CN115062863A - Apple flowering phase prediction method based on crop reference curve and accumulated temperature correction - Google Patents
Apple flowering phase prediction method based on crop reference curve and accumulated temperature correction Download PDFInfo
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Abstract
The invention discloses an apple flowering phase prediction method based on crop reference curve and accumulated temperature correction, which specifically comprises the following steps: step 1: establishing a crop reference curve library, and extracting a crop reference curve according to ground sampling points; step 2: recording the accumulated temperature before the historical flowering beginning; and step 3: calculating the vegetation index with high spatial resolution and clear pixels; and 4, step 4: fitting the high-spatial-resolution vegetation index by using a reference curve, and fitting the high-resolution vegetation index of the historical year with a crop reference curve; and 5: and calculating the predicted flowering phase. The invention provides an apple flowering phase prediction method based on a crop reference curve and accumulated temperature correction according to the characteristics that accumulated temperature has important influence on fruit tree phenology and vegetation index has obvious indication effect on phenology, and the method is suitable for high-spatial-resolution apple tree flowering phase prediction in areas with complex planting structures.
Description
Technical Field
The invention relates to the technical field of agricultural remote sensing, in particular to a method for predicting the flowering beginning of apples based on crop reference curves and accumulated temperature correction, and is suitable for large-range high-precision prediction research of the flowering beginning.
Background
The planting area and the yield of the apples in China are respectively increased to 42.24 percent and 45.54 percent of the world, the planting area of the apples is the first place around the world, but the single yield of the apples is only 18.94 tons/hectare and the apples are the 31 st place in the world. Apple is an important economic crop in Shaanxi province, and is one of important post industries for rural development and farmer income increase in Shaanxi province. Multiple climate indexes of the loess plateau area in the north of China are suitable for apple trees to grow, so that the area becomes a main production area of high-quality apples in Shaanxi. The initial flowering period of the apples is an important phenological index of apple fruit development, and meteorological conditions in the period have profound influence on the formation and development of the fruits, but because the region belongs to the climatic characteristics of temperate continental climate, the loess plateau has complicated shape and morphology and fragile climatic conditions, and is easily influenced by cold flow in the flowering period of the apples, so that low-temperature weather is easy to appear. The freezing damage of the flowering phase of the fruit trees can be caused to a great extent in low-temperature weather, if the control is not timely, the flower buds of the apples can be frozen or pollination is influenced due to low activity of insects, so that the quality of the apples is poor or the yield of the apples is reduced in a large area, and the freezing damage of the flowering phase becomes one of main meteorological disasters influencing the yield and quality of the apples. Meanwhile, accurate flowering phase information has important values for field management such as pest and disease prevention and fertilization, and provides reference information for local development of tourism agricultural activities. Therefore, the method has guiding significance for fruit growers to conduct agricultural production activities by accurately monitoring the initial flowering phase of the apples and developing flowering phase forecasting services, provides references for preventing and controlling freezing damage and guaranteeing quality and yield of the apples for agricultural management departments, provides data support for futures trading, insurance and the like, and provides guarantee for healthy development of apple industry.
At present, the fruit tree phenological information prediction method mainly comprises a field observation method, a model simulation method and a satellite remote sensing method.
1) The field observation method is a traditional method for monitoring the phenological period of the fruit tree, the observation is mainly carried out by eyes and recorded by hands, and the obtained observation data is the first data of the phenological period research of the fruit tree. The field observation method is simple and easy to implement, the result is accurate and reliable, but subjective factors are mixed in the observation, the observation coverage is small, and a large amount of manpower, material resources and time are consumed for developing large-area observation.
2) The model simulation method mainly comprises a statistical analysis model and a process mechanism model mainly based on fruit tree growth rule analysis. The statistical analysis method is a research method for predicting the phenological period by analyzing the change trend of the phenological period of the fruit trees and the correlation of the influence factors thereof in a period of time. The process mechanism model is that assuming that the growth and development of the fruit trees are mainly controlled by factors such as temperature, illumination and the like, the dormancy and dormancy-releasing stage of the fruit trees is usually considered, and the fruit trees can only generate accumulated temperature reaching the critical value required by the generation of a climatic event. And the temperature and illumination data are often large in scale, so that high-resolution accurate prediction is difficult to realize.
