A drought monitoring method and system based on Moderate Resolution Imaging Spectroradiometer (MODIS) data

Abstract

The present invention relates to a drought monitoring method and system based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, belonging to the field of drought monitoring. Based on the Temperature Vegetation Dryness Index (TVDI) theory, the hyperspectral vegetation drought index is used to replace the Normalized Difference Vegetation Index (NDVI), and a characteristic space of hyperspectral vegetation drought index - land surface temperature is constructed, and the Temperature Hyperspectral Vegetation Drought Index (THVDI) is proposed. The THVDI constructs a characteristic space that is more similar to a triangle by using the hyperspectral vegetation drought index and the land surface temperature, overcomes the saturation problem of the TVDI in areas with high vegetation coverage, and improves the accuracy of crop drought monitoring.

Description

Technical Field

[0001] The present invention relates to the field of drought monitoring, and particularly to a drought monitoring method and system based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. Background Art

[0002] Drought is the costliest natural disaster causing water and food security problems worldwide and is also considered one of the most complex but least understood natural disasters. Given the severe impact of drought on agriculture, it is crucial and urgent to develop effective means for timely monitoring of agricultural drought events.

[0003] Traditional drought monitoring methods mainly monitor drought conditions by measuring soil moisture content at limited drought measurement sites. The sampling speed is slow, the scope is limited, and it costs a large amount of manpower and material resources, making it difficult to meet the current requirements for real-time drought monitoring. In recent years, remote sensing technology has provided a fast, economical, and non-destructive spatio-temporal measurement method for drought monitoring.

[0004] Studies have shown that the feature space composed of vegetation index and land surface temperature is closely related to soil moisture status: the slope of land surface temperature (LST) and normalized difference vegetation index (NDVI) is negatively correlated with crop water index and soil moisture, and LST / NDVI increases with the increase of drought intensity. Based on this characteristic, Sandholt proposed the Temperature Vegetation Drought Index (TVDI) based on the NDVI-LST triangular feature space, which has been widely studied and applied in the field of drought monitoring due to its obvious physical meaning.

[0005] However, NDVI will saturate in areas with high vegetation coverage, resulting in the NDVI-LST feature space being mostly trapezoidal rather than triangular in practical applications, reducing the accuracy of crop drought monitoring. Therefore, the accuracy of crop drought monitoring based on the temperature and vegetation index method needs to be improved. Summary of the Invention

[0006] The purpose of the present invention is to provide a drought monitoring method and system based on Moderate Resolution Imaging Spectroradiometer (MODIS) data to improve the accuracy of crop drought monitoring.

[0007] To achieve the above purpose, the present invention provides the following solutions:

[0008] A drought monitoring method based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, comprising:

[0009] Obtain the spectral reflectance image and the land surface brightness temperature image of the crop planting area;

[0010] According to the spectral reflectance image of the crop planting area, calculate the hyperspectral vegetation drought index of each crop planting position in the crop planting area, and form the hyperspectral vegetation drought index image of the crop planting area;

[0011] According to the land surface brightness temperature image of the crop planting area, calculate the land surface temperature of each crop planting position in the crop planting area, and form the land surface temperature image of the crop planting area;

[0012] According to the hyperspectral vegetation drought index image and the land surface temperature image, construct a feature space with the hyperspectral vegetation drought index as the horizontal axis and the land surface temperature as the vertical axis;

[0013] Extract the dry edge equation and the wet edge equation from the feature space by linear fitting;

[0014] According to the dry edge equation and the wet edge equation, determine the temperature hyperspectral vegetation drought index value of each crop planting position in the crop planting area, and obtain the temperature hyperspectral vegetation drought index image of the crop planting area.

