Drought disaster monitoring system

Abstract

The invention discloses a drought disaster monitoring system, and relates to the technical field of drought disaster monitoring, and the system comprises a DISR downscaling unit which is used for obtaining a first scale conversion factor based on remote sensing data, and carrying out the downscaling of the original soil moisture content based on the first scale conversion factor to obtain a first soil moisture content; the DIT downscaling unit is used for obtaining a second scale conversion factor based on the remote sensing data, and downscaling the original soil moisture content based on the second scale conversion factor to obtain a second soil moisture content; the DISR correction unit is used for spatially aggregating the first soil moisture content to obtain a third soil moisture content, obtaining a correction coefficient based on the second soil moisture content and the third soil moisture content, and correcting the first soil moisture content based on the correction coefficient to obtain a fourth soil moisture content; the monitoring result unit is used for obtaining a monitoring result based on the fourth soil moisture content. The problem that an existing spatial resolution downscaling system cannot meet the requirements of high spatial resolution, high accuracy and reasonable spatial distribution at the same time is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of drought disaster monitoring, and in particular to a drought disaster monitoring system. Background Art

[0002] Drought monitoring is of great significance for ensuring agricultural production. At present, global-scale soil moisture remote sensing products based on remote sensing technology are often used for drought monitoring, but the spatial resolution of these remote sensing products is low, such as the European Space Agency's Climate Change Initiative (ESA CCI), which is difficult to meet the application needs of regional scales. Spatial downscaling is one of the ideas to improve the spatial resolution of these remote sensing products.

[0003] At present, a commonly used downscaling method is to construct a spatial scale conversion factor through a drought index that is highly correlated with soil moisture content. However, to construct a drought index, such as TVDI (temperature vegetation drought index), it is usually necessary to introduce LST (surface temperature data) as an auxiliary parameter, and the low spatial resolution of LST, such as MOD11A1, is a surface temperature / emissivity product collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite, with a spatial resolution of 1km, making it impossible for the downscaling method based on DIT (temperature drought index) to reduce the soil moisture remote sensing product to a lower spatial scale.

[0004] Modified Perpendicular Drought Index (MPDI) is also a commonly used drought monitoring indicator. Since the construction of MPDI only requires surface reflectance data in the red and near-infrared bands, the spatial resolution of surface reflectance data is higher than that of temperature data. For example, MOD09GQ is one of the MODIS satellite data products, which is an 8-day synthetic level 3 data product with a spatial resolution of 250m. Therefore, this enables the downscaling method based on DISR (reflectance drought index) to reduce the soil moisture remote sensing product to a lower spatial scale. However, soil moisture is usually highly correlated with surface temperature. Since surface temperature information is not introduced, the results obtained by the DISR downscaling method have a higher spatial resolution, but the accuracy and rationality of the spatial distribution of the results are not as high as those of the DIT downscaling method. Summary of the invention

[0005] In order to solve the problem that the spatial resolution downscaling system of the existing drought monitoring remote sensing products cannot simultaneously meet the requirements of high spatial resolution, high accuracy and reasonable spatial distribution, the present invention provides a drought disaster monitoring system, which includes:

[0006] Acquisition unit: used to acquire remote sensing data of the target area;

[0007] DISR downscaling unit: used for obtaining an improved vertical drought index based on the remote sensing data, obtaining a first scale conversion factor based on the improved vertical drought index, and downscaling the original soil moisture content based on the first scale conversion factor to obtain a first soil moisture content;

[0008] DIT downscaling unit: used for obtaining a temperature vegetation drought index based on the remote sensing data, obtaining a second scale conversion factor based on the temperature vegetation drought index, and downscaling the original soil moisture content based on the second scale conversion factor to obtain a second soil moisture content;

[0009] DISR correction unit: used for spatially aggregating the first soil moisture content to obtain a third soil moisture content, obtaining a correction coefficient based on the second soil moisture content and the third soil moisture content, and correcting the first soil moisture content based on the correction coefficient to obtain a fourth soil moisture content;

[0010] A monitoring result unit is used to obtain a monitoring result based on the fourth soil moisture content.

