Method and device for retrieving lai from synergistic multi-angular remote sensing observations

Abstract

The application discloses a LAI inversion method and device based on cooperative multi-angle remote sensing observation. The method comprises the following steps: determining a plurality of view angle combinations; determining the theoretical difference of specified radiation corresponding to the plurality of view angle combinations based on the model corresponding to the plurality of view angle combinations; determining a target view angle combination based on the theoretical difference of specified radiation corresponding to the plurality of view angle combinations; and obtaining the inversion result of the leaf area index based on the model corresponding to the target view angle combination. The method uses a plurality of observation angles as the reference basis of the LAI inversion process, effectively reduces the uncertainty of the LAI inversion result, determines the target view angle combination based on the theoretical difference of specified radiation corresponding to the plurality of view angle combinations, eliminates the influence of invalid observation angle combinations on the LAI inversion process, and effectively improves the accuracy of the LAI inversion result.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method and apparatus for LAI inversion based on collaborative multi-angle remote sensing observations. Background Technology

[0002] The Leaf Area Index (LAI) is a measure of the total leaf area per unit area in an ecosystem and is an important parameter for simulating terrestrial ecological processes, hydrothermal cycles, and biogeochemical cycles.

[0003] Currently, LAI (Local Area Inversion) is typically obtained using satellite remote sensing data; specifically, it is derived by inverting models based on satellite remote sensing data as parameters. However, the accuracy of LAI inversion results obtained based on existing models is relatively low. Summary of the Invention

[0004] This application provides a method and apparatus for LAI inversion based on collaborative multi-angle remote sensing observations, with the aim of improving the accuracy of LAI inversion.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] A collaborative multi-angle remote sensing observation method for LAI inversion includes:

[0007] Determine multiple viewpoint combinations; the viewpoint combinations include multiple viewpoint parameters; the viewpoint parameters are used to characterize multiple observation angles;

[0008] Based on models corresponding to multiple viewpoint combinations, the theoretical differences in the specified radiation corresponding to the multiple viewpoint combinations are determined; the models are used to characterize the correspondence between leaf area index and specified radiation; the theoretical differences are used to characterize the differences between simulated values ​​and observed values; the simulated values ​​are obtained by simulating specified radiation based on the models; the observed values ​​are obtained by remote sensing observation of specified radiation.

[0009] The target viewpoint combination is determined based on the theoretical differences in the specified radiation corresponding to the multiple viewpoint combinations.

[0010] Based on the model corresponding to the target view combination, the inversion result of the leaf area index is obtained.

[0011] Optionally, the target viewpoint combination is determined based on the theoretical differences in the specified radiation corresponding to the multiple viewpoint combinations, including:

[0012] From the multiple perspective combinations, the perspective combination with a theoretical difference less than a first threshold is selected as the target perspective combination.

[0013] Optionally, from the multiple said viewpoint combinations, a viewpoint combination with a theoretical difference less than a first threshold is selected as the target viewpoint combination, including:

[0014] From the multiple perspective combinations, the perspective combination with a theoretical difference less than a first threshold is selected as the first perspective combination;

[0015] For each selected first viewpoint combination, obtain the theoretical difference of the specified radiation corresponding to each viewpoint parameter in the first viewpoint combination;

[0016] If the theoretical difference corresponding to each of the aforementioned viewpoint parameters is less than the first threshold, then the first viewpoint combination is determined as the target viewpoint combination.

[0017] Optionally, if the theoretical difference corresponding to each of the aforementioned viewpoint parameters is less than the first threshold, then the first viewpoint combination is determined as the target viewpoint combination, including:

[0018] If the theoretical difference corresponding to each of the aforementioned viewpoint parameters is less than the first threshold, then the first viewpoint combination is determined as the second viewpoint combination.

[0019] For each selected second viewpoint combination, obtain the difference between the specified radiations corresponding to each viewpoint parameter in the second viewpoint combination;

[0020] If the difference between the specified radiations corresponding to each of the aforementioned viewpoint parameters is less than or equal to the second threshold, then the second viewpoint combination is determined as the target viewpoint combination.

