CN115810170A - Method for monitoring drought by using multi-source remote sensing data - Google Patents
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Abstract
The invention discloses a method for monitoring drought by using multi-source remote sensing data, which comprises the steps of selecting and processing vegetation indexes, surface temperature and TRMM precipitation data acquired by a medium-resolution imaging spectrometer in a meteorological satellite through remote sensing monitoring of a monitoring area, and establishing an iTVPDI index of drought monitoring on the preprocessed data through a spatial distance regression method; and the drought degree is expressed, and the drought monitoring by utilizing the multi-source remote sensing data is realized. The method for monitoring the drought comprises the steps of acquiring the earth surface temperature, the vegetation index and TRMM precipitation data of the atmospheric product by using a medium-resolution imaging spectrometer, monitoring the drought from three aspects of temperature, precipitation and vegetation, and having the advantages of high monitoring precision, wide space range, more consideration factors, multi-azimuth drought monitoring, comprehensiveness and better monitoring effect than other methods.
Description
Technical Field
The invention belongs to the technical field of drought monitoring, and relates to a method for monitoring drought by using multi-source remote sensing data.
Background
Drought has a significant impact on natural environment changes, human production and life, and social and economic development. Meanwhile, the influence of human beings on the cause and process of drought events and after drought disasters has been studied for a long time. In the 80's of the 20 th century, the World Meteorological Organization (WMO) defined drought as a persistent, abnormal deficit of precipitation. With the continuous understanding and exploration of drought-causing factors, the formation mechanism and process of drought cannot be scientifically reflected by only defining the drought through a factor of precipitation, and the natural process of drought needs to be re-known from the perspective of water resource supply and demand balance. Therefore, the academic world mainly classifies drought into four categories according to different water supply and demand balance relations: weather drought, agricultural drought, hydrological drought, and socioeconomic drought. Weather drought refers to the phenomenon of water deficit caused by imbalance between precipitation reduction and surface evapotranspiration in a short time; agricultural drought is a phenomenon that the water deficiency in vegetation caused by soil water stress limits the growth of vegetation or reduces the yield of crops; hydrodrought refers to reduced surface runoff or inadequate groundwater supply; the social economic drought refers to the phenomenon of abnormal water shortage caused by rainfall, uneven distribution of surface water and underground water and unbalance between water demands of human society, and the occurrence of the social economic drought has close relation with meteorological drought, agricultural drought and hydrological drought. The meteorological drought is the leading factor causing the following three types of drought, and the four types of drought are mutually related. Generally, studies on drought have focused mainly on three aspects, namely, meteorological drought, agricultural drought, and hydrodrought. The determination of whether the drought is disastrous is mainly based on the adverse impact of the drought event on human production life, economic development and natural environment within the duration time of the drought event.
In recent years, the construction of remote sensing drought monitoring indexes gradually becomes a research hotspot, and a plurality of experts and scholars provide the remote sensing drought monitoring indexes with certain monitoring efficacy. The vegetation growth condition of an area is an important index for representing the hydrological condition, the climatic condition and the ecological environment of the area. Drought has a significant effect on the growth of vegetation in a particular area, which, in turn, reflects the severity of the drought in that area. With the continuous research on the occurrence mechanism of the drought event, the evaluation on the actual drought condition cannot be met by a single remote sensing index. Therefore, scholars at home and abroad propose a series of drought indexes based on temperature-vegetation characteristic space, drought indexes based on ground surface evapotranspiration, drought indexes based on multi-drought factor coupling and the like. The main remote sensing drought monitoring indexes are as follows: kogan research finds that the evaluation effect of drought is better than that of a single Index evaluation result by using VCI or TCI by comprehensively evaluating Temperature Condition Index (TCI) and Vegetation Condition Index (VCI). The drought index after the combination of the VCI and the TCI is called a Vegetation Health Index (VHI), the VHI is a coefficient in which two sums are 1 determined according to certain prior knowledge or multiple experiments, and the VCI and the TCI are multiplied by corresponding coefficients respectively and then added to obtain the drought index. In 2002, sandholt et al establish NDVI and LST feature spaces, construct a temperature-vegetation Drought Index (TVDI), and by using the Index, more soil and vegetation moisture information can be obtained, and the method is widely applied to agricultural Drought. The Temperature-Vegetation Drought Index (TVDI) is one of the most widely used Drought monitoring indexes, which is a unitary linear regression equation that fits dry and wet sides respectively according to a triangular feature space between the surface Temperature and the normalized Vegetation Index, and the slope and intercept of the dry and wet sides are applied to the construction of the TVDI. In the 80 s of the 20 th century, idso and the like established Crop water stress Index (Crop Waterstress Index, CWSI) according to the relation between Potential Evapotranspiration (PET) and actual Evapotranspiration (ET) of the earth surface and soil water, and well monitored the earth surface water condition.