[0011] The present invention reduces soil moisture content to a lower spatial scale through a downscaling method based on DISR, introduces surface temperature data based on a downscaling method based on DIT to improve the accuracy of downscaling, uses the results obtained based on the DIT downscaling method to correct the results based on the DISR downscaling method, and combines the respective advantages of the two downscaling methods to obtain a downscaled soil moisture content with high spatial resolution, high accuracy and reasonable spatial distribution, thereby improving the accuracy of drought disaster monitoring.

[0012] Furthermore, the DISR downscaling unit specifically includes:

[0013] The first unit is used to obtain the vegetation coverage and the soil line slope based on the remote sensing data, and obtain the improved vertical drought index based on the vegetation coverage and the soil line slope;

[0014] The second unit is used for spatially aggregating the improved vertical drought index to obtain an aggregated improved vertical drought index;

[0015] The third unit is used to obtain the first scale conversion factor based on the improved vertical drought index and the aggregated improved vertical drought index;

[0016] The fourth unit is used to downscale the original soil moisture content based on the first scale conversion factor to obtain the first soil moisture content.

[0017] Furthermore, the DIT downscaling unit specifically includes:

[0018] The fifth unit is used to obtain the temperature vegetation drought index based on the remote sensing data;

[0019] The sixth unit is used for spatially aggregating the temperature vegetation drought index to obtain an aggregated temperature vegetation drought index;

[0020] The seventh unit is used to obtain the second scale conversion factor based on the temperature vegetation drought index and the aggregated temperature vegetation drought index;

[0021] Unit 8: downscaling the original soil moisture content based on the second scale conversion factor to obtain the second soil moisture content.

[0022] Furthermore, the first calculation method of the vegetation coverage is:

[0023] FVC=(NDVI-NDVI soil ) / (NDVI veg -NDVI soil );

[0024] Among them, FVC represents vegetation coverage, NDVI represents normalized difference vegetation index, and NDVI soil Indicates the NDVI value of a pixel composed entirely of bare soil. veg Indicates the NDVI value of pixels completely covered by vegetation;

[0025] The second calculation method of the soil line slope is:

[0026] M=(R nir -l) / R red ;

[0027] Among them, R red and R nir They represent the reflectance of the red and near-red bands after atmospheric correction, M represents the slope of the soil line, and l represents the intercept;

[0028] The third calculation method of the improved vertical drought index is:

[0029]

[0030] MPDI stands for Modified Vertical Drought Index, R v,red and R v,nir All represent constants.

[0031] Furthermore, the fourth calculation method of the aggregated improved vertical drought index is:

[0032]

[0033] in, represents the aggregated improved vertical drought index, n1 represents the number of MPDI pixels, and MPDI represents the improved vertical drought index;

[0034] The fifth calculation method of the first scale conversion factor is:

[0035]

[0036] SSCF DISR represents the first scale conversion factor;

[0037] The sixth calculation method of the first soil moisture content is:

[0038] SM DISR =SSCF DISR *SM coarse ;

[0039] Among them, SM DISR Indicates the first soil moisture content, SM coarse Indicates the original soil moisture content.

[0040] Furthermore, the seventh calculation method of the temperature vegetation drought index is:

[0041]

[0042] TVDI stands for Temperature Vegetation Drought Index, LST stands for Land Surface Temperature, and LST max and LST min They respectively represent the maximum and minimum surface temperatures corresponding to NDVI in the LST-NDVI feature space.

[0043] Furthermore, the eighth calculation method of the aggregated temperature vegetation drought index is:

[0044]

[0045] in, represents the aggregated temperature vegetation drought index, TVDI represents the temperature vegetation drought index, and n2 represents the number of TVDI pixels;

[0046] The ninth calculation method of the second scale conversion factor is:

[0047]

[0048] Among them, SSCF DIT represents the second scale conversion factor;

[0049] The tenth calculation method of the second soil moisture content is:

[0050] SM DIT =SSCF DIT *SM coarse ;

[0051] Among them, SM DIT Represents the second soil moisture content, SM coarse Indicates the original soil moisture content.

