Crop drought monitoring method, device, storage medium and equipment

Abstract

The present application discloses a crop drought monitoring method, device, storage medium and equipment, the method comprising: obtaining remote sensing data of crops in a preset historical time period; using vegetation index and surface temperature to calculate the remote sensing drought index of crops in the preset historical time period; obtaining the remote sensing drought index of each observation period from the remote sensing drought index of crops in the preset historical time period; obtaining the total primary productivity of each observation period from the total primary productivity of crops in the preset historical time period; substituting the remote sensing drought index and total primary productivity of each observation period into a drought assessment model to calculate the overall drought index. The remote sensing drought index and total primary productivity of each observation period are used as references, which has strong objectivity and can fully consider the drought conditions of crops in different growth periods. Compared with the prior art, the method can accurately assess the correlation between drought conditions and crop yields.

Description

Technical Field

[0001] The present application relates to the field of ecological monitoring technology, and in particular to a crop drought monitoring method, device, storage medium and equipment. Background Art

[0002] In the context of global climate change, agricultural drought events are frequent and food security is seriously threatened. Therefore, estimating the future yield trend of crops has become an indispensable means. Corn, as one of the most important types of crops, is extremely susceptible to climate change. Therefore, assessing the drought conditions in corn-growing areas is of great significance for estimating the future yield trend of crops.

[0003] Existing drought assessment methods mainly use meteorological data (such as precipitation, temperature, etc.) monitored by ground stations to analyze and derive the drought index for crop-growing areas. This method is not only inaccurate, but also only applicable to crops in small planting areas, and has low applicability. Summary of the invention

[0004] The present application provides a crop drought monitoring method, device, storage medium and equipment, aiming to improve the accuracy of drought assessment of crops in a large-scale planting area.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] A crop drought monitoring method, comprising:

[0007] Acquiring remote sensing data of crops within a preset historical time period; the remote sensing data includes vegetation index, surface temperature, and total primary productivity;

[0008] Calculating the remote sensing drought index of the crop in the preset historical time period by using the vegetation index and the surface temperature;

[0009] From the remote sensing drought index of the crop in the preset historical time period, obtaining the remote sensing drought index of each observation period; each observation period is determined according to the change law of the growth cycle of the crop;

[0010] Obtaining the total primary productivity of each observation period from the total primary productivity of the crop in the preset historical time period;

[0011] The remote sensing drought index and the total primary productivity of each observation period are substituted into the drought assessment model to calculate the overall drought index; the drought assessment model includes 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 negatively correlated with the yield of the crop.

[0012] Optionally, the step of obtaining remote sensing data of crops within a preset historical time period includes:

[0013] Acquire remote sensing data of the crop planting area collected by a medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.

[0014] Optionally, the remote sensing data also includes evaporation stress;

[0015] The step of calculating the remote sensing drought index of the crop in the preset historical time period by using the vegetation index and the surface temperature includes:

[0016] The remote sensing drought index of the crop in the preset historical time period is calculated by using the vegetation index and the evaporation stress.

[0017] Optionally, also include:

[0018] Obtaining historical meteorological data of the area where the crop is planted;

[0019] Utilizing the historical meteorological data, calculating the meteorological drought index of the crop within the preset historical time period;

[0020] Obtaining the meteorological drought index of each observation period from the meteorological drought index of the crop in the preset historical time period;

[0021] The meteorological drought index and the total primary productivity of each observation period are substituted into the drought assessment model to calculate the overall drought index.

[0022] A crop drought monitoring device, comprising:

[0023] A data acquisition unit, 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;

[0024] An index calculation unit, used to calculate the remote sensing drought index of the crop in the preset historical time period by using the vegetation index and the surface temperature;

[0025] An index query unit, 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 of the observation periods is determined according to the change law of the growth cycle of the crop;

[0026] A productivity query unit, used for obtaining the total primary productivity of each observation period from the total primary productivity of the crop in the preset historical time period;

[0027] The drought assessment unit is used to substitute the remote sensing drought index and the total primary productivity of each observation period into the drought assessment model to calculate the overall drought index; the drought assessment model includes 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 negatively correlated with the yield of the crop.

