Method and system for continuous monitoring of vegetation optical depth and canopy water content based on global navigation satellite system transmission signals

Abstract

This invention discloses a method and system for continuous monitoring of vegetation optical thickness and canopy moisture based on transmission signals from a Global Navigation Satellite System (GNSS). By deploying GNSS receiving modules above and below the vegetation canopy, GNSS signals from both modules are simultaneously acquired. Vegetation optical thickness is inverted, and multi-satellite data is fused. The fused time series is decomposed into low-frequency structural components and high-frequency dynamic components. The high-frequency dynamic components are then separated into plant internal water, canopy water retention, and residual noise, thus decoupling vegetation structural changes from short-term moisture dynamics and enabling monitoring of canopy moisture content and its dynamic changes. This method offers advantages such as non-destructive operation, high temporal resolution, and automated continuous monitoring, and can be widely applied to the continuous monitoring of vegetation structure and moisture status in forests and farmland.

Description

Technical Field

[0001] This invention relates to the fields of ecological environment monitoring and microwave remote sensing technology, specifically to a method and system for continuous monitoring of vegetation optical thickness and canopy moisture based on Global Navigation Satellite System (GNSS) transmitted signals. Background Technology

[0002] Canopy moisture content and biomass have always been important parameters for assessing carbon and water cycles and vegetation health in terrestrial ecosystems. Traditional vegetation moisture monitoring methods mainly rely on destructive sampling (drying and weighing), sap flow observation, or leaf water potential measurement. These methods essentially obtain point information at the scale of a single leaf, branch, or tree. Moreover, the sampling process itself alters the canopy structure, making it difficult to extrapolate the observation results to the community scale. Furthermore, the aforementioned traditional vegetation moisture observation methods also suffer from drawbacks such as low automation, high reliance on manual labor, and difficulty in continuous operation. For example, drying and weighing requires periodic felling of samples, sap flow requires drilling holes to install probes and frequent calibration, and leaf water potential measurement requires manual leaf-by-leaf operation. These methods not only damage the integrity of vegetation but are also limited by field working conditions, usually only obtaining snapshot-like data at discrete moments.

[0003] With the continuous development of remote sensing technology, microwave remote sensing technology has been widely used in vegetation moisture monitoring in recent years. However, existing satellite microwave remote sensing equipment (such as SMOS and SMAP) is limited by the satellite revisit cycle and transit time, and can usually only provide observation data at the diurnal scale or fixed local time. The temporal resolution is coarse, and it is easy to miss rapid dynamic processes such as rainfall events, canopy water retention and reevaporation, and intra-day water cycling driven by vegetation transpiration. Therefore, it is difficult to reflect local-scale vegetation moisture changes. Research has found that Global Navigation Satellite Systems (GNSS) operate in the L-band frequency range and are sensitive to vegetation canopies. When GNSS signals pass through the vegetation canopy, the signal strength will be significantly attenuated. The degree of attenuation is closely related to vegetation moisture content and canopy structure. Therefore, due to the sensitivity of GNSS L-band signals to vegetation canopies, it has gradually become the mainstream technology for continuous vegetation moisture monitoring. Current research mainly uses the following methods: One method is the GNSS Reflection Signal Method (GNSS-R), which primarily utilizes the left-hand circularly polarized GNSS signal reflected from the vegetation surface. By analyzing the reflected signal power, polarization characteristics, or phase delay, it retrieves vegetation water content and biomass. However, in this process, the reflected signal interacts with the vegetation-soil composite medium, making it susceptible to coupling interference from soil background humidity, surface roughness, and topographic slope. Furthermore, its effective reflective zone (Fresnel zone) spatial resolution is limited by the satellite elevation angle, making it difficult to accurately separate the influence of vegetation canopy and soil signals. Another method is the GNSS Direct Signal Transmission / Attenuation Method, which mainly uses receivers deployed below the vegetation canopy to capture the direct signal penetrating the canopy. It utilizes signal-to-noise ratio (SNR) attenuation or polarization loss to retrieve vegetation optical thickness (VOD) and canopy moisture. Although this method can obtain VOD parameters consistent with the physical meaning of satellite microwave remote sensing, the obtained transmission signal is easily affected by the receiver. Gain fluctuations, changes in atmospheric water vapor content, and multipath interference are all factors to consider. Furthermore, existing research largely remains at the single-point experimental stage, lacking automated quality control and standardized inversion processes for multi-constellation, multi-frequency signals. Another method, GNSS-IR, primarily utilizes the interference pattern formed by the direct signal at the receiver antenna and the reflected signal from the ground surface—that is, the SNR oscillation characteristics—to extract vegetation height and canopy structure information. However, this method is sensitive to antenna installation height, vegetation type, and growth phase. Its interference model typically assumes a homogeneous vegetation medium, which significantly deviates from the heterogeneity of the actual canopy vertical structure, limiting its applicability in complex forest stands or throughout the entire growth period of crops.

[0004] In microwave remote sensing monitoring, vegetation optical thickness (VOD) is primarily used to characterize vegetation water content and biomass. VOD, as a comprehensive parameter, is influenced by both vegetation structure (biomass, leaf area index, and stem characteristics) and vegetation water status. Existing VOD studies largely focus on obtaining total VOD time series data, failing to effectively distinguish between long-term structural background and short-term water variation, leading to ambiguity in parameter interpretation. Furthermore, current VOD research mainly focuses on monitoring living vegetation water (stem water and leaf water), lacking effective means to identify the crucial hydrological flux of canopy intercepted water (free water adhering to the leaf surface during rainfall). In fact, the presence of intercepted water after rainfall significantly increases VOD observations; failure to distinguish it from internal plant water will lead to an overestimation of vegetation water content.

