Multicomponent concentration remote sensing collaborative inversion method for suspended particulate matters in offshore water body

Abstract

The invention discloses an offshore water body suspended particulate matter multi-component concentration remote sensing collaborative inversion method which comprises the following steps: collecting an actually measured data set including hyperspectral remote sensing reflectivity data, total suspended particulate matter concentration TSM, organic suspended particulate matter concentration OSM and inorganic suspended particulate matter concentration ISM; carrying out pretreatment; calculating spectral characteristic quantities representing ISM and OSM concentrations according to the MODIS waveband remote sensing reflectivity data; inputting the ISM and OSM concentration data and the corresponding spectral characteristic quantity into an XGBoost algorithm for training, and constructing a remote sensing inversion model of the ISM and OSM concentration; evaluating the precision of the model by using an independent verification sample set and a satellite-ground matching data set; the method provides important technical means and data support for ocean water quality, ecological environment evaluation and related biogeochemical process research.

Description

Technical Field

[0001] The invention relates to the technical field of seawater environment, and in particular to a remote sensing collaborative inversion method for multi-component concentration of suspended particles in offshore waters. Background Art

[0002] Suspended particulate matter is a general term for all suspended bodies in seawater, including phytoplankton, suspended sediment and organic debris. As an important component of coastal waters, it plays a key role in maintaining marine ecological balance, regulating material and energy cycles, and environmental monitoring. Suspended particulate matter in water bodies mainly includes organic particulate matter (such as phytoplankton, microorganisms and organic debris, etc.) and inorganic particulate matter (such as inorganic debris, clay minerals, etc.). Among them, inorganic particulate matter is an important carrier of nutrients, affecting water transparency, sediment transport and aquatic ecosystems, while organic particulate matter reflects the nutrient level in seawater and provides an important reference for the assessment of marine ecological environment changes. Therefore, mastering the distribution of suspended particulate matter in coastal waters and the characteristics of its organic and inorganic components has important scientific value for in-depth understanding of marine ecological environment research and material cycles.

[0003] The concentration of total suspended particulate matter (TSM), inorganic suspended particulate matter (ISM) and organic suspended particulate matter (OSM) are used to characterize the content of total suspended particulate matter and its components (organic and inorganic) in water bodies. Affected by multiple factors such as tides, ocean currents and human activities, TSM, ISM and OSM in coastal waters show significant spatiotemporal variability. Field surveys usually use the ignition weighing method to determine the concentrations of total, organic and inorganic suspended particulate matter, but this method is time-consuming and labor-intensive, and the data is discrete, making it difficult to characterize its spatiotemporal distribution characteristics. In contrast, optical satellite remote sensing can provide large-scale, long-term observation data, and has unique advantages in monitoring the concentrations of suspended particulate matter and its components. At present, many researchers use satellite remote sensing technology to carry out remote sensing detection research on TSM and ISM, and have proposed a series of remote sensing inversion models. These models are mainly based on satellite remote sensing reflectivity (R rs ) and the concentration of suspended particulate matter, and invert it using empirical models or machine learning methods. Currently, optical satellite remote sensing technology has been widely used in the remote sensing inversion of total suspended particulate matter (TSM) and inorganic suspended particulate matter (ISM) in marine water bodies, but there are few reports on inversion algorithms for marine organic suspended particulate matter (OSM). In addition, most of the existing remote sensing monitoring algorithms focus on a single parameter (such as TSM or ISM), and lack remote sensing methods that can collaboratively invert TSM, ISM, and OSM. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a remote sensing collaborative inversion method for multi-component concentration of suspended particulate matter in near-shore waters, to solve the technical problems of the existing methods such as lack of OSM inversion research and insufficient multi-parameter collaborative inversion capabilities, and to achieve the synchronous acquisition of concentration information of suspended particulate matter and its components (organic and inorganic) in near-shore waters.

