CN116843217A - Agricultural drought monitoring method based on European spatial distance method - Google Patents
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Abstract
The invention relates to the field of agricultural disaster monitoring in remote sensing application, in particular to an agricultural drought monitoring method based on an European space distance method. Based on the existing remote sensing drought index, a space distance model is constructed by calculating the correlation coefficients of different drought factors and soil water content, so that the model length of a drought space vector is obtained, and the model length is used as a drought evaluation index. Compared with the traditional remote sensing drought index, the method considers various factors of drought occurrence, and has better space consistency with the real situation of the drought occurrence; compared with the traditional data aggregation mode, a single data driving mode is not used, simple linear mapping is avoided, the method can be better adapted to the time-space change of drought, and better robustness is achieved.
Description
Technical Field
The invention relates to the field of agricultural disaster monitoring in remote sensing application, in particular to a soil moisture content monitoring technology based on satellite remote sensing data, and particularly relates to an agricultural drought monitoring method based on an European space distance method.
Background
Drought is a frequent weather hazard that affects multiple departments of social production, with the most serious risk of drought to agricultural production. Agricultural drought is typically caused by precipitation anomalies, air temperature anomalies, and other factors that cause a reduction in soil moisture, which occurs when the moisture content of the soil is reduced such that the demand for moisture by the crop is not met throughout the growing season. The method is significant in enhancing drought early warning capability, optimizing resource management and scheduling, and supporting decision making and disaster risk management.
As drought is the result of interaction of multiple factors, the occurrence of agricultural drought often shows multi-level influence, and the traditional method for carrying out drought monitoring by adopting single remote sensing data has certain limitation and uncertainty no matter optical remote sensing or microwave remote sensing. Different sensors can provide remote sensing information of different characteristics of ground objects, and certain complementarity exists between the sensors, so that the defect of a single data source can be overcome to a certain extent by the cooperative utilization of multi-source data, and the remote sensing drought monitoring precision and practicability are improved. But how to effectively couple the information of the data sources, the advantages of different sensors are exerted to the greatest extent. The method is a problem to be solved urgently in current remote sensing drought monitoring.
The existing agricultural drought change monitoring method mostly adopts a data-driven machine learning method or a simple linear mapping method in a data collection mode, and the obtained quantitative index has strong regionality and poor mobility, can not reflect the space-time change of drought, and causes the lack of effectiveness in soil moisture change monitoring and evaluation.
Disclosure of Invention
The invention aims to provide an agricultural drought monitoring method based on an European spatial distance method, which aims to solve the problem that the soil moisture content change monitoring and evaluation lack effectiveness caused by the fact that the existing agricultural drought monitoring method cannot react to the time-space change of drought.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an agricultural drought monitoring method based on European spatial distance method comprises the following steps:
step 1, acquiring near infrared earth surface reflectivity of a region to be monitored, red light wave band earth surface reflectivity data, land surface temperature observation values and rainfall radar data;
step 2, calculating a normalized vegetation index by utilizing the near infrared surface reflectivity and the red wave band surface reflectivity, and calculating a vegetation state index according to the normalized vegetation index; calculating a temperature condition index according to the land surface temperature observation value; calculating a standardized rainfall vapor emission index according to rainfall radar data;
step 3, calculating pearson correlation coefficients between the vegetation state index, the temperature condition index and the standardized rainfall vapor emission index and the soil water content respectively, and normalizing the pearson correlation coefficients between the indexes and the soil water content;
step 4, constructing a space distance model, mapping three representative variables of a vegetation state index, a temperature condition index and a standardized precipitation evaporation index to the same characteristic space, and setting the vegetation state index, the temperature condition index and the standardized precipitation evaporation index as two-by-two orthogonal coordinate axes in the European space in the characteristic space; and carrying out normalization result values on pearson correlation coefficients between each index and the soil water content, using the normalized result values as coordinate values, constructing a spatial distance drought index vector through the coordinate values, and calculating the modular length of the drought space vector as a drought evaluation index.