Although the single remote sensing index and the comprehensive remote sensing index make great contribution to drought monitoring, the drought disaster forming principle in index construction is complex because the drought disaster forming principle is restricted by a plurality of conditions. For example, the water content of soil is reduced due to long-term rainfall balance, the capacities of river runoff, pools and reservoirs are reduced, and drought monitoring by a single remote sensing drought index is not free from one-sidedness, so that the problem can be well solved by establishing the comprehensive remote sensing drought index.
Disclosure of Invention
The invention aims to provide a method for effectively monitoring drought by using multi-source remote sensing data, which solves the problem of constraint of drought monitoring in the prior art and monitors drought more comprehensively and accurately.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a method for monitoring drought by utilizing multi-source remote sensing data is carried out according to the following steps:
1) Selecting vegetation indexes, surface temperature and TRMM precipitation data obtained by remote sensing monitoring of a monitoring area by a medium-resolution imaging spectrometer in a meteorological satellite;
2) Processing the selected vegetation index, the surface temperature and TRMM precipitation data:
for vegetation index and surface temperature:
calling an MRT software batch processing tool, splicing images of original images, selecting Albers projection by projection transformation, converting a file into a tif format, resampling to 1km resolution by using an ArcGIS tool, removing invalid values in batches by using an Arcpy tool, and restoring real values of the images to obtain preprocessed data;
for TRMM precipitation data:
performing image rotation, header file editing and coordinate definition on the original data through ENVI to obtain preprocessed data;
3) Establishing an iTVPDI index of drought monitoring on the preprocessed data by a space distance regression method:
selecting precipitation quantity, surface temperature and vegetation index which can represent precipitation deficit, soil moisture deficit and vegetation growth condition in the whole time sequence as main input variables, establishing precipitation-surface temperature-vegetation index three-dimensional space, and constructing a time-sequence iTVPDI index by using a space distance regression model to represent the drought degree, thereby realizing monitoring the drought by using multi-source remote sensing data.
The monitoring method of the invention utilizes a Moderate Resolution Imaging spectrometer (MODIS) to obtain the surface temperature, the vegetation index and TRMM precipitation data of the atmospheric product, monitors drought from three aspects of temperature-precipitation-vegetation, has high monitoring precision, wide space range, more consideration factors and multi-azimuth monitoring drought, has comprehensiveness and better monitoring effect than other methods.
The monitoring method of the invention has the following advantages:
1) The spatial distance regression method is used, so that the synthetic index range is 0-1, the grading color setting is more convenient, and the theorem is convenient to set.
2) The resolution ratio of the used remote sensing data is controlled to be 1km, and the precision is improved, so that the drought condition can be better monitored.
3) The three factors of vegetation, temperature and rainfall are analyzed, and the accuracy of drought monitoring is greatly improved.
Drawings
FIG. 1 is a diagram of a spatial distance regression model.
FIG. 2 is the spatial distribution of iTVPDI mean values from 1 month to 12 months in 2001-2020.
FIG. 3 is a graph of iTVPDI versus TVDI and CWSI correlations.
FIG. 4 is a graph of trend analysis relating iTVPDI to SPI.
FIG. 5 is a graph of the correlation analysis of iTVPDI with GPP.
FIG. 6 is a graph of the dependence of iTVPDI on soil moisture content.
FIG. 7 is a graph of the correlation of iTVPDI with grain yield.
Detailed Description
The invention is described in detail below with reference to the drawings and the detailed description.
