvement in Kappa is selected to complete the optimization and validation of the urban flooding classification model.

[0011] Preferably, in step one, the bare soil period is from April to May, and the crop growing period is from June to September; the pretreatment specifically includes radiation calibration, atmospheric correction, and geometric correction.

[0012] Preferably, in step one, the wavelengths used for calculating NDWI are 0.559–0.570 μm for the green band and 0.833–0.864 μm for the near-infrared band; the wavelengths used for calculating NDVI are 0.650–0.680 μm for the red band and 0.833–0.864 μm for the near-infrared band; in Sentinel-2A satellite remote sensing imagery, the green band corresponds to band B3, the red band to band B4, and the near-infrared band to band B8; the calculation formulas for NDWI and NDVI are as follows:

[0013]

[0014] .

[0015] Preferably, in step two, the two windows of the dual-window linkage mode load a true color combination and a false color combination of images respectively. The true color combination is bands B4, B3, and B2, and the false color combination is bands B8, B4, and B3.

[0016] Preferably, in step two, the five-level labels are: Level 0 no waterlogging, Level 1 mild waterlogging, Level 2 moderate waterlogging, Level 3 severe waterlogging, and Level 4 extremely severe waterlogging, based on the differences in the color brightness and spatial continuity of farmland water bodies.

[0017] Preferably, in step three, the calculation formulas for ΔNDWI and ΔNDVI are as follows:

[0018]

[0019]

[0020] in, The image dates are from early April during the bare soil period. The image date is mid-April during the bare soil period. It is the bare soil period NDWI value of date It is the bare soil period NDWI value of date It represents the change in soil moisture during the bare soil period. Image dates for the growing season in early June. Image dates in late June during the growing season. It is the crop growth period NDVI value of the date It is the crop growth period NDVI value of the date It is a quantity of vegetation change during the crop growth period.

[0021] Preferably, in step four, the center wavelength of the red-edge band B5 of the Sentinel-2A satellite remote sensing image is 705 nm, the center wavelength of band B6 is 740 nm, and the center wavelength of band B7 is 783 nm; the formula for calculating the red-edge area index is:

[0022]

[0023] in, It is the red-edge area index. It is the surface reflectance of band B5. It is the surface reflectance of band B6. It is the surface reflectance of band B7. It is the difference in center wavelength between bands B5 and B6. It is the difference in center wavelength between bands B6 and B7.

[0024] Preferably, in step five, the spatial resolution of the DEM is 6m, the extracted terrain factors include slope, TWI and FA, and the resolution after resampling of the terrain factors is 10m.

[0025] Preferably, in step five, terrain factor extraction is completed through the SAGA GIS platform, and pixel-level concatenation is completed through the Google Earth Engine platform.

[0026] Preferably, in step six, the division of multidimensional feature vectors adopts a stratified random sampling method; the multiple feature combination schemes include spectral single-factor combination, spectral-topography joint combination, and spectral-process-topography fully coupled combination.

[0027] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention accurately depicts the evolution trajectory of waterlogging with different growth stages by constructing soil moisture change processes during the bare soil period and crop growth change processes during the growing season, overcoming the limitation that a single time phase cannot reflect process differences; by constructing a red-edge area index, it enhances the spectral differences of waterlogging, distinguishes easily confused land types during the bare soil period, and avoids interference from vegetation canopy shading during the growing season; by using topographic factors to reflect the influence of topography on water depth and water retention duration, it improves the accuracy of identifying spatial heterogeneity of waterlogging; and by employing a random forest algorithm, it improves the accuracy of waterlogging classification. Attached Figure Description

[0028] Figure 1 This is a flowchart of the method of the present invention;

[0029] Figure 2 This is a time series curve of NDWI for bare soil.

[0030] Figure 3 A time series curve of NDVI during the reproductive period;

[0031] Figure 4 This is a graph showing the changes in image bands. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see the appendix Figure 1The present invention provides an embodiment of a rapid extraction method for low-lying and waterlogged farmland based on spectral-process-topographic synergy, comprising the following steps: Step 1, satellite remote sensing image acquisition and preprocessing; Step 2, waterlogged area hierarchical labeling; Step 3, process quantity extraction; Step 4, red edge area index calculation; Step 5, topographic factor processing and feature combination; Step 6, model training and optimal feature subset selection.