[0028] Optionally, the data acquisition unit is specifically used for:

[0029] Acquire remote sensing data of the crop planting area collected by a medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.

[0030] Optionally, the remote sensing data also includes evaporation stress;

[0031] The index calculation unit is also used to calculate the remote sensing drought index of the crop in the preset historical time period by using the vegetation index and the evaporation stress.

[0032] Optionally, the drought assessment unit is further used for:

[0033] Obtaining historical meteorological data of the area where the crop is planted;

[0034] Utilizing the historical meteorological data, calculating the meteorological drought index of the crop within the preset historical time period;

[0035] Obtaining the meteorological drought index of each observation period from the meteorological drought index of the crop in the preset historical time period;

[0036] The meteorological drought index and the total primary productivity of each observation period are substituted into the drought assessment model to calculate the overall drought index.

[0037] A computer-readable storage medium includes a stored program, wherein the program is executed by a processor to execute the crop drought monitoring method.

[0038] A crop drought monitoring device comprises: a processor, a memory and a bus; the processor and the memory are connected via the bus;

[0039] The memory is used to store programs, and the processor is used to run programs, wherein the program executes the crop drought monitoring method when the processor runs it.

[0040] The technical solution provided by the present application obtains remote sensing data of crops within a preset historical time period. The remote sensing drought index of crops within a preset historical time period is calculated using the vegetation index and the surface temperature. From the remote sensing drought index of crops within the preset historical time period, the remote sensing drought index of each observation period is obtained. From the total primary productivity of crops within the preset historical time period, the total primary productivity of each observation period is obtained. The remote sensing drought index and the total primary productivity of each observation period are substituted into the drought assessment model to calculate the overall drought index. The remote sensing drought index and the total primary productivity of each observation period are used as references, which has a strong objectivity, can fully consider the drought conditions of crops in different growth stages, and incorporate the drought conditions and the total primary productivity that can reflect the crop yield into the linear regression model to evaluate the correlation, and obtain the overall drought index. Compared with the prior art, the present application can accurately evaluate the correlation between drought conditions and crop yields. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1a A schematic diagram of a method for monitoring crop drought provided in an embodiment of the present application;

[0043] Figure 1b An overall drought index distribution map provided in an embodiment of the present application;

[0044] Figure 1c An overall drought index and crop yield distribution diagram provided in an embodiment of the present application;

[0045] Figure 2 A schematic diagram of another crop drought monitoring method provided in an embodiment of the present application;

[0046] Figure 3 A schematic diagram of the architecture of a crop drought monitoring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] like Figure 1a As shown, it is a flow chart of a crop drought monitoring method provided in an embodiment of the present application, which includes the following steps.

[0049] S101: Acquire remote sensing data of a crop planting area 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.

[0050] The types of remote sensing data include but are not limited to: MOD13A1 data, MYD11A1 data, MOD16A2 data, MCD12Q1 data, MOD17 data, and PML_V2 data.

[0051] 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 described in detail here. In addition, MOD13A1 data, MYD11A1 data, MOD16A2 data, MCD12Q1 data, MOD17 data, and PML_V2 data are all existing publicly available remote sensing data types and will not be described in detail here.

[0052] In the embodiment of the present application, the values ​​of the preset historical time period, the preset time resolution and the preset spatial resolution can be set by a technician according to actual conditions.

[0053] In addition, different types of remote sensing data contain different observations. Specifically, MOD13A1 data include vegetation index (NDVI), MYD11A1 data include land surface temperature (LST), MOD16A2 data include evaporative stress (ET / PET, ET stands for evapotranspiration, PET stands for potential evapotranspiration), MCD12Q1 data include classification products (LC_Typel), MOD17 data include gross primary productivity (GPP), and PML_V2 data include gross primary productivity.

[0054] It should be emphasized that although both MOD17 data and PML_V2 data contain total primary productivity, the specific values ​​of the total primary productivity contained in the two are different.

[0055] Optionally, historical meteorological data and agricultural statistics of the area where the crops are grown can also be obtained.

