CN115855151A - Crop drought monitoring method, device, storage medium and equipment - Google Patents
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Abstract
The application discloses a crop drought monitoring method, a device, a storage medium and equipment, wherein the method comprises the following steps: acquiring remote sensing data of crops in a preset historical time period; calculating to obtain a remote sensing drought index of the crops in a preset historical time period by utilizing the vegetation index and the surface temperature; obtaining the remote sensing drought index of each observation period from the remote sensing drought indexes of crops in a preset historical time period; acquiring the total primary productivity of each observation period from the total primary productivity of the crops in a preset historical time period; and substituting the remote sensing drought index and the total primary productivity of each observation period into a drought evaluation model, and calculating to obtain the overall drought index. The remote sensing drought index and the total primary productivity of each observation period are used as reference bases, the objectivity is strong, the drought conditions of crops in different growth periods can be fully considered, and compared with the prior art, the method can accurately evaluate the correlation between the drought conditions and the crop yield.
Description
Technical Field
The application relates to the technical field of ecological monitoring, in particular to a crop drought monitoring method, a crop drought monitoring device, a crop drought monitoring storage medium and crop drought monitoring equipment.
Background
Under the global climate change background, agricultural drought events occur frequently, and the grain safety is seriously threatened, so that the prediction of the future yield trend of crops becomes an indispensable means. As the most important type of crops, the corn is very easily influenced by climate change, so that the evaluation of the drought condition of a corn planting area has important significance for estimating the future yield trend of the crops.
In the existing drought evaluation mode, the drought index of a crop planting area is obtained by analyzing meteorological data (such as rainfall, air temperature and the like) monitored by a ground station, so that the accuracy is low, the drought evaluation mode is only suitable for crops in a small-range planting area, and the applicability is low.
Disclosure of Invention
The application provides a crop drought monitoring method, a crop drought monitoring device, a crop storage medium and crop drought monitoring equipment, and aims to improve the accuracy of drought evaluation of crops in a large-range planting area.
In order to achieve the above object, the present application provides the following technical solutions:
a crop drought monitoring method comprising:
acquiring remote sensing data of crops in a preset historical time period; the remote sensing data comprises a vegetation index, a surface temperature and total primary productivity;
calculating the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the surface temperature;
acquiring remote sensing drought indexes of each observation period from the remote sensing drought indexes of the crops in the preset historical time period; each observation period is determined according to the growth cycle change rule of the crops;
obtaining a total primary productivity for each of the observation periods from a total primary productivity of the crop over the preset historical time period;
substituting the remote sensing drought index and the total primary productivity of each observation period into a drought evaluation model, and calculating to obtain an overall drought index; the drought evaluation model comprises a linear regression model based on the remote sensing drought index of each observation period as an independent variable and the sum of the total primary productivity of each observation period as a dependent variable; the global drought index is inversely related to the yield of the crop.
Optionally, the obtaining of remote sensing data of the crops in a preset historical time period includes:
and acquiring remote sensing data of the planting area where the crops are located, which is acquired by the medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.
Optionally, the remote sensing data further includes evaporation stress;
the remote sensing drought index of the crops in the preset historical time period is calculated by utilizing the vegetation index and the surface temperature, and the remote sensing drought index comprises the following steps:
and calculating the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the evaporation stress.
Optionally, the method further includes:
acquiring historical meteorological data of a planting area where the crops are located;
calculating the weather drought index of the crops in the preset historical time period by using the historical weather data;
acquiring the meteorological drought index of each observation period from the meteorological drought indexes of the crops in the preset historical time period;
and substituting the meteorological drought index and the total primary productivity of each observation period into the drought evaluation model, and calculating to obtain the overall drought index.
