CN117541086A - Method and device for determining salinization degree, electronic equipment and storage medium - Google Patents
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/55—Specular reflectivity
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- G—PHYSICS
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Abstract
The invention provides a method and a device for determining salinization degree, electronic equipment and a storage medium, and relates to the technical field of remote sensing data processing. The method comprises the following steps: acquiring first remote sensing data of a target planting area in a first preset time period, and acquiring second remote sensing data of the target planting area in a second preset time period of a previous year, wherein the previous year is the last year of a current year, and the current year is the current year of which the salinization degree is to be determined; determining a first salinity index of each subarea in the target planting area based on the first remote sensing data, and determining a first maximum spectrum index of each subarea in the target planting area based on the second remote sensing data, wherein the first maximum spectrum index is the maximum spectrum index in a second preset time period; the salinity level of each sub-region is determined based on the first salinity index of each sub-region and the first maximum spectral index of each sub-region. The invention can realize the low-cost and rapid determination of the salinization degree of a large-area.
Description
Technical Field
The present invention relates to the field of remote sensing data processing technologies, and in particular, to a method and apparatus for determining salinization degree, an electronic device, and a storage medium.
Background
With rapid development of technology, the requirements of people on the yield and quality of crops are higher and higher. The soil salinization degree has a larger influence on the yield and quality of crops, for example, if the soil salinization degree is high, a certain influence can be brought to the emergence and growth of cotton, and the cotton has the problems of low emergence rate, dead seedlings and the like. Therefore, it is necessary to perform plant protection work on an area where the degree of salinization of the soil is high, so as to reduce the degree of salinization of the soil. However, the traditional plant protection operation modes are quantitative plant protection operation modes (indiscriminate plant protection operation modes), so that the plant protection cost is high, and the environment is further polluted; based on the method, the salinization degree needs to be determined firstly, and then variable plant protection operation is performed based on different salinization degrees, so that the cost is reduced, and the environmental pollution is reduced.
At present, the conductivity, the salinity content, the pH value and the like of field soil are tested by sampling under a manual line, and then the salinization degree of the soil is determined. However, manual determination of the degree of salinization requires a lot of manpower and material resources, and is inefficient, and the determination of the degree of salinization in a large area cannot be performed, and too much reliance on manual experience results in a decrease in the accuracy of the determination of the degree of salinization.
Disclosure of Invention
The invention provides a method, a device, electronic equipment and a storage medium for determining the salinization degree, which are used for solving the defects that the determination cost of the salinization degree is high, the determination efficiency is low and the salinization degree cannot be determined in a large-area in the prior art, and realizing the low-cost and rapid determination of the salinization degree in the large-area.
The invention provides a method for determining salinization degree, which comprises the following steps:
acquiring first remote sensing data of a target planting area in a first preset time period, and acquiring second remote sensing data of the target planting area in a second preset time period of a previous year, wherein the previous year is the last year of a current year, and the current year is the current year of which the salinization degree is to be determined;
determining a first salinity index of each subarea in the target planting area based on the first remote sensing data, and determining a first maximum spectrum index of each subarea in the target planting area based on the second remote sensing data, wherein the first maximum spectrum index is the maximum spectrum index in the second preset time period;
determining the salinity degree of each subarea based on the first salinity index of each subarea and the first maximum spectrum index of each subarea.
According to the method for determining the salinization degree provided by the invention, the determining of the first salinity index of each subarea in the target planting area based on the first remote sensing data comprises the following steps:
determining a target remote sensing image from the multi-period first remote sensing image of the first remote sensing data;
determining the blue light wave band reflectivity, the first red light wave band reflectivity and the first near infrared wave band reflectivity of each subarea based on the target remote sensing image;
a first salinity index for each of the subregions is determined based on the blue band reflectivity, the first red band reflectivity, and the first near infrared band reflectivity for each of the subregions.
According to the method for determining the salinization degree provided by the invention, the calculation formula of the first salinity index is as follows:
；
where SI represents the first salinity index, B represents the blue-band reflectivity, R represents the first red-band reflectivity, and NIR represents the first near-infrared-band reflectivity.
According to the method for determining the salinization degree provided by the invention, the first maximum spectrum index of each subarea in the target planting area is determined based on the second remote sensing data, and the method comprises the following steps:
Determining a plurality of second red light wave band reflectivities and a plurality of second near infrared wave band reflectivities of each subarea based on a plurality of periods of second remote sensing images in the second remote sensing data, wherein the first period of second remote sensing images is used for determining one second red light wave band reflectivity and one second near infrared wave band reflectivity;
determining a plurality of spectral indexes of each subarea based on a plurality of second red light wave band reflectivities and a plurality of second near infrared wave band reflectivities of each subarea, wherein the second remote sensing image is used for determining one spectral index;
and screening the first maximum spectral indexes of each subarea from the plurality of spectral indexes of each subarea.
According to the method for determining the salinization degree provided by the invention, the salinization degree of any one of the subareas is determined based on the following steps:
determining the salinity degree of the subarea based on the first salinity index of the subarea and the first maximum spectrum index of the subarea under the condition that the first maximum spectrum index of the subarea is larger than or equal to a preset spectrum index;
and under the condition that the first maximum spectrum index of the subarea is smaller than a preset spectrum index, determining that the subarea is a crop-free area, and determining that the salinity degree of the subarea is a preset salinity degree.
According to the method for determining the salinization degree provided by the invention, the determining the salinization degree of the subarea based on the first salinization index of the subarea and the first maximum spectrum index of the subarea comprises the following steps:
normalizing the first salinity index of the subarea to obtain a normalized first salinity index, and normalizing the first maximum spectrum index of the subarea to obtain a normalized first maximum spectrum index;
determining a difference between the normalized first maximum spectral index and 1;
determining a sum of squares of the difference and the normalized first salinity index;
and determining the salinity degree of the subareas based on the square sum.
According to the method for determining the salinization degree provided by the invention, the determining the salinization degree of each subarea is based on the first salinization index of each subarea and the first maximum spectrum index of each subarea, and then the method further comprises the following steps:
if the current moment is within a third preset time period of the current year, acquiring third remote sensing data of the target planting area within the third preset time period;
Determining a second maximum spectrum index of each subarea in the target planting area based on the third remote sensing data, wherein the second maximum spectrum index is the maximum spectrum index in the third preset time period;
and updating the salinity degree of each subarea based on the first salinity index of each subarea and the second maximum spectrum index of each subarea.