[0056] Among them, historical meteorological data includes meteorological station data and meteorological reanalysis data within a preset historical time period. Specifically, meteorological station data includes but is not limited to: air pressure, wind speed, sunshine hours, precipitation, relative humidity, average temperature, minimum temperature and maximum temperature. Meteorological reanalysis data includes monthly temperature and monthly precipitation. Agricultural statistical data includes the planting area, yield and disaster situation of crops within a preset historical time period.

[0057] It should be noted that meteorological station data can be obtained from the Meteorological Information Center, meteorological reanalysis data can be obtained from the GEE platform (a cloud computing platform for processing satellite remote sensing image data and other earth observation data), and agricultural statistical data can be obtained from the agricultural statistical platform of the crop-growing area.

[0058] S102: Calculate the remote sensing drought index of crops within a preset historical time period using remote sensing data.

[0059] Among them, remote sensing drought indices include but are not limited to: Normalized Vegetation Water Supply Index (NVSWI), Temperature Vegetation Drought Index (TVDI), Vegetation Health Index (VHI), and Drought Severity Index (DSI).

[0060] Specifically, the vegetation index and surface temperature in the remote sensing data are used to calculate the normalized vegetation water supply index. The calculation process is shown in formulas (1) and (2).

[0061]

[0062]

[0063] In formulas (1) and (2), NDVI and LST are collected at the same time and with the same pixel resolution. VSWI stands for vegetation water supply index, which reflects that when crops are affected by drought, the vegetation canopy reduces transpiration by closing some stomata, resulting in an increase in crop canopy temperature and a decrease in NDVI. min Represents the minimum value of VSWI in the preset historical time period. max Indicates the maximum value of VSWI in the preset historical time period.

[0064] Specifically, the temperature vegetation drought index is calculated using the vegetation index and surface temperature in the remote sensing data. The calculation process is shown in formulas (3), (4) and (5).

[0065]

[0066] LST max =a dry +b dry NDVI (4)

[0067] LST min =a wet +b wet NDVI (5)

[0068] In formulas (3), (4) and (5), a dry and b dry represents the coefficient of the linear fitting equation of the dry edge, a wet and b wet represents the coefficients of the wet edge linear fit equation, correspondingly, LST max is obtained by linear fitting of NDVI and LST according to the dry edge. min It 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 edge, and the larger the TVDI value is, the more serious the soil drought is. Conversely, the smaller the TVDI value is, the higher the soil moisture content is.

[0069] Specifically, the vegetation health index is calculated using the vegetation index and surface temperature in the remote sensing data. The calculation process is shown in formulas (6), (7) and (8).

[0070]

[0071]

[0072] VHI=a×VCI+(1-a)×TCI (8)

[0073] In formulas (6), (7) and (8), VCI stands for vegetation condition index, TCI stands for temperature condition index, and NDVI stands for max Represents the maximum value of NDVI in the preset historical time period. min Represents the minimum value of NDVI in the preset historical time period, LST max is obtained by linear fitting of NDVI and LST according to the dry edge. min It 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).

[0074] Specifically, the drought severity index is calculated using the vegetation index and evaporation stress in the remote sensing data. The calculation process is shown in formulas (9), (10), (11) and (12).

[0075]

[0076]

[0077] Z=Z ET / PET +Z NDVI (11)

[0078]

[0079] In formulas (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 standardized value of ET / PET, Z NDVI represents the standardized value of NDVI, Represents the mean value of Z.

[0080] Optionally, historical meteorological data may be used to calculate the meteorological drought index of crops within a preset historical time period.

[0081] 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 its impact on the growth and development of later crops. Specifically, this embodiment starts from the day when the growth stage of the crop begins, pushes forward 50 days to the early growth stage of the crop, and calculates the water deficit index in units of 10 days. The calculation process is shown in formulas (13), (14), (15) and (16).

[0082] CWDI=aCWDI i +bWD i-1 +cWDI i-2 +dCWDI i-3 +eCWDI i-4 (13)

[0083]

[0084] ET c =ET0×K c (15)

[0085]

[0086] In formula (13), CWDI irepresents 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), CWDI i-1 represents the water deficit index of the i-1th time unit (specifically, it can be the water deficit index of the past 11 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 to 30 days), 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 to 40 days), CWDI i-4 The water deficit index of the i-4th time unit (specifically, it can be the water index of the past 41 days and 50 days), 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.