[0015] Optionally, the obtaining of the spectral reflectance image and the land surface brightness temperature image of the crop planting area specifically includes:

[0016] Obtain a plurality of MOD09A1 images and a plurality of MYD11A2 images covering the monitoring area; the product data in the MOD09A1 images is the surface spectral reflectance including the visible light to short-wave infrared bands; the product data in the MYD11A2 images is the average land surface temperature including the brightness temperature band;

[0017] Mosaic a plurality of MOD09A1 images to obtain the mosaicked MOD09A1 image, and mosaic a plurality of MYD11A2 images to obtain the mosaicked MYD11A2 image;

[0018] Use the crop classification product to extract the spectral reflectance image of the crop planting area in the monitoring area from the mosaicked MOD09A1 image, and extract the land surface brightness temperature image of the crop planting area from the mosaicked MYD11A2 image.

[0019] Optionally, the mosaicking of a plurality of MOD09A1 images to obtain the mosaicked MOD09A1 image, and the mosaicking of a plurality of MYD11A2 images to obtain the mosaicked MYD11A2 image specifically includes:

[0020] Perform reprojection processing on each MOD09A1 image and each MYD11A2 image;

[0021] After mosaicking all the reprojected MOD09A1 images into a single whole image, resampling is performed to obtain the mosaicked MOD09A1 image;

[0022] Mosaic all the reprojected MYD11A2 images into a single whole image to obtain the mosaicked MYD11A2 image.

[0023] Optionally, the calculation formula for the hyperspectral vegetation drought index at the crop planting location is

[0024] HVDI = (R NIR - R Red ) / R SWIR

[0025] In the formula, HVDI is the hyperspectral vegetation drought index at the crop planting location, and R NIR is the near-infrared band reflectance at the crop planting location in the 2nd band of the spectral reflectance image, and R Red is the red light band reflectance at the crop planting location in the 1st band of the spectral reflectance image, and R SWIR is the shortwave infrared band reflectance at the crop planting location in the 5th band of the spectral reflectance image.

[0026] Optionally, the calculation formula for the surface temperature at the crop planting location is

[0027] LST = DN × 0.02 - 273

[0028] In the formula, LST is the surface temperature at the crop planting location, and DN is the pixel gray value corresponding to the crop planting location.

[0029] Optionally, constructing a feature space with the hyperspectral vegetation drought index as the horizontal axis and the surface temperature as the vertical axis based on the hyperspectral vegetation drought index image and the surface temperature image specifically includes:

[0030] Select the pixels in the hyperspectral vegetation drought index image where the hyperspectral vegetation drought index is greater than or equal to 0.2, and divide the hyperspectral vegetation drought index in the hyperspectral vegetation drought index image into multiple intervals with a step size of 0.01;

[0031] Determine the maximum and minimum values of the hyperspectral vegetation drought index in each interval, as well as the orbital row and column numbers corresponding to the maximum and minimum values in the hyperspectral vegetation drought index image;

[0032] According to the orbital row and column numbers corresponding to the maximum and minimum values, obtain the temperature values corresponding to the orbital row and column numbers from the surface temperature image;

[0033] Taking the maximum and minimum values of the hyperspectral vegetation drought index in all intervals as the horizontal axis data points, and the corresponding temperature values as the vertical axis data points, a feature space with the hyperspectral vegetation drought index as the horizontal axis and the land surface temperature as the vertical axis is constructed.

[0034] Optionally, the wet edge equation is LST min = a1 + b1×HVDI; where LST min is the lowest land surface temperature corresponding to the hyperspectral vegetation drought index, a1 and b1 are the intercept and slope of the wet edge equation respectively, and HVDI is the hyperspectral vegetation drought index at the crop planting location;

[0035] The dry edge equation is LST max = a2 + b2×HVDI; where LST max is the highest land surface temperature corresponding to the hyperspectral vegetation drought index, a2 and b2 are the intercept and slope of the dry edge equation respectively, and HVDI is the hyperspectral vegetation drought index at the crop planting location.

