CN111982837A - Conversion method of vegetation ecological parameter remote sensing estimation model - Google Patents
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Abstract
The invention discloses a method for converting a vegetation ecological parameter remote sensing estimation model, which comprises the following steps: establishing a spectral response function relation model of the used sensor and the sensor to be used; establishing a standby sensor expression suitable for a used sensor according to the spectral response function relation model; establishing a remote sensing estimation conversion model of the used sensors of the vegetation ecological parameters according to the expression of the sensors to be used; estimating vegetation ecological parameters by using a sensor to be used based on a remote sensing estimation conversion model; the vegetation ecological parameter remote sensing estimation models among different sensors are converted, so that the early-stage work such as training sample selection is reduced, the vegetation ecological parameter remote sensing estimation model conversion among different sensors is realized quickly and efficiently, and the regional vegetation ecological parameters can be accurately estimated.
Description
Technical Field
The invention relates to a conversion method of a vegetation ecological parameter remote sensing estimation model, and belongs to the field of vegetation ecological parameter remote sensing inversion.
Background
The method has important practical significance for scientifically managing the vegetation ecosystem, maintaining the balance of the vegetation ecosystem and promoting sustainable development by developing remote sensing accurate estimation of land vegetation ecological parameters such as biomass, leaf area index, vegetation coverage and the like.
The most common remote sensing method for estimating vegetation ecological parameters is to construct a statistical regression model by using vegetation indexes. In the vegetation index, a red band strongly absorbing green plants and a near infrared band highly reflecting green plants are generally selected. Among them, NDVI, which is the ratio of the difference between the near infrared band reflectivity and the red band reflectivity and the sum of the two, is the most commonly used index of remote sensing vegetation. The method has the core that a statistical regression model of the vegetation index and the vegetation ecological parameters is established, remote sensing estimation of the vegetation ecological parameters is researched by utilizing the established statistical regression model, and from the published research results, numerous vegetation ecological parameter remote sensing estimation models are established in domestic and foreign researches based on the vegetation indexes of different sensors, and are widely recognized and applied in the industry.
However, in practical research and application, different sensor remote sensing data are often used for carrying out remote sensing estimation on vegetation ecological parameters, and when the vegetation ecological parameters are estimated and researched by using the remote sensing data of other sensors, the vegetation index based on the sensor cannot be directly applied to a regression model constructed based on vegetation of the used sensor, which is mainly due to the fact that the radiation performance of the sensor to be used is different from that of the used sensor, and the band widths of the sensor in a near infrared band and a red band are also different.
In order to solve the problems, a series of research works have been carried out, and the most common solution at present is to select a large number of sample training pixels for research, establish a statistical relationship between the vegetation index of the remote sensing sensor to be utilized and the vegetation index of the utilized remote sensing sensor, and use the statistical relationship to realize the conversion of vegetation ecological parameters of the unutilized sensor by utilizing a sensor vegetation index estimation model, however, the method has a great defect, and on one hand, the method needs to carry out a large number of early-stage works such as training sample selection and the like, which is time-consuming and labor-consuming; on the other hand, the established statistical relationship lacks the intrinsic mechanicalness and can not be transplanted and expanded.
Therefore, it is necessary to invent a fast and efficient method for converting vegetation ecological parameter remote sensing estimation models among different sensors, so as to accurately estimate vegetation ecological parameters by using the converted models.
Disclosure of Invention
The invention aims to provide a method for converting a vegetation ecological parameter remote sensing estimation model, which aims to solve the defects of the prior art.
In order to achieve the purpose, the invention provides the following scheme:
a method for converting a vegetation ecological parameter remote sensing estimation model comprises the following steps:
s1, establishing a spectral response function relation model of a used sensor and a sensor to be used;
s2, establishing a standby sensor expression applicable to a used sensor according to the spectral response function relation model;
s3, establishing a vegetation ecological parameter used sensor remote sensing estimation conversion model according to a to-be-used sensor expression;
s4, estimating a conversion model based on remote sensing of a used sensor, and estimating ecological parameters of the vegetation in the area by using a sensor to be used;
preferably, before establishing the spectral response function relationship model of the used sensor and the sensor to be used in S1, the following steps are also required:
preferably, a model of the spectral response function of the sensor to be used and a model of the spectral response function of the sensor already used are established.
Preferably, the spectral response function of the sensor to be used is a function part of the spectral interval of the near infrared band and the red band of the sensor to be used;
preferably, the spectral response function of the used sensor is a function of the near infrared band, red band spectral region of the used sensor.
Preferably, the near infrared band of the sensor to be used is: 841-876 nm;
preferably, the red band of the sensor to be used is: 620-670 nm;
preferably, the used sensors have near infrared bands of: 815 + 915 nm;
preferably, the red bands of the used sensors are: 600-700 nm.
Preferably, the spectral response function relationship model comprises:
preferably, the near infrared band spectral response function relation models of the sensor to be used and the sensor used;
preferably, the red band spectral response function relationship models of the sensor to be used and the sensor already in use.
