CN113962248A

CN113962248A - Method and device for determining biomass on grassland and storage medium

Info

Publication number: CN113962248A
Application number: CN202111449069.9A
Authority: CN
Inventors: 孙丹峰; 焦心; 孙敏轩
Original assignee: China Agricultural University
Current assignee: China Agricultural University
Priority date: 2021-12-01
Filing date: 2021-12-01
Publication date: 2022-01-21
Anticipated expiration: 2041-12-01
Also published as: CN113962248B

Abstract

The application discloses a method, a device and a storage medium for determining biomass on grassland. The method comprises the following steps: determining a pixel set corresponding to a preset area on a grassland, wherein the pixel set comprises a plurality of pixels corresponding to the preset area; selecting a pixel to be evaluated for evaluating aboveground biomass from the pixel set; determining remote sensing surface energy characteristic information corresponding to the pixel to be evaluated according to surface reflectivity remote sensing data and thermal infrared remote sensing data corresponding to the pixel set, wherein the remote sensing surface energy characteristic information is used for evaluating the aboveground biomass; and determining the aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing surface energy characteristic information.

Description

Method and device for determining biomass on grassland and storage medium

Technical Field

The application relates to the technical field of remote sensing science, in particular to a method, a device and a storage medium for determining biomass on grassland.

Background

Grassland, as a widely distributed type of ground cover with important economic and ecological functions, is one of the most important natural resources. The overground biomass of the grassland reflects the productivity of the grassland, provides a basic food source for animal husbandry and marks the health of the ecological system of the grassland. Therefore, accurate monitoring of the biomass on the grassland and the spatial-temporal dynamic change of the biomass on the grassland are of great significance for sustainable macroscopic and accurate grassland management, understanding of response of the grassland to climate change and protecting and repairing the grassland ecosystem.

On a fine scale, the biomass on the grassland is mainly monitored by a method of directly harvesting in a fixed-area sample based on a certain sampling strategy. On the macro scale, biomass is estimated by constructing a grassland biomass estimation model based on remote sensing vegetation indexes such as normalized vegetation indexes, difference vegetation indexes, soil adjustment indexes and the like and ground survey data in the same period. In addition, the mechanism model is also used for researching biomass, and key parameters of plant growth are obtained mainly by simulating the biological physiological process of plants and analyzing influence factors of a simulation object. The researches are widely applied to the estimation of the biomass on the grassland, and an important foundation is laid for the research of the estimation of the biomass on the grassland.

However, in the conventional method system, since the on-site direct harvesting method requires a large amount of resources such as manpower, material resources, and financial resources, it is difficult to develop the method on a macroscopic large scale. The mechanism model has the defects that the biomass estimation is carried out on a macroscopic scale due to the fact that parameters of the mechanism model are complex and difficult to obtain, and meanwhile, the difference between estimation results of different mechanism models is large. Although the estimation model based on the remote sensing vegetation index is widely applied on a large scale, the influence of the soil background of a vegetation density saturated area and a low vegetation density area becomes a main defect influencing the precision of the model, in addition, the current vegetation index mainly focuses on the optical difference of chlorophyll and a canopy structure, and other factors influencing biomass such as water content and the like are not considered.

Aiming at the technical problems that a large amount of manpower and material resources are needed to be spent on monitoring the biomass on the grassland and the accuracy of a model is easily influenced in the prior art, an effective solution is not provided at present.

Disclosure of Invention

The embodiment of the disclosure provides a method, a device and a storage medium for determining biomass on a grassland, so as to at least solve the technical problems that monitoring of the biomass on the grassland needs to cost a large amount of manpower and material resources and the accuracy of a model is easily influenced in the prior art.

According to an aspect of an embodiment of the present disclosure, there is provided a method of determining biomass on a grassland, including: determining a pixel set corresponding to a preset area on a grassland, wherein the pixel set comprises a plurality of pixels corresponding to the preset area; selecting a pixel to be evaluated for evaluating aboveground biomass from the pixel set; determining remote sensing surface energy characteristic information corresponding to the pixel to be evaluated according to surface reflectivity remote sensing data and thermal infrared remote sensing data corresponding to the pixel set, wherein the remote sensing surface energy characteristic information is used for evaluating the aboveground biomass; and determining the aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing surface energy characteristic information.

According to another aspect of the embodiments of the present disclosure, there is also provided a storage medium including a stored program, wherein the method described above is performed by a processor when the program is executed.

There is also provided, in accordance with another aspect of the embodiments of the present disclosure, apparatus for determining biomass on a grassland, including: the image element set determining module is used for determining an image element set corresponding to a preset area on a grassland, wherein the image element set comprises a plurality of image elements corresponding to the preset area; the pixel selection module to be evaluated is used for selecting a pixel to be evaluated for evaluating the aboveground biomass from the pixel set; the remote sensing surface energy characteristic information determining module is used for determining remote sensing surface energy characteristic information corresponding to the pixel to be evaluated according to surface reflectivity remote sensing data and thermal infrared remote sensing data corresponding to the pixel set, and the remote sensing surface energy characteristic information is used for evaluating the aboveground biomass; and the aboveground biomass determining module is used for determining aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing surface energy characteristic information.

There is also provided, in accordance with another aspect of the embodiments of the present disclosure, apparatus for determining biomass on a grassland, including: a processor; and a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: determining a pixel set corresponding to a preset area on a grassland, wherein the pixel set comprises a plurality of pixels corresponding to the preset area; selecting a pixel to be evaluated for evaluating aboveground biomass from the pixel set; determining remote sensing surface energy characteristic information corresponding to the pixel to be evaluated according to surface reflectivity remote sensing data and thermal infrared remote sensing data corresponding to the pixel set, wherein the remote sensing surface energy characteristic information is used for evaluating the aboveground biomass; and determining the aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing surface energy characteristic information.

