Remote sensing estimation method and system of CDOM absorption spectrum, medium and equipment

Abstract

The invention discloses a CDOM absorption spectrum remote sensing estimation method and system, a medium and equipment. The method comprises the following steps: acquiring actually measured remote sensing reflectivity spectrums of a plurality of point locations on the water surface of an estuary in a flood season and CDOM absorption spectrums of water body samples of the corresponding point locations; the maximum correlation coefficient of the actually measured remote sensing reflectivity spectrum in the first waveband range and the maximum correlation coefficient of the CDOM absorption spectrum in the second waveband range are calculated, the spectral gradient of the actually measured remote sensing reflectivity spectrum in the first waveband range is calculated to serve as the reflectivity short-wave spectral gradient, and the spectral slope of the CDOM absorption spectrum in the second waveband range is calculated to serve as the CDOM short-wave spectral slope; the spectral slope of the CDOM absorption spectrum in the third wave band range is calculated as the CDOM long-wave spectral slope, and a CDOM absorption spectrum inversion model is established; and acquiring a spectrum satellite remote sensing reflectivity image of the river mouth in the flood season, and inputting the image into the model pixel by pixel to obtain the spatial distribution of the CDOM absorption spectrum. According to the invention, the inversion precision can be significantly improved.

Description

Technical Field

[0001] The invention relates to the technical field of water quality remote sensing inversion, and more specifically to a remote sensing estimation method, system, medium and equipment for CDOM absorption spectrum. Background Art

[0002] Chromophoric Dissolved Organic Matter (CDOM) is the light-absorbing part of soluble organic matter (DOM) in water. It has significant light absorption in the ultraviolet region (UV, 250nm-400nm), which can affect the light field of the upper water body, causing light attenuation in the ultraviolet and blue light ranges in the water, triggering a variety of photochemical reactions and the release of inorganic carbon, and changing the microbial utilization of organic matter. Due to its strong chemical, biological and optical activities, its absorption spectrum is currently widely used as a tracer of DOM composition, source and activity in water bodies, which has an important enlightening role in understanding the marine biogeochemical system and various processes of the global carbon cycle.

[0003] In water quality remote sensing inversion, CDOM is considered to be one of the main photoactive components in water bodies due to its light absorption capacity in the ultraviolet-visible range, and together with chlorophyll and suspended particles, it determines the water surface reflectance. The optical properties of the open sea (Class I water bodies) are mainly dominated by plankton. Both CDOM and suspended particles are products of plankton, and their absorption coefficients vary in coordination with chlorophyll concentration. For coastal and inland water bodies (Class II water bodies), the main sources of CDOM and suspended matter are terrestrial inputs, and their concentrations vary relatively independently of chlorophyll concentrations. In this case, the remote sensing inversion algorithm needs to consider more unknown variables, which greatly increases the uncertainty of CDOM remote sensing inversion.

[0004] During the flood season, frequent high-intensity rainfall in the estuary leads to increased runoff, which, on the one hand, transports a large amount of terrestrial CDOM and suspended particles, and on the other hand, makes it difficult for chlorophyll to be enriched in the estuary. CDOM and suspended particles together become the dominant factors in the optical characteristics of estuarine water bodies. The absorption spectra of CDOM and suspended particles are similar, both of which decay exponentially with the increase of wavelength, and the absorption intensity is usually comparable. In order to accurately estimate the CDOM absorption spectrum from remote sensing reflectance, it is particularly important to eliminate the interference of suspended particles.

[0005] The existing two types of water body inversion algorithms, whether semi-analytical models based on physical mechanisms or statistical models based on empirical relationships, all use the absorption coefficient of CDOM at 412nm or 443nm as the inversion target and the remote sensing reflectivity below 500nm as the input parameter. The selection of these two band ranges cannot effectively distinguish the absorption effect of CDOM and suspended particles, and the noise interference introduced by the low absorption intensity of CDOM above 400nm and the low signal-to-noise ratio of remote sensing reflectivity below 500nm causes the inversion algorithm to be unable to achieve a high inversion accuracy.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the invention

[0007] In view of this, the present invention provides a remote sensing estimation method, system, medium and equipment for CDOM absorption spectrum that solves at least some of the above-mentioned technical problems, which can truly invert the spatial distribution of CDOM in estuaries during the flood season and help improve the inversion accuracy.

[0008] To achieve the above object, the technical solution adopted by the present invention is:

[0009] In a first aspect, the present invention provides a remote sensing estimation method for CDOM absorption spectrum, the method comprising the following steps:

[0010] Step 1: Obtain the measured remote sensing reflectance spectra of several points on the estuary water surface during the flood season, and obtain the CDOM absorption spectra of water samples at the corresponding points;

[0011] Step 2: Calculate the maximum correlation coefficient between the measured remote sensing reflectance spectrum in the first band and the CDOM absorption spectrum in the second band, and record the CDOM absorption wavelength and remote sensing reflectance wavelength under the maximum correlation coefficient as the optimal band of CDOM absorption coefficient and the optimal band of reflectance, respectively;

[0012] Step 3: Calculate the spectral gradient of the measured remote sensing reflectance spectrum in the first band range, and the spectral slopes of the CDOM absorption spectrum in the second band range and the third band range, as the reflectance shortwave spectral gradient, the CDOM shortwave spectral slope and the CDOM longwave spectral slope respectively;

[0013] Step 4: Construct a CDOM absorption spectrum inversion model based on the optimal band reflectance, the optimal band CDOM absorption coefficient, the reflectance short-wave spectrum gradient, the CDOM short-wave spectrum slope, and the CDOM long-wave spectrum slope;

[0014] Step 5: Obtain the spectral satellite remote sensing reflectance image of the estuary during the flood season, select the satellite remote sensing reflectance corresponding to the optimal reflectance band, input the CDOM absorption spectrum inversion model, and obtain the spatial distribution map of the CDOM absorption spectrum.