Further, the calculation formula of the normalized vegetation index in the step 2 is as follows:
in the formula (1), ρ NIR Surface reflectivity representing near infrared band; ρ Red The earth surface reflectivity of the red light wave band is represented; NDVI represents a normalized vegetation index with a value in the range of [0,1]]In between, higher values indicate better local vegetation growth. Calculation of vegetation state indexThe formula is:
in formula (2), NDVI i Normalized vegetation index NDVI observations, NDVI, representing current time max And NDVI min Representing the maximum and minimum values, respectively, of the normalized vegetation index NDVI that can be observed locally over a period of time.
Further, the calculation formula of the temperature condition index in the step 2 is as follows:
in formula (3), LST i Land surface temperature observations, LST, representing current time max And LST min Representing the maximum and minimum observations, respectively, of land surface temperature that can be observed locally over a period of time.
Further, the calculation formula of the standardized precipitation evaporation index in the step 2 is as follows:
in formula (4), SPI represents a normalized precipitation emission index, c i (i=0, 1, 2) and d i (i=1, 2, 3) are constants, respectively, t is a variable determined by the precipitation amount, and is obtained by the formula (5):
in the formula (5), P is a gamma distribution function of the precipitation fitting value, and the probability density distribution function is subjected to standard normalization treatment.
Further, the calculation formula of the pearson correlation coefficient between each index and the soil water content in the step 3 is as follows:
in the formula (6), r represents a pearson correlation coefficient, x and y respectively represent different variables, and N represents the number of variable sequences; TCI and soil moisture content are inversely related, and VCI and SPI are positively related to soil moisture content.
Further, the equation for calculating the modular length of the drought space vector is:
in the formula (7), VCI, TCI and SPI respectively represent vegetation state index, temperature condition index and standardized rainfall vapor-out index, r i ' represents the normalized result of the pearson correlation coefficient between the remote sensing index i and the soil moisture content, which always satisfies the formula (8):
the invention provides an agricultural drought monitoring method based on an European space distance method, which is based on the existing remote sensing drought index, and a space distance model is constructed by calculating the correlation coefficients of different drought factors and soil water content, so that the model length of a drought space vector is obtained and is used as a drought evaluation index. Compared with the traditional remote sensing drought index, the method considers various factors of drought occurrence, and has better space consistency with the real situation of the drought occurrence; compared with the traditional data aggregation mode, a single data driving mode is not used, simple linear mapping is avoided, the method can be better adapted to the time-space change of drought, and better robustness is achieved.
Drawings
FIG. 1 is a schematic diagram showing the distribution of precipitation curve Γ;
FIG. 2 is a schematic diagram of drought coordinate space;
FIG. 3 is a plot of spatial distance drought index versus soil root effective moisture content correlation coefficient.
Detailed Description
The following details the technical scheme of the invention with reference to the embodiments and the drawings.
The agricultural drought monitoring method based on the European spatial distance method provided by the embodiment comprises the following steps:
and step 1, acquiring near infrared earth surface reflectivity of a region to be monitored, red light wave band earth surface reflectivity data, land surface temperature observation values and rainfall radar data.
Step 2, calculating a normalized vegetation index by utilizing the near infrared surface reflectivity and the red wave band surface reflectivity, and calculating a vegetation state index according to the normalized vegetation index; calculating a temperature condition index according to the land surface temperature observation value; and calculating a standardized rainfall vapor emission index according to rainfall radar data.
The normalized vegetation index (Normalized Difference Vegetation Index, NDVI) is one of important parameters reflecting crop growth vigor and nutrition information, and the calculation formula of the normalized vegetation index is:
in the formula (1), ρ NIR Surface reflectivity representing near infrared band; ρ Red The earth surface reflectivity of the red light wave band is represented; NDVI represents a normalized vegetation index with a value in the range of [0,1]]In between, higher values indicate better local vegetation growth.
