Chlorophyll fluorescence observation system based on laser radar

Abstract

The invention relates to the technical field of vegetation remote sensing observation, in particular to a chlorophyll fluorescence observation system based on laser radar, which comprises a target evaluation module used for responding to an observation evaluation condition to determine an observation analysis strategy of each observation target; the observation analysis module is used for determining the dominant direction of the mirror surface of the observation target and determining an inversion execution strategy of the observation target in response to the inversion execution condition; the dominant inversion module is used for determining a pre-estimation execution wave band set and determining observation pre-estimation data based on the polarization reflection parameters of the pre-estimation execution wave bands; the multi-parameter inversion module is used for determining an inversion weight coefficient of each observation wave band and determining whether to adjust the inversion weight index of each observation wave band in response to a weight analysis condition; the auxiliary analysis module is used for responding to the auxiliary analysis condition to determine the auxiliary analysis mode of the observation target, and the reliability of the fluorescence observation result of the observation target is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing observation of vegetation, and in particular to a chlorophyll fluorescence observation system based on lidar. Background Art

[0002] Nowadays, chlorophyll fluorescence observation and research on vegetation crops are widely used in the tracking of photosynthesis and the monitoring of vegetation stress. Chlorophyll fluorescence observation can effectively reflect the actual photosynthesis of crops, thereby providing effective data basis for the subsequent determination of the growth state of vegetation crops. However, during the chlorophyll fluorescence observation of vegetation crops, the leaf reflection and branch state of the crops can both interfere with the acquired fluorescence data. Therefore, how to effectively analyze the fluorescence data by analyzing the actual state and directional reflection characteristics of vegetation crops to ensure the evaluation quality of the photosynthetic productivity of crops is an urgent problem to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN108693154A discloses a method for accurately retrieving the sun-induced chlorophyll fluorescence of shaded and sunlit leaves in a vegetation canopy using spectral data of the vegetation canopy obtained by a multi-angle observation system, belonging to the research field of methods for obtaining vegetation remote sensing inversion parameters. The steps are as follows: establishment of a multi-angle hyperspectral observation system; acquisition of multi-angle hyperspectral data; calculation of solar incident and canopy reflected radiance; calculation of canopy reflectance and inversion of chlorophyll fluorescence; measurement of leaf reflectance using a leaf clip; calculation of the proportion of shaded and sunlit leaves at different observation angles using the ratio of canopy reflectance and leaf reflectance and combining with a geometric optical model; and fitting of the fluorescence of shaded and sunlit leaves by the least squares method. However, the above solution has the following problems: it fails to specifically analyze the fluorescence data obtained during the observation based on the crop state and directional reflection characteristics of different observation targets, resulting in poor reliability of the subsequent fluorescence observation results of vegetation crops. Summary of the Invention

[0004] Therefore, the present invention provides a chlorophyll fluorescence observation system based on lidar to overcome the problem in the prior art that the fluorescence data of crops obtained during the observation cannot be specifically analyzed based on the crop state and directional reflection characteristics of different observation targets, resulting in poor reliability of the subsequent fluorescence observation results of vegetation crops.

[0005] To achieve the above object, the present invention provides a chlorophyll fluorescence observation system based on lidar, comprising: A target evaluation module, which is used to respond to the observation evaluation condition to determine the observation analysis strategy for each observation target, that is, to perform inversion prediction analysis on the fluorescence observation data of the observation target by using the observation feature analysis method, or to perform rationality analysis on the fluorescence observation data of the observation target by using the auxiliary compliance analysis method; An observation analysis module, which is connected to the target evaluation module, is used to determine the specular dominant direction of the observation target, and respond to the inversion execution condition to determine the inversion execution strategy of the observation target as determining the set of predicted execution bands based on the approximate stability index and the proportion of the dominant reflection component, or determining the inversion weight coefficient of each observation band based on the observation variation difference degree and the feature coincidence degree; A dominant inversion module, which is connected to the observation analysis module, is used to determine the set of predicted execution bands and determine the observation prediction data based on the polarization reflection parameters of the predicted execution bands; A multi-parameter inversion module, which is connected to the observation analysis module, is used to determine the inversion weight coefficient of each observation band, and respond to the weight analysis condition to determine whether to adjust the inversion weight index of each observation band; An auxiliary analysis module, which is connected to the target evaluation module, is used to respond to the auxiliary analysis condition to determine the auxiliary analysis method for the observation target in the secondary observation analysis state as determining whether to perform growth limit warning on the observation target according to the reference observation stability coefficient, or determining the observation quality index of the observation target according to the conflict difference proportion and the change correlation parameter.

[0006] Furthermore, the target evaluation module determines the reflection complexity coefficient of each observation target according to the echo difference parameter and the echo standard difference parameter; The target evaluation module determines the observation complexity coefficient of each observation target according to the target element richness index and the canopy complexity index.