[0036] Optionally, determining the temperature hyperspectral vegetation drought index value of each crop planting location in the crop planting area according to the dry edge equation and the wet edge equation specifically includes:

[0037] Determining the hyperspectral vegetation drought index of each pixel in the land surface temperature image in the hyperspectral vegetation drought index image;

[0038] According to the hyperspectral vegetation drought index of each pixel, determining the highest land surface temperature and the lowest land surface temperature corresponding to each pixel in the dry edge equation and the wet edge equation respectively;

[0039] According to the land surface temperature of each pixel in the land surface temperature image, the highest land surface temperature and the lowest land surface temperature corresponding to each pixel, using the formula THVDI = (LST - LST min ) / (LST max - LST min ) to calculate the temperature hyperspectral vegetation drought index value of each pixel, as the temperature hyperspectral vegetation drought index value of the crop planting location corresponding to each pixel; where THVDI is the temperature hyperspectral vegetation drought index value of the crop planting location, and LST is the land surface temperature of the crop planting location.

[0040] Optionally, the range of the temperature hyperspectral vegetation drought index value is between 0 - 1; the closer the temperature hyperspectral vegetation drought index value is to 1, the more serious the drought degree is.

[0041] A drought monitoring system based on Moderate Resolution Imaging Spectroradiometer (MODIS) data includes:

[0042] An image acquisition module, configured to acquire the spectral reflectance image and the land surface brightness temperature image of the crop planting area;

[0043] A hyperspectral vegetation drought index calculation module, which is used to calculate the hyperspectral vegetation drought index of each crop planting position in the crop planting area according to the spectral reflectance image of the crop planting area, and form the hyperspectral vegetation drought index image of the crop planting area;

[0044] A land surface temperature calculation module, which is used to calculate the land surface temperature of each crop planting position in the crop planting area according to the land surface brightness temperature image of the crop planting area, and form the land surface temperature image of the crop planting area;

[0045] A feature space construction module, which is used to construct a feature space with the hyperspectral vegetation drought index as the horizontal axis and the land surface temperature as the vertical axis according to the hyperspectral vegetation drought index image and the land surface temperature image;

[0046] A linear fitting module, which is used to extract the dry edge equation and the wet edge equation from the feature space through linear fitting;

[0047] A temperature hyperspectral vegetation drought index value determination module, which is used to determine the temperature hyperspectral vegetation drought index value of each crop planting position in the crop planting area according to the dry edge equation and the wet edge equation, and obtain the temperature hyperspectral vegetation drought index image of the crop planting area.

[0048] According to the specific embodiments provided by the present invention, the following technical effects are disclosed by the present invention:

[0049] The present invention discloses a drought monitoring method and system based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. Based on the Temperature Vegetation Drought Index (TVDI) theory, the hyperspectral vegetation drought index is used to replace the Normalized Difference Vegetation Index (NDVI), and a feature space of hyperspectral vegetation drought index - land surface temperature is constructed. The temperature hyperspectral vegetation drought index is proposed. The temperature hyperspectral vegetation drought index constructs a more triangular feature space by using the hyperspectral vegetation drought index and the land surface temperature, overcomes the saturation problem of the temperature vegetation drought index in high vegetation coverage areas, and improves the accuracy of crop drought monitoring. Description of the Drawings

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is a flowchart of the drought monitoring method based on Moderate Resolution Imaging Spectroradiometer (MODIS) data provided by the embodiment of the present invention;

[0052] Figure 2Schematic diagram of the drought monitoring method based on Medium Resolution Imaging Spectrometer (MODIS) data provided by the embodiments of the present invention;

[0053] Figure 3 Schematic diagram of the feature space provided by the embodiments of the present invention;

[0054] Figure 4 Comparison diagram of the NDVI-LST feature space and the HVDI-LST feature space provided by the embodiments of the present invention; Figure 4 In (a), it is the NDVI-LST feature space in May 2017, Figure 4 In (b), it is the HVDI-LST feature space in May 2017, Figure 4 In (c), it is the NDVI-LST feature space in June 2017, Figure 4 In (d), it is the HVDI-LST feature space in June 2017, Figure 4 In (e), it is the NDVI-LST feature space in July 2017, Figure 4 In (f), it is the HVDI-LST feature space in July 2017, Figure 4 In (g), it is the NDVI-LST feature space in August 2017, Figure 4 In (h), it is the HVDI-LST feature space in August 2017, Figure 4 In (i), it is the NDVI-LST feature space in September 2017, Figure 4 In (j), it is the HVDI-LST feature space in September 2017;