[0021] Optionally, based on the model corresponding to the target viewpoint combination, the inversion result of the leaf area index is obtained, including:

[0022] Based on the model corresponding to the target viewpoint combination, determine the simulated value of the specified radiation corresponding to the target viewpoint combination;

[0023] Remote sensing observations are performed based on the observation angles shown by the target viewpoint combination to obtain the observed values ​​of the specified radiation corresponding to the target viewpoint combination.

[0024] The inversion result of the leaf area index is determined based on the difference between the simulated and observed values ​​of the specified radiation corresponding to the target viewpoint combination.

[0025] A collaborative multi-angle remote sensing observation LAI inversion device includes:

[0026] A viewing angle determination unit is used to determine multiple viewing angle combinations; the viewing angle combination includes multiple viewing angle parameters; the viewing angle parameters are used to characterize multiple observation angles.

[0027] The difference determination unit is used to determine the theoretical difference of a specified radiation corresponding to multiple viewpoint combinations based on the model corresponding to the multiple viewpoint combinations; the model is used to characterize the correspondence between leaf area index and specified radiation; the theoretical difference is used to characterize the difference between simulated values ​​and observed values; the simulated values ​​are obtained by simulating specified radiation based on the model; the observed values ​​are obtained based on remote sensing observation of specified radiation;

[0028] The combination selection unit is used to determine the target viewpoint combination based on the theoretical differences between the specified radiations corresponding to the multiple viewpoint combinations.

[0029] The model inversion unit is used to obtain the inversion result of the leaf area index based on the model corresponding to the target view combination.

[0030] Optionally, the combination selection unit is specifically used for:

[0031] From the multiple perspective combinations, the perspective combination with a theoretical difference less than a first threshold is selected as the target perspective combination.

[0032] Optionally, the combination selection unit is specifically used for:

[0033] From the multiple perspective combinations, the perspective combination with a theoretical difference less than a first threshold is selected as the first perspective combination;

[0034] For each selected first viewpoint combination, obtain the theoretical difference of the specified radiation corresponding to each viewpoint parameter in the first viewpoint combination;

[0035] If the theoretical difference corresponding to each of the aforementioned viewpoint parameters is less than the first threshold, then the first viewpoint combination is determined as the target viewpoint combination.

[0036] Optionally, the combination selection unit is specifically used for:

[0037] If the theoretical difference corresponding to each of the aforementioned viewpoint parameters is less than the first threshold, then the first viewpoint combination is determined as the second viewpoint combination.

[0038] For each selected second viewpoint combination, obtain the difference between the specified radiations corresponding to each viewpoint parameter in the second viewpoint combination;

[0039] If the difference between the specified radiations corresponding to each of the aforementioned viewpoint parameters is less than or equal to the second threshold, then the second viewpoint combination is determined as the target viewpoint combination.

[0040] Optionally, the model inversion unit is specifically used for:

[0041] Based on the model corresponding to the target viewpoint combination, determine the simulated value of the specified radiation corresponding to the target viewpoint combination;

[0042] Remote sensing observations are performed based on the observation angles shown by the target viewpoint combination to obtain the observed values ​​of the specified radiation corresponding to the target viewpoint combination.

[0043] The inversion result of the leaf area index is determined based on the difference between the simulated and observed values ​​of the specified radiation corresponding to the target viewpoint combination.

[0044] Based on the scheme described in this application, multiple viewpoint combinations are determined, and based on the models corresponding to these viewpoint combinations, the theoretical differences in the specified radiation corresponding to each viewpoint combination are determined. Based on the theoretical differences in the specified radiation corresponding to the multiple viewpoint combinations, a target viewpoint combination is determined. Based on the model corresponding to the target viewpoint combination, the inversion result of the leaf area index (LAI) is obtained. This application utilizes multiple observation angles as references in the LAI inversion process, effectively reducing the uncertainty of the LAI inversion results. By determining the target viewpoint combination based on the theoretical differences in the specified radiation corresponding to the multiple viewpoint combinations, the influence of invalid observation angle combinations on the LAI inversion process is eliminated, thus effectively improving the accuracy of the LAI inversion results. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A flowchart illustrating a collaborative multi-angle remote sensing observation-based LAI inversion method provided in this application embodiment;

[0047] Figure 2 A flowchart illustrating another collaborative multi-angle remote sensing observation-based LAI inversion method provided in this application embodiment;