[0005] Technical solution: The remote sensing collaborative inversion method for multi-component concentration of suspended particulate matter in offshore waters described in the present invention comprises the following steps: (1) Collect measured data sets, including hyperspectral remote sensing reflectance data, total suspended particulate matter concentration (TSM), organic suspended particulate matter concentration (OSM), and inorganic suspended particulate matter concentration (ISM); and perform preprocessing; (2) Calculate the spectral characteristics representing the concentrations of ISM and OSM based on the MODIS band remote sensing reflectance data; (3) The ISM and OSM concentration data and their corresponding spectral features were input into the XGBoost algorithm for training to construct remote sensing inversion models of ISM and OSM concentrations; (4) Evaluate the model accuracy using an independent validation sample set and a satellite-ground matching dataset Furthermore, in step (1), the preprocessing is as follows: based on the spectral response function of the MODIS sensor, the measured hyperspectral remote sensing reflectance data is converted into remote sensing reflectance data corresponding to the MODIS band. ; Through the sampling time and longitude and latitude information, the converted The data are matched with TSM, OSM and ISM concentration data.

[0006] Furthermore, the MODIS bands include 10 bands of 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678 nm, and the conversion of remote sensing reflectance data is through the formula: ; In the formula, R rs (λ M ) is the MODIS sensor λ M The remote sensing reflectivity at the band; λ1 and λ2 are the band ranges of the band; R rs (λ) is the measured hyperspectral remote sensing reflectance; SRF(λ) is the spectral response function of the MODIS sensor.

[0007] Furthermore, in step (2), the ISM feature quantities include: Reflectance values ​​and band ratios in bands 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678; OSM feature quantities include: The logarithmic transformed values ​​in bands 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678, as well as their band differences and band combination ratios.

[0008] Furthermore, in step (3), the total suspended particulate matter concentration TSM is obtained by the following formula: ; Among them, TSM, OSM and ISM are the total, organic and inorganic suspended particulate matter concentrations in the water body, respectively.

[0009] Furthermore, in step (3), the training parameters of the XGBoost model are optimized through cross-validation, including learning rate, tree depth, and regularization coefficient.

[0010] Furthermore, in step (4), the model is applied to long-term MODIS remote sensing images to generate a remote sensing product set of TSM, ISM and OSM concentrations, and their spatiotemporal variation characteristics are analyzed.

[0011] Furthermore, in step (4), the indicators for model accuracy verification include root mean square error RMSE, mean absolute error MAE and determination coefficient R 2 .

[0012] An electronic device described in the present invention includes a memory, a processor, and a computer program stored in the memory, and the processor implements the steps of any one of the methods when executing the program.

[0013] A computer-readable storage medium according to the present invention stores a computer program, and when the program is executed by a processor, the steps of any one of the methods are implemented.

[0014] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: The present invention combines the XGBoost method to develop a remote sensing collaborative inversion method for suspended particulate matter and its component concentrations suitable for complex offshore optical water bodies, and evaluates the accuracy of the model. In addition, the method was successfully applied to long-term MODIS remote sensing images to generate remote sensing product sets of TSM, ISM and OSM for offshore water bodies, and reveal their spatiotemporal variation characteristics. The method of the present invention can be extended to other ocean areas and other optical satellite sensors, and can provide long-term, multi-parameter high-quality remote sensing thematic products, providing important technical means and data support for marine water quality, ecological environment assessment and related biogeochemical process research. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the present invention; Figure 2 The invention is to construct and verify the suspended particulate matter and its component concentration model using the measured data set; wherein, the attached Figure 2 (a) is TSM model training; Figure 2 (b) is the ISM model training; Figure 2 (c) in the figure is OSM model training; Figure 2 (d) in the figure is TSM model verification; Figure 2 (e) in the figure is the ISM model verification; Figure 2 (f) in the figure is OSM model verification; Figure 3 The satellite remote sensing inversion accuracy assessment of suspended particulate matter and its component concentrations of the present invention; wherein, Figure 3 (a) is TSM satellite-ground matching verification; Figure 3 (b) is the ISM satellite-ground matching verification; Figure 3 (c) in the figure is the OSM satellite-ground matching verification. DETAILED DESCRIPTION

[0016] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0017] like Figure 1 As shown, an embodiment of the present invention provides a remote sensing collaborative inversion method for multi-component concentration of suspended particles in offshore waters, comprising the following steps: Step 1: Organize and collect offshore survey data sets, including remote sensing reflectance spectrum, total suspended particulate matter concentration, organic suspended particulate matter concentration, and inorganic suspended particulate matter concentration. According to the spectral response function of the MODIS sensor, based on the measured hyperspectral remote sensing reflectance data, the corresponding 10 bands are obtained. According to the sampling time and the longitude and latitude of the station, the measured R rs (λ M ) are matched with the suspended particulate matter and its component concentrations (TSM, ISM and OSM).

