CN115855151A - Crop drought monitoring method, device, storage medium and equipment - Google Patents

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

Crop drought monitoring method, device, storage medium and equipment

technical field

The present application relates to the technical field of ecological monitoring, in particular to a crop drought monitoring method, device, storage medium and equipment.

Background technique

In the context of global climate change, agricultural droughts occur frequently and food security is seriously threatened. Therefore, predicting the future yield trend of crops has become an indispensable means. As one of the most important types of crops, maize is extremely vulnerable to climate change. Therefore, it is of great significance to evaluate the drought situation in the maize planting area to predict the future yield trend of crops.

The existing drought assessment method mainly uses the meteorological data (such as precipitation, temperature, etc.) monitored by ground stations to analyze the drought index of the crop planting area. Not only is the accuracy low, but it is only suitable for crops in small-scale planting areas. Applicability is low.

Contents of the invention

The present application provides a crop drought monitoring method, device, storage medium and equipment, with the purpose of improving the accuracy of drought assessment of crops in a wide range of planting areas.

In order to achieve the above object, the application provides the following technical solutions:

A crop drought monitoring method, comprising:

Obtain remote sensing data of crops in a preset historical time period; the remote sensing data includes vegetation index, surface temperature, and total primary productivity;

Using the vegetation index and the surface temperature to calculate the remote sensing drought index of the crop in the preset historical time period;

Obtain the remote sensing drought index of each observation period from the remote sensing drought index of the crop in the preset historical time period; each observation period is determined according to the change law of the growth cycle of the crop;

Obtaining the total primary productivity of each of the observation periods from the total primary productivity of the crops in the preset historical time period;

Substituting the remote sensing drought index and total primary productivity in each of the observation periods into the drought assessment model to calculate the overall drought index; the drought assessment model includes the remote sensing drought index based on each of the observation periods as an independent variable, and each of the The sum of the total primary productivity in the observation period is used as a linear regression model of the dependent variable; the overall drought index is negatively correlated with the yield of the crops.

Optionally, the acquisition of remote sensing data of crops within a preset historical time period includes:

Obtain the remote sensing data of the planting area of the crops collected by the medium-resolution imaging spectrometer in the preset historical time period according to the preset time resolution and preset spatial resolution.

Optionally, the remote sensing data also includes evaporation stress;

The remote sensing drought index of the crops in the preset historical time period is calculated by using the vegetation index and the surface temperature, including:

Using the vegetation index and the evaporative stress, calculate the remote sensing drought index of the crop in the preset historical time period.

Optionally, also include:

Obtain historical meteorological data of the planting area where the crop is located;

Using the historical meteorological data to calculate the meteorological drought index of the crops in the preset historical time period;

Obtaining the meteorological drought index of each observation period from the meteorological drought index of the crop in the preset historical time period;

The meteorological drought index and total primary productivity in each observation period are substituted into the drought assessment model to calculate the overall drought index.

A crop drought monitoring device, comprising:

The data acquisition unit is used to acquire remote sensing data of crops in a preset historical time period; the remote sensing data includes vegetation index, surface temperature, and total primary productivity;

An index calculation unit, configured to use the vegetation index and the surface temperature to calculate the remote sensing drought index of the crop in the preset historical time period;

The index query unit is used to obtain the remote sensing drought index of each observation period from the remote sensing drought index of the crop in the preset historical time period; each observation period is determined according to the change law of the growth cycle of the crop ;

A productivity query unit, configured to obtain the total primary productivity of each of the observation periods from the total primary productivity of the crops in the preset historical time period;

The drought assessment unit is used to substitute the remote sensing drought index and total primary productivity in each of the observation periods into the drought assessment model to calculate the overall drought index; the drought assessment model includes the remote sensing drought index based on each of the observation periods As an independent variable, the sum of the total primary productivity in each observation period is used as a linear regression model of the dependent variable; the overall drought index is negatively correlated with the yield of the crops.

Optionally, the data acquisition unit is specifically used for:

Obtain the remote sensing data of the planting area of the crops collected by the medium-resolution imaging spectrometer in the preset historical time period according to the preset time resolution and preset spatial resolution.

Optionally, the remote sensing data also includes evaporation stress;

The index calculation unit is also used to: use the vegetation index and the evaporative stress to calculate the remote sensing drought index of the crops in the preset historical time period.

