CN116523859B - Coastal zone vegetation weather parameter satellite image calibration method combined with ground observation - Google Patents
Coastal zone vegetation weather parameter satellite image calibration method combined with ground observation Download PDFInfo
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Abstract
The invention relates to a satellite image calibration method for vegetation weathers parameters of a coastal zone combined with ground observation, which comprises the following steps: acquiring a ground camera photo and a satellite remote sensing image; respectively carrying out data preprocessing and fitting a GCC time sequence observation curve and an NDVI time sequence observation curve on the ground camera photo and the satellite remote sensing image; and calculating each pixel climatic parameter covering the image of the research area by applying the optimal threshold value, thereby obtaining the climatic spatial distribution. The beneficial effects of the invention are as follows: according to the invention, the object-weather curve inflection point is used for automatically acquiring object-weather parameter information of the ground object-weather photo, which is densely and continuously observed, and satellite image object-weather parameter information which is sparsely observed and is unevenly distributed is extracted through a continuously-changing threshold value so as to match the object-weather parameter information of the photo, thereby constructing a time sequence object-weather data set which is attached to the sense of real object-weather.
Description
Technical Field
The invention relates to the technical field of remote sensing time sequence processing, in particular to a satellite image calibration method for vegetation weathers parameters of a coastal zone combined with ground observation.
Background
Vegetation weather is a biological phenomenon that natural plants are influenced by genetic factors and surrounding environments to generate periodical changes, and is an important ecosystem parameter for representing the dynamic state of an ecosystem and the response of the ecological system to the environmental changes. The coastal zone is in the land and ocean system staggered transition zone, and is one of the most productive and valuable ecosystems worldwide. Climate (especially temperature) changes have a significant impact on coastal zone vegetation climate; at the same time, the vegetation climate change will affect the coastal zone ecosystem structural function and further react to the coastal climate system. Therefore, the method has important significance in improving the accuracy of the global land-sea ecological model on carbon, water and energy cycle simulation and prediction and the like for long-term observation and accurate description of vegetation waiting periods of coastal zones.
The satellite images can acquire a large-scale space continuous earth observation, so that the research on the relation between vegetation weather and climate change on the national, intercontinental and even global scale becomes possible. In order to ensure sufficient observation quantity, the high-time-resolution satellite image synthesized product is firstly used for observing the weather information. However, the above-mentioned image products have mixed pixel climate information because of a relatively coarse spatial resolution (250 m to 8 km), and are difficult to be applied to a coastal wetland region with a narrow distribution range and complex ground feature elements. The medium-fraction (less than or equal to 30 m) satellite images are gradually used for acquiring vegetation climate information of the coastal wetland. Notably, the extraction of vegetation weather parameters based on the medium-resolution satellite images is mainly determined by fitting a time-series weather curve of remote sensing observation and extracting change characteristic nodes of the weather curve. However, the time resolution of the middle satellite image is generally low, and the influence of cloud and rain weather in the coastal zone is also caused, so that the effective annual observation quantity is small, and the accuracy of extracting the vegetation weather parameters in the coastal zone is often difficult to guarantee.
The use of a ground weather camera (Phenocam) to obtain continuous observation of the visible light range of vegetation becomes one of the important means for remote sensing weather observation in recent years. At present, the weather cameras are gradually distributed in a plurality of countries and ecological systems worldwide, the rapid development of the weather camera observation network promotes the research of the coastal wetland weather observed by the ground camera to be increased. While ground observation with a weatherometer can provide accurate vegetation weatherometer information at a specific location, spatial dispersion of observations presents a significant challenge in constructing a large range of weatherometer data sets.
Therefore, the method fully plays the ground-satellite observation advantages, and combines the ground observation to determine the coastal zone vegetation weather parameters of the satellite image, thereby being beneficial to realizing the coastal zone vegetation weather monitoring with large range, long period and high precision and providing scientific reference for the coastal zone resource development and ecological protection.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a satellite image calibration method for vegetation weather parameters of a coastal zone combined with ground observation.