[0055] Figure 5 Schematic diagram of the drought situation monitoring in Northeast China in 2017 provided by the embodiments of the present invention; Figure 5 In (a), it is the drought situation monitoring map in Northeast China in May 2017, Figure 5 In (b), it is the drought situation monitoring map in Northeast China in June 2017, Figure 5 In (c), it is the drought situation monitoring map in Northeast China in July 2017, Figure 5 In (d), it is the drought situation monitoring map in Northeast China in August 2017, Figure 5 In (e), it is the drought situation monitoring map in Northeast China in September 2017;

[0056] Figure 6 Schematic diagram of the comparison of THVDI with precipitation, PET, and CWDI in four regions of Northeast China provided by the embodiments of the present invention; Figure 6 In (a), it is the THVDI schematic diagram of four regions in Northeast China from May to September, Figure 6 In (b), it is the precipitation schematic diagram of four regions in Northeast China from May to September,Figure 6 In (c) is the PET schematic diagram of four regions in Northeast China from May to September, Figure 6 In (d) is the CWDI schematic diagram of four regions in Northeast China from May to September. Specific implementation manners

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0058] The purpose of the present invention is to provide a drought monitoring method and system based on Moderate Resolution Imaging Spectroradiometer (MODIS) data to improve the accuracy of crop drought monitoring.

[0059] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0060] In order to effectively overcome the saturation of the TVDI index in high vegetation coverage areas, based on MODIS (Moderate Resolution Imaging Spectroradiometer) images, the embodiments of the present invention provide a drought monitoring method based on Moderate Resolution Imaging Spectroradiometer data. Based on the TVDI theory, the Hyperspectral Vegetation Drought Index (HVDI) is used to replace NDVI, a HVDI-LST feature space is constructed, and the Temperature Hyperspectral Vegetation Drought Index (THVDI) is proposed to overcome the limitations of TVDI in high vegetation coverage areas and improve the accuracy of crop drought monitoring.

[0061] Figure 1 This is the flowchart of the drought monitoring method based on Moderate Resolution Imaging Spectroradiometer data provided by the embodiments of the present invention. A drought monitoring method based on Moderate Resolution Imaging Spectroradiometer data provided by the embodiments of the present invention includes the following steps:

[0062] Step S1, obtain the spectral reflectance image and the land surface brightness temperature image of the crop planting area.

[0063] Refer to Figure 2, obtain MOD09A1 and MYD11A2 product data covering the monitoring area. The track and row numbers of the products are h25v03, h25v04, h26v03, h26v04, h27v04, and h27v05. Among them, the MOD09A1 product data is a MODIS product at the A1 processing level with a resolution of 500m and an 8-day synthesis covering the visible to shortwave infrared bands. The MOD09A1 product includes the surface spectral reflectance of bands 1-7. MYD11A2 is a MODIS product at the A2 processing level with a resolution of 1km and an 8-day synthesis covering the brightness temperature band, that is, the 8-day average land surface temperature. The steps for data preprocessing include: (1) Use the MRT (MODIS Reprojection Tool) software to perform reprojection processing on the MOD09A1 and MYD11A2 images respectively, and the projection uses the latitude-longitude projection; (2) Use the MRT software to perform image mosaicking on the reprojected MOD09A1 and MYD11A2 images respectively, and mosaic them into a single whole image; (3) Use the resampling tool in the ArcGIS software to resample the spatial resolution of the mosaicked MOD09A1 image to 1km, and the output image is in the GEOTIFF format. (4) Use the masking tool in the ArcGIS software to extract the crop planting area in the monitoring area from the resampled MOD09A1 image and the mosaicked MYD11A2 image using the crop classification product. Among them, the crop classification product is a 10m crop type map of the main crops in the monitoring area.

[0064] After preprocessing, the MOD09A1 product data is the spectral reflectance image data of the crop planting area in the monitoring area, and the MYD11A2 product data is the surface brightness temperature image data of the crop planting area in the monitoring area.