[0007] Furthermore, if the observation evaluation condition responded by the target evaluation module is that the observation target is in the primary observation analysis state, it is determined that the observation analysis module performs inversion prediction analysis on the fluorescence observation data of the observation target by using the observation feature analysis method; The observation analysis module responds to the feature analysis condition, determines the specular dominant direction of the observation target according to the observation polarization parameter, and determines the inversion execution strategy of the observation target based on the dominant direction proportion; The feature analysis condition is that the target evaluation module determines that the observation analysis module performs inversion prediction analysis on the fluorescence observation data of the observation target by using the observation feature analysis method, and the primary observation analysis state is that the reflection complexity coefficient of the observation target is greater than the preset reflection complexity coefficient or the observation complexity coefficient is greater than the preset observation complexity coefficient.

[0008] Further, the inversion execution condition responded by the observation and analysis module is that the dominant direction ratio is greater than the preset dominant direction ratio, then it is determined that the dominant inversion module determines the estimated execution band set based on the approximate stability index and the dominant reflection component ratio; The dominant inversion module responds to the estimated execution condition, and determines the observation estimated data based on the polarization reflection parameters of each estimated execution band in the estimated execution band set; The estimated execution condition is that the observation target completes the determination of the estimated execution band set.

[0009] Further, the inversion execution condition responded by the observation and analysis module is that the dominant direction ratio is less than or equal to the preset dominant direction ratio, then it is determined that the multi-parameter inversion module determines the inversion weight coefficient of each observation band based on the observation variation degree and the feature coincidence degree; The inversion weight coefficient has a negative correlation with the observation variation degree, and the inversion weight coefficient has a positive correlation with the feature coincidence degree.

[0010] Further, the multi-parameter inversion module responds to the multi-parameter execution condition, performs band weight analysis on each mirror dominant direction, and determines whether to adjust the inversion weight index of each observation band according to the dominant reflection component ratio; The weight analysis condition responded by the multi-parameter inversion module is that the dominant reflection component ratio of an observation band is less than the preset dominant reflection component ratio, then the inversion weight index of this observation band is increased and adjusted according to the dominant reflection component ratio; The multi-parameter execution condition is that the observation and analysis module determines that the multi-parameter inversion module determines the inversion weight coefficient of each observation band.

[0011] Further, the observation evaluation condition responded by the target evaluation module is that the observation target is in the secondary observation analysis state, then it is determined that the auxiliary analysis module uses the auxiliary compliance analysis method to perform a rationality analysis on the fluorescence observation data of the observation target; The auxiliary analysis module responds to the auxiliary correction condition and determines the auxiliary analysis method based on the distribution feature correlation degree of the conflict data points; The auxiliary correction condition is that the target evaluation module determines that the auxiliary analysis module uses the auxiliary compliance analysis method to correct the observation data of the observation target, and the secondary observation analysis state is that the reflection complexity coefficient of the observation target is less than or equal to the preset reflection complexity coefficient and the observation complexity coefficient is less than or equal to the preset observation complexity coefficient.

[0012] Further, the auxiliary analysis condition responded by the auxiliary analysis module is that the distribution feature correlation degree of the observation target is greater than the preset distribution feature correlation degree, then the observation stability coefficient of each conflict data point is detected, and it is determined whether to issue a growth restriction warning for the observation target according to the reference observation stability coefficient; The observation stability coefficient is determined according to the intensity change degree of the fluorescence intensity of the reflection data acquired each time during the observation evaluation stage.

[0013] Furthermore, if the restriction analysis condition responded by the auxiliary analysis module is that the reference observation stability coefficient is less than or equal to the preset reference observation stability coefficient, it is determined that a growth restriction warning is issued for the observation target; If the limiting analysis condition of the auxiliary analysis module response is that the reference observation stability coefficient is greater than the preset reference observation stability coefficient, the observation quality index of the observation target is determined based on the conflict difference ratio; The observation quality index is negatively correlated with the conflict difference ratio.

[0014] Furthermore, if the auxiliary analysis condition responded by the auxiliary analysis module is that the distribution feature correlation of the observed target is less than or equal to the preset distribution feature correlation, the observation quality index of the observed target is determined according to the conflict difference ratio and the change association parameter; The observation quality index is positively correlated with the change association parameter, and the observation quality index is negatively correlated with the conflict difference ratio.

[0015] Compared with the prior art, the beneficial effect of the present invention lies in that the technical scheme of the present invention determines the observation and analysis state of each observation target according to the reflection complexity coefficient and the observation complexity coefficient, and determines the observation and analysis strategy of each observation target according to the observation and analysis state, so that the analysis process performed on the acquired observation data is more in line with the actual state of the observation target. The present invention ensures the reliability of the fluorescence observation results of the observation target, thereby improving the estimated quality of the photosynthesis productivity of vegetation crops.

[0016] Furthermore, the present invention determines the observation and analysis status of each observed crop based on the reflection complexity coefficient and the observation complexity coefficient, so as to characterize the degree to which the observation results are affected by the leaf directional reflection characteristics during the chlorophyll fluorescence observation of the observation target and the degree to which the branch and leaf segmentation interferes with the analysis process of the observation results, thereby determining the reliability of the acquired observation data and the dominant cause of the deviation, providing a preliminary basis for determining the observation and analysis strategy of each observation target in a targeted manner, and ensuring that the executed analysis process is more in line with the actual state of the observation target.