Optionally, the drought assessment unit is also used for:

Obtain historical meteorological data of the planting area where the crop is located;

Using the historical meteorological data to calculate the meteorological drought index of the crops in the preset historical time period;

Obtaining the meteorological drought index of each observation period from the meteorological drought index of the crop in the preset historical time period;

The meteorological drought index and total primary productivity in each observation period are substituted into the drought assessment model to calculate the overall drought index.

A computer-readable storage medium, the computer-readable storage medium includes a stored program, wherein, when the program is executed by a processor, the method for monitoring crop drought is executed.

A crop drought monitoring device, comprising: a processor, a memory, and a bus; the processor is connected to the memory through the bus;

The memory is used to store a program, and the processor is used to run the program, wherein, when the program is run by the processor, the crop drought monitoring method is executed.

The technical solution provided by this application acquires remote sensing data of crops in a preset historical time period. Using the vegetation index and surface temperature, the remote sensing drought index of the crops in the preset historical time period is calculated. The remote sensing drought index of each observation period is obtained from the remote sensing drought index of crops in a preset historical time period. The total primary productivity of each observation period is obtained from the total primary productivity of crops in a preset historical time period. The remote sensing drought index and total primary productivity in each observation period were substituted into the drought assessment model to calculate the overall drought index. Taking the remote sensing drought index and total primary productivity of each observation period as a reference basis, it has strong objectivity, can fully consider the drought conditions in different growth periods of crops, and incorporate the drought conditions and total primary productivity that can reflect crop yields into a linear model. The correlation relationship is evaluated in the regression model to obtain the overall drought index. Compared with the prior art, the present application can accurately evaluate the correlation between the drought situation and the crop yield.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present application. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

Figure 1a is a schematic flow diagram of a crop drought monitoring method provided in the embodiment of the present application;

Fig. 1 b is a kind of overall drought index distribution map that the embodiment of the present application provides;

Fig. 1c is a distribution diagram of overall drought index and crop yield provided by the embodiment of the present application;

Fig. 2 is a schematic flow chart of another crop drought monitoring method provided by the embodiment of the present application;

Fig. 3 is a schematic structural diagram of a crop drought monitoring device provided in an embodiment of the present application.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some of the embodiments of the application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

As shown in Fig. 1a, it is a schematic flowchart of a crop drought monitoring method provided in the embodiment of the present application, including the following steps.

S101: Obtain the remote sensing data of the planting area where the crops are collected by a Moderate-resolution Imaging Spectroradiometer (MODIS) within a preset historical time period according to a preset time resolution and a preset spatial resolution.

Among them, the types of 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 functional implementation principle of MODIS is common knowledge familiar to those skilled in the art, and will not be repeated here. In addition, MOD13A1 data, MYD11A1 data, MOD16A2 data, MCD12Q1 data, MOD17 data, and PML_V2 data are all existing public remote sensing data types, and will not be repeated here.

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 technicians according to actual conditions.

In addition, different types of remote sensing data contain different observations. Specifically, MOD13A1 data includes vegetation index (NDVI), MYD11A1 data includes land surface temperature (LST), and MOD16A2 data includes evaporative stress (ET/PET, ET stands for evapotranspiration, PET stands for potential evapotranspiration), MCD12Q1 data includes categorical products (LC_Typel), MOD17 data includes gross primary productivity (GPP), and PML_V2 data includes gross primary productivity.

It should be emphasized that although both MOD17 data and PML_V2 data include total primary productivity, the specific values of total primary productivity included in the two are different.

Optionally, historical meteorological data and agricultural statistical data of the planting area where the crops are located can also be obtained.

Wherein, the historical meteorological data includes weather station data and meteorological reanalysis data within a preset historical time period. Specifically, weather station data include but are not limited to: air pressure, wind speed, sunshine hours, precipitation, relative humidity, average temperature, minimum temperature and maximum temperature, etc. Meteorological reanalysis data include monthly temperature and monthly precipitation. Agricultural statistics include the acreage, yield and damage of crops in a preset historical time period.

It should be noted that weather station data can be obtained from the Meteorological Information Center, meteorological reanalysis data can be obtained from the GEE platform (a cloud computing platform that processes satellite remote sensing image data and other earth observation data), and agricultural statistical data can be obtained from crop planting areas Obtained from the affiliated agricultural statistics platform.

S102: Using the remote sensing data, calculate the remote sensing drought index of the crops in the preset historical time period.