[0087] In the embodiment of the present application, the time unit is specifically set to the past 10 days, and i is a positive integer.

[0088] In formula (14), ET ci represents the cumulative water demand of the ith time unit, P i Represents the cumulative precipitation in the i-th time unit.

[0089] In formula (15), ET c represents water demand, ET0 represents reference evapotranspiration, K c Represents the crop coefficient.

[0090] In formula (16), Δ represents the slope of the saturated water pressure curve, Rn represents the net radiation of the surface, G represents the soil heat flux, γ represents the psychrometric constant, T represents the average temperature, U2 represents the wind speed at a height of 2 m, and e s represents the saturated water vapor pressure, e a Represents the actual water vapor pressure.

[0091] It should be noted that the slope of the saturated water vapor pressure curve, net surface radiation, soil heat flux, psychrometric constant, average temperature, wind speed at 2 m, saturated water vapor pressure and actual water vapor pressure are all observed quantities in historical meteorological data.

[0092] By analyzing the normalized vegetation water supply index, temperature vegetation drought index, vegetation health index, drought severity index, and water deficit index, the results show that the temperature vegetation drought index has a high correlation with the water deficit index, and the fluctuation trend of the temperature vegetation drought index is consistent with that of the 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.

[0093] S103: Determine multiple observation periods according to the growth cycle of crops.

[0094] Among them, taking corn as an example, the corn growth cycle from corn planting to maturity is divided into 9 observation periods with an interval of 16 days.

[0095] S104: Obtaining remote sensing drought indexes of various observation periods from remote sensing drought indexes of crops in a preset historical time period.

[0096] Among them, in order to improve the accuracy of subsequent drought assessments, the remote sensing drought index for each observation period is: the average value of the remote sensing drought index of multiple crop planting areas in the same observation period.

[0097] Optionally, the meteorological drought index for each observation period may also be obtained from the meteorological drought index of crops in a preset historical time period.

[0098] S105: Obtain the total primary productivity of each observation period from the total primary productivity of crops in a preset historical time period.

[0099] Among them, the crop yield shown in the agricultural statistical data is used as a reference, and the PML_V2 data and MOD17 data are analyzed for yield, and the analysis results are obtained: the correlation between the total primary productivity shown in the PML_V2 data set and the crop yield is higher than the correlation between the total primary productivity and the crop yield shown in the MOD17 data. In order to improve the accuracy of drought assessment, this embodiment preferentially selects the total primary productivity shown in the PML_V2 data as the total primary productivity used in the drought assessment process.

[0100] It should be noted that there is a linear relationship between total primary productivity and crop yield. Therefore, monitoring total primary productivity is actually monitoring crop yield.

[0101] S106: Substitute the remote sensing drought index and total primary productivity of each observation period into the drought assessment model to calculate the overall drought index.

[0102] The drought assessment model includes a linear regression model based on the remote sensing drought index of each observation period as the independent variable and the sum of the total primary productivity of each observation period as the dependent variable. Specifically, the specific expression of the drought assessment model is shown in formulas (17), (18) and (19).

[0103] Y=β0+β1X1+β2X2+…+β j X j +ε, j=1,2,3,...,n (17)

[0104]

[0105]

[0106] In formulas (17), (18) and (19), Y represents the sum of the total primary productivity in each observation period, β0 is a preset constant, and X1, X2, ..., X j Represent the remote sensing drought index of each observation period, β1, β2, ..., β j are the regression coefficients of the remote sensing drought index in each observation period, n is a positive integer, ε represents the random experimental error, W i represents the weight of the remote sensing drought index in each observation period, and S represents the overall drought index.