[0017] Furthermore, in the present invention, for the observation target in a type of observation and analysis state, the observation feature analysis method is used to perform inversion and estimation analysis on the fluorescence observation data of the observation target, and a targeted inversion execution strategy is determined according to the proportion of the dominant direction, so that the inversion process of the chlorophyll content based on the reflection data obtained by the observation is more in line with the actual scene. The present invention improves the reliability of the inversion results of the chlorophyll content of the observation target.

[0018] Furthermore, for the observation targets in the secondary observation and analysis state in the present invention, an auxiliary compliance analysis method is adopted to analyze the rationality of the fluorescence observation data of the observation targets. The observation data of such observation targets is not continuously interfered. If there are conflicting data points, it is necessary to analyze its rationality, determine the auxiliary analysis method according to the correlation degree of the distribution characteristics of the conflicting data points. If the correlation degree of the distribution characteristics is large, it indicates that their growth limiting conditions are similar. Determine whether there are growth limiting factors according to their observation stability, and determine whether to give an early warning. If the correlation degree of the distribution characteristics is large or the observation stability is relatively good, make a targeted judgment on the observation quality of the observation targets. The present invention ensures the reliability of the fluorescence observation results of the observation targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a module connection diagram of the chlorophyll fluorescence observation system based on lidar of the present invention; Figure 2 It is a flowchart of the target evaluation module of the present invention for determining the observation and analysis strategies of each observation target in response to the observation evaluation conditions; Figure 3 It is a flowchart of the observation analysis module of the present invention for determining the inversion execution strategy of the observation target in response to the inversion execution conditions; Figure 4 It is a flowchart of the auxiliary analysis module of the present invention for determining the auxiliary analysis method in response to the auxiliary analysis conditions. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0022] It should be noted that in the description of the present invention, the terms indicating the direction or positional relationship such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention.

[0023] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "linkage" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] Please refer to Figures 1 to 4 As shown, the present invention provides a chlorophyll fluorescence observation system based on lidar, including: A target evaluation module, which is used to respond to the observation evaluation condition to determine the observation analysis strategy for each observation target. The strategy can be to perform inversion prediction analysis on the fluorescence observation data of the observation target by using the observation feature analysis method, or to perform rationality analysis on the fluorescence observation data of the observation target by using the auxiliary compliance analysis method; An observation analysis module, which is connected to the target evaluation module, is used to determine the specular dominant direction of the observation target, and respond to the inversion execution condition to determine the inversion execution strategy for the observation target. The strategy can be to determine the set of prediction execution bands based on the approximate stability index and the proportion of the dominant reflection component, or to determine the inversion weight coefficient of each observation band based on the observation variation difference degree and the feature coincidence degree; A dominant inversion module, which is connected to the observation analysis module, is used to determine the set of prediction execution bands and determine the observation prediction data based on the polarization reflection parameters of the prediction execution bands; A multi-parameter inversion module, which is connected to the observation analysis module, is used to determine the inversion weight coefficient of each observation band and respond to the weight analysis condition to determine whether to adjust the inversion weight index of each observation band; An auxiliary analysis module, which is connected to the target evaluation module, is used to respond to the auxiliary analysis condition to determine the auxiliary analysis method for the observation target in the secondary observation analysis state. The method can be to determine whether to perform growth restriction early warning on the observation target according to the reference observation stability coefficient, or to determine the observation quality index of the observation target according to the conflict difference ratio and the change correlation parameter.

[0025] Among them, the present invention is used to correct the chlorophyll fluorescence observation data of the obtained observation target to ensure an accurate determination of the growth situation of vegetation crops. In the present invention, the target observation area is the area covered by various vegetation crops that need to be observed for fluorescence. In the present invention, the observation target is the vegetation crop that needs to be observed for chlorophyll fluorescence. The fluorescence observation data obtained in the present invention includes, but is not limited to, the fluorescence reflection signal and the fluorescence intensity of the fluorescence reflection data; In the present invention, a cyclic observation and evaluation period is applied. The duration of the observation and evaluation period can be determined by the user himself. A duration of the observation and evaluation period is provided. The observation and evaluation period is 10 days. At the end of each observation and evaluation period, the target evaluation module determines the observation and analysis states of each observation target according to the reflection complexity coefficient and the observation complexity coefficient, and determines the observation and analysis strategies of each observation target according to the observation and analysis states. In the present invention, there are several observation management records. Any one of the observation management records records at least one reflection complexity coefficient, observation complexity coefficient, observation polarization parameter, dominant direction ratio, dominant reflection component ratio, inversion band coefficient, observation conflict coefficient, distribution feature correlation degree, reference observation stability coefficient, and observation quality index during the calibration process of the chlorophyll fluorescence observation results of the observation target. And each observation management record corresponds to a qualified mark, and the qualified mark records whether the reliability of the fluorescence observation results of the observation target meets the user's requirements. It can be understood that the user can determine whether the reliability of the fluorescence observation results of the observation target meets the requirements according to the self-set index. For example, the self-set index can be, but is not limited to, the productivity evaluation difference index, and the productivity evaluation difference index is the difference value between the crop photosynthesis productivity determined based on the fluorescence observation data and the crop photosynthesis productivity determined based on the normalized difference vegetation index.