Among them, remote sensing drought indices include, but are not limited to: Normalized Difference Vegetation Water Supply Index (NVSWI), Temperature Vegetation Drought Index (TVDI), Vegetation Health Index (VHI), and Drought Severity Index (DSI).

Specifically, the normalized vegetation water supply index is calculated by using the vegetation index in the remote sensing data and the surface temperature, and the calculation process is shown in formulas (1) and (2).

In formulas (1) and (2), NDVI and LST have the same acquisition time and the same pixel resolution, and VSWI stands for vegetation water supply index, which reflects that when crops are drought-stricken, the vegetation canopy decreases by closing some stomata Transpiration results in an increase in crop canopy temperature and a decrease in NDVI. VSWI _min represents the minimum value of VSWI in the preset historical time period, and VSWI _max represents the maximum value of VSWI in the preset historical time period.

Specifically, the temperature vegetation drought index is calculated by using the vegetation index in the remote sensing data and the surface temperature, and the calculation process is shown in formulas (3), (4) and (5).

LST _max ＝a _dry +b _dry NDVI (4)

LST _min ＝a _wet +b _wet NDVI (5)

In formulas (3), (4) and (5), a _dry and b _dry represent the coefficients of the dry edge linear fitting equation, a _wet and b _wet represent the coefficients of the wet edge linear fitting equation, correspondingly, LST _max is obtained by linear fitting of NDVI and LST according to the dry edge, and LST _min is obtained by linear fitting of NDVI and LST according to the wet edge. Generally speaking, the lower the soil moisture at any point in the preset area, the closer the LST is to the dry side, and the larger the value of TVDI, the more severe the soil drought. Conversely, the smaller the value of TVDI, the higher the soil moisture content .

Specifically, the vegetation health index is calculated by using the vegetation index in the remote sensing data and the surface temperature, and the calculation process is shown in formulas (6), (7) and (8).

VHI=a×VCI+(1-a)×TCI (8)

In the formulas (6), (7) and (8), VCI represents the vegetation condition index, TCI represents the temperature condition index, NDVI _max represents the maximum value of NDVI in the preset historical time period, and NDVI _min represents the maximum value of NDVI in the preset historical time period. The minimum value in the time period, LST _max is obtained by linear fitting of NDVI and LST according to the dry edge, LST _min is obtained by linear fitting of NDVI and LST according to the wet edge, and a is the preset contribution coefficient (specifically, it can be set to 0.5 ).

Specifically, the drought severity index is calculated using the vegetation index and evaporative stress in the remote sensing data, and the calculation process is shown in formulas (9), (10), (11) and (12).

Z＝ _ZET/PET + _ZNDVI (11)

代表ET/PET在预设历史时间段内的平均值，/>

代表NDVI在预设历史时间段内的平均值，σ_ET/PET代表ET/PET的标准差，σ_NDVI代表NDVI的标准差，σ_Z代表Z的标准差，Z_ET/PET代表ET/PET的标准化值，Z_NDVI代表NDVI的标准化值，/>

代表Z的平均值。In equations (9), (10), (11) and (12),

Represents the average value of ET/PET in the preset historical time period, />

Represents the average value of NDVI in the preset historical time period, σ _ET/PET represents the standard deviation of ET/PET, σ _NDVI represents the standard deviation of NDVI, σ _Z represents the standard deviation of Z, Z _ET/PET represents the standard deviation of ET/PET Normalized value, Z _NDVI represents the normalized value of NDVI, />

Represents the mean value of Z.

Optionally, historical meteorological data can also be used to calculate the meteorological drought index of crops in a preset historical time period.

Among them, the meteorological drought index includes but is not limited to the water deficit index (CWDI), which takes into account the cumulative effect of water deficit and the impact on the growth and development of later crops. Concretely, the present embodiment counts from the day when the growth stage of crops begins, pushes 50 days to the early stage of crop growth, and calculates the water deficit index with 10 days as a unit, and the calculation process is as 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 formula (13), CWDI _i represents the water deficit index of the i-th time unit (specifically, it can be the water deficit index of the past 1 to 10 days), and CWDI _i-1 represents the water deficit index of the i-1th time unit. Water deficit index (specifically, it can be the water deficit index of the past 11 days to 20 days), CWDI _i-2 represents the water deficit index of the i-2th time unit (specifically, it can be the water deficit index of the past 21 days to 30 days Deficit Index), CWDI _i-3 represents the water deficit index of the i-3th time unit (specifically, it can be the water deficit index of the past 31 days to 40 days), CWDI _i-4 represents the water deficit index of the i-4th time unit Water deficit index (specifically, it can be the moisture index of the past 41 days and 50 days), a, b, c, d and e all represent the cumulative weight index, specifically, the value of a can be 0.3, and 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 the past 10 days, and i is a positive integer.

