CN111931987A - Prediction method for rice maturity period in time series LAI curve integral area region - Google Patents
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Abstract
The invention belongs to the technical field of rice maturity prediction, and discloses a rice maturity prediction method in a time sequence LAI curve integral area, which classifies the types of crops in a research area to obtain a planting area map of rice; obtaining a Sentinel-2LAI product in the rice growth period, and reconstructing an LAI time sequence; acquiring the heading stage of the rice when the LAI reaches the peak value; extracting a Sentinel-2LAI time sequence, and calculating a rice maturity threshold; constructing a CSDM curve simulating an LAI time sequence track; simulating an LAI curve based on a CSDM curve simulating an LAI time sequence track; forming a LAI integral curve in the whole growth period; forecasting the maturation period in the first 15 days, and calculating the integral area ratio R by using the LAI integral curve of the whole growth period day by day in the forecasting intervalpre(ii) a Generating a regional rice maturity prediction space distribution map to guide the timeliness of riceAnd (6) harvesting.
Description
Technical Field
The invention belongs to the technical field of rice maturity prediction, and particularly relates to a rice maturity prediction method in a time sequence LAI curve integral area.
Background
Currently, the closest prior art: the agricultural remote sensing system is a comprehensive technology for agricultural application, such as agricultural resource investigation, current land utilization state analysis, agricultural pest and disease monitoring, crop yield estimation and the like by using a remote sensing technology, and can predict crop pests and diseases by acquiring crop image data, including crop growth conditions and forecast. The remote sensing technology is combined with various agricultural subjects and the technology thereof, and the technology is a very comprehensive technology for serving agricultural development. The method mainly comprises the steps of investigating land resources by using a remote sensing technology, investigating and analyzing the current land utilization situation, monitoring and analyzing the growth of crops, predicting plant diseases and insect pests, estimating the yield of the crops and the like. Is one of the largest users of current remote sensing applications. The remote sensing technology is utilized to monitor the crop planting area and the crop growth information, rapidly monitor and evaluate disaster information such as agricultural drought and plant diseases and insect pests, estimate crop yield in the global range, the national range and the regional range, and provide information for grain supply quantity analysis and prediction early warning. The remote sensing satellite can rapidly and accurately acquire ground information, and by combining other modern high and new technologies such as a Geographic Information System (GIS), a Global Positioning System (GPS) and the like, the agricultural condition information collection and analysis can be timed, quantified and positioned, the objectivity is strong, the agricultural condition information collection and analysis is not interfered by people, the agricultural decision is convenient, and the development of precise agriculture becomes possible. Basic principle of crop remote sensing: the reflectivity and the combination of the reflectivity of the red wave band and the near infrared wave band of the remote sensing image have better correlation with the leaf area index, the solar photosynthetic effective radiation and the biomass of crops. The crop types are distinguished through the earth surface information recorded by the satellite sensor, yield forecasting models under different conditions are established, agricultural knowledge and remote sensing observation data are integrated, and remote sensing monitoring and forecasting of crop yields are achieved. The image data can be downloaded and obtained from the remote sensing market, and thematic information product monitoring and service reports can be obtained regularly through various large terminal products, and meanwhile, the defects that manual data collection is time-consuming and labor-consuming and has certain destructiveness are avoided.
In summary, the problems of the prior art are as follows: in the prior art, no related method for predicting the rice maturity by using remote sensing data exists, and the conventional rice maturity prediction is mainly realized manually and is inaccurate.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a rice maturity prediction method in a time series LAI curve integral area region.
The invention is realized in such a way that a rice maturity period prediction method in a time series LAI curve integral area region comprises the following steps:
step one, adopting Sentinel-2 data to combine with field survey to classify the crop types in a research area, and obtaining a planting area map of 20-meter grid unit rice;
secondly, obtaining a Sentinel-2LAI product in the rice growth period, and reconstructing an LAI time sequence;
step three, acquiring the heading stage of the rice when the LAI reaches the peak value;
fourthly, extracting a Sentinel-2LAI time sequence aiming at the growth period of the rice in the last three years, and calculating a rice maturity threshold;
fifthly, constructing a CSDM curve simulating an LAI time sequence track by adopting an actual Sentinel-2LAI curve from the rice seedling emergence stage to the heading stage; simulating an LAI curve based on a CSDM curve simulating an LAI time sequence track; forming a LAI integral curve in the whole growth period;
sixthly, forecasting the maturation period in the first 15 days, and calculating the integral area ratio R by using the LAI integral curve of the whole growth period day by day in a forecasting intervalpre；
And seventhly, repeating the sixth step by 20 m grid units to generate a regional rice maturity prediction spatial distribution map and guide timely harvesting of rice.