[0026] Specifically, the target evaluation module determines the reflection complexity coefficient of each observation target according to the echo difference parameter and the echo standard difference parameter. The target evaluation module determines the observation complexity coefficient of each observation target according to the target element richness index and the canopy complexity index.

[0027] Among them, for a single observation target, the reflection complexity coefficient = ln (echo difference parameter × echo standard difference parameter), and the echo difference parameter , where m is the number of observation incident angles involved in monitoring the observation target, hj is the echo intensity measured at the j-th observation incident angle, h0 is the average value of the echo intensities measured at each observation incident angle, and the echo standard deviation parameter is the average value of the standard deviation values of each observation incident angle. For a single observation incident angle, the standard deviation value is the absolute value of the difference between the echo intensity measured at this observation incident angle and the standard echo intensity of this observation incident angle. The standard echo intensity is the echo intensity of the standard reflector at this observation incident angle. The observation incident angle is the included angle formed between the incident wave and the horizontal plane where the observation target is located. The echo intensity is the intensity of the reflected wave. How to detect the echo intensity and the standard echo intensity of each observation incident angle is easily understood by those skilled in the art and will not be elaborated here. The observation complexity coefficient is determined according to the target element richness index and the canopy complexity index of the observation target of this crop category. Observation complexity coefficient = ln (canopy complexity index / target element richness index). The canopy complexity index is the absolute value of the difference between the maximum and minimum values of the leaf inclination angles obtained for the observation target during the current observation and evaluation period. The target element richness index is the average value of the chlorophyll content obtained for the leaves of the crop category where the observation target is located during the growth stage corresponding to the current observation and evaluation period in the observation management record. How to determine the leaf inclination angle of the leaves of the key target is already mastered by those skilled in the art and will not be elaborated here.

[0028] Specifically, when the observation evaluation condition responded by the target evaluation module is that the observation target is in a first-class observation analysis state, it is determined that the observation analysis module uses the observation feature analysis method to perform inversion prediction analysis on the fluorescence observation data of the observation target; The observation analysis module responds to the feature analysis condition, determines the specular dominant direction of the observation target according to the observation polarization parameter, and determines the inversion execution strategy of the observation target based on the dominant direction ratio; The feature analysis condition is that the target evaluation module determines that the observation analysis module uses the observation feature analysis method to perform inversion prediction analysis on the fluorescence observation data of the observation target. The first-class observation analysis state is that the reflection complexity coefficient of the observation target is greater than the preset reflection complexity coefficient or the observation complexity coefficient is greater than the preset observation complexity coefficient.

[0029] Among them, the values of the preset reflection complexity coefficient and the preset observation complexity coefficient can be determined by the user according to the actual working scenario. For example, the user can set them according to the observation management record. The higher the reliability requirement of the user for the fluorescence observation result of the observation target, the smaller the value of the preset reflection complexity coefficient and the smaller the value of the preset observation complexity coefficient. A method for obtaining the value of the preset reflection complexity coefficient is provided. The observation management record that uses the observation feature analysis method to perform inversion prediction analysis on the fluorescence observation data of the observation target is recorded as the evaluation reference record. The average value of the reflection complexity coefficients of the observation target in the evaluation reference record that meets the validity of the correction result of the fluorescence observation data of the observation target by the user is recorded as the preset reflection complexity coefficient. A method for obtaining the value of the preset observation complexity coefficient is provided. The average value of the observation complexity coefficients of the observation target in the evaluation reference record that meets the validity of the correction result of the fluorescence observation data of the observation target by the user is recorded as the preset observation complexity coefficient; If the observation target is in a first-class observation analysis state, it indicates that the process of chloroplast fluorescence observation for this type of observation target is continuously affected. Through the observation feature analysis method, inversion prediction analysis is performed on the fluorescence observation data. According to the proportion of the dominant direction of the observation target, a targeted inversion execution strategy is determined to screen the acquired observation data to determine the data used for chlorophyll content inversion prediction to ensure the reliability of the inversion result. For a single observation target in the first-class observation analysis state, according to the proportion of the dominant reflection component of the crop category corresponding to the observation target in the observation management record in each observation direction, the observation polarization parameter of each observation direction is determined. Each observation direction corresponds to only a single observation incident angle. For a single observation direction, the observation polarization parameter is the average value of the proportion of the reference dominant reflection component determined for the crop category corresponding to the observation target in the observation management record in this observation direction. The proportion of the reference dominant reflection component is the average value of the proportions of the dominant reflection components of each observation band in this observation direction. The observation direction with the observation polarization parameter greater than the preset observation polarization parameter is recorded as the specular dominant direction. The proportion of the dominant direction = the number of specular dominant directions determined in the current observation evaluation period / the number of observation directions used in the current observation evaluation period; The value of the preset observation polarization parameter can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the reliability requirement of the user for the fluorescence observation result of the observation target, the smaller the value of the preset observation polarization parameter. A method for obtaining the value of the preset observation polarization parameter is provided. The average value of the observation polarization parameters of each specular dominant direction in the observation management record that meets the reliability requirement of the fluorescence observation result of the observation target by the user is recorded as the preset observation polarization parameter.