In formula (14), ET _ci represents the cumulative water demand of the i-th time unit, and P _i represents the cumulative precipitation of the i-th time unit.

In formula (15), ET _c represents water demand, ET ₀ represents reference evapotranspiration, and K _c represents crop coefficient.

In 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 psychrometer constant, T represents the average temperature, U2 represents the wind speed at a height of 2m, and e _s represents Saturated water pressure, e _a represents the actual water pressure.

It should be noted that the slope of the saturated water pressure curve, surface net radiation, soil heat flux, psychrometer constant, average temperature, wind speed at a height of 2m, saturated water pressure, and actual water pressure are all observations in historical meteorological data .

After analyzing the normalized vegetation water supply index, temperature vegetation drought index, vegetation health index, drought severity index, and water deficit index, the analysis results are obtained: the correlation between the temperature vegetation drought index and the water deficit index is high, and the temperature The fluctuation trend of vegetation drought index was consistent with that of vegetation health index and drought severity index. Therefore, the temperature vegetation drought index can be used as the best choice for remote sensing drought index.

S103: Determine a plurality of observation periods according to the change law of the growth cycle of the crops.

Among them, taking corn as an example, with an interval of 16 days, the corn growth cycle is divided into 9 observation periods from planting to maturity.

S104: Obtain the remote sensing drought index of each observation period from the remote sensing drought index of the crops in the preset historical time period.

Among them, in order to improve the accuracy of subsequent drought assessment, the remote sensing drought index in each observation period is: the average value of remote sensing drought indices in multiple crop planting areas in the same observation period.

Optionally, the meteorological drought index of each observation period can also be obtained from the meteorological drought index of crops in a preset historical time period.

S105: Obtain the total primary productivity of each observation period from the total primary productivity of crops in a preset historical time period.

Among them, using the crop yield shown in the agricultural statistics data as a reference, the yield analysis was carried out on the PML_V2 data and the MOD17 data, and the analysis results were obtained: the correlation between the total primary productivity and the crop yield shown in the PML_V2 data set was higher than that shown in the MOD17 data. Correlation between primary productivity and crop yield. In order to improve the accuracy of the drought assessment, this embodiment preferably selects the total primary productivity shown in the PML_V2 data as the total primary productivity used in the drought assessment process.

It should be noted that there is a linear relationship between total primary productivity and crop yield. Therefore, the monitoring of total primary productivity is essentially monitoring crop yield.

S106: Substitute the remote sensing drought index and total primary productivity in each observation period into the drought assessment model to calculate the overall drought index.

Among them, the drought assessment model includes a linear regression model based on the remote sensing drought index in each observation period as the independent variable, and the sum of the total primary productivity in each observation period as the dependent variable. Specifically, the specific expression of the drought assessment model, such as the formula (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 formulas (17), (18) and (19), Y represents the sum of total primary productivity in each observation period, β ₀ is a preset constant, and X ₁ , X ₂ ,...,X _j represent the β ₁ , β ₂ ,..., β _j are the regression coefficients of the remote sensing drought indices in each observation period, n is a positive integer, ε represents the random experiment error, W _i represents the remote sensing drought index in each observation period The weight of the drought index, S represents the overall drought index.

Specifically, taking the remote sensing drought index as the temperature vegetation drought index as an example, and substituting the temperature vegetation drought index of maize in the nine observation periods shown in Table 1 into the drought assessment model, the temperature vegetation drought in each observation period in Table 1 is obtained The weight of the index is based on the corn yield shown in the agricultural statistics. The drought during DOY177-DOY256 had a serious impact on the GPP of corn, and TVDI and GPP were negatively correlated. After selecting the weights of DOY177, DOY193, DOY209, DOY225 and DOY241, the overall drought index is calculated as:

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 R² Coefficient -88.4 160.9 1.3 325.3^** -597.1^** -405.7^** -710.0^** -1199.2^** --638.5^** 0.61

In the above Table 1, * indicates that there is a significant correlation at the first preset confidence level (that is, the overall drought index and total primary productivity), ** indicates that there is a significant correlation at the second preset confidence level, and R ² represents the coefficient of determination of the drought assessment model.

