A GNSS-R ocean macroalgae detection method and system based on wave height suppression

Abstract

The application discloses a GNSS-R ocean macroalgae detection method and system based on wave height suppression, first acquires marine geophysical data, extracts the effective wave height and wind speed of each specular reflection point; and based on the effective wave height and wind speed, point by point, the wave height suppression index is calculated; then the detection power of the wave height suppression index is verified by using the wind speed stratification effect quantity analysis; when the wave height suppression index is effective, the mean value and standard deviation of the wave height suppression index under the corresponding wind speed are inquired from the pre-established clean water body background reference table, and then the algal index is calculated according to the mean value and standard deviation of the wave height suppression index; finally, the calculated algal index is input into the pre-trained logistic calibration model, and the algal existence probability of the observation point is output. The application introduces the wave height suppression index, eliminates the dominant influence of the wind speed on the SWH through the wind speed stratification strategy, separates the suppression signal from the background noise, finally outputs the standardized existence probability, and realizes the effective detection of the ocean macroalgae.

Description

Technical Field

[0001] This invention relates to the field of marine remote sensing data processing of spaceborne GNSS reflected signals (GNSS-R), and more specifically, to a GNSS-R method and system for detecting large marine algae based on wave height suppression. It utilizes the wave height suppression effect caused by large algae covering the sea surface to calculate the wave height suppression index from CYGNSS satellite data, thereby achieving all-weather detection and probabilistic identification of large marine algae. Background Technology

[0002] Large-scale outbreaks of marine macroalgae have become a global marine ecological problem. Taking Sargassum as an example, large-scale algal blooms have occurred annually in the tropical Atlantic since 2011, forming a Sargassum belt spanning the Atlantic and severely impacting the ecology, tourism, and fisheries of the Caribbean Sea and West Africa. Current operational monitoring relies on optical satellite sensors such as MODIS, which identify algae by calculating the Floating Algae Index (FAI). However, optical sensor signals cannot penetrate cloud cover and only operate during the day. The tropical Atlantic is typically cloudy year-round, with cloud cover reaching over 45% during peak summer algal blooms, severely limiting effective optical observation.

[0003] CYGNSS is a GNSS-R constellation consisting of eight microsatellites that receive GPS L1 band (approximately 19 cm wavelength) signals reflected from the sea surface. It has all-weather, all-time observation capabilities and collects tens of thousands of sea surface data daily. However, CYGNSS is not an imaging sensor; a single observation is an integral measure of sea surface roughness within a kilometer-scale footprint, and it cannot directly "see" algae.

[0004] Therefore, it can be seen that existing methods cannot effectively detect algae through non-imaging observations. Summary of the Invention

[0005] The key to this invention lies in converting non-imaging GNSS-R ocean observation data into detection signals for large marine algae, overcoming the bottleneck of optical sensors being limited by clouds and sunlight, and providing an all-weather, all-time, probabilistically outputtable method and system for detecting large marine algae. Specifically, this invention proposes that large algae covering the sea surface form a natural viscoelastic surface layer, which dampens short gravity-capillary waves, leading to a systematic reduction in the effective wave height (SWH) at a given wind speed—that is, the wave height suppression effect. This invention directly defines the ratio of SWH to the cooperative positioning wind speed as the Wave Height Suppression Index (WHSI). The WHSI in algae-covered sea areas is significantly lower than that in clean sea areas. Based on this index, algae signals can be directly extracted from CYGNSS data, achieving all-weather detection.

[0006] To achieve the above objectives, the first aspect of the present invention provides a GNSS-R method for detecting large marine algae based on wave height suppression, comprising: Acquire marine geophysical data and extract the effective wave height and wind speed at each specular reflection point; Based on the significant wave height and wind speed, the wave height suppression index is calculated point by point. The wave height suppression index is used to indicate the significant wave height driven by a unit wind speed. The effectiveness of the wave height suppression index was verified by wind speed stratification effect analysis. If the wave height suppression index is effective, the mean and standard deviation of the wave height suppression index at the corresponding wind speed are retrieved from the pre-established clean water body background reference table based on the wind speed at the observation point, and the algae index is calculated based on the mean and standard deviation of the wave height suppression index. The calculated algae index is input into a pre-trained logistic calibration model, which outputs the probability of algae presence at the observation point.