3) The satellite remote sensing method is mainly determined by the tissue structure, biochemical components and morphological characteristics of the fruit trees according to the spectral characteristics of the fruit trees, and mainly comprises leaf color, cell structure, plant moisture content and the like, and the characteristics are closely related to the phenological period of the fruit trees. The satellite-borne multispectral sensor can carry out continuous multi-temporal observation on vegetation, makes up the defect of discontinuous observation of the traditional vegetation phenology and makes it possible to observe the vegetation phenology in a large scale. The physiological and biochemical characteristics of the fruit trees in different phenological periods can influence the absorption, reflection and transmission of the fruit trees on the spectrum, so that the spectral response curves generate certain differences, the variation of the vegetation spectrum is obtained through different sensors, and the spectral index of the phenological variation is calculated, thereby realizing phenological monitoring and prediction. The method for predicting the crop phenology by scholars at home and abroad based on remote sensing generally comprises two methods: one is that the remote sensing data (such as vegetation index) based on time series tracks the growth process of crops, the phenological period of crops is determined according to the characteristic change in the growth process of crops, for example, after fitting or filtering the time series data, the time node of phenological is judged by using methods such as threshold, derivation, sliding average and the like according to the curve form of vegetation index time series in different phenological periods, so as to realize the monitoring or prediction of the phenological of crops; the other method is to quantify the physiological and biochemical indicating factors in the crop growth process through remote sensing data, for example, the LAI, the plant height and the like of the crop are inverted through the remote sensing data, related researches also comprise that the change of the physiological and biochemical parameters of the fruit trees in different phenological periods is quantified through calculating the optical thickness of microwave vegetation, sunlight-induced chlorophyll fluorescence and the like, and thus the monitoring and the prediction of the phenological periods of the crop are realized.
The satellite remote sensing method is suitable for wide-range phenological prediction, but because the change of the vegetation index of the apples before the initial flowering phase is difficult to be accurately reflected on the satellite scale and is used for directly predicting larger errors, the method firstly detects historical apple initial flowering phase data, and then corrects the historical initial flowering phase detection result calculated based on the vegetation index by means of predicting the current year temperature, so as to achieve the purpose of accurately predicting the initial flowering phase of the apples with large scale and high resolution.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for predicting the initial flowering phase of apples based on a crop reference curve and accumulated temperature correction aiming at the defects of the prior art; the crop reference curve is combined with the multi-source remote sensing data, the translation amount of the vegetation index in time is calculated, and the meteorological data are used for improving the prediction result, so that the high-precision apple initial flowering stage prediction is carried out.
The technical scheme of the invention is as follows:
in order to solve the technical problems, the invention provides an apple flowering phase prediction method based on crop reference curve and accumulated temperature correction
A method for predicting the flowering phase of apples based on crop reference curve and accumulated temperature correction comprises the following steps: step 1: establishing a crop reference curve library, establishing a crop extraction crop reference curve according to ground sampling points, and recording the initial flowering phase t of the sampling points 0 (ii) a Step 2: recording accumulated temperature before a historical flowering-beginning period, and selecting an optimal accumulated temperature correction threshold value in the flowering-beginning period according to historical meteorological data;and step 3: calculating a vegetation index with high spatial resolution, preprocessing the remote sensing image with high resolution, and calculating the vegetation index of a clear sky pixel; and 4, step 4: fitting the vegetation index with high spatial resolution by using a reference curve, fitting the vegetation index with high resolution in the historical years with a crop reference curve, and establishing a parameter delta t 0 The method is used for recording the translation amount of the curve fitted by different pixels relative to the reference curve in time according to the translation amount delta t 0 The initial flowering phase t of the reference curve 0 Calculating to obtain an initial detection florescence t 0 +Δt 0 (ii) a And 5: calculating and predicting the flowering phase through accumulated temperature correction, and calculating the date difference delta t when the accumulated temperature is higher than 415 ℃ between two years by using meteorological data, so that the predicted flowering phase of the pixel is t 0 +Δt 0 +Δt。
The method, the step 1 comprises the following steps:
step A: investigating the spatial distribution of fruit trees in a research area on the spot, drawing out the vector boundary of the fruit trees in a high-definition satellite image, and recording the initial flowering phases t of the fruit trees in different areas 0 ；
And B: carrying out pretreatment such as re-projection on MODIS data with low spatial resolution, keeping a geographic coordinate system and a projection coordinate system of the MODIS data consistent with a vector boundary of an apple, then calculating to obtain the NDVI of the MODIS data, searching a pure apple pixel in the MODIS data by combining the vector boundary of the fruit tree, and extracting the NDVI time sequence curve of the pixel;
and C: extracting a plurality of NDVI time sequence curves, removing abnormal values in the curves, interpolating blank values by adopting a Savitzky-Golay filtering method, taking the interpolated curves as reference curves, and taking a set of the reference curves as an apple reference curve library.
