Accurate extraction method of maturity information based on the whole life cycle of crop development and maturity

Abstract

The present invention discloses a method for accurately extracting maturity information based on the whole life cycle of crop development and maturity. First, a vegetation index and browning index time series data set is established and time series difference is performed. Then, the maturity period, growth peak period, seedling period and growth period of candidate crops are determined; then, according to the vegetation browning index index of candidate crops in seedling and maturity period, a crop development and maturity index is designed, and finally, a crop growth period discrimination standard is established based on the index to realize the extraction of maturity information. The present invention cleverly reveals the process of crop maturity and harvesting by using the time series characteristics of the browning index, breaking away from the research idea of ​​mainly relying on vegetation index time series data to determine growth peaks for a long time, and can effectively overcome the interference caused by weed growth in cultivated land areas and winter wheat growth peaks. At the same time, there is no need to set constraints or threshold parameters according to the characteristics of crop planting patterns in different regions, and it has high-precision large-scale application and promotion capabilities.

Description

Technical Field

[0001] The present invention relates to the field of large-scale agricultural remote sensing monitoring technology, and in particular to a method for accurately extracting maturity information based on the entire life cycle of crop development and maturity. Background Art

[0002] As the most direct and effective way to increase crop yields, multiple cropping plays an important role in ensuring the implementation of my country's food security strategy. Most areas of my country, especially the North China Plain and its southern areas, generally have the climatic conditions required for multiple cropping. The proportion of multiple cropping in my country is twice the world average. Remarkable results have been achieved by increasing the intensity of agricultural planting through multiple cropping. my country's agricultural production uses less than one-third of the world's per capita arable land to feed 22% of the world's population. However, with the continuous and rapid development of my country's economy, the continuous transfer of rural labor has brought certain challenges to multiple cropping. Many areas that have implemented multiple cropping for a long time have changed to single cropping or even abandoned land. For example, the double-season rice planting that was once popular in the middle and lower reaches of the Yangtze River has gradually been replaced by single-season rice. Crop information is not only related to crop yields, but also closely related to the carbon sequestration function of farmland ecosystems.

[0003] Accurately acquiring information on the spatiotemporal distribution of crop maturity is of great significance for ensuring the sustainable development of agriculture. Traditional agricultural surveys such as crop maturity and crop planting types are time-consuming and costly, and are usually sample surveys that are difficult to achieve full coverage. With the continuous enrichment of time-series remote sensing data sources, their temporal, spatial, and spectral resolutions are constantly improving, bringing rare opportunities for agricultural remote sensing monitoring. However, agricultural monitoring based on time-series remote sensing data is currently based on vegetation index time-series data sets. Crop maturity identification based solely on vegetation index time-series data sets faces at least the following two challenges: on the one hand, the growth peaks formed by weeds during the seasonal fallow of cultivated land are very similar to the growth peaks of crops, and it is difficult to distinguish them simply based on vegetation indices; on the other hand, winter crops such as winter wheat usually have two growth peaks during the crop growth period, and restrictions need to be set to eliminate the winter growth peaks. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method for accurately extracting maturity information based on the entire life cycle of crop development and maturity, combine the vegetation index and the browning index, determine the complete process of crops from growth to maturity, and establish a multi-ripening system identification method based on the entire life cycle of growth and maturity.

[0005] To achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a method for accurately extracting ripeness information based on the whole life cycle of crop development and maturity: comprising the following steps:

[0006] Step S01: Establish a time series dataset of vegetation index and browning index in the study area;

[0007] Step S02: respectively establishing enhanced vegetation index and browning index difference time series data sets;

[0008] Step S03: Obtaining the maturity period of candidate crops;

[0009] Step S04: obtaining the peak growth period of candidate crops;

[0010] Step S05: obtaining the seedling stage of candidate crops;

[0011] Step S06: Determine the growth period of candidate crops;

[0012] Step S07: Designing a crop maturity index;

[0013] Step S08: Establishing a criterion for determining the growth period of crops;

[0014] Step S09: constructing a process method for the familiarization identification technology;

[0015] Step S10: Obtain the spatial distribution map of the production process in the study area.

