CN113345004B - Estimation method and device for vegetation leaf area index - Google Patents
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Abstract
The invention provides a vegetation leaf area index estimation method and device, which relate to the technical field of data processing and comprise the following steps: acquiring a surface reflectivity image of a region to be detected, wherein the surface reflectivity image is a high-resolution remote sensing image after radiometric calibration and processing and atmospheric correction processing are completed; constructing a first lookup table based on metadata of the high-resolution remote sensing image; based on a preset lookup table, performing three-dimensional linear interpolation on the first lookup table to obtain a first lookup table after interpolation processing, wherein the preset lookup table is used for representing the reflectivity of a canopy corresponding to a target parameter, and the target parameter comprises: presetting a parameter value set of a first parameter and a parameter range of the presetting parameter; based on the first lookup table and the earth surface reflectivity image after interpolation processing, the vegetation leaf area index of the high-resolution remote sensing image is determined, and the technical problem that the spatial resolution of the existing vegetation leaf area index product is low is solved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a vegetation leaf area index estimation method and device.
Background
The Leaf Area Index (LAI) is one of the key parameters characterizing vegetation structure, which quantifies the degree of involvement of Leaf Area in the ecosystem. LAI plays a crucial role in vegetation photosynthesis, respiration, rainfall uptake, and is also considered as one of the essential parameters in the field of global climate change research. LAI is defined as the multiple of the total plant leaf area per unit area of land. It is related to the density, structure (single or multiple layers) of vegetation, biological characteristics of trees (branch angle, leaf angle, shade resistance, etc.) and environmental conditions (light, moisture, soil nutrient status).
At present, methods for LAI inversion by satellite remote sensing data are roughly classified into three categories: the first is an empirical model, i.e., a statistical relationship between LAI and canopy reflectivity or vegetation index (e.g., NDVI, etc.) is established. The method has the advantages of simplicity, feasibility and high calculation efficiency. However, in the case of dense vegetation, the vegetation index saturates rapidly, resulting in a large LAI estimation bias; although the saturation problem under dense vegetation can be overcome to some extent by Near Infrared (NIR) bands compared to vegetation index, LAI inversion relying only on single band reflectivity has strong sensitivity to atmospheric conditions and soil background, resulting in instability of LAI estimation. In addition, the empirical model between LAI-VI often needs to model different vegetation types differently, and there is a limitation in actual operation under the condition that ground measured data is limited.
The second method is model inversion, that is, under the condition of known canopy-direction reflectivity, the leaf or canopy radiation transmission model is solved reversely to obtain the LAI value of one of the model input parameters. Compared with the first method, the method is more complex and lower in calculation efficiency, but the problem of vegetation index saturation is solved according to inversion of the physical model, and estimation accuracy of the LAI is guaranteed to the greatest extent. One typical model in this class of methods is the PROSAIL model, which is a combination of the leaf optical property model prospectt and the canopy directional reflectance model SAIL. Related studies have demonstrated the reliability and stability of the PROSAIL model.
A third type of method is LAI estimation using LiDAR (LiDAR) or microwave data. The LiDAR estimation method comprises the steps of firstly utilizing indexes such as laser penetration indexes to invert the proportion of gaps (gap fraction) of the canopy, and then solving LAI according to the correlation between the LAI and the proportion of the gaps of the canopy; or the biophysical parameters of vegetation such as canopy coverage, canopy height, leaf density and the like are obtained according to the direct observation of LiDAR, and then the LAI estimation is carried out through a geometric model between the parameters and the LAI. The microwave radar estimation method well makes up the defect that data is incomplete due to the fact that an optical image is affected by clouds, and estimates the LAI by using a simple empirical relationship between a radar backscattering signal and the LAI. Since the current remote sensing satellite observation still mainly uses an optical sensor, the application range of the LiDAR or microwave radar estimation method is relatively narrow.
Over the last two decades, several medium resolution (250-7000 m) global LAI products have been produced, however, there is little research on LAI for high spatial resolution (within 20 m) satellite imagery. For some local ecological studies such as precision agriculture, the spatial resolution of these LAI products is too low to grasp the spatial differences of crop LAIs in small-scale farmland. The reliability of these products has become less reliable due to the mixing of multiple crops, even farmland and non-farmland surfaces, where heterogeneity is high.
No effective solution has been proposed to the above problems.