[0015] Further, the first waveband ranges from 400nm to 600nm, the second waveband ranges from 250nm to 400nm, and the third waveband ranges from 250nm to 700nm.

[0016] Furthermore, in step 2, the calculation formula of the correlation coefficient is:

[0017]

[0018] in, represents the correlation coefficient, X represents the measured remote sensing reflectance of each wavelength in the first band, and Y represents the CDOM absorption coefficient of each wavelength in the second band; It represents the average value of the measured remote sensing reflectance of each wavelength in the first band at all sampling points; It represents the average value of the CDOM absorption coefficient of each wavelength in the second band at all sampling points; the correlation coefficients of the measured remote sensing reflectivity of each wavelength in the first band and the CDOM absorption coefficient of each wavelength in the second band are calculated one by one to obtain a correlation coefficient matrix, and the maximum value of the correlation coefficient matrix element and its row and column number are calculated.

[0019] Furthermore, in step 3, the short-wave spectral gradient of the reflectance is the growth rate from the trough to the peak of the measured remote sensing reflectance spectrum within the first band, and the calculation formula is as follows:

[0020]

[0021] in, represents the short-wave spectral gradient of reflectance, and They represent the maximum and minimum values ​​of the measured remote sensing reflectance spectrum in the first band, and Respectively and The corresponding wavelength.

[0022] Furthermore, in step 3, the calculation formula of the CDOM spectrum slope is:

[0023]

[0024] Wherein, λ represents any wavelength in the second wavelength range or the third wavelength range, represents the absorption spectrum of CDOM at wavelength λ, represents the absorption spectrum of CDOM at the optimal absorption coefficient band λ0, exp represents the exponential relationship, S g represents the CDOM spectrum slope;

[0025] The CDOM absorption spectra in the second and third bands were fitted according to the calculation formula to obtain the CDOM short-wave spectrum slope. and the CDOM long-wave spectrum slope .

[0026] Furthermore, in step 4, the CDOM absorption spectrum inversion model constructed includes:

[0027]

[0028]

[0029]

[0030]

[0031] in, represents the remote sensing reflectance of the optimal band, λ1 represents the optimal band of reflectance, log represents the logarithmic relationship, A, B, C, D, E, and F are model parameters obtained by data fitting.

[0032] Furthermore, in step 5, the spectral satellite remote sensing reflectance is the water body reflectance obtained by a multi-spectral or hyperspectral remote sensing satellite, and has at least 3 bands within the first band range.

[0033] In a second aspect, the present invention further provides a remote sensing estimation system for CDOM absorption spectrum, which is applied to the above-mentioned remote sensing estimation method for CDOM absorption spectrum to perform CDOM absorption spectrum inversion, and the system comprises:

[0034] The spectrum acquisition module is used to obtain the measured remote sensing reflectance spectra of several points on the estuary water surface during the flood season, and obtain the CDOM absorption spectra of the water samples at the corresponding points;

[0035] A calculation module is used to calculate the maximum correlation coefficient between the measured remote sensing reflectance spectrum in the first band and the CDOM absorption spectrum in the second band, and record the CDOM absorption wavelength and remote sensing reflectance wavelength under the maximum correlation coefficient as the optimal band of CDOM absorption coefficient and the optimal band of reflectance, respectively;

[0036] and calculating the spectral gradient of the measured remote sensing reflectance spectrum in the first waveband range and the spectral slopes of the CDOM absorption spectrum in the second waveband range and the third waveband range, as the reflectance shortwave spectral gradient, the CDOM shortwave spectral slope and the CDOM longwave spectral slope, respectively;

[0037] A model building module is used to build a CDOM absorption spectrum inversion model based on the optimal band reflectance, the optimal band CDOM absorption coefficient, the reflectance short-wave spectrum gradient, the CDOM short-wave spectrum slope and the CDOM long-wave spectrum slope;

[0038] The spatial distribution generation module is used to obtain the spectral satellite remote sensing reflectance image of the estuary during the flood season, select the satellite remote sensing reflectance corresponding to the optimal reflectance band, input the CDOM absorption spectrum inversion model, and obtain the spatial distribution map of the CDOM absorption spectrum.

[0039] In a third aspect, the present invention further provides a storage medium having stored thereon one or more programs readable by a computing device, wherein the one or more programs include instructions, and when the instructions are executed by the computing device, the computing device executes the remote sensing estimation method of a CDOM absorption spectrum as described above.

[0040] In a fourth aspect, an embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the above-mentioned remote sensing estimation method of CDOM absorption spectrum.

[0041] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0042] 1. The present invention solves the confusion interference caused by the similar absorption characteristics of suspended particles in the existing CDOM remote sensing inversion algorithm, and the noise interference caused by the low signal-to-noise ratio of the CDOM absorption coefficient and the remote sensing reflectivity, thereby improving the inversion accuracy.