[0048] Figure 3 A schematic diagram illustrating the value range of LAI inversion results provided in this application embodiment;

[0049] Figure 4 This is a schematic diagram of a data processing application scenario provided in an embodiment of this application;

[0050] Figure 5 A data histogram provided for an embodiment of this application;

[0051] Figure 6Another data histogram provided for embodiments of this application;

[0052] Figure 7 Another data histogram provided in the embodiments of this application;

[0053] Figure 8 A schematic diagram of the architecture of a collaborative multi-angle remote sensing observation LAI inversion device provided in this application embodiment;

[0054] Figure 9 This is a schematic diagram of the architecture of an electronic device provided in an embodiment of this application. Detailed Implementation

[0055] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0057] This solution can be used in a wide range of general-purpose or specialized computing environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor devices, and distributed computing environments that include any of the above devices.

[0058] like Figure 1 The diagram shown is a flowchart of a collaborative multi-angle remote sensing observation LAI inversion method provided in this application embodiment, including the following steps.

[0059] S101: Determine multiple viewpoint combinations.

[0060] The viewpoint combination includes multiple viewpoint parameters, which are used to characterize multiple observation angles.

[0061] The so-called observation angle is the angle referenced by the satellite during remote sensing observation. It is common knowledge in the field of remote sensing observation. For the embodiments of this application, the observation angles included in the view parameters include the solar zenith angle (SZ), the solar azimuth angle (SA), the view zenith angle (VZ), and the view azimuth angle (VA).

[0062] It should be noted that the number of view parameters included in each view combination can be set by technicians according to the actual situation, and this application embodiment does not limit it.

[0063] Specifically, the specific implementation method for determining multiple viewpoint combinations includes, but is not limited to: maintaining SA at 0° and SZ at 16.5° as constants for each viewpoint parameter, sampling every 30° in the direction to which VA belongs to obtain 7 VAs in the range of [0°, 180°], and sampling every 4° in the direction to which VZ belongs to obtain 16 VZs in the range of [0°, 67.5°]. Combining the 7 VAs and 16 VZs and introducing them into the constants SA and SZ, multiple viewpoint combinations can be obtained.

[0064] Specifically, assuming the viewpoint combination includes two viewpoint parameters, each viewpoint parameter includes VA, VZ, SA, and SZ, meaning the viewpoint combination includes eight observation angles.

[0065] S102: Based on the models corresponding to multiple viewpoint combinations, determine the theoretical differences of the specified radiation corresponding to multiple viewpoint combinations.

[0066] The model characterizes the correspondence between leaf area index and specified radiation, while the theoretical difference characterizes the difference between simulated and observed values. Simulated values ​​are obtained based on model simulations of specified radiation, while observed values ​​are obtained based on remote sensing observations of specified radiation.

[0067] In the embodiments of this application, the model can specifically be a three-dimensional radiative transfer model. The three-dimensional radiative transfer model is common knowledge in the field of remote sensing technology. Specifically, the three-dimensional radiative transfer model used in this application can be the 3D-RTM model used in the MODIS LAI operational algorithm.

[0068] It should be noted that the 3D radiative transfer model uses scene parameters such as LAI and viewpoint combination as input parameters and specified radiation as output parameters. Specifically, the specified radiation is used to characterize the reflectivity of a specified object, such as canopy reflectivity or the reflectivity of a specified spectrum.

[0069] It is important to note the specific implementation method for using a three-dimensional radiative transfer model to simulate a specified radiation and obtain its simulated value. Specifically, a three-dimensional radiative transfer model can be constructed based on the band response function of the spectrum shown by the MODIS sensor to simulate the reflectance of the spectrum shown by the MODIS sensor. Furthermore, the specific implementation method for obtaining the observed value of a specified radiation based on remote sensing observation can be achieved by remotely sensing the specified radiation using a MODIS sensor.

[0070] S103: Determine the target viewpoint combination based on the theoretical differences in the specified radiation corresponding to multiple viewpoint combinations.

[0071] The specific implementation method for determining the target viewpoint combination is based on the theoretical differences in the specified radiation corresponding to multiple viewpoint combinations. (See also...) Figure 2 The steps shown are explained below.

[0072] S104: Based on the model corresponding to the target view combination, obtain the inversion result of leaf area index.