[0065] Taking the drought monitoring in Northeast China as an example, its latitude and longitude range is 38°40' - 53°20'N, 111°37' - 135°5'E. The administrative regions include Heilongjiang Province, Jilin Province, Liaoning Province, and Hulunbuir City, Xing'an League, Chifeng, and Tongliao in Inner Mongolia Autonomous Region, with an area of approximately 1,245,202 Km 2 . The terrain of the study area is mainly mountains and plains. Among them, the Northeast Plain is rich in black soil resources, with fertile soil and is an important grain base in China. The average annual precipitation is about 600mm, gradually decreasing from east to west. The east is humid and the west is semi-humid. The main crop types in the study area are corn and soybeans, and the growth period is from May to September. Obtain MOD09A1 and MYD11A2 product data covering Northeast China. The crop classification product is a 10m crop type map of the main crops (corn, soybeans, and rice) in Northeast China in 2017. After a series of data preprocessing, finally obtain the spectral reflectance image and surface brightness temperature image of the corn and soybean planting areas in Northeast China.

[0066] Step S2: According to the spectral reflectance image of the crop planting area, calculate the hyperspectral vegetation drought index for each crop planting position in the crop planting area, and form the hyperspectral vegetation drought index image of the crop planting area.

[0067] Using the preprocessed MOD09A1 product data, use the band calculation tool in ENVI software to calculate the hyperspectral vegetation drought index. In the band calculation tool, input the formula of HVDI, select the corresponding bands in the MOD09A1 product data, and the calculation result is the HVDI image. The HVDI calculation formula is as follows:

[0068] HVDI = (R NIR - R Red ) / R SWIR

[0069] In the formula, R NIR represents the near-infrared band reflectance of the second band of MOD09A1, R Red represents the red band reflectance of the first band of MOD09A1, and R SWIR represents the shortwave infrared band reflectance of the fifth band of MOD09A1.

[0070] Step S3: According to the land surface brightness temperature image of the crop planting area, calculate the land surface temperature for each crop planting position in the crop planting area, and form the land surface temperature image of the crop planting area.

[0071] Using the preprocessed MYD11A2 product data, use the band calculation tool in ENVI software to calculate the land surface temperature (LST). In the band calculation tool, input the formula of LST, select the corresponding bands in the MYD11A2 product data, and the calculation result is the LST image. The LST calculation formula is as follows:

[0072] LST = DN × 0.02 - 273

[0073] LST is the true land surface temperature (°C), and DN is the pixel gray value.

[0074] Step S4: According to the hyperspectral vegetation drought index image and the land surface temperature image, construct a feature space with the hyperspectral vegetation drought index as the horizontal axis and the land surface temperature as the vertical axis.

[0075] In the ENVI IDL programming environment, select the pixels with HVDI ≥ 0.2 in the HVDI image, divide HVDI into several intervals with a step of 0.01, obtain the maximum and minimum values of HVDI in each interval, as well as the row and column numbers in the HVDI image, and obtain the corresponding temperature values of the LST image, so as to construct the HVDI-LST feature space with HVDI as the horizontal axis and LST as the vertical axis, as Figure 3 shown.

[0076] Step S5, extract the dry edge equation and the wet edge equation from the feature space by linear fitting.

[0077] Extract the dry edge and wet edge equations by linear fitting:

[0078] LST min = a1 + b1 × HVDI

[0079] LST max = a2 + b2 × HVDI

[0080] In the formula, LST min , LST max are the lowest and highest land surface temperatures corresponding to HVDI respectively, a1 and b1 are the intercept and slope of the wet edge equation, and a2 and b2 are the intercept and slope of the dry edge equation.

[0081] Figure 3 The wet edge shown is only a schematic diagram. When b1 in the wet edge equation is not equal to 0, the wet edge is a slant line.