[0034] In step one above, cloudless Sentinel-2A satellite remote sensing images of the target area during the bare soil period and crop growing season are selected. After preprocessing the images with radiometric calibration, atmospheric correction, and geometric correction, the NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) of each image period are calculated. The index (Normalized Difference Vegetation Index) is used to calculate NDWI. The bare soil period is from April to May, and the crop growing season is from June to September. The wavelengths used for calculating NDWI are 0.559–0.570 μm for the green band and 0.833–0.864 μm for the near-infrared band. The wavelengths used for calculating NDVI are 0.650–0.680 μm for the red band and 0.833–0.864 μm for the near-infrared band. In Sentinel-2A satellite remote sensing imagery, the green band corresponds to band B3, the red band to band B4, and the near-infrared band to band B8. The calculation formulas for NDWI and NDVI are as follows:

[0035]

[0036] ;

[0037] In step two above, the preprocessed remote sensing images are cropped according to the vector range of farmland plots in the target area. Images after rainfall during the crop growing season are selected, and visual interpretation is performed using a dual-window linkage mode. Low-lying and waterlogged areas in farmland are labeled with five levels of tags, and a plot-level tag vector library is generated. In the dual-window linkage mode, the two windows load the true color combination and the false color combination of the image, respectively. The true color combination is bands B4, B3, and B2, and the false color combination is bands B8, B4, and B3. The five levels of tags are: Level 0 No waterlogging, Level 1 Mild waterlogging, Level 2 Moderate waterlogging, Level 3 Severe waterlogging, and Level 4 Extremely severe waterlogging. The labeling is based on the differences in the color brightness and spatial continuity of farmland water bodies.

[0038] In step three above, time-series curves of NDWI during the bare soil period and NDVI during the crop growing season are plotted, and the soil moisture change process quantity ΔNDWI during the bare soil period and the crop vegetation change process quantity ΔNDVI during the growing season are obtained based on the time-series curves; the calculation formulas for ΔNDWI and ΔNDVI are as follows:

[0039]

[0040]

[0041] in, The image dates are from early April during the bare soil period. The image date is mid-April during the bare soil period. It is the bare soil period NDWI value of date It is the bare soil period NDWI value of date It represents the change in soil moisture during the bare soil period. Image dates for the growing season in early June. Image dates in late June during the growing season. It is the crop growth period NDVI value of the date It is the crop growth period NDVI value of the date It is a quantity related to vegetation changes during the crop growing season;

[0042] In step four above, the Red-Edge Area Index (REA) is calculated using the trapezoidal integral principle based on the red-edge bands B5, B6, and B7 of the Sentinel-2A satellite remote sensing imagery. The center wavelengths of red-edge bands B5, B6, and B7 are 705 nm, 740 nm, and 783 nm, respectively. The formula for calculating the Red-Edge Area Index is as follows:

[0043]

[0044] in, It is the red-edge area index. It is the surface reflectance of band B5. It is the surface reflectance of band B6. It is the surface reflectance of band B7. It is the difference in center wavelength between bands B5 and B6. It is the difference in center wavelength between bands B6 and B7;

[0045] In step five above, a DEM (Digital Elevation Model) with a spatial resolution of 6m is used as the terrain source. Terrain factors are extracted through the SAGA GIS platform. The terrain factors include slope, TWI (Topographic Wetness Index), and FA (Flow Accumulation). The terrain factors are resampled to a resolution of 10m that is consistent with the spectral characteristics of the Sentinel-2A satellite remote sensing image. Then, the terrain factors are concatenated with the image bands, NDWI, NDVI, and ΔNDWI, ΔNDVI obtained in step three, and the red edge area index obtained in step four on the Google Earth Engine platform at the pixel level to form a multidimensional feature vector.

