CN114782816A - Remote sensing extraction method for crop multiple cropping index - Google Patents

Remote sensing extraction method for crop multiple cropping index Download PDF

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CN114782816A
CN114782816A CN202210462211.1A CN202210462211A CN114782816A CN 114782816 A CN114782816 A CN 114782816A CN 202210462211 A CN202210462211 A CN 202210462211A CN 114782816 A CN114782816 A CN 114782816A
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刘冰宣
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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

一种作物复种指数遥感提取方法A Remote Sensing Extraction Method of Crop Multiple Cropping Index

技术领域technical field

本发明涉及农业和遥感技术领域，特别是涉及一种作物复种指数遥感提取方法。The invention relates to the technical fields of agriculture and remote sensing, in particular to a remote sensing extraction method of crop multiple cropping index.

背景技术Background technique

复种指数是指一定时期内(一般为1年)在同一块耕地面积上种植农作物的平均次数，即年内耕地面积上种植农作物的平均次数，数值上等于年内耕地上农作物总播种面积与耕地面积之比。复种指数等于耕地上全年内农作物的总播种面积与耕地面积之比。是反映耕地利用程度的指标，用百分数表示。计算公式为：复种指数＝全年播种(或移栽)作物的总面积÷耕地总面积×100％。它反映复种程度的高低，用来比较不同年份、不同地区和不同生产单位之间耕地的利用情况。获得农田的复种指数和分布信息对作物长势、产量的估算和农田管理等方面十分重要。因此，精准的农田的复种指数的时空分布对于作物估产、科学管理和水资源科学分配至关重要。The multiple cropping index refers to the average number of crops planted on the same arable land area within a certain period (usually 1 year), that is, the average number of crops planted on the arable land area during the year, and the value is equal to the total sown area of crops on the arable land in the year and the arable land area. Compare. The multiple cropping index is equal to the ratio of the total sown area of crops to the area of arable land in the whole year. It is an index reflecting the degree of cultivated land utilization, expressed as a percentage. The calculation formula is: multiple cropping index = total area of crops sown (or transplanted) in the whole year ÷ total area of arable land × 100%. It reflects the level of multiple cropping and is used to compare the utilization of cultivated land between different years, different regions and different production units. Obtaining the multiple cropping index and distribution information of farmland is very important for crop growth, yield estimation and farmland management. Therefore, the accurate spatiotemporal distribution of the multiple cropping index of farmland is very important for crop yield estimation, scientific management and scientific allocation of water resources.

目前，获得时空分布的农田复种指数信息的方法包括人工统计方法和遥感方法，其中遥感方法包括土地利用分析方法、时间序列的分析方法等。其中，人工统计方法是较早使用的一种方法，主要通过人工实地统计并汇总上报获得农田的复种信息。该方法的问题存在不够客观并且难以获得时空动态的复种指数分布信息。遥感方法是国内外应用最普遍的方法，具有客观、快速、成本低等优势。遥感方法中的土地利用方法是通过遥感技术方法获得的耕地信息，并结合经验进行估计，该方法可以有效的获得耕地分布和经验信息，然而需要结合当地的种植习惯和结合上报数据才能识别。时间序列的提取方法主要根据作物的时间序列变化特征研发信息提取方法统计分析作物的复种指数。然而，受空间分辨率的限制，对于地块破碎或者小的区域难以推广应用。At present, the methods for obtaining the information of cropland multiple cropping index distributed in time and space include manual statistical methods and remote sensing methods, among which remote sensing methods include land use analysis methods, time series analysis methods, and the like. Among them, the manual statistical method is a method used earlier, mainly through manual on-the-spot statistics and summary reporting to obtain the multi-cropping information of farmland. The problem of this method is that it is not objective enough and it is difficult to obtain the information of the spatiotemporal dynamics of the exponential distribution of multiple crops. Remote sensing method is the most widely used method at home and abroad, and has the advantages of objectivity, rapidity and low cost. The land use method in the remote sensing method is the cultivated land information obtained by the remote sensing technology method and estimated based on the experience. This method can effectively obtain the cultivated land distribution and experience information, but it needs to combine the local planting habits and the reported data to identify. The time series extraction method is mainly based on the time series change characteristics of crops to develop information extraction methods to statistically analyze the multiple cropping index of crops. However, due to the limitation of spatial resolution, it is difficult to popularize and apply for fragmented or small areas.