Further, in the first step, the classifying the crop types in the research area using the Sentinel-2 data in combination with the field survey comprises:
(1) collecting Sentinel-2 data and obtaining ground data of a research area through field investigation;
(2) obtaining a synthesis time sequence with a certain time length by a data synthesis method;
(3) extracting classification characteristics corresponding to each crop in a research area;
(4) and (4) classifying the crops in the research area based on the characteristics corresponding to each crop obtained in the step (3).
Further, in step (1), the ground data includes geographical location and type of crop.
Further, in the second step, the LAI time series reconstruction method includes:
reconstructing the LAI time sequence by adopting a filtering algorithm;
the filter formula is:
wherein, Yj+1Represents the value in a window on the original LAI curve, m is the radius of the window, N is the convolution number, the width of the window is 2m +1, represents the LAI value in the center of the filtered window, CiFilter coefficients representing the ith LAI value.
Further, in the fourth step, the method for calculating the rice maturity threshold value comprises:
calculating the percentage mean value R of the integral area from heading stage to maturation stage to the total integral area from emergence to maturationaveAs the rice maturity threshold.
Further, the fifth step specifically includes:
1) adopting a canopy structure dynamic model CSDM curve from the heading period to the mature period;
2) combining meteorological observation data and meteorological set forecast data as input of CSDM, and constructing a CSDM curve simulating an LAI time sequence track;
3) simulating an LAI curve based on a CSDM curve simulating an LAI time series track; constitute the LAI integral curve of the whole growth period.
Further, in the sixth step, the maturity forecast includes: calculating integral area ratio R by using LAI integral curve of whole growth period day by day in forecast intervalpreWhen R ispre≥RaveThe corresponding date is the rice maturity period.
Another object of the present invention is to provide a rice maturity prediction system based on time series LAI curve integrated area region for implementing the method for predicting rice maturity of time series LAI curve integrated area region, the rice maturity prediction system based on time series LAI curve integrated area region comprising:
the data acquisition module is used for acquiring Sentinel-2 data and acquiring ground data of a research area in combination with field investigation;
the classification module is used for classifying the types of the crops in the research area;
the planting area map generating module is used for generating a planting area map of the 20-meter grid unit rice based on the crop type classification result;
the time sequence reconstruction module is used for obtaining a Sentinel-2LAI product in the rice growth period and reconstructing an LAI time sequence;
the rice heading stage determining module is used for acquiring a rice heading stage when LAI reaches a peak value;
the time sequence extraction module is used for extracting a Sentinel-2LAI time sequence based on the growth period of rice in three years;
the rice maturity threshold module is used for calculating a rice maturity threshold based on the extracted time sequence;
the CSDM curve generation module is used for constructing a CSDM curve simulating an LAI time sequence track by adopting an actual Sentinel-2LAI curve from the rice seedling emergence stage to the heading stage;
the LAI integral curve generating module is used for simulating an LAI curve based on the generated CSDM curve and by taking the CSDM curve for simulating an LAI time sequence track as a basis; forming a LAI integral curve in the whole growth period;
a maturity stage forecasting module for calculating an integral area ratio R by day-by-day in a forecasting interval with a full growth stage LAI integral curvepreAnd determining RpreJudging whether the rice is mature or not according to the size of the rice maturation threshold value, and forecasting the maturation period;
the prediction spatial distribution map generation module is used for generating a prediction spatial distribution map of the rice maturity period of the region;
and the suggestion generation module is used for guiding the timely harvesting of the rice based on the generated rice maturity prediction spatial distribution map.