[0043] 2. The present invention adopts an inversion strategy from the shortwave range to the longwave range. It can invert the absorption coefficient and spectral slope of CDOM in the shortwave range based on the remote sensing reflectivity above 500nm, and then use the autocorrelation of the CDOM absorption spectrum to extrapolate the CDOM spectral slope from the shortwave range to the longwave range, thereby obtaining the absorption spectrum of CDOM in the full band. Since suspended particulate matter has no absorption characteristics in the shortwave band, this strategy can effectively avoid the confusion and interference of suspended particulate matter. This strategy greatly reduces the use of shortwave band remote sensing reflectivity and longwave band CDOM absorption coefficient during modeling, effectively avoiding the noise interference caused by the low absorption of CDOM in the longwave band and the low signal-to-noise ratio of remote sensing reflectivity in the shortwave band, and can significantly improve the remote sensing inversion accuracy of CDOM absorption spectrum.

[0044] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0045] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0048] Figure 1 A schematic flow chart of a method for remote sensing estimation of CDOM absorption spectrum provided in an embodiment of the present invention.

[0049] Figure 2 A schematic diagram of the location distribution of sampling points for a water surface remote sensing test provided in an embodiment of the present invention.

[0050] Figure 3 A schematic diagram of the spatial distribution of CDOM absorption spectrum obtained by satellite remote sensing inversion provided in an embodiment of the present invention.

[0051] Figure 4 A schematic diagram of the spatial distribution of CDOM absorption spectra measured at an estuary during flood season provided in an embodiment of the present invention.

[0052] Figure 5 The wavelength-by-wavelength CDOM absorption coefficient (a) provided in the embodiment of the present invention g ) Schematic diagram of the mean absolute percentage error (MAPD) ​​and root mean square error (RMSE) of the inversion results.

[0053] Figure 6 A schematic diagram showing a comparison between the measured and inverted values ​​of the full-band CDOM absorption coefficient of a water sample provided in an embodiment of the present invention.

[0054] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0056] In the description of the present invention, it should be noted that: in some processes described in the specification and drawings of this application, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be performed in the order in which they appear in this document or may be performed in parallel. In addition, various serial numbers are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0057] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Embodiment 1:

[0059] The embodiment of the present invention provides a remote sensing estimation method for CDOM absorption spectrum, which can solve the confusion interference caused by similar absorption characteristics of suspended particles in the existing CDOM remote sensing inversion algorithm, and the noise interference caused by the low signal-to-noise ratio of CDOM absorption coefficient and remote sensing reflectivity in the range of 400-500nm, which helps to significantly improve the inversion accuracy. The general process of the method is as follows Figure 1 As shown, it mainly includes the following steps:

[0060] Step 1, obtaining the measured remote sensing reflectance spectra of several points on the estuary water surface during the flood season, and obtaining the CDOM absorption spectra of water samples at the corresponding points;

[0061] Step 2, calculating the maximum correlation coefficient between the measured remote sensing reflectance spectrum in the first band and the CDOM absorption spectrum in the second band, recording the CDOM absorption wavelength at the maximum correlation coefficient as the CDOM absorption coefficient optimal band λ0, and the remote sensing reflectance wavelength at the maximum correlation coefficient as the reflectance optimal band λ1;

[0062] Step 3: Calculate the spectral gradient of the measured remote sensing reflectance spectrum in the first band as the reflectance short-wave spectral gradient R rs _Gradient (first band range), calculate the spectral slope of the CDOM absorption spectrum in the second band range as the CDOM short-wave spectrum slope S g (second band range), calculate the spectral slope of the CDOM absorption spectrum in the third band range as the CDOM long-wave spectrum slope S g (third waveband range), the third waveband range is larger than the second waveband range;

[0063] Step 4: CDOM absorption coefficient based on the optimal band , optimal band remote sensing reflectivity , reflectance short-wave spectral gradient R rs _Gradient (first band range), CDOM shortwave spectrum slope S g (second band range) and CDOM long-wave spectrum slope S g (the third band range), the CDOM absorption spectrum inversion model was established by using the data fitting method, among which, based on the remote sensing reflectance of the optimal band Establishing the optimal band CDOM absorption coefficient The fitting model is based on the reflectance short-wave spectral gradient R rs _Gradient (first band range) establishes the CDOM shortwave spectrum slope S g The fitting model of (second band range) is based on the CDOM shortwave spectrum slope S g (Second band range) Establish the CDOM long-wave spectrum slope S g (Third band range) fitting model, based on the optimal band CDOM absorption coefficient and the CDOM long-wave spectrum slope S g (Third band range) An exponential relationship is used to restore the absorption coefficient of CDOM in the third band range;

[0064] Step 5, obtain the multispectral or hyperspectral satellite remote sensing reflectance image of the estuary during the flood season, select the satellite remote sensing reflectance image corresponding to the reflectance optimal band λ1, and input the remote sensing reflectance of all pixels in the satellite remote sensing reflectance image into the CDOM absorption spectrum inversion model one by one to obtain the spatial distribution map of the CDOM absorption spectrum of the estuary during the flood season.

[0065] In a specific embodiment, the first waveband range is 400nm-600nm, the second waveband range is 250nm-400nm, and the third waveband range is 250nm-700nm. The first waveband range 400nm-600nm is the optimal waveband range of the water surface remote sensing reflectance for remote sensing inversion of CDOM absorption spectrum, and the second waveband range 250nm-400nm is the optimal waveband range of the CDOM absorption spectrum that can be inverted through the water surface remote sensing reflectance. The reason for the above selection is that after the correlation analysis of experimental data and the analysis of the physical and chemical characteristics of CDOM, the correlation between the water surface remote sensing reflectance and the CDOM absorption spectrum is the strongest in these two waveband ranges, and the data signal-to-noise ratio is high, the spatial difference is significant, and the inversion accuracy is high. The third waveband range of 250nm-700nm is the entire waveband range where CDOM has spectral characteristics, and it is also the general waveband range for conventional measurement experiments of CDOM spectral characteristics.