随着遥感技术的发展，遥感数据空间分辨率有提高的趋势，而且中高分辨率的卫星发射的数量大幅度增加，使得应用中高分辨率获得农田的复种的时空分布信息成为可能。With the development of remote sensing technology, the spatial resolution of remote sensing data tends to increase, and the number of medium and high-resolution satellite launches has increased significantly, making it possible to obtain the spatiotemporal distribution information of farmland multi-cropping with medium and high resolution.

针对上述问题，考虑到目前提取中模型方法的局限性，本发明综合考虑作物的物候信息和时间序列的NDVI特征，提出一种作物复种指数的遥感提取方法及系统。In view of the above problems and considering the limitations of the current extraction model methods, the present invention comprehensively considers the phenological information of crops and the NDVI characteristics of time series, and proposes a remote sensing extraction method and system for crop multiple cropping index.

发明内容SUMMARY OF THE INVENTION

本发明提供一种作物复种指数遥感提取方法，结合不同的作物物候信息，利用中高分辨率的遥感数据获得关键物候期的时间节点的NDVI数值，建立作物复种指数，达到精准识别复种指数时空分布的目的。The invention provides a remote sensing extraction method for crop multiple cropping index, which combines different crop phenology information, uses medium and high-resolution remote sensing data to obtain NDVI values of time nodes of key phenological periods, establishes crop multiple cropping index, and achieves accurate identification of the temporal and spatial distribution of multiple cropping index. Purpose.

为实现上述目的，本发明提供了如下方案：For achieving the above object, the present invention provides the following scheme:

一种作物复种指数遥感提取方法，包括：A remote sensing extraction method for crop multiple cropping index, comprising:

采集高时间分辨率遥感数据，获取作物种植区域，构建NDVI-t二维直角坐标系，得到时间序列的NDVI的作物物候曲线；Collect high temporal resolution remote sensing data, obtain crop planting area, construct NDVI-t two-dimensional rectangular coordinate system, and obtain time series NDVI crop phenology curve;

基于所述时间序列的NDVI的作物物候曲线和所述高时间分辨率遥感数据，判断作物种植茬数，基于所述作物种植茬数获取所述种植区域的复种指数。Based on the NDVI crop phenology curve of the time series and the high-time-resolution remote sensing data, the number of crop planting stubble is determined, and the multiple cropping index of the planting area is obtained based on the crop planting stubble number.

优选地，基于所述高时间分辨率遥感数据计算作物识别指数，若所述作物识别指数不小于预设阈值a4，则确定农田覆盖信息，通过所述农田覆盖信息确定所述待监测区域，根据所述待监测区域的范围和时间段，选取作物种植、生长和收割的遥感数据，确定所述作物种植区域；其中，所述预设阈值a4根据遥感影像的类型和农田覆盖区域下垫面的特征，综合确定。Preferably, the crop identification index is calculated based on the high temporal resolution remote sensing data, and if the crop identification index is not less than a preset threshold a 4 , the farmland coverage information is determined, and the to-be-monitored area is determined by the farmland coverage information, According to the scope and time period of the to-be-monitored area, select the remote sensing data of crop planting, growth and harvesting to determine the crop planting area; wherein, the preset threshold a 4 is based on the type of remote sensing image and the field coverage area. The characteristics of the surface are comprehensively determined.

优选地，所述作物识别指数的计算公式为：Preferably, the calculation formula of the crop identification index is:

Figure BDA0003620764100000031
Figure BDA0003620764100000031

其中，CRI为作物识别指数，NDVIi为覆盖同一区域的第i景数据的NDVI值；i为参与计算的遥感影像的第i景数据；n1为遥感影像的景数；

Figure BDA0003620764100000032
为时间序列的NDVI均值，
Figure BDA0003620764100000033
NDVI为归一化植被指数，Rnir为近红外波段反射率，Rr为红波段的反射率。Among them, CRI is the crop identification index, NDVI i is the NDVI value of the ith scene data covering the same area; i is the ith scene data of the remote sensing image participating in the calculation; n 1 is the scene number of the remote sensing image;
Figure BDA0003620764100000032
is the mean NDVI of the time series,
Figure BDA0003620764100000033
NDVI is the normalized vegetation index, R nir is the reflectance in the near-infrared band, and R r is the reflectance in the red band.