In the disclosed embodiment, the biomass on the grassland is estimated from the energy perspective by remotely sensing the surface energy characteristic information. Compared with the traditional method for remotely sensing the vegetation index, the method provided by the invention can be used for more quickly and accurately acquiring the biomass on the grassland. In addition, because the invention estimates the biomass on the grassland through the thermal infrared remote sensing data, the invention can realize the physical expression of the professional concept through the remote sensing ground surface energy characteristic information based on the energy balance of the grassland vegetation in the growing period, realize the standard unification and ensure the universality of the method and the comparability of the result under different space-time scales. Therefore, the technical problems that a large amount of manpower and material resources are needed to be spent on monitoring the biomass on the grassland and the accuracy of the model is easily influenced in the prior art are solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:

fig. 1 is a hardware block diagram of a computing device for implementing the method according to embodiment 1 of the present disclosure;

FIG. 2A is a schematic view of a system for monitoring biomass on a grassland according to embodiment 1 of the present disclosure;

FIG. 2B is a schematic block diagram of a system for monitoring biomass on a grassland according to embodiment 1 of the present disclosure;

FIG. 3 is a schematic flow chart of a method for determining biomass on a grassland according to the first aspect of embodiment 1 of the present disclosure;

FIG. 4 is a schematic diagram of a part of a pixel of a surface reflectance image acquired by the surface reflectance image sensor according to embodiment 1 of the present disclosure;

fig. 5 is a schematic diagram of surface temperature timing information and a surface temperature reference curve of each of the surface matrix pure pixels according to embodiment 1 of the present disclosure;

FIG. 6 is a schematic diagram of a reference surface temperature curve and a related surface temperature curve of a pixel to be evaluated according to embodiment 1 of the present disclosure;

FIG. 7 is a schematic diagram of a scatter plot for determining surface matrix clear pixels according to embodiment 1 of the present disclosure;

FIG. 8 is a schematic view of an apparatus for determining biomass on grassland according to embodiment 2 of the present disclosure; and

FIG. 9 is a schematic diagram of an apparatus for determining biomass on grassland according to embodiment 3 of the present disclosure.

Detailed Description

In order to make those skilled in the art better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. It is to be understood that the described embodiments are merely exemplary of some, and not all, of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

According to the present embodiment, there is provided an embodiment of a method of determining biomass on a grassland, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than here.

The method embodiments provided by the present embodiment may be executed in a mobile terminal, a computer terminal, a server or a similar computing device. FIG. 1 shows a block diagram of a hardware architecture of a computing device for implementing a method for determining biomass on a grassland. As shown in fig. 1, the computing device may include one or more processors (which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, and a transmission device for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computing device may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuitry may be a single, stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computing device. As referred to in the disclosed embodiments, the data processing circuit acts as a processor control (e.g., selection of a variable resistance termination path connected to the interface).

The memory can be used for storing software programs and modules of application software, such as program instructions/data storage devices corresponding to the method for determining the biomass on the grassland in the embodiment of the disclosure, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, namely, the method for determining the biomass on the grassland of the application program is realized. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory located remotely from the processor, which may be connected to the computing device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device is used for receiving or transmitting data via a network. Specific examples of such networks may include wireless networks provided by communication providers of the computing devices. In one example, the transmission device includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computing device.

It should be noted here that in some alternative embodiments, the computing device shown in fig. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that FIG. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in a computing device as described above.

Fig. 2A is a schematic view of a system for monitoring biomass on a grassland according to the present embodiment. Referring to fig. 2A, the system includes: a satellite 200 and a server 100 in communication with the satellite 200. The satellite 200 collects remote sensing data of the monitored grassland area through a remote sensing sensor, and transmits the remote sensing data to the server 100. The server 100 acquires the remote sensing data from the satellite 200 and processes the received remote sensing data, so that the biomass on the grassland is evaluated and calculated, and the monitoring of the biomass on the grassland is realized. In addition, as further shown in FIG. 2B, the remote sensing sensors disposed on the satellite 200 include a surface reflectance image sensor 210 and a thermal infrared image sensor 220. The surface reflectivity image sensor 210 collects surface reflectivity remote sensing data of the grassland in a surface reflectivity image mode; the thermal infrared image sensor 220 collects thermal infrared remote sensing data of the grassland in a thermal infrared image mode. And the satellite 200 transmits the remote sensing data collected by the surface reflectivity image sensor 210 and the thermal infrared image sensor 220 to the server 100 so that the server 100 monitors the biomass on the grassland.

It should be noted that the server 100 in the system may be adapted to the above-described hardware configuration.

Under the above operating environment, according to a first aspect of the present embodiment, there is provided a method of determining biomass on a grassland, which is implemented by the server 100 shown in fig. 2A and 2B. Fig. 3 shows a flow diagram of the method, which, with reference to fig. 3, comprises:

s302: determining a pixel set corresponding to a preset area on a grassland, wherein the pixel set comprises a plurality of pixels corresponding to the preset area;

s304: selecting a pixel to be evaluated for evaluating aboveground biomass from the pixel set;

s306: determining remote sensing surface energy characteristic information corresponding to the pixel to be evaluated according to surface reflectivity remote sensing data and thermal infrared remote sensing data corresponding to the pixel set, wherein the remote sensing surface energy characteristic information is used for evaluating the aboveground biomass; and

s308: and determining the aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing surface energy characteristic information.

Specifically, the server 100 receives surface reflectance remote sensing data from the surface reflectance image sensor 210 of the satellite 200. Since the surface reflectance remote sensing data is acquired in the form of an image by the surface reflectance image sensor 210, the server 100 may determine a set of pixels corresponding to a predetermined area on the grassland, for example, based on the pixels of the surface reflectance image acquired by the surface reflectance image sensor 210. For example, the server 100 may determine a plurality of image elements in the surface reflectance image corresponding to the predetermined area as the set of image elements. Accordingly, the remote sensing data of the surface reflectivity of the predetermined area is determined by a plurality of image elements in the image element set (S302).

When determining the above-ground biomass in the predetermined area, the server 100 may determine the above-ground biomass on a pixel-by-pixel basis, for example. That is, the server 100 first determines the aboveground biomass of the region corresponding to each image element, and thus analyzes the aboveground biomass of the region corresponding to each image element. Alternatively, the server 100 may further analyze the total aboveground biomass of the predetermined area according to the aboveground biomass of the area corresponding to each image element. Therefore, the server 100 selects a pixel to be evaluated from the set of pixels in order to determine the aboveground biomass of the region corresponding to the pixel to be evaluated (S304).