The method, the step 2 comprises the following steps:
step D: and counting accumulated temperatures before the initial flowering phase of the historical ground sample points, and finding out an optimal accumulated temperature value, so that when the optimal accumulated temperature is in different years and different areas are used as the initial flowering phase accumulated temperature values of the apples, the average relative error of each sample point is minimum.
The method, the step 3 comprises the following steps:
step E: based on clear sky Sentinel-2 surface reflectivity data, NDVI data of 10m spatial resolution is obtained through calculation, NDVI is masked by using fruit tree vector boundaries, and only the NDVI data of the fruit trees are reserved.
The method, the step 4 comprises the following steps:
step F: analyzing all image pixels of the data and the determining coefficient R of all curves in the MODIS crop reference curve library obtained in the step C through the Sentinel-2 time series NDVI data obtained by calculation in the step E 2 Will determine the coefficient R 2 And (4) taking the maximum curve as the optimal reference curve of the image pixel of the Sentinel-2 time sequence NDVI data, and then further fitting the Sentinel-2 time sequence NDVI data and the MODIS optimal reference curve in the step E by the following formula to obtain a Sentinel-2 fitting curve.
L(x)＝a×M(x+Δt 0 )+b
Wherein x represents a day of the year, L (x) represents a Sentiniel-2 time series NDVI curve function, M (x) is an optimal MODIS reference curve, Δ t 0 Representing the possible time offset between the two curves, ranging between ± 30 days, and a and b are fitting parameters calculated using the least squares method.
G: and F, when the curve is fitted according to the step F, the offset delta t of each pixel relative to the reference curve in time is recorded and calculated 0 At this point, according to the initial flowering phase t of its reference curve 0 Obtaining the detection flowering time delta t of the historical years of the pixel 0 +t 0 。
The method, the step 5 comprises the following steps:
step H: calculating the date t when the predicted year reaches the optimal accumulated temperature according to the weather forecast data of the predicted year and the historical weather data of the detected year 1 (ii) a Simultaneously calculating the date t when the detection year reaches the optimal accumulated temperature 2 If the space range is large, the regional statistics needs to be carried out, and the date of reaching the optimal accumulated temperature for two years is calculatedDifference Δ t ═ t 1 -t 2 ；
Step I: and G, according to the optimal accumulated temperature threshold date difference delta t obtained in the step H, the initial detection florescence delta t obtained in the step G 0 +t 0 Correcting to obtain predicted flowering phase T ═ T 0 +Δt 0 +Δt。
The invention provides an apple flowering phase prediction method based on a crop reference curve and accumulated temperature correction according to the characteristics that accumulated temperature has important influence on fruit tree phenology and vegetation index has obvious indication effect on phenology, and the method is suitable for high-spatial-resolution apple tree flowering phase prediction in areas with complex planting structures. Searching pure pixels of MODIS data by using ground survey data, establishing a crop reference curve library, and determining the optimal accumulated temperature threshold value of the apple tree reaching the initial flowering phase through historical data; obtaining an initial fitting curve by comparing the correlation between an MODIS crop reference curve and a Sentinel-2 original NDVI value, reserving the NDVI effective value of original Sentinel data to the maximum extent by methods such as linear interpolation and fitting of a do-it-yourself function, further reducing the overfitting phenomenon of the initial curve to obtain a final fitting curve, simultaneously recording fitting parameters, and determining the detection flowering phase of the historical year according to the initial flowering phase of the selected optimal reference curve and the time offset of the fitting curve; and calculating the time offset of the apple trees of the historical years and the predicted years to reach the optimal accumulated temperature threshold value of the initial flowering phase through weather forecast data, and correcting the initial predicted flowering phase by using the offset so as to obtain the final predicted flowering phase. The method comprehensively considers the indicating effect of the vegetation index on the phenology, simultaneously uses the accumulated temperature for secondary correction, can fully utilize the space-time resolution advantage of the remote sensing data, simultaneously considers the influence of the meteorological data on the phenology of the vegetation, and can apply the generated high-space-time-resolution apple flowering-stage prediction drawing to the apple flowering-stage prediction of a complex planting structure.