In a first aspect, a method for calibrating satellite images of vegetation weathers parameters of a coastal zone in combination with ground observation is provided, including:
Step 1, acquiring a data source, wherein the data source comprises a ground camera photo from the same year and a satellite remote sensing image;
Step 2, carrying out data preprocessing on the ground camera photo, including green coordinate index GCC calculation and percentile value denoising, and constructing photo GCC time sequence observation;
Step 3, fitting a photo GCC time sequence observation curve by using a harmonic function, and extracting the time corresponding to the inflection point of the curve as a real earth surface weather parameter;
Step4, combining the vision analysis and GPS correction to determine the corresponding vision range of the ground camera photo;
Step 5, carrying out data preprocessing on the satellite remote sensing image, including normalized difference vegetation index NDVI calculation and inverse distance weighted synthesis, and constructing an image NDVI time sequence observation in a corresponding view range;
Step 6, fitting an image NDVI time sequence observation curve by using a harmonic function, acquiring image weather parameters through different thresholds, and selecting the threshold closest to the ground surface real weather parameters as an optimal threshold;
And 7, calculating each pixel climatic parameter of the image of the coverage research area by applying an optimal threshold value, so as to obtain the climatic spatial distribution.
Preferably, step 2 includes:
Step 2.1, calculating a greenness coordinate index, wherein the formula is as follows:
Wherein GCC represents the green degree coordinate index, and Band Blue、BandGreen、BandRed corresponds to blue light, green light and red light wave bands in the ground camera photo respectively;
Step 2.2, calculating GCC for all photos acquired every day, and counting the average value of the GCC of vegetation parts in the photos;
step 2.3, ascending order of GCC mean value is ordered, and a numerical value corresponding to the set quantile is selected to be output as an effective value;
And 2.4, sequencing the effective values according to the acquisition date to obtain photo GCC time sequence observation.
Preferably, step 3 includes:
step 3.1, fitting a photo GCC time sequence observation curve by using a harmonic function, wherein the calculation formula is as follows:
Wherein, The method is characterized in that the method is used for estimating a harmonic function of GCC, t is julian day, w is annual frequency, and a constant 2 pi/365 is taken; i is the harmonic function order, a i and b i are harmonic function parameters for describing the periodic variation in years; c is the intercept, used to describe the change in annual trends; solving harmonic function parameters by adopting a general least square method;
Step 3.2, calculating a second derivative of the harmonic function curve, and solving a corresponding time vector T when the second derivative is zero; the time vector T includes two time points, denoted as t= (T1, T2), the former time point T1 is the ground-observed vegetation period weather parameter SOS ground, and the latter time point T2 is the ground-observed vegetation period weather parameter EOS ground.
Preferably, step 4 includes:
Step 4.1, combining the ground camera height, azimuth angle, inclination angle and digital elevation model parameters, and obtaining a rough vision range by utilizing the vision analysis;
And 4.2, the observer holds the GPS to move along the boundary of the rough vision range, and the position feedback correction of the observer in the photo is carried out to determine the fine vision range.
Preferably, step 5 includes:
step 5.1, calculating a normalized difference vegetation index, wherein the formula is as follows:
Wherein, NDVI represents normalized difference vegetation index, band Red、BandNIR corresponds to red light and near infrared Band in satellite remote sensing image respectively;
step 5.2, synthesizing the satellite images NDVI in the photo view range by using an inverse distance weighting mode, wherein the calculation formula is as follows:
Wherein, For the synthesized satellite image NDVI, i is an ith satellite image pixel falling into the view range of the weather photo, NDVI i is the corresponding NDVI of the pixel i, and d i is the distance from the pixel i to the ground camera;
Step 5.3, will And sequencing according to the acquisition date to obtain the satellite image NDVI time sequence observation.