[0052] Furthermore, the eleventh calculation method of the third soil moisture content is:

[0053]

[0054] in, Indicates the third soil moisture content, SM DISR represents the first soil moisture content, n3 represents SM DISR Number of pixels.

[0055] Furthermore, the twelfth calculation method of the correction coefficient is:

[0056]

[0057] Where F is the correction factor, SM DIT represents the second soil moisture content, represents the third soil moisture content, and Respectively represent SM DIT The minimum and maximum values ​​of and Respectively The minimum and maximum values ​​of .

[0058] Furthermore, the thirteenth calculation method of the fourth soil moisture content is:

[0059] SM final =F*SM DISR ;

[0060] Among them, SM final represents the fourth soil moisture content, F represents the correction factor, SM DISR Indicates the first soil moisture content.

[0061] The results obtained based on the DIT downscaling method were used to correct the results based on the DISR downscaling method. By combining the respective advantages of the two downscaling methods, the downscaled soil moisture content with high spatial resolution, high accuracy and reasonable spatial distribution was obtained.

[0062] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0063] The present invention reduces soil moisture content to a lower spatial scale through a DISR-based downscaling method, thereby improving the spatial resolution of the downscaling product and making it more suitable for small and medium-scale applications; a DIT-based downscaling method introduces surface temperature data to improve the accuracy of downscaling; the results obtained based on the DIT downscaling method are used to correct the results based on the DISR downscaling method, and the respective advantages of the two downscaling methods are combined to obtain a downscaled soil moisture content with high spatial resolution, high accuracy and reasonable spatial distribution, thereby improving the accuracy of drought disaster monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation on the embodiments of the present invention;

[0065] Figure 1 It is a structural schematic diagram of a drought disaster monitoring system in the present invention;

[0066] Figure 2 This is a schematic diagram of the input remote sensing data. a represents the MOD09GQ surface reflectance red band data; b represents the MOD09GQ surface reflectance near-infrared band data; c represents the MOD 1 1A1 surface temperature data; d represents the ESA CCI soil moisture product;

[0067] Figure 3 It is a schematic diagram of the results of the improved vertical drought index MPDI;

[0068] Figure 4 Aggregate Improved Vertical Drought Index Schematic diagram of the results;

[0069] Figure 5 is the first scale conversion factor SSCF DISR Schematic diagram of the results;

[0070] Figure 6 is the first soil moisture content SM DISR Schematic diagram of the results;

[0071] Figure 7 This is a schematic diagram of the results of the Temperature Vegetation Drought Index TVDI;

[0072] Figure 8 is the aggregate temperature vegetation drought index Schematic diagram of the results;

[0073] Fig. 9 is the second scale conversion factor SSCF DIT Schematic diagram of the results;

[0074] Fig.10is the second soil moisture content SM DIT Schematic diagram of the results;

[0075] Fig.11 The third soil moisture content Schematic diagram of the results;

[0076] Fig.12 It is a schematic diagram of the result of the correction factor F;

[0077] Fig.13 The fourth soil moisture content SM final Schematic diagram of the results. DETAILED DESCRIPTION

[0078] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0079] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those within the scope of this description. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0080] Embodiment 1

[0081] refer to Figure 1 This embodiment provides a drought disaster monitoring system, the system comprising:

[0082] Acquisition unit: used to acquire remote sensing data of the target area; in this embodiment, the remote sensing data may include surface reflectivity data, surface temperature data, soil moisture content product, vegetation coverage data, vegetation index data and other data related to drought disasters;

[0083] DISR downscaling unit: used for obtaining an improved vertical drought index based on the remote sensing data, obtaining a first scale conversion factor based on the improved vertical drought index, and downscaling the original soil moisture content based on the first scale conversion factor to obtain a first soil moisture content;

[0084] DIT downscaling unit: used for obtaining a temperature vegetation drought index based on the remote sensing data, obtaining a second scale conversion factor based on the temperature vegetation drought index, and downscaling the original soil moisture content based on the second scale conversion factor to obtain a second soil moisture content;

[0085] DISR correction unit: used for spatially aggregating the first soil moisture content, aggregating the first soil moisture content obtained based on DISR downscaling to the spatial scale of the second soil moisture content, obtaining a third soil moisture content, obtaining a correction coefficient based on the second soil moisture content and the third soil moisture content, and correcting the first soil moisture content based on the correction coefficient to obtain a fourth soil moisture content;

[0086] A monitoring result unit is used to obtain a monitoring result based on the fourth soil moisture content.