Preferably, establishing a standby sensor spectral response function integral model and a used sensor spectral response function integral model according to the standby sensor spectral response function model and the used sensor spectral response function model;
preferably, a near-infrared band spectral response function relation model of the sensor to be used and the sensor to be used is established according to the spectral response function integral model of the sensor to be used and the spectral response function integral model of the sensor to be used;
preferably, a red-band spectral response function relation model of the standby sensor and the used sensor is established according to the standby sensor spectral response function integral model and the used sensor spectral response function integral model.
Preferably, S2 includes:
and establishing the spectral response function relation model, and establishing a near infrared band reflectivity unit and a red band reflectivity unit of the sensor to be used.
Preferably, a first relation model is obtained according to the near-infrared band reflectivity unit of the sensor to be utilized and the near-infrared band spectral response function relation models of the sensor to be utilized and the sensor used;
preferably, a second relation model is obtained according to the red waveband reflectivity unit of the sensor to be utilized and the near infrared waveband spectral response function relation models of the sensor to be utilized and the sensor to be utilized;
preferably, the standby sensor expression applicable to the used sensor is derived from the first relational model and the second relational model.
Preferably, the used sensor remote sensing estimation conversion model and the standby sensor remote sensing estimation expression model of the used sensor remote sensing estimation model are the same model.
The invention discloses the following technical effects:
compared with the prior art, the technical scheme of the invention has the advantages that the vegetation ecological parameter remote sensing estimation models among different sensors are converted, so that a large amount of preliminary work such as training sample selection is reduced, the vegetation ecological parameter remote sensing estimation models among different sensors are converted quickly and efficiently, and the vegetation ecological parameters are accurately estimated by using the converted models.
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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 diagram of the present invention;
FIG. 2 is based on NDVIMODISIn 2019, in 8 mid-month, green biomass in the region of interest in inner Mongolia grassland.
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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within 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.
Example 1: the vegetation ecological parameter remote sensing estimation model conversion method between different sensors provided by the invention is further explained in detail based on the application case of the conversion of the vegetation green biomass remote sensing estimation model of the inner Mongolia grassland region of interest between the FY-3AMERSI sensor and the MODIS sensor, and the specific method comprises the following steps:
step one, acquiring a spectral response function of an MODIS sensor
Acquiring a spectral response function of the MODIS sensor, and extracting function parts of the spectral response function in spectral intervals of a near infrared band (841-876nm) and a red band (620-670nm) of the MODIS sensor.
Step two, calculating the spectral response function integral value of the MODIS sensor
And (4) performing integral calculation according to the function part of the spectral interval of the near infrared band and the red band of the MODIS sensor obtained in the first step to obtain the spectral response function integral value A of the near infrared band and the red band of the MODIS sensor, which is 32.4, and B of the spectral response function integral value B of the near infrared band and the red band of the MODIS sensor, which is 39.3.
Step three, acquiring a spectral response function of the MERSI sensor
Acquiring a spectral response function of the MERSI sensor, and extracting function parts of the spectral response function in spectral intervals of a near infrared band (815-.
Step four, calculating the spectral response function integral value of the MERSI sensor
And integrating the spectrum part of the spectrum interval of the near infrared band and the red band of the MERSI sensor obtained in the step three to obtain the spectrum response function integral value C of the near infrared band and the red band of the MERSI sensor which is 59.4 and D of 50.0.
Step five, establishing the relation between the spectral response functions of the MERSI sensor and the MODIS sensor
According to the MODIS sensor near-infrared band and red band spectral response function integral value A, B calculated in the second step and the MERSI sensor near-infrared band and red band spectral response function integral value C, D calculated in the fourth step, the ratio of the MERSI sensor to the MODIS sensor near-infrared band and red band spectral response functions is calculated, and the formula is as follows:
in the formula, a is the ratio (1.8) of the integrated values of the near-infrared band spectral response functions of the MERSI sensor and the MODIS sensor, and b is the ratio (1.3) of the integrated values of the red band spectral response functions of the MERSI sensor and the MODIS sensor.
Step six, establishing NDVI suitable for MERSI sensorMERSIMODIS sensor NDVIMODISExpression formula
Establishing NDVI suitable for the MERSI sensor according to the ratio of the near infrared band spectral response function integral value to the red band spectral response function integral value of the MERSI sensor to the MODIS sensor established in the step fiveMERSIMODIS sensor NDVIMODISThe expression, the formula is as follows:
in the formula, NIR and Red are the reflectivity of the MODIS sensor in the near infrared band and the Red band.
Step seven, establishing the NDVI based on the MODIS sensorMODISGreen biomass MERSI sensor NDVI of inner Mongolia grassland region of interestMERSIRemote sensing estimation conversion model
NDVI established at step six for the MERSI sensorMERSIMODIS sensor NDVIMODISBased on the expression, the NDVI based on the MODIS sensor is establishedMODISGreen biomass MERSI sensor NDVI of inner Mongolia grassland region of interestMERSIAnd remote sensing estimation conversion model.
In this example, the green biomass MERSI sensor NDVI on the pixel scale of the region of interestMERSIThe remote sensing estimation model is as follows:
y＝849.1NDVIMERSI-110.1 (4)
wherein y is the grassland green biomass (g/m2) of the pixel scale of the region of interest.