[0046] In step six above, a stratified random sampling method is used to divide the multidimensional feature vectors obtained in step five into a training set and an independent validation set. Multiple feature combination schemes are constructed to drive the training of the random forest model and the evaluation of out-of-bag error. The overall accuracy (OA), Kappa coefficient, and feature importance sequence are output simultaneously. By comparing the recognition performance under different combinations, the optimal feature subset with the smallest out-of-bag error and significant improvement in Kappa is selected to complete the optimization and validation of the urban flooding classification model. Among them, the multiple feature combination schemes include spectral single-factor combination, spectral-topography joint combination, and spectral-process-topography fully coupled combination.

[0047] Experimental example:

[0048] To verify the effectiveness of this method, an experiment was conducted using Friendship Farm as the target area, employing the method proposed in the embodiments. Nine different feature combination schemes were designed for the experiment. The experimental results are shown in Table 1 and Appendix 2. Figure 2 - Appendix Figure 4 The experimental results show that the feature combination of "bare soil period image + NDWI + REA + topographic factor + ΔNDWI; growing season image + NDVI + REA + topographic factor + ΔNDVI" has the best effect, with an OA of 0.73 and a Kappa coefficient of 0.66. It can most accurately identify and classify low-lying waterlogged areas in Youyi Farm.

[0049] Table 1

[0050]

[0051] Based on the above, the advantages of this invention are as follows: When used, it accurately depicts the evolution trajectory of waterlogging with different growth stages by constructing soil moisture change processes during the bare soil period and crop growth change processes during the growing season, overcoming the limitation that relying solely on a single time phase cannot reflect process differences; it constructs a red edge area index based on the Sentinel-2A red edge band, which is sensitive to leaf moisture, surface water accumulation, and crop stress response, significantly enhancing the spectral differences of different levels of waterlogging, effectively distinguishing easily confused land types during the bare soil period, while avoiding the shading interference of vegetation canopy on water surface information during the growing season; it accurately reflects the influence of topography on water depth and water retention duration by incorporating topographic factors, improving the accuracy of identifying spatial heterogeneity of waterlogging; and it drives iterative training of the random forest model through multi-feature combination, selecting the optimal feature subset, avoiding the rigidity of fixed thresholds, and significantly improving the accuracy of waterlogging classification and model robustness, providing more reliable technical support for accurately identifying low-lying waterlogging in farmland.

[0052] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A rapid extraction method for low-lying waterlogged areas in farmland based on spectral-process-topographic synergy, comprising the following steps: Step 1: Satellite remote sensing image acquisition and preprocessing; Step 2: Hierarchical labeling of flooded areas; Step 3: Extraction of process quantities; Step 4: Calculation of red-edge area index; Step 5: Topographic factor processing and feature combination; Step 6: Model training and selection of optimal feature subset; Its characteristics are: In step one above, cloudless Sentinel-2A satellite remote sensing images of the bare soil period and crop growth period in the target area are selected. After image preprocessing, the NDWI and NDVI of each period of image are calculated. In step two above, the preprocessed remote sensing images are cropped according to the vector range of farmland plots in the target area, images after rainfall during the crop growing season are selected, and visual interpretation is performed using a dual-window linkage mode. Five-level labels are added to low-lying and waterlogged areas of farmland, and a plot-level label vector library is generated. In step three above, time series curves of NDWI during the bare soil period and NDVI during the crop growing period are plotted, and the soil moisture change process ΔNDWI during the bare soil period and the crop vegetation change process ΔNDVI during the growing period are obtained based on the time series curves. In step four above, the red edge area index is calculated using the trapezoidal integral principle based on the red edge bands B5, B6 and B7 of Sentinel-2A satellite remote sensing imagery. In step five above, topographic factors are extracted using DEM as the topographic source. The topographic factors are then resampled to a resolution consistent with the spectral characteristics of Sentinel-2A satellite remote sensing images. They are then concatenated with image bands, NDWI, NDVI, and the ΔNDWI, ΔNDVI obtained in step three and the red edge area index obtained in step four at the pixel level to form a multidimensional feature vector. In step six above, the multidimensional feature vectors obtained in step five are divided into training set and independent validation set. Multiple feature combination schemes are constructed to drive the training of random forest model and the evaluation of out-of-bag error. OA, Kappa coefficients and feature importance sequences are output simultaneously. By comparing the recognition performance under different combinations, the optimal feature subset with the smallest out-of-bag error and significant improvement in Kappa is selected to complete the optimization and validation of the urban flooding classification model.