[0107] Specifically, taking the remote sensing drought index as the temperature vegetation drought index as an example, the temperature vegetation drought index of the 9 observation periods of corn shown in Table 1 is substituted into the drought assessment model to obtain the weights of the temperature vegetation drought index of each observation period in Table 1. From the corn yield shown in agricultural statistical data, it is known that the drought in the DOY177-DOY256 period had a more serious impact on the GPP of corn, and TVDI was negatively correlated with GPP. After selecting the weights of DOY177, DOY193, DOY209, DOY225 and DOY241, the overall drought index was calculated as follows:

[0108] TVDI S =0.17*TVDI 177 +0.11*TVDI 193 +0.2*TVDI 209 +0.34*TVDI 225 +0.18*TVDI 241 .

[0109] Table 1

[0110] DOY 113 129 145 161 177 193 209 225 241 <![CDATA[R 2 ]]> Coefficient -88.4 160.9 1.3 <![CDATA[325.3 ** ]]> <![CDATA[-597.1 ** ]]> <![CDATA[-405.7 ** ]]> <![CDATA[-710.0 ** ]]> <![CDATA[-1199.2 ** ]]> <![CDATA[--638.5 ** ]]> 0.61

[0111] In Table 1 above, * indicates a significant correlation at the first preset confidence level (i.e., overall drought index and total primary productivity), ** indicates a significant correlation at the second preset confidence level, R 2 represents the coefficient of determination of the drought assessment model.

[0112] 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 found in Figure 1b shown.

[0113] Generally speaking, the crop yield shown in agricultural statistics can be used to verify the monitoring effect (i.e. 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, trend yield is affected by non-natural factors such as social economy and production technology, meteorological yield is caused by natural factors such as drought and floods, and random yield is also called random noise, which can generally be ignored. 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 meteorological yield of corn is obtained from the yield data of corn in a preset historical time period using the sliding average method.

[0114] 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 found in Figure 1c That is to say, the overall drought index can accurately predict the changes in crop yields.

[0115] Optionally, the meteorological drought index and gross primary productivity of each observation period can be substituted into the drought assessment model to calculate the overall drought index.

[0116] In summary, the remote sensing drought index and total primary productivity in each observation period are used as reference basis, which has strong objectivity and can fully consider the drought conditions in different growth stages of crops. The drought conditions and the total primary productivity that can reflect the crop yield are included in the linear regression model to evaluate the correlation and obtain the overall drought index. Compared with the prior art, this embodiment can accurately evaluate the correlation between drought conditions and crop yield.

[0117] It should be noted that S101 mentioned in the above embodiment is an optional implementation 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. To this end, the process mentioned in the above embodiment can be summarized as follows: Figure 2 The method described.

[0118] like Figure 2 As shown, it is a flow chart of another crop drought monitoring method provided in an embodiment of the present application, which includes the following steps.

[0119] S201: Acquire remote sensing data of crops within a preset historical time period.

[0120] Among them, remote sensing data include vegetation index, surface temperature, and gross primary productivity.

[0121] S202: Calculate the remote sensing drought index of crops in a preset historical time period using the vegetation index and the surface temperature.

[0122] S203: Obtaining the remote sensing drought index of each observation period from the remote sensing drought index of crops in a preset historical time period.

[0123] Among them, each observation period is determined according to the changing laws of the growth cycle of crops.

[0124] S204: Obtain the total primary productivity of each observation period from the total primary productivity of crops in a preset historical time period.

[0125] S205: Substitute the remote sensing drought index and total primary productivity of each observation period into the drought assessment model to calculate the overall drought index.

[0126] Among them, the drought assessment model includes a linear regression model based on the remote sensing drought index of each observation period as the independent variable and the sum of the total primary productivity of each observation period as the dependent variable; the overall drought index is negatively correlated with crop yields.

[0127] In summary, the remote sensing drought index and total primary productivity in each observation period are used as reference basis, which has strong objectivity and can fully consider the drought conditions in different growth stages of crops. The drought conditions and the total primary productivity that can reflect the crop yield are included in the linear regression model to evaluate the correlation and obtain the overall drought index. Compared with the prior art, this embodiment can accurately evaluate the correlation between drought conditions and crop yield.

[0128] Corresponding to the crop drought monitoring method provided in the above-mentioned embodiment of the present application, the embodiment of the present application also provides a crop drought monitoring device.

[0129] like Figure 3 , which is a schematic diagram of the architecture of a crop drought monitoring device provided in an embodiment of the present application, including the units shown below.