Disclosure of Invention
In view of the above, the present invention provides a method for estimating a vegetation leaf area index, so as to alleviate the technical problem of low spatial resolution of the existing vegetation leaf area index product.
In a first aspect, an embodiment of the present invention provides a method for estimating a vegetation leaf area index, including: acquiring a surface reflectivity image of a region to be detected, wherein the surface reflectivity image is a high-resolution remote sensing image after radiometric calibration and processing and atmospheric correction processing are completed;
constructing a first lookup table based on metadata of the high-resolution remote sensing image, wherein the first lookup table comprises current parameter values of first parameters corresponding to pixels in the high-resolution remote sensing image, and the first parameters comprise: sun zenith angle, observation zenith angle and relative azimuth angle; based on a preset lookup table, performing three-dimensional linear interpolation on the first lookup table to obtain a first lookup table after interpolation processing, wherein the preset lookup table is used for representing the canopy reflectivity corresponding to the target parameter, and the target parameter includes: the preset parameter value set of the first parameter and the parameter range of the preset parameter; and determining the vegetation leaf area index of the high-resolution remote sensing image based on the first lookup table after interpolation processing and the earth surface reflectivity image.
Further, the method further comprises: inputting the preset parameter value set of the first parameter and the parameter range of the preset parameter into a PROSAIL model to obtain a preset lookup table, wherein the preset parameter at least comprises: structural coefficient, chlorophyll content, carotene content, brown pigment content, equivalent water thickness, leaf unit area mass, soil reflectivity, leaf area index, hot spot coefficient and average leaf inclination angle.
Further, inputting the target parameter set into the PROSAIL model to obtain a preset lookup table, including: a determining step, wherein a first subset is determined based on a preset parameter value set of the first parameter, wherein the first subset comprises: a preset sun zenith angle, a preset observation zenith angle and a preset relative azimuth angle; a generation step, wherein the parameter value of each preset parameter is randomly generated based on the parameter range of the preset parameter to obtain a second subset; a processing step, inputting the first subset and the second subset into a PROSAIL model to obtain an output spectrum, and performing weighted average on the output spectrum by using a spectral response function to obtain a sub-canopy reflectivity; a construction step of constructing a sub lookup table using the first subset, the second subset and the sub-canopy reflectivity; and repeatedly executing the determining step, the generating step, the processing step and the constructing step for preset times to obtain a preset number of sub lookup tables, and constructing the preset lookup table based on the preset number of sub lookup tables.
Further, based on a preset lookup table, performing three-dimensional linear interpolation on the first lookup table to obtain a first lookup table after interpolation processing, including: determining a target parameter value in a preset parameter value set of the first parameter based on the current parameter value of the first parameter, wherein the target parameter value is a preset parameter value adjacent to the current parameter value; constructing the target lookup table based on the target parameter value, wherein the target lookup table is a lookup table containing the target parameter value in a preset lookup table; and performing three-dimensional difference processing on the current parameter value of the first parameter by using the target lookup table to obtain the interpolated first lookup table.
Further, based on the first lookup table after the interpolation processing, the preset lookup table and the earth surface reflectivity image, determining a vegetation leaf area index of the high-resolution remote sensing image, including: determining the canopy reflectivity corresponding to the pixel in the high-resolution remote sensing image based on the surface reflectivity image; calculating the root mean square error between the canopy reflectivity of the pixel in the high-resolution remote sensing image and the canopy reflectivity of the first lookup table after interpolation processing; determining a sub lookup table in the first lookup table after interpolation processing based on the root mean square error, wherein the sub lookup table in the preset lookup table is the sub lookup table corresponding to the minimum value of the root mean square error in the preset lookup table; determining the vegetation leaf area index corresponding to a sub lookup table in the preset lookup table as the initial vegetation leaf area index of the pixel; and calculating the ratio of the initial vegetation leaf area index and the aggregation index of the pixel to obtain the vegetation leaf area index of the high-resolution remote sensing image.
Further, the canopy reflectivity comprises: blue light band image reflectivity, green light band image reflectivity, red light band image reflectivity and near infrared light band image reflectivity.
Further, the root mean square error is calculated by the formula
WhereinN bthe number of the wave bands is 4,
the reflectivity of the b-th wave band corresponding to the pixel in the high-resolution remote sensing image,
is the b-th band of the look-up table reflectivity.