Another object of the present invention is to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the method for predicting rice maturity in the area of the integrated area of the time series LAI curve when executed on an electronic device.
It is another object of the present invention to provide a computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the method for predicting rice maturity in the area of the integrated area of the LAI curve over the time series.
In summary, the advantages and positive effects of the invention are: the method can effectively overcome the technical defects of inaccurate prediction and large error of the existing rice maturity period, accurately and effectively predict the rice maturity period, and prepare for guiding the dispatching of regional crop agricultural machinery and preventing emergencies.
The invention constructs a rice maturity prediction method in a time series LAI curve integral area, generates a simulation LAI curve with prediction performance, finally establishes a maturity prediction model based on the integral area, predicts the crop maturity, and solves the problem that the current maturity prediction cannot meet the accurate agricultural requirements in terms of spatial resolution and prediction timeliness.
Drawings
Fig. 1 is a flowchart of a rice maturity prediction method in a time series LAI curve integral area region according to an embodiment of the present invention.
FIG. 2 is a flowchart of a method for classifying types of crops in a research area according to an embodiment of the present invention.
Fig. 3 is a flowchart of a method for generating an LAI integral curve according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a rice maturity prediction system based on a time series LAI curve integral area region according to an embodiment of the present invention.
In the figure: 1. a data acquisition module; 2. a classification module; 3. a planting area map generation module; 4. a time series reconstruction module; 5. a rice heading stage determining module; 6. a time series extraction module; 7. a rice maturity threshold module; 8. a CSDM curve generation module; 9. an LAI integral curve generation module; 10. a maturity stage forecasting module; 11. a prediction space distribution map generation module; 12. a suggestion generation module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a method for predicting the rice maturity of a time series LAI curve integral area region, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for predicting rice maturity in the time series LAI curve integrated area region provided by the embodiment of the present invention includes the following steps:
s101: classifying the crop types in the research area by combining the Sentinel-2 data with the field survey to obtain a planting area map of the 20-meter grid unit rice;
s102: obtaining a Sentinel-2LAI product in the rice growth period, and reconstructing an LAI time sequence by adopting a filtering algorithm to eliminate the influence of cloud;
s103: acquiring a specific date when the LAI reaches a peak value, namely a heading date of rice by adopting a dynamic threshold method;
s104: aiming at the growth period of rice in three years, a Sentinel-2LAI time sequence is extracted, and the percentage mean value R of the integral area from heading period to mature period to the total integral area from emergence period to mature period is calculatedaveAs a rice maturity threshold;
s105: adopting an actual Sentinel-2LAI curve from the rice seedling emergence stage to the heading stage, adopting a canopy structure dynamic model CSDM curve from the heading stage to the mature stage, and combining meteorological observation data and meteorological set forecast data as input of CSDM to construct a CSDM curve simulating an LAI time sequence track; simulating an LAI curve based on a CSDM curve simulating an LAI time sequence track; forming a LAI integral curve in the whole growth period;
s106: forecasting the maturation period 15 days in advance, and calculating the integral area ratio R by using the LAI integral curve of the whole growth period day by day in the forecasting intervalpreWhen R ispre≥RaveThen, the corresponding date is the mature period of the rice;
s107: and repeating the step S106 by the grid unit of 20 meters, generating a regional rice maturity prediction space distribution map, and guiding the rice harvesting in time.
As shown in fig. 2, in step S101, the classifying the crop types in the research area by using Sentinel-2 data in combination with the field survey according to the embodiment of the present invention includes:
s201, collecting Sentinel-2 data and obtaining ground data of a research area through field investigation;
s202, obtaining a synthesis time sequence with a certain time length through a data synthesis method;
s203, extracting classification characteristics corresponding to each crop in the research area;
and S204, classifying the crops in the research area based on the characteristics corresponding to the crops obtained in the step S203.
In step S201, the ground data provided by the embodiment of the present invention includes the geographical location and type of the crop.
In step S102, the LAI time series reconstruction method provided in the embodiment of the present invention includes:
reconstructing the LAI time sequence by adopting a filtering algorithm;
the filter formula is:
wherein, Yj+1Represents the value in a window on the original LAI curve, m is the radius of the window, N is the convolution number, the width of the window is 2m +1, represents the LAI value in the center of the filtered window, CiFilter coefficients representing the ith LAI value.