[0005] In summary, current technologies for vegetation moisture monitoring primarily rely on destructive sampling, making it difficult to widely apply the results. The existing VOD ground validation network remains extremely sparse, and current validation methods largely depend on flux tower eddy covariance systems or handheld microwave radiometers, making continuous automated observation difficult. Furthermore, VOD retrieved from satellite microwave remote sensing mainly verifies the feasibility of using GNSS for vegetation monitoring at the principle level, and still suffers from immature multi-constellation, multi-frequency signal fusion processing mechanisms, a lack of universality in transmitted and reflected signal separation algorithms, an incomplete automated processing chain from raw observation data to VOD and canopy moisture parameters, and inadequate systematic error correction and field calibration schemes for long-term continuous observation. These limitations make it difficult to meet the requirements for all-weather, automated, and high-temporal-resolution continuous monitoring of vegetation structure and moisture status in various ecosystems such as forests and farmland. Therefore, there is an urgent need for a vegetation optical thickness and canopy moisture monitoring system that can balance automation, continuity, and stability. Summary of the Invention

[0006] To address the aforementioned issues, this application provides a method and system for continuous monitoring of vegetation optical thickness and canopy moisture based on transmitted signals from a Global Navigation Satellite System (GNSS). By acquiring GNSS signal attenuation information through upper and lower GNSS receiving structures set up on the vegetation canopy, vegetation optical thickness inversion and canopy moisture dynamic monitoring are achieved.

[0007] This invention provides a method for continuous monitoring of vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, specifically including the following steps: S1. System deployment: At the observation target, the first GNSS receiving module is deployed above the vegetation canopy as a reference observation end to collect the GNSS signal above, and the second GNSS receiving module is deployed below the vegetation canopy as a transmission observation end to collect the GNSS signal below. Furthermore, in order to ensure the consistency of the observed canopy volume, the first GNSS receiving module and the second GNSS receiving module are set to have the same or similar projection range in the horizontal position; S2. Synchronously acquire GNSS signals received from above and below. The first GNSS receiving module and the second GNSS receiving module synchronously acquire GNSS signals from multiple satellites and multiple frequency points from above and below, and record the signal strength information. GNSS signals include signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR). Satellite geometric information, observation time, frequency information, etc.; Satellite geometric information may include satellite azimuth, satellite elevation, satellite number, etc. S3. GNSS signal matching: During the same observation time, the GNSS signals received above and below with the same satellite number and frequency are matched to form corresponding GNSS signal pairs and recorded and stored. S4. Data preprocessing: Preprocess the obtained GNSS signal. Preprocessing includes removing anomalous data, which may include low-elevation satellite signals, signals with severe multipath interference, anomalous jump data, and missing data. Preferably, only satellite signals with elevation angles higher than a set threshold can be retained; S5. Signal attenuation calculation: Calculate signal attenuation based on the difference between the GNSS signal received above and the GNSS signal received below. S6. Inversion of vegetation optical thickness: Inversion of vegetation optical thickness based on the attenuation model of electromagnetic waves in vegetation. Specifically, vegetation optical thickness can be retrieved based on the Beer-Lambert attenuation model: ; in, The angle of incidence of the satellite signal. Transmittance; Step S7: Multi-satellite data fusion. The vegetation optical thickness (VOD) data obtained from multiple satellites observed simultaneously are inverted separately, and the vegetation optical thickness (VOD) data obtained from different satellites are fused to obtain multi-satellite inversion fusion data. Step S8: Canopy moisture estimation. The dynamics of vegetation canopy moisture are estimated using the obtained multi-satellite inversion fusion data. Step S81: First, arrange the multi-satellite inversion and fusion data results obtained in step S7 according to a preset time interval to form a continuous inversion and fusion data time series: Step S82: Extracting low-frequency components from the inverted fusion data, decomposing the continuous inverted fusion data time series into low-frequency structural components and high-frequency dynamic components; Step S83: Extraction of high-frequency dynamic components from inversion fusion data. After obtaining low-frequency structural components, high-frequency dynamic components are obtained based on the inversion fusion data and the corresponding low-frequency structural components obtained in step S82, and smoothing filtering is performed on the high-frequency dynamic components. Step S84: Decompose the smoothed high-frequency dynamic components into plant internal water, canopy water retention, and residual noise. Step S9: Output the final observation results of the canopy moisture estimation; and remotely transmit the final observation results to the server through the communication module to realize long-term ecological monitoring; The final observation results include time series of inverted and fused data of vegetation optical thickness, time series of vegetation canopy water change, signal quality indicators, and equipment operating status; among which, the time series of vegetation canopy water change includes low-frequency structural components and high-frequency dynamic components, and the high-frequency dynamic components include plant internal water items and canopy water interception items. This application also proposes a continuous monitoring system for vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, which mainly includes the following modules: The first GNSS receiving module is installed in the area above the vegetation canopy to receive reference signals from GNSS satellites that have not been attenuated by the vegetation canopy. The second GNSS receiver module is installed in the area below the vegetation canopy to receive GNSS signals that have passed through the vegetation canopy. The first GNSS receiving module and the second GNSS receiving module synchronously acquire and store GNSS signals; The collected GNSS signals include signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR). ), satellite number (PRN), satellite azimuth, satellite elevation, observation time, frequency information, GNSS constellation type, etc.; The first GNSS receiver module can receive navigation system signals, including GPS, BeiDou (BDS), GLONASS, and Galileo. The first GNSS receiving module includes a GNSS receiving antenna at the top of the canopy and a GNSS receiver above the canopy; the second GNSS receiving module includes a GNSS receiving antenna at the bottom of the canopy and a GNSS receiver below the canopy. The first GNSS receiving module and the second GNSS receiving module use the same type of receiver, the same sampling frequency, and the same frequency point configuration to ensure good comparability of the observed signals.