[0087] The DISR downscaling unit specifically includes:

[0088] The first unit is used to obtain the vegetation coverage and the slope of the soil line based on the remote sensing data, and the vegetation coverage can be calculated by using a pixel binary model based on the normalized vegetation index NDVI; based on the vegetation coverage and the slope of the soil line, the improved vertical drought index is obtained;

[0089] The second unit is used to spatially aggregate the improved vertical drought index to obtain an aggregated improved vertical drought index; and aggregate the obtained high spatial resolution MPDI data to the spatial scale of low spatial resolution soil moisture content.

[0090] The third unit is used to obtain the first scale conversion factor based on the improved vertical drought index and the aggregated improved vertical drought index;

[0091] The fourth unit is used to downscale the original soil moisture content based on the first scale conversion factor to obtain the first soil moisture content.

[0092] The DIT downscaling unit specifically includes:

[0093] The fifth unit is used to obtain the temperature vegetation drought index based on the remote sensing data;

[0094] Unit 6: for spatially aggregating the temperature vegetation drought index to obtain an aggregated temperature vegetation drought index; aggregating the obtained high spatial resolution TVDI data to the spatial scale of low spatial resolution soil moisture content.

[0095] The seventh unit is used to obtain the second scale conversion factor based on the temperature vegetation drought index and the aggregated temperature vegetation drought index;

[0096] Unit 8: downscaling the original soil moisture content based on the second scale conversion factor to obtain the second soil moisture content.

[0097] R red As the horizontal axis, R nirAs the vertical axis, "R nir -R red ” Spectral space, soil line is “R nir -R red "R of pixels with different soil moisture content in bare soil area in spectral space v,ed With R v,nir Combined, soil line slope represents the slope of the soil line.

[0098] Downscaling: A method of decomposing large-scale, low-resolution images into small-scale, high-resolution images.

[0099] Spatial aggregation: A method of aggregating small-scale, high-resolution images into large-scale, low-resolution images.

[0100] The vertical drought index (PDI) is a soil moisture monitoring model based on the surface spectral characteristics, which is mainly used to monitor drought conditions in areas with bare soil and less vegetation coverage.

[0101] The Modified Vertical Drought Index (MPDI) is an improved drought monitoring method that aims to more accurately reflect the degree of surface climate drought. MPDI is optimized based on the traditional Vertical Drought Index (PDI) and takes into account more factors, especially soil moisture and vegetation growth characteristics, thereby improving its applicability and accuracy in areas with vegetation cover.

[0102] The Normalized Difference Vegetation Index (NDVI) is an index that quantifies vegetation by measuring the difference in reflectance between the near-infrared band and the red band. veg is the NDVI value of pure vegetation pixels, which should be 1 in theory; NDVI soil is the NDVI value of pure bare soil pixel, which should be 0 in theory.

[0103] Feature space: The space where the feature vector is located. Each feature corresponds to a one-dimensional coordinate in the feature space.

[0104] A pixel is a grid unit formed by discretizing ground information. Each square grid unit represents a pixel. A pixel is the basic unit of a scanned image and is represented by a brightness value. The size of a pixel is closely related to the spatial resolution of a remote sensing image. The smaller the pixel, the greater the spatial resolution.