In a second aspect, an embodiment of the present invention further provides an apparatus for estimating vegetation leaf area index, including: the device comprises an acquisition unit, a construction unit, an interpolation unit and a determination unit, wherein the acquisition unit is used for acquiring a surface reflectivity image of a region to be detected, and the surface reflectivity image is a high-resolution remote sensing image after radiometric calibration and processing and atmospheric correction processing are completed; the construction unit is configured to construct a first lookup table based on metadata of the high-resolution remote sensing image, where the first lookup table includes a current parameter value of a first parameter corresponding to a pixel in the high-resolution remote sensing image, and the first parameter includes: sun zenith angle, observation zenith angle and relative azimuth angle; the interpolation unit is used for performing three-dimensional linear interpolation on the first lookup table based on a preset lookup table to obtain a first lookup table after interpolation processing, wherein the preset lookup table is used for representing the reflectivity of the canopy corresponding to the target parameter; and the determining unit is used for determining the vegetation leaf area index of the high-resolution remote sensing image based on the first lookup table after interpolation processing and the earth surface reflectivity image.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory is used to store a program that supports the processor to execute the method in the first aspect, and the processor is configured to execute the program stored in the memory.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method in the first aspect.
In the embodiment of the invention, a surface reflectivity image of a region to be detected is obtained, wherein the surface reflectivity image is a high-resolution remote sensing image after radiation calibration and processing and atmospheric correction processing are completed; constructing a first lookup table based on metadata of the high-resolution remote sensing image, wherein the first lookup table comprises current parameter values of first parameters corresponding to pixels in the high-resolution remote sensing image, and the first parameters comprise: sun zenith angle, observation zenith angle and relative azimuth angle; based on a preset lookup table, performing three-dimensional linear interpolation on the first lookup table to obtain a first lookup table after interpolation processing, wherein the preset lookup table is used for representing the canopy reflectivity corresponding to the target parameter, and the target parameter includes: the preset parameter value set of the first parameter and the parameter range of the preset parameter; based on the first lookup table after interpolation processing, the preset lookup table and the earth surface reflectivity image, the vegetation leaf area index of the high-resolution remote sensing image is determined, the purpose of generating a high-resolution vegetation leaf area index product is achieved, the technical problem that the spatial resolution of the existing vegetation leaf area index product is low is further solved, and therefore the technical effect of supporting application research of precision agriculture and the like is achieved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method for estimating vegetation leaf area index according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an estimation apparatus for vegetation leaf area index according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
in accordance with an embodiment of the present invention, there is provided an embodiment of a method for estimating a vegetation leaf area index, wherein the steps illustrated in the flowchart of the accompanying drawings may be performed in a computer system such as a set of computer-executable instructions, and wherein although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that illustrated herein.
Fig. 1 is a flowchart of a method for estimating vegetation leaf area index according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S102, obtaining a surface reflectivity image of a region to be detected, wherein the surface reflectivity image is a high-resolution remote sensing image after completing radiometric calibration and processing and atmospheric correction processing;
step S104, constructing a first lookup table based on metadata of the high-resolution remote sensing image, wherein the first lookup table comprises current parameter values of first parameters corresponding to pixels in the high-resolution remote sensing image, and the first parameters comprise: sun zenith angle, observation zenith angle and relative azimuth angle;
step S106, based on a preset lookup table, performing three-dimensional linear interpolation on the first lookup table to obtain a first lookup table after interpolation processing, wherein the preset lookup table is used for representing the canopy reflectivity corresponding to the target parameter, and the target parameter includes: the preset parameter value set of the first parameter and the parameter range of the preset parameter;
and S108, determining the vegetation leaf area index of the high-resolution remote sensing image based on the first lookup table after interpolation processing and the earth surface reflectivity image.
In the embodiment of the invention, a surface reflectivity image of a region to be detected is obtained, wherein the surface reflectivity image is a high-resolution remote sensing image after radiation calibration and processing and atmospheric correction processing are completed; constructing a first lookup table based on metadata of the high-resolution remote sensing image, wherein the first lookup table comprises current parameter values of first parameters corresponding to pixels in the high-resolution remote sensing image, and the first parameters comprise: sun zenith angle, observation zenith angle and relative azimuth angle; based on a preset lookup table, performing three-dimensional linear interpolation on the first lookup table to obtain a first lookup table after interpolation processing, wherein the preset lookup table is used for representing the canopy reflectivity corresponding to the target parameter, and the target parameter includes: the preset parameter value set of the first parameter and the parameter range of the preset parameter; based on the first lookup table after interpolation processing, the preset lookup table and the earth surface reflectivity image, the vegetation leaf area index of the high-resolution remote sensing image is determined, the purpose of generating a high-resolution vegetation leaf area index product is achieved, the technical problem that the spatial resolution of the existing vegetation leaf area index product is low is further solved, and therefore the technical effect of supporting application research of precision agriculture and the like is achieved.