[0030] Specifically, the inversion execution condition responded by the observation and analysis module is that the dominant direction ratio is greater than the preset dominant direction ratio, then it is determined that the dominant inversion module determines the estimated execution band set based on the approximate stability index and the dominant reflection component ratio; The dominant inversion module responds to the estimated execution condition and determines the observation estimated data based on the polarization reflection parameters of each estimated execution band in the estimated execution band set; The estimated execution condition is that the observation target completes the determination of the estimated execution band set.

[0031] Among them, for the value of the preset dominant direction ratio, the user can determine it according to the actual working scenario. For example, the user can set it according to the observation management record, and a method for obtaining the value of the preset dominant direction ratio is provided. The observation management record for determining the observation estimated data based on the polarization reflection parameters of each estimated execution band is recorded as the dominant inversion record, and the minimum value of the dominant direction ratio in the dominant inversion record that meets the user's reliability requirement for the fluorescence observation result of the observation target is recorded as the preset dominant direction ratio; When the dominant direction ratio is greater than the preset dominant direction ratio, the mirror dominant direction with the smallest reference dominant reflection component ratio is recorded as the estimated analysis direction, and the estimated execution band set of the estimated analysis direction is determined according to the approximate stability index. Any determined estimated execution band set includes three observation bands, and it is ensured that the approximate stability index of the determined estimated execution band set is the maximum value. For a single estimated execution fluctuation set, the approximate stability index = 1 / (band difference parameter + polarization reflection difference parameter). The band difference parameter is the absolute value of the difference between the maximum wavelength and the minimum wavelength in the estimated execution fluctuation set, and the polarization reflection difference parameter is the absolute value of the difference between the maximum value and the minimum value of the polarization reflection parameters of each observation band in the estimated execution band set. The observation estimated data , y1, y2, and y3 are respectively the reciprocals of the dominant reflection component ratios corresponding to each observation band in the determined estimated execution band set. The determined observation estimated data can be used as the spectral index for the chlorophyll content determined by inversion for the observation target. How to determine the chlorophyll content based on the spectral index is easily understood by those skilled in the art and will not be elaborated here.

[0032] Specifically, the inversion execution condition responded by the observation and analysis module is that the dominant direction ratio is less than or equal to the preset dominant direction ratio, then it is determined that the multi-parameter inversion module determines the inversion weight coefficient of each observation band based on the observation variation degree and the feature coincidence degree; The inversion weight coefficient has a negative correlation with the observation variation degree, and the inversion weight coefficient has a positive correlation with the feature coincidence degree.

[0033] Specifically, the multi-parameter inversion module responds to the multi-parameter execution condition, performs band weight analysis for each mirror dominant direction, and determines whether to adjust the inversion weight index for each observation band according to the proportion of the dominant reflection component; The weight analysis condition responded by the multi-parameter inversion module is that the proportion of the dominant reflection component of an observation band is less than the preset proportion of the dominant reflection component, and then the inversion weight index for this observation band is increased according to the proportion of the dominant reflection component; The multi-parameter execution condition is that the observation analysis module determines the inversion weight coefficients of each observation band determined by the multi-parameter inversion module.

[0034] Among them, when the proportion of the dominant direction is less than or equal to the preset proportion of the dominant direction, for a single mirror dominant direction, based on the observation variation difference degree and the feature coincidence degree, determine the inversion weight coefficients of each observation band of this mirror dominant direction, and detect the proportion of the dominant reflection component of each observation band during the observation of this mirror dominant direction. The inversion weight coefficient of any observation band of this mirror dominant direction has a positive correlation with the reflection effective parameter. The reflection effective parameter = ln(feature coincidence degree / observation variation difference degree), the feature coincidence degree = the absolute value of the difference between the maximum wavelength and the minimum wavelength of the coincidence range of this observation band and the observation feature band / the absolute value of the difference between the maximum wavelength and the minimum wavelength of this observation band. The observation feature band is the set of observation bands that are all inversion preferred bands corresponding to the crop category of this observation target in the observation management record. The observation variation difference degree is the absolute value of the difference between the maximum value and the minimum value of the proportion of the dominant reflection component of each observation band of this mirror dominant direction; For a single observation band, if the proportion of the dominant reflection component of this observation band is less than the preset proportion of the dominant reflection component, then the inversion weight index is increased according to the proportion of the dominant reflection component. The increase value of the inversion weight index has a negative correlation with the proportion of the dominant reflection component. For a single observation band, the proportion of the dominant reflection component = polarized reflection component / (polarized reflection component + non-polarized reflection component). How to extract the polarized reflection component and the non-polarized reflection component from the reflection data obtained for each observation band is easily understood by those skilled in the art and will not be elaborated here; For the observation bands where the proportion of the dominant reflection component is relatively small, after separating the specular reflection component, the reflection data obtained from such observation bands is relatively complete. Therefore, the inversion weight coefficients for such observation bands are further adjusted to further improve the reliability of the determined observation results. The value of the preset proportion of the dominant reflection component can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirement for the reliability of the fluorescence observation results of the observation target, the smaller the value of the preset proportion of the dominant reflection component. A method for determining the value of the preset proportion of the dominant reflection component is provided. The observation management record that increases the inversion weight index for the observation band according to the proportion of the dominant reflection component is recorded as the weight regulation record. The average value of the proportion of the dominant reflection component of each observation band in the weight regulation record that meets the user's requirement for the reliability of the fluorescence observation results of the observation target is recorded as the preset proportion of the dominant reflection component; The observation bands with inversion weight coefficients greater than the preset inversion weight coefficient are recorded as the preferred inversion bands of the observation target. The observation results of the chlorophyll content of the observation target in the current observation evaluation period are determined based on the reflection data obtained from the preferred inversion bands. This is easily understood by those skilled in the art and will not be elaborated here. The value of the preset inversion weight coefficient can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirement for the reliability of the fluorescence observation results of the observation target, the larger the value of the preset inversion weight coefficient. A method for determining the value of the preset inversion weight coefficient is provided. The average value of the inversion band coefficients of each preferred inversion band in the observation management record that meets the user's requirement for the reliability of the fluorescence observation results of the observation target is recorded as the preset inversion band coefficient.