[0105] Embodiment 2

[0106] On the basis of the first embodiment, in this embodiment, the first calculation method of the vegetation coverage is:

[0107] FVC=(NDVI-NDVI soil ) / (NDVI veg -NDVI soil ); (1)

[0108] Among them, FVC represents vegetation coverage, NDVI represents normalized difference vegetation index, and NDVI soil Indicates the NDVI value of a pixel composed entirely of bare soil. veg Indicates the NDVI value of pixels completely covered by vegetation;

[0109] The second calculation method of the soil line slope is:

[0110] M=(R nir -l) / R red ; (2)

[0111] Among them, R red and R nir Respectively represent the reflectance of the red and near-red bands after atmospheric correction. red As the horizontal axis, R nir As the vertical axis, "R nir -R red ” Spectral space, soil line is “R nir -R red "R of pixels with different soil moisture content in bare soil area in spectral space v，ed With R v，nir Combination, M represents the slope of the soil line and l represents the intercept.

[0112] The third calculation method of the improved vertical drought index is:

[0113]

[0114] MPDI stands for Modified Vertical Drought Index, R v，red and R v，nir They all represent constants that can be calculated from remote sensing data of known vegetation areas.

[0115] The fourth calculation method of the aggregated improved vertical drought index is:

[0116]

[0117] in, represents the aggregated improved vertical drought index, n1 represents the number of MPDI pixels, that is, the number of high spatial resolution MPDI pixels contained in a certain pixel of low spatial resolution remote sensing data, and MPDI represents the improved vertical drought index;

[0118] The fifth calculation method of the first scale conversion factor is:

[0119]

[0120] SSCF DISRrepresents the first scale conversion factor;

[0121] The sixth calculation method of the first soil moisture content is:

[0122] SM DISR =SSCF DISR *SM coarse ; (6)

[0123] Among them, SM DISR represents the first soil moisture content, i.e., the high spatial resolution soil moisture content after DISR downscaling, SM coarse Represents the original soil moisture content, that is, the original low spatial resolution soil moisture content.

[0124] Embodiment 3

[0125] Based on the above embodiment, in this embodiment, the seventh calculation method of the temperature vegetation drought index is:

[0126]

[0127] TVDI stands for Temperature Vegetation Drought Index, LST stands for Land Surface Temperature, and LST max and LST min They respectively represent the highest and lowest surface temperatures corresponding to NDVI in the LST-NDVI feature space. The LST-NDVI feature space is a prior art in this field.

[0128] The eighth calculation method of the aggregate temperature vegetation drought index is:

[0129]

[0130] in, represents the aggregated temperature vegetation drought index, TVDI represents the temperature vegetation drought index, and n2 represents the number of TVDI pixels, that is, the number of high spatial resolution TVDI pixels contained in a certain pixel of low spatial resolution remote sensing data;

[0131] The ninth calculation method of the second scale conversion factor is:

[0132]

[0133] Among them, SSCF DIT represents the second scale conversion factor;

[0134] The tenth calculation method of the second soil moisture content is:

[0135] SM DIT =SSCF DIT *SM coarse ; (10)

[0136] Among them, SM DIT Represents the second soil moisture content, SM coarse Indicates the original soil moisture content.

[0137] Embodiment 4

[0138] On the basis of the above embodiment, in this embodiment, the eleventh calculation method of the third soil moisture content is:

[0139]

[0140] in, Indicates the third soil moisture content, SM DISR represents the first soil moisture content, n3 represents SM DISR The number of pixels, SM DIT SM contained in the pixel DISR Number of pixels.

[0141] The twelfth calculation method of the correction coefficient is:

[0142]

[0143] Where F is the correction factor, SM DIT represents the second soil moisture content, represents the third soil moisture content, and Respectively represent SM DIT The minimum and maximum values ​​of the pixels, and Respectively The minimum and maximum values ​​of the cells.

[0144] The thirteenth calculation method of the fourth soil moisture content is:

[0145] SM final =F*SM DISR ; (13)

[0146] Among them, SM final represents the fourth soil moisture content, F represents the correction factor, SM DISR Indicates the first soil moisture content.