Will be suitable for use in the MERSI sensor NDVIMERSIMODIS sensor NDVIMODISSubstituting the obtained value into the formula (4) to obtain the NDVI based on the MODIS sensorMODISThe remote sensing estimation conversion model of the NDVI of the green biomass MERSI sensor in the region of interest has the following formula:
in the formula, y is the grassland green biomass (g/m2) of the pixel scale of the region of interest, and NIR and Red are the reflectivity of the MODIS sensor near infrared band and Red band;
step eight, based on MODIS sensor NDVIMODISRemote sensing estimation of green biomass of interested period of interested region of inner Mongolia grassland
NDVI (modified mode noise suppression) based on MODIS (moderate resolution imaging spectroradiometer) sensor established according to step sevenMODISGreen biomass MERSI sensor NDVI of inner Mongolia grassland region of interestMERSIRemote sensing estimation conversion model for developing NDVI based on MODIS sensorMODISRemote sensing estimation of green biomass in 2019, mid-8 months in the region of interest in inner Mongolia grassland (FIG. 2).
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the scope of the present invention is defined by the claims.
Claims (10)
1. A method for converting a vegetation ecological parameter remote sensing estimation model is characterized by comprising the following steps:
s1, establishing a spectral response function relation model of a used sensor and a sensor to be used;
s2, establishing a standby sensor expression applicable to a used sensor according to the spectral response function relation model;
s3, establishing a vegetation ecological parameter used sensor remote sensing estimation conversion model according to the expression of the sensor to be used;
and S4, estimating the ecological parameters of the vegetation in the area by using the sensors to be used based on the used sensor remote sensing estimation conversion model.
2. The method for transforming the vegetation ecological parameter remote sensing estimation model according to claim 1, wherein the method comprises the following steps:
before establishing the spectral response function relationship model of the used sensor and the sensor to be used in step S1, it is further required to:
and establishing a standby sensor spectral response function model and a used sensor spectral response function model.
3. The method for transforming the vegetation ecological parameter remote sensing estimation model according to claim 2, wherein the method comprises the following steps:
the spectral response function of the sensor to be used is a function part of the spectral interval of the near infrared band and the red band of the sensor to be used;
the spectral response function of the used sensor is a function part of the spectral interval of the used sensor in the near infrared band and the red band.
4. The method for transforming the vegetation ecological parameter remote sensing estimation model according to claim 3, wherein the method comprises the following steps:
the near-infrared wave band of the sensor to be used is as follows: 841-876 nm;
the red wave band of the sensor to be used is as follows: 620-670 nm;
the near-infrared wave band of the used sensor is as follows: 815 + 915 nm;
the red wave band of the used sensor is as follows: 600-700 nm.
5. The method for transforming the vegetation ecological parameter remote sensing estimation model according to claim 2, wherein the method comprises the following steps:
the spectral response functional relationship model comprises:
the near-infrared band spectral response function relation models of the standby sensor and the used sensor;
red band spectral response function relationship models of the sensor to be used and the sensor already used.
6. The method for transforming the vegetation ecological parameter remote sensing estimation model according to claim 5, wherein the method comprises the following steps:
and establishing a standby sensor spectral response function integral model and a used sensor spectral response function integral model according to the standby sensor spectral response function model and the used sensor spectral response function model.
7. The method for transforming the vegetation ecological parameter remote sensing estimation model according to claim 6, wherein the method comprises the following steps:
establishing a near-infrared band spectral response function relation model of the sensor to be used and the sensor to be used according to the spectral response function integral model of the sensor to be used and the spectral response function integral model of the sensor to be used;
and establishing a red wave band spectral response function relation model of the standby sensor and the used sensor according to the spectral response function integral model of the standby sensor and the spectral response function integral model of the used sensor.
8. The method for transforming the vegetation ecological parameter remote sensing estimation model according to claim 1, wherein the method comprises the following steps:
the step S2 includes:
and establishing the spectral response function relation model, and establishing a near infrared band reflectivity unit and a red band reflectivity unit of the sensor to be used.
9. The method for transforming the vegetation ecological parameter remote sensing estimation model according to claim 8, wherein the method comprises the following steps:
obtaining a first relation model with the near-infrared band spectral response function relation models of the sensor to be used and the used sensor according to the near-infrared band reflectivity unit of the sensor to be used;
obtaining a second relation model with the near-infrared band spectral response function relation models of the sensor to be used and the used sensor according to the red-band reflectivity unit of the sensor to be used;
and obtaining an expression of the standby sensor suitable for the used sensor through the first relational model and the second relational model.
10. The method for transforming the vegetation ecological parameter remote sensing estimation model according to claim 1, wherein the method comprises the following steps:
the used sensor remote sensing estimation conversion model and the standby sensor remote sensing estimation expression model of the used sensor remote sensing estimation model are the same model.
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| CN113989661A (en) * | 2021-10-15 | 2022-01-28 | 中国气象科学研究院 | Normalized vegetation index conversion method of MERSI-2 and MODIS |
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