Preferably, step 6 includes:
step 6.1, fitting an image NDVI time sequence observation curve by using a harmonic function, wherein the calculation formula is as follows:
Wherein, The method is characterized by taking a harmonic function estimated value of NDVI, wherein t is julian day, w is annual frequency, and a constant of 2 pi/365 is taken; i is the harmonic function order, a i and b i are harmonic function parameters for describing the periodic variation in years; c is the intercept, used to describe the change in annual trends; solving harmonic function parameters by adopting a general least square method;
Step 6.2, defining a vegetation period weather parameter SOS satellite,x1 acquired by a satellite image as a corresponding time of an ascending x1% of a harmonic function curve from a minimum value to a maximum value, and defining a vegetation degradation period weather parameter EOS satellite,x2 acquired by a satellite remote sensing image as a corresponding time of a descending x2% of the harmonic function curve from the maximum value to the minimum value;
and 6.3, taking x1 and x2 as undetermined thresholds, and trying with the step length of 5 from 20 to 80, wherein when the satellite image vegetation weather is closest to the ground observation vegetation weather, the x1 and x2 are the optimal thresholds, and the formula is as follows:
|SOSsatellite,x1-SOSground|→min，x1＝20，25，…，80
|EOSsatellite,x2-EOSground|→min，x2＝20，25，…，80。
In a second aspect, a satellite image calibration device for a vegetation weatherometer of a coastal zone in joint ground observation is provided, and the satellite image calibration method for the vegetation weatherometer of the coastal zone in joint ground observation in any aspect is executed, and includes:
the acquisition module is used for acquiring a data source, wherein the data source comprises a ground camera photo from the same year and a satellite remote sensing image;
The first construction module is used for carrying out data preprocessing on the photos of the ground camera, and comprises the steps of calculating a green coordinate index GCC and denoising a percentile value, so as to construct a photo GCC time sequence observation;
the first fitting module is used for fitting a photo GCC time sequence observation curve by using a harmonic function and extracting the time corresponding to the inflection point of the curve as a real earth surface weather parameter;
the correction module is used for combining the visibility analysis and GPS correction to determine the corresponding view range of the ground camera photo;
the second construction module is used for carrying out data preprocessing on the satellite remote sensing image, and comprises normalized difference vegetation index NDVI calculation and inverse distance weighted synthesis, and constructing an image NDVI time sequence observation in a corresponding view range;
the second fitting module is used for fitting an image NDVI time sequence observation curve by using a harmonic function, acquiring image weather parameters through different thresholds, and selecting the threshold closest to the ground surface real weather parameters as an optimal threshold;
And the calculation module is used for applying an optimal threshold value to calculate each pixel climatic parameter of the image of the coverage research area so as to obtain the climatic space distribution.
In a third aspect, a computer storage medium having a computer program stored therein is provided; the computer program when run on a computer causes the computer to execute the satellite image calibration method for vegetation parameters of the coastal zone in combination with ground observation according to any one of the first aspects.
The beneficial effects of the invention are as follows: the invention innovatively provides a satellite image coastal zone vegetation weather rating method combined with ground observation, which automatically acquires densely-observed continuous ground weather photo weather parameter information by using a weather curve inflection point, extracts satellite image weather parameter information which is sparsely observed and is unevenly distributed by continuously changing a threshold value to match the photo weather parameter information, and thereby constructs a time sequence weather data set which is fit with the sense of real weather. The data set has more definite biophysical connotation, can refract photosynthesis and carbon sink efficiency annual progress, and is further hopeful to serve the monitoring and evaluation work of the ecological environment of the coastal zone. In addition, as important vegetation biophysical information, a satellite image object data set with large range, long period and high precision is expected to expand research contents of cross subjects such as ocean, ecology, environment and the like.
Drawings
FIG. 1 is a schematic view of Landsat-8OLI images and a ground camera photograph of the natural protection area of Dandelion crane in 2020;
FIG. 2 is a schematic diagram of a daily land photograph pretreatment process;
FIG. 3 is a schematic diagram of a process for matching the vegetation ground observation weathers with the satellite image weathers in the coastal zone;
FIG. 4 is a schematic diagram of a process for matching the field of view of a terrestrial camera with satellite image pixels;
FIG. 5 is a schematic view showing the spatial distribution of the climate parameters in the natural protection zone of the Dan Ding Ji in 2020;
FIG. 6 is a flow chart of a method for satellite image calibration of the vegetation weathers parameters of the coastal zone in combination with ground observation.
Detailed Description
The invention is further described below with reference to examples. The following examples are presented only to aid in the understanding of the invention. It should be noted that it will be apparent to those skilled in the art that modifications can be made to the present invention without departing from the principles of the invention, and such modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
Example 1:
a satellite image calibration method for vegetation weathers of coastal zone combined with ground observation is shown in fig. 6, and comprises the following steps:
and step 1, acquiring a data source, wherein the data source comprises a ground camera photo from the same year and a satellite remote sensing image.