[0007] In one implementation, the wave height suppression index is calculated point-by-point based on the effective wave height and wind speed, including:

[0008] in, represents the wave height suppression index, Indicates the significant wave height. This indicates the ERA5 10-meter cooperative positioning wind speed.

[0009] In one implementation, wind speed stratification effect quantity analysis is used to verify the detection efficacy of the wave height suppression index, including: The observation data is divided into several equal-width intervals according to wind speed; The standardized mean difference effect of wave height suppression index between algae-infested water bodies and clean water bodies and the weighted composite value of each interval were calculated separately within each interval. If the weighted composite value of each interval is negative and the signs of each interval are consistent, then the wave height suppression index is determined to be effective in detecting algae.

[0010] In one implementation, the standardized mean difference effect of the wave height suppression index between algae-infested and clean water bodies and the weighted composite value for each interval are calculated separately, including: Wave height inhibition index values ​​were extracted for algae-infested water bodies and clean water bodies, respectively. The standardized mean difference effect size of the wave height inhibition index between algae-infested and clean water bodies was calculated using the formula:

[0011] In the formula, target and reference represent the categories of algae-infested water bodies and clean water bodies, respectively. This represents the mean value of the wave height inhibition index for algae growth in water bodies. This represents the mean value of the wave height suppression index for clean water bodies. The standard deviation of the wave height inhibition index for algae growth is represented by the following: The standard deviation of the wave height suppression index for clean water bodies. This represents the standardized mean difference effect size for the k-th interval; Calculate the weighted composite value for each interval using the formula:

[0012] in, This is the weighted composite value. This represents the weight of the k-th interval. , These represent the number of samples from algae-rich water bodies and the number of samples from clean water bodies, respectively.

[0013] In one implementation, the clean water body background reference table is constructed in the following ways: All samples from historical observation data in which the algae index of the optical sensor is below the cleaning threshold are considered as clean water samples. The wind speed is divided into several equal-width intervals according to a preset interval. The mean and standard deviation of the wave height suppression index are calculated for each interval to construct a background reference table for clean water bodies.

[0014] In one implementation, the algae index is calculated based on the mean and standard deviation of the wave height suppression index, including:

[0015] In the formula, This indicates the value of the algae index. Indicates wind speed The mean of the corresponding wave height suppression index, Indicates wind speed The standard deviation of the corresponding wave height suppression index, WHSI represents the wave height suppression index.

[0016] In one implementation, the calculated algae index is input into a pre-trained logistic calibration model, which outputs the probability of algae presence at the observation point, including:

[0017] In the formula This represents the conditional probability that large algae exist at the observation point given a given algae index value. This indicates the presence of algae. Indicates the algae index value; β0 represents the intercept term, β1 represents... The regression coefficients.

[0018] Based on the same inventive concept, a second aspect of this invention provides a GNSS-R method for detecting large marine algae based on wave height suppression, comprising: The data acquisition module is used to acquire marine geophysical data and extract the effective wave height and wind speed of each mirror reflection point; The wave height suppression index calculation module is used to calculate the wave height suppression index point by point based on the effective wave height and wind speed. The wave height suppression index is used to indicate the effective wave height driven by a unit wind speed. The wind speed stratification verification module is used to verify the detection efficacy of the wave height suppression index by analyzing the wind speed stratification effect quantity. The algae index calculation module is used to look up the mean and standard deviation of the wave height suppression index at the corresponding wind speed from a pre-established clean water background reference table based on the wind speed at the observation point if the wave height suppression index is effective, and then calculate the algae index based on the mean and standard deviation of the wave height suppression index. The probability calibration and determination module is used to input the calculated algae index into a pre-trained logistic calibration model and output the probability of algae presence at the observation point. Based on the same inventive concept, a third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is used to implement the GNSS-R method for detecting large marine algae based on wave height suppression as described in the first aspect.

[0019] Based on the same inventive concept, a fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it is used to implement the GNSS-R method for detecting large marine algae based on wave height suppression as described in the first aspect.