A crop drought monitoring device comprising:
the data acquisition unit is used for acquiring remote sensing data of crops in a preset historical time period; the remote sensing data comprises a vegetation index, a surface temperature and total primary productivity;
the index calculation unit is used for calculating the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the surface temperature;
the index query unit is used for acquiring the remote sensing drought index of each observation period from the remote sensing drought indexes of the crops in the preset historical time period; each observation period is determined according to the growth cycle change rule of the crops;
a productivity query unit for obtaining the total primary productivity of each observation period from the total primary productivity of the crops in the preset historical time period;
the drought evaluation unit is used for substituting the remote sensing drought index and the total primary productivity of each observation period into a drought evaluation model to calculate and obtain an overall drought index; the drought evaluation model comprises a linear regression model based on the remote sensing drought index of each observation period as an independent variable and the sum of the total primary productivity of each observation period as a dependent variable; the global drought index is inversely related to the yield of the crop.
Optionally, the data obtaining unit is specifically configured to:
and acquiring remote sensing data of the planting area where the crops are located, which is acquired by the medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.
Optionally, the remote sensing data further includes evaporation stress;
the index calculation unit is further configured to: and calculating the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the evaporation stress.
Optionally, the drought evaluation unit is further configured to:
acquiring historical meteorological data of a planting area where the crops are located;
calculating the weather drought index of the crops in the preset historical time period by using the historical weather data;
acquiring the weather drought index of each observation period from the weather drought indexes of the crops in the preset historical time period;
and substituting the meteorological drought index and the total primary productivity of each observation period into the drought evaluation model, and calculating to obtain the overall drought index.
A computer-readable storage medium comprising a stored program, wherein the program, when executed by a processor, performs the crop drought monitoring method.
A crop drought monitoring device comprising: a processor, memory, and a bus; the processor and the memory are connected through the bus;
the memory is used for storing a program, and the processor is used for executing the program, wherein the program is executed by the processor to execute the crop drought monitoring method.
According to the technical scheme, the remote sensing data of the crops in the preset historical time period are acquired. And calculating to obtain the remote sensing drought index of the crops in the preset historical time period by utilizing the vegetation index and the surface temperature. And obtaining the remote sensing drought index of each observation period from the remote sensing drought indexes of the crops in the preset historical time period. The total primary productivity for each observation period is obtained from the total primary productivity of the crop over a preset historical period. And substituting the remote sensing drought index and the total primary productivity of each observation period into a drought evaluation model, and calculating to obtain the overall drought index. The remote sensing drought index and the total primary productivity of each observation period are taken as reference bases, the objectivity is strong, the drought conditions of crops in different growth periods can be fully considered, the drought conditions and the total primary productivity capable of reflecting the crop yield are brought into a linear regression model to evaluate the correlation relation, the overall drought index is obtained, and compared with the prior art, the method and the device for evaluating the drought conditions and the crop yield can accurately evaluate the correlation between the drought conditions and the crop yield.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1a is a schematic flow chart of a crop drought monitoring method provided in an embodiment of the present application;
FIG. 1b is a graph of the overall drought index profile provided in the examples of the present application;
FIG. 1c is a graph of overall drought index and crop yield provided in accordance with an embodiment of the present application;
FIG. 2 is a schematic flow chart of another crop drought monitoring method provided in the embodiments of the present application;
fig. 3 is a schematic diagram of an architecture of a crop drought monitoring device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all 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 application.
As shown in fig. 1a, a schematic flow chart of a crop drought monitoring method provided in an embodiment of the present application includes the following steps.
S101: acquiring remote sensing data of a planting area where crops are located, wherein the remote sensing data are acquired by a middle-resolution Imaging spectrometer (MODIS) according to a preset time resolution and a preset spatial resolution within a preset historical time period.
The types of the remote sensing data include but are not limited to: MOD13A1 data, MYD11A1 data, MOD16A2 data, MCD12Q1 data, MOD17 data, PML _ V2 data.
It should be noted that the principle of implementing the functionality of the MODIS is common knowledge familiar to those skilled in the art, and is not described herein again. In addition, the MOD13A1 data, the MYD11A1 data, the MOD16A2 data, the MCD12Q1 data, the MOD17 data, and the PML _ V2 data are all remote sensing data types disclosed in the prior art, and are not described herein again.