[0035] Specifically, when the observation evaluation condition responded by the target evaluation module is that the observation target is in the secondary observation analysis state, it is determined that the auxiliary analysis module uses the auxiliary compliance analysis method to analyze the rationality of the fluorescence observation data of the observation target; The auxiliary analysis module responds to the auxiliary correction condition and determines the auxiliary analysis method based on the distribution feature correlation of the conflict data points; The auxiliary correction condition is that the target evaluation module determines that the auxiliary analysis module uses the auxiliary compliance analysis method to correct the observation data of the observation target. The secondary observation analysis state is that the reflection complexity coefficient of the observation target is less than or equal to the preset reflection complexity coefficient and the observation complexity coefficient is less than or equal to the preset observation complexity coefficient.

[0036] Among them, if the observation target is in the second-class observation and analysis state, it indicates that the interference during the chlorophyll fluorescence observation of such an observation target is relatively small, and it is relatively easy to divide the branches and leaves of the observation target based on the acquired point cloud data to determine the growth position information corresponding to the fluorescence observation data of each part obtained. Therefore, an auxiliary compliance analysis method is used to correct the fluorescence observation data of the observation target to assist in verifying the fluorescence observation data of each part obtained, further ensuring the reliability of the acquired fluorescence observation data; When using the auxiliary compliance analysis method to correct the observation data of the observation target, the correlation degree of the distribution characteristics of the conflict data points of the observation target is detected, and the auxiliary analysis method of the observation target is determined according to the distribution characteristic correlation degree. The conflict data points are the observation points where the observation conflict coefficient is greater than the preset observation conflict coefficient. For a single observation point, the observation conflict coefficient is the average value of the absolute value of the difference between the fluorescence intensity of the reflection data obtained at this observation point and the fluorescence intensity of the reflection data obtained within the conflict evaluation range. The distance between any observation point within the conflict evaluation range and the observation point for which the observation conflict coefficient is determined is less than the preset conflict evaluation distance. The value of the preset conflict evaluation distance can be set by the user according to the actual working scenario. A value of the preset conflict evaluation distance is provided, which is five times the maximum length of the leaf corresponding to the observation target; The value of the preset observation conflict coefficient can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirement for the reliability of the fluorescence observation result of the observation target, the smaller the value of the preset observation conflict coefficient. A method for determining the value of the preset observation conflict coefficient is provided, which is to record the minimum value of the observation conflict coefficients of each conflict data point in the observation management record that meets the user's requirement for the reliability of the fluorescence observation result of the observation target as the preset observation conflict coefficient; The distribution characteristic correlation degree is determined according to the distribution position index of each conflict data point. For a single conflict data point, the distribution position index is the distance between the position connecting this conflict data point and the main trunk of the observed crop. The distribution characteristic correlation degree = 1 / (distribution fluctuation parameter + distribution position difference parameter). The distribution position difference parameter = (the maximum value of the distribution position indices of each conflict data point - the minimum value of the distribution position indices of each conflict data point) / the average value of the distribution position indices of each conflict data point. The distribution fluctuation parameter , where n is the number of conflict data points of the observation target, fi is the distribution position index of the i-th conflict data point, and f0 is the average value of the distribution position indices of each conflict data point.

[0037] Specifically, the auxiliary analysis condition responded by the auxiliary analysis module is that the correlation degree of the distribution characteristics of the observation target is greater than the preset distribution characteristics correlation degree. Then, the observation stability coefficient of each conflict data point is detected, and it is determined whether to give a growth restriction warning for the observation target according to the reference observation stability coefficient; The observation stability coefficient is determined according to the intensity change degree of the fluorescence intensity of the reflection data obtained each time during the observation evaluation stage.