And 2, carrying out data preprocessing on the ground camera photo, including calculation of a green coordinate index GCC and denoising of a percentile value, and constructing a photo GCC time sequence observation.
The step 2 comprises the following steps:
Step 2.1, calculating a greenness coordinate index, wherein the formula is as follows:
Wherein GCC represents the green degree coordinate index, and Band Blue、BandGreen、BandRed corresponds to blue light, green light and red light wave bands in the ground camera photo respectively;
Step 2.2, calculating GCC for all photos acquired every day, and counting the average value of the GCC of vegetation parts in the photos;
Step 2.3, ascending order of GCC mean value is ordered, and a numerical value corresponding to the set quantile is selected to be output as an effective value; in this embodiment, the quantile may be set to 90%
And 2.4, sequencing the effective values according to the acquisition date to obtain photo GCC time sequence observation.
In the step 2, the method calculates the GCC average value of vegetation parts in photos acquired every day, and selects a specific quantile as the effective value output of the same day after ascending order sorting, so that the influence of illumination and atmospheric condition differences at different moments on the GCC value can be avoided.
And step 3, fitting a photo GCC time sequence observation curve by using a harmonic function, and extracting the time corresponding to the inflection point of the curve as the real earth surface weather parameter.
The step 3 comprises the following steps:
step 3.1, fitting a photo GCC time sequence observation curve by using a harmonic function, wherein the calculation formula is as follows:
Wherein, The method is characterized in that the method is used for estimating a harmonic function of GCC, t is julian day, w is annual frequency, and a constant 2 pi/365 is taken; i is the harmonic function order, a i and b i are harmonic function parameters for describing the periodic variation in years; c is the intercept, used to describe the change in annual trends; and solving harmonic function parameters by adopting a general least square method.
As known by combining the subsequent step 6, the application adopts the harmonic function as a fitting curve for the time series observation of the ground photo and the satellite image, so the application comprehensively considers the periodic and trend change characteristics of the vegetation in the coastal zone.
Step 3.2, calculating a second derivative of the harmonic function curve, and solving a time vector T corresponding to zero (curve inflection point) of the second derivative; the time vector T includes two time points, denoted as t= (T1, T2), the former time point T1 is the ground-observed vegetation period weather parameter SOS ground, and the latter time point T2 is the ground-observed vegetation period weather parameter EOS ground. According to the application, the corresponding time when the GCC fitting curve inflection point is solved, and the time is used as the vegetation growth period and the fading period physical parameters of ground observation, so that the real sensory result of the vegetation change of the ground surface can be attached
And 4, combining the visibility analysis and GPS correction to determine the corresponding view range of the ground camera photo, and obtaining the view range of the ground camera from thick to thin.
Step 4 comprises:
Step 4.1, combining the ground camera height, azimuth angle, inclination angle and digital elevation model parameters, and obtaining a rough vision range by utilizing the vision analysis;
And 4.2, the observer holds the GPS to move along the boundary of the rough vision range, and the position feedback correction of the observer in the photo is carried out to determine the fine vision range.
And 5, performing data preprocessing on the satellite remote sensing image, including normalized difference vegetation index NDVI calculation and inverse distance weighted synthesis, and constructing an image NDVI time sequence observation in a corresponding view range.
The step 5 comprises the following steps:
step 5.1, calculating a normalized difference vegetation index, wherein the formula is as follows:
Wherein, NDVI represents normalized difference vegetation index, band Red、BandNIR corresponds to red light and near infrared Band in satellite remote sensing image respectively;
step 5.2, synthesizing the satellite images NDVI in the photo view range by using an inverse distance weighting mode, wherein the calculation formula is as follows:
Wherein, For the synthesized satellite image NDVI, i is an ith satellite image pixel falling into the view range of the weather photo, NDVI i is the corresponding NDVI of the pixel i, and d i is the distance from the pixel i to the ground camera;
Step 5.3, will And sequencing according to the acquisition date to obtain the satellite image NDVI time sequence observation.