Then, the server 100 determines remote sensing data corresponding to the pixels in the pixel set, that is, the surface reflectance remote sensing data and the thermal infrared remote sensing data corresponding to the predetermined area, from the received surface reflectance remote sensing data and thermal infrared remote sensing data. Then, the server 100 determines the remote sensing surface energy characteristic information corresponding to the pixel to be evaluated according to the surface reflectivity remote sensing data and the thermal infrared remote sensing data corresponding to the predetermined area so as to evaluate the aboveground biomass corresponding to the evaluation pixel (S306). The remote sensing surface energy characteristic information will be described in detail later.

Then, the server 100 determines the aboveground biomass corresponding to the pixel to be evaluated according to the determined remote sensing surface energy characteristic information (S308). The method for determining the aboveground biomass corresponding to the pixel to be evaluated by the server 100 according to the remote sensing surface energy characteristic information will be described in detail below.

As described in the background art, in the conventional method system for monitoring biomass on the grassland, the on-site direct harvest method requires a large amount of resources such as manpower, material resources, and financial resources, and thus is difficult to develop on a macro scale. The mechanism model has the defects that the biomass estimation is carried out on a macroscopic scale due to the fact that parameters of the mechanism model are complex and difficult to obtain, and meanwhile, the difference between estimation results of different mechanism models is large. Although the estimation model based on the remote sensing vegetation index is widely applied on a large scale, the influence of the soil background of a vegetation density saturated area and a low vegetation density area becomes a main defect influencing the precision of the model, in addition, the current vegetation index mainly focuses on the optical difference of chlorophyll and a canopy structure, and other factors influencing biomass such as water content and the like are not considered.

In view of the above, the invention provides a method for estimating the biomass on the grassland from the energy perspective by remotely sensing the surface energy characteristic information based on thermal infrared remote sensing data. Compared with the traditional method for remotely sensing the vegetation index, the method provided by the invention can be used for more quickly and accurately acquiring the biomass on the grassland. In addition, because the invention estimates the biomass on the grassland through the thermal infrared remote sensing data, the invention can realize the physical expression of the professional concept through the remote sensing ground surface energy characteristic information based on the energy balance of the grassland vegetation in the growing period, realize the standard unification and ensure the universality of the method and the comparability of the result under different space-time scales. Therefore, the technical problems that a large amount of manpower and material resources are needed to be spent on monitoring the biomass on the grassland and the accuracy of the model is easily influenced in the prior art are solved.

Optionally, the operation of determining the remote sensing earth surface energy characteristic information corresponding to the pixel to be evaluated according to the earth surface reflectivity remote sensing data and the thermal infrared remote sensing data corresponding to the pixel set comprises: determining a surface matrix pure pixel for describing surface matrix information from the pixel set according to the surface reflectivity remote sensing data; determining reference surface temperature time sequence data of reference surface temperature corresponding to a preset area according to the thermal infrared remote sensing data corresponding to the surface matrix pure pixels; determining relevant earth surface temperature time sequence data of relevant earth surface temperatures of the pixel to be evaluated according to the thermal infrared remote sensing data corresponding to the pixel to be evaluated; and determining remote sensing earth surface energy characteristic information according to the reference earth surface temperature time sequence data and the related earth surface temperature time sequence data.

As is well known, the thermal infrared remote sensing data can reflect the surface temperature information of the monitored area. The server 100 can determine the remote sensing surface energy characteristic information based on the surface temperature related to the pixel to be evaluated and the reference temperature of the predetermined area.

Specifically, the server 100 first determines surface matrix pure pixels from the set of pixels according to the surface reflectance image (i.e., surface reflectance remote sensing data). Wherein fig. 4 shows a schematic diagram of a portion of the image elements of the surface reflectance image acquired by the surface reflectance image sensor 210. Referring to fig. 4, the surface reflectivity image includes a pure pixel and a mixed pixel. Wherein the mixed image element contains information of a plurality of end members (i.e. surface reflectivity data of a plurality of surface components), and the pure image element contains information of only a single end member (i.e. surface reflectivity data of a single surface component). Thus, when determining the reference temperature of the predetermined area, the server 100 first determines pure surface matrix pixels containing only surface matrix information from the set of pixels corresponding to the predetermined area.

Then, the server 100 may determine the pixels corresponding to the surface matrix pure pixels from the received thermal infrared image (i.e., thermal infrared remote sensing data) by, for example, an image pixel matching method, thereby determining the thermal infrared remote sensing data corresponding to the surface matrix pure pixels, and further determining the surface temperature corresponding to the surface matrix pure pixels. Further, the server 100 may determine the time sequence information of the earth surface temperature corresponding to the pure image element of the earth surface matrix according to the thermal infrared remote sensing data of different time points corresponding to the pure image element of the earth surface matrix, and determine the time sequence data of the reference earth surface temperature of the predetermined area according to the time sequence information of the earth surface temperature corresponding to the pure image element of the earth surface matrix.

Referring to FIG. 5, the server 100 determines, for example, 3 surface matrix clear pixels 1-3 from the set of pixels. In addition, the surface temperature time sequence information corresponding to the surface matrix pure pixels 1-3 is shown in a curve form in fig. 5, and is determined according to the thermal infrared remote sensing data corresponding to the surface matrix pure pixels 1-3. The server 100 may then determine reference surface temperature time series data for the predetermined area, for example, by averaging the surface temperatures of the 3 surface matrix clear pixels at the same time. Wherein the reference surface temperature time series data is shown in the form of a graph in fig. 5.

Then, the server 100 determines the pixels of the thermal infrared image corresponding to the pixels to be evaluated according to an image pixel matching method, and further determines the thermal infrared remote sensing data corresponding to the pixels to be evaluated. And further, the server 100 may determine the relevant earth surface temperature time sequence data corresponding to the pixel to be evaluated according to the thermal infrared remote sensing data corresponding to the pixel to be evaluated at different times.

Then, the server 100 may determine the remote sensing surface energy characteristic information corresponding to the pixel to be evaluated according to the reference surface temperature time sequence data and the relevant surface temperature time sequence data.

Because the reference earth surface temperature time sequence data and the related earth surface temperature time sequence data contain the earth surface temperature information of the preset area at different time and the earth surface temperature information of the area corresponding to the pixel to be evaluated at different time, the remote sensing earth surface energy characteristic information extracted according to the reference earth surface temperature time sequence data and the related earth surface temperature time sequence data not only reflects the earth surface temperature characteristics of the area corresponding to the preset area and the pixel to be evaluated, but also reflects the change of the earth surface temperature of the area corresponding to the preset area and the pixel to be evaluated along with time. Therefore, the biomass on the grassland can be more accurately evaluated by using the remote sensing surface energy characteristic information extracted according to the technical scheme of the invention.