Comparing the current NDVI with the value range of the current annual contemporaneous observation value to obtain a vegetation state index (Vegetation Condition Index, VCI), wherein the value range of the vegetation state index is between [0,1], and the lower value and the higher value respectively represent poor and good vegetation growth condition, and the calculation formula of the vegetation state index is as follows:
in formula (2), NDVI i Normalized vegetation index NDVI observations, NDVI, representing current time max And NDVI min Representing the maximum and minimum values, respectively, of the normalized vegetation index NDVI that can be observed locally over a period of time.
In the embodiment, the vegetation state index VCI is introduced into the construction space distance model, so that the deviation of the normalized vegetation index NDVI generated by the geographic position and the local ecological system is eliminated, and the detection precision is improved.
The temperature condition index (Temperature Condition Index, TCI) is defined similarly to VCI, but mainly emphasizes the relationship of temperature to plant growth, typically expressed as a high temperature detrimental to vegetation growth when the vegetation is subject to water stress. The calculation formula of the temperature condition index is as follows:
in formula (3), LST i Land surface temperature observations, LST, representing current time max And LST min Representing the maximum and minimum observations, respectively, of land surface temperature that can be observed locally over a period of time.
The normalized precipitation vapor emission index (Standardized Precipitation Index, SPI) compares the severity of weather drought by estimating the off-state distribution of precipitation. Rainfall radar can be used for estimating the rainfall in remote sensing observation, so SPI can be used for remote sensing agriculture drought monitoring. The calculation formula of the standardized rainfall vapor emission index SPI is as follows:
in formula (4), SPI represents a normalized precipitation emission index, c i (i=0, 1, 2) and d i (i=1, 2, 3) are constants, respectively, t is a variable determined by the precipitation amount, and is obtained by the formula (5):
in the formula (5), P is a probability density distribution function after the normalized gamma distribution function of the precipitation fitting value is subjected to standard normalization, and specifically refer to fig. 1.
Step 3, respectively calculating a vegetation state index, a temperature condition index, a pearson correlation coefficient between a standardized rainfall vapor emission index and the water content of soil by using a formula (6):
in the formula (6), r represents a pearson correlation coefficient, x and y respectively represent different variables, and N represents the number of variable sequences; TCI and soil moisture content are inversely related, and VCI and SPI are positively related to soil moisture content.
And 4, constructing a space distance model, and calculating a space distance drought index.
Three representative variables of a vegetation state index, a temperature condition index and a standardized precipitation evaporation index are mapped to the same characteristic space, and in the characteristic space, the vegetation state index, the temperature condition index and the standardized precipitation evaporation index are set to be two-by-two orthogonal coordinate axes in the European space. As shown in fig. 2, pearson correlation coefficients between each index and the soil moisture content are normalized to obtain a result value, and a spatial distance drought index vector is constructed from the coordinate values as shown in formula (7:
in the formula (7), VCI, TCI and SPI respectively represent vegetation state index, temperature condition index and standardized rainfall vapor-out index, r i ' represents the normalized result of the pearson correlation coefficient between the remote sensing index i and the soil moisture content, which always satisfies the formula (8):
fig. 3 is a pearson correlation coefficient result graph between the method and the effective water content of the crop root, and it can be seen from the graph that the vector projection index obtained by the method has higher correlation with the effective water content of the crop root, so that the space-time resolution and the prediction accuracy are effectively improved.
In summary, the agricultural drought monitoring method based on the European spatial distance method provided by the invention adopts the remote sensing drought index to replace direct soil water content observation, and three representative variables of temperature, vegetation growth and precipitation are mapped to the same characteristic space, so that not only are different factors in the drought process comprehensively considered, but also the defect that a drought mechanism is difficult to reflect by simple linear weighting is overcome, and meanwhile, the three indexes adopted have certain descriptive capacity on local space-time characteristics, have higher robustness, and promote soil moisture variation monitoring and evaluation effectiveness. In the whole drought monitoring process of the agricultural rows, the method is realized by adopting a nonlinear data calling mode, and machine learning is not needed.