[0130] 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.

[0131] Optionally, the data acquisition unit 100 is specifically used to obtain remote sensing data of the planting area where the crops are located, collected by a medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.

[0132] The index calculation unit 200 is used to calculate the remote sensing drought index of crops in a preset historical time period by using the vegetation index and the surface temperature.

[0133] Optionally, the remote sensing data also includes evaporative stress.

[0134] The index calculation unit 200 is also used to calculate the remote sensing drought index of crops within a preset historical time period using the vegetation index and evaporation stress.

[0135] 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 crops in a preset historical time period; each observation period is determined according to the change law of the growth cycle of crops.

[0136] The productivity query unit 400 is used to obtain the total primary productivity of each observation period from the total primary productivity of crops in a preset historical time period.

[0137] The drought assessment unit 500 is used to substitute the remote sensing drought index and total primary productivity of each observation period into the drought assessment model to calculate the overall drought index; the drought assessment model includes 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 negatively correlated with the yield of crops.

[0138] Optionally, the drought assessment unit 500 is also used to: obtain historical meteorological data of the crop planting area; use the historical meteorological data to calculate the meteorological drought index of the crops in a preset historical time period; obtain the meteorological drought index of each observation period from the meteorological drought index of the crops in the preset historical time period; substitute the meteorological drought index and total primary productivity of each observation period into the drought assessment model to calculate the overall drought index.

[0139] In summary, the remote sensing drought index and total primary productivity in each observation period are used as reference basis, which has strong objectivity and can fully consider the drought conditions in different growth stages of crops. The drought conditions and the total primary productivity that can reflect the crop yield are included in the linear regression model to evaluate the correlation and obtain the overall drought index. Compared with the prior art, this embodiment can accurately evaluate the correlation between drought conditions and crop yield.

[0140] The present application also provides a computer-readable storage medium, which includes a stored program, wherein the program executes the crop drought monitoring method provided by the present application.

[0141] 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 via a bus, the memory is used to store programs, and the processor is used to run the programs, wherein when the programs are run, the crop drought monitoring method provided by the present application is executed, including the following steps:

[0142] Acquiring remote sensing data of crops within a preset historical time period; the remote sensing data includes vegetation index, surface temperature, and total primary productivity;

[0143] Calculating the remote sensing drought index of the crop in the preset historical time period by using the vegetation index and the surface temperature;

[0144] From the remote sensing drought index of the crop in the preset historical time period, obtaining the remote sensing drought index of each observation period; each observation period is determined according to the change law of the growth cycle of the crop;

[0145] Obtaining the total primary productivity of each observation period from the total primary productivity of the crop in the preset historical time period;

[0146] The remote sensing drought index and the total primary productivity of each observation period are substituted into the drought assessment model to calculate the overall drought index; the drought assessment model includes 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 negatively correlated with the yield of the crop.

[0147] Specifically, based on the above embodiment, the step of obtaining remote sensing data of crops within a preset historical time period includes:

[0148] Acquire remote sensing data of the crop planting area collected by a medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.

[0149] Specifically, based on the above embodiment, the remote sensing data further includes evaporation stress;

[0150] The step of calculating the remote sensing drought index of the crop in the preset historical time period by using the vegetation index and the surface temperature includes:

[0151] The remote sensing drought index of the crop in the preset historical time period is calculated by using the vegetation index and the evaporation stress.

[0152] Specifically, based on the above embodiment, it also includes:

[0153] Obtaining historical meteorological data of the area where the crop is planted;

[0154] Utilizing the historical meteorological data, calculating the meteorological drought index of the crop within the preset historical time period;

[0155] Obtaining the meteorological drought index of each observation period from the meteorological drought index of the crop in the preset historical time period;

[0156] The meteorological drought index and the total primary productivity of each observation period are substituted into the drought assessment model to calculate the overall drought index.

[0157] If the functions described in the method of the embodiment 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 storage medium readable by a computing device. 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, which is stored in a storage medium and includes several instructions for a computing device (which can be a personal computer, server, mobile computing device or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories, random access memories, magnetic disks or optical disks.