Optionally, the operation of determining the remote sensing surface energy characteristic information according to the reference surface temperature time series data and the relevant surface temperature time series data includes: generating a corresponding reference surface temperature curve according to the reference surface temperature time sequence data; generating a corresponding relevant earth surface temperature curve according to the relevant earth surface temperature time sequence data; and determining remote sensing surface energy characteristic information according to the reference surface temperature curve and the related surface temperature curve.

Specifically, the server 100 may determine the reference surface temperature time series data of the predetermined area by averaging the surface temperatures of the 3 surface matrix pure pixels at the same time according to the above. The server 100 then fits the reference surface temperature time series data to obtain the reference surface temperature profile shown in fig. 5. In addition, the reference surface temperature curve obtained by fitting is also shown in fig. 6g(t)In which the time variable istMay be, for example, a month in this embodiment (hereinafter for time variables in other formulas "t", the units are also months, and are not described in detail herein). Of course time varianttThe units of (a) may also be days or weeks, as can be determined by one skilled in the art based on the frequency with which the satellite acquires data.

In addition, the server 100 may first compare the relevant surface temperature time series data corresponding to the pixels to be evaluatedg’(t)Performing first order differential operation to obtain first order differential function of relevant earth surface temperature time sequence datah(t)：

Whereing’(t)According to the relevant earth surface temperature time sequence data corresponding to the pixel to be evaluated,h(t)is a function of time series data of the relevant earth surface temperatureg’(t)And performing first order differentiation to obtain a first order differentiation function.

The server 100 then applies the first order differential functionh(t)Integral operation is carried out, so that a relevant earth surface temperature curve corresponding to relevant earth surface temperature time sequence data corresponding to the pixel to be evaluated is obtainedf(t)：

Wherein the content of the first and second substances,f(t)and C is a constant, and the temperature value is obtained by substituting the temperature values corresponding to different times in the relevant earth surface temperature time sequence data of the pixel to be evaluated into the formula (2).

Thus, according to the above method, the server 100 calculates a reference surface temperature curve corresponding to the predetermined areag(t)And a correlated surface temperature curve corresponding to the pixel to be evaluatedf(t). Wherein FIG. 6 shows a reference surface temperature profileg(t)And associated surface temperature profilef(t)Schematic representation of (a). The server 100 then follows the reference surface temperature profileg(t)And associated surface temperature profilef(t)And determining remote sensing earth surface energy characteristic information.

Optionally, the operation of determining the remote sensing surface energy characteristic information according to the reference surface temperature curve and the related surface temperature curve includes: determining remote sensing earth surface energy characteristic information according to the reference earth surface temperature curve and the related earth surface temperature curve, and determining at least one piece of the following remote sensing earth surface energy characteristic information corresponding to the pixel to be evaluated: a start time for indicating a start time of a growing period of vegetation on the grassland of a corresponding area of the pixel to be evaluated; an end time for indicating an end time of the growing period; a growth period duration indicating a duration from a start time to an end time; the earth surface maximum temperature is used for indicating the maximum temperature value of the reference earth surface temperature; the growth period amplitude is used for indicating the difference value between the maximum temperature value and the minimum temperature value of the relevant earth surface temperature in the growth period; the temperature difference amplitude is used for indicating the difference between the maximum value and the minimum value of the difference value between the phase-moment related earth surface temperature and the reference earth surface temperature in the growing period; a temperature difference increase rate for indicating a rate at which the difference between the contemporaneous correlated surface temperature and the reference surface temperature during the growth period increases from a preset first temperature difference to a preset second temperature difference; a temperature difference reduction rate for indicating a rate at which the difference between the same time-dependent surface temperature and the reference surface temperature during the growth period is reduced from a preset third temperature difference to a preset fourth temperature difference; a vegetation period integral indicative of a difference between an integral of the reference surface temperature profile and an integral of the associated surface temperature profile during the vegetation period; and a production rate indicating a ratio between the integral of the growth period and the length of the growth period.

Specifically, FIG. 6 illustrates a reference surface temperature profileg(t)And associated surface temperature profilef(t)So that the server 100 can be based on the reference surface temperature profileg(t)And associated surface temperature profilef(t)And determining remote sensing surface energy characteristic information used for calculating the aboveground biomass corresponding to the pixel to be evaluated.

Wherein the remote sensing surface energy characteristic information comprises at least one of:

a start time indicating a start time of a growth period of an organism on the grassland of the corresponding area of the pixel to be evaluated, corresponding to a time corresponding to the characteristic point a shown in fig. 6;

an end time for indicating an end time of the growth period, corresponding to a time corresponding to the characteristic point b shown in fig. 6;

a growth period duration indicating a duration from the start time to the end time, corresponding to a time range c shown in fig. 6, in which a characteristic point d within the time range c is a turning point of the growth period;

a surface maximum temperature indicating a maximum temperature value of the reference surface temperature, which corresponds to a temperature value corresponding to the characteristic point g shown in fig. 6;

the growth phase amplitude, which indicates the difference between the maximum and minimum temperature values of the relevant surface temperature during the growth phase, corresponds to the temperature range f shown in fig. 6;

the temperature difference amplitude is used for indicating the difference between the maximum value and the minimum value of the difference value between the phase-moment related earth surface temperature and the reference earth surface temperature in the growing period;

a temperature difference increase rate for indicating a rate at which the difference between the contemporaneous correlated surface temperature and the reference surface temperature increases from a preset first temperature difference to a second temperature difference during the growth period;

a temperature difference reduction rate for indicating a rate at which the difference between the same time-dependent surface temperature and the reference surface temperature during the growth period is reduced from a preset third temperature difference to a preset fourth temperature difference;

integral of the growing period, indicating the difference between the integral of the reference surface temperature curve and the integral of the associated surface temperature curve during the growing period, corresponds to the reference surface temperature curve corresponding to the label "e" shown in fig. 6g(t)And associated surface temperature profilef(t)The area between; and

production rate, indicating the ratio between the integral of the growth phase and the length of the growth phase.