In step S104, the method for calculating the rice maturity threshold provided by the embodiment of the present invention includes:
calculating the percentage mean value R of the integral area from heading stage to maturation stage to the total integral area from emergence to maturationaveAs the rice maturity threshold.
As shown in fig. 3, step S105 provided in the embodiment of the present invention specifically includes:
s301, adopting a canopy structure dynamic model CSDM curve from the heading stage to the mature stage;
s302, combining meteorological observation data and meteorological set forecast data as input of CSDM, and constructing a CSDM curve simulating an LAI time sequence track;
s303, simulating an LAI curve based on the CSDM curve simulating the LAI time sequence track; constitute the LAI integral curve of the whole growth period.
In step S106, the maturity prediction provided by the embodiment of the present invention includes: calculating integral area ratio R by using LAI integral curve of whole growth period day by day in forecast intervalpreWhen R ispre≥RaveThe corresponding date is the rice maturity period.
As shown in fig. 4, the rice maturity prediction system based on the time series LAI curve integrated area region according to the embodiment of the present invention includes:
the data acquisition module 1 is used for acquiring Sentinel-2 data and acquiring ground data of a research area in combination with field investigation;
the classification module 2 is used for classifying the types of the crops in the research area;
the planting area map generating module 3 is used for generating a planting area map of the 20-meter grid unit rice based on the crop type classification result;
the time sequence reconstruction module 4 is used for obtaining a Sentinel-2LAI product in the rice growth period and reconstructing an LAI time sequence;
the rice heading stage determining module 5 is used for acquiring a rice heading stage when LAI reaches a peak value;
the time sequence extraction module 6 is used for extracting a Sentinel-2LAI time sequence based on the growth period of rice in three years;
a rice maturity threshold module 7 for calculating a rice maturity threshold based on the extracted time sequence;
the CSDM curve generation module 8 is used for constructing a CSDM curve simulating an LAI time sequence track by adopting an actual Sentinel-2LAI curve from the rice seedling emergence stage to the heading stage;
an LAI integral curve generating module 9, configured to simulate, based on the generated CSDM curve, an LAI curve based on a CSDM curve that simulates an LAI time series trajectory; forming a LAI integral curve in the whole growth period;
a maturity forecast module 10 for passing through the forecast intervalCalculating integral area ratio R by using LAI integral curve of whole growth period day by daypreAnd determining RpreJudging whether the rice is mature or not according to the size of the rice maturation threshold value, and forecasting the maturation period;
the prediction spatial distribution map generating module 11 is used for generating a prediction spatial distribution map of the rice maturity period of the region;
and the suggestion generation module 12 is used for guiding the timely harvesting of the rice based on the generated rice maturity prediction spatial distribution map.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A rice maturity prediction method in a time series LAI curve integral area is characterized by comprising the following steps:
step one, adopting Sentinel-2 data to combine with field survey to classify the crop types in a research area, and obtaining a planting area map of 20-meter grid unit rice;
secondly, obtaining a Sentinel-2LAI product in the rice growth period, and reconstructing an LAI time sequence;
step three, acquiring the heading stage of the rice when the LAI reaches the peak value;
fourthly, extracting a Sentinel-2LAI time sequence aiming at the growth period of the rice in the last three years, and calculating a rice maturity threshold;
fifthly, constructing a CSDM curve simulating an LAI time sequence track by adopting an actual Sentinel-2LAI curve from the rice seedling emergence stage to the heading stage; simulating an LAI curve based on a CSDM curve simulating an LAI time sequence track; forming a LAI integral curve in the whole growth period;
sixthly, forecasting the maturation period in the first 15 days, and calculating the integral area ratio R by using the LAI integral curve of the whole growth period day by day in a forecasting intervalpre；
And seventhly, repeating the sixth step by 20 m grid units to generate a regional rice maturity prediction spatial distribution map and guide timely harvesting of rice.