Drawings
FIG. 1 is a flow chart of algorithm implementation;
FIG. 2 is a schematic diagram of the principle of flowering detection based on a crop reference curve; solid line: a crop reference curve; dot: high resolution image upper pairVegetation index on date; Δ t 0 : and (4) deviation of the reference curve and the detected year in physical and physical climate after curve fitting.
FIG. 3 is the final florescence prediction in 2021 in the study area;
Detailed Description
The present invention will be described in detail with reference to specific examples.
Example 1:
a method for predicting the beginning flowering phase of apples based on crop reference curve and accumulated temperature correction comprises the following steps:
a: the research area of this example is located in Yanan city 6 county, Shaanxi province, including: luochuan county, Huangling county, Fuxian county, Yangxuan county, Yichuan county, Huanglong county, the area is the main area for planting the apples in Shaanxi, and the area of the apples in Yangan city, Fugan city, full city in 2019 is 323.65 ten thousand mu. According to 2018 land survey combined with Google high-definition satellite images, a 6-county fruit tree distribution diagram in Yanan city in Shaanxi province is drawn, meanwhile, the initial flowering phases of orchards in different areas in 2018-2020 are recorded, and the initial flowering phases t of 18 land sample points in three years are recorded 0 ；。
B: the MCD43A4 product in MODIS data is selected from remote sensing data with low spatial resolution, preprocessing such as re-projection is carried out on original data by using MRT (MODIS reproduction tool), the re-projection is UTM (UNIVERSAL TRANSVERSE MERCRARTOR GRID STSTEM, UNIVERSAL lateral-axis Mercator grid system), the time range is 2018 to 2020, the finally obtained data has the spatial resolution of 500 meters and the time resolution of 1 day, and NDVI data are obtained through calculation. According to an apple spatial distribution map obtained by ground investigation, selecting a pixel completely containing an apple forest in an NDVI layer of the MODIS as a sample point, namely the sample point is an apple pure pixel.
C: taking 5 sample points as an example in Luchuan county of Yanan city and extracting NDVI time sequences of the sample points as an initial reference curve, an S-G filtering method is used for removing and filling abnormal values and vacancy values of the NDVI time sequences so as to ensure the continuity and rationality of the NDVI time sequences. And repeating the steps B-C for many times to obtain a plurality of reference curves, and establishing an apple crop reference curve library according to the reference curves.
D: according to the data of the initial flowering phase of the apples obtained by ground sampling from 2018 to 2021, the data is analyzed by combining the taken historical air temperature data, so that the purpose of monitoring the initial flowering phase of the apples is achieved. Where the historical air temperature data was ERA5, which was a fifth generation global climate atmospheric re-analysis data set providing a variety of weather data including air temperature with a temporal resolution of 1 day and a spatial resolution of about 0.1 °. The accumulated values (more than 3 ℃) of the air temperatures before 18 initial flowering stages corresponding to the three years are counted, the initial flowering stages of the apples are artificially set when the accumulated temperature of more than 3 ℃ reaches 415 ℃ through observing the accumulated temperature and the accumulated temperature values of the three years, the real initial flowering stages are compared with the accumulated temperature of 415 ℃, and when the threshold 415 ℃ is used as the initial flowering stage, good prediction precision (average error is 2.4 days) can be obtained, the threshold is accurate and effective, and the method is suitable for predicting the initial flowering stages of the apples in the key area in Shaanxi.
E: and B, downloading Sentinel-2 data, calculating the NDVI of the region based on the surface reflectivity of the clear and empty pixel, and masking the non-fruit tree region in the research region through the fruit tree distribution diagram in Yanan city 6 county in Shanxi province manufactured in the step A.
F: according to the Sentinel-2 NDVI data obtained by calculation in the step E, analyzing all image pixels of the data and all curve decision coefficients R in the MODIS crop reference curve sample library obtained in the step C 2 The curve with the maximum coefficient is used as the optimal reference curve for fitting the sentinel NDVI and the MODIS reference curve to obtain the sentinel fitting curve. L (x) axm (x + Δ t) 0 )+b
Wherein x represents a day of the year, L (x) represents a Sentiniel-2 time series NDVI curve function, M (x) is an optimal MODIS reference curve, Δ t 0 The time offset possibly existing between the two curves is shown, the range is +/-30 days, a and b are fitting parameters obtained by calculation through a least square method, and fig. 2 is a schematic diagram of a flowering phase detection principle based on a crop reference curve.
