CN114782816B - Remote sensing extraction method for crop multiple cropping index - Google Patents
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Abstract
The invention relates to a remote sensing extraction method of crop multiple cropping indexes, which comprises the following steps: acquiring high-time-resolution remote sensing data, acquiring a crop planting area, and constructing an NDVI-t two-dimensional rectangular coordinate system to obtain a time-series NDVI crop phenological curve; and judging the crop planting stubble number based on the crop phenological curve of the NDVI of the time sequence and the high-time resolution remote sensing data, and acquiring the multiple cropping index of the planting area based on the crop planting stubble number. Compared with the traditional method, the method has the advantages of scientificity and higher precision, combines different crop phenological information, utilizes the medium-high resolution remote sensing data to obtain the NDVI value of the time node of the key phenological period, establishes the crop multiple cropping index, and achieves the purpose of accurately identifying the spatial-temporal distribution of the multiple cropping index.
Description
Technical Field
The invention relates to the technical field of agriculture and remote sensing, in particular to a remote sensing extraction method for crop multiple cropping indexes.
Background
The multiple cropping index is the average number of times of planting crops on the same cultivated land area in a certain period (generally 1 year), namely the average number of times of planting crops on the cultivated land area in a year, and the numerical value is equal to the ratio of the total seeding area of the crops on the cultivated land in the year to the cultivated land area. The multiple cropping index is equal to the ratio of the total seeding area of the crops to the cultivated area in the whole year on the cultivated land. Is an index reflecting the utilization degree of cultivated land and is expressed by percentage. The calculation formula is as follows: replanting index = total area of crop sown (or transplanted) all year round ÷ total area of cultivated land × 100%. It reflects the degree of multiple cropping and is used for comparing the utilization conditions of cultivated land among different years, different regions and different production units. Obtaining the multiple cropping index and the distribution information of the farmland is very important for the aspects of crop growth vigor, yield estimation, farmland management and the like. Therefore, accurate space-time distribution of the multiple cropping index of the farmland is crucial to crop yield assessment, scientific management and scientific allocation of water resources.
At present, methods for obtaining farmland multi-cropping index information of space-time distribution comprise a manual statistical method and a remote sensing method, wherein the remote sensing method comprises a land utilization analysis method, a time sequence analysis method and the like. The manual statistical method is an earlier used method, and mainly obtains the multiple cropping information of the farmland through manual field statistics, summarization and reporting. The method has the problems of being not objective enough and being difficult to obtain the multi-species index distribution information of space-time dynamic state. The remote sensing method is the most common method applied at home and abroad, and has the advantages of objectivity, rapidness, low cost and the like. The land utilization method in the remote sensing method is to estimate farmland information obtained by a remote sensing technology method and combine experience, and the method can effectively obtain farmland distribution and experience information, but can be identified by combining local planting habits and combining reported data. The time sequence extraction method mainly develops an information extraction method according to the time sequence change characteristics of the crops and statistically analyzes the multiple cropping indexes of the crops. However, due to the limitation of spatial resolution, the method is difficult to be popularized and applied to the broken or small region of the land.
With the development of remote sensing technology, the spatial resolution of remote sensing data tends to be improved, and the number of satellite emissions with medium and high resolution is greatly increased, so that it is possible to obtain multiple kinds of space-time distribution information of farmlands by applying medium and high resolution.
Aiming at the problems, the invention provides a remote sensing extraction method and a remote sensing extraction system for crop multiple cropping indexes, which comprehensively consider the phenological information of crops and the NDVI characteristics of time sequences in consideration of the limitation of the current extraction method.
Disclosure of Invention
The invention provides a remote sensing extraction method of a crop multiple cropping index, which combines different crop phenological information, utilizes medium-high resolution remote sensing data to obtain an NDVI (normalized difference vegetation index) value of a time node of a key phenological period, establishes the crop multiple cropping index and achieves the purpose of accurately identifying the spatial-temporal distribution of the multiple cropping index.