According to the method for determining the salinization degree provided by the invention, the updating of the salinization degree of each subarea based on the first salinization index of each subarea and the second maximum spectrum index of each subarea comprises the following steps:
acquiring fourth remote sensing data of the target planting area in a first preset time period of the current year when the first remote sensing data are the remote sensing data of the previous year and the current moment is after the first preset time period of the current year;
determining a second salinity index of each subarea in the target planting area based on the fourth remote sensing data;
and updating the salinity degree of each subarea based on the second salinity index of each subarea and the second maximum spectrum index of each subarea.
The invention also provides a device for determining the salinization degree, which comprises the following steps:
the data acquisition module is used for acquiring first remote sensing data of a target planting area in a first preset time period and acquiring second remote sensing data of the target planting area in a second preset time period of a previous year, wherein the previous year is the last year of a current year, and the current year is the current year of which the salinization degree is to be determined;
the index determining module is used for determining a first salinity index of each subarea in the target planting area based on the first remote sensing data, and determining a first maximum spectrum index of each subarea in the target planting area based on the second remote sensing data, wherein the first maximum spectrum index is the maximum spectrum index in the second preset time period;
the degree determining module is used for determining the salinity degree of each subarea based on the first salinity index of each subarea and the first maximum spectrum index of each subarea.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor implements the method for determining the salinization degree according to any of the above methods when executing the program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of determining the degree of salinization as described in any of the above.
According to the method, the device, the electronic equipment and the storage medium for determining the salinization degree, provided by the invention, the first remote sensing data of the target planting area in the first preset time period is obtained, and the second remote sensing data of the target planting area in the second preset time period of the previous year is obtained, so that the salinization degree can be determined in a large area based on the remote sensing data, namely, the salinization degree can be determined in a large range, and the determination efficiency of the salinization degree is improved; acquiring second remote sensing data of the target planting area in a second preset time period of the previous year, wherein the previous year is the year previous to the current year, and the current year is the year of the current degree of salinization to be determined, so that the salinization degree can be determined at any time, and the determination flexibility of the salinization degree is improved; based on the first remote sensing data, determining a first salinity index of each subarea in the target planting area, and based on the second remote sensing data, determining a first maximum spectrum index of each subarea in the target planting area, thereby determining a relevant index of each subarea based on the remote sensing data, and improving the accuracy of determining the salinization degree compared with the large-area with only one index; the first maximum spectrum index is the maximum spectrum index in the second preset time period, so that the first maximum spectrum index is ensured to represent the index which corresponds to the best growth vigor, and the accuracy of determining the salinization degree is further improved; and comprehensively determining the salinity degree of each subarea based on the first salinity index of each subarea and the first maximum spectrum index of each subarea, thereby improving the accuracy of determining the salinity degree. By means of the method, compared with the manual determination of the salinization degree, the method can reduce the determination cost of the salinization degree, improve the determination efficiency of the salinization degree, and improve the determination accuracy of the salinization degree without depending on manual experience.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for determining salinization degree according to the present invention;
FIG. 2 is a second flow chart of the method for determining salinization degree according to the present invention;
FIG. 3 is a third flow chart of the method for determining salinization degree according to the present invention;
fig. 4 is a schematic structural diagram of a device for determining salinization degree provided by the invention;
fig. 5 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
With rapid development of technology, the requirements of people on the yield and quality of crops are higher and higher. The soil salinization degree has a larger influence on the yield and quality of crops, for example, if the soil salinization degree is high, a certain influence can be brought to the emergence and growth of cotton, and the cotton has the problems of low emergence rate, dead seedlings and the like. Therefore, it is necessary to perform plant protection work on an area where the salinization degree of the soil is high so as to reduce the salinization degree of the soil; for example, growers generally perform winter irrigation or spring irrigation to reduce the salt content of soil and increase the emergence rate of cotton. However, the traditional plant protection operation modes are quantitative plant protection operation modes (indiscriminate plant protection operation modes), so that the plant protection cost is high, and the environment is further polluted; for example, the irrigation water amount for pressing saline-alkali in cotton fields is more than 50% of the annual irrigation quota of the cotton fields, along with winter-free spring irrigation, a proper amount of seedling water is irrigated after sowing, namely, a dry sowing and wet emergence technology appears, so that water resources can be saved, production cost is reduced, but the irrigation of the seedling water in fields, the addition of soil improvement agents for areas with high salinization degree and other agricultural operations cannot be accurately implemented at present, and most of the situations are indiscriminate treatment. Based on this, in order to more accurately carry out irrigation and related plant protection operations according to the difference of salinization degrees, the determination of the salinization degrees needs to be carried out firstly, and then variable plant protection operations are carried out based on different salinization degrees, so that the cost is reduced and the environmental pollution is reduced.
At present, the conductivity, the salinity content, the pH value and the like of field soil are tested by sampling under a manual line, and then the salinization degree of the soil is determined. However, manual determination of the degree of salinization requires a lot of manpower and material resources, and is inefficient, and the determination of the degree of salinization in a large area cannot be performed, and too much reliance on manual experience results in a decrease in the accuracy of the determination of the degree of salinization.
In view of the above problems, the present invention proposes the following embodiments. Fig. 1 is a schematic flow chart of a method for determining a salinization degree according to the present invention, as shown in fig. 1, where the method for determining a salinization degree includes:
step 110, obtaining first remote sensing data of a target planting area in a first preset time period, and obtaining second remote sensing data of the target planting area in a second preset time period of the previous year.
The previous year is the year previous to the current year, and the current year is the year of which the salinization degree is currently to be determined.
Here, the target planting area is a planting area where the degree of salinization is to be determined, for example, it is a cotton planting area.
Here, the first preset time period may be the first preset time period of the current year, or may be the first preset time period of the previous year. It will be appreciated that the determined first salinity index is more accurate in the case where the first preset time period is the first preset time period of the current year. For example, for cotton crops, the first preset time period is 4 months of the year.
In an embodiment, if the current time is after a first preset time period of the current year, acquiring first remote sensing data of the target planting area in the first preset time period of the current year; and if the current moment is before the first preset time period of the current year, acquiring first remote sensing data of the target planting area in the first preset time period of the previous year.
It should be appreciated that, considering that the current time is mostly before the second preset time period of the current year, the second remote sensing data of the target planting area in the second preset time period of the previous year is acquired, so that the salinization degree can be determined no matter what time. For example, for cotton crops, the current time is mostly before the bud period, and the second preset time period is located at or after the bud period, e.g., the second preset time period is 7 months 1 day to 8 months 30 days.