At the moment, the initial fitting curve and the sentinel data NDVI value of the target pixel area have a better fitting relationship, butThe method is characterized in that part of original sentinel NDVI values cannot be well fitted to an initial fitting curve, the difference value between the initial fitting curve and the original sentinel NDVI values is calculated, a linear interpolation method is adopted to fit a linear interpolation result to the initial curve so as to reduce the difference between the initial fitting curve and the original sentinel NDVI values, finally, a Gaussian function in an IDL programming platform is used for further smoothing the optimized fitting curve, the curve contains all available original sentinel NDVI data as far as possible, and the curve at the moment is a final fitting curve. Thus, the final fitting parameter Δ t is obtained 0 A and b.
G: according to the offset delta t of each pixel in F relative to the optimal reference curve in time 0 And the value t of the optimal reference curve at the beginning of flowering 0 The detection result delta t of the initial flowering phase of each pixel apple tree in 2019 can be obtained 0 +t 0 And the detection results were verified by using the ground sample, and the average error was about 0.83 days as shown in table 1. From the ground verification results, the method based on the crop reference curve can well detect the beginning flowering phase of the apples in the historical years, and is high in precision.
TABLE 1 comparison of detection results of beginning flowering phase of representative counties in Shaanxi province with ground data
H: according to weather forecast data of the current year and historical weather data of the detected year, the time difference of 2019 years when the accumulated temperature of a research area in 2021 reaches 415 ℃ is calculated and recorded as delta T, and the predicted value T of each pixel in the flowering phase of 2021 is T 0 +Δt 0 + Δ t. In the research area, the accumulated temperature is calculated and detected from 1 month and 1 day in the year, and the accumulated temperature is accumulated (accumulated temperature) when the temperature reaches above 3 ℃, and if the blooming period of Luochuan land data in 2019 is 4 months and 11 days (101 th day), the accumulated temperature is accumulated to 101 th day. By calculating the accumulated temperature, the optimal accumulated temperature threshold value before the initial flowering phase of Luochuan is 415 ℃, the accumulated temperature of Yichuan in the north is 420 ℃ and the accumulated temperatures of other counties during flowering are all within 410-The effect was within one day, so the best threshold for incipient flowering in 2021 in the study area was rocky 415 ℃. According to the acquired station meteorological data, the detection result is corrected by combining with meteorological forecast data, the accumulated temperature of Luochuan reaching 415 ℃ in 2021 is calculated to be 98 th day, which is 3 days earlier than that in 2019, so that the predicted value of the 2021 initial flowering phase is obtained by subtracting 3 days from the high-precision initial flowering phase detection result based on the reference curve in 2019. Fig. 3 shows the final estimated initial flowering stage result in 2021 years by taking lovage, huangling, fuxian as an example, and the estimated result is subjected to accuracy evaluation based on ground verification data, and as a result, as shown in table 2, the average error is 2.67 days before the integrated temperature correction, and the average error is reduced to 1.67 days after the integrated temperature correction is used. From the overall prediction results, the low-latitude areas of the whole research area have earlier initial flowering phase, and the high-latitude areas have later initial flowering phase; meanwhile, the flowering phase of the east part is later, the flowering phase of the west part is earlier, the real situation of field investigation is met, and in general, the precision is improved after the optimal accumulated temperature threshold value is used for correction. From the detail diagram of the right prediction result, it can be seen that the phenological differences exist among different orchards under the spatial resolution of 10m, which indicates that the method not only can monitor the phenological of the apple trees more accurately, but also can identify the phenological differences among the orchards. Therefore, the apple flowering phase prediction method based on the crop reference curve and accumulated temperature correction is accurate and effective.