In an embodiment of the present invention, the method further includes the steps of:
step S201, inputting the preset parameter value set of the first parameter and the parameter range of the preset parameter into a PROSAIL model to obtain a preset lookup table, where the preset parameter at least includes: structural coefficient, chlorophyll content, carotene content, brown pigment content, equivalent water thickness, leaf unit area mass, soil reflectivity, leaf area index, hot spot coefficient and average leaf inclination angle.
Specifically, step S201 includes the following steps:
step S11, determining, based on the preset parameter value set of the first parameter, a first subset, where the first subset includes: a preset sun zenith angle, a preset observation zenith angle and a preset relative azimuth angle;
step S12, a generation step, in which parameter values of each preset parameter are randomly generated based on the parameter range of the preset parameter to obtain a second subset;
step S13, a processing step, namely inputting the first subset and the second subset into a PROSAIL model to obtain an output spectrum, and performing weighted average on the output spectrum by using a spectral response function to obtain the reflectivity of the sub-canopy;
step S14, a construction step, wherein a sub lookup table is constructed by utilizing the first subset, the second subset and the reflectivity of the sub canopy;
step S15, repeating the determining step for a preset number of times, the generating step, the processing step, and the constructing step to obtain a preset number of sub lookup tables, and constructing the preset lookup table based on the preset number of sub lookup tables.
Because the target parameter LAI (vegetation leaf area index) is one of the input parameters of the PROSAIL model, and the canopy reflectivity output by PROSAIL can be obtained by the satellite observation image, theoretically, the LAI value under the given observation situation can be obtained by only reversing the model. However, in the actual data production process, because the calculation efficiency of the radiation transmission model is low, the radiation transmission model is not suitable for performing the model inverse operation on all pixels of the image in real time to obtain the LAI.
In an embodiment of the present invention, the LAI is inverted using an offline simulation to build a look-up table. The input parameter list and its parameter ranges for the PROSAIL model are shown in table 1 below.
TABLE 1
Specifically, a random value is generated in a parameter range of each non-fixed-value PROSAIL input parameter (i.e., a preset parameter) in table 1, a scene (scenario) is defined by a combination (i.e., a second subset) of the parameters, and the scene is input into a PROSAIL model, so that the canopy reflectivity under the scene is obtained. The process is repeated for a plurality of times (after trial and error experiments, the method is finally repeated for a plurality of timesN_simuSet as 50,000), the canopy reflectivities under various scenes are obtained, and finally, the canopy reflectivities under various scenes and the first parameters and the preset parameters corresponding to various scenes are utilized to construct a preset lookup table, as shown in table 2.
TABLE 2
The reflectance values of 4 bands, as shown in the last four columns of table 2, are obtained by weighted averaging the output spectrum of the model using the spectral response function of the GF-1 PMS sensor, and if a lookup table suitable for the WFV sensor needs to be established, weighted averaging is performed using the spectral response function of the WFV sensor. In order to simulate various scenes as much as possible, random values of the parameters are generated by adopting uniform distribution.
It should be noted that, since the remote sensing image itself provides the accurate values of the three parameters SZA, VZA, and RAA, the three parameters do not participate in the random generation when creating the lookup table, but are set to a fixed set of values (i.e., the first subset), and a lookup table for the other parameters is created for each combination of SZA-VZA-RAA. According to the sensitivity analysis, in the range of the spectrum sensitive to the change of the angle, the angle is set to be a small step length; in the range where the variation is insensitive, a long step is set. The three angles are set as described in table 3 in the present application.
TABLE 3
With such an arrangement, it is substantially ensured that the simulated spectrum exhibits a uniform variation as the angle grows within the collection.
In the embodiment of the present invention, step S104 includes the following steps:
step S21, determining a target parameter value in a preset parameter value set of the first parameter based on the current parameter value of the first parameter, where the target parameter value is a preset parameter value adjacent to the current parameter value;
step S22, constructing the first lookup table based on the target parameter value;
and step S23, performing three-dimensional difference processing on the current parameter value of the first parameter by using the target lookup table to obtain the interpolated first lookup table.