In step 5, the application synthesizes the satellite images NDVI in the photo view range by using an inverse distance weighting mode, and then sequences the synthesized NDVI according to the acquisition date to obtain the time series observation of the satellite images NDVI so as to fully consider the imaging characteristics of the near-large and far-small photos of the object,
And 6, fitting an image NDVI time sequence observation curve by using a harmonic function, acquiring image weather parameters through different thresholds, and selecting the threshold closest to the ground surface real weather parameters as an optimal threshold.
The step 6 comprises the following steps:
step 6.1, fitting an image NDVI time sequence observation curve by using a harmonic function, wherein the calculation formula is as follows:
Wherein, The method is characterized by taking a harmonic function estimated value of NDVI, wherein t is julian day, w is annual frequency, and a constant of 2 pi/365 is taken; i is the harmonic function order, a i and b i are harmonic function parameters for describing the periodic variation in years; c is the intercept, used to describe the change in annual trends; solving harmonic function parameters by adopting a general least square method;
Step 6.2, defining a vegetation period weather parameter SOS satellite,x1 acquired by a satellite image as a corresponding time of an ascending x1% of a harmonic function curve from a minimum value to a maximum value, and defining a vegetation degradation period weather parameter EOS satellite,x2 acquired by a satellite remote sensing image as a corresponding time of a descending x2% of the harmonic function curve from the maximum value to the minimum value;
and 6.3, taking x1 and x2 as undetermined thresholds, and trying with the step length of 5 from 20 to 80, wherein when the satellite image vegetation weather is closest to the ground observation vegetation weather, the x1 and x2 are the optimal thresholds, and the formula is as follows:
|SOSsatellite,x1-SOSground|→min，x1＝20，25，…，80
|EOSsatellite,x2-EOSground|→min，x2＝20，25，…，80
In step 6, the application adopts a continuous change threshold segmentation method to determine the satellite image weather parameters closest to the ground observation weather parameters, thereby ensuring the accuracy of satellite image vegetation weather extraction.
And 7, calculating each pixel climatic parameter of the image of the coverage research area by applying an optimal threshold value, so as to obtain the climatic spatial distribution.
Example 2:
The natural protection area of the Dan top crane is positioned on the coast between dou Dragon harbor and New ocean harbor in salt city of Jiangsu province, and is considered to be one of the coastal zone wetlands which remain relatively intact in China. In order to make the content and advantages of the invention more clear, the following description uses the natural protection area of the dan crane as a case and the accompanying drawings to illustrate the specific implementation process of the invention, and the detailed steps are as follows:
step 1, as shown in fig. 1, downloading cloud-free coverage Landsat-8 OLI and Landsat-7 ETM + image 21 scenes in 2020 of a natural protection area covering a Dandelion crane; in addition, as shown in part (a) of fig. 2, a land camera photo 9862 of the year 2020 of the north bank of the natural protection area of the dan-top crane is obtained.
And 2, calculating GCC for all the ground photos collected every day, counting vegetation average values in the photos, and outputting 90% quantiles as GCC effective values of the same day after ascending order sequencing. Taking 5/13/2020 as an example, as shown in part (b) of fig. 2, 24 land photographs GCC are calculated first, and then the average ascending order of vegetation GCC is counted, wherein 90% of the digits correspond to 0.375, which is the effective value of GCC on 13/2020. Photo GCC time series observations are obtained by sequencing the GCC effective values daily in 2020 according to the dates.
Step 3, as shown in part (a) of fig. 3, solving a harmonic function fitting curve by using a least square method through GCC time series observation in 2020, wherein the specific expression is as follows:
The expression of the second derivative curve is further calculated as:
let the second derivative curve expression be zero, solve the time vector T, obtain the vegetation growth period weathered parameter SOS ground of ground observation as 157, and the vegetation decay period weathered parameter EOS ground of ground observation as 289, as shown in part (b) of fig. 3.
And 4, obtaining a rough visual range by a Viewshed tool of which the ground camera height is 2.5m, the azimuth angle is 285 degrees, the inclination angle is 30 degrees, the digital elevation model is downloaded from USGS Earth Explorer, and related parameters and data are input into ArcGIS 10.3 software. On this basis, the observer moves along the rough viewing area boundary, the relative position of the observer and the viewing area boundary in the photo is recorded, and the fine viewing area range is obtained by correcting the observer GPS information as shown in fig. 4.