In the embodiment of the present application, the respective values of the preset historical time period, the preset time resolution and the preset spatial resolution may be set by a technician according to an actual situation.
In addition, the observed quantities contained in the different types of remote sensing data may vary, and specifically, the MOD13A1 data includes vegetation index (NDVI), the MYD11A1 data includes surface temperature (LST), the MOD16A2 data includes evaporation stress (ET/PET, ET stands for evaporation, PET stands for potential evaporation), the MCD12Q1 data includes classification product (LC _ type), the MOD17 data includes total primary productivity (GPP), and the PML _ V2 data includes total primary productivity.
It should be emphasized that both the MOD17 data and the PML _ V2 data include the total primary productivity, but the specific values of the total primary productivity included in the both are different.
Optionally, historical meteorological data and agricultural statistical data of the planting area where the crops are located can be obtained.
The historical meteorological data comprises meteorological site data and meteorological reanalysis data in a preset historical time period. Specifically, the meteorological site data includes, but is not limited to: air pressure, wind speed, sunshine hours, precipitation, relative humidity, average temperature, minimum temperature, maximum air temperature and the like. The weather reanalysis data includes monthly air temperature and monthly precipitation. The agricultural statistical data comprise planting area, yield and disaster situations of crops in a preset historical time period.
It should be noted that the meteorological site data may be acquired from a meteorological information center, the meteorological reanalysis data may be acquired from a GEE platform (a cloud computing platform that processes satellite remote sensing image data and other earth observation data), and the agricultural statistical data may be acquired from an agricultural statistical platform to which the crop planting area belongs.
S102: and calculating the remote sensing drought index of the crops in the preset historical time period by using the remote sensing data.
Wherein, the remote sensing drought index includes but is not limited to: normalized vegetation water supply index (NVSWI), temperature Vegetation Drought Index (TVDI), vegetation Health Index (VHI), drought Severity Index (DSI).
Specifically, the vegetation index and the earth surface temperature in the remote sensing data are used for calculating to obtain the normalized vegetation water supply index, and the calculation process is shown as formulas (1) and (2).
In the formulas (1) and (2), the respective acquisition time of NDVI and LST is the same, the pixel resolution is the same, and VSWI represents the vegetation water supply index, which reflects that when crops are drought, the vegetation canopy reduces the transpiration amount by closing part of the air holes, so that the temperature of the vegetation canopy is increased, the NDVI is reduced, and the VSWI min Representing the minimum value of VSWI in a predetermined historical time period, VSWI max Representing the maximum value of VSWI over a preset historical period of time.
Specifically, the vegetation index and the earth surface temperature in the remote sensing data are utilized to calculate the temperature vegetation drought index, and the calculation process is shown as formulas (3), (4) and (5).
LST max ＝a dry +b dry NDVI (4)
LST min ＝a wet +b wet NDVI (5)
In the formulae (3), (4) and (5), a dry And b dry Coefficient representing a linear fitting equation of the dry edge, a wet And b wet Representing coefficients of a wet-edge linear fit equation, corresponding to LST max To be derived from NDVI and LST by a dry edge linear fit, LST min Are obtained from NDVI and LST according to a wet-edge linear fit. Generally, the lower the soil moisture at any point in the preset area, the closer the LST is to the dry edge, the larger the TVDI value is, the more severe the soil drought condition is, and conversely, the smaller the TVDI value is, the higher the soil moisture content is.
Specifically, the vegetation index and the earth surface temperature in the remote sensing data are used for calculating the vegetation health index, and the calculation process is shown as formulas (6), (7) and (8).
VHI＝a×VCI+(1-a)×TCI (8)
In equations (6), (7) and (8), VCI represents the vegetation condition index, TCI represents the temperature condition index, NDVI max Representing the maximum value of NDVI over a predetermined historical period, NDVI min Representing the minimum value of NDVI over a predetermined historical period, LST max To be derived from NDVI and LST by a dry edge linear fit, LST min To be obtained from NDVI and LST by a wet-edge linear fit, a is a preset contribution coefficient (which may be set to 0.5).