[0073] The specific methods for obtaining the inversion results of LAI include, but are not limited to, lookup table method and optimization method.

[0074] The so-called lookup table method involves pre-calculating the array of corresponding input parameters and specified radiation. When calculating LAI, a cost function is constructed to find the input parameters that minimize the difference between the simulated and observed values ​​of the specified radiation. Specifically, in the lookup table method, the cost function shown in formula (1) is used to measure the deviation between the observed reflectance (i.e., the observed value of the specified radiation) obtained by the MODIS sensor in the red and near-red bands and the model simulated reflectance (i.e., the simulated value of the specified radiation) stored in the table. The LAIs with deviations less than a specified value are selected from the table to construct an acceptable solution set. Based on the frequency of occurrence of LAI values ​​in the acceptable solution set, the weighted average of all LAI values ​​is calculated as the LAI inversion result.

[0075]

[0076] In formula (1), r represents the deviation between observed reflectance and simulated emissivity. λl (Ω, Ω0) represents the observed reflectance. σ represents the simulated emissivity. r (l) represents the standard deviation, h r Represents a specified value.

[0077] Optionally, the specific implementation process of obtaining the LAI inversion result based on the model corresponding to the target view combination includes: determining the simulated value of the specified radiation corresponding to the target view combination based on the model corresponding to the target view combination; conducting remote sensing observations according to the observation angle shown by the target view combination to obtain the observed value of the specified radiation corresponding to the target view combination; and determining the inversion result of the leaf area index based on the difference between the simulated value and the observed value of the specified radiation corresponding to the target view combination.

[0078] The so-called optimization method includes, but is not limited to, ant colony algorithm, genetic algorithm, Bayesian network, etc. The optimization method is similar to gradient descent in machine learning, which finds the parameter combination (i.e., target view combination, LAI) that minimizes the simulated value and observed value of a specified radiation by continuously wandering in the parameter space.

[0079] In the embodiments of this application, since remote sensing observation signals under different observation angles have significant differences and complementarity, using multiple observation angles as input parameters of the model can effectively constrain the LAI inversion process, reduce the uncertainty of LAI inversion results, and improve the accuracy of inversion results. In addition, in order to avoid invalid observation angle combinations from affecting the accuracy of LAI inversion, the target viewpoint combination is determined based on the theoretical differences of the specified radiation corresponding to multiple viewpoint combinations. This can effectively screen out observation angle combinations that do not affect the accuracy of LAI inversion and improve the accuracy of LAI inversion.

[0080] Based on the process shown in S101-S104 above, multiple observation angles are used as references for the leaf area index (LAI) inversion process, which effectively reduces the uncertainty of the LAI inversion results. Based on the theoretical differences of the specified radiation corresponding to multiple viewpoint combinations, the target viewpoint combination is determined, eliminating the influence of invalid observation angle combinations on the LAI inversion process, thereby effectively improving the accuracy of the LAI inversion results.

[0081] like Figure 2 The diagram shown is a flowchart of another collaborative multi-angle remote sensing observation LAI inversion method provided in this application embodiment, including the following steps.

[0082] S201: From multiple perspective combinations, select the perspective combination with theoretical differences less than a first threshold as the first perspective combination.

[0083] Among them, the viewpoint combination with a theoretical difference less than the first threshold means that the LAI corresponding to the viewpoint combination can exhibit a small deviation in all observation directions (i.e., the solar direction and the observation direction).

[0084] Specifically, assuming that the viewpoint combination includes two viewpoint parameters, denoted as the first viewpoint parameter and the second viewpoint parameter respectively, then based on the cost function shown in formula (2), the viewpoint combination with a theoretical difference less than the first threshold is selected from multiple viewpoint combinations as the first viewpoint combination.

[0085]

[0086] In formula (2), The theoretical differences in radiation corresponding to the combination of representative viewpoints, r λl (Ω j ,Ω0) represents the observed value of the specified radiation, σ represents the simulated value of the specified radiation. r (j,l) represents the standard deviation, h r This represents the first threshold.

[0087] S202: For each selected first-view combination, obtain the theoretical difference of the specified radiation corresponding to each view parameter in the first-view combination.