Wherein, for example, the server 100 may be based on a correlated surface temperature profilef(t)First order differential function ofD(t)To determine the characteristic information start time and end time. Specifically, the server 100 first determines a preliminarily formulated start time and end time according to formula (3) and formula (4) described below:

t _s={𝑡∣D(t-2)>i and D(t-1)>i and D(t)<i} （3）；

t _e= {𝑡∣D(t)<j and D(t+1) >j and D(t+2)>j} （4）。

whereint _sAndt _ethe preliminarily planned starting and ending times of the growth period,iandjthe first-order differential value thresholds of the earth surface temperature are respectively corresponding to the starting time and the ending time of the growth period.

Then, the server 100 calculates a preliminarily planned growth period duration according to the following formula (5)Δt：

Δt = t _e-t _s （5）。

Then, the clothesThe server determines the starting time as the characteristic information of the remote sensing earth surface energy according to the formula (6) and the formula (7)T _sAnd end timeT _e：

WhereinT _s、T _eRespectively the finally determined starting time (corresponding to the point a in figure 6) and the ending time (corresponding to the point b in figure 6) as the characteristic information of the remote sensing surface energy,kthe shortest growth period.

Thus, in the above manner, the server 100 can compare the calculated start timeT _sAnd end timeT _eAnd correspondingΔtThe information is used as the starting time, the ending time and the growing period duration in the remote sensing earth surface energy characteristic information.

Additionally, the server 100 may further determine a reference surface temperature profile, a correlated surface temperature profile, and a start time based on the determined reference surface temperature profileT _sAnd end timeT _eAnd determining the highest earth surface temperature and the growth period amplitude as the characteristic information of the remote sensing earth surface energy, which is not described herein again.

In addition, the server 100 may calculate the magnitude of the temperature difference as the characteristic information of the remote sensing surface energy according to the following formula (8):

wherein the content of the first and second substances,lis the magnitude of the temperature difference.

Further, assuming that the first temperature difference value and the fourth temperature difference value are 20% of the magnitude of the temperature difference, and the second temperature difference value and the third temperature difference value are 80% of the magnitude of the temperature difference (of course, the first temperature difference value to the fourth temperature difference value may be set to other values, which are only exemplary illustration here), and the first temperature difference value and the fourth temperature difference value may take different values, and the second temperature difference value and the third temperature difference value may take different values, the server 100 may calculate the rate of increase in the temperature difference and the rate of decrease in the temperature difference according to equations (9) and (10) shown below:

wherein, thereinr ₁Andr ₂respectively, a temperature difference increase rate and a temperature difference decrease rate;lis the temperature difference amplitude;t ₁andt ₂the times corresponding to the temperature difference reaching the 20% and 80% magnitude of the temperature difference in the side where the temperature difference between the reference surface temperature profile and the associated surface temperature profile increases with time (e.g., the left side as shown in fig. 6), respectively;t ₃andt ₄the times corresponding to the temperature difference reaching 20% and 80% in the side where the temperature difference between the reference surface temperature profile and the associated surface temperature profile decreases with time (e.g., the right side as shown in fig. 6), respectively.

Further, the server 100 may detect the feature point during the growing period according to the following formula, whereinT _sAndT _eare not equal to 0, andT ₁、T ₂∈（T _s 、T _e）：

T ₁= {𝑡∣D(t)=0 and D(t-1)>0 and D(t+1)< 0} （11）

T ₂= {t∣D(t)=0 and D(t-1)<0 and D(t+1)> 0} （12）

whereinT ₁AndT ₂the times of the characteristic point of the first growth phase and the characteristic point of the second growth phase, respectively. And whereinT ₂Corresponding to the time corresponding to turning point d shown in fig. 6.

The server 100 may then calculate the growing period integral as the remote sensing surface energy characteristic information according to the formula shown below:

whereinIntThe integration of the growing period is taken as the characteristic information of the remote sensing surface energy.

And further, if the growing period that the server 100 can determine includes a plurality of vegetation waiting periods, the growing period characteristic point calculated above may be usedT ₂The method comprises the following steps of segmenting a growth period, performing piecewise fitting on an earth surface temperature curve of the growth period, and calculating the integral of the growth period, wherein a specific technical formula is as follows:

（14）

wherein the content of the first and second substances,f ₁(t) Is prepared from (a)T _s,T ₂) A fitting function of the relevant surface temperature curve of the pixel to be evaluated in the time period,f ₂(t) Is prepared from (a)T ₂,T _e) And fitting a function of the relevant surface temperature curve of the pixel to be evaluated in the time period. Therein aboutf ₁(t) Andf ₂(t) The determination method can refer to the relevant surface temperature curvef(t)The determination method is not described herein.

Accordingly, the server 100 may determine the production rate as the remote sensing surface energy characteristic information according to the following formula:

(15) (ii) a Or

（16）

Wherein VPI is the production rate as one of the remotely sensed surface energy signature information.

Therefore, by the mode, the remote sensing surface energy characteristic information is determined according to the reference surface temperature curve and the relevant surface temperature curve corresponding to the pixel to be evaluated, so that the difference of biomass energy balance on different ground of the grassland can be quantized more accurately by the remote sensing surface energy characteristic information, and the influence of each factor on the biomass on the ground of the grassland is comprehensively reflected. Therefore, the biomass on the grassland can be more accurately evaluated by utilizing the remote sensing surface energy characteristic information extracted by the method.

Optionally, the operation of determining the aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing surface energy characteristic information includes: and determining the aboveground biomass according to the remote sensing earth surface energy characteristic information by utilizing a preset random forest model.

Random forest is a tree-based ensemble learning method that works by building a set of regression trees and averaging the results. During the training process, a random forest algorithm generates a plurality of trees. Each tree in the forest is independent and constructed based on self-sampling of the original training data. Each tree grows to the maximum size without pruning, but the self-sampling approach allows the random forest to better solve the over-fit problem. In terms of use, unlike most machine learning methods, only two parameters need to be set, including the number of regression trees grown in the forest and the number of feature variables selected. Meanwhile, based on the error of regression prediction, the importance of each characteristic variable can be calculated by a random forest algorithm.

Therefore, according to the method of the present invention, after extracting the remote sensing surface energy characteristic information, the server 100 inputs the remote sensing surface energy characteristic information into a pre-trained random forest model, so as to determine the aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing surface energy characteristic information.