Specifically, the drought severity index is calculated by using the vegetation index and the evaporation stress in the remote sensing data, and the calculation process formulas (9), (10), (11) and (12) are shown.
Z＝Z ET/PET +Z NDVI (11)
In the formulas (9), (10), (11) and (12),
represents the average value of ET/PET over a preset history period, in conjunction with a timer>
Representing the average, σ, of NDVI over a predetermined historical period ET/PET Represents the standard deviation, σ, of ET/PET NDVI Represents the standard deviation, σ, of NDVI Z Represents the standard deviation of Z, Z ET/PET Normalized value, Z, representing ET/PET NDVI Standardized value representing NDVI>
Represents the average value of Z.
Optionally, historical meteorological data can be used to calculate a meteorological drought index of the crop in a preset historical time period.
The meteorological drought index includes, but is not limited to, a water deficit index (CWDI) which takes into account the cumulative effect of water deficit and the influence on later-stage crop growth and development. Specifically, in this embodiment, from the beginning of the growing period of the crop, the water deficit index is calculated by taking 10 days as a unit by pushing 50 days to the early growth period of the crop, and the calculation process is shown in the formulas (13), (14), (15) and (16).
CWDI＝aCWDI i +bCWDI i-1 +cCWDI i-2 +dCWDI i-3 +eCWDI i-4 (13)
ET c ＝ET 0 ×K c (15)
In equation (13), CWDI i Represents the water deficit index of the ith time unit (specifically, the water deficit index of the past 1 to 10 days), CWDI i-1 Represents water deficit index of i-1 time unit (specifically water deficit index of 11 days to 20 days in the past), CWDI i-2 Water deficit index representing the i-2 time unit (specifically, water deficit index from the last 21 days to 30 days), CWDI i-3 Water deficit index representing the i-3 time unit (specifically, the water deficit index of the last 31 days to 40 days), CWDI i-4 The water deficit index (specifically, the water deficit index in the last 41 days and 50 days) of the (i-4) th time unit, a, b, c, d and e all represent cumulative weight indexes, specifically, the value of a can be 0.3, the value of b can be 0.25, the value of c can be 0.2, the value of d can be 0.15, and the value of e can be 0.1.
In the embodiment of the present application, the time unit is specifically set to 10 days in the past, and i is a positive integer.
In the formula (14), ET ci Represents the cumulative water demand, P, of the ith time unit i Representing the cumulative precipitation for the ith time unit.
In the formula (15), ET c Representing water demand, ET 0 Represents a reference evapotranspiration, K c Representing the crop coefficient.
In the formula (16), Δ represents the slope of the saturated water pressure curve, rn represents the net surface radiation, G represents the soil heat flux, γ represents the psychrometric chart constant, T represents the average air temperature, and U2 represents the generationTABLE 2m high wind speed, e s Represents saturated water pressure, e a Representing the actual water pressure.
Note that the slope of the saturated water pressure curve, the net surface radiation, the soil heat flux, the psychrometer constant, the average air temperature, the wind speed at 2m height, the saturated water pressure, and the actual water pressure are all observed quantities in the historical meteorological data.
Analyzing the normalized vegetation water supply index, the temperature vegetation drought index, the vegetation health index, the drought severity index and the water deficit index to obtain an analysis result: the correlation between the temperature vegetation drought index and the water deficit index is high, and the temperature vegetation drought index is consistent with the fluctuation trend of the vegetation health index and the drought severity index. Therefore, the temperature vegetation drought index can be used as the optimal selection of the remote sensing drought index.
S103: and determining a plurality of observation periods according to the growth cycle change rule of the crops.
In the case of corn, the growth cycle of corn is divided into 9 observation periods from corn planting to maturity at intervals of 16 days.