[0066] In one embodiment, the reflectance short-wave spectral gradient R rs _Gradient (first band range) is the growth rate of the measured remote sensing reflectance spectrum from the trough to the peak within the first band range. The calculation formula is as follows:

[0067]

[0068] Among them, R rs _Gradient represents the short-wave spectral gradient of reflectance, and They represent the maximum and minimum values ​​of the measured remote sensing reflectance spectrum in the first band, and Respectively and The corresponding wavelength.

[0069] In one embodiment, the calculation formula of CDOM spectrum slope is:

[0070]

[0071] Wherein, λ is any wavelength within the second wavelength range or the third wavelength range, is the absorption spectrum of CDOM at wavelength λ, is the absorption spectrum of CDOM at the optimal absorption coefficient band λ0, exp is the exponential relationship, S g is the CDOM spectrum slope, and the CDOM absorption spectra in the second band and the third band are The data is fitted according to the calculation formula to obtain the CDOM shortwave spectrum slope S g (second band range) and CDOM long-wave spectrum slope Sg (Third band range).

[0072] In a specific embodiment, the constructed CDOM absorption spectrum inversion model is:

[0073]

[0074]

[0075]

[0076]

[0077] Wherein, log is the logarithmic relationship, A, B, C, D, E, and F are model parameters, which are obtained by fitting the data in step 3.

[0078] In a specific embodiment, the spectral satellite remote sensing reflectance is the water body reflectance obtained by a multi-spectral or hyperspectral remote sensing satellite, and has at least 3 bands within the first band range.

[0079] Combine the following Figures 1 to 6 As shown, the implementation mode and working principle of the method of the present invention are introduced in detail:

[0080] This embodiment proposes a remote sensing estimation method for the absorption spectrum of colored soluble organic matter in estuary water bodies during the flood season, which adopts an inversion strategy from ultraviolet to visible light, that is, firstly, the absorption coefficient and spectral slope of CDOM in the ultraviolet band (250nm-400nm) are inverted based on the remote sensing reflectivity above 500nm, and then the CDOM spectral slope is extrapolated from the ultraviolet band (250nm-400nm) to the visible light band (250nm-700nm) using the autocorrelation of the CDOM absorption spectrum, and then the absorption spectrum of CDOM in the full band is obtained. Since suspended particulate matter has no absorption characteristics in the ultraviolet band, this strategy can effectively avoid the confusion interference of suspended particulate matter. This strategy significantly reduces the use of remote sensing reflectance below 500nm and CDOM absorption coefficient in the visible light band (400nm-700nm) during modeling, effectively avoiding the noise interference caused by the low absorption of CDOM in the visible light band and the low signal-to-noise ratio of remote sensing reflectance below 500nm, and can significantly improve the remote sensing inversion accuracy of CDOM absorption spectrum. Figure 1 As shown, the method includes the following steps S1 to S5:

[0081] S1, obtain the measured remote sensing reflectance spectra of several points on the estuary water surface during the flood season, and obtain the CDOM absorption spectra of water samples at the corresponding points.

[0082] In this embodiment, if Figure 2As shown in the figure, 56 points were selected at a certain estuary, and a water surface remote sensing experiment was carried out during the flood season (May). The remote sensing reflectance spectrum above the water surface in the range of 400-900nm was obtained by measuring the radiation on the water. At the same time, water samples were collected and filtered, and the CDOM absorption spectrum in the range of 250nm-700nm was measured in the laboratory.

[0083] S2, calculate the maximum correlation coefficient between the measured remote sensing reflectance spectrum in the range of 400nm-600nm and the CDOM absorption spectrum in the range of 250nm-400nm, record the CDOM absorption wavelength and remote sensing reflectance wavelength under the maximum correlation coefficient, and record them as the CDOM absorption coefficient optimal band λ0 and the reflectance optimal band λ1, respectively.

[0084] In this embodiment, the Pearson correlation coefficient is calculated band by band for the measured remote sensing reflectance spectrum in the range of 400nm-600nm and the CDOM absorption spectrum in the range of 250nm-400nm, and a correlation coefficient matrix with a dimension of 201*151 is generated. The maximum value of the correlation coefficient matrix is ​​obtained, and the maximum correlation coefficient is 0.86. The row and column numbers corresponding to 0.86 are 197 and 41 respectively, and the CDOM absorption wavelength λ0 under this correlation coefficient is 290nm, and the remote sensing reflectance wavelength λ1 is 596nm.

[0085] S3, calculate the spectral gradient of the measured remote sensing reflectance spectrum in the range of 400nm-600nm, and the spectral slope of the CDOM absorption spectrum in the range of 250nm-400nm and 250nm-700nm, which are defined as the reflectance shortwave spectral gradient, CDOM shortwave spectral slope and CDOM longwave spectral slope, respectively.

[0086] The short-wave spectral gradient of reflectance is the growth rate of the measured remote sensing reflectance spectrum from any wavelength in the range of 400-450nm to the peak in the range of 400nm-600nm, which is calculated according to the following formula:

[0087] (1)

[0088] Among them, R rs _Gradient represents the short-wave spectral gradient of reflectance, and They represent the maximum and minimum values ​​of the measured remote sensing reflectance spectrum in the first band, and Respectively and The corresponding wavelength.