Drought monitoring modes are generally divided into two types, the first type is drought monitoring based on meteorological site data: the method has the advantages of short data acquisition period, high data precision and good monitoring effect on the site coverage area. Due to the fact that drought occurs regionally, the coverage range of a station is limited, and the drought range cannot be covered in all directions in space. Weather drought monitoring based on weather stations also requires a large amount of manpower and financial resources to realize complex requirements of distribution of observation stations, timely data acquisition, complex database establishment, management of acquired weather data and the like. The second method is drought monitoring based on remote sensing data: the remote sensing data has the advantages of good timeliness, high resolution, global coverage of observation range, easy acquisition and processing of data and the like.
The invention provides a method for effectively monitoring drought by using multi-source remote sensing data, which comprises the following steps:
1) selecting vegetation index (MOD 11A 2), surface temperature (MOD 13A 3) and TRMM precipitation data which are obtained by remote sensing monitoring of a medium-resolution imaging spectrometer (MODIS) in a meteorological satellite on a selected monitoring area;
the selected vegetation index (MOD 11A 2), the surface temperature (MOD 13A 3) and TRMM rainfall data are unified in space and time by using a maximum value synthesis method (time) and a resampling method (space) in ArcGIS, the time precision is month scale, and the space resolution is 1km.
2) Processing the selected vegetation index, the surface temperature and TRMM precipitation data:
for vegetation index and surface temperature:
calling an MRT software batch processing tool, splicing images of original images, selecting Albers projection by projection transformation, converting a file into a tif format, resampling to 1km resolution by using an ArcGIS tool, and then removing invalid values in batches and reducing real values of the images by using the Arcpy tool to obtain preprocessed data;
for TRMM precipitation data:
performing image rotation, head file editing and coordinate definition on the original data through a remote sensing image processing platform (ENVI) to obtain preprocessed data;
3) Establishing an iTVPDI index of drought monitoring on the preprocessed data by a spatial distance regression method;
the specific method for establishing the iTVPDI index comprises the following steps: selecting precipitation deficiency, soil moisture deficiency and precipitation of vegetation growth conditions, surface temperature (LST) and vegetation index (NDVI) in a whole time sequence (the whole time sequence is a research period) as main input variables, establishing a precipitation-surface temperature-vegetation index three-dimensional space, and constructing a time-sequential iTVPDI index by utilizing a space distance regression model as shown in figure 1;
in fig. 1, a three-dimensional rectangular coordinate system is established with point O as the origin of coordinates, as shown in fig. 1a. In the three-dimensional rectangular coordinate system, the X axis represents vegetation index, the Y axis represents earth surface temperature, and the Z axis represents precipitation, as shown in FIG. 1b; according to the selected precipitation, the earth surface temperature and the vegetation index, a three-dimensional space is established in the three-dimensional rectangular coordinate system, the three-dimensional space is a cuboid, one vertex of the cuboid coincides with the origin of coordinates, and three edges, which intersect at the vertex coinciding with the origin of coordinates, in the cuboid are respectively located on an X axis, a Y axis and a Z axis. The point D farthest from the origin of coordinates O on the Y axis is the driest point; the farthest point from point D in the rectangular parallelepiped is point W in the XOZ plane, and point W is the wettest point. A line segment DW represents a dry edge and a wet edge, and is an index for judging the dry and wet conditions, and the closer to the point W, the wet edge represents the wetting; the closer to point D, the dry edge indicates drought. In the created space regression model, the unit side length is 1, and the corresponding DW with dry and wet sides has a value range of 0 to
(ii) a Due to the fact that
It is an infinite loop decimal that is not favorable for grading the drought index. For grading and controlling the drought index within an integral interval, the drought index is controlled by [ (actual value-minimum value)V (maximum-minimum)]×
The normalization method controls the value range of the index to be between 0 and 1 (formula 1 to 3), and after treatment, the grading of the drought index is normalized into an integer, which is convenient for the following theorem, and the specific formula is as follows:
in the formula (I), the compound is shown in the specification,Normalizedexpressing the normalized value, and marking as N (C), wherein C respectively expresses NDVI, LST and TRMM in different formulas;minwhich represents the minimum value of the sum of the values,maxthe maximum value is indicated.