[0016] In a preferred embodiment: in the step S02, in the study area, the vegetation index time series data set is time-differentiated pixel by pixel and period by period to form vegetation index difference time series data DVI; similarly, the browning index time series data set is time-differentiated pixel by pixel and period by period to form browning index difference time series data DBRI; the vegetation index difference time series data is multiplied by a coefficient C to further obtain enhanced vegetation index difference time series data CDVI; CDVI=C×DVI, where C is a constant, and the value of C is 30.

[0017] In a preferred embodiment: in the step S03, based on the browning index differential time series data set DBRI, the local maximum value of the browning index differential time series curve is searched pixel by pixel, and it is determined whether it is greater than 0, and the local maximum values ​​of the browning index differential time series curve with values ​​greater than 0 are retained in turn and determined as the maturity period of the candidate crop.

[0018] In a preferred embodiment: in the step S04, the intersection of the enhanced vegetation index difference time series curve and the browning index difference time series curve is determined as the peak growth period of the candidate crop.

[0019] In a preferred embodiment: in the step S05, first, all the candidate crop peak growth periods are searched pixel by pixel, and then the corresponding candidate crop maturity periods are searched backward one by one based on the candidate crop peak growth period, and finally the candidate crop seedling period is dynamically acquired based on the principle of equal spacing between the candidate crop seedling period, peak growth period and maturity period.

[0020] In a preferred embodiment: in the step S06, the candidate crop seedling stage is searched pixel by pixel, and the time period determined by the candidate crop seedling stage and the maturity stage is determined as the candidate crop growth period.

[0021] In a preferred embodiment: in the step S07, the vegetation browning index is designed using the enhanced vegetation index difference time series data and the browning index difference time series data; the vegetation browning index of the candidate crop seedling stage and the vegetation browning index of the candidate crop maturity stage are calculated in sequence, and on this basis, the crop development and maturity index GM is designed, and its calculation formula is: GM=(CDVIl-DBRIl)+(DBRIh-CDVIh); wherein DBRIh and DVIh are the values ​​of the browning index difference time series curve and the enhanced vegetation index difference time series curve corresponding to the crop maturity stage, respectively; DBRIl and CDVIl are the values ​​of the browning index difference time series curve and the enhanced vegetation index difference time series curve corresponding to the crop seedling stage, respectively.

[0022] In a preferred embodiment: in the step S08, a criterion for distinguishing the crop growth period is established based on the crop development maturity index GM; a candidate crop growth period must satisfy GM>λ to become a real crop growth period, where λ is a constant term.

[0023] In a preferred embodiment: in the step S09, according to the rules established in step S08, all candidate crop growing periods are judged pixel by pixel, and the number NL of crop growing periods occurring in the pixel in the year is recorded; the research units with NL values ​​of 0, 1, 2, and 3 are respectively determined as fallow, single-cropping, double-cropping, and triple-cropping.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] (1) The present invention is ingeniously conceived. Starting from the temporal variation characteristics of the browning index, the present invention firmly grasps the characteristic that crops complete a complete life cycle only after they are mature and harvested, which helps to cleverly break away from the long-standing research idea of ​​estimating crop growth peaks based on vegetation indices;

[0026] (2) The method designed by the present invention can effectively overcome the interference caused by weed growth in cultivated land areas and the winter growth peak of winter wheat. It does not need to set constraints or threshold parameters based on the characteristics of crop planting patterns in different regions, and has high-precision large-scale application and promotion capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The figure is a flow chart of the implementation of the preferred embodiment of the present invention.

[0028] Figure 2 This is the time series signal diagram of EVI2 and BRI index of winter wheat-corn and weeds.

[0029] Figure 3 This is the differential time series signal diagram of EVI2 and BRI index of winter wheat-corn and weeds.

[0030] Figure 4 Design a schematic diagram for the indicator.