[0008] Furthermore, the GNSS receiving antenna and GNSS receiver can be the PolaNt-X MF.v2 model receiving antenna and the PolaRx5 model receiver from the Belgian company Septentrio. The data processing and storage module is used to match the GNSS signals received from above and below to form corresponding GNSS signal pairs, and to perform data preprocessing, signal attenuation calculation, vegetation optical thickness inversion, multi-satellite data fusion, and canopy moisture estimation on the GNSS signal pairs. The data processing and storage module includes a signal matching module, an observation quality control module, a signal attenuation calculation module, a vegetation optical thickness inversion module, a time series fusion module, and a canopy moisture estimation module. The signal matching module is used to match the GNSS signals received from above and below to form corresponding GNSS signal pairs. The observation quality control module can perform data preprocessing, preprocessing the acquired GNSS signal pairs and removing abnormal data. Abnormal data may include low elevation angle satellite signals, signals with severe multipath interference, abnormal jump data, and missing data. The signal attenuation calculation module is used to calculate signal attenuation based on the difference between the GNSS signal received above and the GNSS signal received below. Among them, the vegetation optical thickness inversion module inverts the vegetation optical thickness based on the attenuation model of electromagnetic waves in vegetation; Among them, the time-series fusion module is used for multi-satellite data fusion. It performs vegetation optical thickness inversion on the data obtained from multiple satellites observed at the same time, and fuses the vegetation optical thickness VOD data obtained from different satellites to obtain multi-satellite inversion fusion data. Among them, the canopy moisture estimation module is used to estimate the dynamics of vegetation canopy moisture based on the obtained multi-satellite inversion fusion data; Furthermore, it also includes a control system and a communication module for remote transmission of observation data; the communication methods can include wired communication, 4 / 5G communication, LoRa communication and satellite communication, with 4 / 5G communication being preferred; The power supply module is used for long-term system operation and can be powered by mains power, solar power, or battery power; specifically, the power supply module is a solar panel.

[0009] Compared with the prior art, the advantages provided by the present invention include: 1. This invention utilizes two GNSS receiving modules deployed above and below the canopy to invert vegetation optical thickness (VOD) by leveraging the transmission attenuation differences of L-band signals as they penetrate the entire canopy volume. The transmission path includes the cumulative absorption and scattering effects of signals from all leaves, branches, and trunks within the canopy, enabling the acquisition of VOD and moisture change information at the overall canopy scale, rather than at the single-leaf or single-branch scale. This cumulative observation based on the transmission path avoids the spatial limitations of single-point sampling and fills the observation gap between the satellite-level and leaf-level scales, significantly improving the representativeness of ecological observations. It achieves cumulative observation of overall optical thickness at the canopy scale, overcoming the spatial representativeness bottleneck of traditional single-point destructive sampling.

[0010] 2. This invention constructs a non-contact automated monitoring architecture to achieve long-term, continuous, and maintenance-free observation of vegetation moisture status. Based on the sensitivity of GNSS signals in the L-band to vegetation canopy, only a pair of GNSS receiving antennas and receivers need to be deployed above and below existing observation facilities such as tower bases or flux stations. There is no need to contact the vegetation tissue. It can achieve all-weather unattended operation by relying on solar power and remote communication modules such as 4G / 5G. Through automatic matching of signals above and below, multi-satellite fusion, and data processing, the original GNSS observation signals are directly converted into VOD and canopy moisture time series, which significantly reduces the cost of manual sampling and maintenance, and provides a universal solution for long-term continuous monitoring of multiple ecosystems such as forests and farmland.

[0011] 3. This invention relies on multi-satellite GNSS signal sources to achieve high temporal resolution continuous monitoring and accurately capture rapid moisture dynamics. Utilizing signal resources from multiple constellations, satellites, and frequencies such as GPS, BeiDou, GLONASS, and Galileo, combined with synchronous continuous acquisition by upper and lower receiving modules, this invention can achieve high temporal resolution monitoring output compared to existing satellite VOD products that typically only provide daily-scale or fixed transit time information. This continuous observation capability enables the system to fully record the rapid response changes of VOD during rainfall events, the rapid decay process of canopy water interception after rain, and the diurnal moisture rhythm driven by the evapotranspiration-replenishment mechanism. It is more suitable for analyzing rainfall events, evapotranspiration processes, and diurnal variations, providing corresponding observational support for understanding the rapid conversion of vegetation moisture at the rainfall event scale and the daily scale.