[0082] Taking the growth period of corn and soybean in Northeast China from May to September in 2017 as an example, construct the HVDI-LST and NDVI-LST feature spaces ( Figure 4 ). It can be seen from Figure 4 that the shape of the HVDI-LST space is closer to a triangle than that of the NDVI-LST space. In practical applications, due to the easy saturation of NDVI and its insensitivity to high vegetation coverage, the NDVI-LST space is close to a trapezoid. The results show that HVDI can improve the sensitivity to high vegetation coverage. The linear fitting effect of the dry and wet edges of the HVDI-LST space is better than that of the NDVI-LST space. The determination coefficients R 2 of the dry edge of the NDVI-LST space are 0.90, 0.78, 0.72, 0.45 and 0.37 respectively, and the determination coefficients R 2 of the dry edge of the HVDI-LST space are 0.91, 0.88, 0.90, 0.82 and 0.53 respectively. The determination coefficients R 2 of the wet edge of the NDVI-LST space are 0.77, 0.01, 0.00, 0.00 and 0.49 respectively, and the determination coefficients R 2 of the wet edge of the HVDI-LST space are 0.80, 0.26, 0.52, 0.24 and 0.35 respectively.

[0083] Step S6, according to the dry edge equation and the wet edge equation, determine the temperature hyperspectral vegetation drought index values of each crop planting position in the crop planting area, and obtain the temperature hyperspectral vegetation drought index image of the crop planting area.

[0084] In the ENVI IDL programming environment, based on the dry edge and wet edge equations of the HVDI-LST feature space, calculate THVDI and obtain the THVDI image. The formula is as follows:

[0085] THVDI = (LST - LST min ) / (LST max - LST min )

[0086] In the formula, THVDI is the temperature hyperspectral vegetation drought index value, and LST is the land surface temperature of the pixel. The range of THVDI values is between 0 and 1. The closer the THVDI value is to 1, the more severe the drought degree; the closer the THVDI value is to 0, the lighter the drought degree or no drought.

[0087] As Figure 5 shown, taking the corn and soybean planting areas in Northeast China from May to September 2017 as an example, calculate THVDI and monitor the drought situation. Figure 5 The larger the THVDI value in

[0088] , the darker the gray scale and the more severe the drought degree. The results show that the western part of Northeast China experienced a large-scale drought in May, which may be caused by scarce precipitation in the previous months of 2017. Then the crops began to grow rapidly, increasing the demand for water, and the scope of drought further expanded. The drought in July almost covered the entire Northeast China, which may be due to insufficient precipitation, high evapotranspiration caused by rising temperatures, and increased water demand of crops. From August to September, except for the continuous drought in the southwest, the drought situation in Northeast China eased. Figure 6) Due to the spatial differences in drought occurrence in the Northeast region, the Northeast region is divided into four regions: Heilongjiang, Jilin, Liaoning, and Inner Mongolia for comparative analysis. From May to July, THVDI shows a trend of increasing drought, which is caused by the continuous low precipitation and the increase in crop PET due to rising temperatures. CWDI also shows the drought situation during this period. From August to September, THVDI shows a significant alleviation of drought, which is due to the significant increase in precipitation in August and the decrease in temperature, resulting in a decrease in crop PET, which is consistent with the results of CWDI. Among the four regions in the Northeast, THVDI shows that Inner Mongolia has the most severe drought, which is caused by low precipitation and high PET throughout the growth period of the crops. Followed by the drought in Liaoning and Jilin, and the drought in Heilongjiang is the least severe. In addition, the distribution of drought in the four regions may be related to the crop types. Some studies have shown that soybeans have stronger drought resistance than corn because during the growth process of soybeans, the small leaf area results in less transpiration of the plants and less water is required. The northern and eastern parts of Heilongjiang are soybean planting areas, and the drought tolerance of soybeans plays a regulatory role in drought. Jilin and Liaoning are mainly corn planting areas and have poor drought resistance. Although there is a wide area of soybean planting in Inner Mongolia, the drought is more severe, which may be mainly due to the intolerance of soybeans to high temperatures.

[0089] Based on MODIS images, the present invention proposes a temperature hyperspectral vegetation drought index (THVDI). The THVDI index uses HVDI and LST to construct a feature space that is more similar to a triangle, overcomes the easy saturation of TVDI in areas with high vegetation coverage, and improves the accuracy of drought monitoring.