The random forest model used in the present invention may be created by setting two parameters, i.e., the number of regression trees and the number of feature variables in the random forest model, for example. In this embodiment, when creating the random forest model, the number of regression trees may be set to 100, for example. Meanwhile, since 10 different pieces of remote sensing surface energy characteristic information (i.e., start time, end time, growth period duration, maximum surface temperature, growth period amplitude, temperature difference increase rate, temperature difference decrease rate, growth period integral, and production rate) are described above, the present embodiment can set the number of characteristic variables to 10, for example. Thus, a random forest model can be created from the two parameters described above and trained. Of course, the setting of the above parameters is only exemplary, and those skilled in the art can set the above two parameters according to actual situations.

Wherein, in the training process, for example, the training can be performedR ²、RMSEAndMAEthe three statistical indexes evaluate the accuracy of the model:

wherein the content of the first and second substances,nis the number of the verification samples that are,

verifying the predicted value of the aboveground biomass of the sample,

is an actual measurement value for predicting the aboveground biomass of a sample,

is the average of the actual measurements of biomass on the ground of the prediction sample.

Therefore, the biomass on the grassland can be predicted more accurately by utilizing the random forest model according to the remote sensing surface energy characteristic information obtained based on the thermal infrared remote sensing data.

Optionally, the operation of determining the surface matrix pure image elements from the image element set according to the surface reflectivity remote sensing data includes: performing data dimension reduction on the surface reflectivity remote sensing data by using a principal component analysis algorithm, and determining wave band data of a predetermined dimension; and extracting end members of the geometrical vertexes of the wave band data with the preset dimensionality to determine the surface matrix pure pixel.

In particular, principal component analysis is a method of removing unnecessary information between bands, compressing image information of multiple bands to a few converted bands more effective than the original bands. And the dimensionality reduction of the data is realized through principal component analysis. For example, more than 95% of the original total bands are compressed into three primary bands (the primary bands are not limited to 3, and may be other number of bands such as 2 or 4), and the converted bands are very small in correlation, that is, the band including 95% of the data information may be used as the primary component band.

Further, as shown in fig. 7, a two-dimensional scattergram is constructed by using image bands with small correlation, such as the first two bands of the principal component analysis transformation result, as the X, Y axes. In the ideal case, the scatter diagram is a triangle, and according to the mathematical description of the linear mixed model, the geometric positions of the pure end members are distributed at three vertexes of the triangle, and the points inside the triangle are linear combinations of the three vertexes, i.e., mixed pixels, as shown in fig. 7. Based on this principle, we can select end-member spectra on a two-dimensional scattergram. In the actual process of selecting end members, a convex area around the scatter diagram is often selected, and then an average spectrum on the original image corresponding to the area is obtained as an end member spectrum.

In addition, for example, taking a water body endpoint as an example, taking the water body endpoint as a center of a circle, a pixel within a radius 1 (the radius 1 is merely illustrated, and may be other numerical values, and is defined according to a user requirement) is taken as a pure pixel, that is, the closer the pixel is to the water body endpoint, the purer the pixel is. The principle of sand and vegetation end points refers to the selection of pure pixels of water end points.

In this way, the server 100 performs data dimension reduction on the surface reflectance remote sensing data by using a principal component analysis algorithm, and determines waveband data of a predetermined number of wavebands. Then, the server 100 performs geometric vertex end member extraction on the wave band data of the preset dimensionality by using the two-dimensional scatter diagram to determine the surface matrix pure pixel.

Optionally, the operation of generating a corresponding reference surface temperature curve according to the reference surface temperature time series data includes: arranging the reference earth surface temperature time sequence data according to a time sequence; and performing S-G filtering on the arrayed reference surface temperature time sequence data to generate a reference surface temperature curve.

Specifically, the server 100 first arranges the reference surface temperatures corresponding to the respective time points in time order, thereby generating preliminary time series data of the reference surface temperatures with respect to the time order. The server 100 then smoothes and reconstructs the time series data through S-G filtering, thereby generating a higher quality reference surface temperature curveg(t)。

Further, the server 100 may generate the correlated surface temperature time series data described above with reference to the above methodg’ (t). Specifically, the server 100 first arranges the relevant surface temperatures corresponding to the respective time points in time order, thereby generating preliminary relevant surface temperature time series data. The server 100 then smoothes and reconstructs the preliminarily generated correlated surface temperature time series data through S-G filtering, thereby generating the correlated surface temperature time series data described aboveg’(t). The server 100 then reconciles the correlated surface temperature time series datag’(t)Performing first order differential operation and integral operation to generate higher quality correlated earth surface temperature curvef(t)。

Further, referring to fig. 1, according to a second aspect of the present embodiment, there is provided a storage medium. The storage medium comprises a stored program, wherein the method of any of the above is performed by a processor when the program is run.

Therefore, the biomass on the grassland is estimated by the embodiment from the energy perspective through remote sensing of the surface energy characteristic information. Compared with the traditional method for remotely sensing the vegetation index, the method can acquire the biomass on the grassland more quickly and accurately. In addition, the biomass on the grassland is estimated through the thermal infrared remote sensing data, so that the physical expression of the professional concept can be realized through the remote sensing ground surface energy characteristic information based on the energy balance in the growing period of the grassland vegetation, the standardization and the unification can be realized, and the universality of the embodiment under different space-time scales and the result comparability are ensured. Therefore, the technical problems that a large amount of manpower and material resources are needed to be spent on monitoring the biomass on the grassland and the accuracy of the model is easily influenced in the prior art are solved.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

Example 2

Fig. 8 shows a device 800 for determining biomass on grassland according to the present embodiment, which device 800 corresponds to the method according to the first aspect of embodiment 1. Referring to fig. 8, the apparatus 800 includes: the image element set determining module 810 is configured to determine an image element set corresponding to a predetermined area on a grassland, where the image element set includes a plurality of image elements corresponding to the predetermined area; a to-be-evaluated pixel selection module 820, configured to select a to-be-evaluated pixel for evaluating aboveground biomass from the pixel set; the remote sensing surface energy characteristic information determining module 830 is used for determining remote sensing surface energy characteristic information corresponding to the pixel to be evaluated according to surface reflectivity remote sensing data and thermal infrared remote sensing data corresponding to the pixel set, and the remote sensing surface energy characteristic information is used for evaluating the aboveground biomass; and the aboveground biomass determining module 840 is used for determining aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing earth surface energy characteristic information.