优选地，获取所述作物种植区域的过程包括：Preferably, the process of obtaining the crop planting area includes:

ZWI＝A1∪A2∪...∪An ZWI=A 1 ∪A 2 ∪...∪A n

式中ZWI为种植区的最大面积；A1，A2…An为基于遥感影像统计得到的以像素为单元的集合。In the formula, ZWI is the maximum area of the planting area; A1, A2...An is a set of pixels based on remote sensing image statistics.

优选地，所述得到时间序列的NDVI的作物物候曲线的过程包括：Preferably, the process of obtaining the crop phenology curve of the time series NDVI includes:

基于所述作物种植区域建立所述NDVI-t二维直角坐标系，通过所述NDVI-t二维直角坐标系获取同一时间序列不同作物生育期NDVI和时间的关系，进而得到时间序列的NDVI的作物物候曲线。The NDVI-t two-dimensional rectangular coordinate system is established based on the crop planting area, and the relationship between the NDVI and time of different crop growth stages in the same time series is obtained through the NDVI-t two-dimensional rectangular coordinate system, and then the NDVI of the time series is obtained. Crop phenology curves.

优选地，基于所述时间序列的NDVI的作物物候曲线和所述高时间分辨率遥感数据，确定不同作物类型的NDVI的作物物候曲线最大值NDV Imax和中值

Figure BDA0003620764100000041
Preferably, based on the crop phenology curve of the NDVI of the time series and the high temporal resolution remote sensing data, determine the maximum value NDV I max and the median value of the crop phenology curve of the NDVI of different crop types
Figure BDA0003620764100000041

优选地，判断作物种植茬数的过程包括：Preferably, the process of judging the number of crop stubble includes:

基于所述物候曲线获取作物种植起点信息和作物成熟起点信息，确定同一像素作物的种植茬数。The crop planting starting point information and the crop maturity starting point information are obtained based on the phenology curve, and the planting stubble number of the same pixel crop is determined.

优选地，基于所述同一像素作物的种植茬数，设定所述物候曲线达到中值的点NDVIp10、NDVIp11、NDVIp20、NDVIp21、NDVIp30、NDVIp31，若包括越冬作物，则取值NDV Ip4。Preferably, based on the planting stubble number of the same pixel crop, set the points NDVI p10 , NDVI p11 , NDVI p20 , NDVI p21 , NDVI p30 , and NDVI p31 at which the phenological curve reaches the median value. Value NDV I p4 .

优选地，所述复种指数的计算公式为：Preferably, the calculation formula of the multiple cropping index is:

Figure BDA0003620764100000042
Figure BDA0003620764100000042

式中，Kij为第i年第j种作物种植情况，n为统计的年数，m为作物种植的茬数，FI为复种指数。In the formula, K ij is the planting situation of the jth crop in the ith year, n is the number of statistical years, m is the stubble number of crops planted, and FI is the multiple cropping index.

本发明的有益效果为：The beneficial effects of the present invention are:

本发明相比较传统的方法更具有科学性和更高的精度，结合不同的作物物候信息，利用中高分辨率的遥感数据获得关键物候期的时间节点的NDVI数值，建立作物复种指数，达到精准识别复种指数时空分布的目的。Compared with the traditional method, the present invention is more scientific and more precise, combines different crop phenological information, and utilizes medium and 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 accurate identification. The purpose of multiplying exponential spatiotemporal distributions.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings required in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some of the present invention. In the embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative labor.

图1为本发明实施例的一种作物复种指数遥感提取方法的流程示意图。FIG. 1 is a schematic flowchart of a remote sensing extraction method for crop multiple cropping index according to an embodiment of the present invention.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本发明作进一步详细的说明。In order to make the above objects, features and advantages of the present invention more clearly understood, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

参照附图1，一种作物复种指数遥感提取方法，包括：With reference to accompanying drawing 1, a kind of crop multiple cropping index remote sensing extraction method, comprises:

获取高时间分辨率遥感数据，得到农田覆盖信息，通过所述农田覆盖信息确定待监测区域；Obtaining high temporal resolution remote sensing data, obtaining farmland coverage information, and determining the area to be monitored through the farmland coverage information;

基于所述待监测区域确定作物种植区域，构建NDVI-t二维直角坐标系，得到时间序列的NDVI的作物物候曲线；Determine the crop planting area based on the to-be-monitored area, construct the NDVI-t two-dimensional Cartesian coordinate system, and obtain the time-series NDVI crop phenology curve;

基于所述时间序列的NDVI的作物物候曲线和所述高时间分辨率遥感数据，得到作物种植茬数，基于所述作物种植茬数得到所述待监测区域的复种指数。Based on the NDVI crop phenology curve of the time series and the high temporal resolution remote sensing data, the number of crop planting stubble is obtained, and the multiple cropping index of the area to be monitored is obtained based on the crop planting stubble number.