In the embodiment of the present invention, after determining the current parameter value of the first parameter corresponding to the pixel in the high resolution remote sensing image, each angle is compared with each angle in the preset parameter value set of the first parameter, and lookup tables under two angles (i.e., target parameter values) adjacent to the current parameter value are taken out to form a set of 8 lookup tables (i.e., the first lookup table). For example, if the three angles of the test image are SZA = 69.53 °, VZA = 80.51 °, and RAA = 58.00 °, the following 8 lookup tables are taken:
sza_00_vza_80_raa_40_LUT、sza_75_vza_80_raa_40_LUT、sza_00_vza_83_raa_40_LUT、sza_75_vza_83_raa_40_LUT、sza_00_vza_80_raa_180_LUT、sza_75_vza_80_raa_180_LUT、sza_00_vza_83_raa_180_LUT、sza_75_vza_83_raa_180_LUT。
and performing three-dimensional linear interpolation on the reflectivity of each waveband of all 50000 line scenes in the preset lookup table according to the proportion of the image angle between adjacent positions in the angle list to obtain a first lookup table after interpolation, namely sza _69.53_ vza _80.51_ raa _58.00_ LUT.
In the embodiment of the present invention, step S110 includes the following steps:
step S31, determining the canopy reflectivity corresponding to the pixel in the high-resolution remote sensing image based on the earth surface reflectivity image;
step S32, calculating the root mean square error between the canopy reflectivity of the pixel in the high-resolution remote sensing image and the canopy reflectivity of the first lookup table after interpolation processing;
step S33, determining a sub lookup table in the first lookup table after the interpolation processing based on the root mean square error, where the sub lookup table in the preset lookup table is the sub lookup table corresponding to the minimum value of the root mean square error in the preset lookup table;
step S34, determining the vegetation leaf area index corresponding to the sub lookup table in the preset lookup table as the initial vegetation leaf area index of the pixel;
and step S35, calculating the ratio of the initial vegetation leaf area index and the aggregation index of the pixel to obtain the vegetation leaf area index of the high-resolution remote sensing image.
In the embodiment of the present invention, after the first lookup table after interpolation processing is obtained, for each pixel on the high-resolution remote sensing image, the surface reflectivities of the pixel in 4 bands of blue, green, red, and near infrared are compared with the reflectivities in the first lookup table after interpolation processing, and a scene closest to the pixel is found, so as to find the LAI value under the scene, that is, the LAI value is determined as the effective LAI value of the pixel (that is, the initial vegetation leaf area index of the pixel).
The difference degree between the image pixel reflectivity and the reflectivity in the lookup table can be measured by using the root mean square error of the surface reflectivity of 4 wave bands, and the scene with the minimum RMSE is the closest scene.
Wherein,N bthe number of the wave bands is 4,R b, Mis the reflection of the image pixel in the b-th band,R b, Lis the b-th band of the look-up table reflectivity. The LAI value searched by the method is used as the effective LAI value of the PROSAIL modelAnd dividing by the aggregation index of 0.71 to obtain the vegetation leaf area index of the high-resolution remote sensing image.
Example two:
the embodiment of the invention further provides a vegetation leaf area index estimation device, which is used for executing the vegetation leaf area index estimation method provided by the embodiment of the invention, and the following is a specific description of the vegetation leaf area index estimation device provided by the embodiment of the invention.
As shown in fig. 2, fig. 2 is a schematic view of the apparatus for estimating a vegetation leaf area index, and the apparatus for estimating a vegetation leaf area index includes: an acquisition unit 10, a construction unit 20, an interpolation unit 30 and a determination unit 40.
The acquiring unit 10 is configured to acquire a surface reflectance image of the area to be detected, where the surface reflectance image is a high-resolution remote sensing image after radiometric calibration and processing and atmospheric correction processing are completed;
the constructing unit 20 is configured to construct a first lookup table based on metadata of the high-resolution remote sensing image, where the first lookup table includes current parameter values of a first parameter corresponding to a pixel in the high-resolution remote sensing image, and the first parameter includes: sun zenith angle, observation zenith angle and relative azimuth angle;
the interpolation unit 30 is configured to perform three-dimensional linear interpolation on the first lookup table based on a preset lookup table to obtain a first lookup table after interpolation processing, where the preset lookup table is used to represent the canopy reflectivity corresponding to the target parameter;
the determining unit 40 is configured to determine a vegetation leaf area index of the high-resolution remote sensing image based on the interpolated first lookup table and the earth surface reflectance image.