TABLE 22021 florescence prediction vs 2021 ground data
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (6)
1. A method for predicting the beginning flowering phase of apples based on crop reference curve and accumulated temperature correction is characterized by comprising the following steps:
step 1: selectingTaking a crop reference curve, extracting the crop reference curve from the MODIS data according to the ground sampling points, and recording the initial flowering phase t of the sampling points 0 ；
Step 2: recording accumulated temperature before a historical flowering-beginning period, and selecting an optimal accumulated temperature threshold value of the flowering-beginning period according to historical meteorological data;
and step 3: calculating vegetation indexes of high-spatial-resolution clear pixels, preprocessing the high-resolution remote sensing image, and calculating the vegetation indexes of the clear pixels;
and 4, step 4: fitting the vegetation index with high spatial resolution by using a crop reference curve, fitting the vegetation index with high spatial resolution of the historical year with the crop reference curve, and establishing a parameter delta t 0 The method is used for recording the translation amount of the fitted curve of different pixels relative to a reference curve in time according to the translation amount delta t 0 The initial flowering phase t of the reference curve 0 Calculating to obtain an initial detection flowering phase t 0 +Δt 0 ；
And 5: calculating the optimal accumulated temperature threshold time difference between the predicted year and the detected year by combining with meteorological data, correcting the initial detected flowering phase obtained in the step 4, calculating to obtain a predicted flowering phase, and calculating the date difference delta t when the accumulated temperature is higher than 415 ℃ between two years by using the meteorological data, so that the predicted flowering phase of the pixel is t 0 +Δt 0 +Δt。
2. The method according to claim 1, wherein the step 1 comprises the steps of:
step A: investigating spatial distribution of apple trees in a research area on the spot, drawing vector boundaries of the fruit trees in high-definition satellite images, and recording the initial flowering phases t of the fruit trees in different areas 0 ；
And B: preprocessing MODIS data with low spatial resolution including reprojection to keep a geographic coordinate system and a projection coordinate system consistent with a vector boundary of an apple, then calculating to obtain an NDVI of the MODIS data, searching a pure apple pixel in the MODIS data by combining a fruit tree vector boundary, and extracting an NDVI time sequence curve of the pixel;
step C: repeatedly extracting a plurality of NDVI time sequence curves, removing abnormal values in the curves, interpolating blank values by adopting a Savitzky-Golay filtering method, taking the interpolated curves as reference curves, and taking a set of the reference curves as an apple reference curve library.
3. The method according to claim 2, wherein the step 2 comprises the steps of:
step D: and counting accumulated temperature before the initial flowering phase of the historical ground sample points, and finding out an optimal accumulated temperature threshold value, so that when the optimal accumulated temperature threshold value is used as the initial flowering phase accumulated temperature value of the apple in different years and different areas, the average relative error of each sample point is minimum.
4. The method according to claim 3, wherein the step 3 comprises the steps of:
step E: and calculating the NDVI data of the research area under clear sky based on the Sentinel-2 surface reflectivity data in the apple growth cycle time.
5. The method according to claim 4, wherein the step 4 comprises the steps of:
step F: analyzing all image pixels of the data and the determining coefficient R of all curves in the MODIS crop reference curve library obtained in the step C through the Sentinel-2 time sequence NDVI data obtained by calculation in the step E 2 Will determine the coefficient R 2 And taking the maximum curve as the optimal reference curve of the image pixel of the Sentinel-2 time sequence NDVI data, and then further combining the Sentinel-2 time sequence NDVI data in the step E and the MODIS optimal reference curve through the following formula to obtain a Sentinel-2 fitting curve.
L(x)＝a×M(x+Δt 0 )+b
Wherein x represents a day of the year, L (x) represents a Sentiniel-2 time series NDVI curve function, M (x) is an optimal MODIS reference curve, Δ t 0 Represents the possible time offset between the two curves, ranging from + -30 daysAnd a and b are fitting parameters calculated by using a least square method.
Step G: and F, recording the time offset of each pixel relative to the reference curve when the curve is fitted according to the step F, and recording the time offset as delta t 0 At this point, according to the initial flowering phase t of its reference curve 0 Obtaining the initial detection flowering phase delta t of the pixel 0 +t 0 。
6. The method of claim 5, wherein the step 5 comprises the steps of:
step H: calculating the date t when the predicted year reaches the optimal accumulated temperature according to the weather forecast data of the predicted year and the historical weather data of the detected year 1 (ii) a Simultaneously calculating the date t when the detection year reaches the optimal accumulated temperature 2 If the space range is large, regional statistics needs to be carried out, and the date difference delta t, of the apple trees reaching the optimal accumulated temperature threshold value in the detection year and the prediction year is calculated at the moment 1 -t 2 ；
Step I: and G, according to the optimal accumulated temperature date difference delta t obtained in the step H, carrying out initial detection on the flowering phase delta t obtained in the step G 0 +t 0 Correcting to obtain predicted flowering phase T ═ T 0 +Δt 0 +Δt。
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