Step 5, calculating NDVI for 21 scene Landsat images, and synthesizing pixels positioned in the field of view of the ground camera by adopting an inverse distance weighting formulaTaking Landsat-8OLI of 7.6.2020 as an example, the pixel NDVI composition value/>0.431. All images/>And obtaining the time series observation of the image NDVI according to the date sequence.
Step 6, part (c) of fig. 3 shows the fitting of the harmonic function of the NDVI time series of the satellite image and the matching of the physical parameters thereof, and the fitting curve of the harmonic function is solved by using the least square method through the NDVI time series observation in 2020, and the specific expression is as follows:
During successive attempts from 20 to 80 at step 5 of the pending thresholds x1, x2, it was found that SOS satellite,45 =159 is closest to SOS ground when x1=45, EOS satellite,40 =286 is closest to EOS ground when x2=40, so 45 and 40 are the optimal thresholds for x1 and x2, respectively.
Step 7, applying x1=45 and x2=40 to calculate the weather parameters of each pixel of the image covering the natural protection area of the dan-top crane, as shown in the parts (a) and (b) of fig. 5, to obtain the weather parameter spatial distributions of the vegetation period (SOS satellite,45) and the regression period (EOS satellite,40), respectively.
Claims (5)
1. The method for calibrating the satellite images of the vegetation weathers of the coastal zone by combining ground observation is characterized by comprising the following steps of:
Step 1, acquiring a data source, wherein the data source comprises a ground camera photo from the same year and a satellite remote sensing image;
Step 2, carrying out data preprocessing on the ground camera photo, including green coordinate index GCC calculation and percentile value denoising, and constructing photo GCC time sequence observation;
the step 2 comprises the following steps:
Step 2.1, calculating a greenness coordinate index, wherein the formula is as follows:
Wherein GCC represents the green degree coordinate index, and Band Blue、BandGreen、BandRed corresponds to blue light, green light and red light wave bands in the ground camera photo respectively;
Step 2.2, calculating GCC for all photos acquired every day, and counting the average value of the GCC of vegetation parts in the photos;
step 2.3, ascending order of GCC mean value is ordered, and a numerical value corresponding to the set quantile is selected to be output as an effective value;
step 2.4, sorting the effective values according to the acquisition date to obtain photo GCC time sequence observation;
Step 3, fitting a photo GCC time sequence observation curve by using a harmonic function, and extracting the time corresponding to the inflection point of the curve as a real earth surface weather parameter;
Step4, combining the vision analysis and GPS correction to determine the corresponding vision range of the ground camera photo;
Step 5, carrying out data preprocessing on the satellite remote sensing image, including normalized difference vegetation index NDVI calculation and inverse distance weighted synthesis, and constructing an image NDVI time sequence observation in a corresponding view range;
The step 5 comprises the following steps:
step 5.1, calculating a normalized difference vegetation index, wherein the formula is as follows:
Wherein, NDVI represents normalized difference vegetation index, band Red、BandNIR corresponds to red light and near infrared Band in satellite remote sensing image respectively;
step 5.2, synthesizing the satellite images NDVI in the photo view range by using an inverse distance weighting mode, wherein the calculation formula is as follows:
Wherein, For the synthesized satellite image NDVI, i is an ith satellite image pixel falling into the view range of the weather photo, NDVI i is the corresponding NDVI of the pixel i, and d i is the distance from the pixel i to the ground camera;
Step 5.3, will Sequencing according to the acquisition date to obtain a satellite image NDVI time sequence observation;
Step 6, fitting an image NDVI time sequence observation curve by using a harmonic function, acquiring image weather parameters through different thresholds, and selecting the threshold closest to the ground surface real weather parameters as an optimal threshold;
The step 6 comprises the following steps:
step 6.1, fitting an image NDVI time sequence observation curve by using a harmonic function, wherein the calculation formula is as follows:
Wherein, The method is characterized by taking a harmonic function estimated value of NDVI, wherein t is julian day, w is annual frequency, and a constant of 2 pi/365 is taken; i is the harmonic function order, a i and b i are harmonic function parameters for describing the periodic variation in years; c is the intercept, used to describe the change in annual trends; solving harmonic function parameters by adopting a general least square method;
Step 6.2, defining a vegetation period weather parameter SOS satellite,x1 acquired by a satellite image as a corresponding time of an ascending x1% of a harmonic function curve from a minimum value to a maximum value, and defining a vegetation degradation period weather parameter EOS satellite,x2 acquired by a satellite remote sensing image as a corresponding time of a descending x2% of the harmonic function curve from the maximum value to the minimum value;
and 6.3, taking x1 and x2 as undetermined thresholds, and trying with the step length of 5 from 20 to 80, wherein when the satellite image vegetation weather is closest to the ground observation vegetation weather, the x1 and x2 are the optimal thresholds, and the formula is as follows:
|SOSsatellite,x1-SOSground|→min,x1＝20,25,…,80
|EOSsatellite,x2-EOSground|→min,x2＝20,25,…,80
And 7, calculating each pixel climatic parameter of the image of the coverage research area by applying an optimal threshold value, so as to obtain the climatic spatial distribution.