Optionally, the remote sensing surface energy characteristic information determining module 830 includes: the surface matrix pure pixel determining submodule is used for determining the surface matrix pure pixel from the pixel set according to the surface reflectivity remote sensing data; the reference surface temperature time sequence data determining submodule is used for determining reference surface temperature time sequence data of reference surface temperature corresponding to a preset area according to the thermal infrared remote sensing data corresponding to the surface matrix pure pixels; the relevant earth surface temperature time sequence data determining submodule is used for determining relevant earth surface temperature time sequence data of relevant earth surface temperature of the pixel to be evaluated according to the thermal infrared remote sensing data corresponding to the pixel to be evaluated; and the remote sensing earth surface energy characteristic information determining submodule is used for determining remote sensing earth surface energy characteristic information according to the reference earth surface temperature time sequence data and the related earth surface temperature time sequence data.

Optionally, the remote sensing surface energy characteristic information determining submodule includes: the reference surface temperature curve determining unit is used for generating a corresponding reference surface temperature curve according to the reference surface temperature time sequence data; the relevant earth surface temperature curve determining unit is used for generating a corresponding relevant earth surface temperature curve according to the relevant earth surface temperature time sequence data; and the remote sensing earth surface energy characteristic information determining unit is used for determining remote sensing earth surface energy characteristic information according to the reference earth surface temperature curve and the related earth surface temperature curve.

Optionally, the remote sensing surface energy characteristic information determining unit includes a remote sensing surface energy characteristic information determining subunit, and determines at least one of the following remote sensing surface energy characteristic information corresponding to the pixel to be evaluated: a start time for indicating a start time of a growing period of vegetation on the grassland of a corresponding area of the pixel to be evaluated; an end time for indicating an end time of the growing period; a growth period duration indicating a duration from a start time to an end time; the earth surface maximum temperature is used for indicating the maximum temperature value of the reference earth surface temperature; the growth period amplitude is used for indicating the difference value between the maximum temperature value and the minimum temperature value of the relevant earth surface temperature in the growth period; the temperature difference amplitude is used for indicating the difference between the maximum value and the minimum value of the difference value between the phase-moment related earth surface temperature and the reference earth surface temperature in the growing period; a temperature difference increase rate for indicating a rate at which the difference between the contemporaneous correlated surface temperature and the reference surface temperature during the growth period increases from a preset first temperature difference to a preset second temperature difference; a temperature difference reduction rate for indicating a rate at which the difference between the same time-dependent surface temperature and the reference surface temperature during the growth period is reduced from a preset third temperature difference to a preset fourth temperature difference; a vegetation period integral indicative of a difference between an integral of the reference surface temperature profile and an integral of the associated surface temperature profile during the vegetation period; and a production rate indicating a ratio between the integral of the growth period and the length of the growth period.

Optionally, the above-ground biomass determination module comprises an above-ground biomass determination submodule, and is used for determining the above-ground biomass according to the remote sensing earth surface energy characteristic information by using a preset random forest model.

Optionally, the sub-module for determining the surface matrix pure pixels comprises: the principal component analysis unit is used for performing data dimension reduction on the surface reflectivity remote sensing data by using a principal component analysis algorithm and determining wave band data of a preset dimension; and the surface matrix pure pixel determining unit is used for extracting the end members of the geometrical vertexes of the wave band data of the preset dimensionality and determining the surface matrix pure pixel.

Optionally, the reference surface temperature profile determination unit comprises: the data combination subunit is used for arranging the reference earth surface temperature time sequence data according to a time sequence; and the S-G filtering subunit is used for carrying out S-G filtering on the arrayed reference surface temperature time sequence data to generate the reference surface temperature curve.

Example 3

Fig. 9 shows an apparatus 900 for determining biomass on grassland according to the first aspect of the present embodiment, the apparatus 900 corresponding to the method according to the first aspect of the embodiment 1. Referring to fig. 9, the apparatus 900 includes: a processor 910; and a memory 920, coupled to the processor 910, for providing instructions to the processor 910 to process the following steps: determining a pixel set corresponding to a preset area on a grassland, wherein the pixel set comprises a plurality of pixels corresponding to the preset area; selecting a pixel to be evaluated for evaluating aboveground biomass from the pixel set; determining remote sensing surface energy characteristic information corresponding to the pixel to be evaluated according to surface reflectivity remote sensing data and thermal infrared remote sensing data corresponding to the pixel set, wherein the remote sensing surface energy characteristic information is used for evaluating the aboveground biomass; and determining the aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing surface energy characteristic information.

Optionally, the operation of determining the remote sensing earth surface energy characteristic information corresponding to the pixel to be evaluated according to the earth surface reflectivity remote sensing data and the thermal infrared remote sensing data corresponding to the pixel set comprises: determining a surface matrix pure pixel from the pixel set according to surface reflectivity remote sensing data; determining reference surface temperature time sequence data of reference surface temperature corresponding to a preset area according to the thermal infrared remote sensing data corresponding to the surface matrix pure pixels; determining relevant earth surface temperature time sequence data of relevant earth surface temperatures of the pixel to be evaluated according to the thermal infrared remote sensing data corresponding to the pixel to be evaluated; and determining remote sensing earth surface energy characteristic information according to the reference earth surface temperature time sequence data and the related earth surface temperature time sequence data.

Optionally, the operation of determining the remote sensing surface energy characteristic information according to the reference surface temperature curve and the related surface temperature curve includes: determining remote sensing earth surface energy characteristic information according to the reference earth surface temperature curve and the related earth surface temperature curve, and determining at least one piece of the following remote sensing earth surface energy characteristic information corresponding to the pixel to be evaluated: a start time for indicating a start time of a growth period of an above-grassland organism of a corresponding area of the pixel to be evaluated; an end time for indicating an end time of the growing period; a growth period duration indicating a duration from a start time to an end time; the earth surface maximum temperature is used for indicating the maximum temperature value of the reference earth surface temperature; the growth period amplitude is used for indicating the difference value between the maximum temperature value and the minimum temperature value of the relevant earth surface temperature in the growth period; the temperature difference amplitude is used for indicating the difference between the maximum value and the minimum value of the difference value between the phase-moment related earth surface temperature and the reference earth surface temperature in the growing period; a temperature difference increase rate for indicating a rate at which the difference between the contemporaneous correlated surface temperature and the reference surface temperature during the growth period increases from a preset first temperature difference to a preset second temperature difference; a temperature difference reduction rate for indicating a rate at which the difference between the same time-dependent surface temperature and the reference surface temperature during the growth period is reduced from a preset third temperature difference to a preset fourth temperature difference; a vegetation period integral indicative of a difference between an integral of the reference surface temperature profile and an integral of the associated surface temperature profile during the vegetation period; and a production rate indicating a ratio between the integral of the growth period and the length of the growth period.