In the embodiment of the invention, a surface reflectivity image of a region to be detected is obtained, wherein the surface reflectivity image is a high-resolution remote sensing image after radiation calibration and processing and atmospheric correction processing are completed; constructing a first lookup table based on metadata of the high-resolution remote sensing image, wherein the first lookup table comprises current parameter values of first parameters corresponding to pixels in the high-resolution remote sensing image, and the first parameters comprise: sun zenith angle, observation zenith angle and relative azimuth angle; based on a preset lookup table, performing three-dimensional linear interpolation on the first lookup table to obtain a first lookup table after interpolation processing, wherein the preset lookup table is used for representing the canopy reflectivity corresponding to the target parameter, and the target parameter includes: the preset parameter value set of the first parameter and the parameter range of the preset parameter; based on the first lookup table after interpolation processing, the preset lookup table and the earth surface reflectivity image, the vegetation leaf area index of the high-resolution remote sensing image is determined, the purpose of generating a high-resolution vegetation leaf area index product is achieved, the technical problem that the spatial resolution of the existing vegetation leaf area index product is low is further solved, and therefore the technical effect of supporting application research of precision agriculture and the like is achieved.
Example three:
an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory is used to store a program that supports the processor to execute the method described in the first embodiment, and the processor is configured to execute the program stored in the memory.
Referring to fig. 3, an embodiment of the present invention further provides an electronic device 100, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.
The Memory 61 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 62 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus.
The memory 61 is used for storing a program, the processor 60 executes the program after receiving an execution instruction, and the method executed by the apparatus defined by the flow process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 60, or implemented by the processor 60.
The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 60. The Processor 60 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 61, and the processor 60 reads the information in the memory 61 and, in combination with its hardware, performs the steps of the above method.
Example four:
the embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the method in the first embodiment.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A vegetation leaf area index estimation method is characterized by comprising the following steps:
acquiring a surface reflectivity image of a region to be detected, wherein the surface reflectivity image is a high-resolution remote sensing image after completing radiometric calibration processing and atmospheric correction processing;
constructing a first lookup table based on metadata of the high-resolution remote sensing image, wherein the first lookup table comprises current parameter values of first parameters corresponding to pixels in the high-resolution remote sensing image, and the first parameters comprise: sun zenith angle, observation zenith angle and relative azimuth angle;
based on a preset lookup table, performing three-dimensional linear interpolation on the first lookup table to obtain a first lookup table after interpolation processing, wherein the preset lookup table is used for representing the reflectivity of a canopy corresponding to a target parameter, and the target parameter comprises: the preset parameter value set of the first parameter and the parameter range of the preset parameter;
determining a vegetation leaf area index of the high-resolution remote sensing image based on the interpolated first lookup table and the earth surface reflectivity image;
the three-dimensional linear interpolation is carried out on the first lookup table based on a preset lookup table to obtain the first lookup table after interpolation processing, and the method comprises the following steps:
determining a target parameter value in a preset parameter value set of the first parameter based on the current parameter value of the first parameter, wherein the target parameter value is a preset parameter value adjacent to the current parameter value;
constructing a target lookup table based on the target parameter value, wherein the target lookup table is a lookup table containing the target parameter value in the preset lookup table;
performing three-dimensional difference processing on the current parameter value of the first parameter by using the target lookup table to obtain the first lookup table after interpolation processing;
wherein, based on the first lookup table after the interpolation processing, the preset lookup table and the earth surface reflectivity image, determining the vegetation leaf area index of the high-resolution remote sensing image, comprising:
determining the canopy reflectivity corresponding to the pixel in the high-resolution remote sensing image based on the surface reflectivity image;
calculating the root mean square error between the canopy reflectivity of the pixel in the high-resolution remote sensing image and the canopy reflectivity of the first lookup table after interpolation processing;
determining a sub lookup table in the first lookup table after interpolation processing based on the root mean square error, wherein the sub lookup table in the preset lookup table is the sub lookup table corresponding to the minimum value of the root mean square error in the preset lookup table;
determining the vegetation leaf area index corresponding to a sub lookup table in the preset lookup table as the initial vegetation leaf area index of the pixel;
and calculating the ratio of the initial vegetation leaf area index and the aggregation index of the pixel to obtain the vegetation leaf area index of the high-resolution remote sensing image.