[0147] Embodiment 5

[0148] refer to Figure 1-Figure 13 , based on the above embodiment, in this embodiment:

[0149] Acquisition unit: Acquisition of remote sensing data of the target area, including:

[0150] MOD09GQ surface reflectance red band data ( Figure 2 a) and near-infrared band data ( Figure 2 b), spatial resolution 250m;

[0151] MOD1 1A1 surface temperature data ( Figure 2 c), spatial resolution 1km;

[0152] ESA CCI soil moisture product ( Figure 2 d), spatial resolution 25km.

[0153] DISR downscaling unit:

[0154] (1) Unit 1: MPDI calculation. According to formulas (1)-(3), the MPDI results with a spatial resolution of 250 m are calculated based on the MOD09GQ surface reflectance data. Figure 3 As shown;

[0155] (2) Unit 2: MPDI spatial aggregation. According to formula (4), the MPDI results obtained in step (1) are spatially aggregated to a resolution of 25 km to obtain the aggregated Data, such as Figure 4 As shown;

[0156] (3) Unit 3: Calculation of DISR spatial scale conversion factor. According to formula (5), calculate the spatial scale conversion factor SSCF based on the DISR downscaling method. DISR ,like Figure 5 As shown;

[0157] (4) Unit 4: DISR spatial downscaling. According to formula (6), calculate the 250m spatial resolution soil moisture result SM after downscaling based on the DISR method. DISR ,like Figure 6 shown.

[0158] DIT downscaling unit:

[0159] (5) Unit 5: TVDI calculation. According to formula (7), the 1km spatial resolution TVDI result is calculated based on the MOD09GQ surface reflectivity data and the MOD11A1 surface temperature data, as follows: Figure 7 As shown;

[0160] (6) Unit 6: TVDI spatial aggregation. According to formula (8), the TVDI results obtained in step (5) are spatially aggregated to a resolution of 25 km to obtain the aggregated Data, such as Figure 8 As shown;

[0161] (7) Unit 7: Calculation of DIT spatial scale conversion factor. According to formula (9), calculate the spatial scale conversion factor SSCF obtained based on the DIT downscaling method. DIT ,like Fig. 9 As shown;

[0162] (8) Unit 8: DIT spatial downscaling, according to formula (10), calculate the 1km spatial resolution soil moisture result SM based on DIT downscaling DIT ,like Fig.10 As shown;

[0163] DISR correction unit:

[0164] (9)SM DISR Spatial aggregation: According to formula (11), the 250m soil moisture product SM obtained in step (4) after downscaling based on the DISR method is DISR Aggregation to SM DIT On the spatial scale of like Fig.11 As shown;

[0165] (10) Calculation of correction factor F: Based on formula (12), combined with the SM obtained in step (8) DIT , and the one obtained in step (9) Calculate SM DISR The correction factor F is as follows: Fig.12 As shown;

[0166] (11)SM DISR Correction: Based on formula (13), based on the correction factor F obtained in step (10), the SM in step (4) is DISR Make corrections and get the final result SM final ,like Fig.13 shown.

[0167] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0168] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A drought disaster monitoring system, characterized in that: The system comprises: Acquisition unit: used to acquire remote sensing data of the target area; DISR downscaling unit: used for obtaining an improved vertical drought index based on the remote sensing data, obtaining a first scale conversion factor based on the improved vertical drought index, and downscaling the original soil moisture content based on the first scale conversion factor to obtain a first soil moisture content; DIT downscaling unit: used for obtaining a temperature vegetation drought index based on the remote sensing data, obtaining a second scale conversion factor based on the temperature vegetation drought index, and downscaling the original soil moisture content based on the second scale conversion factor to obtain a second soil moisture content; DISR correction unit: used for spatially aggregating the first soil moisture content to obtain a third soil moisture content, obtaining a correction coefficient based on the second soil moisture content and the third soil moisture content, and correcting the first soil moisture content based on the correction coefficient to obtain a fourth soil moisture content; A monitoring result unit is used to obtain a monitoring result based on the fourth soil moisture content.