[0031] Figure 5 This is the spatial distribution map of the study area. DETAILED DESCRIPTION

[0032] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0034] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application; as used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0035] The present invention provides a method for accurately extracting ripeness information based on the entire life cycle of crop development and maturity, such as Figure 1 As shown, firstly, the vegetation index EVI2 and browning index BRI time series data set of the study area are established, and the crop maturity harvest index is designed. According to the fact that the multi-cropping system planting area has two or more crop maturity harvest processes in a year, the discrimination rules are established to realize the mapping of the multi-cropping system planting area in the study area. The method for accurately extracting the crop maturity information based on the whole life cycle of crop development and maturity specifically includes the following steps:

[0036] Step S01: Establish a time series dataset of vegetation index and browning index in the study area

[0037] Based on the reflectance data of the sentinel band, the vegetation index EVI2 and the browning index BRI were calculated pixel by pixel and period by period to form the EVI2 and BRI index time series data set of the study area in the study year. On the basis of removing the cloud interference data period by period, the Whittaker Smoother data smoothing method was used to establish the daily smoothed EVI2 and BRI index time series data set pixel by pixel in the study year. For the winter wheat-corn rotation points and weed points, the EVI2 and BRI index time series curves established within the year are shown in Figure 2 As a new type of optical remote sensing satellite, Sentinel-2 has multiple advantages, including wide bandwidth, high temporal and spatial resolution, and free sharing. Its unique red light edge band is very effective for monitoring vegetation growth and health status.

[0038] Vegetation index: Vegetation index is a factor that characterizes the growth status and spatial distribution density of vegetation. Common vegetation indices include NDVI and EVI2. NDVI is the normalized vegetation index, and its full name is Normalized Difference Vegetation Index. EVI2 is the enhanced vegetation index, and its full name is Enhanced Vegetation Index. The calculation formula of EVI2 index is:

[0039] Where ρRed and ρNIR are the reflectances of the red and near-infrared bands of the Sentinel images, respectively.

[0040] Browning Reflectance Index: Phenolic compounds are important pigments in the growth and development of crops. The Browning Reflectance Index can well reveal the changes in the content level of phenolic compounds in the crop canopy. The calculation formula of the Browning Reflectance Index (BRI) is:

[0041]

[0042] Among them, ρ Green , VRE1 , NIR Represents the reflectance of the green light, first red edge and near infrared bands of Sentinel-2 data. The higher the phenolic compound content, the higher the BRI value; conversely, the lower the phenolic compound content, the lower the BRI value.

[0043] The growth of crops usually goes through a full life cycle from germination, growth, flowering, fruiting, to maturity. During the growth period of crops, the vegetation index usually experiences a process of gradually rising from the seedling stage, reaching a peak during the heading stage, and then gradually declining as the crops mature, showing an inverse U-shaped change characteristic. The change process of the browning index time series curve is exactly the opposite of the vegetation index, and it shows a U-shaped change characteristic during the growth period of crops. That is, the browning index is relatively high during the seedling and maturity stages of crops, and the value is low during the heading stage of crops.

[0044] Step S02: Establishing enhanced vegetation index and browning index difference time series data set

[0045] In the study area, the vegetation index time series data set is time-series differentiated pixel by pixel and period by period to form the vegetation index difference time series data DVI (Differenced VI). Similarly, the browning index time series data set is time-series differentiated pixel by pixel and period by period to form the browning index difference time series data DBRI (Differenced BRI). Considering the difference between the numerical range of the vegetation index and the numerical range of the browning index, the vegetation index difference time series data is multiplied by a coefficient C to further obtain the enhanced vegetation index difference time series data CDVI.

[0046] CDVI=C×DVI.

[0047] In the above formula, C is a constant, and the value of C is 30.

[0048] Based on the enhanced vegetation index difference time series data and browning index difference time series data, for winter wheat-corn and weed points, the corresponding vegetation index and browning index difference time series curves are shown in Figure 3 .

[0049] Although the browning index and the vegetation index generally show opposite change characteristics, the two are not simply linearly negatively correlated. The browning index differential time series curve can well monitor the maturity process of crops. When the crops mature, the browning index differential time series curve shows an obvious local maximum. The browning index is particularly helpful in eliminating the interference of winter wheat growth peaks and weeds, which is manifested in two aspects: (1) Although winter wheat usually shows an obvious winter growth peak on the vegetation index time series curve, the browning index time series curve only shows a periodic change characteristic once during the entire winter wheat growth period; (2) Unlike all crops, the browning index time series curve does not show a very obvious periodic change characteristic during the weed growth period. The reason is that compared with crops, various weeds grow in the cultivated area, and the growth, flowering, fruiting and maturity of different weeds are uneven and alternate.

