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Crop classification method based on time sequence NDVI and LST

A classification method, crop technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems that affect the classification accuracy of crops, easy saturation, and low sensitivity in high vegetation density areas

Inactive Publication Date: 2019-08-13
INST OF REMOTE SENSING & DIGITAL EARTH CHINESE ACADEMY OF SCI +2
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Problems solved by technology

However, the NDVI value is easily saturated, and the sensitivity to high vegetation density areas is reduced, thus affecting the classification accuracy of crops

Method used

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  • Crop classification method based on time sequence NDVI and LST
  • Crop classification method based on time sequence NDVI and LST

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Embodiment Construction

[0010] The present invention "a crop classification method based on time series NDVI and LST" will be further described below in conjunction with examples, according to the implementation process (such as figure 1 shown), the detailed implementation details are as follows.

[0011] Step 1: Take Barton County, Kansas, USA as the experimental area. The main crops in the experimental area are corn, alfalfa, soybean, winter wheat and sorghum. The Landsat-8 satellite images covering the experimental area from June to December 2016 (one period per month) were obtained, and the NDVI was extracted using the OLI surface reflectance data of the 7 periods of Landsat-8. The NDVI calculation method is as follows:

[0012] NDVI=(NDVI-R)⁄(NDVI+R)

[0013] Among them, NIR stands for near-infrared band reflectance, and R stands for red light band reflectance.

[0014] The NDVI of the seven phases are superimposed in chronological order to form the NDVI time series.

[0015] At the same tim...

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Abstract

The invention discloses a crop classification method based on time sequence NDVI and LST. The method comprises the following steps: 1) obtaining remote sensing image data containing a red light band,a near infrared band and a thermal infrared band, constructing a remote sensing image time sequence covering a crop growth period, and calculating to obtain an NDVI time sequence and an LST time sequence; (2) in order to enhance the difference between different crops, for each LST in step 1), firstly calculating LST mean vaue of LST, and then using the mean value to adjust (the formula is ALST(i,j)=LST(i,j)-LST mean, with i, j being the row number and column number of each pixel respectively, and finally supermisoing all the adjusted ALSTs in chronological order to form an ALST time series.; 3) obtaining crop sample data through a field survey or historical map, and 4) taking the NDVI time sequence, the ALST time sequence and the sample data as input, and classifying the crops in the research area by adopting a random forest classifier to form a crop classification result map.

Description

technical field [0001] The present invention is a crop remote sensing fine classification technology, and proposes a crop classification method based on time series NDVI and LST, which can reflect the growth characteristics of different crops by making full use of NDVI time series and LST time series, and effectively improve the precision of fine classification of crops. It provides a new idea for the fine classification of crops. Background technique [0002] Accurate spatial distribution of crops is an important basis for crop growth monitoring and crop yield estimation, and is an important basis for the country to formulate food planning and economic policies. Traditional crop monitoring methods are mostly regional surveys, which consume a lot of manpower, material and financial resources, and it is difficult to obtain large-scale crop information in a timely manner. Due to its fast speed and wide range, remote sensing is widely used in crop classification to obtain the ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/188G06F18/24
Inventor 占玉林陈昕然顾行发余涛杨健臧文乾黄祥志李娟
Owner INST OF REMOTE SENSING & DIGITAL EARTH CHINESE ACADEMY OF SCI
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