[0012] 4. This invention constructs a low-frequency-high-frequency dual-layer decomposition and three-term separation mechanism to decouple vegetation structure changes from short-term water fluctuations, thereby improving the explanatory power of parameters. Since VOD parameters are simultaneously affected by factors such as vegetation structure, biomass, and water, this invention constructs a VOD time series decomposition system, extracts the daily minimum value sequence, and smoothly fits it to extract low-frequency structural components reflecting slow changes in biomass and structure. Furthermore, the remaining high-frequency dynamic components are decomposed into plant internal water terms (based on the diurnal variation template constructed using multi-day evaluation of diurnal VOD cycles during rainfall periods), canopy intercepted water terms (pulse signals during rainfall periods), and residual noise terms (random fluctuations under statistical constraints). By utilizing this decoupling mechanism from low-frequency structural components and high-frequency dynamic components to plant internal water terms and canopy intercepted water terms, long-term structural changes and short-term water changes can be separated. It can independently track structural evolution caused by vegetation growth (weekly-monthly scale) and physiological state changes caused by water stress / recovery (hourly-daily scale), significantly improving the ecological explanatory power of VOD parameters.

[0013] 5. This invention establishes a GNSS-VOD identification channel for post-rain canopy water interception, expanding the application scenarios of ecohydrology. Through the three-component decomposition of high-frequency dynamic components (plant internal water component, canopy intercepted water component, and residual noise component), combined with the event triggering mechanism of rainfall sensors and the setting of post-rain buffer periods, this invention is sensitive to the increase process of post-rain canopy water interception, realizing the quantitative calculation of the canopy intercepted water component within the GNSS transmission signal framework. This invention can be used to monitor the rapid accumulation of intercepted water during rainfall events, the evaporation attenuation process after rainfall stops, and wetting events such as dew attachment. Through the above content, the hydrological bias in traditional VOD inversion is corrected, and at the same time, it provides a new observation dimension for studying the impact of canopy interception on precipitation redistribution, forest microclimate, and soil moisture replenishment, expanding the ecohydrological application scenarios of traditional GNSS-VOD.

[0014] 6. This invention constructs ground verification nodes that match the scale of satellite VOD products, supporting remote sensing product calibration and ecological model improvement. Currently, VOD ground verification networks are still relatively lacking. This invention acquires VOD parameters through a dual GNSS receiver module transmission architecture. Utilizing the L-band frequency range of GNSS and its sensitivity to vegetation canopy, it accumulates data across the vegetation canopy to obtain the system output: inversion and fusion data time series of vegetation optical thickness, vegetation canopy moisture change-related time series, signal quality indicators, and equipment operating status information. This data can be imported into a database in real time via a communication module, providing long-term, stable, and high-temporal-density ground anchor points for improving VOD inversion algorithms and calibrating vegetation parameters in the ground verification system, thus promoting the construction of a satellite-ground collaborative observation system.

[0015] In summary, this invention systematically solves multiple technical problems in existing vegetation moisture monitoring processes, such as insufficient spatial representativeness, low temporal resolution, strong destructiveness, ambiguous parameter interpretation, immature multi-constellation and multi-frequency signal fusion processing mechanisms, and lack of universality in the transmission and reflection signal separation algorithm, through a complete technical chain of dual GNSS receiver module signal acquisition, multi-satellite VOD fusion, and time series hierarchical decomposition. It provides a ground vegetation optical thickness and canopy moisture monitoring solution that can be engineered, automated, operated continuously for a long time, and directly interfaced with satellite remote sensing for the study of water and carbon cycles in various ecosystems. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1This is a schematic diagram of the observation of transmitted signals from the Global Navigation Satellite System based on this application; Figure 2 This is a schematic diagram of the GNSS-VOD system framework of this application; Figure 3 This is a flowchart of the data acquisition and processing for this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] This invention provides a method for continuous monitoring of vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, specifically including the following steps: S1. System deployment: At the observation target, the first GNSS receiving module is deployed above the vegetation canopy as a reference observation end to collect the GNSS signal above, and the second GNSS receiving module is deployed below the vegetation canopy as a transmission observation end to collect the GNSS signal below. Furthermore, in order to ensure the consistency of the observed canopy volume, the first GNSS receiving module and the second GNSS receiving module are set to have the same or similar projection range in the horizontal position; The observation targets are the target vegetation sample plots; S2. Synchronously acquire and record GNSS signals received from above and below by using the first and second GNSS receiving modules to acquire and store multiple satellite and frequency point GNSS signals from above and below. GNSS signals include SNR and carrier-to-noise ratio. Satellite geometric information, observation time, frequency information, etc.; SNR is the signal-to-noise ratio, which characterizes the overall level of power attenuation of GNSS signals after passing through the canopy. Generally, the greater the canopy water and biomass, the stronger the dielectric absorption and volume scattering of L-band signals, and the lower the corresponding SNR. Carrier-to-noise ratio (CNR) is the ratio of carrier signal power C to noise power spectral density N0 (noise power within the bandwidth). It typically eliminates system bias caused by differences in receiver processing bandwidth and is a standardized signal quality indicator in the GNSS field. Satellite geometric information refers to the spatial position of the satellite relative to the receiver. Satellite geometric information may include satellite azimuth, satellite elevation, satellite number, etc. Furthermore, it can also collect GNSS constellation types; S3. GNSS signal matching: During the same observation time, the GNSS signals received above and below with the same satellite number and frequency are matched to form corresponding GNSS signal pairs and recorded and stored. S4. Data preprocessing: Preprocess the obtained GNSS signal pairs. Preprocessing includes removing anomalous data, which may include low-elevation satellite signals, signals with severe multipath interference, anomalous jump data, and missing data. Preferably, only satellite signals with elevation angles higher than a set threshold can be retained; S5. Signal attenuation calculation: Calculate signal attenuation based on the difference between the received GNSS signal from above and the received GNSS signal from below. ; Further, the transmittance is calculated: ; in, , , , These are the strength of the GNSS signal received above, the strength of the GNSS signal received below, the GNSS signal difference, and the transmittance; where a transmittance of 1 indicates that the signal is completely penetrated, and a transmittance of 0 indicates that the signal is completely blocked. S6. Inversion of vegetation optical thickness: Inversion of vegetation optical thickness based on the attenuation model of electromagnetic waves in vegetation. Specifically, the Beer-Lambert attenuation model can be used to invert the optical thickness of vegetation: ; ; in, λ is the incident angle of the satellite signal, and λ is the satellite elevation angle. Transmittance; When the satellite elevation angle is low, the propagation path of the GNSS signal in the vegetation canopy becomes longer, making it susceptible to multipath interference and the influence of canopy lateral heterogeneity. Therefore, before performing VOD inversion, the incident angle of the satellite signals involved in the calculation can be screened. Preferably, only those with λ greater than 30° are retained, which can effectively reduce the multipath interference and external scattering effects of low elevation angle satellite signals, thereby improving the stability of single satellite VOD inversion. Step S7: Multi-satellite data fusion. The vegetation optical thickness (VOD) data obtained from multiple satellites observed simultaneously are inverted separately, and the vegetation optical thickness (VOD) data obtained from different satellites are fused to obtain multi-satellite inversion fusion data. Among them, arithmetic average fusion or weighted average fusion algorithms can be used to obtain more stable VOD estimates; Step S71: Perform arithmetic averaging and fusion on the VOD data obtained from the inversion; ; Where N is the number of valid satellite samples in the current time window. This method is simple to implement and is suitable for situations where the observation conditions are relatively uniform and the number of samples is sufficient. Step S72: Perform weighted average fusion on the VOD data obtained from the inversion; ; in, For the first The weight of each satellite sample; Preferably, the weights can be determined based on one or more of the following factors: (1) Determine the weights based on signal quality, and assign higher weights to samples with higher SNR and lower noise. (2) Based on the stability of the incident angle, the weights are determined, and samples with medium to high satellite elevation angles (i.e., small to medium incident angles) are given higher weights in order to reduce the residual error caused by extreme large incident angles. (3) Based on historical stability, the weights are determined, and samples with smaller historical variance and better repeatability of satellite trajectories are given higher weights.