[0038] Among them, the value of the preset distribution characteristics correlation degree can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirement for the reliability of the fluorescence observation result of the observation target, the larger the value of the preset characteristic correlation degree. A method for obtaining the value of the preset observation conflict coefficient is provided. The observation management record for determining whether to give a growth restriction warning for the observation target according to the observation stability coefficient of each conflict data point is recorded as the warning evaluation record. The minimum value of the distribution characteristics correlation degree of the observation target in the warning evaluation record that meets the user's requirement for the reliability of the fluorescence observation result of the observation target is recorded as the preset distribution characteristics correlation degree; The reference observation stability coefficient is the average value of the observation stability coefficients of each conflict data point of the observation target. For a single conflict data point, the observation stability coefficient = 1 / the average value of each intensity change degree during the stability evaluation stage. The intensity change degree of the fluorescence intensity of the reflection data obtained each time during the observation evaluation stage is detected. For the fluorescence intensity of the reflection data obtained once, the intensity change degree = |the fluorescence intensity of the reflection data obtained this time - the fluorescence intensity of the reflection data obtained last time| / the fluorescence intensity of the reflection data obtained last time. How to determine the fluorescence intensity of the reflection data obtained each time is easy for those skilled in the art to understand. The end time of the observation evaluation stage is the end time of the current observation management cycle. The duration of the observation evaluation stage can be set by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirement for the reliability of the fluorescence observation result of the observation target, the longer the duration of the observation evaluation stage. A duration of the observation evaluation stage is provided. The duration of the observation evaluation stage is 5 times the observation evaluation period.

[0039] Specifically, the restriction analysis condition responded by the auxiliary analysis module is that the reference observation stability coefficient is less than or equal to the preset reference observation stability coefficient, then it is determined that a growth restriction warning is given for the observation target; The restriction analysis condition responded by the auxiliary analysis module is that the reference observation stability coefficient is greater than the preset reference observation stability coefficient, then the observation quality index of the observation target is determined based on the conflict difference ratio; The observation quality index and the conflict difference ratio have a negative correlation.

[0040] Among them, the value of the preset reference observation stability coefficient can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. A method for obtaining the value of the preset reference observation stability coefficient is provided. The observation management record for growth limit warning of the observation target is recorded as the warning reference record, and the maximum value of the reference observation stability coefficient in the warning reference record that meets the user's reliability requirement for the fluorescence observation result of the observation target is recorded as the preset reference observation stability coefficient. When the reference observation stability coefficient is greater than the preset reference observation stability coefficient, the proportion of conflict differences for the observation target is detected. The proportion of conflict differences = the number of conflict data points that do not exist simultaneously in the current observation evaluation period and the previous observation management period / the number of conflict data points in the current observation evaluation period.

[0041] Specifically, when the auxiliary analysis condition responded to by the auxiliary analysis module is that the distribution feature correlation degree of the observation target is less than or equal to the preset distribution feature correlation degree, the observation quality index of the observation target is determined according to the proportion of conflict differences and the change correlation parameter; The observation quality index and the change correlation parameter are in a positive correlation relationship, and the observation quality index and the proportion of conflict differences are in a negative correlation relationship.

[0042] Among them, when the distribution feature correlation degree is less than or equal to the preset distribution feature correlation degree, the observation quality index and the quality reference coefficient are in a positive correlation relationship. The quality reference coefficient = the change correlation parameter / the proportion of conflict differences. The change correlation parameter is the average value of the change correlation degrees of each newly added conflict data point in the current observation evaluation period. For a single newly added conflict data point, the change correlation degree is the number of conflict data points within the conflict evaluation range of this conflict data point in the previous observation evaluation period. If the observation quality index is less than the preset observation quality index, an observation quality warning is sent to the user, indicating that the determined observation result is abnormal; The value of the preset observation quality index can be determined by the user according to the actual working scenario. For example, the user can set it according to the observation management record. The higher the user's requirement for the reliability of the fluorescence observation result of the observation target, the larger the value of the preset observation quality index. A method for obtaining the value of the preset observation quality index is provided. The observation management record for which an observation quality warning is sent to the user is recorded as the quality reference record, and the minimum value of the observation quality index in the quality reference record that meets the user's reliability requirement for the fluorescence observation result of the observation target is recorded as the preset observation quality index.

[0043] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, those skilled in the art can easily understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0044] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A lidar-based chlorophyll fluorescence observation system, characterized in that, Including: A target evaluation module, which is used to respond to the observation evaluation condition to determine that the observation analysis strategy for each observation target is to perform inversion prediction analysis on the fluorescence observation data of the observation target by using the observation feature analysis method, or to perform rationality analysis on the fluorescence observation data of the observation target by using the auxiliary compliance analysis method; An observation analysis module, which is connected to the target evaluation module, is used to determine the specular dominant direction of the observation target, and respond to the inversion execution condition to determine that the inversion execution strategy for the observation target is to determine the set of predicted execution bands based on the approximate stability index and the proportion of the dominant reflection component, or to determine the inversion weight coefficient of each observation band based on the observation variation difference degree and the feature coincidence degree; A dominant inversion module, which is connected to the observation analysis module, is used to determine the set of predicted execution bands and determine the observation prediction data based on the polarization reflection parameters of the predicted execution bands; A multi-parameter inversion module, which is connected to the observation analysis module, is used to determine the inversion weight coefficient of each observation band, and respond to the weight analysis condition to determine whether to adjust the inversion weight index of each observation band; An auxiliary analysis module, which is connected to the target evaluation module, is used to respond to the auxiliary analysis condition to determine that the auxiliary analysis method for the observation target in the secondary observation analysis state is to determine whether to perform growth limit warning on the observation target according to the reference observation stability coefficient, or to determine the observation quality index of the observation target according to the conflict difference ratio and the change correlation parameter.