In order to achieve the purpose, the invention provides the following scheme:
a remote sensing extraction method for crop multiple cropping indexes comprises the following steps:
acquiring high-time-resolution remote sensing data, acquiring a crop planting area, and constructing an NDVI-t two-dimensional rectangular coordinate system to obtain a time-series NDVI crop phenological curve;
and judging the crop planting stubble number based on the crop phenological curve of the NDVI of the time sequence and the high-time-resolution remote sensing data, and acquiring the multiple cropping index of the planting area based on the crop planting stubble number.
Preferably, the high time resolution remote sensing data meter is based onCalculating crop identification index if the crop identification index is not less than a preset threshold value a 4 Determining farmland coverage information, determining the area to be monitored according to the farmland coverage information, selecting remote sensing data of crop planting, growth and harvesting according to the range and time period of the area to be monitored, and determining the crop planting area; wherein the preset threshold a 4 And comprehensively determining according to the type of the remote sensing image and the characteristics of the underlying surface of the farmland coverage area.
Preferably, the crop identification index is calculated by the formula:
wherein CRI is crop identification index, NDVI i NDVI value of ith scene data covering the same area; i is ith scene data of the remote sensing image participating in calculation; n is a radical of an alkyl radical 1 The number of scenes of the remote sensing image;is the average value of the NDVI over a time series,NDVI is the normalized vegetation index, R nir Is the reflectivity of the near infrared band, R r The reflectance of the red band.
Preferably, the process of obtaining the crop planting area comprises:
ZWI=A 1 ∪A 2 ∪...∪A n
in the formula, ZWI is the maximum area of a planting area; a1 A2 8230an is a set which is obtained based on remote sensing image statistics and takes pixels as units.
Preferably, the process of obtaining a time-series crop phenology curve of NDVI comprises:
and establishing the NDVI-t two-dimensional rectangular coordinate system based on the crop planting area, and acquiring the relation between NDVI of different crop growth periods and time in the same time sequence through the NDVI-t two-dimensional rectangular coordinate system so as to obtain a crop phenological curve of the NDVI of the time sequence.
Preferably, the maximum value NDV I of the crop phenology curve of the NDVI of different crop types is determined based on the crop phenology curve of the NDVI of the time series and the high-time resolution remote sensing data max And median value
Preferably, the process of judging the number of crop planting stubbles comprises the following steps:
and acquiring crop planting starting point information and crop maturity starting point information based on the phenological curve, and determining the number of planted stubbles of crops with the same pixel.
Preferably, the NDVI of the point where the phenological curve reaches the median is set based on the number of planted stubbles of the same pixel crop p10 、NDVI p11 、NDVI p20 、NDVI p21 、NDVI p30 、NDVI p31 If overwintering crops are included, the value NDV I is taken p4 。
Preferably, the formula for calculating the multiple cropping index is as follows:
in the formula, K ij The planting condition of the j-th crop in the ith year is shown, n is the counted number of years, m is the number of stubbles planted by the crop, and FI is a multiple cropping index.
The invention has the beneficial effects that:
compared with the traditional method, the method has the advantages of scientificity and higher precision, different crop phenology information is combined, the NDVI value of the time node of the key phenology period is obtained by using the remote sensing data with medium and high resolution, the crop multiple cropping index is established, and the purpose of accurately identifying the time-space distribution of the multiple cropping index is achieved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a remote sensing extraction method of crop multiple cropping index according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Referring to the attached figure 1, the remote sensing extraction method of the crop multiple cropping index comprises the following steps:
acquiring high-time-resolution remote sensing data to obtain farmland coverage information, and determining a region to be monitored according to the farmland coverage information;
determining a crop planting area based on the area to be monitored, and constructing an NDVI-t two-dimensional rectangular coordinate system to obtain a time-series NDVI crop phenological curve;
and obtaining crop planting stubble number based on the crop phenological curve of the NDVI of the time sequence and the high-time-resolution remote sensing data, and obtaining the multiple cropping index of the area to be monitored based on the crop planting stubble number.