[0089] In this embodiment, since the measured remote sensing reflectivity has a large signal noise at 380nm-420nm, the lower edge wavelength is set to 420nm, that is, λ(min) =420nm, R rs(min) =R rs (420). Then search for R in the range of 420-600nm rs(max) ,The search results show that the actual peak positions of the 56 points are basically concentrated around 580nm.

[0090] The CDOM spectral slope is obtained by fitting the CDOM absorption spectra in the range of 250nm-400nm and 250nm-700nm to the following formulas:

[0091] (2)

[0092] Among them, λ0 is set to the optimal band of CDOM absorption coefficient, that is, 290nm.

[0093] S4, based on the optimal band of reflectance, the optimal band of CDOM absorption coefficient, the short-wave spectrum gradient of reflectance, the slope of CDOM short-wave spectrum, and the slope of CDOM long-wave spectrum, a CDOM absorption spectrum inversion model is established.

[0094] First, based on the optimal band remote sensing reflectance (Rrs(596)), The fitting results show that the linear fitting has the highest fitting accuracy (its determination coefficient can reach 0.74), so the remote sensing inversion relationship for the optimal band of CDOM absorption coefficient is established as follows:

[0095] (3)

[0096] Secondly, based on the short-wave spectral gradient of reflectance (R rs _Gradient), The data was fitted. The fitting results showed that the power law relationship had the highest fitting accuracy (its determination coefficient was 0.53), so the remote sensing inversion relationship of the CDOM shortwave spectrum slope was established as follows:

[0097] (4)

[0098] Again based on the CDOM shortwave spectrum slope ( ),right The data was fitted. The fitting results showed that the logarithmic relationship had the highest fitting accuracy (its determination coefficient was 0.97), so the extrapolated relationship of the slope of the CDOM long-wave spectrum was established as:

[0099] (5)

[0100] Finally, the CDOM absorption spectrum in the range of 250-700nm is restored through the CDOM index relationship:

[0101] (6)

[0102] In this embodiment, the CDOM absorption coefficient in the ultraviolet band shows a positive correlation with the reflectivity in the visible light band, while the CDOM short-wave spectrum slope shows a negative correlation with the reflectivity short-wave spectrum gradient. This is because CDOM has fluorescent properties and can be excited under the irradiation of solar ultraviolet radiation to emit fluorescence in the visible light range. As the excitation wavelength increases, the maximum radiation of CDOM fluorescence will gradually increase. The increase in the excitation wavelength means that the band range of CDOM strong absorption effect increases, and the spectral slope in this band range decreases accordingly. At the same time, the increase in CDOM fluorescence radiation will cause the remote sensing reflectivity of the water body to increase in the visible light band, and the spectral gradient will increase accordingly.

[0103] S5, obtaining a multispectral or hyperspectral satellite remote sensing reflectance image of the estuary during the flood season, selecting a satellite remote sensing reflectance image corresponding to the optimal reflectance band, inputting all pixels of the image into the CDOM absorption spectrum inversion model one by one, and obtaining a spatial distribution map of the estuary CDOM absorption spectrum.

[0104] In this embodiment, four types of multispectral or hyperspectral satellite data are obtained and the inversion model is used for spatial distribution remote sensing generation. Satellite remote sensing images include Suomi NPP VIIRS, Sentinel-3A OLCI, ISS HICO and Landsat 8 OLI data products (satellite and payload parameters are shown in Table 1). The spatial resolution of all satellite data is better than 1 km, and for areas less than 2000 km 2 It is a better choice than the old generation satellite data MODIS and SeaWiFS.

[0105] Table 1 Multispectral or hyperspectral satellite payload parameters used in this example

[0106]

[0107] Images with cloud coverage less than 20% in the flood season (April and May) from 2012 to 2018 and the study area of ​​22°-23°N and 113.5°-114.5°E were selected for download to match the season with the measured data. For the Landsat 8 OLI data with a 16-day revisit period, the time range is relaxed to March to June.

[0108] The atmospheric correction and normalized surface reflectance calculation of OLCI 1B data were performed using the Class II Water Region / Coastal Color Processing Module (C2RCC) of SNAP. The atmospheric correction and surface reflectance calculation of HICO 1B data were performed using the ENVI 5.2 FLAASH module. All remote sensing reflectances were normalized. VIIRS, OLCI, and HICO data were geometrically corrected using ENVI 5.2. The near-infrared reflectance threshold method was used to extract the water area, and the threshold was set to 10%, 15%, or 20% depending on the specific situation. The NIR Black algorithm was used to further eliminate the influence of the atmosphere and the water surface. Finally, pixels with negative reflectance in the visible light band were removed.

[0109] After preprocessing, the CDOM absorption coefficient and spectral slope are remotely sensed and inverted using formulas (1), (3)-(5). rs (596) According to the strategies listed in Table 2, the standard band values ​​of the four sensors are respectively corresponded. The remote sensing reflectance of all pixels of the standard band images of the four sensors or their algebraic weighted average images is input into formula (3) pixel by pixel to calculate the corresponding reflectance of each image pixel. .