The drought was highest at point D in fig. 1, where itvdi =1. The drought values were lowest at point W in fig. 1, where itvdi =0.
The iTVPDI corresponds to the drought severity level, as shown in Table 1.
TABLE 1 drought grading for iTVPDI
Application of iTVPDI to drought monitoring in China
The iTVPDI is used for drought monitoring in China areas in 2001-2020. As shown in fig. 2, the monitoring results show that: the Qinghai-Tibet region is dry compared to the northwest region, while the south and Central China regions are wet. The northeast region is wettest in July, and is drought in 3-4 and 9-12 months. The Qinghai-Tibet region is wet in July and the northwest region is dry as a whole. According to different regional statistics, the iTVPDI value is lower in Qinghai-Tibet areas and North China areas, and the iTVPDI value is higher in Central China and south China areas. The iTVPDI value of the northwest region is lower compared with that of the Tibet region, and the iTVPDI values of the east region, the northeast region and the southwest region are 0.5141-0.5499.
By combining the value law of iTVPDI, the drought appears in 5-8 months as a whole, 10 months to the next 2 months, and the curve analysis from 1-3 months shows that the spring drought has higher frequency in China and the drought is more moderate from summer to autumn. The iTVPDI values fluctuate between 0.47 and 0.55 by analysis over a 240 month time series between 2001 and 2020, with the maximum occurring at 7 months per year and the minimum at 12 months. The high and low fluctuation trend is shown in 1-6 months. In summary, one of the reasons for the significant difference between the northwest region and the east China region is that the rising of the Qinghai-Tibet plateau leads to the failure of the warm and humid air stream in the Indian ocean to be transported to the northwest region of China, thus causing the drought climate in the northwest region, and in addition, most of the northwest region is desert and bare soil region, the soil moisture content is low, which is not suitable for the growth of part of vegetation, and most of the vegetation and shrubs in desert. In east China, due to climate, the plants are wet and rainy all the year round and are suitable for crop growth, so that drought rarely occurs in east China.
The iTVPDI index accords with the actual drought condition distribution by using the iTVPDI index to monitor and analyze the drought in China, and the iTVPDI index has certain accuracy in drought monitoring.
The index established by the invention is subjected to precision verification
In order to test the precision of the drought monitoring index in the monitoring method, the precision is verified from two aspects: firstly, the precision of the drought index is analyzed by comparing with the drought index in the prior art; and secondly, carrying out correlation analysis on the vegetation productivity GPP, the soil water content SM and the grain yield so as to test the correlation between the vegetation productivity GPP, the soil water content SM and the grain yield and the iTVPDI index.
1. Comparison with drought index in the prior art
1) Calculation of validation index:
TVDI index calculation:
TVDI=(LST i －LST min )/(LST max －LST min ) （5）
LST max =a 1 +b 1 ×NDVI （6）
LST min = a 2 +b 2 ×NDVI （7）
in the formula (5), the first and second groups of the chemical reaction materials are selected from the group consisting of,LST i image of LST raster image for monthA meta value;LST min is the minimum value in the pixels of the same vegetation index;LST max is the maximum value in the pixels of the same vegetation index; in the formulas (6) and (7), a 1 、b 1 Respectively the intercept and the slope of the dry edge in the feature space; a is 2 、b 2 Respectively the intercept and the slope of the wet edge in the feature space; the TVDI value range is 0 to 1, and the higher the TVDI value, the more drought.
And (3) index calculation:
CWSI=1－(ET/PET) （8）
(8) In the formula (I), the compound is shown in the specification,ETactual evapotranspiration for a month;PETpotential evapotranspiration;CWSIthe range of (a) is from 0 to 1,CWSIa lower value of (a) indicates a higher drought.
And (3) index calculation:
assuming that x is the precipitation of a month, the probability density function is:
(9) In the formula (I), the compound is shown in the specification,xin order to reduce the amount of water in the water,βandγscale parameters and shape parameters, Γ: (x) In order to be the probability of the gamma distribution,β＞0，γ> 0 and can be estimated by maximum likelihood:
(10-12) in the formula,x i and
respectively representing the average value of the precipitation of a certain month and the precipitation of the whole time sequence, wherein n is the length of the time sequence;
respectively, represent values of the shape parameter and the scale parameter estimated by the maximum likelihood method.AIn order to be a probability parameter,ito representiAnd (4) month.