基于所述高时间分辨率遥感数据计算作物识别指数：Calculate the crop identification index based on the high temporal resolution remote sensing data:

Figure BDA0003620764100000061
Figure BDA0003620764100000061

其中，CRI为作物识别指数，NDVIi为覆盖同一区域的第i景数据的NDVI值；i为参与计算的遥感影像的第i景数据；n1为遥感影像的景数；

Figure BDA0003620764100000062
为时间序列的NDVI均值，
Figure BDA0003620764100000063
NDVI为归一化植被指数，Rnir为近红外波段反射率，Rr为红波段的反射率。Among them, CRI is the crop identification index, NDVI i is the NDVI value of the ith scene data covering the same area; i is the ith scene data of the remote sensing image participating in the calculation; n 1 is the scene number of the remote sensing image;
Figure BDA0003620764100000062
is the mean NDVI of the time series,
Figure BDA0003620764100000063
NDVI is the normalized vegetation index, R nir is the reflectance in the near-infrared band, and R r is the reflectance in the red band.

若所述作物识别指数CRI不小于预设阈值a4，则确定所述农田覆盖信息，通过所述农田覆盖信息确定所述待监测区域，其中，所述预设阈值a4根据遥感影像的类型和农田覆盖区域下垫面的特征，综合确定。If the crop identification index CRI is not less than a preset threshold a 4 , the farmland coverage information is determined, and the to-be-monitored area is determined by the farmland coverage information, wherein the preset threshold a 4 is based on the type of remote sensing image And the characteristics of the underlying surface of the farmland coverage area are comprehensively determined.

根据所述待监测区域的范围和时间段，选取适宜的时间序列的遥感数据，确定作物种植区域：According to the scope and time period of the area to be monitored, select appropriate time series remote sensing data to determine the crop planting area:

ZWI＝A1∪A2∪...∪An ZWI=A 1 ∪A 2 ∪...∪A n

式中ZWI为种植区的最大面积；A1，A2…An为基于遥感影像统计得到的以像素为单元的集合。In the formula, ZWI is the maximum area of the planting area; A1, A2...An is a set of pixels based on remote sensing image statistics.

进一步优化方案，基于所述作物种植区域建立所述NDVI-t二维直角坐标系，其中NDVI作为纵轴，时间t作为横轴。In a further optimization scheme, the NDVI-t two-dimensional Cartesian coordinate system is established based on the crop planting area, wherein NDVI is used as the vertical axis and time t is used as the horizontal axis.

通过所述NDVI-t二维直角坐标系得到同一时间序列不同作物生育期NDVI和时间的关系，进而得到时间序列的NDVI的作物物候曲线。时间序列的NDVI的作物物候曲线为了确定作物生长的关键节点，后面的是根据此曲线来确定各时间点Through the NDVI-t two-dimensional rectangular coordinate system, the relationship between NDVI and time of different crop growth stages in the same time series is obtained, and then the crop phenology curve of the time series NDVI is obtained. Crop phenology curve of time series NDVI In order to determine the key nodes of crop growth, the following is to determine each time point according to this curve

进一步优化方案，基于所述时间序列的NDVI的作物物候曲线和所述高时间分辨率遥感数据，确定不同作物类型的NDVI的作物物候曲线最大值NDV Imax和中值

Figure BDA0003620764100000071
Further optimization scheme, based on the crop phenology curve of the NDVI of the time series and the high temporal resolution remote sensing data, determine the maximum value NDV I max and the median value of the crop phenology curve of the NDVI of different crop types
Figure BDA0003620764100000071

基于作物种植起点信息和作物成熟起点信息，确定同一像素作物的种植茬数。基于所述作物的种植茬数，设定所述物候曲线达到中值的点NDVIp10、NDV Ip11、NDV Ip20、NDVIp21、NDV Ip30、NDV Ip31，若包括越冬作物，则取值NDV Ip4。物候曲线呈现正弦曲线，取两侧中点。Based on the crop planting starting point information and the crop maturity starting point information, the planting stubble number of the same pixel crop is determined. Based on the number of planting stubble of the crop, set the points NDVI p10 , NDV I p11 , NDV I p20 , NDVI p21 , NDV I p30 , and NDV I p31 at which the phenological curve reaches the median value. If overwintering crops are included, the values are NDV I p4 . The phenology curve presents a sine curve, taking the midpoint of both sides.