[0050] Step S03: Obtaining the maturity period of candidate crops

[0051] Based on the browning index differential time series data set DBRI, the local maximum of the browning index differential time series curve is searched pixel by pixel, and it is judged whether it is greater than 0, and only the local maximum value with a value greater than 0 is retained. The local maximum values ​​of the browning index differential time series curve with a value greater than 0 are retained in turn and determined as the maturity period of the candidate crops. At the same time, the maturity periods of the candidate crops searched within a year are recorded in chronological order as: MB1, MB2, ..., MBi.

[0052] Step S04: Obtain the peak growth period of candidate crops

[0053] The intersection of the enhanced vegetation index difference time series curve and the browning index difference time series curve is determined as the candidate crop growth peak period. Based on the vegetation index difference time series dataset DVI and the browning index difference time series dataset DBRI, the intersection of the enhanced vegetation index difference time series curve and the browning index difference time series curve is searched pixel by pixel, and it is defined as the candidate crop growth peak period. The searched crop growth peak periods are recorded pixel by pixel in chronological order as ND1, ND2, ..., NDj (see Figure 4 ). The number of intersection points of the vegetation-browning index difference time series curve, usually in the range of [0,4].

[0054] Step S05: Obtaining the seedling stage of candidate crops

[0055] Based on the candidate crop peak growth period and the candidate crop maturity period, the candidate crop seedling period is dynamically obtained. First, all candidate crop peak growth periods are searched pixel by pixel, and then the corresponding candidate crop maturity period is searched backwards according to the candidate crop peak growth period. Finally, the candidate crop seedling period is dynamically obtained according to the principle of equal spacing between the candidate crop seedling period, peak growth period and maturity period. If the maturity period of the selected crop cannot be found in the backward search during the peak growth period of a candidate crop, it is directly skipped and the search continues in the next candidate crop peak growth period.

[0056] Step S06: Determine the growth period of candidate crops

[0057] Based on the seedling stage and maturity stage of the candidate crops, the candidate crop growth period is determined. The candidate crop seedling stage is searched pixel by pixel one by one, and the time period determined by the seedling stage and maturity stage of the candidate crops is determined as the candidate crop growth period.

[0058] Step S07: Designing crop maturity index

[0059] The enhanced vegetation index difference time series data and browning index difference time series data were used to design vegetation browning index. The vegetation browning index of candidate crops at seedling stage and the vegetation browning index of candidate crops at maturity stage were calculated in sequence. On this basis, the crop development and maturity index GM was designed. The calculation formulas are as follows:

[0060] Vegetation browning index of candidate crops at seedling stage: ΔDDl=CDVIl-DBRIl.

[0061] Vegetation browning index of candidate crops at maturity stage: ΔDDh=DBRIh-CDVIh.

[0062] Crop development and maturity index GM: GM = ΔDDl + ΔDDh

[0063] Among them, ΔDDh is the vegetation browning amount index of the candidate crops in the seedling stage, DBRIh and DVIh are the values ​​of the browning index difference time series curve and the enhanced vegetation index difference time series curve corresponding to the crop maturity stage, respectively; ΔDDl is the vegetation browning amount index of the candidate crops in the maturity stage, DBRIl and CDVIl are the values ​​of the browning index difference time series curve and the enhanced vegetation index difference time series curve corresponding to the crop seedling stage, respectively.

[0064] Step S08: Establishing a criterion for determining the growth period of crops

[0065] Based on the crop maturity index GM, a criterion for distinguishing the crop growth period is established. A candidate crop growth period must satisfy GM>λ to become a real crop growth period. In this embodiment, the value of the constant term λ is 3.0. The threshold λ can be appropriately adjusted in actual applications in different regions.

[0066] Step S09: Constructing a process method for the technical process of familiarization

[0067] According to the rules established in step S08, all candidate crop growth periods are identified pixel by pixel, and the number NL of crop growth periods that appear in the pixel in the year is recorded. The research units with NL values ​​of 0, 1, 2, and 3 are respectively determined as fallow, single cropping, double cropping, and triple cropping.

[0068] Step S10: Obtaining the spatial distribution map of the research area

[0069] Taking Henan Province as an example, the spatial distribution map of the study area is shown in Figure 5 .