Specifically, taking the temperature vegetation drought index as an example, the overall drought index of crops in the preset planting area from 2007 to 2020 can be seen in Figure 1b.

Generally speaking, the crop yield shown in agricultural statistics can be used to verify the monitoring effect (ie accuracy) of the overall drought index. Specifically, taking corn as an example, the actual yield of corn can be regarded as the sum of trend yield, meteorological yield, and random yield. Among them, the trend yield is affected by unnatural factors such as social economy and production technology, and the meteorological yield is influenced by natural factors such as drought and flood. The random output is also called random noise, which is generally negligible. In order to study the relationship between meteorological changes and corn yield, the trend yield can be removed from the actual yield to obtain the meteorological yield of corn. Specifically, the moving average method is used to obtain the yield data of corn in the preset historical time period. weather production.

In the embodiment of the present application, the overall drought index is negatively correlated with the meteorological yield. Specifically, the correlation between the overall drought index and the meteorological yield can be seen in FIG. 1c. In other words, using the overall drought index, changes in crop yields can be accurately predicted.

Optionally, the meteorological drought index and total primary productivity in each observation period can also be substituted into the drought assessment model to calculate the overall drought index.

To sum up, taking the remote sensing drought index and total primary productivity in each observation period as a reference basis has strong objectivity, can fully consider the drought conditions in different growth periods of crops, and combine the drought conditions with the total yield that can reflect crop yields. The primary productivity is incorporated into the linear regression model to evaluate the correlation, and the overall drought index is obtained. Compared with the prior art, this embodiment can accurately evaluate the correlation between the drought situation and the crop yield.

It should be noted that S101 mentioned in the above embodiment is an optional implementation manner of the crop drought monitoring method shown in the embodiment of the present application. In addition, S103 mentioned in the above embodiment is also an optional implementation of the crop drought monitoring method shown in the embodiment of the present application. For this reason, the processes mentioned in the above embodiments can be summarized as the method shown in FIG. 2 .

As shown in FIG. 2 , it is a schematic flowchart of another crop drought monitoring method provided in the embodiment of the present application, including the following steps.

S201: Obtain remote sensing data of crops in a preset historical time period.

Among them, remote sensing data include vegetation index, surface temperature, and total primary productivity.

S202: Using the vegetation index and the surface temperature, calculate the remote sensing drought index of the crops in the preset historical time period.

S203: Obtain the remote sensing drought index of each observation period from the remote sensing drought index of the crops in the preset historical time period.

Wherein, each observation period is determined according to the change law of the growth cycle of the crops.

S204: Obtain the total primary productivity of each observation period from the total primary productivity of crops in a preset historical time period.

S205: Substituting the remote sensing drought index and total primary productivity in each observation period into the drought assessment model to calculate the overall drought index.

Among them, the drought assessment model includes a linear regression model based on the remote sensing drought index in each observation period as the independent variable, and the sum of total primary productivity in each observation period as the dependent variable; the overall drought index is negatively correlated with the crop yield.

To sum up, taking the remote sensing drought index and total primary productivity in each observation period as a reference basis has strong objectivity, can fully consider the drought conditions in different growth periods of crops, and combine the drought conditions with the total yield that can reflect crop yields. The primary productivity is incorporated into the linear regression model to evaluate the correlation, and the overall drought index is obtained. Compared with the prior art, this embodiment can accurately evaluate the correlation between the drought situation and the crop yield.

Corresponding to the crop drought monitoring method provided in the above embodiment of the present application, the embodiment of the present application further provides a crop drought monitoring device.

As shown in FIG. 3 , it is a schematic structural diagram of a crop drought monitoring device provided in the embodiment of the present application, including the following units.

The data acquisition unit 100 is used to acquire remote sensing data of crops within a preset historical time period; the remote sensing data includes vegetation index, surface temperature, and total primary productivity.

Optionally, the data acquisition unit 100 is specifically configured to: acquire the remote sensing data of the planting area of the crops collected by the medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.

The index calculation unit 200 is used to calculate the remote sensing drought index of the crops in the preset historical time period by using the vegetation index and the surface temperature.

Optionally, the remote sensing data also includes evaporation stress.

The index calculation unit 200 is also used to calculate the remote sensing drought index of crops in a preset historical time period by using the vegetation index and evaporative stress.