[0110] Table 2 R rs (596) Correspondence strategy with the standard band values ​​of four sensors

[0111]

[0112] In the embodiment of the present invention, in the inversion S g (250-400), taking into account the available spectral range of each satellite data and the atmospheric correction effect, for VIIRS, HICO, OLCI and OLI data, the calculation of R rs _Gradient band lower edge λ min Set to 445nm, 415.5nm, 400nm and 443nm respectively. The upper edge of the band of all sensors λ max The reflectance spectrum slope of each pixel in each remote sensing image is calculated according to formula (1), and on this basis, the S of each pixel in each remote sensing image is calculated according to formulas (4)-(5). g (250-400) and S g (250-700), and then calculate the total absorption coefficient of CDOM in the range of 250nm-700nm for each pixel in each remote sensing image according to formula (6). Each pixel value is arranged according to longitude and latitude, and the spatial distribution map of the CDOM absorption coefficient of the estuary area is obtained.

[0113] Figure 3The inversion of four types of multispectral or hyperspectral satellite remote sensing data shows a g (290) and S g (250-400), each image represents a obtained by inverting images from different sensors at different times g (290) and S g (250-400), different colors represent a g (290) and S g The size of (250-400) is in m. -1 and nm -1 , blue represents the minimum value, and red represents the maximum value. It can be seen that all satellite image inversion results reflect the a of the estuary during the flood season well. g (290) and S g (250-400) main distribution patterns (e.g. Figure 4 As shown): The upper and western sides of the estuary have higher a g (290) and lower S g (250-400), while the opposite is true on the east side of the estuary and its offshore waters. Since this method adopts an inversion strategy from ultraviolet to visible light, that is, firstly, the absorption coefficient and spectral slope of CDOM in the ultraviolet band (250nm-400nm) are inverted based on the remote sensing reflectance above 500nm, and then the CDOM spectral slope is extrapolated from the ultraviolet band (250nm-400nm) to the visible light band (250nm-700nm) using the autocorrelation of the CDOM absorption spectrum, and then the absorption spectrum of CDOM in the full band is obtained. Since suspended particles have no absorption characteristics in the ultraviolet band, this strategy can effectively avoid the confusion interference of suspended particles. This strategy significantly reduces the use of remote sensing reflectance below 500nm and CDOM absorption coefficient in the visible light band (400nm-700nm) when modeling, effectively avoiding the noise interference caused by the low absorption of CDOM in the visible light band and the low signal-to-noise ratio of remote sensing reflectance below 500nm, and significantly improving the ability of satellite remote sensing to depict the spatial distribution characteristics of estuarine CDOM.

[0114] In this embodiment, the water surface remote sensing test and satellite remote sensing inversion application of a certain estuary area are taken as an example. It transports more than 87 million tons of suspended particles (mainly mineral particles) and 30 million tons of dissolved substances to the northern part of the South China Sea every year. The optical properties of the estuary water body are significantly affected by the estuary runoff, especially during the flood season. Multiple bio-optical measurements of the estuary water body found that its annual average chlorophyll concentration is generally less than 5 mg / m 3 , and the concentration of suspended mineral particles during the flood season can reach 50-100 mg / L. Based on this estimate, the absorption coefficient of phytoplankton at 440 nm is a ph (440) The average level is usually less than 0.1m-1 , and the absorption coefficient of suspended mineral particles at 440nm is a d (440) is greater than 0.5m -1 , can reach 1m during flood season -1 ; At the same time, the absorption coefficient of CDOM at 440nm is g (440) is also much larger than a ph (440), average level is 0.3-0.5m -1 In this case, chlorophyll is no longer the main component that determines the optical characteristics of the water in this area, and suspended particulate matter and CDOM input from land become the dominant factors affecting the water color of the estuary.

[0115] Application and verification of technical effects:

[0116] The application and verification of technical effects are implemented in two ways: model accuracy evaluation and satellite remote sensing inversion application verification. The details are as follows:

[0117] Inversion accuracy evaluation: It is mainly estimated through three statistical quantities, including mean absolute percentage error (MAPD), root mean square error (RMSE), and determination coefficient (R 2 ), the calculation formula is as follows:

[0118] (7)

[0119] (8)

[0120] (9)

[0121] Among them, C alg and C insitu are the inversion value and the measured value respectively; N represents the number of sampling points.

[0122] Due to the limited number of measured values, the K-fold cross-validation method was used to evaluate the accuracy of the algorithms, and the statistical mean obtained by K-fold cross-validation was used to measure the performance of the algorithm. 54 measured values ​​were used, and the k value was set to 6, so each group had 9 observations. The cross-validation results (Table 3) show that a g (290), S g (250-400) and S g The average values ​​of mean absolute percentage error (MAPD) ​​of (250-700) are ~31%, ~5.3% and ~6%, respectively. The cross-validation results of the six groups are similar, reflecting the good generalization ability of the model.

[0123] Table 3 k-fold cross validation results

[0124]

[0125] According to the inversion of a g (290) and S g (250-700) Reconstruction of the CDOM absorption spectrum in the range of 250-700 nm. Figure 5 The wavelength-by-wavelength CDOM absorption coefficient (a g ), the horizontal axis represents the wavelength in nm, the left vertical axis and the blue line represent the mean absolute percentage error (MAPD) ​​in percentage, and the right vertical axis and the green line represent the root mean square error (RMSE) in m -1 The mean absolute percentage error (MAPD) ​​is stable at about 30% below 450nm, and increases with increasing wavelength above 450nm, mainly affected by the low measurement signal-to-noise ratio caused by the reduced absorption of CDOM in the long-wavelength band; the root mean square error (RMSE) is consistent with the trend of the CDOM absorption coefficient spectral curve, and decreases exponentially with increasing wavelength. Figure 6 The comparison between the measured and inverted values ​​of the full-band CDOM absorption coefficient of all water samples is shown. The horizontal axis is all sampling points, the vertical axis is the wavelength, the unit is nm, and the different colors represent the size of the CDOM absorption coefficient, the unit is m -1 , blue represents the minimum absorption coefficient, and red represents the maximum absorption coefficient. It can be seen that the distribution of the true value and the inversion value is highly consistent. Figure 5 and Figure 6 The accuracy analysis results show that the inversion accuracy of this inversion model for the CDOM absorption coefficient in the strong absorption band (250nm-500nm) can be stabilized at around 70%, demonstrating a relatively high inversion accuracy.