Precipitation for a yearx 0 In other words, random variablesx＜x 0 The probability of (c) is:
when the precipitation is 0, the probability is:
F(x=0)=m/n（14）
(14) In the formula (I), the compound is shown in the specification,mis composed ofxA number of samples of =0 is calculated,nis the total number of samples.
Carrying out normalization standard processing on the distribution probability of the Gamma to obtain:
the calculation formula of the SPI obtained by approximate solution is as follows:
(16) In the formula (I), the compound is shown in the specification,
when is coming into contact withFWhen the carbon content is more than 0.5,S=1; when in useFWhen the content of the organic acid is less than 0.5,S=-1，Sis a probability coefficient;c 0 、c 1 、c 2 2.515517, 0.802853, 0.010328, respectively.d 1 、d 2 、d 3 1.432788, 0.189269, 0.001308, respectively. A smaller value of SPI indicates a more drought, while a larger value of SPI indicates a more humid state.
2) Comparative analysis of results
The Chinese area of 2001-2020 is selected for comparison and verification, and the result is shown in FIG. 3: the iTVPDI and the TVDI are in a significant negative correlation relationship, and the correlation coefficient is between-0.5 and-0.9, as shown in FIG. 3a, which shows that the iTVPDI is more accurate in monitoring the soil water content condition, i.e. the iTVPDI is more suitable for agricultural drought monitoring. The iTVPDI and the CWSI have a remarkable negative correlation, the correlation coefficient is-0.5 to-0.93, and the graph is shown in a figure 3b; the iTVPDI has good monitoring effect on monitoring water balance. SPI indexes of five time scales of 1 month, 3 months, 6 months, 9 months and 12 months are selected and respectively compared with iTVPDI, as shown in figure 4, the degree of fit between iTVPDI and SPI-3 is the most fit in the SPI indexes of the five time scales, and SPI-3 represents the drought index of the short-term vegetation rainfall deficit condition and has the strongest response to the water content of soil. This shows that the iTVPDI can monitor the decrease of the soil moisture content caused by short-term rainfall deficit, and the soil moisture content has certain influence on the normal growth of vegetation in the growing season of the vegetation, so the iTVPDI is more suitable for monitoring short-term drought.
2. Correlation analysis with vegetation productivity GPP, soil moisture content SM and grain yield
Soil moisture content (SM) is a direct indicator of drought. The GPP, which is an index characterizing the maximum carbon flux and carbon input of the terrestrial ecosystem, has a sensitive and well documented incidence of drought events. Grain yield data is also important feedback data for monitoring agricultural drought. The total primary productivity GPP, the soil water content SM and the grain yield per unit area are utilized to carry out correlation coefficient analysis through Pearson correlation coefficient and iTVPDI, and the analysis result is shown in a graph 5~7 and a table 2. As can be seen from fig. 5, the itvdi and GPP exhibit a negative correlation, with a pearson correlation coefficient of 0.93 at maximum. FIG. 6 shows that there is a positive correlation between iTVPDI and soil moisture content, and that R is less than 0.05 2 =0.62. FIG. 7 illustrates that iTVPDI is negatively correlated to grain yield with Pearson correlation coefficient up to 0.96.
By taking the whole country as a research area, the iTVPDI and the correlation coefficient of the three test indexes show that the iTVPDI has a good effect on drought monitoring. The index has the best monitoring effect on monitoring short-term drought and agricultural drought caused by rainfall deficit, the monitoring method does not depend on ground station observation data, has strong practicability, can be used for carrying out real-time accurate large-range monitoring in space by utilizing remote sensing data, and is superior to the drought monitoring method in the prior art.