(1)对于一年最多只种一茬庄稼的区域，采用：(1) For areas where at most one crop is grown a year, use:

如果同时满足：If both:

Figure BDA0003620764100000072
Figure BDA0003620764100000072

且：

Figure BDA0003620764100000073
tp11和tp10之间的NDVI的最大值满足条件为：|NDVImax1-NDVImax|≤a1，则确定为一茬庄稼。因此，相应的像素值设定为1。and:
Figure BDA0003620764100000073
The maximum value of NDVI between t p11 and t p10 satisfies the condition: |NDVI max1 -NDVI max |≤a 1 , then it is determined as a crop. Therefore, the corresponding pixel value is set to 1.

式中，t10和t11分别为tp10点(第一茬作物种植期的中点)前后得到的遥感影像的时间点；t20和t21分别为tp11点(第一茬作物成熟期的中点)前后得到的遥感影像的时间点,；NDVImax1为该时间段的NDVI峰值。In the formula, t 10 and t 11 are the time points of remote sensing images obtained before and after t p10 (the midpoint of the first crop planting period); t 20 and t 21 are respectively t p11 (the first crop maturity period). The time point of the remote sensing image obtained before and after the midpoint), and NDVI max1 is the NDVI peak value of this time period.

(2)对于一年可能种两茬庄稼的区域，采用：(2) For areas where two crops may be grown in a year, use:

在(1)的基础上，如果同时满足：On the basis of (1), if both:

Figure BDA0003620764100000081
Figure BDA0003620764100000081

且：

Figure BDA0003620764100000082
tp21和tp20之间的NDVI的最大值满足条件为：|NDV Imax2-NDV Imax|≤a2，则确定为第二茬庄稼。因此，相应的像素值设定为2。and:
Figure BDA0003620764100000082
The maximum value of NDVI between t p21 and t p20 satisfies the condition: |NDV I max2 -NDV I max |≤a 2 , then it is determined as the second crop. Therefore, the corresponding pixel value is set to 2.

式中，t30和t31分别为tp20点(第二茬作物种植期的中点)前后得到的遥感影像的时间点；t40和t41分别为tp21点(第二茬作物成熟期的中点)前后得到的遥感影像的时间点,；NDVImax2为该时间段的NDVI峰值。In the formula, t 30 and t 31 are the time points of the remote sensing images obtained before and after t p20 (the midpoint of the second crop planting period); t 40 and t 41 are respectively t p21 (the second crop maturity period). The time point of the remote sensing image obtained before and after the midpoint); NDVI max2 is the NDVI peak value of this time period.

(3)对于一年可能种三茬庄稼的区域，采用：(3) For areas where three crops may be planted in a year, use:

在(2)的基础上，如果同时满足：On the basis of (2), if both:

Figure BDA0003620764100000083
Figure BDA0003620764100000083

Figure BDA0003620764100000091
Figure BDA0003620764100000091

且：

Figure BDA0003620764100000092
tp31和tp30之间的NDVI的最大值满足条件为：|NDV Imax3-NDV Imax|≤a3，则确定为第三茬庄稼3。因此，相应的像素值设定为3。and:
Figure BDA0003620764100000092
The maximum value of NDVI between t p31 and t p30 satisfies the condition: |NDV I max3 -NDV I max |≤a 3 , then it is determined as the third crop 3. Therefore, the corresponding pixel value is set to 3.

式中，t50和t51分别为tp30点(第三茬作物种植期的中点)前后得到的遥感影像的时间点；t60和t61分别为tp31点(第三茬作物成熟期的中点)前后得到的遥感影像的时间点,；NDVImax3为该时间段的NDVI峰值。In the formula, t 50 and t 51 are the time points of the remote sensing images obtained before and after t p30 (the midpoint of the third crop planting period), respectively; t 60 and t 61 are respectively t p31 (the third crop maturity period). The time point of the remote sensing image obtained before and after the midpoint), and NDVI max3 is the NDVI peak value of this time period.