The index query unit 300 is used to obtain the remote sensing drought index of each observation period from the remote sensing drought index of the crops in the preset historical time period; each observation period is determined according to the change law of the growth cycle of the crops.

The productivity query unit 400 is used to obtain the total primary productivity of crops in each observation period from the total primary productivity of crops in a preset historical time period.

The drought assessment unit 500 is used to substitute the remote sensing drought index and total primary productivity in each observation period into the drought assessment model to calculate the overall drought index; the drought assessment model includes the remote sensing drought index based on each observation period as an independent variable, each The sum of gross primary productivity over the observation period was used as the dependent variable in a linear regression model; the overall drought index was negatively correlated with crop yields.

Optionally, the drought assessment unit 500 is also used to: obtain historical meteorological data of the planting area where the crops are located; use the historical meteorological data to calculate the meteorological drought index of the crops in the preset historical time period; From the meteorological drought index in the section, the meteorological drought index of each observation period is obtained; the meteorological drought index and total primary productivity of each observation period are substituted into the drought assessment model to calculate the overall drought index.

To sum up, taking the remote sensing drought index and total primary productivity in each observation period as a reference basis has strong objectivity, can fully consider the drought conditions in different growth periods of crops, and combine the drought conditions with the total yield that can reflect crop yields. The primary productivity is incorporated into the linear regression model to evaluate the correlation, and the overall drought index is obtained. Compared with the prior art, this embodiment can accurately evaluate the correlation between the drought situation and the crop yield.

The present application also provides a computer-readable storage medium, and the computer-readable storage medium includes a stored program, wherein the program executes the crop drought monitoring method provided in the present application.

The present application also provides a crop drought monitoring device, including: a processor, a memory and a bus. The processor and the memory are connected through a bus, the memory is used to store the program, and the processor is used to run the program, wherein, when the program is running, the above method for monitoring crop drought provided by the present application is executed, including the following steps:

Obtain remote sensing data of crops in a preset historical time period; the remote sensing data includes vegetation index, surface temperature, and total primary productivity;

Using the vegetation index and the surface temperature to calculate the remote sensing drought index of the crop in the preset historical time period;

Obtain the remote sensing drought index of each observation period from the remote sensing drought index of the crop in the preset historical time period; each observation period is determined according to the change law of the growth cycle of the crop;

Obtaining the total primary productivity of each of the observation periods from the total primary productivity of the crops in the preset historical time period;

Substituting the remote sensing drought index and total primary productivity in each of the observation periods into the drought assessment model to calculate the overall drought index; the drought assessment model includes the remote sensing drought index based on each of the observation periods as an independent variable, and each of the The sum of the total primary productivity in the observation period is used as a linear regression model of the dependent variable; the overall drought index is negatively correlated with the yield of the crops.

Specifically, on the basis of the above-mentioned embodiments, the acquisition of remote sensing data of crops within a preset historical time period includes:

Obtain the remote sensing data of the planting area of the crops collected by the medium-resolution imaging spectrometer in the preset historical time period according to the preset time resolution and preset spatial resolution.

Specifically, on the basis of the above embodiments, the remote sensing data also includes evaporation stress;

The remote sensing drought index of the crops in the preset historical time period is calculated by using the vegetation index and the surface temperature, including:

Using the vegetation index and the evaporative stress, calculate the remote sensing drought index of the crop in the preset historical time period.

Specifically, on the basis of the foregoing embodiments, it also includes:

Obtain historical meteorological data of the planting area where the crop is located;

Using the historical meteorological data to calculate the meteorological drought index of the crops in the preset historical time period;

Obtaining the meteorological drought index of each observation period from the meteorological drought index of the crop in the preset historical time period;

The meteorological drought index and total primary productivity in each observation period are substituted into the drought assessment model to calculate the overall drought index.

If the functions described in the methods of the embodiments of the present application are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computing device-readable storage medium. Based on this understanding, the part of the embodiment of the present application that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, the software product is stored in a storage medium, and includes several instructions to make a A computing device (which may be a personal computer, a server, a mobile computing device or a network device, etc.) executes all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media capable of storing program codes such as U disk, mobile hard disk, read-only memory, random access memory, magnetic disk or optical disk.

Each embodiment in this specification is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same or similar parts of each embodiment can be referred to each other.

The above 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 general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the present application will not 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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