[0126] Satellite remote sensing inversion verification: Due to the lack of matching data between field observations and satellite observations on the same day, images collected by different sensors on the same day were selected for cross-comparison: HICO and VIIRS (13 / 04 / 20), OLI and VIIRS (2016 / 03 / 26), OLCI and VIIRS (2017 / 04 / 02). Figure 3 It can be seen that the a obtained by inversion of different sensors in the same phase g (290) and S g (250-400) has good consistency in both numerical range and distribution pattern, and is consistent with the actual distribution of CDOM in estuaries reflected by measured data (e.g. Figure 4 This shows that the inversion model is not affected by different sensor types and can truly invert the spatial distribution of CDOM in the estuary during the flood season.

[0127] From the description of the above embodiments, those skilled in the art can know that the embodiments of the present invention provide a remote sensing estimation method for the absorption spectrum of colored soluble organic matter, which solves the confusion interference caused by the similar absorption characteristics of suspended particles in the existing CDOM remote sensing inversion algorithm, and the noise interference caused by the low signal-to-noise ratio of the CDOM absorption coefficient and the remote sensing reflectivity in the range of 400nm-500nm, and significantly improves the inversion accuracy.

[0128] Embodiment 2:

[0129] The embodiment of the present invention further provides a remote sensing estimation system for CDOM absorption spectrum, which is applied to the remote sensing estimation method for CDOM absorption spectrum described in the above embodiment 1 to perform remote sensing estimation of CDOM absorption spectrum. The system comprises:

[0130] The spectrum acquisition module is used to obtain the measured remote sensing reflectance spectra of several points on the estuary water surface during the flood season, and obtain the CDOM absorption spectra of the water samples at the corresponding points;

[0131] A calculation module is used to calculate the maximum correlation coefficient between the measured remote sensing reflectance spectrum in the first band and the CDOM absorption spectrum in the second band, and record the CDOM absorption wavelength and remote sensing reflectance wavelength under the maximum correlation coefficient as the optimal band of CDOM absorption coefficient and the optimal band of reflectance, respectively;

[0132] and calculating the spectral gradient of the measured remote sensing reflectance spectrum in the first waveband range and the spectral slopes of the CDOM absorption spectrum in the second waveband range and the third waveband range, as the reflectance shortwave spectral gradient, the CDOM shortwave spectral slope and the CDOM longwave spectral slope, respectively;

[0133] A model building module is used to build a CDOM absorption spectrum inversion model based on the optimal band reflectance, the optimal band CDOM absorption coefficient, the reflectance short-wave spectrum gradient, the CDOM short-wave spectrum slope and the CDOM long-wave spectrum slope;

[0134] The spatial distribution generation module is used to obtain the spectral satellite remote sensing reflectance image of the estuary during the flood season, select the satellite remote sensing reflectance corresponding to the optimal reflectance band, input the CDOM absorption spectrum inversion model, and obtain the spatial distribution map of the CDOM absorption spectrum.

[0135] The implementation principle and technical effects of a remote sensing estimation system for CDOM absorption spectrum provided in an embodiment of the present invention are the same as those of the aforementioned method embodiment. For the sake of brief description, for parts not mentioned in this embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment, which will not be repeated here.

[0136] Embodiment 3:

[0137] An embodiment of the present invention further provides a storage medium on which one or more programs readable by a computing device are stored. The one or more programs include instructions. When the instructions are executed by the computing device, the computing device executes a remote sensing estimation method for CDOM absorption spectrum in embodiment 1.

[0138] Examples of computer-readable storage media include: read-only memory (ROM), random-access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random-access memory (DRAM), static random-access memory (SRAM), flash memory, non-volatile memory, CD-ROM, DVD-ROM, Blu-ray or optical disk storage, hard disk drive (HDD), solid-state drive (SSD), card-type memory (such as a multimedia card, a secure digital (SD) card, or an extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state disk, and any other device configured to store a computer program and any associated data, data files, and data structures in a non-transitory manner and provide the computer program and any associated data, data files, and data structures to a processor or computer so that the processor or computer can execute the computer program. The computer program in the above-mentioned computer-readable storage medium can be executed in an environment deployed in a computer device such as a client, a host, an agent device, a server, etc. In addition, in one example, the computer program and any associated data, data files, and data structures are distributed on a networked computer system, so that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner by one or more processors or computers.

[0139] Embodiment 4:

[0140] Reference Figure 7 As shown, an embodiment of the present invention further provides an electronic device for remote sensing estimation of CDOM absorption spectrum, which may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10.

[0141] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect the various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11, and calls the data stored in the memory 11, so as to execute various functions of the electronic device and process data.

[0142] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, devices or computer program products, etc. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0143] It should be noted that the word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several distinct components, and by means of a suitably programmed computer.