(4)如果有越冬作物的话：(4) If there are overwintering crops:

若秋季种植，次年春夏季收割，则确定为越冬作物。因此，结合物候资料，确定种植发芽期或者次年开始生长期的时间点。If it is planted in autumn and harvested in the following spring and summer, it is determined as a winter crop. Therefore, combined with the phenological data, determine the time point of the planting germination period or the beginning of the growth period in the following year.

Figure BDA0003620764100000093
确定为越冬作物。
Figure BDA0003620764100000093
Determined as a winter crop.

对于一般情况下复种指数的计算：For the calculation of the multiple cropping index in general:

Figure BDA0003620764100000094
Figure BDA0003620764100000094

式中，Kij为第i年第j种作物种植情况，n为统计的年数，m为作物种植的茬数，FI为复种指数。若第i年第j茬庄稼种植，则Kj取值为1，否则为零In the formula, K ij is the planting situation of the jth crop in the ith year, n is the number of statistical years, m is the stubble number of crops planted, and FI is the multiple cropping index. If the jth crop in the ith year is planted, the value of K j is 1, otherwise it is zero

本发明相比较传统的方法更具有科学性和更高的精度，结合不同的作物物候信息，利用中高分辨率的遥感数据获得关键物候期的时间节点的NDVI数值，建立作物复种指数，达到精准识别复种指数时空分布的目的。Compared with the traditional method, the present invention is more scientific and more precise, combines different crop phenological information, and utilizes medium and 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 accurate identification. The purpose of multiplying exponential spatiotemporal distributions.

以上所述的实施例仅是对本发明优选方式进行的描述，并非对本发明的范围进行限定，在不脱离本发明设计精神的前提下，本领域普通技术人员对本发明的技术方案做出的各种变形和改进，均应落入本发明权利要求书确定的保护范围内。The above-mentioned embodiments are only descriptions of the preferred modes of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, those of ordinary skill in the art can make various Variations and improvements should fall within the protection scope determined by the claims of the present invention.

Claims (9)