2. The method of claim 1, further comprising:
inputting the preset parameter value set of the first parameter and the parameter range of the preset parameter into a PROSAIL model to obtain a preset lookup table, wherein the preset parameter at least comprises: structural coefficient, chlorophyll content, carotene content, brown pigment content, equivalent water thickness, leaf unit area mass, soil reflectivity, leaf area index, hot spot coefficient and average leaf inclination angle.
3. The method of claim 2, wherein inputting the target parameter set into the PROSAIL model to obtain the predetermined look-up table comprises:
a determining step, wherein a first subset is determined based on a preset parameter value set of the first parameter, wherein the first subset comprises: a preset sun zenith angle, a preset observation zenith angle and a preset relative azimuth angle;
a generation step, wherein the parameter value of each preset parameter is randomly generated based on the parameter range of the preset parameter to obtain a second subset;
a processing step, inputting the first subset and the second subset into a PROSAIL model to obtain an output spectrum, and performing weighted average on the output spectrum by using a spectral response function to obtain a sub-canopy reflectivity;
a construction step of constructing a sub lookup table using the first subset, the second subset and the sub-canopy reflectivity;
and repeatedly executing the determining step, the generating step, the processing step and the constructing step for preset times to obtain a preset number of sub lookup tables, and constructing the preset lookup table based on the preset number of sub lookup tables.
4. The method of claim 1,
the canopy reflectivity comprises: blue light band image reflectivity, green light band image reflectivity, red light band image reflectivity and near infrared light band image reflectivity.
5. The method of claim 4,
the root mean square error is calculated by the formula
WhereinN bthe number of the wave bands is 4,
the reflectivity of the b-th wave band corresponding to the pixel in the high-resolution remote sensing image,
is the b-th band of the look-up table reflectivity.
6. An apparatus for estimating vegetation leaf area index, comprising: an acquisition unit, a construction unit, an interpolation unit and a determination unit, wherein,
the acquisition unit is used for acquiring a surface reflectivity image of the area to be detected, wherein the surface reflectivity image is a high-resolution remote sensing image after completing radiometric calibration processing and atmospheric correction processing;
the construction unit is configured to construct a first lookup table based on metadata of the high-resolution remote sensing image, where the first lookup table includes a current parameter value of a first parameter corresponding to a pixel in the high-resolution remote sensing image, and the first parameter includes: sun zenith angle, observation zenith angle and relative azimuth angle;
the interpolation unit is configured to perform three-dimensional linear interpolation on the first lookup table based on a preset lookup table to obtain a first lookup table after interpolation processing, where the preset lookup table is used to represent a canopy reflectivity corresponding to a target parameter, and the target parameter includes: the preset parameter value set of the first parameter and the parameter range of the preset parameter;
the determining unit is used for determining the vegetation leaf area index of the high-resolution remote sensing image based on the first lookup table after interpolation processing and the earth surface reflectivity image;
wherein the interpolation unit is configured to:
determining a target parameter value in a preset parameter value set of the first parameter based on the current parameter value of the first parameter, wherein the target parameter value is a preset parameter value adjacent to the current parameter value;
constructing a target lookup table based on the target parameter value, wherein the target lookup table is a lookup table containing the target parameter value in the preset lookup table;
performing three-dimensional difference processing on the current parameter value of the first parameter by using the target lookup table to obtain the first lookup table after interpolation processing;
wherein the determining unit is configured to:
determining the canopy reflectivity corresponding to the pixel in the high-resolution remote sensing image based on the surface reflectivity image;
calculating the root mean square error between the canopy reflectivity of the pixel in the high-resolution remote sensing image and the canopy reflectivity of the first lookup table after interpolation processing;
determining a sub lookup table in the first lookup table after interpolation processing based on the root mean square error, wherein the sub lookup table in the preset lookup table is the sub lookup table corresponding to the minimum value of the root mean square error in the preset lookup table;
determining the vegetation leaf area index corresponding to a sub lookup table in the preset lookup table as the initial vegetation leaf area index of the pixel;
and calculating the ratio of the initial vegetation leaf area index and the aggregation index of the pixel to obtain the vegetation leaf area index of the high-resolution remote sensing image.
7. An electronic device comprising a memory for storing a program that enables a processor to perform the method of any of claims 1 to 5 and a processor configured to execute the program stored in the memory.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1 to 5.
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