[0144] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0145] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A remote sensing estimation method for CDOM absorption spectrum, characterized in that: The method comprises the following steps: Step 1: Obtain the measured remote sensing reflectance spectra of several points on the estuary water surface during the flood season, and obtain the CDOM absorption spectra of water samples at the corresponding points; Step 2: Calculate the maximum correlation coefficient between the measured remote sensing reflectance spectrum in the first band and the CDOM absorption spectrum in the second band, and record the CDOM absorption wavelength and remote sensing reflectance wavelength under the maximum correlation coefficient as the optimal band of CDOM absorption coefficient and the optimal band of reflectance, respectively; Step 3: Calculate the spectral gradient of the measured remote sensing reflectance spectrum in the first band range, and the spectral slopes of the CDOM absorption spectrum in the second band range and the third band range, as the reflectance shortwave spectral gradient, the CDOM shortwave spectral slope and the CDOM longwave spectral slope respectively; Step 4: Construct a CDOM absorption spectrum inversion model based on the optimal band reflectance, the optimal band CDOM absorption coefficient, the reflectance short-wave spectrum gradient, the CDOM short-wave spectrum slope, and the CDOM long-wave spectrum slope; Step 5: Obtain the spectral satellite remote sensing reflectance image of the estuary during the flood season, select the satellite remote sensing reflectance corresponding to the optimal reflectance band, input the CDOM absorption spectrum inversion model, and obtain the spatial distribution map of the CDOM absorption spectrum.

2. The remote sensing estimation method of CDOM absorption spectrum according to claim 1, characterized in that: The first waveband ranges from 400nm to 600nm, the second waveband ranges from 250nm to 400nm, and the third waveband ranges from 250nm to 700nm.

3. The remote sensing estimation method of CDOM absorption spectrum according to claim 2, characterized in that: In step 2, the calculation formula of the correlation coefficient is: ； in, represents the correlation coefficient, X represents the measured remote sensing reflectance of each wavelength in the first band, and Y represents the CDOM absorption coefficient of each wavelength in the second band; It represents the average value of the measured remote sensing reflectance of each wavelength in the first band at all sampling points; It represents the average value of the CDOM absorption coefficient of each wavelength in the second band at all sampling points.

4. The remote sensing estimation method of CDOM absorption spectrum according to claim 3, characterized in that: In step 3, the short-wave spectral gradient of the reflectivity is the growth rate of the measured remote sensing reflectivity spectrum from the trough to the peak within the first band, and the calculation formula is as follows: ； in, represents the short-wave spectral gradient of reflectance, and They represent the maximum and minimum values ​​of the measured remote sensing reflectance spectrum in the first band, and Respectively and The corresponding wavelength.

5. The remote sensing estimation method of CDOM absorption spectrum according to claim 4, characterized in that: In step 3, the calculation formula of the CDOM spectrum slope is: ； Wherein, λ represents any wavelength in the second wavelength range or the third wavelength range, represents the absorption spectrum of CDOM at wavelength λ, represents the absorption spectrum of CDOM at the optimal absorption coefficient band λ0, exp represents the exponential relationship, S g represents the CDOM spectrum slope; The CDOM absorption spectra in the second and third bands were fitted according to the calculation formula to obtain the CDOM short-wave spectrum slope. and the CDOM long-wave spectrum slope .

6. The remote sensing estimation method of CDOM absorption spectrum according to claim 5, characterized in that: In step 4, the CDOM absorption spectrum inversion model constructed includes: ； ； ； ； in, represents the remote sensing reflectance of the optimal band, λ1 represents the optimal band of reflectance, log represents the logarithmic relationship, A, B, C, D, E, and F are model parameters obtained by data fitting.

7. The remote sensing estimation method of CDOM absorption spectrum according to claim 1, characterized in that: In step 5, the spectral satellite remote sensing reflectance is the water body reflectance obtained by a multi-spectral or hyperspectral remote sensing satellite, and has at least 3 bands within the first band range.

8. A remote sensing estimation system for CDOM absorption spectrum, characterized in that: When applied, a remote sensing estimation method for CDOM absorption spectrum according to any one of claims 1 to 7 is performed to invert the CDOM absorption spectrum. The system comprises: The spectrum acquisition module is used to obtain the measured remote sensing reflectance spectra of several points on the estuary water surface during the flood season, and obtain the CDOM absorption spectra of the water samples at the corresponding points; A calculation module is used to calculate the maximum correlation coefficient between the measured remote sensing reflectance spectrum in the first band and the CDOM absorption spectrum in the second band, and record the CDOM absorption wavelength and the remote sensing reflectance wavelength under the maximum correlation coefficient as the optimal band of CDOM absorption coefficient and the optimal band of reflectance, respectively; and calculating the spectral gradient of the measured remote sensing reflectance spectrum in the first waveband range and the spectral slopes of the CDOM absorption spectrum in the second waveband range and the third waveband range, as the reflectance shortwave spectral gradient, the CDOM shortwave spectral slope and the CDOM longwave spectral slope, respectively; A model building module is used to build a CDOM absorption spectrum inversion model based on the optimal band reflectance, the optimal band CDOM absorption coefficient, the reflectance short-wave spectrum gradient, the CDOM short-wave spectrum slope and the CDOM long-wave spectrum slope; The spatial distribution generation module is used to obtain the spectral satellite remote sensing reflectance image of the estuary during the flood season, select the satellite remote sensing reflectance corresponding to the optimal reflectance band, input the CDOM absorption spectrum inversion model, and obtain the spatial distribution map of the CDOM absorption spectrum.

9. A storage medium having stored thereon one or more programs readable by a computing device, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform a remote sensing estimation method for CDOM absorption spectrum according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement a remote sensing estimation method of CDOM absorption spectrum according to any one of claims 1 to 7.

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