[0158] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0159] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be 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 present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring crop drought, characterized in that: include: Acquiring remote sensing data of crops within a preset historical time period; the remote sensing data includes vegetation index, surface temperature, and total primary productivity; Calculating the remote sensing drought index of the crop in the preset historical time period by using the vegetation index and the surface temperature; From the remote sensing drought index of the crop in the preset historical time period, obtaining the remote sensing drought index of each observation 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 observation period from the total primary productivity of the crop in the preset historical time period; Substituting the remote sensing drought index and the total primary productivity of each observation period into the drought assessment model, the overall drought index is calculated; the drought assessment model includes 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 negatively correlated with the yield of the crop; The drought assessment model is: Y=β0+β1X1+β2X2+…+β j X j +ε, j=1,2,3,...,n; Among them, Y represents the sum of the total primary productivity of each observation period, β0 is a preset constant, X1, X2, ..., X j Represent the remote sensing drought index of each observation period, β1, β2, ..., β j are the regression coefficients of the remote sensing drought index in each observation period, n is a positive integer, ε represents the random experimental error, W i represents the weight of the remote sensing drought index in each observation period, and S represents the overall drought index.

2. The method according to claim 1, characterized in that The step of obtaining remote sensing data of crops within a preset historical time period includes: Acquire remote sensing data of the crop planting area collected by a medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.

3. The method according to claim 1, characterized in that The remote sensing data also includes evaporation stress; The step of calculating the remote sensing drought index of the crop in the preset historical time period by using the vegetation index and the surface temperature includes: The remote sensing drought index of the crop in the preset historical time period is calculated by using the vegetation index and the evaporation stress.

4. The method according to claim 1, characterized in that Also includes: Obtaining historical meteorological data of the area where the crop is planted; Utilizing the historical meteorological data, calculating the meteorological drought index of the crop within 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 the total primary productivity of each observation period are substituted into the drought assessment model to calculate the overall drought index.

5. A crop drought monitoring device, characterized in that: include: A data acquisition unit, 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; An index calculation unit, used to calculate the remote sensing drought index of the crop in the preset historical time period by using the vegetation index and the surface temperature; An index query unit, 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 of the observation periods is determined according to the change law of the growth cycle of the crop; A productivity query unit, used for obtaining the total primary productivity of each observation period from the total primary productivity of the crop in the preset historical time period; A drought assessment unit, used for substituting the remote sensing drought index and the total primary productivity of each observation period into a drought assessment model to calculate an overall drought index; the drought assessment model includes 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 negatively correlated with the yield of the crop; The drought assessment model is: Y=β0+β1X1+β2X2+…+β j X j +ε, j=1,2,3,...,n; Among them, Y represents the sum of the total primary productivity of each observation period, β0 is a preset constant, X1, X2, ..., X j Represent the remote sensing drought index of each observation period, β1, β2, ..., β j are the regression coefficients of the remote sensing drought index in each observation period, n is a positive integer, ε represents the random experimental error, W i represents the weight of the remote sensing drought index in each observation period, and S represents the overall drought index.

6. The device according to claim 5, characterized in that The data acquisition unit is specifically used for: Acquire remote sensing data of the crop planting area collected by a medium-resolution imaging spectrometer within a preset historical time period according to a preset time resolution and a preset spatial resolution.

7. The device according to claim 5, characterized in that The remote sensing data also includes evaporation stress; The index calculation unit is also used to calculate the remote sensing drought index of the crop in the preset historical time period by using the vegetation index and the evaporation stress.

8. The device according to claim 5, characterized in that The drought assessment unit is also used to: Obtaining historical meteorological data of the area where the crop is planted; Utilizing the historical meteorological data, calculating the meteorological drought index of the crop within 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 the total primary productivity of each observation period are substituted into the drought assessment model to calculate the overall drought index.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program is executed by a processor to execute the crop drought monitoring method according to any one of claims 1 to 4.

10. A crop drought monitoring device, characterized in that: include: processor, memory, and bus; The processor is connected to the memory via the bus; The memory is used to store programs, and the processor is used to run programs, wherein the program, when run by the processor, executes the crop drought monitoring method according to any one of claims 1 to 4.

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