Calculating a crop identification index based on the high temporal resolution remote sensing data:
wherein CRI is crop identification index, NDVI i NDVI value of ith scene data covering the same area; i is ith scene data of the remote sensing image participating in calculation; n is 1 The number of scenes of the remote sensing image is;is the average value of NDVI in a time series,NDVI is the normalized vegetation index, R nir Is the reflectivity of the near infrared band, R r The reflectance of the red band.
If the crop identification index CRI is not less than a preset threshold value a 4 Determining the farmland coverage information, and determining the region to be monitored according to the farmland coverage information, wherein the preset threshold value a 4 And comprehensively determining according to the type of the remote sensing image and the characteristics of the underlying surface of the farmland coverage area.
Selecting suitable time series remote sensing data according to the range and the time period of the area to be monitored, and determining a crop planting area:
ZWI=A 1 ∪A 2 ∪...∪A n
in the formula, ZWI is the maximum area of a planting area; a1 A2 8230an is a set which is obtained based on remote sensing image statistics and takes pixels as units.
And further optimizing the scheme, establishing the NDVI-t two-dimensional rectangular coordinate system based on the crop planting area, wherein the NDVI is taken as a longitudinal axis, and the time t is taken as a transverse axis.
And obtaining the relation between the NDVI and time of different crop growth periods in the same time sequence through the NDVI-t two-dimensional rectangular coordinate system, and further obtaining a crop phenological curve of the NDVI in the time sequence. Time series NDVI crop climatic Curve to determine key nodes for crop growth, the time points are determined from this curve
Further optimizing the scheme, determining the maximum value NDV I of the crop phenological curve of the NDVI of different crop types based on the crop phenological curve of the NDVI of the time sequence and the high-time resolution remote sensing data max And median value
And determining the planting stubbles of the same pixel crop based on the crop planting starting point information and the crop maturity starting point information. Setting a point NDVI at which the phenological curve reaches a median value based on the number of planted stubbles of the crop p10 、NDV I p11 、NDV I p20 、NDV I p21 、NDV I p30 、NDV I p31 If overwintering crops are included, the value NDV I is taken p4 . The phenological curve presents a sine curve, and the middle points on the two sides are taken.
(1) For an area with at most one crop in one year, the following steps are adopted:
if both:
and:t p11 and t p10 The maximum value of NDVI in between satisfies the condition: | NDVI max1 -NDVI max |≤a 1 And determining the crop as a crop. Therefore, the corresponding pixel value is set to 1.
In the formula, t 10 And t 11 Are each t p10 Time points of remote sensing images obtained before and after the point (the midpoint of the first crop planting period); t is t 20 And t 21 Are each t p11 Time points of remote sensing images obtained before and after the point (the midpoint of the mature period of the first crop); NDVI max1 The NDVI peak for this time period.
(2) For an area with two possible crops in a year, the following are adopted:
on the basis of (1), if the following conditions are satisfied:
and:t p21 and t p20 The maximum value of NDVI in between satisfies the condition: i NDV I max2 -NDV I max |≤a 2 And determining the crop as the second crop. Therefore, the corresponding pixel value is set to 2.
In the formula, t 30 And t 31 Are each t p20 Time points of remote sensing images obtained before and after the point (the midpoint of the second crop planting period); t is t 40 And t 41 Are each t p21 Time points of remote sensing images obtained before and after the point (the midpoint of the mature period of the second crop); NDVI max2 The NDVI peak for this time period.
(3) For an area where three crops are likely to be planted in a year, the following are adopted:
on the basis of (2), if the following conditions are satisfied:
and:t p31 and t p30 The maximum value of NDVI in between satisfies the condition: i NDV I max3 -NDV I max |≤a 3 And then the crop is determined as the third crop 3. Therefore, the corresponding pixel value is set to 3.
In the formula, t 50 And t 51 Are each t p30 Time points of remote sensing images obtained before and after the point (the midpoint of the third crop planting period); t is t 60 And t 61 Are each t p31 Time points of remote sensing images obtained before and after the point (the midpoint of the mature period of the third crop); NDVI max3 The NDVI peak for this time period.