2. The rapid extraction method for low-lying waterlogged areas in farmland based on spectral-process-topographic synergy as described in claim 1, characterized in that: In step one, the bare soil period is from April to May, and the crop growing period is from June to September; the pretreatment specifically includes radiation calibration, atmospheric correction, and geometric correction.

3. The rapid extraction method for low-lying waterlogged areas in farmland based on spectral-process-topographic synergy as described in claim 1, characterized in that: In step one, the wavelengths used for calculating NDWI are 0.559–0.570 μm for the green band and 0.833–0.864 μm for the near-infrared band; the wavelengths used for calculating NDVI are 0.650–0.680 μm for the red band and 0.833–0.864 μm for the near-infrared band. In Sentinel-2A satellite remote sensing imagery, the green band corresponds to band B3, the red band to band B4, and the near-infrared band to band B8. The calculation formulas for NDWI and NDVI are as follows: ， 。 4. The rapid extraction method for low-lying waterlogged areas in farmland based on spectral-process-topographic synergy as described in claim 1, characterized in that: In step two, the two windows of the dual-window linkage mode load the true color combination and the false color combination of the image respectively. The true color combination is bands B4, B3, and B2, and the false color combination is bands B8, B4, and B3.

5. The rapid extraction method for low-lying waterlogged areas in farmland based on spectral-process-topographic synergy as described in claim 1, characterized in that: In step two, the five-level labels are: Level 0 no waterlogging, Level 1 mild waterlogging, Level 2 moderate waterlogging, Level 3 severe waterlogging, and Level 4 extremely severe waterlogging. The labeling is based on the differences in the color brightness and spatial continuity of farmland water bodies.

6. The rapid extraction method for low-lying waterlogged areas in farmland based on spectral-process-topographic synergy as described in claim 1, characterized in that: In step three, the calculation formulas for ΔNDWI and ΔNDVI are as follows: ， ， in, The image dates are from early April during the bare soil period. The image date is mid-April during the bare soil period. It is the bare soil period NDWI value of date It is the bare soil period NDWI value of date It represents the change in soil moisture during the bare soil period. Image dates for the growing season in early June. Image dates in late June during the growing season. It is the crop growth period NDVI value of the date It is the crop growth period NDVI value of the date It is a quantity of vegetation change during the crop growth period.

7. The rapid extraction method for low-lying waterlogged areas in farmland based on spectral-process-topographic synergy as described in claim 1, characterized in that: In step four, the center wavelength of the red-edge band B5 in the Sentinel-2A satellite remote sensing image is 705 nm, the center wavelength of band B6 is 740 nm, and the center wavelength of band B7 is 783 nm; the formula for calculating the red-edge area index is: ， in, It is the red-edge area index. It is the surface reflectance of band B5. It is the surface reflectance of band B6. It is the surface reflectance of band B7. It is the difference in center wavelength between bands B5 and B6. It is the difference in center wavelength between bands B6 and B7.

8. The rapid extraction method for low-lying waterlogged areas in farmland based on spectral-process-topographic synergy as described in claim 1, characterized in that: In step five, the spatial resolution of the DEM is 6m, and the extracted terrain factors include slope, TWI and FA. The resolution of the terrain factors after resampling is 10m.

9. The rapid extraction method for low-lying waterlogged areas in farmland based on spectral-process-topographic synergy as described in claim 1, characterized in that: In step five, terrain factor extraction is completed through the SAGA GIS platform, and pixel-level concatenation is completed through the Google Earth Engine platform.

10. The rapid extraction method for low-lying waterlogged areas in farmland based on spectral-process-topographic synergy as described in claim 1, characterized in that: In step six, the division of multidimensional feature vectors adopts a stratified random sampling method; multiple feature combination schemes include spectral single-factor combination, spectral-topography joint combination, and spectral-process-topography fully coupled combination.

Images