2. The method of claim 1, wherein the step of classifying the crop types in the research area using the Sentinel-2 data in combination with the field survey comprises:
(1) collecting Sentinel-2 data and obtaining ground data of a research area through field investigation;
(2) obtaining a synthesis time sequence with a certain time length by a data synthesis method;
(3) extracting classification characteristics corresponding to each crop in a research area;
(4) and (4) classifying the crops in the research area based on the characteristics corresponding to each crop obtained in the step (3).
3. The method for predicting rice maturity of time series LAI curve integrated area as claimed in claim 2 wherein in step (1) said ground data includes geographical location and type of crop.
4. The method for predicting rice maturity of time series LAI curve integrated area of claim 1, wherein in the second step, the LAI time series reconstruction method comprises:
reconstructing the LAI time sequence by adopting a filtering algorithm;
the filter formula is:
wherein, Yj+1Represents the value in a window on the original LAI curve, m is the radius of the window, N is the convolution number, the width of the window is 2m +1, represents the LAI value in the center of the filtered window, CiFilter coefficients representing the ith LAI value.
5. The method for predicting rice maturity of time series LAI curve integrated area according to claim 1, wherein in the fourth step, the method for calculating rice maturity threshold comprises:
calculating the percentage mean value R of the integral area from heading stage to maturation stage to the total integral area from emergence to maturationaveAs the rice maturity threshold.
6. The method for predicting the rice maturity of the time series LAI curve integrated area region according to claim 1, wherein the fifth step comprises:
1) adopting a canopy structure dynamic model CSDM curve from the heading period to the mature period;
2) combining meteorological observation data and meteorological set forecast data as input of CSDM, and constructing a CSDM curve simulating an LAI time sequence track;
3) simulating an LAI curve based on a CSDM curve simulating an LAI time series track; constitute the LAI integral curve of the whole growth period.
7. The method for predicting the rice maturity of the time series LAI curve integrated area region according to claim 1, wherein in the sixth step, the maturity prediction comprises: calculating integral area ratio R by using LAI integral curve of whole growth period day by day in forecast intervalpreWhen R ispre≥RaveThe corresponding date is the rice maturity period.
8. A rice maturity prediction system based on time series LAI curve integrated area region for implementing the rice maturity prediction method of claim 1 to 7, wherein the rice maturity prediction system based on time series LAI curve integrated area region comprises:
the data acquisition module is used for acquiring Sentinel-2 data and acquiring ground data of a research area in combination with field investigation;
the classification module is used for classifying the types of the crops in the research area;
the planting area map generating module is used for generating a planting area map of the 20-meter grid unit rice based on the crop type classification result;
the time sequence reconstruction module is used for obtaining a Sentinel-2LAI product in the rice growth period and reconstructing an LAI time sequence;
the rice heading stage determining module is used for acquiring a rice heading stage when LAI reaches a peak value;
the time sequence extraction module is used for extracting a Sentinel-2LAI time sequence based on the growth period of rice in three years;
the rice maturity threshold module is used for calculating a rice maturity threshold based on the extracted time sequence;
the CSDM curve generation module is used for constructing a CSDM curve simulating an LAI time sequence track by adopting an actual Sentinel-2LAI curve from the rice seedling emergence stage to the heading stage;
the LAI integral curve generating module is used for simulating an LAI curve based on the generated CSDM curve and by taking the CSDM curve for simulating an LAI time sequence track as a basis; forming a LAI integral curve in the whole growth period;
a maturity stage forecasting module for calculating an integral area ratio R by day-by-day in a forecasting interval with a full growth stage LAI integral curvepreAnd determining RpreJudging whether the rice is mature or not according to the size of the rice maturation threshold value, and forecasting the maturation period;
the prediction spatial distribution map generation module is used for generating a prediction spatial distribution map of the rice maturity period of the region;
and the suggestion generation module is used for guiding the timely harvesting of the rice based on the generated rice maturity prediction spatial distribution map.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the method for predicting rice maturity in the time series LAI curve integrated area region according to any one of claims 1 to 7 when executed on an electronic device.
10. A computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the method for predicting rice maturity in the time series LAI curve integrated area region according to any one of claims 1 to 7.
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