(4) If there are overwintering crops:
if the plants are planted in autumn and harvested in spring and summer of the next year, the plants are determined as overwintering crops. Therefore, the time point of the germination period or the growth period starting in the next year of the planting is determined by combining the physical and weather data.
For the calculation of the generally multiple seed index:
in the formula, K ij The planting condition of the jth crop in the ith year is n, m is the number of stubbles planted by the crop, and FI is a multiple cropping index. If the j th crop of the ith year is planted, K j A value of 1, otherwise zero
Compared with the traditional method, the method has the advantages of scientificity and higher precision, different crop phenology information is combined, the NDVI value of the time node of the key phenology period is obtained by using the remote sensing data with medium and high resolution, the crop multiple cropping index is established, and the purpose of accurately identifying the time-space distribution of the multiple cropping index is achieved.
The above-described embodiments are only intended to describe the preferred embodiments of the present invention, and not to limit the scope of the present invention, and various modifications and improvements made to the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.
Claims (4)
1. A remote sensing extraction method for crop multiple cropping indexes is characterized by comprising the following steps:
acquiring high-time-resolution remote sensing data, acquiring a crop planting area, and constructing an NDVI-t two-dimensional rectangular coordinate system to obtain a time-series NDVI crop phenological curve;
the process for obtaining the time series NDVI crop phenological curve comprises the following steps:
establishing the NDVI-t two-dimensional rectangular coordinate system based on the crop planting area, and acquiring the relationship between NDVI and time of different crop growth periods in the same time sequence through the NDVI-t two-dimensional rectangular coordinate system so as to obtain a crop phenological curve of the NDVI in the time sequence;
judging the crop planting stubble number based on the crop phenological curve of the NDVI of the time sequence and the high-time resolution remote sensing data, and acquiring a multiple cropping index of the planting area based on the crop planting stubble number;
determining the maximum value NDVI of the crop phenological curve of the NDVI of different crop types based on the crop phenological curve of the NDVI of the time sequence and the high-time resolution remote sensing data max And median value
The process for judging the number of the crop planting stubbles comprises the following steps:
acquiring crop planting starting point information and crop maturity starting point information based on the phenological curve, and determining the number of planted stubbles of crops with the same pixel;
setting the point NDVI at which the phenological curve reaches the median value based on the number of planted stubbles of the same pixel crop p10 、NDVI p11 、NDVI p20 、NDVI p21 、NDVI p30 、NDVI p31 If overwintering crops are included, the value NDVI is taken p4 ;
The formula for calculating the multiple cropping index is as follows:
in the formula, K ij The planting condition of the j-th crop in the ith year is shown, n is the counted number of years, m is the number of stubbles planted by the crop, and FI is a multiple cropping index.
2. The remote sensing extraction method for crop multi-cropping index according to claim 1, characterized in that the method is based onCalculating a crop identification index from the high-time-resolution remote sensing data, and if the crop identification index is not less than a preset threshold value a 4 Determining farmland coverage information, determining a region to be monitored according to the farmland coverage information, selecting remote sensing data of crop planting, growth and harvesting according to the range and time period of the region to be monitored, and determining the crop planting region; wherein the preset threshold a 4 And comprehensively determining according to the type of the remote sensing image and the characteristics of the underlying surface of the farmland coverage area.
3. The remote sensing extraction method for the crop multi-cropping index according to claim 2, characterized in that the calculation formula of the crop identification index is as follows:
wherein CRI is crop identification index, NDVI i NDVI value of ith scene data covering the same area; i is ith scene data of the remote sensing image participating in calculation; n is 1 The number of scenes of the remote sensing image is;is the average value of NDVI in a time series,NDVI is the normalized vegetation index, R nir Is the reflectivity of the near infrared band, R r The reflectance of the red band.
4. The remote sensing extraction method for the crop multiple cropping index according to claim 2, characterized in that the process of obtaining the crop planting area comprises:
ZWI=A 1 ∪A 2 ∪...∪A n
in the formula, ZWI is the maximum area of a planting area; a. The 1 ,A 2 …A n Is a set which is obtained based on remote sensing image statistics and takes pixels as unitsAnd (6) mixing.
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