S104: and obtaining the remote sensing drought index of each observation period from the remote sensing drought indexes of the crops in the preset historical time period.
In order to improve the accuracy of subsequent drought evaluation, the remote sensing drought index of each observation period is as follows: average value of remote sensing drought index of multiple crop planting areas in the same observation period.
Optionally, the weather drought index of each observation period can be obtained from the weather drought indexes of the crops in the preset historical time period.
S105: the total primary productivity for each observation period is obtained from the total primary productivity of the crops in the preset historical period.
Wherein, with the crop yield shown by the agricultural statistical data as a reference, performing yield analysis on the PML _ V2 data and the MOD17 data to obtain an analysis result: the correlation between total primary productivity and crop yield shown by the PML _ V2 data set was higher than that shown by MOD17 data. In order to improve the accuracy of drought assessment, the total primary productivity indicated by PML _ V2 data is preferably selected as the total primary productivity adopted in the drought assessment process.
It should be noted that there is a linear relationship between total primary productivity and crop yield, and for this reason, monitoring of total primary productivity is also essential in monitoring crop yield.
S106: and substituting the remote sensing drought index and the total primary productivity of each observation period into a drought evaluation model, and calculating to obtain the integral drought index.
Wherein, the drought evaluation model comprises a linear regression model based on the remote sensing drought indexes of each observation period as independent variables and the sum of the total primary productivities of each observation period as dependent variables, in particular, the specific expression form of the drought evaluation model is shown in formulas (17), (18) and (19).
Y＝β 0 +β 1 X 1 +β 2 X 2 +…+β j X j +ε， j＝1,2,3,...,n (17)
In the formulae (17), (18) and (19), Y represents the sum of the total primary productivities of the respective observation periods, β 0 Is a predetermined constant, X 1 ,X 2 ,...,X j Respectively representing the remote sensing drought index, beta, of each observation period 1 ,β 2 ,...,β j The regression coefficients of the remote sensing drought index of each observation period are respectively, n is a positive integer, epsilon represents a random experiment error, W i Represents the weight of the remote sensing drought index of each observation period, and S represents the overall drought index.
Specifically, taking a remote sensing drought index as an example of a temperature vegetation drought index, substituting the temperature vegetation drought indexes of 9 observation periods of the corn shown in table 1 into a drought evaluation model to obtain the weight of the temperature vegetation drought index of each observation period in table 1, knowing from the corn yield shown by agricultural statistical data that drought in DOY177-DOY256 periods has a relatively serious influence on the GPP of the corn, and TVDI is negatively related to the GPP. Selecting the weights of DOY177, DOY193, DOY209, DOY225 and DOY241, and calculating to obtain the overall drought index as follows:
TVDI S ＝0.17*TVDI 177 +0.11*TVDI 193 +0.2*TVDI 209 +0.34*TVDI 225 +0.18*TVDI 241 。
TABLE 1
| DOY | 113 | 129 | 145 | 161 | 177 | 193 | 209 | 225 | 241 | R 2 |
| Coefficient | -88.4 | 160.9 | 1.3 | 325.3 ** | -597.1 ** | -405.7 ** | -710.0 ** | -1199.2 ** | --638.5 ** | 0.61 |
In table 1 above, indicates a significant correlation at the first predetermined confidence level (i.e., overall drought index versus total primary productivity), indicates a significant correlation at the second predetermined confidence level, and R 2 The determination coefficients of the drought evaluation model are represented.
Specifically, taking the temperature vegetation drought index as an example, the overall drought index of the crops in the preset planting area from 2007 to 2020 can be seen in fig. 1 b.
Generally, the monitoring effect (i.e., accuracy) of the overall drought index can be verified using crop yield as indicated by agricultural statistics. Specifically, taking corn as an example, the actual yield of corn can be regarded as the sum of a trend yield, a meteorological yield and a random yield, wherein the trend yield is influenced by unnatural factors such as social economy, production technology and the like, the meteorological yield is caused by natural factors such as drought, flooding and the like, and the random yield is also called random noise and can be generally ignored. In order to research the relation between the meteorological change and the corn yield, the trend yield can be removed from the actual yield to obtain the meteorological corn yield, and specifically, the meteorological corn yield is obtained from the yield data of the corn in a preset historical time period by using a moving average method.