2. A drought disaster monitoring system according to claim 1, characterized in that: The DISR downscaling unit specifically includes: The first unit is used to obtain the vegetation coverage and the soil line slope based on the remote sensing data, and obtain the improved vertical drought index based on the vegetation coverage and the soil line slope; The second unit is used for spatially aggregating the improved vertical drought index to obtain an aggregated improved vertical drought index; The third unit is used to obtain the first scale conversion factor based on the improved vertical drought index and the aggregated improved vertical drought index; The fourth unit is used to downscale the original soil moisture content based on the first scale conversion factor to obtain the first soil moisture content.

3. A drought disaster monitoring system according to claim 2, characterized in that: The DIT downscaling unit specifically includes: The fifth unit is used to obtain the temperature vegetation drought index based on the remote sensing data; The sixth unit is used for spatially aggregating the temperature vegetation drought index to obtain an aggregated temperature vegetation drought index; The seventh unit is used to obtain the second scale conversion factor based on the temperature vegetation drought index and the aggregated temperature vegetation drought index; Unit 8: downscaling the original soil moisture content based on the second scale conversion factor to obtain the second soil moisture content.

4. A drought disaster monitoring system according to claim 3, characterized in that: The first calculation method of the vegetation coverage is: FVC=(NDVI-NDVI soil ) / (NDVI veg -NDVI soil ); Among them, FVC represents vegetation coverage, NDVI represents normalized difference vegetation index, and NDVI soil Indicates the NDVI value of a pixel composed entirely of bare soil. veg Indicates the NDVI value of pixels completely covered by vegetation; The second calculation method of the soil line slope is: M=(R nir -l) / R red ; Among them, R red and R nir They represent the reflectance of the red and near-red bands after atmospheric correction, M represents the slope of the soil line, and l represents the intercept; The third calculation method of the improved vertical drought index is: MPDI stands for Modified Vertical Drought Index, R v，red and R v，nir All represent constants.

5. A drought disaster monitoring system according to claim 4, characterized in that: The fourth calculation method of the aggregated improved vertical drought index is: in, represents the aggregated improved vertical drought index, n1 represents the number of MPDI pixels, and MPDI represents the improved vertical drought index; The fifth calculation method of the first scale conversion factor is: SSCF DISR represents the first scale conversion factor; The sixth calculation method of the first soil moisture content is: SM DISR =SSCF DISR *SM coarse ; Among them, SM DISR Indicates the first soil moisture content, SM coarse Indicates the original soil moisture content.

6. A drought disaster monitoring system according to claim 5, characterized in that: The seventh calculation method of the temperature vegetation drought index is: TVDI stands for Temperature Vegetation Drought Index; LST stands for Land Surface Temperature; LST max and LST min They respectively represent the maximum and minimum surface temperatures corresponding to NDVI in the LST-NDVI feature space.

7. A drought disaster monitoring system according to claim 6, characterized in that: The eighth calculation method of the aggregate temperature vegetation drought index is: in, represents the aggregated temperature vegetation drought index, TVDI represents the temperature vegetation drought index, and n2 represents the number of TVDI pixels; The ninth calculation method of the second scale conversion factor is: Among them, SSCF DIT represents the second scale conversion factor; The tenth calculation method of the second soil moisture content is: SM DIT =SSCF DIT *SM coarse ; Among them, SM DIT Represents the second soil moisture content, SM coarse Indicates the original soil moisture content.

8. A drought disaster monitoring system according to claim 7, characterized in that: The eleventh calculation method of the third soil moisture content is: in, Indicates the third soil moisture content, SM DISR represents the first soil moisture content, n3 represents SM DISR Number of pixels.

9. A drought disaster monitoring system according to claim 8, characterized in that: The twelfth calculation method of the correction coefficient is: Where F is the correction factor, SM DIT represents the second soil moisture content, represents the third soil moisture content, and Respectively represent SM DIT The minimum and maximum values ​​of and Respectively The minimum and maximum values ​​of .

10. A drought disaster monitoring system according to claim 9, characterized in that: The thirteenth calculation method of the fourth soil moisture content is: SM final =F*SM DISR ; Among them, SM final represents the fourth soil moisture content, F represents the correction factor, SM DISR Indicates the first soil moisture content.

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