[0019] Step S8: Canopy moisture estimation. The dynamics of vegetation canopy moisture are estimated using the obtained multi-satellite inversion fusion data. Since vegetation optical thickness is closely related to vegetation water content, this application uses inverted VOD data to detect vegetation water content; Step S81: First, arrange the multi-satellite inversion and fusion data results obtained in step S7 according to a preset time interval to form a continuous inversion and fusion data time series: ; in, This indicates the observation time, where the time resolution can be selected as 1 minute, 5 minutes, 30 minutes, or 1 hour, etc. Preferably, a 30-minute time resolution can be used to generate a continuous inversion and fusion data time series to balance signal stability and the ability to characterize diurnal variations in canopy moisture. Step S82: Extracting low-frequency components from the inverted fusion data, decomposing the continuous inverted fusion data time series into low-frequency structural components and high-frequency dynamic components; Among them, low-frequency structural components mainly reflect the slow changes in vegetation canopy on a weekly to monthly scale, which may include changes in vegetation biomass, leaf area index, branch and leaf structure, and seasonal phenological changes. Preferably, low-frequency structural components It is obtained by smoothing and fitting the daily minimum value sequence in the continuous inversion fusion data time series; Specifically, a daily minimum value sequence can be constructed from the minimum values ​​in the daily inverted fused data time series, and fitted using smoothed splines or a generalized additive model to obtain the low-frequency structural components: ; in, The function represents a smoothed time trend. Preferably, during rainfall events, the minimum value of the inverted fusion data is masked to avoid the impact of rainwater retention on the rise of low-frequency background terms. Specifically, the impact of rainfall can be masked by using hourly rainfall greater than or equal to 1 mm and extending it backward by 48 hours, and then fitting the low-frequency structural components with the daily minimum value. Step S83: Extraction of high-frequency dynamic components from inversion fusion data. After obtaining low-frequency structural components, high-frequency dynamic components are obtained based on the inversion fusion data and the corresponding low-frequency structural components obtained in step S82. ; Among them, high-frequency dynamic components It can reflect responses to rainfall events, changes in evapotranspiration, and canopy water retention, etc. Furthermore, to reduce the impact of measurement noise, it is preferable to perform smoothing filtering on the high-frequency dynamic components. A Savitzky-Golay filter can be used for smoothing filtering. ; The filter window length is preferably 5 to 15 hours, and the polynomial order is preferably 2. Furthermore, in existing studies on intercepted water, an 11-hour window and a second-order Savitzky-Golay filter can be used to smooth high-frequency dynamic components. Step S84: Smooth the high-frequency dynamic components Decomposed into the plant's internal water content Canopy water interception project and residual noise term ; ; Among them, the internal water content of plants The estimation is based on the construction of a typical diurnal variation template of plant internal water using multi-day average diurnal VOD cycles during periods without rainfall. For example, suppose the average daily cycle of the high-frequency dynamic components of all rainless days in a certain month is: ; in, Indicates the hourly position within a day; Indicates the number of days without rainfall; The internal water content of plants is: ; in This represents the hour position corresponding to time t; The physical significance of the above settings is that, under no-rainfall conditions, the high-frequency dynamic components are mainly dominated by the diurnal variation of plant internal water, which is usually higher in the early morning and at night and lower at noon, consistent with transpiration water loss and nighttime replenishment. Furthermore, to improve seasonal adaptability, it is preferable to calculate separately by month or by phenological stage. In each month, a multi-day average diurnal VOD cycle template of rainless days is reconstructed, which is also a typical diurnal variation template of plant internal water. Given that during a period of no rainfall and relatively stable vegetation moisture, it can be considered that... The value is approximately 0, at which point the residual noise term is... It can be done during periods without rain. To obtain: ; Furthermore, the obtained residual noise term can also be... By estimating the standard deviation, the corresponding noise statistical characteristics can be obtained. And in addition to the periods of no rainfall and relatively stable vegetation moisture mentioned above, The above statistical characteristics will still be used; Furthermore, It can also be updated through real-time sliding window filtering; Specifically, let the period without rainfall be... The residual sequence is obtained by subtracting the high-frequency dynamic components from the internal water term of the plant. : ; The standard deviation of the residual sequence is as follows: ; ; in, This represents the number of valid samples during periods without rainfall. This represents the mean of the residual sequence; furthermore, the residual sequences can be constructed separately for each month or phenological stage, and the corresponding standard deviations can be calculated. To adapt to noise level drift caused by seasonal changes; During periods of rainfall, based on the aforementioned noise statistical characteristics Determine the residual noise term ,in or ; After obtaining the plant internal water term and residual noise term, the canopy retained water term can be expressed as: ; Furthermore, when a rainfall sensor is also present, the time period that meets the following conditions can be identified as a rainfall / wet canopy period: ; in, Let be the rainfall at time t. Preset rainfall threshold; Furthermore, 1 mm / h can be selected; Furthermore, a buffer period of 12 to 48 hours can be added after the rainfall ends to ensure coverage of the canopy's water interception and evaporation phase; during this wet canopy period, if A value significantly greater than zero indicates the accumulation of free water on the surface of the plant canopy; Furthermore, When the water level gradually decreases over time and recovers to near zero, it indicates the presence of a process where water trapped in the plant canopy is re-evaporated. generally, It increases rapidly after a rainfall event, and its average magnitude can reach the diurnal variation within the plant (the plant's internal water content). Amplitudes 1.5 to 2 times greater can separate water trapped in the canopy from water inside the plant; Step S9: Output the final observation results of the canopy moisture estimation; and remotely transmit the final observation results to the server through the communication module to realize long-term ecological monitoring; The final observation results include time series of inverted and fused data of vegetation optical thickness, time series of vegetation canopy water change, signal quality indicators, and equipment operating status; among which, the time series of vegetation canopy water change includes low-frequency structural components and high-frequency dynamic components, and the high-frequency dynamic components include plant internal water items and canopy water interception items. This application also proposes a continuous monitoring system for vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, combined with the attached... Figure 1 It mainly includes the following modules: 1. GNSS satellites 2. Canopy Top GNSS Receiving Antenna 3. GNSS receiver above the canopy 4. Forest 5. Canopy-bottom GNSS receiving antenna 6. GNSS receiver below the canopy 7. Data processing and storage module 8. Control system and communication module 9. Solar panels and waterproof boxes; Among them, the GNSS receiving antenna at the top of the canopy and the GNSS receiver above the canopy together constitute the GNSS receiving module above the canopy, which is the first GNSS receiving module; the GNSS receiving antenna at the bottom of the canopy and the GNSS receiver below the canopy together constitute the GNSS receiving module below the canopy, which is the second GNSS receiving module. The first GNSS receiver module is installed above the vegetation canopy to receive reference signals from GNSS satellites that have not been attenuated by the vegetation canopy. The first GNSS receiver module can receive navigation system signals, including GPS, BeiDou (BDS), GLONASS, and Galileo. Among them, an unobstructed area can be selected above the vegetation canopy, such as the top of the flux tower, the horizontal arm of the observation tower, or the top of the independent observation support; Preferably, the operating frequency band is the L-band (L1), but it can also be extended to L2, L5 and other frequency points.