In the embodiment of the present application, the overall drought index is negatively correlated with the meteorological output, and specifically, the correlation between the overall drought index and the meteorological output can be seen in fig. 1 c. That is, the change in crop yield can be accurately estimated using the overall drought index.
Optionally, the meteorological drought index and the total primary productivity of each observation period may be substituted into the drought evaluation model to calculate the overall drought index.
In summary, the remote sensing drought index and the total primary productivity in each observation period are used as reference bases, so that the method has strong objectivity, can fully consider the drought conditions of crops in different growth periods, and brings the drought conditions and the total primary productivity capable of reflecting the crop yield into a linear regression model for evaluating the correlation relationship to obtain the overall drought index.
It should be noted that, in the above embodiment, reference is made to S101, which is an alternative implementation manner of the crop drought monitoring method shown in the embodiment of the present application. In addition, S103 mentioned in the above embodiments is also an optional implementation manner of the crop drought monitoring method shown in the embodiments of the present application. For this reason, the flow mentioned in the above embodiment can be summarized as the method described in fig. 2.
As shown in fig. 2, a schematic flow chart of another crop drought monitoring method provided in the embodiment of the present application includes the following steps.
S201: and acquiring remote sensing data of crops in a preset historical time period.
Wherein the remote sensing data comprises vegetation index, surface temperature, total primary productivity.
S202: and calculating to obtain the remote sensing drought index of the crops in the preset historical time period by utilizing the vegetation index and the surface temperature.
S203: and obtaining the remote sensing drought index of each observation period from the remote sensing drought indexes of the crops in the preset historical time period.
Wherein each observation period is determined according to the growth cycle variation rule of the crops.
S204: the total primary productivity for each observation period is obtained from the total primary productivity of the crops in the preset historical period.
S205: and substituting the remote sensing drought index and the total primary productivity of each observation period into a drought evaluation model, and calculating to obtain the overall drought index.
The drought evaluation model comprises a linear regression model which takes remote sensing drought indexes of all observation periods as independent variables and takes the sum of total primary productivity of all observation periods as dependent variables; the overall drought index is inversely related to the yield of the crop.
In summary, the remote sensing drought index and the total primary productivity in each observation period are used as reference bases, so that the method has strong objectivity, can fully consider the drought conditions of crops in different growth periods, and brings the drought conditions and the total primary productivity capable of reflecting the crop yield into a linear regression model for evaluating the correlation relationship to obtain the overall drought index.
Corresponding to the crop drought monitoring method provided by the embodiment of the application, the embodiment of the application also provides a crop drought monitoring device.
As shown in fig. 3, a schematic diagram of an architecture of a crop drought monitoring device provided in the embodiment of the present application includes the following units.
The data acquisition unit 100 is used for acquiring remote sensing data of crops in a preset historical time period; the remote sensing data includes vegetation index, surface temperature, total primary productivity.
Optionally, the data obtaining unit 100 is specifically configured to: and acquiring remote sensing data of the planting area where the crops are located, which is acquired by the medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.
And the index calculation unit 200 is used for calculating the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the earth surface temperature.
Optionally, the remote sensing data further comprises evaporation stress.
The index calculation unit 200 is further configured to: and calculating the remote sensing drought index of the crops in a preset historical time period by utilizing the vegetation index and the evaporation stress.
The index query unit 300 is used for acquiring the remote sensing drought index of each observation period from the remote sensing drought indexes of the crops in the preset historical time period; each observation period is determined according to the growth cycle change rule of the crops.
A productivity query unit 400 for obtaining the total primary productivity for each observation period from the total primary productivity of the crops in the preset historical period.