2. The chlorophyll fluorescence observation system based on lidar according to claim 1, characterized in that, The target evaluation module determines the reflection complexity coefficient of each observation target according to the echo difference parameter and the echo standard difference parameter; The target evaluation module determines the observation complexity coefficient of each observation target according to the target element richness index and the canopy complexity index.

3. The chlorophyll fluorescence observation system based on lidar according to claim 2, wherein If the observation evaluation condition responded by the target evaluation module is that the observation target is in the primary observation analysis state, it is determined that the observation analysis module performs inversion prediction analysis on the fluorescence observation data of the observation target by using the observation feature analysis method; The observation analysis module responds to the feature analysis condition, determines the specular dominant direction of the observation target according to the observation polarization parameter, and determines the inversion execution strategy of the observation target based on the dominant direction proportion; The feature analysis condition is that the target evaluation module determines that the observation analysis module performs inversion prediction analysis on the fluorescence observation data of the observation target by using the observation feature analysis method, and the primary observation analysis state is that the reflection complexity coefficient of the observation target is greater than the preset reflection complexity coefficient or the observation complexity coefficient is greater than the preset observation complexity coefficient.

4. The chlorophyll fluorescence observation system based on lidar according to claim 3, characterized in that, If the inversion execution condition responded by the observation analysis module is that the dominant direction proportion is greater than the preset dominant direction proportion, it is determined that the dominant inversion module determines the set of predicted execution bands based on the approximate stability index and the proportion of the dominant reflection component; The dominant inversion module responds to the prediction execution condition and determines the observation prediction data based on the polarization reflection parameters of each predicted execution band in the set of predicted execution bands; The prediction execution condition is that the observation target completes the determination of the set of predicted execution bands.

5. The chlorophyll fluorescence observation system based on lidar according to claim 4, characterized in that, When the inversion execution condition responded by the observation and analysis module is that the dominant direction ratio is less than or equal to the preset dominant direction ratio, it is determined that the multi-parameter inversion module determines the inversion weight coefficients of each observation band based on the observation variation difference degree and the feature coincidence degree; The inversion weight coefficient has a negative correlation with the observation variation difference degree, and the inversion weight coefficient has a positive correlation with the feature coincidence degree.

6. The chlorophyll fluorescence observation system based on lidar according to claim 5, characterized in that, The multi-parameter inversion module responds to the multi-parameter execution condition, performs band weight analysis for each mirror dominant direction, and determines whether to adjust the inversion weight index of each observation band according to the dominant reflection component ratio; When the weight analysis condition responded by the multi-parameter inversion module is that the dominant reflection component ratio of an observation band is less than the preset dominant reflection component ratio, the inversion weight index of this observation band is increased and adjusted according to the dominant reflection component ratio; The multi-parameter execution condition is that the observation and analysis module determines that the multi-parameter inversion module determines the inversion weight coefficients of each observation band.

7. The chlorophyll fluorescence observation system based on lidar according to claim 6, wherein, When the observation evaluation condition responded by the target evaluation module is that the observation target is in the second-class observation and analysis state, it is determined that the auxiliary analysis module uses the auxiliary compliance analysis method to perform a rationality analysis on the fluorescence observation data of the observation target; The auxiliary analysis module responds to the auxiliary correction condition and determines the auxiliary analysis method based on the distribution feature correlation degree of the conflict data points; The auxiliary correction condition is that the target evaluation module determines that the auxiliary analysis module uses the auxiliary compliance analysis method to correct the observation data of the observation target. The second-class observation and analysis state means that the reflection complexity coefficient of the observation target is less than or equal to the preset reflection complexity coefficient and the observation complexity coefficient is less than or equal to the preset observation complexity coefficient.

8. The chlorophyll fluorescence observation system based on lidar according to claim 1, characterized in that, When the auxiliary analysis condition responded by the auxiliary analysis module is that the distribution feature correlation degree of the observation target is greater than the preset distribution feature correlation degree, the observation stability coefficient of each conflict data point is detected, and it is determined whether to issue a growth limit warning for the observation target according to the reference observation stability coefficient; The observation stability coefficient is determined according to the intensity change degree of the fluorescence intensity of the reflection data obtained each time during the observation evaluation stage.

9. The chlorophyll fluorescence observation system based on lidar according to claim 8, characterized in that, When the limit analysis condition responded by the auxiliary analysis module is that the reference observation stability coefficient is less than or equal to the preset reference observation stability coefficient, it is determined to issue a growth limit warning for the observation target; When the limit analysis condition responded by the auxiliary analysis module is that the reference observation stability coefficient is greater than the preset reference observation stability coefficient, the observation quality index of the observation target is determined based on the conflict difference ratio; The observation quality index has a negative correlation with the conflict difference ratio.

10. The chlorophyll fluorescence observation system based on lidar according to claim 9, wherein, When the auxiliary analysis condition responded by the auxiliary analysis module is that the distribution feature correlation degree of the observation target is less than or equal to the preset distribution feature correlation degree, the observation quality index of the observation target is determined according to the conflict difference ratio and the change correlation parameter; The observation quality index has a positive correlation with the change correlation parameter, and the observation quality index has a negative correlation with the conflict difference ratio.

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