[0020] The second GNSS receiver module is installed in the area below the vegetation canopy to receive GNSS signals that have passed through the vegetation canopy; the installation height can be set to 0.5 to 3 meters above the ground. The first GNSS receiving module and the second GNSS receiving module use the same type of receiver, the same sampling frequency, and the same frequency point configuration to ensure good comparability of the observed signals.

[0021] Furthermore, the GNSS receiving antenna and GNSS receiver can be the PolaNt-X MF.v2 model receiving antenna and the PolaRx5 model receiver from the Belgian company Septentrio. The first GNSS receiving module and the second GNSS receiving module synchronously acquire and store GNSS signals, wherein the acquired GNSS signals include signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR). ), satellite number (PRN), satellite azimuth, satellite elevation, observation time, frequency information, GNSS constellation type, etc.; Furthermore, the acquired GNSS signals can be processed, including time synchronization, data recording, data caching, and data storage. Furthermore, to meet the needs of monitoring daily changes in vegetation moisture, the observation time resolution can be from 1 second to 30 minutes; The data processing and storage module includes a signal matching module, an observation quality control module, a signal attenuation calculation module, a vegetation optical thickness inversion module, a time series fusion module, and a canopy moisture estimation module. It is used to match the GNSS signals received above and below to form corresponding GNSS signal pairs, and to perform data preprocessing, signal attenuation calculation, vegetation optical thickness inversion, multi-satellite data fusion, and canopy moisture estimation on the GNSS signal pairs. The signal matching module is used to match the GNSS signals received from above and below to form corresponding GNSS signal pairs. The observation quality control module can perform data preprocessing, preprocessing the acquired GNSS signal pairs and removing abnormal data. Abnormal data may include low elevation angle satellite signals, signals with severe multipath interference, abnormal jump data, and missing data. The signal attenuation calculation module is used to calculate signal attenuation based on the difference between the GNSS signal received above and the GNSS signal received below. Among them, the vegetation optical thickness inversion module inverts the vegetation optical thickness based on the attenuation model of electromagnetic waves in vegetation; Among them, the time-series fusion module is used for multi-satellite data fusion. It performs vegetation optical thickness inversion on the data obtained from multiple satellites observed at the same time, and fuses the vegetation optical thickness VOD data obtained from different satellites to obtain multi-satellite inversion fusion data. Among them, the canopy moisture estimation module is used to estimate the dynamics of vegetation canopy moisture based on the obtained multi-satellite inversion fusion data; Furthermore, it also includes a control system and a communication module for remote transmission of observation data; the communication methods can include wired communication, 4 / 5G communication, LoRa communication and satellite communication, with 4 / 5G communication being preferred; The power supply module is used for long-term system operation and can be powered by mains power, solar power, or battery power; the power supply module is a solar panel.

[0022] Considering the low power consumption of the GNSS receiver and antenna, solar panels are preferred for power supply, and a corresponding waterproof enclosure structure is provided; wherein, the GNSS receiver (3) above the canopy, the GNSS receiver (6) below the canopy, the data processing and storage module (7), and the control system and communication module (8) are all encapsulated in the waterproof enclosure (9); As attached Figure 1As shown, the GNSS satellite (1) continuously transmits L-band microwave signals to the Earth's surface. When the GNSS signal propagates to the vegetation observation area, part of the signal is first received by the GNSS receiving antenna (2) above the canopy and then by the GNSS receiver (3) above the canopy. At this time, the signal has not yet been attenuated by the vegetation canopy, so it can be used as a reference signal. The other part of the signal passes through the vegetation canopy forest (4) and is received by the GNSS receiving antenna (5) below the canopy and then by the GNSS receiver (6) below the canopy. During the propagation process, the GNSS signal will be absorbed and scattered by the vegetation leaves, branches and canopy water, so a certain degree of signal attenuation will occur. The degree of attenuation is closely related to factors such as vegetation water content, vegetation biomass, canopy structure and satellite incident angle. Therefore, by comparing the difference in GNSS signal intensity received above and below the canopy, the optical thickness of the vegetation canopy can be inverted.

[0023] Furthermore, the data transmitted from the GNSS receiver (3) above the canopy and the GNSS receiver (6) below the canopy are sent to the data processing and storage module (7), which is controlled by the control system and communication module (8) to generate the final observation results. The final observation results include the time series of inversion and fusion data of vegetation optical thickness, the time series related to vegetation canopy moisture change, signal quality indicators, equipment operating status, etc. The time series related to vegetation canopy moisture change includes low-frequency structural components and high-frequency dynamic components. The high-frequency dynamic components include the plant internal water item and the canopy intercepted water item. As attached Figure 2 As shown, the GNSS receiver above the canopy (3), the GNSS receiver below the canopy (6), the data processing and storage module (7), and the control system and communication module (8) are all enclosed in a waterproof box (9); the waterproof box is further powered by a solar panel, which provides a 12V DC power supply.

[0024] like Figure 3 As shown, during data acquisition and processing, this invention collects GNSS signals received from the upper and lower receivers. Combining satellite identification numbers and satellite geometric information, the collected GNSS signals are matched to form GNSS signal pairs. Abnormal data is removed, and transmittance is calculated based on the corresponding upper and lower GNSS signals. Vegetation optical thickness is inverted, multi-satellite data is fused, and finally, canopy moisture is estimated and the results are output.

[0025] In summary, this invention discloses a method and system for continuous monitoring of vegetation optical thickness and canopy moisture based on transmission signals from a Global Navigation Satellite System (GNSS). By deploying GNSS receiving modules above and below the vegetation canopy, GNSS signals from these modules are simultaneously acquired. Vegetation optical thickness is inverted, and multi-satellite data is fused. The fused time series is decomposed into low-frequency structural components and high-frequency dynamic components. The high-frequency dynamic components are then separated into plant internal water, canopy water retention, and residual noise, thus decoupling vegetation structural changes from short-term moisture dynamics and monitoring canopy moisture content and its dynamic changes. This method offers advantages such as non-destructive operation, high temporal resolution, and automated continuous monitoring, and can be widely applied to continuous monitoring of vegetation structure and moisture status in forests and farmland. This invention is universally applicable to various ecological environments, including forest ecosystem monitoring, farmland crop moisture monitoring, grassland ecosystem research, orchard water management, and ground verification of satellite VOD products. It can also be used in conjunction with relevant observation equipment, such as soil moisture sensors, eddy covariance systems, sap flow sensors, and rainfall sensors, to further study vegetation water cycle processes.