The drought evaluation unit 500 is used for substituting the remote sensing drought index and the total primary productivity of each observation period into a drought evaluation model to calculate and obtain an overall drought index; the drought evaluation model comprises a linear regression model based on the remote sensing drought index of each observation period as an independent variable and the sum of the total primary productivity of each observation period as a dependent variable; the overall drought index is inversely related to the yield of the crop.
Optionally, the drought evaluation unit 500 is further configured to: acquiring historical meteorological data of a planting area where crops are located; calculating to obtain a weather drought index of the crops in a preset historical time period by using historical weather data; acquiring the meteorological drought index of each observation period from the meteorological drought index of crops in a preset historical time period; and substituting the meteorological drought index and the total primary productivity of each observation period into a drought evaluation model, and calculating to obtain the overall drought index.
In summary, the remote sensing drought index and the total primary productivity of each observation period are used as reference bases, so that the method has strong objectivity, can fully consider the drought conditions of crops in different growth periods, and brings the drought conditions and the total primary productivity capable of reflecting the crop yield into the linear regression model for evaluating the correlation relationship to obtain the overall drought index.
The present application also provides a computer readable storage medium comprising a stored program, wherein the program performs the crop drought monitoring method provided herein above.
The application also provides a crops drought monitoring facilities includes: a processor, memory, and a bus. The processor is connected with the memory through a bus, the memory is used for storing programs, and the processor is used for running the programs, wherein when the programs are run, the crop drought monitoring method provided by the application comprises the following steps:
acquiring remote sensing data of crops in a preset historical time period; the remote sensing data comprises a vegetation index, a surface temperature and total primary productivity;
calculating the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the surface temperature;
acquiring remote sensing drought indexes of each observation period from the remote sensing drought indexes of the crops in the preset historical time period; each observation period is determined according to the growth cycle variation rule of the crops;
obtaining total primary productivity for each of the observation periods from total primary productivity of the crops over the preset historical period;
substituting the remote sensing drought index and the total primary productivity of each observation period into a drought evaluation model, and calculating to obtain an overall drought index; the drought evaluation model comprises a linear regression model based on the remote sensing drought index of each observation period as an independent variable and the sum of the total primary productivity of each observation period as a dependent variable; the global drought index is inversely related to the yield of the crop.
Specifically, on the basis of the above embodiment, the acquiring remote sensing data of the crops in the preset historical time period includes:
and acquiring remote sensing data of the planting area where the crops are located, which is acquired by the medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.
Specifically, on the basis of the above embodiment, the remote sensing data further includes an evaporation stress;
the remote sensing drought index of the crops in the preset historical time period is calculated by utilizing the vegetation index and the surface temperature, and the remote sensing drought index comprises the following steps:
and calculating the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the evaporation stress.
Specifically, on the basis of the above embodiment, the method further includes:
acquiring historical meteorological data of a planting area where the crops are located;
calculating the weather drought index of the crops in the preset historical time period by using the historical weather data;
acquiring the meteorological drought index of each observation period from the meteorological drought indexes of the crops in the preset historical time period;
and substituting the meteorological drought index and the total primary productivity of each observation period into the drought evaluation model, and calculating to obtain the overall drought index.
The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic or optical disk, etc. for storing program codes.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A crop drought monitoring method, comprising:
acquiring remote sensing data of crops in a preset historical time period; the remote sensing data comprises a vegetation index, a surface temperature and total primary productivity;
calculating the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the surface temperature;
acquiring the remote sensing drought index of each observation period from the remote sensing drought indexes of the crops in the preset historical time period; each observation period is determined according to the growth cycle change rule of the crops;
obtaining total primary productivity for each of the observation periods from total primary productivity of the crops over the preset historical period;
substituting the remote sensing drought index and the total primary productivity of each observation period into a drought evaluation model, and calculating to obtain an overall drought index; the drought evaluation model comprises a linear regression model based on the remote sensing drought index of each observation period as an independent variable and the sum of the total primary productivity of each observation period as a dependent variable; the global drought index is inversely related to the yield of the crop.