Claims (4)
1. A method for monitoring drought by using multi-source remote sensing data is characterized by comprising the following steps:
1) Selecting vegetation indexes, surface temperature and TRMM precipitation data obtained by remote sensing monitoring of a monitoring area by a medium-resolution imaging spectrometer in a meteorological satellite;
2) Processing the selected vegetation index, the surface temperature and TRMM precipitation data:
for vegetation index and surface temperature:
calling an MRT software batch processing tool, splicing images of original images, selecting Albers projection by projection transformation, converting a file into a tif format, resampling to 1km resolution by using an ArcGIS tool, removing invalid values in batches by using an Arcpy tool, and restoring real values of the images to obtain preprocessed data;
for TRMM precipitation data:
performing image rotation, header file editing and coordinate definition on the original data through ENVI to obtain preprocessed data;
3) Establishing an iTVPDI index of drought monitoring on the preprocessed data by a space distance regression method:
selecting precipitation quantity, surface temperature and vegetation index which can represent precipitation deficit, soil moisture deficit and vegetation growth condition in the whole time sequence as main input variables, establishing precipitation-surface temperature-vegetation index three-dimensional space, and constructing a time-sequence iTVPDI index by using a space distance regression model to represent the drought degree, thereby realizing monitoring the drought by using multi-source remote sensing data.
2. The method for monitoring the drought according to the multi-source remote sensing data of claim 1, wherein in the step 1), the vegetation index, the surface temperature and the TRMM rainfall data are unified in time and space by using a maximum value synthesis method and a resampling method in ArcGIS, the time precision is a monthly scale, and the spatial resolution is 1km.
3. The method for monitoring the drought according to the multi-source remote sensing data of claim 1, wherein in the step 3), the establishment of the spatial distance regression model comprises the following steps: establishing a three-dimensional rectangular coordinate system by taking the O point as a coordinate origin; in the three-dimensional rectangular coordinate system, an X axis represents a vegetation index, a Y axis represents a ground surface temperature, and a Z axis represents precipitation; establishing a three-dimensional space in a three-dimensional rectangular coordinate system according to the selected precipitation, the surface temperature and the vegetation index, wherein the three-dimensional space is a cuboid, one vertex of the cuboid is coincided with an origin of coordinates, and three edges, which are intersected at the vertex coincided with the origin of coordinates, in the cuboid are respectively positioned on an X axis, a Y axis and a Z axis; the point D farthest from the origin of coordinates O on the Y axis is the dryest point and represents the highest drought degree, the point farthest from the point D in the cuboid is the point W in the XOZ plane, the point W is the wetest point, and the drought value is the lowest; a line segment DW represents a dry edge and a wet edge which are used for judging the dry and wet conditions, and the closer the line segment DW is to a point W, the dry edge represents the wet condition; the closer to point D, the dry edge indicates drought.
4. The method for monitoring the drought according to claim 3, wherein the unit side length is 1, and the range of the corresponding DW with the dry and wet side is 0 to E in the created spatial regression model
(ii) a Due to the fact that
An infinite endless decimal is not beneficial to grading the drought index; in order to control the drought index in an integer interval in a grading way, the drought index is extracted through (actual value-minimum value/maximum value-minimum value)
The normalization method controls the value range of the index to be between 0 and 1 (formula 1~3), and after treatment, the grading of the drought index is normalized into an integer, which is convenient for the following theorem, and the specific formula is as follows:
in the formula (I), the compound is shown in the specification,Normalizedthe normalized values are shown as N (C), and C is shown as NDVI, LST and TRMM;minwhich represents the minimum value of the sum of the values,maxthe maximum value is indicated.
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| CN116680548A (en) * | 2023-08-03 | 2023-09-01 | 南京信息工程大学 | Time sequence drought causal analysis method for multi-source observation data |
| CN118427740A (en) * | 2024-06-27 | 2024-08-02 | 水利部交通运输部国家能源局南京水利科学研究院 | Information fusion-based seamless precision evaluation method and system for satellite and analysis precipitation product |
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| CN116680548A (en) * | 2023-08-03 | 2023-09-01 | 南京信息工程大学 | Time sequence drought causal analysis method for multi-source observation data |
| CN116680548B (en) * | 2023-08-03 | 2023-10-13 | 南京信息工程大学 | Time sequence drought causal analysis method for multi-source observation data |
| CN118427740A (en) * | 2024-06-27 | 2024-08-02 | 水利部交通运输部国家能源局南京水利科学研究院 | Information fusion-based seamless precision evaluation method and system for satellite and analysis precipitation product |
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