[0090] The present invention also provides a drought monitoring system based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, including:

[0091] An image acquisition module for acquiring the spectral reflectance image and the land surface brightness temperature image of the crop planting area;

[0092] A hyperspectral vegetation drought index calculation module for calculating the hyperspectral vegetation drought index of each crop planting location in the crop planting area based on the spectral reflectance image of the crop planting area, and forming a hyperspectral vegetation drought index image of the crop planting area;

[0093] A land surface temperature calculation module for calculating the land surface temperature of each crop planting location in the crop planting area based on the land surface brightness temperature image of the crop planting area, and forming a land surface temperature image of the crop planting area;

[0094] A feature space construction module for constructing a feature space with the hyperspectral vegetation drought index as the horizontal axis and the land surface temperature as the vertical axis based on the hyperspectral vegetation drought index image and the land surface temperature image;

[0095] A linear fitting module for extracting the dry edge equation and the wet edge equation from the feature space through linear fitting;

[0096] A temperature hyperspectral vegetation drought index value determination module, which is used to determine the temperature hyperspectral vegetation drought index value of each crop planting position in the crop planting area according to the dry edge equation and the wet edge equation, and obtain the temperature hyperspectral vegetation drought index image of the crop planting area.

[0097] The drought monitoring system based on Moderate Resolution Imaging Spectroradiometer (MODIS) data provided by the embodiments of the present invention and the drought monitoring method based on MODIS data described in the above embodiments have similar working principles and beneficial effects, so they will not be elaborated here. For specific content, reference can be made to the introduction of the above method embodiments.

[0098] In this specification, the embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple. For related parts, reference can be made to the description in the method part.

[0099] Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention. At the same time, for those of ordinary skill in the art, based on the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A drought monitoring method based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, characterized in that, Including: Obtaining a spectral reflectance image and a land surface brightness temperature image of a crop planting area; Calculating the hyperspectral vegetation drought index of each crop planting location in the crop planting area according to the spectral reflectance image of the crop planting area, and forming a hyperspectral vegetation drought index image of the crop planting area; Calculating the land surface temperature of each crop planting location in the crop planting area according to the land surface brightness temperature image of the crop planting area, and forming a land surface temperature image of the crop planting area; Constructing a feature space with the hyperspectral vegetation drought index as the horizontal axis and the land surface temperature as the vertical axis according to the hyperspectral vegetation drought index image and the land surface temperature image; Extracting a dry edge equation and a wet edge equation from the feature space by linear fitting; Determining the temperature hyperspectral vegetation drought index value of each crop planting location in the crop planting area according to the dry edge equation and the wet edge equation, and obtaining a temperature hyperspectral vegetation drought index image of the crop planting area; The calculation formula for the hyperspectral vegetation drought index of the crop planting location is HVDI = (R NIR - R Red ) / R SWIR In the formula, HVDI is the hyperspectral vegetation drought index at the crop planting position, and R NIR is the near-infrared band reflectance at the crop planting position in the second band of the spectral reflectance image, and R Red is the red band reflectance at the crop planting position in the first band of the spectral reflectance image, and R SWIR is the short-wave infrared band reflectance at the crop planting position in the fifth band of the spectral reflectance image; The calculation formula for the land surface temperature of the crop planting location is LST = DN × 0.02 - 273 In the formula, LST is the land surface temperature of the crop planting location, and DN is the pixel gray value corresponding to the crop planting location; The constructing a feature space with the hyperspectral vegetation drought index as the horizontal axis and the land surface temperature as the vertical axis according to the hyperspectral vegetation drought index image and the land surface temperature image specifically includes: Selecting pixels in the hyperspectral vegetation drought index image with a hyperspectral vegetation drought index greater than or equal to 0.2, and dividing the hyperspectral vegetation drought index in the hyperspectral vegetation drought index image into multiple intervals with a step size of 0.01; Determining the maximum and minimum values of the hyperspectral vegetation drought index in each interval, and the orbital row and column numbers corresponding to the maximum and minimum values in the hyperspectral vegetation drought index image; Obtaining the temperature values corresponding to the orbital row and column numbers from the land surface temperature image according to the orbital row and column numbers corresponding to the maximum and minimum values; Constructing a feature space with the hyperspectral vegetation drought index as the horizontal axis and the land surface temperature as the vertical axis with the maximum and minimum values of the hyperspectral vegetation drought index in all intervals as the horizontal axis data points and the corresponding temperature values as the vertical axis data points.