1.一种作物复种指数遥感提取方法，其特征在于，包括：1. a crop multiple cropping index remote sensing extraction method, is characterized in that, comprises: 采集高时间分辨率遥感数据，获取作物种植区域，构建NDVI-t二维直角坐标系，得到时间序列的NDVI的作物物候曲线；Collect high temporal resolution remote sensing data, obtain crop planting area, construct NDVI-t two-dimensional rectangular coordinate system, and obtain time series NDVI crop phenology curve; 基于所述时间序列的NDVI的作物物候曲线和所述高时间分辨率遥感数据，判断作物种植茬数，基于所述作物种植茬数获取所述种植区域的复种指数。Based on the NDVI crop phenology curve of the time series and the high-time-resolution remote sensing data, the number of crop planting stubble is determined, and the multiple cropping index of the planting area is obtained based on the crop planting stubble number. 2.根据权利要求1所述的作物复种指数遥感提取方法，其特征在于，基于所述高时间分辨率遥感数据计算作物识别指数，若所述作物识别指数不小于预设阈值a4，则确定农田覆盖信息，通过所述农田覆盖信息确定所述待监测区域，根据所述待监测区域的范围和时间段，选取作物种植、生长和收割的遥感数据，确定所述作物种植区域；其中，所述预设阈值a4根据遥感影像的类型和农田覆盖区域下垫面的特征，综合确定。2. The method for extracting crop multiple cropping index by remote sensing according to claim 1, wherein the crop identification index is calculated based on the high temporal resolution remote sensing data, and if the crop identification index is not less than a preset threshold a 4 , it is determined Farmland coverage information, the area to be monitored is determined by the farmland coverage information, and according to the scope and time period of the area to be monitored, remote sensing data of crop planting, growth and harvesting is selected to determine the crop planting area; The preset threshold a4 is comprehensively determined according to the type of remote sensing image and the characteristics of the underlying surface of the farmland coverage area. 3.根据权利要求2所述的作物复种指数遥感提取方法，其特征在于，所述作物识别指数的计算公式为：3. crop multiple cropping index remote sensing extraction method according to claim 2, is characterized in that, the calculation formula of described crop identification index is:
Figure FDA0003620764090000011
Figure FDA0003620764090000011
 其中，CRI为作物识别指数，NDVIi为覆盖同一区域的第i景数据的NDVI值；i为参与计算的遥感影像的第i景数据；n1为遥感影像的景数；
Figure FDA0003620764090000012
为时间序列的NDVI均值，
Figure FDA0003620764090000013
NDVI为归一化植被指数，Rnir为近红外波段反射率，Rr为红波段的反射率。Among them, CRI is the crop identification index, NDVI i is the NDVI value of the ith scene data covering the same area; i is the ith scene data of the remote sensing image participating in the calculation; n 1 is the scene number of the remote sensing image;
Figure FDA0003620764090000012
is the mean NDVI of the time series,
Figure FDA0003620764090000013
NDVI is the normalized vegetation index, R nir is the reflectance in the near-infrared band, and R r is the reflectance in the red band. 4.根据权利要求2所述的作物复种指数遥感提取方法，其特征在于，获取所述作物种植区域的过程包括：4. crop multiple cropping index remote sensing extraction method according to claim 2, is characterized in that, the process that obtains described crop planting area comprises: ZWI＝A1∪A2∪...∪An ZWI=A 1 ∪A 2 ∪...∪A n 式中ZWI为种植区的最大面积；A1，A2…An为基于遥感影像统计得到的以像素为单元的集合。In the formula, ZWI is the maximum area of the planting area; A1, A2...An is a set of pixels based on remote sensing image statistics. 5.根据权利要求1所述的作物复种指数遥感提取方法，其特征在于，所述得到时间序列的NDVI的作物物候曲线的过程包括：5. crop multiple cropping index remote sensing extraction method according to claim 1, is characterized in that, the described process that obtains the crop phenology curve of the NDVI of time series comprises: 基于所述作物种植区域建立所述NDVI-t二维直角坐标系，通过所述NDVI-t二维直角坐标系获取同一时间序列不同作物生育期NDVI和时间的关系，进而得到时间序列的NDVI的作物物候曲线。The NDVI-t two-dimensional rectangular coordinate system is established based on the crop planting area, and the relationship between the NDVI and time of different crop growth stages in the same time series is obtained through the NDVI-t two-dimensional rectangular coordinate system, and then the NDVI of the time series is obtained. Crop phenology curves. 6.根据权利要求5所述的作物复种指数遥感提取方法，其特征在于，基于所述时间序列的NDVI的作物物候曲线和所述高时间分辨率遥感数据，确定不同作物类型的NDVI的作物物候曲线最大值NDVImax和中值
Figure FDA0003620764090000021
6. crop multiple cropping index remote sensing extraction method according to claim 5, is characterized in that, based on the crop phenology curve of the NDVI of described time series and described high temporal resolution remote sensing data, determine the crop phenology of the NDVI of different crop types Curve maximum NDVI max and median
Figure FDA0003620764090000021
 7.根据权利要求1所述的作物复种指数遥感提取方法，其特征在于，判断作物种植茬数的过程包括：7. crop multiple cropping index remote sensing extraction method according to claim 1, is characterized in that, the process of judging crop planting stubble number comprises: 基于所述物候曲线获取作物种植起点信息和作物成熟起点信息，确定同一像素作物的种植茬数。The crop planting starting point information and the crop maturity starting point information are obtained based on the phenology curve, and the planting stubble number of the same pixel crop is determined. 8.根据权利要求7所述的作物复种指数遥感提取方法，其特征在于，基于所述同一像素作物的种植茬数，设定所述物候曲线达到中值的点NDVIp10、NDVIp11、NDVIp20、NDVIp21、NDVIp30、NDVIp31，若包括越冬作物，则取值NDV Ip4。8. The method for extracting multiple cropping indices by remote sensing according to claim 7, wherein, based on the planting stubble number of the same pixel crop, the points NDVI p10 , NDVI p11 and NDVI p20 at which the phenological curve reaches the median value are set , NDVI p21 , NDVI p30 , NDVI p31 , if winter crops are included, the value is NDVI p4 . 9.根据权利要求1所述的作物复种指数遥感提取方法，其特征在于，所述复种指数的计算公式为：9. crop multiple cropping index remote sensing extraction method according to claim 1, is characterized in that, the calculation formula of described multiple cropping index is:
Figure FDA0003620764090000031
Figure FDA0003620764090000031
 式中，Kij为第i年第j种作物种植情况，n为统计的年数，m为作物种植的茬数，FI为复种指数。In the formula, K ij is the planting situation of the jth crop in the ith year, n is the number of statistical years, m is the stubble number of crops planted, and FI is the multiple cropping index.
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