Step 120, determining a first salinity index of each subarea in the target planting area based on the first remote sensing data, and determining a first maximum spectrum index of each subarea in the target planting area based on the second remote sensing data.
The first maximum spectrum index is the maximum spectrum index in the second preset time period.
It will be appreciated that the remote sensing data includes remote sensing data for each sub-region of the target planting region such that the salinity index and the spectral index for each sub-region can be determined. For example, a sub-region is a pel region of a target planting region.
Here, the first salinity index is used to characterize the soil salinization level. The first maximum spectral index (vegetation index) is used to characterize the growth vigor of crops, such as NDVI (Normalized Difference Vegetation Index, normalized vegetation index) and EVI (Enhanced Vegetation Index ), among others.
In an embodiment, a target remote sensing image is determined from a multi-stage first remote sensing image of the first remote sensing data; and determining a first salinity index of each subarea based on the target remote sensing image. The optimal primary target remote sensing image is determined from the multiple primary first remote sensing images, or the primary target remote sensing image is randomly determined from the multiple primary first remote sensing images.
In another embodiment, a plurality of salinity indexes of each sub-region are determined based on a plurality of periods of first remote sensing images in the first remote sensing data, wherein the first period of first remote sensing images is used for determining one salinity index; and screening out the first salinity index with the maximum value from the salinity indexes of the subareas. The maximum first salinity index is the maximum salinity index in a first preset time period.
Illustratively, the first salinity index is calculated as follows:
；
where SI represents the first salinity index, B represents the reflectance of the blue band, R represents the reflectance of the red band, and NIR represents the reflectance of the near infrared band.
In a specific embodiment, a plurality of spectrum indexes of each subarea are determined based on a plurality of periods of second remote sensing images in the second remote sensing data, wherein one period of second remote sensing image is used for determining one spectrum index; and screening the first maximum spectrum index of each subarea from the spectrum indexes of each subarea.
Step 130, determining the salinity degree of each subarea based on the first salinity index of each subarea and the first maximum spectrum index of each subarea.
In consideration of the fact that salt tends to gather on the surface of the soil in the salinized area, white salt frost and salt crust are formed on the surface layer of the soil in severe cases, the spectral reflectivities of the salinized soil and the non-salinized soil surface layer are obviously different, and therefore the salt degree is determined based on the first maximum spectral index, and the accuracy of determining the salt degree is improved.
In one embodiment, the salinity level of any one sub-region is determined based on the following steps: and comprehensively determining the salinity degree of the subarea based on the first salinity index of the subarea and the first maximum spectrum index of the subarea.
In another embodiment, the salinity level of any one sub-region is determined based on the following steps: under the condition that the first maximum spectrum index of the subarea is larger than or equal to a preset spectrum index, comprehensively determining the salinity degree of the subarea based on the first salinity index of the subarea and the first maximum spectrum index of the subarea; and determining the salinity degree of the subarea to be the preset salinity degree under the condition that the first maximum spectrum index of the subarea is smaller than the preset spectrum index.
Illustratively, the salinity level is determined based on the following steps: and determining a difference value between the first maximum spectrum index and a preset maximum spectrum index, determining a square sum of the difference value and the first salinity index, and determining the salinity degree based on the square sum.
In an embodiment, normalizing the first salinity index of each subarea to obtain each normalized first salinity index, and normalizing the first maximum spectrum index of each subarea to obtain each normalized first maximum spectrum index; and determining the salinity degree of each subarea based on each normalized first salinity index and each normalized first maximum spectrum index.
Illustratively, the normalized formula is as follows:
；
in the method, in the process of the invention,for the value to be normalized, +.>For normalized value, ++>Is->Corresponding minimum value, < >>Is->Corresponding maximum value.
Further, the salinity level of each subarea is determined based on the salinity level of each subarea and the salinization level grading standard. Illustratively, the degree of salinization grading criteria are shown in the following table:
in the table, SDI indicates the salinity level, and 5 is a preset salinity level or a maximum salinity level. Wherein, salinity level 1 represents non-salinization, salinity level 2 represents light salinization, salinity level 3 represents moderate salinization, salinity level 4 represents heavy salinization, and salinity level 5 represents no crop.
In one embodiment, a prescription map is generated based on the salinity level of each sub-region.
Further, a prescription map is generated based on the salinity level of each sub-region. It should be appreciated that the prescription map may guide plant protection operations (farming operations).
According to the method for determining the salinization degree, which is provided by the embodiment of the invention, the first remote sensing data of the target planting area in the first preset time period is obtained, and the second remote sensing data of the target planting area in the second preset time period of the previous year is obtained, so that the salinization degree can be determined in a large area based on the remote sensing data, namely, the salinization degree can be determined in a large range, and the determination efficiency of the salinization degree is improved; acquiring second remote sensing data of the target planting area in a second preset time period of the previous year, wherein the previous year is the year previous to the current year, and the current year is the year of the current degree of salinization to be determined, so that the salinization degree can be determined at any time, and the determination flexibility of the salinization degree is improved; based on the first remote sensing data, determining a first salinity index of each subarea in the target planting area, and based on the second remote sensing data, determining a first maximum spectrum index of each subarea in the target planting area, thereby determining a relevant index of each subarea based on the remote sensing data, and improving the accuracy of determining the salinization degree compared with the large-area with only one index; the first maximum spectrum index is the maximum spectrum index in the second preset time period, so that the first maximum spectrum index is ensured to represent the index which corresponds to the best growth vigor, and the accuracy of determining the salinization degree is further improved; and comprehensively determining the salinity degree of each subarea based on the first salinity index of each subarea and the first maximum spectrum index of each subarea, thereby improving the accuracy of determining the salinity degree. By means of the method, compared with the manual determination of the salinization degree, the method can reduce the determination cost of the salinization degree, improve the determination efficiency of the salinization degree, and improve the determination accuracy of the salinization degree without depending on manual experience.
Based on any of the above embodiments, fig. 2 is a second flowchart of the method for determining salinization degree provided by the present invention, as shown in fig. 2, in step 120, a first salinity index of each sub-region in the target planting region is determined based on the first remote sensing data, which includes:
step 121, determining a target remote sensing image from the multi-stage first remote sensing image of the first remote sensing data.
In one embodiment, an optimal target remote sensing image is determined from a plurality of first remote sensing images of the first remote sensing data. The target remote sensing image is the best quality remote sensing image in the multi-period first remote sensing image.
In another embodiment, the target remote sensing image is randomly determined from the multiple-stage first remote sensing image of the first remote sensing data.
Step 122, determining the blue light band reflectivity, the first red light band reflectivity and the first near infrared band reflectivity of each sub-region based on the target remote sensing image.
Considering that the blue light wave band and the red light wave band of visible light obviously reflect the salinity of soil, and considering that the common cultivated land and the area with higher salinization degree have certain differences in the aspect of spectral characteristics, the area with higher salinization degree has stronger spectral reflection than the normal cultivated land in the visible light wave band and the near infrared wave band; therefore, the blue light wave band reflectivity, the first red light wave band reflectivity and the first near infrared wave band reflectivity are determined based on the remote sensing image so as to comprehensively determine the salinity index, namely, the salinity index is used as a main index for judging soil salinity, so that the accuracy of determining the salinity index is improved, and finally, the accuracy of determining the salinization degree is improved.
Step 123, determining a first salinity index of each sub-region based on the blue light wave band reflectivity, the first red light wave band reflectivity and the first near infrared wave band reflectivity of each sub-region.
It should be appreciated that the blue band reflectivity, the first red band reflectivity, and the first near infrared band reflectivity of one sub-region are used to determine a first salinity index of one sub-region.
In one embodiment, the first salinity index of any one of the subregions is determined based on the steps of: a first salinity index of the sub-region is determined based on a sum of squares of the blue band reflectivity, the first red band reflectivity, and the first near-infrared band reflectivity of the sub-region.
The first salinity index is calculated by the following formula:
；
where SI represents the first salinity index, B represents the blue-band reflectivity, R represents the first red-band reflectivity, and NIR represents the first near-infrared-band reflectivity.
According to the method for determining the salinization degree, the target remote sensing image is determined from the multi-period first remote sensing image of the first remote sensing data, so that the blue light wave band reflectivity, the first red light wave band reflectivity and the first near infrared wave band reflectivity of each subarea are determined based on the target remote sensing image, the first salinity index of each subarea is determined more accurately based on the blue light wave band reflectivity, the first red light wave band reflectivity and the first near infrared wave band reflectivity which can fully represent the salinity degree, and the determination accuracy of the salinity index is improved.
Based on any of the above embodiments, fig. 3 is a third flowchart of the method for determining salinization degree according to the present invention, as shown in fig. 3, in step 120, a first maximum spectrum index of each sub-region in the target planting region is determined based on the second remote sensing data, which includes:
step 124, determining a plurality of second red light band reflectivities and a plurality of second near infrared band reflectivities of each sub-region based on the multi-period second remote sensing image in the second remote sensing data.
The first remote sensing image is used for determining the reflectivity of the second red light wave band and the reflectivity of the second near infrared wave band.
The red light wave band and the near infrared wave band are considered to better reflect the growth situation of vegetation, so that the second red light wave band reflectivity and the second near infrared wave band reflectivity are determined based on the remote sensing image, the determination accuracy of the first maximum spectrum index is improved, and the determination accuracy of the salinization degree is further improved.
Step 125, determining a plurality of spectral indexes of each sub-region based on the plurality of second red band reflectivities and the plurality of second near infrared band reflectivities of each sub-region.
The first remote sensing image is used for determining one spectrum index.
Illustratively, the spectral index is an NDVI index, and the calculation formula of the spectral index is as follows:
；
in the method, in the process of the invention,indicating the second near infrared band reflectivity, +.>Representing the second red band reflectivity.
Step 126, screening the first maximum spectral indexes of each subarea from the plurality of spectral indexes of each subarea.
Specifically, the first maximum spectral index of any sub-region is determined based on the steps of: and screening the largest spectrum index from the spectrum indexes in the subarea as a first largest spectrum index.
According to the method for determining the salinization degree, which is provided by the embodiment of the invention, the first maximum spectrum index of each subarea can be accurately obtained through the method, so that the index which is best corresponding to the growth condition and is represented by the first maximum spectrum index is accurately ensured, and finally, the accuracy for determining the salinization degree is further improved.
Based on any of the above embodiments, in the method, the salinity level of any of the subregions is determined based on the steps of:
determining the salinity degree of the subarea based on the first salinity index of the subarea and the first maximum spectrum index of the subarea under the condition that the first maximum spectrum index of the subarea is larger than or equal to a preset spectrum index;
And under the condition that the first maximum spectrum index of the subarea is smaller than a preset spectrum index, determining that the subarea is a crop-free area, and determining that the salinity degree of the subarea is a preset salinity degree.
Considering that no crop area does not need to perform plant protection operation, determining the subarea as a crop area based on the fact that the first maximum spectrum index of the subarea is larger than or equal to a preset spectrum index; and determining the subarea as a crop-free area under the condition that the first maximum spectral index of the subarea is smaller than a preset spectral index. And determining the salinity degree of the subarea based on the first salinity index of the subarea and the first maximum spectrum index of the subarea under the condition that the crop area exists, otherwise, directly taking the salinity degree of the subarea as the preset salinity degree.
Here, the preset spectrum index may be set according to actual needs, for example, 0.25. The preset salinity level can be set according to actual needs, for example, 5.
In one embodiment, the salinity level is determined based on the following steps: and determining a difference value between the first maximum spectrum index and a preset maximum spectrum index, determining a square sum of the difference value and the first salinity index, and determining the salinity degree based on the square sum.
According to the method for determining the salinization degree, whether the subarea is a crop area or not can be determined firstly, and if the subarea is the crop area, the salinity degree of the subarea is accurately determined based on the first salinity index of the subarea and the first maximum spectrum index of the subarea; and if the crop-free area is the crop-free area, determining the salinity degree of the subarea as a preset salinity degree. Based on the method, the accuracy of determining the salinization degree is improved, and the follow-up plant protection operation on the crop-free area is ensured not to be carried out, so that the agricultural production cost is reduced, and the environmental pollution is reduced.
Based on any of the foregoing embodiments, in the method, the determining the salinity level of the subregion based on the first salinity index of the subregion and the first maximum spectral index of the subregion includes:
normalizing the first salinity index of the subarea to obtain a normalized first salinity index, and normalizing the first maximum spectrum index of the subarea to obtain a normalized first maximum spectrum index;
determining a difference between the normalized first maximum spectral index and 1;
determining a sum of squares of the difference and the normalized first salinity index;
And determining the salinity degree of the subareas based on the square sum.
It should be understood that, after the normalization of the first maximum spectrum index, the preset maximum spectrum index is 1. Since the greater the spectral index, the lesser the salinity level should be, based on which the difference between the normalized first maximum spectral index and 1 is determined first.
Since the first salinity index and the first maximum spectrum index are normalized, the sum of squares of the difference and the normalized first salinity index can be determined.
The square root of the sum of squares may be determined as the salinity level of the sub-area, or the sum of squares may be directly determined as the salinity level of the sub-area.
The salt degree is calculated by the following formula:
；
wherein SDI represents the salt content,represents the normalized first maximum spectral index, and SI represents the normalized first salinity indexA number.
According to the method for determining the salinization degree, the first salinization indexes of the sub-areas are normalized in the mode to obtain normalized first salinization indexes, and the first maximum spectrum indexes of the sub-areas are normalized to obtain normalized first maximum spectrum indexes, so that the salinization degree of each sub-area can be determined by the sub-areas and the sub-areas better, and the accuracy of determining the salinization degree is improved; the difference value between the normalized first maximum spectrum index and 1 is determined, so that the square sum of the difference value and the normalized first salinity index is determined, the salinity degree of the subarea is determined based on the square sum, the salinity degree can be accurately obtained by combining the difference value and the normalized first salinity index, and the accuracy of determining the salinity degree is further improved.
Based on any of the above embodiments, after the step 130, the method further includes:
if the current moment is within a third preset time period of the current year, acquiring third remote sensing data of the target planting area within the third preset time period;
determining a second maximum spectrum index of each subarea in the target planting area based on the third remote sensing data, wherein the second maximum spectrum index is the maximum spectrum index in the third preset time period;
and updating the salinity degree of each subarea based on the first salinity index of each subarea and the second maximum spectrum index of each subarea.
Here, the current time is the time at which the degree of salinization is currently to be determined.
It should be appreciated that since the previously determined salinity level of each sub-area is determined based on the second remote sensing data within the second preset time period of the previous year, the previously determined salinity level of each sub-area can only guide the plant protection operation before the third preset time period of the current year. Therefore, if the current time is after the third preset time period of the current year, the salinity level of each sub-area needs to be updated based on the latest remote sensing data.
Illustratively, for cotton crops, the third predetermined period of time is the bud period of the current year (e.g., 6 months-7 months). The salinity degree of each subarea is used for guiding plant protection operation (agronomic operation) of cotton before the bud period, and after the bud period, the second maximum spectrum index of each subarea is determined based on the third remote sensing data of the bud period, so that the salinity degree of each subarea is updated.
Here, the second maximum spectral index (vegetation index) is used to characterize the growth vigor of crops, such as NDVI and EVI, etc.
In an embodiment, determining a plurality of spectral indexes of each sub-region based on a plurality of periods of third remote sensing images in the third remote sensing data, wherein one period of the third remote sensing images is used for determining one spectral index; and screening the second maximum spectral indexes of each subarea from the plurality of spectral indexes of each subarea.
In another embodiment, a third target remote sensing image is determined from a multi-stage third remote sensing image of the third remote sensing data; and determining a second maximum spectrum index of each subarea based on the third target remote sensing image. The optimal first-stage third target remote sensing image is determined from the multi-stage third remote sensing images, or the first-stage third target remote sensing image is randomly determined from the multi-stage third remote sensing images.
In one embodiment, the salinity level of any updated sub-region is determined based on the following steps: and comprehensively determining the salinity degree of the updated subarea based on the first salinity index of the subarea and the second maximum spectrum index of the subarea.
In another embodiment, the salinity level of any updated sub-region is determined based on the steps of: under the condition that the second maximum spectrum index of the subarea is larger than or equal to the preset spectrum index, comprehensively determining the salinity degree of the updated subarea based on the first salinity index of the subarea and the second maximum spectrum index of the subarea; and under the condition that the second maximum spectrum index of the subarea is smaller than the preset spectrum index, determining that the salinity degree of the updated subarea is the preset salinity degree.
Illustratively, the salinity level is determined based on the following steps: and determining a difference value between the second maximum spectrum index and a preset maximum spectrum index, determining a square sum of the difference value and the first salinity index, and determining the salinity degree based on the square sum.
In an embodiment, normalizing the first salinity index of each subarea to obtain each normalized first salinity index, and normalizing the second maximum spectrum index of each subarea to obtain each normalized second maximum spectrum index; and determining the salinity degree of each updated subarea based on each normalized first salinity index and each normalized second maximum spectrum index.
Further, the salinity level of each updated subarea is determined based on the salinity level of each updated subarea and the salinization level grading standard.
In one embodiment, an updated prescription map is generated based on the updated salinity levels of the sub-regions.
Further, an updated prescription map is generated based on the updated salinity level of each sub-region.
In addition, the determination manner of the second maximum spectrum index may refer to the first maximum spectrum index, which is not described herein. The updated salinity level may refer to the above-mentioned manner of determining the salinity level, which is not described herein in detail.
According to the method for determining the salinization degree, if the current moment is within the third preset time period of the current year, the third remote sensing data of the target planting area in the third preset time period is obtained, so that the second maximum spectrum index of each subarea in the target planting area is determined more accurately based on the third remote sensing data of the current year, the salinity degree of each subarea is determined more accurately based on the first salinity index of each subarea and the second maximum spectrum index of each subarea, the determination accuracy of the salinization degree is further improved, the accuracy of plant protection operation is further improved finally, the cost is reduced, and the environmental pollution is reduced.
Based on any of the foregoing embodiments, in the method, updating the salinity level of each sub-region based on the first salinity index of each sub-region and the second maximum spectrum index of each sub-region includes:
acquiring fourth remote sensing data of the target planting area in a first preset time period of the current year when the first remote sensing data are the remote sensing data of the previous year and the current moment is after the first preset time period of the current year;
determining a second salinity index of each subarea in the target planting area based on the fourth remote sensing data;
and updating the salinity degree of each subarea based on the second salinity index of each subarea and the second maximum spectrum index of each subarea.
It should be appreciated that in the case where the first telemetry data is the telemetry data of the previous year and the current time is after the first preset time period of the current year, the salinity index may be determined based on the fourth telemetry data (i.e., the most recent telemetry data) of the current year.
Here, the second salinity index is used to characterize the degree of salinization of the soil.
In an embodiment, determining a fourth target remote sensing image from a multi-stage fourth remote sensing image of the fourth remote sensing data; and determining a second salinity index of each subarea based on the fourth target remote sensing image. The optimal first-stage fourth target remote sensing image is determined from the multi-stage fourth remote sensing images, or the first-stage fourth target remote sensing image is randomly determined from the multi-stage fourth remote sensing images.
In another embodiment, a plurality of salinity indexes of each sub-region are determined based on a plurality of periods of fourth remote sensing images in the fourth remote sensing data, wherein one period of the fourth remote sensing images is used for determining one salinity index; and screening out the fourth salinity index with the maximum salinity index of each subarea from the plurality of salinity indexes of each subarea. The maximum fourth salinity index is the maximum salinity index in the first preset time period.
Illustratively, the fourth salt index is calculated as follows:
；
where SI represents the fourth salinity index, B represents the blue band reflectance, R represents the red band reflectance, and NIR represents the near infrared band reflectance.
In one embodiment, the salinity level of any updated sub-region is determined based on the following steps: and comprehensively determining the salinity degree of the updated subarea based on the second salinity index of the subarea and the second maximum spectrum index of the subarea.
In another embodiment, the salinity level of any updated sub-region is determined based on the steps of: under the condition that the second maximum spectrum index of the subarea is larger than or equal to the preset spectrum index, comprehensively determining the salinity degree of the updated subarea based on the second salinity index of the subarea and the second maximum spectrum index of the subarea; and under the condition that the second maximum spectrum index of the subarea is smaller than the preset spectrum index, determining that the salinity degree of the updated subarea is the preset salinity degree.
Illustratively, the salinity level is determined based on the following steps: and determining a difference value between the second maximum spectrum index and a preset maximum spectrum index, determining a square sum of the difference value and the second salinity index, and determining the salinity degree based on the square sum.
In an embodiment, normalizing the second salinity index of each subarea to obtain each normalized second salinity index, and normalizing the second maximum spectrum index of each subarea to obtain each normalized second maximum spectrum index; and determining the salinity degree of each updated subarea based on each normalized second salinity index and each normalized second maximum spectrum index.
In addition, the determination manner of the second salinity index may refer to the determination manner of the first salinity index, which is not described herein in detail. The updated salinity level may refer to the above-mentioned manner of determining the salinity level, which is not described herein in detail.
According to the method for determining the salinization degree, under the condition that the first remote sensing data is the remote sensing data of the previous year and the current moment is after the first preset time period of the current year, the fourth remote sensing data of the target planting area in the first preset time period of the current year is obtained, so that the second salinization index of each subarea in the target planting area is determined more accurately based on the latest fourth remote sensing data, the salinization degree of each subarea is determined more accurately based on the second salinization index of each subarea and the second maximum spectrum index of each subarea, the determination accuracy of the salinization degree is further improved, the accuracy of plant protection operation is further improved finally, the cost is reduced, and environmental pollution is reduced.
Based on the embodiments, the invention can develop different amounts of plant protection operations on the alkali spot area formed by the area with higher salinization degree, thereby reducing the agricultural cost and improving the crop yield. The spatial distribution of the field salinization degree is obtained, and the agricultural operation can be effectively guided. In other words, the method has the advantages of low cost, quick realization of large-scale salinization degree determination, grading, forming a regional prescription, guiding agriculture operation, achieving the effects of reducing agriculture cost and saving resources, and popularization, solving the problem that the salinization degree and the regional area thereof cannot be extracted manually in a large scale, namely realizing different agriculture operations according to the salinization degree or the spatial distribution of alkali spot areas in the agriculture operation, reducing agriculture cost and saving resources.
It should be understood that plant protection operations include irrigation during seedling emergence, improvement of salinization, and the like. Such as biochemical agent, defoliant, etc. For the crop-free area, spraying of any biological agent or pesticide can be omitted, so that cost saving and pollution reduction are facilitated.
The device for determining the salinization degree provided by the invention is described below, and the device for determining the salinization degree described below and the method for determining the salinization degree described above can be correspondingly referred to each other.
Fig. 4 is a schematic structural diagram of a salinization degree determining device provided by the present invention, as shown in fig. 4, where the salinization degree determining device includes:
the data acquisition module 410 is configured to acquire first remote sensing data of a target planting area within a first preset time period, and acquire second remote sensing data of the target planting area within a second preset time period of a previous year, where the previous year is a year previous to a current year, and the current year is a year for which a salinization degree is currently to be determined;
the index determining module 420 is configured to determine, based on the first remote sensing data, a first salinity index of each sub-region in the target planting region, and determine, based on the second remote sensing data, a first maximum spectrum index of each sub-region in the target planting region, where the first maximum spectrum index is a maximum spectrum index within the second preset time period;
the degree determining module 430 is configured to determine the salinity degree of each sub-region based on the first salinity index of each sub-region and the first maximum spectrum index of each sub-region.
According to the salinization degree determining device provided by the embodiment of the invention, the first remote sensing data of the target planting area in the first preset time period is obtained, and the second remote sensing data of the target planting area in the second preset time period of the previous year is obtained, so that the salinization degree can be determined in a large area based on the remote sensing data, namely the salinization degree can be determined in a large area, and the determination efficiency of the salinization degree is improved; acquiring second remote sensing data of the target planting area in a second preset time period of the previous year, wherein the previous year is the year previous to the current year, and the current year is the year of the current degree of salinization to be determined, so that the salinization degree can be determined at any time, and the determination flexibility of the salinization degree is improved; based on the first remote sensing data, determining a first salinity index of each subarea in the target planting area, and based on the second remote sensing data, determining a first maximum spectrum index of each subarea in the target planting area, thereby determining a relevant index of each subarea based on the remote sensing data, and improving the accuracy of determining the salinization degree compared with the large-area with only one index; the first maximum spectrum index is the maximum spectrum index in the second preset time period, so that the first maximum spectrum index is ensured to represent the index which corresponds to the best growth vigor, and the accuracy of determining the salinization degree is further improved; and comprehensively determining the salinity degree of each subarea based on the first salinity index of each subarea and the first maximum spectrum index of each subarea, thereby improving the accuracy of determining the salinity degree. By means of the method, compared with the manual determination of the salinization degree, the method can reduce the determination cost of the salinization degree, improve the determination efficiency of the salinization degree, and improve the determination accuracy of the salinization degree without depending on manual experience.
Based on any of the above embodiments, the index determination module 420 is further configured to:
determining a target remote sensing image from the multi-period first remote sensing image of the first remote sensing data;
determining the blue light wave band reflectivity, the first red light wave band reflectivity and the first near infrared wave band reflectivity of each subarea based on the target remote sensing image;
a first salinity index for each of the subregions is determined based on the blue band reflectivity, the first red band reflectivity, and the first near infrared band reflectivity for each of the subregions.
Based on any of the above embodiments, the calculation formula of the first salinity index is as follows:
；
where SI represents the first salinity index, B represents the blue-band reflectivity, R represents the first red-band reflectivity, and NIR represents the first near-infrared-band reflectivity.
Based on any of the above embodiments, the index determination module 420 is further configured to:
determining a plurality of second red light wave band reflectivities and a plurality of second near infrared wave band reflectivities of each subarea based on a plurality of periods of second remote sensing images in the second remote sensing data, wherein the first period of second remote sensing images is used for determining one second red light wave band reflectivity and one second near infrared wave band reflectivity;
Determining a plurality of spectral indexes of each subarea based on a plurality of second red light wave band reflectivities and a plurality of second near infrared wave band reflectivities of each subarea, wherein the second remote sensing image is used for determining one spectral index;
and screening the first maximum spectral indexes of each subarea from the plurality of spectral indexes of each subarea.
Based on any of the above embodiments, the degree determination module 430 is further configured to:
determining the salinity degree of the subarea based on the first salinity index of the subarea and the first maximum spectrum index of the subarea under the condition that the first maximum spectrum index of the subarea is larger than or equal to a preset spectrum index;
and under the condition that the first maximum spectrum index of the subarea is smaller than a preset spectrum index, determining that the subarea is a crop-free area, and determining that the salinity degree of the subarea is a preset salinity degree.
Based on any of the above embodiments, the degree determination module 430 is further configured to:
normalizing the first salinity index of the subarea to obtain a normalized first salinity index, and normalizing the first maximum spectrum index of the subarea to obtain a normalized first maximum spectrum index;
Determining a difference between the normalized first maximum spectral index and 1;
determining a sum of squares of the difference and the normalized first salinity index;
and determining the salinity degree of the subareas based on the square sum.
Based on any of the above embodiments, the apparatus further comprises:
the remote sensing data acquisition module is used for acquiring third remote sensing data of the target planting area in a third preset time period if the current moment is in the third preset time period of the current year;
the spectrum index determining module is used for determining a second maximum spectrum index of each subarea in the target planting area based on the third remote sensing data, wherein the second maximum spectrum index is the maximum spectrum index in the third preset time period;
and the salinity degree updating module is used for updating the salinity degree of each subarea based on the first salinity index of each subarea and the second maximum spectrum index of each subarea.
Based on any of the above embodiments, the salinity level update module is further configured to:
acquiring fourth remote sensing data of the target planting area in a first preset time period of the current year when the first remote sensing data are the remote sensing data of the previous year and the current moment is after the first preset time period of the current year;
Determining a second salinity index of each subarea in the target planting area based on the fourth remote sensing data;
and updating the salinity degree of each subarea based on the second salinity index of each subarea and the second maximum spectrum index of each subarea.
Fig. 5 illustrates a physical schematic diagram of an electronic device, as shown in fig. 5, which may include: processor 510, communication interface (Communications Interface) 520, memory 530, and communication bus 540, wherein processor 510, communication interface 520, memory 530 complete communication with each other through communication bus 540. Processor 510 may invoke logic instructions in memory 530 to perform a method of determining a degree of salinization, the method comprising: acquiring first remote sensing data of a target planting area in a first preset time period, and acquiring second remote sensing data of the target planting area in a second preset time period of a previous year, wherein the previous year is the last year of a current year, and the current year is the current year of which the salinization degree is to be determined; determining a first salinity index of each subarea in the target planting area based on the first remote sensing data, and determining a first maximum spectrum index of each subarea in the target planting area based on the second remote sensing data, wherein the first maximum spectrum index is the maximum spectrum index in the second preset time period; determining the salinity degree of each subarea based on the first salinity index of each subarea and the first maximum spectrum index of each subarea.
Further, the logic instructions in the memory 530 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In yet another aspect, the present invention provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform a method of determining a degree of salinization provided by the above methods, the method comprising: acquiring first remote sensing data of a target planting area in a first preset time period, and acquiring second remote sensing data of the target planting area in a second preset time period of a previous year, wherein the previous year is the last year of a current year, and the current year is the current year of which the salinization degree is to be determined; determining a first salinity index of each subarea in the target planting area based on the first remote sensing data, and determining a first maximum spectrum index of each subarea in the target planting area based on the second remote sensing data, wherein the first maximum spectrum index is the maximum spectrum index in the second preset time period; determining the salinity degree of each subarea based on the first salinity index of each subarea and the first maximum spectrum index of each subarea.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (11)
1. A method for determining a degree of salinization, comprising:
acquiring first remote sensing data of a target planting area in a first preset time period, and acquiring second remote sensing data of the target planting area in a second preset time period of a previous year, wherein the previous year is the last year of a current year, and the current year is the current year of which the salinization degree is to be determined;
determining a first salinity index of each subarea in the target planting area based on the first remote sensing data, and determining a first maximum spectrum index of each subarea in the target planting area based on the second remote sensing data, wherein the first maximum spectrum index is the maximum spectrum index in the second preset time period;
Determining the salinity degree of each subarea based on the first salinity index of each subarea and the first maximum spectrum index of each subarea.
2. The method for determining a salinization degree according to claim 1, wherein said determining a first salinity index of each sub-region in the target planting region based on the first remote sensing data comprises:
determining a target remote sensing image from the multi-period first remote sensing image of the first remote sensing data;
determining the blue light wave band reflectivity, the first red light wave band reflectivity and the first near infrared wave band reflectivity of each subarea based on the target remote sensing image;
a first salinity index for each of the subregions is determined based on the blue band reflectivity, the first red band reflectivity, and the first near infrared band reflectivity for each of the subregions.
3. The method for determining a degree of salinization according to claim 2, wherein the first salinity index is calculated as follows:
；
where SI represents the first salinity index, B represents the blue-band reflectivity, R represents the first red-band reflectivity, and NIR represents the first near-infrared-band reflectivity.
4. The method of claim 1, wherein determining a first maximum spectral index for each sub-region of the target planting region based on the second remote sensing data comprises:
Determining a plurality of second red light wave band reflectivities and a plurality of second near infrared wave band reflectivities of each subarea based on a plurality of periods of second remote sensing images in the second remote sensing data, wherein the first period of second remote sensing images is used for determining one second red light wave band reflectivity and one second near infrared wave band reflectivity;
determining a plurality of spectral indexes of each subarea based on a plurality of second red light wave band reflectivities and a plurality of second near infrared wave band reflectivities of each subarea, wherein the second remote sensing image is used for determining one spectral index;
and screening the first maximum spectral indexes of each subarea from the plurality of spectral indexes of each subarea.
5. The method of determining the degree of salinization according to claim 1, characterized in that the degree of salinization of any of the subregions is determined based on the steps of:
determining the salinity degree of the subarea based on the first salinity index of the subarea and the first maximum spectrum index of the subarea under the condition that the first maximum spectrum index of the subarea is larger than or equal to a preset spectrum index;
and under the condition that the first maximum spectrum index of the subarea is smaller than a preset spectrum index, determining that the subarea is a crop-free area, and determining that the salinity degree of the subarea is a preset salinity degree.
6. The method of determining a degree of salinization according to claim 5, wherein said determining a degree of salinization of said subregion based on a first salination index of said subregion and a first maximum spectral index of said subregion comprises:
normalizing the first salinity index of the subarea to obtain a normalized first salinity index, and normalizing the first maximum spectrum index of the subarea to obtain a normalized first maximum spectrum index;
determining a difference between the normalized first maximum spectral index and 1;
determining a sum of squares of the difference and the normalized first salinity index;
and determining the salinity degree of the subareas based on the square sum.
7. The method of determining the degree of salinization according to claim 1, wherein the determining the degree of salinization of each subregion based on the first salinity index of each subregion and the first maximum spectral index of each subregion further comprises:
if the current moment is within a third preset time period of the current year, acquiring third remote sensing data of the target planting area within the third preset time period;
Determining a second maximum spectrum index of each subarea in the target planting area based on the third remote sensing data, wherein the second maximum spectrum index is the maximum spectrum index in the third preset time period;
and updating the salinity degree of each subarea based on the first salinity index of each subarea and the second maximum spectrum index of each subarea.
8. The method of determining the salinization level according to claim 7, wherein said updating the salinization level of each subregion based on the first salinization index of each subregion and the second maximum spectral index of each subregion comprises:
acquiring fourth remote sensing data of the target planting area in a first preset time period of the current year when the first remote sensing data are the remote sensing data of the previous year and the current moment is after the first preset time period of the current year;
determining a second salinity index of each subarea in the target planting area based on the fourth remote sensing data;
and updating the salinity degree of each subarea based on the second salinity index of each subarea and the second maximum spectrum index of each subarea.
9. A device for determining the degree of salinization, comprising:
the data acquisition module is used for acquiring first remote sensing data of a target planting area in a first preset time period and acquiring second remote sensing data of the target planting area in a second preset time period of a previous year, wherein the previous year is the last year of a current year, and the current year is the current year of which the salinization degree is to be determined;
the index determining module is used for determining a first salinity index of each subarea in the target planting area based on the first remote sensing data, and determining a first maximum spectrum index of each subarea in the target planting area based on the second remote sensing data, wherein the first maximum spectrum index is the maximum spectrum index in the second preset time period;
the degree determining module is used for determining the salinity degree of each subarea based on the first salinity index of each subarea and the first maximum spectrum index of each subarea.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of determining the degree of salinization according to any of claims 1 to 8 when executing the program.
11. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements a method of determining a degree of salinization according to any of claims 1 to 8.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070020181A1 (en) * | 2002-11-12 | 2007-01-25 | Workman Jerome J | Non-invasive measurement of analytes |
| CN108918820A (en) * | 2018-05-23 | 2018-11-30 | 中国水利水电科学研究院 | The method and apparatus for obtaining arable soil degree of salinity distribution of grades |
| CN114241334A (en) * | 2021-12-20 | 2022-03-25 | 南京大学 | Multi-factor integrated arid region ecological environment remote sensing monitoring method |
| US20230035355A1 (en) * | 2021-07-28 | 2023-02-02 | Downforce Technologies Limited | System and method for natural capital measurement |
| US20230039763A1 (en) * | 2020-01-14 | 2023-02-09 | Centure Application Ltd. | System and method for real-time crop management |
| CN115901634A (en) * | 2022-10-08 | 2023-04-04 | 山东理工大学 | Salinization inversion method based on three-dimensional characteristic space model |
| CN116359137A (en) * | 2023-05-31 | 2023-06-30 | 武汉大学 | Multi-water-area urban ecological environment remote sensing monitoring method |
-
2024
- 2024-01-10 CN CN202410036663.2A patent/CN117541086B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070020181A1 (en) * | 2002-11-12 | 2007-01-25 | Workman Jerome J | Non-invasive measurement of analytes |
| CN108918820A (en) * | 2018-05-23 | 2018-11-30 | 中国水利水电科学研究院 | The method and apparatus for obtaining arable soil degree of salinity distribution of grades |
| US20230039763A1 (en) * | 2020-01-14 | 2023-02-09 | Centure Application Ltd. | System and method for real-time crop management |
| US20230035355A1 (en) * | 2021-07-28 | 2023-02-02 | Downforce Technologies Limited | System and method for natural capital measurement |
| CN114241334A (en) * | 2021-12-20 | 2022-03-25 | 南京大学 | Multi-factor integrated arid region ecological environment remote sensing monitoring method |
| CN115901634A (en) * | 2022-10-08 | 2023-04-04 | 山东理工大学 | Salinization inversion method based on three-dimensional characteristic space model |
| CN116359137A (en) * | 2023-05-31 | 2023-06-30 | 武汉大学 | Multi-water-area urban ecological environment remote sensing monitoring method |
Non-Patent Citations (5)
| Title |
|---|
| LINGLING BIAN等: "Spatiotemporal Changes of Soil Salinization in the Yellow River Delta of China from 2015 to 2019", 《MDPI》, 15 January 2021 (2021-01-15) * |
| 刘静: "基于特征空间的土壤盐渍化遥感模型应用研究", 《中国优秀硕士论文 工程科技Ⅱ辑》, 15 April 2023 (2023-04-15) * |
| 潘肖燕;崔江慧;杨江燕;关瑜;孟泽;刘田书;门明新;陈影;: "基于SDI校正指数的滨海平原盐渍化生态风险评价", 农业资源与环境学报, no. 05, 31 July 2019 (2019-07-31) * |
| 王飞;丁建丽;伍漫春;: "基于NDVI-SI特征空间的土壤盐渍化遥感模型", 农业工程学报, no. 08, 31 August 2010 (2010-08-31), pages 1 - 5 * |
| 陈俊英;姚志华;张智韬;魏广飞;王新涛;韩佳;: "大田葵花土壤含盐量无人机遥感反演研究", 农业机械学报, no. 07, 31 July 2020 (2020-07-31), pages 2 - 12 * |
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