[0070] The present invention combines vegetation index and browning index to determine the complete process of crops from growth to maturity, and establishes a multi-ripening system identification method based on the full life cycle of growth and maturity. The time series data set used is Sentinel-2 MSI optical time series remote sensing data. The spectral index used is vegetation index and browning index based on red edge band. The three red edge bands unique to Sentinel-2 MSI data provide a good opportunity for effectively monitoring the growth process of crops. When crops are sown and seedlings emerge, the seedlings are fragile and easily damaged by stress environments such as strong light. Vegetation accumulates more photoprotective pigments, the content of phenolic compounds increases, and the browning index value is high. As crops grow and develop, plants gradually grow and flourish, the content of phenolic compounds gradually decreases, and the browning index value is low. When crops enter the mature stage, the content of phenolic compounds increases again, causing the browning index value to reach a high value again. The present invention makes full use of the vegetation-pigment periodic change characteristics during the crop growth cycle, designs the crop development maturity index, and establishes a crop maturity identification method that can be applied to a wide range of applications.

[0071] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions do not exceed the scope of the technical solution of the present invention, belong to the protection scope of the present invention.

Claims

1. A method for accurately extracting ripeness information based on the entire life cycle of crop development and maturity, characterized by: The following steps are included: Step S01: Establish a time series dataset of vegetation index and browning index in the study area; Step S02: respectively establishing enhanced vegetation index and browning index difference time series data sets; Step S03: Obtaining the maturity period of candidate crops; Step S04: obtaining the peak growth period of candidate crops; Step S05: obtaining the seedling stage of candidate crops; Step S06: Determine the growth period of candidate crops; Step S07: Designing a crop maturity index; Step S08: Establishing a criterion for determining the growth period of crops; Step S09: constructing a process method for the familiarization identification technology; Step S10: Obtaining a spatial distribution map of the cooking process in the study area; In the step S02, in the study area, the vegetation index time series data set is time-series differentiated pixel by pixel and period by period to form vegetation index differential time series data DVI; similarly, the browning index time series data set is time-series differentiated pixel by pixel and period by period to form browning index differential time series data DBRI; the vegetation index differential time series data is multiplied by a coefficient C to further obtain enhanced vegetation index differential time series data CDVI; CDVI=C×DVI, where C is a constant, and the value of C is 30; In the step S03, based on the browning index difference time series data set DBRI, the local maximum value of the browning index difference time series curve is searched pixel by pixel, and it is determined whether it is greater than 0, and the local maximum values ​​of the browning index difference time series curve with values ​​greater than 0 are retained in turn to determine as the maturity stage of the candidate crop; In the step S04, the intersection of the enhanced vegetation index difference time series curve and the browning index difference time series curve is determined as the peak growth period of the candidate crop; In step S05, first, all candidate crop peak growth periods are searched pixel by pixel, then corresponding candidate crop maturity periods are searched backwards according to the candidate crop peak growth periods, and finally, the candidate crop seedling period is dynamically acquired according to the principle of equal spacing between the candidate crop seedling period, peak growth period and maturity period; In the step S06, the candidate crop seedling stage is searched pixel by pixel, and the time period determined by the candidate crop seedling stage and the maturity stage is determined as the candidate crop growth period; In the step S07, the enhanced vegetation index difference time series data and the browning index difference time series data are used to design the vegetation browning index; the vegetation browning index of the candidate crop seedling stage and the vegetation browning index of the candidate crop maturity stage are calculated in sequence, and on this basis, the crop development and maturity index GM is designed, and its calculation formula is: GM= (CDVIl-DBRIl)+(DBRIh-CDVIh); wherein DBRIh and DVIh are the values ​​of the browning index difference time series curve and the enhanced vegetation index difference time series curve corresponding to the crop maturity stage, respectively; DBRIl and CDVIl are the values ​​of the browning index difference time series curve and the enhanced vegetation index difference time series curve corresponding to the crop seedling stage, respectively; In the step S08, a criterion for distinguishing the crop growth period is established based on the crop development maturity index GM; a candidate crop growth period must satisfy GM >λ to become a real crop growth period, where λ is a constant term; In step S09, according to the rules established in step S08, all candidate crop growing periods are judged pixel by pixel, and the number NL of crop growing periods occurring in the pixel year is recorded; the research units with NL values ​​of 0, 1, 2, and 3 are respectively determined as fallow, single-cropping, double-cropping, and triple-cropping.

Images