2. The method of claim 1, wherein the obtaining remote sensing data of the crop over a predetermined historical period comprises:
and acquiring remote sensing data of the planting area where the crops are located, which is acquired by the medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.
3. The method of claim 1, wherein the remote sensing data further comprises evaporation stress;
the remote sensing drought index of the crops in the preset historical time period is calculated by utilizing the vegetation index and the surface temperature, and the remote sensing drought index comprises the following steps:
and calculating the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the evaporation stress.
4. The method of claim 1, further comprising:
acquiring historical meteorological data of a planting area where the crops are located;
calculating the weather drought index of the crops in the preset historical time period by using the historical weather data;
acquiring the weather drought index of each observation period from the weather drought indexes of the crops in the preset historical time period;
and substituting the meteorological drought index and the total primary productivity of each observation period into the drought evaluation model, and calculating to obtain the overall drought index.
5. A crop drought monitoring device, comprising:
the data acquisition unit is used for acquiring remote sensing data of crops in a preset historical time period; the remote sensing data comprises a vegetation index, a surface temperature and total primary productivity;
the index calculation unit is used for calculating the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the surface temperature;
the index query unit is used for acquiring the remote sensing drought index of each observation period from the remote sensing drought indexes of the crops in the preset historical time period; each observation period is determined according to the growth cycle change rule of the crops;
a productivity query unit for obtaining a total primary productivity for each of the observation periods from a total primary productivity of the crop over the preset historical time period;
the drought evaluation unit is used for substituting the remote sensing drought index and the total primary productivity of each observation period into a drought evaluation model to calculate and obtain an overall drought index; the drought evaluation model comprises a linear regression model based on the remote sensing drought index of each observation period as an independent variable and the sum of the total primary productivity of each observation period as a dependent variable; the global drought index is inversely related to the yield of the crop.
6. The apparatus according to claim 5, wherein the data acquisition unit is specifically configured to:
and acquiring remote sensing data of the planting area where the crops are located, which is acquired by the medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.
7. The apparatus of claim 5, wherein the remote sensing data further comprises an evaporative stress;
the index calculation unit is further configured to: and calculating the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the evaporation stress.
8. The apparatus of claim 5, wherein the drought evaluation unit is further configured to:
acquiring historical meteorological data of a planting area where the crops are located;
calculating the weather drought index of the crops in the preset historical time period by using the historical weather data;
acquiring the weather drought index of each observation period from the weather drought indexes of the crops in the preset historical time period;
and substituting the meteorological drought index and the total primary productivity of each observation period into the drought evaluation model, and calculating to obtain the overall drought index.
9. A computer readable storage medium comprising a stored program, wherein the program, when executed by a processor, performs the crop drought monitoring method of any one of claims 1-4.
10. A crop drought monitoring device, comprising: a processor, a memory, and a bus; the processor and the memory are connected through the bus;
the memory is configured to store a program and the processor is configured to execute the program, wherein the program when executed by the processor performs the crop drought monitoring method according to any one of claims 1-4.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116976516A (en) * | 2023-08-01 | 2023-10-31 | 中国科学院空天信息创新研究院 | Early prediction method for single crop yield in arid region |
| CN118468146A (en) * | 2024-07-15 | 2024-08-09 | 贵州省生态与农业气象中心 | Drought early warning method and system based on soil moisture detection |
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2022
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116976516A (en) * | 2023-08-01 | 2023-10-31 | 中国科学院空天信息创新研究院 | Early prediction method for single crop yield in arid region |
| CN116976516B (en) * | 2023-08-01 | 2024-03-15 | 中国科学院空天信息创新研究院 | Early prediction method for single crop yield in arid region |
| CN118468146A (en) * | 2024-07-15 | 2024-08-09 | 贵州省生态与农业气象中心 | Drought early warning method and system based on soil moisture detection |
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