Optionally, the operation of determining the aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing surface energy characteristic information comprises: and determining the aboveground biomass according to the remote sensing earth surface energy characteristic information by utilizing a preset random forest model.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims

1. A method of determining biomass on a grassland, comprising:

determining a pixel set corresponding to a predetermined area on a grassland, wherein the pixel set comprises a plurality of pixels corresponding to the predetermined area;

selecting a pixel to be evaluated for evaluating aboveground biomass from the pixel set;

determining remote sensing surface energy characteristic information corresponding to the pixel to be evaluated according to surface reflectivity remote sensing data and thermal infrared remote sensing data corresponding to the pixel set, wherein the remote sensing surface energy characteristic information is used for evaluating the aboveground biomass; and

and determining the aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing surface energy characteristic information.

2. The method according to claim 1, wherein the operation of determining the remote sensing earth surface energy characteristic information corresponding to the pixel to be evaluated from the earth surface reflectance remote sensing data and the thermal infrared remote sensing data corresponding to the set of pixels comprises:

determining a surface matrix pure pixel for describing surface matrix information from the pixel set according to the surface reflectivity remote sensing data;

determining reference surface temperature time sequence data of reference surface temperature corresponding to the preset area according to the thermal infrared remote sensing data corresponding to the surface matrix pure pixels;

determining relevant earth surface temperature time sequence data of relevant earth surface temperatures of the pixel to be evaluated according to the thermal infrared remote sensing data corresponding to the pixel to be evaluated; and

and determining the remote sensing earth surface energy characteristic information according to the reference earth surface temperature time sequence data and the related earth surface temperature time sequence data.

3. The method of claim 2, wherein the operation of determining the remotely sensed surface energy characteristic information from the reference surface temperature time series data and the correlated surface temperature time series data comprises:

generating a corresponding reference earth surface temperature curve according to the reference earth surface temperature time sequence data;

generating a corresponding relevant earth surface temperature curve according to the relevant earth surface temperature time sequence data; and

and determining the remote sensing earth surface energy characteristic information according to the reference earth surface temperature curve and the related earth surface temperature curve.

4. The method of claim 3, wherein the operation of determining the remotely sensed surface energy signature information from the reference surface temperature profile and the associated surface temperature profile comprises: determining at least one piece of remote sensing earth surface energy characteristic information corresponding to the pixel to be evaluated according to the reference earth surface temperature curve and the related earth surface temperature curve:

a start time for indicating a start time of a growing period of vegetation on the grassland of the corresponding area of the pixel to be evaluated;

an end time for indicating an end time of the growing period;

a growth period duration indicating a duration from the start time to the end time;

a maximum surface temperature for indicating a maximum temperature value of the reference surface temperature;

a growth period amplitude for indicating a difference between a maximum temperature value and a minimum temperature value of the correlated surface temperature during the growth period;

a temperature differential amplitude indicative of a difference between a maximum and a minimum of the difference between the correlated surface temperature and the reference surface temperature at the same time during the growth period;

a temperature differential increase rate indicative of a rate at which the difference between the correlated surface temperature and the reference surface temperature increases from a preset first temperature difference to a preset second temperature difference at the same time during the growth period;

a temperature differential reduction rate indicative of a rate at which the difference between the correlated surface temperature and the reference surface temperature at the same time during the growth period is reduced from a preset third temperature difference to a preset fourth temperature difference;

a vegetation period integral indicative of a difference between the integrated value of the reference surface temperature profile and the integrated value of the correlated surface temperature profile during the vegetation period; and

a production rate indicating a ratio between the growth phase integral and the growth phase duration.

5. The method according to claim 4, wherein the operation of determining the above-ground biomass corresponding to the pixel to be evaluated from the remotely sensed surface energy characteristic information comprises: and determining the aboveground biomass according to the remote sensing earth surface energy characteristic information by utilizing a preset random forest model.

6. The method of claim 2, wherein the operation of determining surface matrix pure pixels from the set of pixels from the surface reflectance remote sensing data comprises:

performing data dimension reduction on the surface reflectivity remote sensing data by using a principal component analysis algorithm, and determining wave band data of a preset wave band; and

and extracting end members of a geometric vertex of the wave band data of the preset wave band to determine the surface matrix pure pixel.

7. The method of claim 3, wherein the operation of generating a corresponding reference surface temperature profile from the reference surface temperature time series data comprises:

arranging the reference earth surface temperature time sequence data according to a time sequence; and

and performing S-G filtering on the arrayed time sequence data of the reference surface temperature to generate a reference surface temperature curve.

8. A storage medium comprising a stored program, wherein the method of any one of claims 1 to 7 is performed by a processor when the program is run.

9. An apparatus for determining biomass on a grassland, comprising:

a set of image elements determination module (810) for determining a set of image elements corresponding to a predetermined area on a grassland, wherein the set of image elements comprises a plurality of image elements corresponding to the predetermined area;

a to-be-evaluated pixel selection module (820) for selecting a to-be-evaluated pixel for evaluating aboveground biomass from the set of pixels;

the remote sensing surface energy characteristic information determining module (830) is used for determining remote sensing surface energy characteristic information corresponding to the pixel to be evaluated according to surface reflectivity remote sensing data and thermal infrared remote sensing data corresponding to the pixel set, and the remote sensing surface energy characteristic information is used for evaluating the aboveground biomass; and

and the aboveground biomass determining module (840) is used for determining aboveground biomass corresponding to the pixel to be evaluated according to the remote sensing earth surface energy characteristic information.

10. An apparatus for determining biomass on a grassland, comprising:

a processor (910); and

a memory (920) coupled to the processor (910) for providing instructions to the processor (910) to process the following process steps: