CN113591611A - Soil drought remote sensing monitoring method based on geographical zoning - Google Patents

Soil drought remote sensing monitoring method based on geographical zoning Download PDF

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CN113591611A
CN113591611A CN202110788979.3A CN202110788979A CN113591611A CN 113591611 A CN113591611 A CN 113591611A CN 202110788979 A CN202110788979 A CN 202110788979A CN 113591611 A CN113591611 A CN 113591611A
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陈国茜
李素雲
李林
周秉荣
祝存兄
李甫
张娟
石明明
乔斌
史飞飞
曹晓云
李璠
赵彤
校瑞香
赵慧芳
肖建设
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Qinghai Institute Of Meteorology Science
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Abstract

The invention relates to a soil drought remote sensing monitoring method based on geographical zoning, which comprises the following steps: the method comprises the steps of firstly partitioning an area to be detected, then obtaining a soil moisture threshold table of each drought level of each partition based on existing data, finally calculating based on real-time data to obtain the soil moisture of each partition, and determining the real-time drought level of each partition by combining the soil moisture threshold tables of each drought level of each partition. According to the method, the area to be detected is partitioned, so that the influence of regional difference is avoided, and the drought grade of each partition of the area to be detected can be accurately obtained.

Description

Soil drought remote sensing monitoring method based on geographical zoning
Technical Field
The invention relates to the technical field of remote sensing monitoring, in particular to a soil drought remote sensing monitoring method based on geographical zoning.
Background
The soil moisture generally refers to the moisture stored in soil pores of an unsaturated soil layer or a seepage layer, is an important factor for leading the growth vigor and the yield of crops, and is also an important index for drought monitoring; the lack of soil moisture can cause the normal growth and development of vegetation to be hindered, the growth vigor and the yield of crops are influenced, and even the crops are dead harvested due to drought. The traditional soil moisture information acquisition mode is mainly based on ground observation, and the large-range regional soil moisture information acquisition mode is mainly based on satellite remote sensing. The remote sensing monitoring principle of soil moisture is a physical characteristic and radiation theory of soil, and mainly utilizes the reflection or radiation reaction of soil moisture characteristics in different wave bands to estimate the soil moisture; the soil moisture remote sensing can be divided into optical remote sensing and microwave remote sensing from different wave bands, wherein the optical remote sensing has higher spatial resolution and relatively mature technology and is commonly used for regional soil moisture remote sensing inversion, for example, in the Qinghai, a temperature vegetation drought index, an apparent thermal inertia method and a vertical drought index are used in the eastern agricultural area of the Qinghai, the temperature vegetation drought index is used in the Qilian mountain area, the apparent thermal inertia method and a vegetation condition index are used in the three-river source area, and finer soil moisture spatial distribution information is provided.
Aiming at the soil moisture ground observation station and the area where the meteorological station is rare and uneven in distribution, remote sensing monitoring becomes an important means for acquiring the soil moisture information in a large range of the area. In the prior art, the area is basically taken as a whole to be researched or a local area is deeply researched, and the influence of the difference of the climate, the vegetation and the soil area on the remote sensing monitoring precision of the soil moisture is rarely considered, so that the monitoring efficiency of the drought degree is low.
Disclosure of Invention
The invention aims to provide a soil drought remote sensing monitoring method based on geographical zoning, which is used for accurately monitoring the soil drought degree of a region to be detected in real time.
In order to achieve the purpose, the invention provides the following scheme:
a soil drought remote sensing monitoring method based on geographical zoning comprises the following steps:
s1, partitioning the area to be detected based on the historical data set of the area to be detected to obtain a partition set;
s2, calculating based on the historical data set and the remote sensing monitoring model to obtain a historical soil moisture data set of each partition in the partition set;
s3, determining the probability threshold value of each drought grade based on the percentile method; obtaining a soil moisture threshold table of each drought level of each subarea based on the probability threshold of each drought level and the historical soil moisture data set of each subarea;
s4, obtaining real-time soil moisture data of each subarea based on the real-time data of each subarea; and determining the real-time drought level of each subarea based on the soil moisture threshold value of each drought level of each subarea and the real-time soil moisture data of each subarea.
Preferably, the historical data set comprises:
a historical air temperature dataset, a historical precipitation dataset, a historical surface temperature dataset, a historical reflectivity dataset, a historical soil line slope, and a historical soil dataset.
Preferably, the S1 includes:
s11, calculating the historical reflectivity data set to obtain a historical normalized vegetation index set;
s12, calculating the historical soil data set based on a layering weight method to obtain an initial soil attribute data set; analyzing the initial soil attribute data set based on a principal component analysis method to obtain a soil attribute data set;
s13, respectively standardizing the historical air temperature data set, the historical precipitation data set, the historical normalized vegetation index set and the soil attribute data set based on a range standard method to obtain a standard historical air temperature data set, a standard historical precipitation data set, a standard historical normalized vegetation index set and a standard soil attribute data set;
s14, performing unsupervised classification on the standard historical air temperature data set, the standard historical precipitation data set, the standard historical normalized vegetation index set and the standard soil attribute data set based on a repeated self-organizing data analysis technology to obtain an initial partition set; and sorting and merging the initial partition set based on the climate partition of the area to be detected to obtain the partition set.
Preferably, the S2 includes:
s21, calculating based on the historical reflectivity data set of each partition to obtain a historical normalized moisture index set of each partition;
s22, calculating based on the historical normalized vegetation index set of each partition to obtain a historical vegetation condition index set of each partition;
s23, calculating based on the historical reflectivity data set and the historical soil line slope of each partition to obtain a historical vertical drought index set of each partition;
s24, calculating based on the historical normalized vegetation index set and the historical earth surface temperature data set of each partition to obtain a historical temperature vegetation drought index set of each partition;
and S25, based on the remote sensing monitoring model, calculating the historical normalized water index set, the historical vegetation condition index set, the historical vertical drought index set and the historical temperature vegetation drought index set of each partition to obtain a historical soil water index data set of each partition.
Preferably, before the S1, the method further includes:
acquiring an initial historical data set of the area to be detected; the initial historical data set comprises an initial historical air temperature data set, an initial historical precipitation data set, the historical surface temperature data set, the historical reflectivity data set, the historical soil line slope, and the historical soil data set;
carrying out outlier removal and spatial interpolation processing on the initial historical air temperature data set respectively to obtain a historical air temperature data set; and respectively carrying out outlier removal and spatial interpolation processing on the initial historical precipitation data set to obtain the historical precipitation data set.
Preferably, the calculation formula of the normalized vegetation index in the historical normalized vegetation index set is as follows:
Figure BDA0003160124830000031
in the formula: NDVI is a normalized vegetation index, B is a near infrared band reflectivity, A is a red light band reflectivity, mn is an nth monitoring period in an mth year, M is the number of years, and N is the number of monitoring periods in the year.
Preferably, the calculation formula of the historical normalized moisture index centralized normalized moisture index is as follows:
Figure BDA0003160124830000032
in the formula: NDWI is the normalized moisture index, and C is the reflectivity of the short wave infrared band.
Preferably, the calculation formula of the vegetation status index in the historical vegetation status index set is as follows:
Figure BDA0003160124830000033
in the formula:
Figure BDA0003160124830000034
to normalize the vegetation index to a maximum within M years of the nth monitoring period,
Figure BDA0003160124830000035
is the minimum value of the normalized vegetation index within M years of the nth monitoring period.
Preferably, the calculation formula of the vertical drought index in the historical vertical drought index set is as follows:
Figure BDA0003160124830000041
in the formula: PDI is the vertical drought index and S is the soil line slope.
Preferably, the historical temperature vegetation drought index and the centralized temperature vegetation drought index are calculated by the following formula:
Figure BDA0003160124830000042
in the formula: TDVI is the drought index of temperature vegetation, LST is the surface temperature,
Figure BDA0003160124830000043
is the maximum surface temperature within M years of the nth monitoring period,
Figure BDA0003160124830000044
is the minimum value of the surface temperature within M years of the nth monitoring period.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to a soil drought remote sensing monitoring method based on geographical zoning, which comprises the following steps: the method comprises the steps of firstly partitioning an area to be detected, then obtaining a soil moisture threshold table of each drought level of each partition based on existing data, finally calculating based on real-time data to obtain the soil moisture of each partition, and determining the real-time drought level of each partition by combining the soil moisture threshold tables of each drought level of each partition. According to the method, the area to be detected is partitioned, so that the influence of regional difference is avoided, and the drought grade of each partition of the area to be detected can be accurately obtained.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a soil drought remote sensing monitoring method based on geographical zoning.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a soil drought remote sensing monitoring method based on geographical zoning, which is used for accurately monitoring the drought degree of a to-be-detected area in real time.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a flow chart of a soil drought remote sensing monitoring method based on geographical zoning, and as shown in FIG. 1, the invention provides a soil drought remote sensing monitoring method based on geographical zoning, which comprises the following steps:
and step S1, partitioning the area to be detected based on the historical data set of the area to be detected to obtain a partition set. The historical data set includes: a historical air temperature dataset, a historical precipitation dataset, a historical surface temperature dataset, a historical reflectivity dataset, a historical soil line slope, and a historical soil dataset. In this embodiment, the historical air temperature data set and the historical precipitation data set adopt data issued by a climate center of an area to be measured, the historical air temperature data set and the historical precipitation data set both include mxn data, M is a number of years, N is a number of monitoring periods within a year, and M and N are both positive integers greater than 1; the historical surface temperature data set and the historical reflectivity data set adopt MODIS product data or VNP product data of a NASA official website; the historical soil line slope is calculated by adopting a minimum near infrared method based on the historical reflectivity data set; the historical soil data set adopts data published by Beijing university of teachers.
As an alternative embodiment, step S1 of the present invention includes:
and S11, calculating the historical reflectivity data set to obtain a historical normalized vegetation index set. Specifically, the historical reflectivity data set comprises a historical red light band reflectivity set, a historical near infrared band reflectivity set and a historical short wave infrared band reflectivity set; the historical red wave band reflectivity set, the historical near infrared wave band reflectivity set and the historical short wave infrared wave band reflectivity set all comprise M multiplied by N data.
Further, the calculation formula of the normalized vegetation index in the historical normalized vegetation index set is as follows:
Figure BDA0003160124830000051
in the formula: NDVI is a normalized vegetation index, B is a near infrared band reflectivity, A is a red light band reflectivity, mn is an nth monitoring period in an mth year, M is the number of years, and N is the number of monitoring periods in the year.
In order to ensure the integrity and accuracy of data, the method comprises the steps of firstly acquiring an initial historical data set of the area to be detected; the initial historical data set comprises an initial historical air temperature data set, an initial historical precipitation data set, the historical surface temperature data set, the historical reflectivity data set, the historical soil line slope, and the historical soil data set; carrying out outlier removal and spatial interpolation processing on the initial historical air temperature data set respectively to obtain a historical air temperature data set; and respectively carrying out outlier removal and spatial interpolation processing on the initial historical precipitation data set to obtain the historical precipitation data set.
S12, calculating the historical soil data set based on a layering weight method to obtain an initial soil attribute data set; and analyzing the initial soil attribute data set based on a principal component analysis method to obtain a soil attribute data set. The soil property dataset includes M × N data.
And S13, respectively carrying out standardization treatment on the historical air temperature data set, the historical precipitation data set, the historical normalized vegetation index set and the soil attribute data set based on a range standardization method to obtain a standard historical air temperature data set, a standard historical precipitation data set, a standard historical normalized vegetation index set and a standard soil attribute data set.
S14, performing unsupervised classification on the standard historical air temperature data set, the standard historical precipitation data set, the standard historical normalized vegetation index set and the standard soil attribute data set based on a repeated self-organizing data analysis technology to obtain an initial partition set; and sorting and merging the initial partition set based on the climate partition of the area to be detected to obtain the partition set. In this embodiment, the repeated self-organizing data analysis technology is implemented based on an ISO function module of the ArcGIS software, and the classification number is set to 8 and the minimum classification number is set to 80, so as to obtain the initial partition set.
And step S2, calculating based on the historical data set and the remote sensing monitoring model to obtain the historical soil moisture data set of each partition in the partition set.
Specifically, the step S2 includes:
s21, calculating based on the historical reflectivity data set of each partition to obtain a historical normalized moisture index set of each partition; the calculation formula of the historical normalized moisture index centralized normalized moisture index is as follows:
Figure BDA0003160124830000061
in the formula: NDWI is the normalized moisture index, and C is the reflectivity of the short wave infrared band.
S22, calculating based on the historical normalized vegetation index set of each partition to obtain a historical vegetation condition index set of each partition; the calculation formula of the vegetation condition index in the historical vegetation condition index set is as follows:
Figure BDA0003160124830000062
in the formula:
Figure BDA0003160124830000063
to normalize the vegetation index to a maximum within M years of the nth monitoring period,
Figure BDA0003160124830000064
is the minimum value of the normalized vegetation index within M years of the nth monitoring period.
S23, calculating based on the historical reflectivity data set and the historical soil line slope of each partition to obtain a historical vertical drought index set of each partition; the calculation formula of the historical vertical drought index set vertical drought index is as follows:
Figure BDA0003160124830000071
in the formula: PDI is the vertical drought index and S is the soil line slope.
S24, calculating based on the historical normalized vegetation index set and the historical earth surface temperature data set of each partition to obtain a historical temperature vegetation drought index set of each partition; the historical surface temperature data set comprises M x N data; the calculation formula of the historical temperature vegetation drought index and the centralized temperature vegetation drought index is as follows:
Figure BDA0003160124830000072
in the formula: TDVI is the drought index of temperature vegetation, LST is the surface temperature,
Figure BDA0003160124830000073
is the maximum surface temperature within M years of the nth monitoring period,
Figure BDA0003160124830000074
is the minimum value of the surface temperature within M years of the nth monitoring period.
And S25, based on the remote sensing monitoring model, calculating the historical normalized water index set, the historical vegetation condition index set, the historical vertical drought index set and the historical temperature vegetation drought index set of each partition to obtain a historical soil water index data set of each partition.
As an optional implementation manner, the historical data set further includes a site soil moisture data set, the site soil moisture data set is adopted for an area covered by a ground observation site, and the historical soil moisture data set of each partition is obtained by adopting the above method for calculating and combining the site soil moisture data set for an area uncovered by the ground observation site.
Step S3, determining the probability threshold value of each drought grade based on the percentile method; in this example, the drought levels include extra drought, heavy drought, medium drought, light drought, and no drought, and the probability thresholds are shown in table 1; and obtaining a soil moisture threshold value table of each drought level of each subarea based on the probability threshold value of each drought level and the historical soil moisture data set of each subarea. Further, the site soil moisture data sets can be selected to replace the historical soil moisture data sets of the partitions to obtain the soil moisture threshold value tables of the drought levels of the partitions.
TABLE 1 probability threshold for drought level
Figure BDA0003160124830000075
Figure BDA0003160124830000081
Step S4, obtaining real-time soil moisture data of each subarea based on the real-time data of each subarea, wherein the specific calculation method is the same as that in step S2; and determining the real-time drought level of each subarea based on the soil moisture threshold value of each drought level of each subarea and the real-time soil moisture data of each subarea.
The following description will be given with the Qinghai province as the region to be measured:
in this example, the partition set of Qinghai province is shown in Table 2.
TABLE 2 geographical zoning results
Figure BDA0003160124830000082
In practical application, the absolute error of soil water data obtained based on MODIS product data and a remote sensing monitoring model is between 2.6% and 7.8%, and the average absolute error is 5.6%; the root mean square error is between 3.4 and 9.7 percent, the average root mean square error is 7.5 percent, and the method has better application precision. The absolute error of the soil water data obtained based on VNP product data and a remote sensing monitoring model is between 2.1 and 13.7 percent, and the average absolute error is 6.3 percent; the root mean square error is between 2.5 percent and 8.1 percent, the average root mean square error is 5.7 percent, and the method has better application precision.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. A soil drought remote sensing monitoring method based on geographical zoning is characterized by comprising the following steps:
s1, partitioning the area to be detected based on the historical data set of the area to be detected to obtain a partition set;
s2, calculating based on the historical data set and the remote sensing monitoring model to obtain a historical soil moisture data set of each partition in the partition set;
s3, determining the probability threshold value of each drought grade based on the percentile method; obtaining a soil moisture threshold table of each drought level of each subarea based on the probability threshold of each drought level and the historical soil moisture data set of each subarea;
s4, obtaining real-time soil moisture data of each subarea based on the real-time data of each subarea; and determining the real-time drought level of each subarea based on the soil moisture threshold value of each drought level of each subarea and the real-time soil moisture data of each subarea.
2. The remote sensing monitoring method for soil drought based on geographical zoning according to claim 1, wherein the historical data set comprises:
a historical air temperature dataset, a historical precipitation dataset, a historical surface temperature dataset, a historical reflectivity dataset, a historical soil line slope, and a historical soil dataset.