[0018] (1); In the formula, R rs (λ M ) is the MODIS sensor λ M The remote sensing reflectivity at the band; λ1 and λ2 are the band ranges of the band; R rs (λ) is the measured hyperspectral remote sensing reflectance; SRF(λ) is the spectral response function of the MODIS sensor.

[0019] Step 2: Based on the measured data set, calculate the spectral feature quantities that can characterize the ISM concentration and OSM concentration, as shown in Table 1.

[0020] Table 1 Spectral characteristics of ISM and OSM concentrations ; Step 3: Input the measured ISM and OSM concentrations and their corresponding spectral features in Table 1 into XGBoost for model training to obtain the remote sensing inversion method of ISM and OSM concentrations; calculate the TSM concentration according to formula (2).

[0021] (2); Among them, TSM, OSM and ISM are the total, organic and inorganic suspended particulate matter concentrations in the water body, respectively.

[0022] Step 4: Based on the above steps combined with the XGBoost method, a remote sensing collaborative inversion method for offshore suspended particulate matter and its component concentrations is constructed, and the accuracy is verified using measured data and satellite-ground matching datasets, such as Figure 2 and Figure 3 shown.

[0023] Step 5: The developed Pan-Asian method for remote sensing coordinated monitoring of suspended particulate matter and its components was applied to the long-term MODIS satellite remote sensing reflectance products to generate a set of remote sensing products of total, organic and inorganic suspended particulate matter concentrations in the nearshore area, and reveal their spatiotemporal variation characteristics.

[0024] The invention can be applied to offshore water bodies to obtain long-term total, organic and inorganic suspended particle concentrations, and to conduct temporal and spatial variation characteristic analysis.

Claims

1. A remote sensing collaborative inversion method for multi-component concentration of suspended particulate matter in offshore waters, characterized in that: The following steps are involved: (1) Collect measured data sets, including hyperspectral remote sensing reflectance data, total suspended particulate matter concentration (TSM), organic suspended particulate matter concentration (OSM), and inorganic suspended particulate matter concentration (ISM); and perform preprocessing; (2) Calculate the spectral characteristics of ISM and OSM concentrations based on MODIS band remote sensing reflectance data; (3) The ISM and OSM concentration data and their corresponding spectral features were input into the XGBoost algorithm for training to construct a remote sensing inversion model for ISM and OSM concentrations; (4) The model accuracy is evaluated using an independent validation sample set and a satellite-ground matching dataset.

2. The method for remote sensing collaborative inversion of multi-component concentration of suspended particulate matter in offshore waters according to claim 1 is characterized in that: In step (1), the preprocessing is as follows: Based on the spectral response function of the MODIS sensor, the measured hyperspectral remote sensing reflectance data is converted into remote sensing reflectance data corresponding to the MODIS band. ; By sampling time and longitude and latitude information, the converted The data are matched with TSM, OSM and ISM concentration data.

3. The method for remote sensing collaborative inversion of multi-component concentration of suspended particulate matter in offshore waters according to claim 2 is characterized in that: MODIS bands include 10 bands of 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678 nm, and the conversion of remote sensing reflectance data is through the formula: ； In the formula, R rs (λ M ) is the MODIS sensor λ M Remote sensing reflectance at the band; λ1 and λ2 are the band ranges of the current band; R rs (λ) is the measured hyperspectral remote sensing reflectance; SRF(λ) is the spectral response function of the MODIS sensor.

4. The method for remote sensing collaborative inversion of multi-component concentration of suspended particulate matter in offshore waters according to claim 1 is characterized in that: In step (2), ISM feature quantities include: Reflectance values ​​and band ratios in bands 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678; OSM feature quantities include: The logarithmic transformed values ​​in bands 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678, as well as their band differences and band combination ratios.

5. The method for remote sensing collaborative inversion of multi-component concentration of suspended particulate matter in offshore waters according to claim 1 is characterized in that: In step (3), the total suspended particulate matter concentration TSM is obtained by the following formula: Among them, TSM, OSM and ISM are the total, organic and inorganic suspended particulate matter concentrations in the water body, respectively.

6. The method for remote sensing collaborative inversion of multi-component concentration of suspended particulate matter in offshore waters according to claim 1 is characterized in that: In step (3), the training parameters of the XGBoost model are optimized through cross-validation, including learning rate, tree depth, and regularization coefficient.

7. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images