[0020] Compared with the prior art, the advantages and beneficial technical effects of the present invention are as follows: This invention discloses a GNSS-R method for detecting large marine algae based on wave height suppression. The method calculates a wave height suppression index (SWH) based on significant wave height (SWH) and wind speed. Due to the wave height suppression effect, this index is systematically low in algae-covered sea areas, thus indicating the significant wave height driven by unit wind speed. A wind speed stratification strategy is used to eliminate the dominant influence of wind speed on SWH, separating the suppressed signal from background noise. Then, based on the wind speed at the observation point, the mean and standard deviation of the wave height suppression index at the corresponding wind speed are looked up from a pre-established clean water background reference table. An algae index is then calculated based on the mean and standard deviation of the wave height suppression index. Finally, a standardized presence probability is output, thus overcoming the bottleneck of optical sensors being limited by clouds and sunlight, and achieving all-weather, all-time, and probabilistic detection of large marine algae. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart of a GNSS-R method for detecting large marine algae based on wave height suppression provided in an embodiment of the present invention; Figure 2 This is a scatter plot of wind speed for SWH–ERA5 in an embodiment of the present invention; Figure 3 This is a comparative bar chart of wind speed stratification using Cohen's d in an embodiment of the present invention; Figure 4 This is the interval-by-interval Cohen's d curve of WHSI in each wind speed range in this embodiment of the invention. Figure 5 This is a schematic diagram illustrating the change of WHSI inhibition signal with algal density in an embodiment of the present invention; Figure 6 This is a schematic diagram comparing the spatial distribution of 0.25° gridded MODIS FAI and CYGNSS WHSI in an embodiment of the present invention; Figure 7 This is a schematic diagram of the seasonal climate of the multi-year monthly average WHSI in a core area in an embodiment of the present invention; Figure 8 This is a schematic diagram showing the distribution of MODIS cloud coverage and the comparison of the effective observation days of CYGNSS and MODIS in an embodiment of the present invention. Figure 9 This is a schematic diagram of the probability calibration curve of the algae index AI in an embodiment of the present invention. Detailed Implementation

[0023] Example 1 Please see Figure 1 This embodiment provides a GNSS-R method for detecting large marine algae based on wave height suppression, including: S1: Acquire marine geophysical data and extract the effective wave height and wind speed at each mirror reflection point.

[0024] Specifically, S1 is data acquisition. In this embodiment, the marine geophysical data is CYGNSS L2 CDR data (Cyclone Global Navigation Satellite System Level 2 Marine Geophysical Product). GNSS The basic principle of R (Global Navigation Satellite Reflection Remote Sensing) is as follows: GPS transmits navigation signals, which propagate downwards to a point on the sea surface where they undergo specular reflection. The reflected signal is then received upwards by CYGNSS satellites. The specular reflection point SP refers to the location of the specular reflection on the sea surface, geometrically determined by the GPS transmitting antenna, the sea surface, and the CYGNSS receiving antenna. The wind speed refers to the ERA5 10-meter cooperative positioning wind speed; ERA5 is the fifth-generation global atmospheric reanalysis dataset released by the European Centre for Medium-Range Weather Forecasts (ECMWF).

[0025] Preferably, after extracting the effective wave height and wind speed of each mirror reflection point, the present invention further removes invalid values.

[0026] S2: Based on the effective wave height and wind speed, calculate the wave height suppression index point by point. The wave height suppression index is used to indicate the effective wave height driven by a unit wind speed.

[0027] Specifically, S2 is the calculation of the wave height suppression index.

[0028] In one implementation, S2 can be achieved in the following way:

[0029] Among them, among them, represents the wave height suppression index, Indicates the significant wave height. This indicates the ERA510-meter cooperative positioning wind speed. This means taking the larger value between the ERA5 10-meter cooperative positioning wind speed and 0.5 to ensure that the denominator is not zero under near-zero wind speed conditions.

[0030] For clean sea surfaces, the WHSI value is approximately 0.20–0.30. When algae cover the sea surface, the WHSI is lower because the wave height is suppressed.

[0031] S3: The effectiveness of the wave height suppression index was verified by wind speed stratification effect analysis.

[0032] Specifically, S3 is wind speed stratification verification, a preliminary verification of signal validity: before calculating AI and output probability, it confirms whether WHSI can distinguish between algae and clean water, avoiding blindly outputting detection results under spatiotemporal conditions where WHSI is insensitive. Step S2 provides WHSI values, step S4 calculates AI based on a verified reference table, and step S3 serves as a checkpoint between the two, ensuring that AI calculation is based on valid physical signals.

[0033] In one implementation, S3 can be achieved in the following way: S3.1: Divide the observation data into several equal-width intervals according to wind speed; S3.2: Calculate the standardized mean difference effect of wave height suppression index between algae-infested water bodies and clean water bodies, and the weighted composite value of each interval, respectively; S3.3: If the weighted composite value of each interval is negative and the signs of each interval are consistent, then the wave height suppression index is determined to be effective in detecting algae.

[0034] In the specific implementation process, the observed data will be processed according to ERA5 wind speeds ranging from 3.0 to 10.0 ms. - ¹, every 0.5 ms - ¹ Divided into 14 intervals (i.e., [3.0, 3.5), [3.5, 4.0), ..., [9.5, 10.0), each interval is called a wind speed chamber, denoted by the chamber index k). Within each wind speed chamber, algae-rich water (floating algae index calculated by MODIS FAI based on MODIS satellite sensor reflectivity > 0.002) and clean water (MODIS FAI < 0.002) are extracted respectively. Calculate the Cohen's d effect size of the bin using the WHSI value of 0.001. The weighted combined effect size is calculated using the following formula. .

[0035] S3.2 includes: S3.2.1: Extract the wave height inhibition index values ​​for algae-infested water bodies and clean water bodies respectively; S3.2.2: Calculate the standardized mean difference effect size of the wave height suppression index between algae-infested and clean water bodies according to the formula:

[0036] In the formula, target and reference represent the categories of algae-infested water bodies and clean water bodies, respectively. This represents the mean value of the wave height inhibition index for algae growth in water bodies. This represents the mean value of the wave height suppression index for clean water bodies. The standard deviation of the wave height inhibition index for algae growth is represented by the following: The standard deviation of the wave height suppression index for clean water bodies. This represents the standardized mean difference effect size for the k-th interval; Calculate the weighted composite value for each interval using the formula:

[0037] in, This is the weighted composite value. This represents the weight of the k-th interval. , These represent the number of samples from algae-rich water bodies and the number of samples from clean water bodies, respectively.

[0038] Specifically <0 indicates that the WHSI of algae-infested water is lower than that of clean water under this wind speed condition, i.e., there is a wave height suppression effect. The subscript k indicates the wind speed bin index; weight The smaller sample size is selected from both algae-rich and clean water samples to avoid the wind speed chamber dominating the overall results due to significant sample size disparities. Effect size interpretation criteria: | |<0.2 indicates a negligible effect, 0.2~0.5 indicates a small effect, 0.5~0.8 indicates a medium effect, and >0.8 indicates a large effect. If Negative and each wind speed compartment If the signs are consistent (both are negative), it is determined that the wave height inhibition index can effectively detect algae.

[0039] S4: If the wave height suppression index is effective, look up the mean and standard deviation of the wave height suppression index at the corresponding wind speed in the pre-established clean water background reference table based on the wind speed at the observation point, and calculate the algae index based on the mean and standard deviation of the wave height suppression index.

[0040] Specifically, S4 is the calculation of the algae index.

[0041] The construction methods for the clean water body background reference table include: All samples from historical observation data in which the algae index of the optical sensor is below the cleaning threshold are considered as clean water samples. The wind speed is divided into several equal-width intervals according to a preset interval. The mean and standard deviation of the wave height suppression index are calculated for each interval to construct a background reference table for clean water bodies.

[0042] Specifically, dividing wind speed into several equal-width intervals means dividing continuous wind speed measurements into intervals of 0.5 ms. - ¹ The equal-width interval is discretized into several compartments (intervals). Observation points within the same compartment are considered to be under comparable wind speed conditions, thereby eliminating the nonlinear influence of wind speed on SWH. In specific implementation, the MODIS FAI < 0.05 in historical CYGNSS observations is used as the benchmark. Based on all clean water samples with a density of 0.001, the mean and standard deviation of WHSI were calculated for each wind speed chamber to construct a clean water background reference table. The table structure is as follows: each row corresponds to one wind speed chamber, and the fields include the center value of the wind speed chamber (e.g., 3.25, 3.75, ..., 9.75 ms). - ¹) The mean, standard deviation, and sample size of the clean water in the tank.

[0043] The algae index is calculated based on the mean and standard deviation of the wave height inhibition index, including:

[0044] In the formula, This indicates the value of the algae index. Indicates wind speed The mean of the corresponding wave height suppression index, Indicates wind speed The standard deviation of the corresponding wave height suppression index, WHSI represents the wave height suppression index.

[0045] Specifically, for each CYGNSS observation point to be detected, its ERA5 10-meter wind speed w from collaborative positioning is read, and the wind speed bin to which w belongs is determined. For example, w = 5.3 ms - ¹ If the value falls within the interval [5.0, 5.5), then the corresponding value for that warehouse is retrieved from the reference table. and Then, the values ​​are substituted into the algae index formula for calculation. AI is a dimensionless standardized outlier. When AI>0, it means that the WHSI at this point is lower than the expected value of the background at the same wind speed in clean water, that is, there is wave height suppression, indicating that there may be algae coverage. The larger the AI ​​value, the stronger the signal and the more significant the wave height suppression.

[0046] S5: Input the calculated algae index into the pre-trained logistic calibration model, and output the probability of algae presence at the observation point.

[0047] Specifically, S5 is probabilistic calibration and judgment. The pre-trained logistic calibration model is obtained as follows: observation points where the algae index of the optical sensor exceeds the density threshold are considered positive samples, and observation points below the cleanliness threshold are considered negative samples. AI is used as the independent variable for model fitting. The calibration coefficients β0 and β1 are obtained.

[0048] Observation points with an algae index exceeding the algae density threshold are considered positive samples (in which case, A = 1), while observation points with an algae index below the cleanliness threshold are considered negative samples (in which case, A = 0). The logistic model, due to its sigmoid function form, ensures that the output probability value always falls within the [0, 1] interval.

[0049] In practice, steps S1 to S5 are executed sequentially for all CYGNSS observation points, completing the full steps of data acquisition, WHSI calculation, wind speed stratification verification, AI calculation, and logistic probability transformation. The probability of each point is then converted... Aggregated into a regular geographic grid, the mean or median of all observation points P(A) within each grid is taken to generate a gridded probability distribution map. The purpose of this distribution map is: (1) to transform non-imaging discrete point observations into spatially continuous planar products of algal presence probability, providing a readable spatial situation map of large marine algae for operational early warning; (2) to conduct spatial comparison and verification with optical images such as MODIS FAI within the same grid framework. For example, A value >0.5 indicates the presence of large algae in the grid.

[0050] The output of the above logistic calibration model is the point-by-point existence probability. The probability distribution map after grid aggregation provides the spatial distribution of algae at the regional scale, which can be directly used as the input confidence level of the operational early warning system.

[0051] Preferably, the present invention also includes S6 multi-temporal climatological analysis.

[0052] The specific implementation process is as follows: (1) Spatial averaging: Within the target sea area, the median of the WHSI at all observation points for a given month is taken (the median is more resistant to outlier interference than the mean) to obtain the regional representative value for that month. (2) Multi-year aggregation: The regional representative values ​​for all years (e.g., 2020–2023) of that month are aggregated to calculate the multi-year monthly average WHSI, which is used as the seasonal climatological baseline value for that month. (3) Constructing the seasonal climatological curve: The seasonal climatological curve for the whole year is plotted with January–December as the horizontal axis and the multi-year monthly average WHSI as the vertical axis. (4) Identification of outbreak seasons: Periods in which the WHSI is systematically lower than the multi-year annual average (the average of the 12-month average) are identified as algal outbreak seasons. (5) Tracking anomalies year by year: The difference between the WHSI and the climatological baseline value for the same month of each year is calculated as the interannual anomaly. A negative difference indicates that the wave height suppression for that month of that year is stronger than usual (more severe algal coverage), while a positive difference indicates that it is weaker than usual. This allows for the quantitative tracking of interannual variations in algal blooms.

[0053] To more clearly illustrate the specific process of this invention, specific examples are provided below.

[0054] Using *Sargassum fusiforme* as a representative example of large algae, combined with... Figure 1 Process Description. This invention is also applicable to other large floating algae with wave height suppression effects. Data Source: CYGNSS L2 CDR v1.2 (NASA PO.DAAC), MODIS MOD09GA Collection 6.1 (NASA LP DAAC), ERA5 wind speed provided by CYGNSS L2 product embedding.

[0055] (1) Data acquisition and preprocessing Extract the SWH and ERA5 10-meter wind speed for each specular reflection point from the CYGNSS L2 CDR file, and remove missing values.

[0056] (2) WHSI calculation The analysis area is 10°S~40°N and 100°W~10°E. The wave height suppression index is calculated point-by-point. Among them, MODIS MOD09GA uses h10v07~h13v09 as the tile segmentation, where h10v07~h13v09 represents a continuous rectangular tile range, according to... calculate, (1240nm) ≤ 0.03 and reflectivity [0, 0.1] for each band are cloud mask conditions. Then, spatiotemporal matching is performed, matching MODIS pixels for each CYGNSS point with a radius of 24km, and calculating the average FAI value. Among them, 26464 matching points were obtained in July 2020 and 27353 matching points were obtained in July 2021.

[0057] (3) Verification of wind speed stratification like Figure 2 In the SWH–ERA5 wind speed scatter plot, the algae points are systematically lower than the clean water point group, which is direct evidence of the wave height suppression effect. Figure 3 After wind speed stratification, the detection effectiveness of WHSI was compared with other sea surface roughness parameters in the CYGNSS L2 CDR product: mean square slope (MSS), normalized bistatic radar cross section (NBRCS), and leading edge slope (LES), under the same wind speed stratification conditions. Cohen's d is the effect quantity, and its absolute value |d| represents the degree of normalized difference between the distributions of algae and clean water: the larger |d| is, the more obvious the separation between the two distributions. The comparison results show that the comprehensive weighted average of WHSI | | (i.e., weighted Cohen's d) reaches The |d| value is 0.67, the highest among all four CYGNSS parameters; the |d| values ​​for MSS, NBRCS, and LES are all close to zero, indicating no detectable response to algae, confirming that SWH is the only GNSS-R observable that responds to the wave height suppression effect of large algae. Figure 4 As shown, all 14 wind speed ranges All values ​​were negative, with peak values ​​between 4 and 6 ms. - ¹(| | =0.75~0.83). For example... Figure 5 As shown, Cohen's d monotonically increases with the algal density threshold, and the WHSI probability density distributions of different density categories are separated step by step.

[0058] (4) Algae Index Calculation First, we need to correlate space with time, such as... Figure 6 As shown, the 0.25° gridded comparison indicates a spatial inverse correlation between the low WHSI region and the high FAI region. (As...) Figure 7 As shown, the four-year average monthly WHSI in the core Caribbean region shows a systematic minimum from May to August, which coincides with the algal bloom season.

[0059] like Figure 8 As shown, the average cloud coverage of MODIS was 45% in 2020-2021, with about 64 days of clear skies; CYGNSS had about 700 effective days, with sampling density increasing by about 10 times.

[0060] Clean water samples were taken at 0.5 ms. - ¹Establish a clean water body background reference table for each compartment and calculate AI for each detection point.

[0061] (5) Probability calibration A logistic model was fitted with FAI > 0.004 as the true value. When the value is greater than 0.5, the positive rate is 0.72% and the false positive rate is 0.28%. For example... Figure 9 As shown, the calibration curves for all 10 probability compartments are close to the diagonal. In the 5-fold cross-validation, the random forest (features: WHSI, NBRCS / MSS, LES / MSS, incident angle, ERA5 wind speed) has an AUC of 0.978 and an F1 of 0.73.

[0062] In summary, the method provided by this invention can achieve the following technical effects: (1) A Wave Height Suppression Index (WHSI) is defined to convert non-imaging GNSS-R data into a large algae detection signal. WHSI directly quantifies the degree of wave height suppression, with a clear physical meaning: the WHSI in algae-covered sea areas is systematically lower than that in clean sea areas. The wind speed stratification strategy eliminates the dominant influence of wind speed on SWH, enabling reliable extraction of the suppression signal.

[0063] (2) Achieve all-weather, all-time detection. L-band signals penetrate clouds and do not depend on sunlight. According to data statistics from 2020 to 2021, the effective observation days of CYGNSS (about 700 days) are about 10 times that of MODIS clear sky days (about 64 days), with the greatest advantage during the summer outbreak season.

[0064] (3) Achieve probabilistic output. The algae index AI normalizes WHSI anomalies and outputs the probability of algae presence at observation points after logistic calibration, which can be directly integrated into the operational early warning system.

[0065] (4) The detection performance was independently verified. Taking Sargassum as an example, when FAI>0.004 was used as the label, the WHSI wind speed stratification Cohen's d reached 0.96 to 1.09; Random forest cross-validation AUC = 0.978, F1 = 0.73.

[0066] Example 2 Based on the same inventive concept, this embodiment discloses a GNSS-R method for detecting large marine algae based on wave height suppression, including: The data acquisition module is used to acquire marine geophysical data and extract the effective wave height and wind speed of each mirror reflection point; The wave height suppression index calculation module is used to calculate the wave height suppression index point by point based on the effective wave height and wind speed. The wave height suppression index is used to indicate the effective wave height driven by a unit wind speed. The wind speed stratification verification module is used to verify the detection efficacy of the wave height suppression index by analyzing the wind speed stratification effect quantity. The algae index calculation module is used to look up the mean and standard deviation of the wave height suppression index at the corresponding wind speed from a pre-established clean water background reference table based on the wind speed at the observation point if the wave height suppression index is effective, and then calculate the algae index based on the mean and standard deviation of the wave height suppression index. The probability calibration and determination module is used to input the calculated algae index into a pre-trained logistic calibration model and output the probability of algae presence at the observation point.

[0067] Since the system in Embodiment 2 of this invention is the same system used in the GNSS-R marine macroalgae detection method based on wave height suppression in Embodiment 1, those skilled in the art can understand the specific structure and variations of this system based on the method described in Embodiment 1 of this invention, and therefore will not be repeated here. All systems used in the method of Embodiment 1 of this invention fall within the scope of protection of this invention.

[0068] Example 3 Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the GNSS-R method for detecting large marine algae based on wave height suppression as described in Embodiment 1.

[0069] Since the computer-readable storage medium described in Embodiment 3 of this invention is the same computer-readable storage medium used in implementing the GNSS-R method for detecting large marine algae based on wave height suppression in Embodiment 1 of this invention, those skilled in the art can understand the specific structure and variations of this computer-readable storage medium based on the method described in Embodiment 1 of this invention, and therefore will not be repeated here. All computer-readable storage media used in the method of Embodiment 1 of this invention fall within the scope of protection of this invention.

[0070] Example 4 Based on the same inventive concept, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the GNSS-R method for detecting large marine algae based on wave height suppression as described in Embodiment 1.

[0071] Since the computer equipment described in Embodiment 4 of this invention is the same computer equipment used in implementing the GNSS-R method for detecting large marine algae based on wave height suppression in Embodiment 1 of this invention, those skilled in the art can understand the specific structure and variations of this computer equipment based on the method described in Embodiment 1 of this invention, and therefore will not be described again here. All computer equipment used in the method of Embodiment 1 of this invention falls within the scope of protection of this invention.

[0072] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various modifications and variations to the embodiments of the invention without departing from the spirit and scope of the invention. Thus, if these modifications and variations of the embodiments of the invention fall within the scope of the claims of the invention and their equivalents, the invention also intends to include these modifications and variations.

Claims

1. A GNSS-R method for detecting large marine algae based on wave height suppression, characterized in that, include: Acquire marine geophysical data and extract the effective wave height and wind speed at each specular reflection point; Based on the significant wave height and wind speed, the wave height suppression index is calculated point by point. The wave height suppression index is used to indicate the significant wave height driven by a unit wind speed. The effectiveness of the wave height suppression index was verified by wind speed stratification effect analysis. If the wave height suppression index is effective, the mean and standard deviation of the wave height suppression index at the corresponding wind speed are retrieved from the pre-established clean water body background reference table based on the wind speed at the observation point, and the algae index is calculated based on the mean and standard deviation of the wave height suppression index. The calculated algae index is input into a pre-trained logistic calibration model, which outputs the probability of algae presence at the observation point.

2. The GNSS-R method for detecting large marine algae based on wave height suppression as described in claim 1, characterized in that, Based on the significant wave height and wind speed, the wave height suppression index is calculated point by point, including: in, represents the wave height suppression index, Indicates the significant wave height. This indicates the ERA5 10-meter cooperative positioning wind speed.

3. The GNSS-R method for detecting large marine algae based on wave height suppression as described in claim 1, characterized in that, The effectiveness of the wave height suppression index in detecting wave height was verified using wind speed stratification effect analysis, including: The observation data is divided into several equal-width intervals according to wind speed; The standardized mean difference effect of wave height suppression index between algae-infested water bodies and clean water bodies and the weighted composite value of each interval were calculated separately within each interval. If the weighted composite value of each interval is negative and the signs of each interval are consistent, then the wave height suppression index is determined to be effective in detecting algae.

4. The GNSS-R method for detecting large marine algae based on wave height suppression as described in claim 3, characterized in that, Within each interval, the standardized mean difference effect size of the wave height suppression index between algae-infested and clean water bodies, as well as the weighted composite value for each interval, were calculated, including: Wave height inhibition index values ​​were extracted for algae-infested water bodies and clean water bodies, respectively. The standardized mean difference effect size of the wave height inhibition index between algae-infested and clean water bodies was calculated using the formula: In the formula, target and reference represent the categories of algae-infested water bodies and clean water bodies, respectively. This represents the mean value of the wave height inhibition index for algae growth in water bodies. This represents the mean value of the wave height suppression index for clean water bodies. The standard deviation of the wave height inhibition index for algae growth is represented by the following: The standard deviation of the wave height suppression index for clean water bodies. This represents the standardized mean difference effect size for the k-th interval; Calculate the weighted composite value for each interval using the formula: in, This is the weighted composite value. This represents the weight of the k-th interval. , These represent the number of samples from algae-rich water bodies and the number of samples from clean water bodies, respectively.

5. The GNSS-R method for detecting large marine algae based on wave height suppression as described in claim 1, characterized in that, The methods for constructing a background reference table for clean water bodies include: All samples from historical observation data in which the algae index of the optical sensor is below the cleaning threshold are considered as clean water samples. The wind speed is divided into several equal-width intervals according to a preset interval. The mean and standard deviation of the wave height suppression index are calculated for each interval to construct a background reference table for clean water bodies.

6. The GNSS-R method for detecting large marine algae based on wave height suppression as described in claim 1, characterized in that, The algae index is calculated based on the mean and standard deviation of the wave height inhibition index, including: In the formula, This indicates the value of the algae index. Indicates wind speed The mean of the corresponding wave height suppression index, Indicates wind speed The standard deviation of the corresponding wave height suppression index, WHSI represents the wave height suppression index.

7. The GNSS-R method for detecting large marine algae based on wave height suppression as described in claim 1, characterized in that, The calculated algae index is input into a pre-trained logistic calibration model, which outputs the probability of algae presence at each observation point, including: In the formula This represents the conditional probability that large algae exist at the observation point given a given algae index value. This indicates the presence of algae. Indicates the algae index value; β0 represents the intercept term, β1 represents... The regression coefficients.

8. A GNSS-R method for detecting large marine algae based on wave height suppression, characterized in that, include: The data acquisition module is used to acquire marine geophysical data and extract the effective wave height and wind speed of each mirror reflection point; The wave height suppression index calculation module is used to calculate the wave height suppression index point by point based on the effective wave height and wind speed. The wave height suppression index is used to indicate the effective wave height driven by a unit wind speed. The wind speed stratification verification module is used to verify the detection efficacy of the wave height suppression index by analyzing the wind speed stratification effect quantity. The algae index calculation module is used to look up the mean and standard deviation of the wave height suppression index at the corresponding wind speed from a pre-established clean water background reference table based on the wind speed at the observation point if the wave height suppression index is effective, and then calculate the algae index based on the mean and standard deviation of the wave height suppression index. The probability calibration and determination module is used to input the calculated algae index into a pre-trained logistic calibration model and output the probability of algae presence at the observation point.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the GNSS-R method for detecting large marine algae based on wave height suppression as described in any one of claims 1 to 7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the GNSS-R method for detecting large marine algae based on wave height suppression as described in any one of claims 1 to 7.

Images