[0026] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0027] Although preferred embodiments of the present application 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 the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0028] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

Claims

1. A method for continuous monitoring of vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, characterized in that, Includes the following steps: Step S1: System deployment. At the observation target, the first GNSS receiving module is deployed above the vegetation canopy as a reference observation end to collect the GNSS signal above. The second GNSS receiving module is deployed below the vegetation canopy as a transmission observation end to collect the GNSS signal below. Step S2: Synchronously acquire the GNSS signals received from above and below. The first GNSS receiving module and the second GNSS receiving module synchronously acquire the GNSS signals from multiple satellites and multiple frequency points from above and below, and record the signal strength information. Step S3: GNSS signal matching. At the same observation time, the GNSS signals received above and below with the same satellite number and frequency are matched to form corresponding GNSS signal pairs and recorded and stored. Step S4: Data preprocessing, preprocessing the obtained GNSS signal; Step S5: Calculate signal attenuation based on the difference between the received GNSS signal above and the received GNSS signal below; Step S6: Inversion of vegetation optical thickness, based on the attenuation model of electromagnetic waves in vegetation to invert vegetation optical thickness; Step S7: Multi-satellite data fusion. The vegetation optical thickness (VOD) data obtained from multiple satellites observed simultaneously are inverted separately, and the vegetation optical thickness (VOD) data obtained from different satellites are fused to obtain multi-satellite inversion fusion data. Step S8: Canopy moisture estimation. The dynamics of vegetation canopy moisture are estimated using the obtained multi-satellite inversion fusion data. Step S9: Output the final observation results of the canopy moisture estimation; and remotely transmit the final observation results to the server through the communication module to realize long-term ecological monitoring.

2. The method for continuous monitoring of vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, as described in claim 1, is characterized in that... GNSS signals include signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR). .

3. The method for continuous monitoring of vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, as described in claim 1, is characterized in that... The attenuation model is the Beer-Lambert attenuation model: ; in, The angle of incidence of the satellite signal. Transmittance.

4. The method for continuous monitoring of vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, as described in claim 1, is characterized in that... Estimating vegetation canopy water dynamics using the obtained multi-satellite inversion fusion data also includes the following steps: Step S81: Arrange the multi-satellite inversion and fusion data results obtained in step S7 according to a preset time interval to form a continuous inversion and fusion data time series: Step S82: Extracting low-frequency components from the inverted fusion data, decomposing the continuous inverted fusion data time series into low-frequency structural components and high-frequency dynamic components; Step S83: Extraction of high-frequency dynamic components from inversion fusion data. After obtaining low-frequency structural components, high-frequency dynamic components are obtained based on the inversion fusion data and the corresponding low-frequency structural components obtained in step S82, and smoothing filtering is performed on the high-frequency dynamic components. Step S84: Decompose the smoothed high-frequency dynamic components into plant internal water, canopy water retention, and residual noise.

5. The method for continuous monitoring of vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, as described in claim 4, is characterized in that... The smoothing filtering of high-frequency dynamic components can be performed using a Savitzky-Golay filter.

6. The method for continuous monitoring of vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, as described in claim 1, is characterized in that... The first GNSS receiving module and the second GNSS receiving module are configured to have the same or similar projection range in the horizontal position.

7. The method for continuous monitoring of vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, as described in claim 1, is characterized in that... The multi-satellite data fusion employs an arithmetic average fusion or weighted average fusion algorithm to obtain multi-satellite inversion fused data.

8. A continuous monitoring system for vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, applicable to the method described in any one of claims 1-7, characterized in that, Includes the following modules: The first GNSS receiving module is installed in the area above the vegetation canopy to receive reference signals from GNSS satellites that have not been attenuated by the vegetation canopy. The second GNSS receiver module is installed in the area below the vegetation canopy to receive GNSS signals that have passed through the vegetation canopy. The data processing and storage module is used to match the GNSS signals received from above and below to form corresponding GNSS signal pairs, and to perform data preprocessing, signal attenuation calculation, vegetation optical thickness inversion, multi-satellite data fusion, and canopy moisture estimation on the GNSS signal pairs. The control system and communication module are used to realize the remote transmission of observation data; The power supply module is used to provide electrical energy for the long-term operation of the system.

9. A continuous monitoring system for vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, as described in claim 8, is characterized in that... The data processing and storage module includes a signal matching module, an observation quality control module, a signal attenuation calculation module, a vegetation optical thickness inversion module, a time series fusion module, and a canopy moisture estimation module. The signal matching module is used to match the GNSS signals received above and below to form corresponding GNSS signal pairs; The observation quality control module is used to perform data preprocessing, preprocessing the acquired GNSS signal pairs, and removing abnormal data. The signal attenuation calculation module is used to calculate signal attenuation based on the difference between the GNSS signal received above and the GNSS signal received below; The vegetation optical thickness inversion module inverts the vegetation optical thickness based on the attenuation model of electromagnetic waves in vegetation. The temporal fusion module is used for multi-satellite data fusion. It performs vegetation optical thickness inversion on data obtained from multiple satellites observed simultaneously, and then fuses the vegetation optical thickness data obtained from different satellites to obtain multi-satellite inversion fusion data. The canopy moisture estimation module is used to estimate the dynamics of vegetation canopy moisture based on the obtained multi-satellite inversion fusion data.

10. A continuous monitoring system for vegetation optical thickness and canopy moisture based on transmission signals from a global navigation satellite system, as described in claim 8, is characterized in that... The power supply module is a solar panel.

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