[0088] The cost function shown in formula (2) can only ensure that LAI exhibits a small overall deviation in all observation directions, but it cannot ensure that LAI exhibits a small deviation in each viewpoint parameter. Therefore, it is necessary to obtain the theoretical difference of the specified radiation corresponding to each viewpoint parameter in the first viewpoint combination, and use the theoretical difference of the specified radiation corresponding to each viewpoint parameter to determine the theoretical difference corresponding to each viewpoint parameter.

[0089] S203: If the theoretical difference corresponding to each viewpoint parameter is less than the first threshold, then determine the first viewpoint combination as the second viewpoint combination.

[0090] Among them, the first viewpoint combination, based on the fact that the theoretical difference corresponding to each viewpoint parameter is less than the first threshold, serves as the second viewpoint combination, which can ensure that LAI exhibits a small deviation in each viewpoint parameter.

[0091] Specifically, assuming that the viewpoint combination includes two viewpoint parameters, referred to as the first viewpoint parameter and the second viewpoint parameter respectively, then based on the cost function shown in formula (3), the first viewpoint combination whose theoretical difference for each viewpoint parameter is less than the first threshold is selected as the second viewpoint combination.

[0092]

[0093] In formula (3), The theoretical differences corresponding to the first-person perspective parameters. This represents the theoretical differences corresponding to the second-view parameters.

[0094] S204: For each selected second view combination, obtain the difference between the specified radiations corresponding to the various view parameters in the second view combination.

[0095] While the second viewpoint combination can ensure that LAI exhibits small deviations across various viewpoint parameters, there may still be invalid observation angle combinations within the second viewpoint combination. To avoid the impact of invalid observation angle combinations on LAI inversion, it is necessary to determine the differences between the specified radiations corresponding to each viewpoint parameter in the second viewpoint combination in order to eliminate invalid observation angle combinations.

[0096] S205: If the difference between the specified radiations corresponding to each viewpoint parameter is less than or equal to the second threshold, then the second viewpoint combination is determined as the target viewpoint combination.

[0097] Among them, the second view combination based on the difference between the specified radiation corresponding to each view parameter being less than or equal to the second threshold is used as the target view combination, which can ensure that the target view combination will not affect the accuracy of the LAI inversion results.

[0098] Specifically, assuming the viewpoint combination includes two viewpoint parameters, denoted as the first viewpoint parameter and the second viewpoint parameter respectively, then based on the cost function shown in formula (4), the second viewpoint combination whose difference between the specified radiations corresponding to each viewpoint parameter is less than or equal to the second threshold is selected as the target viewpoint combination.

[0099] Δ bias =|Δ r_d1 -Δ r_d2 ≤h bias | (4)

[0100] In formula (4), Δ bias Δ represents the difference between the specified radiation corresponding to the first-viewpoint parameter and the specified radiation corresponding to the second-viewpoint parameter. r_d1 Δ represents the specified radiation corresponding to the first-person perspective parameters. r_d2 h represents the specified radiation corresponding to the second-view parameters. bias This represents the second threshold.

[0101] It should be noted that the inversion results of LAI determined based on the cost functions shown in formulas (2), (3), and (4) above can be found in [reference needed]. Figure 3 As shown.

[0102] Based on the procedures shown in S201-S205 above, the target viewpoint combination can be determined based on the theoretical differences of the specified radiation corresponding to multiple viewpoint combinations, eliminating the influence of invalid observation angle combinations on the LAI inversion process, and thus effectively improving the accuracy of the LAI inversion results.

[0103] It should be noted that the above Figure 1 and Figure 2 The LAI inversion method based on collaborative multi-angle remote sensing observations shown can be specifically applied to... Figure 4 The scenario shown is used to implement LAI inversion and to verify and compare the LAI inversion results.

[0104] Specifically, to verify the model corresponding to the target view combination and obtain the accuracy of the leaf area index (LAI) inversion results, this application compares the LAI inversion results between the model corresponding to the target view combination, the model corresponding to a single view parameter, and existing satellite remote sensing observation data. Specifically, for example... Figure 5 As shown, the root mean square error (RMSE) of the LAI inversion result based on the model corresponding to the target view combination is 0.7145. Figure 6 As shown, the RMSE of the LAI inversion result corresponding to the single-view parameter model is 0.8133. Figure 7 As shown, the RMSE of LAI inversion results based on existing satellite remote sensing data is 0.7937. Clearly, the model provided in this application yields higher accuracy LAI inversion results.