3. The remote sensing monitoring method for soil drought based on geographical zoning according to claim 2, wherein the S1 comprises:
s11, calculating the historical reflectivity data set to obtain a historical normalized vegetation index set;
s12, calculating the historical soil data set based on a layering weight method to obtain an initial soil attribute data set; analyzing the initial soil attribute data set based on a principal component analysis method to obtain a soil attribute data set;
s13, respectively standardizing the historical air temperature data set, the historical precipitation data set, the historical normalized vegetation index set and the soil attribute data set based on a range standard method to obtain a standard historical air temperature data set, a standard historical precipitation data set, a standard historical normalized vegetation index set and a standard soil attribute data set;
s14, performing unsupervised classification on the standard historical air temperature data set, the standard historical precipitation data set, the standard historical normalized vegetation index set and the standard soil attribute data set based on a repeated self-organizing data analysis technology to obtain an initial partition set; and sorting and merging the initial partition set based on the climate partition of the area to be detected to obtain the partition set.
4. The remote sensing monitoring method for soil drought according to claim 3, wherein the S2 comprises:
s21, calculating based on the historical reflectivity data set of each partition to obtain a historical normalized moisture index set of each partition;
s22, calculating based on the historical normalized vegetation index set of each partition to obtain a historical vegetation condition index set of each partition;
s23, calculating based on the historical reflectivity data set and the historical soil line slope of each partition to obtain a historical vertical drought index set of each partition;
s24, calculating based on the historical normalized vegetation index set and the historical earth surface temperature data set of each partition to obtain a historical temperature vegetation drought index set of each partition;
and S25, based on the remote sensing monitoring model, calculating the historical normalized water index set, the historical vegetation condition index set, the historical vertical drought index set and the historical temperature vegetation drought index set of each partition to obtain a historical soil water index data set of each partition.
5. The remote sensing monitoring method for soil drought based on geographical zoning according to claim 2, further comprising before the step of S1:
acquiring an initial historical data set of the area to be detected; the initial historical data set comprises an initial historical air temperature data set, an initial historical precipitation data set, the historical surface temperature data set, the historical reflectivity data set, the historical soil line slope, and the historical soil data set;
carrying out outlier removal and spatial interpolation processing on the initial historical air temperature data set respectively to obtain a historical air temperature data set; and respectively carrying out outlier removal and spatial interpolation processing on the initial historical precipitation data set to obtain the historical precipitation data set.
6. The geographical partitioning-based soil drought remote sensing monitoring method according to claim 4, wherein the historical normalized vegetation index set normalized vegetation index is calculated by the following formula:
Figure FDA0003160124820000021
in the formula: NDVI is a normalized vegetation index, B is a near infrared band reflectivity, A is a red light band reflectivity, mn is an nth monitoring period in an mth year, M is the number of years, and N is the number of monitoring periods in the year.
7. The remote sensing monitoring method for soil drought based on geographical zoning according to claim 6, wherein the calculation formula of the historical normalized water index centralized normalized water index is as follows:
Figure FDA0003160124820000031
in the formula: NDWI is the normalized moisture index, and C is the reflectivity of the short wave infrared band.
8. The geographical zoning-based soil drought remote sensing monitoring method of claim 6, wherein the calculation formula of the vegetation status index in the historical vegetation status index set is as follows:
Figure FDA0003160124820000032
in the formula:
Figure FDA0003160124820000033
to normalize the vegetation index to a maximum within M years of the nth monitoring period,
Figure FDA0003160124820000034
is the minimum value of the normalized vegetation index within M years of the nth monitoring period.
9. The remote sensing monitoring method for soil drought based on geographical zoning according to claim 6, wherein the calculation formula of the historical vertical drought index set vertical drought index is as follows:
Figure FDA0003160124820000035
in the formula: PDI is the vertical drought index and S is the soil line slope.
10. The remote sensing monitoring method for soil drought based on geographical zoning according to claim 6, wherein the calculation formula of the temperature vegetation drought index in the historical temperature vegetation drought index set is as follows:
Figure FDA0003160124820000036
in the formula: TDVI is the drought index of temperature vegetation, LST is the surface temperature,
Figure FDA0003160124820000037
is the maximum surface temperature within M years of the nth monitoring period,
Figure FDA0003160124820000038
is the minimum value of the surface temperature within M years of the nth monitoring period.
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