2. The method for satellite image calibration of coastal zone vegetation weatherometer parameters in combination with ground observation according to claim 1, wherein step 3 comprises:
step 3.1, fitting a photo GCC time sequence observation curve by using a harmonic function, wherein the calculation formula is as follows:
Wherein, The method is characterized in that the method is used for estimating a harmonic function of GCC, t is julian day, w is annual frequency, and a constant 2 pi/365 is taken; i is the harmonic function order, a i and b i are harmonic function parameters for describing the periodic variation in years; c is the intercept, used to describe the change in annual trends; solving harmonic function parameters by adopting a general least square method;
Step 3.2, calculating a second derivative of the harmonic function curve, and solving a corresponding time vector T when the second derivative is zero; the time vector T includes two time points, denoted as t= (T1, T2), the former time point T1 is the ground-observed vegetation period weather parameter SOS ground, and the latter time point T2 is the ground-observed vegetation period weather parameter EOS ground.
3. The method for satellite image calibration of coastal zone vegetation weatherometer parameters in combination with ground observation according to claim 2, wherein step 4 comprises:
Step 4.1, combining the ground camera height, azimuth angle, inclination angle and digital elevation model parameters, and obtaining a rough vision range by utilizing the vision analysis;
And 4.2, the observer holds the GPS to move along the boundary of the rough vision range, and the position feedback correction of the observer in the photo is carried out to determine the fine vision range.
4. A satellite image calibration device for a coastal zone vegetation weather parameter of a joint ground observation, which is used for executing the satellite image calibration method for the coastal zone vegetation weather parameter of the joint ground observation according to any one of claims 1 to 3, and comprises the following steps:
the acquisition module is used for acquiring a data source, wherein the data source comprises a ground camera photo from the same year and a satellite remote sensing image;
The first construction module is used for carrying out data preprocessing on the photos of the ground camera, and comprises the steps of calculating a green coordinate index GCC and denoising a percentile value, so as to construct a photo GCC time sequence observation;
the first fitting module is used for fitting a photo GCC time sequence observation curve by using a harmonic function and extracting the time corresponding to the inflection point of the curve as a real earth surface weather parameter;
the correction module is used for combining the visibility analysis and GPS correction to determine the corresponding view range of the ground camera photo;
the second construction module is used for carrying out data preprocessing on the satellite remote sensing image, and comprises normalized difference vegetation index NDVI calculation and inverse distance weighted synthesis, and constructing an image NDVI time sequence observation in a corresponding view range;
the second fitting module is used for fitting an image NDVI time sequence observation curve by using a harmonic function, acquiring image weather parameters through different thresholds, and selecting the threshold closest to the ground surface real weather parameters as an optimal threshold;
And the calculation module is used for applying an optimal threshold value to calculate each pixel climatic parameter of the image of the coverage research area so as to obtain the climatic space distribution.
5. A computer storage medium, wherein a computer program is stored in the computer storage medium; the computer program when running on a computer causes the computer to execute the satellite image calibration method for the vegetation weatherometer parameters of the coastal zone in combination with the ground observation according to any one of claims 1 to 3.
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