[0105] Corresponding to the LAI inversion method based on collaborative multi-angle remote sensing observation provided in this application, this application also provides an LAI inversion device based on collaborative multi-angle remote sensing observation.

[0106] like Figure 8 The diagram shown is a schematic of the architecture of a collaborative multi-angle remote sensing observation LAI inversion device provided in an embodiment of this application, including the following units.

[0107] The viewpoint determination unit 100 is used to determine multiple viewpoint combinations; the viewpoint combination includes multiple viewpoint parameters; the viewpoint parameters are used to characterize multiple observation angles.

[0108] The difference determination unit 200 is used to determine the theoretical difference of a specified radiation corresponding to multiple viewpoint combinations based on the model corresponding to multiple viewpoint combinations; the model is used to characterize the correspondence between leaf area index and specified radiation; the theoretical difference is used to characterize the difference between simulated values ​​and observed values; the simulated values ​​are obtained based on the model to simulate the specified radiation; the observed values ​​are obtained based on remote sensing observation of the specified radiation.

[0109] The combination selection unit 300 is used to determine the target viewpoint combination based on the theoretical differences of the specified radiation corresponding to multiple viewpoint combinations.

[0110] In the LAI inversion device for collaborative multi-angle remote sensing observation shown, the combination selection unit 300 is specifically used to: select from multiple view combinations the view combination with a theoretical difference less than a first threshold as the target view combination.

[0111] Optionally, the combination selection unit 300 is specifically used to: select a view combination from multiple view combinations whose theoretical difference is less than a first threshold as a first view combination; for each selected first view combination, obtain the theoretical difference of the specified radiation corresponding to each view parameter in the first view combination; if the theoretical difference corresponding to each view parameter is less than the first threshold, then determine the first view combination as the target view combination.

[0112] Optionally, the combination selection unit 300 is specifically used for: if the theoretical difference corresponding to each viewpoint parameter is less than the first threshold, then determining the first viewpoint combination as the second viewpoint combination; for each selected second viewpoint combination, obtaining the difference between the specified radiation corresponding to each viewpoint parameter in the second viewpoint combination; if the difference between the specified radiation corresponding to each viewpoint parameter is less than or equal to the second threshold, then determining the second viewpoint combination as the target viewpoint combination.

[0113] Model inversion unit 400 is used to obtain the inversion result of leaf area index based on the model corresponding to the target view combination.

[0114] In the LAI inversion device for collaborative multi-angle remote sensing observation shown, the model inversion unit 400 is specifically used for: determining the simulated value of the specified radiation corresponding to the target view combination based on the model corresponding to the target view combination; conducting remote sensing observations according to the observation angles shown by the target view combination to obtain the observed value of the specified radiation corresponding to the target view combination; and determining the inversion result of the leaf area index based on the difference between the simulated value and the observed value of the specified radiation corresponding to the target view combination.

[0115] Based on the units shown above, multiple observation angles are used as references for the leaf area index (LAI) inversion process, which effectively reduces the uncertainty of the LAI inversion results. Based on the theoretical differences of the specified radiation corresponding to multiple viewpoint combinations, the target viewpoint combination is determined, eliminating the influence of invalid observation angle combinations on the LAI inversion process, thereby effectively improving the accuracy of the LAI inversion results.

[0116] This application also provides a computer-readable storage medium including a stored program, wherein the program executes the LAI inversion method for collaborative multi-angle remote sensing observations provided in this application.

[0117] This application also provides an electronic device, such as Figure 9 As shown, it includes a processor, a memory, and a bus. The processor and memory are connected via the bus. The memory is used to store programs, and the processor is used to run the programs. When the programs run, they execute the LAI inversion method for collaborative multi-angle remote sensing observations provided in this application.