While the invention has been described with respect to specific embodiments thereof, it will be understood by those skilled in the art that any of the features disclosed in this specification, unless otherwise indicated, may be substituted for those illustrated and other equivalent or similarly accomplished; all of the features disclosed, or all of the steps in a method or process, may be combined in any combination, except mutually exclusive features or/and steps.
Claims (6)
1. An agricultural drought monitoring method based on an European space distance method is characterized by comprising the following steps of:
step 1, acquiring near infrared earth surface reflectivity of a region to be monitored, red light wave band earth surface reflectivity data, land surface temperature observation values and rainfall radar data;
step 2, calculating a normalized vegetation index by utilizing the near infrared surface reflectivity and the red wave band surface reflectivity, and calculating a vegetation state index according to the normalized vegetation index; calculating a temperature condition index according to the land surface temperature observation value; calculating a standardized rainfall vapor emission index according to rainfall radar data;
step 3, calculating pearson correlation coefficients between the vegetation state index, the temperature condition index and the standardized rainfall vapor emission index and the soil water content respectively, and normalizing the pearson correlation coefficients between the indexes and the soil water content;
step 4, constructing a space distance model, mapping three representative variables of a vegetation state index, a temperature condition index and a standardized precipitation evaporation index to the same characteristic space, and setting the vegetation state index, the temperature condition index and the standardized precipitation evaporation index as two-by-two orthogonal coordinate axes in the European space in the characteristic space; and carrying out normalization result values on pearson correlation coefficients between each index and the soil water content, using the normalized result values as coordinate values, constructing a spatial distance drought index vector through the coordinate values, and calculating the modular length of the drought space vector as a drought evaluation index.
2. The agricultural drought monitoring method based on the European spatial distance method according to claim 1, wherein the calculation formula of the normalized vegetation index in the step 2 is:
in the formula (1), ρ NIR Surface reflectivity representing near infrared band; ρ Red The earth surface reflectivity of the red light wave band is represented; NDVI represents a normalized vegetation index with a value in the range of [0,1]]In between, higher values indicate better local vegetation growth. The calculation formula of the vegetation state index is as follows:
in formula (2), NDVI i Normalized vegetation index NDVI observations, NDVI, representing current time max And NDVI min Representing the maximum and minimum values, respectively, of the normalized vegetation index NDVI that can be observed locally over a period of time.
3. The agricultural drought monitoring method based on the European space distance method according to claim 1, wherein the calculation formula of the temperature condition index in the step 2 is as follows:
in formula (3), LST i Land surface temperature observations, LST, representing current time max And LST min Representing the maximum and minimum observations, respectively, of land surface temperature that can be observed locally over a period of time.
4. The agricultural drought monitoring method based on the European space distance method according to claim 1, wherein the calculation formula of the standardized precipitation evaporation index in the step 2 is as follows:
in formula (4), SPI represents a normalized precipitation emission index, c i (i=0, 1, 2) and d i (i=1, 2, 3) are constants, respectively, t is a variable determined by the precipitation amount, and is obtained by the formula (5):
in the formula (5), P is a gamma distribution function of the precipitation fitting value, and the probability density distribution function is subjected to standard normalization treatment.
5. The agricultural drought monitoring method based on the European spatial distance method according to claim 1, wherein the pearson correlation coefficient calculation formula between each index and the soil water content in step 3 is:
in the formula (6), r represents a pearson correlation coefficient, x and y respectively represent different variables, and N represents the number of variable sequences; TCI and soil moisture content are inversely related, and VCI and SPI are positively related to soil moisture content.
6. An agricultural drought monitoring method based on the Euclidean space distance method according to any one of claims 1-5, wherein the formula for calculating the modular length of the drought space vector is:
in the formula (7), VCI, TCI and SPI respectively represent vegetation state index, temperature condition index and standardized rainfall vapor-out index, r i ' represents the normalized result of the pearson correlation coefficient between the remote sensing index i and the soil moisture content, which always satisfies the formula (8):
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