[0118] Furthermore, the functions described above in the embodiments of this application can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SOCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0119] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

[0120] While several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0121] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for LAI inversion based on collaborative multi-angle remote sensing observations, characterized in that, include: Determine multiple viewpoint combinations; the viewpoint combinations include multiple viewpoint parameters; the viewpoint parameters are used to characterize multiple observation angles; Based on the models corresponding to multiple viewpoint combinations, the theoretical differences in the specified radiation corresponding to the multiple viewpoint combinations are determined; the models are used to characterize the correspondence between leaf area index and specified radiation. The theoretical difference is used to characterize the difference between the simulated values ​​and the observed values; the simulated values ​​are obtained based on the model simulating the specified radiation; the observed values ​​are obtained based on remote sensing observations of the specified radiation. The target viewpoint combination is determined based on the theoretical differences in the specified radiation corresponding to the multiple viewpoint combinations. Based on the model corresponding to the target view combination, the inversion result of the leaf area index is obtained; The determination of the target viewpoint combination, based on the theoretical differences in the specified radiation corresponding to the multiple viewpoint combinations, includes: From the multiple perspective combinations, the perspective combination with a theoretical difference less than a first threshold is selected as the first perspective combination; For each selected first viewpoint combination, obtain the theoretical difference of the specified radiation corresponding to each viewpoint parameter in the first viewpoint combination; If the theoretical difference corresponding to each of the aforementioned viewpoint parameters is less than the first threshold, then the first viewpoint combination is determined as the second viewpoint combination. For each selected second viewpoint combination, obtain the difference between the specified radiations corresponding to each viewpoint parameter in the second viewpoint combination; If the difference between the specified radiations corresponding to each of the aforementioned viewpoint parameters is less than or equal to the second threshold, then the second viewpoint combination is determined as the target viewpoint combination to avoid the influence of invalid observation angle combinations on LAI inversion.

2. The method according to claim 1, characterized in that, Based on the model corresponding to the target viewpoint combination, the inversion result of the leaf area index is obtained, including: Based on the model corresponding to the target viewpoint combination, determine the simulated value of the specified radiation corresponding to the target viewpoint combination; Remote sensing observations are performed based on the observation angles shown by the target viewpoint combination to obtain the observed values ​​of the specified radiation corresponding to the target viewpoint combination. The inversion result of the leaf area index is determined based on the difference between the simulated and observed values ​​of the specified radiation corresponding to the target viewpoint combination.

3. A collaborative multi-angle remote sensing observation LAI inversion device, characterized in that, include: A viewing angle determination unit is used to determine multiple viewing angle combinations; the viewing angle combination includes multiple viewing angle parameters; the viewing angle parameters are used to characterize multiple observation angles. The difference determination unit is used to determine the theoretical difference of a specified radiation corresponding to multiple viewpoint combinations based on the model corresponding to the multiple viewpoint combinations; the model is used to characterize the correspondence between leaf area index and specified radiation; The theoretical difference is used to characterize the difference between the simulated values ​​and the observed values; the simulated values ​​are obtained based on the model simulating the specified radiation; the observed values ​​are obtained based on remote sensing observations of the specified radiation. The combination selection unit is used to determine the target viewpoint combination based on the theoretical differences between the specified radiations corresponding to the multiple viewpoint combinations. The model inversion unit is used to obtain the inversion result of the leaf area index based on the model corresponding to the target view combination. Specifically, the combination selection unit is used for: From the multiple perspective combinations, the perspective combination with a theoretical difference less than a first threshold is selected as the first perspective combination; For each selected first viewpoint combination, obtain the theoretical difference of the specified radiation corresponding to each viewpoint parameter in the first viewpoint combination; If the theoretical difference corresponding to each of the aforementioned viewpoint parameters is less than the first threshold, then the first viewpoint combination is determined as the second viewpoint combination. For each selected second viewpoint combination, obtain the difference between the specified radiations corresponding to each viewpoint parameter in the second viewpoint combination; If the difference between the specified radiations corresponding to each of the aforementioned viewpoint parameters is less than or equal to the second threshold, then the second viewpoint combination is determined as the target viewpoint combination to avoid the influence of invalid observation angle combinations on LAI inversion.

4. The apparatus according to claim 3, characterized in that, The model inversion unit is specifically used for: Based on the model corresponding to the target viewpoint combination, determine the simulated value of the specified radiation corresponding to the target viewpoint combination; Remote sensing observations are performed based on the observation angles shown by the target viewpoint combination to obtain the observed values ​​of the specified radiation corresponding to the target viewpoint combination. The inversion result of the leaf area index is determined based on the difference between the simulated and observed values ​​of the specified radiation corresponding to the target viewpoint combination.

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