2. The drought monitoring method based on medium-resolution imaging spectrometer data according to claim 1, wherein The obtaining a spectral reflectance image and a land surface brightness temperature image of the crop planting area specifically includes: Obtaining a plurality of MOD09A1 images and a plurality of MYD11A2 images covering the monitoring area; the product data in the MOD09A1 images is the surface spectral reflectance including visible to short-wave infrared bands; the product data in the MYD11A2 images is the average land surface temperature including the brightness temperature band; Mosaicking a plurality of MOD09A1 images to obtain a mosaicked MOD09A1 image, and mosaicking a plurality of MYD11A2 images to obtain a mosaicked MYD11A2 image; Extracting the spectral reflectance image of the crop planting area in the monitoring area from the mosaicked MOD09A1 image by using a crop classification product, and extracting the land surface brightness temperature image of the crop planting area from the mosaicked MYD11A2 image.

3. The drought monitoring method based on medium-resolution imaging spectrometer data according to claim 2, characterized in that, Splicing multiple MOD09A1 images to obtain a spliced MOD09A1 image, and splicing multiple MYD11A2 images to obtain a spliced MYD11A2 image, specifically including: Performing reprojection processing on each MOD09A1 image and each MYD11A2 image; Splicing all the reprojected MOD09A1 images into a whole image and then performing resampling to obtain a spliced MOD09A1 image; Splicing all the reprojected MYD11A2 images into a whole image to obtain a spliced MYD11A2 image.

4. The drought monitoring method based on medium-resolution imaging spectrometer data according to claim 1, wherein The wet edge equation is LST min = a1 + b1 × HVDI; where LST min is the lowest land surface temperature corresponding to the hyperspectral vegetation drought index, a1 and b1 are the intercept and slope of the wet edge equation respectively, and HVDI is the hyperspectral vegetation drought index at the crop planting location; The dry edge equation is LST max = a2 + b2 × HVDI; where LST max is the maximum land surface temperature corresponding to the hyperspectral vegetation drought index, a2 and b2 are the intercept and slope of the dry edge equation respectively, and HVDI is the hyperspectral vegetation drought index at the crop planting location.

5. The drought monitoring method based on medium-resolution imaging spectrometer data according to claim 4, characterized in that Determining the temperature hyperspectral vegetation drought index value at each crop planting position in the crop planting area according to the dry edge equation and the wet edge equation, specifically including: Determining the hyperspectral vegetation drought index of each pixel in the surface temperature image in the hyperspectral vegetation drought index image; Determining the highest surface temperature and the lowest surface temperature corresponding to each pixel in the dry edge equation and the wet edge equation respectively according to the hyperspectral vegetation drought index of each pixel; According to the land surface temperature of each pixel in the land surface temperature image, the highest land surface temperature and the lowest land surface temperature corresponding to each pixel, use the formula THVDI=(LST - LST min ) / (LST max -LST min ) to calculate the temperature hyperspectral vegetation drought index value of each pixel, which is used as the temperature hyperspectral vegetation drought index value of the crop planting position corresponding to each pixel; in the formula, THVDI is the temperature hyperspectral vegetation drought index value of the crop planting position, and LST is the land surface temperature of the crop planting position.

6. The drought monitoring method based on medium-resolution imaging spectrometer data according to claim 1, characterized in that The range of the temperature hyperspectral vegetation drought index value is between 0 and 1; the closer the temperature hyperspectral vegetation drought index value is to 1, the more serious the drought degree is.

7. A drought monitoring system based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, characterized in that, Including: An image acquisition module for acquiring the spectral reflectance image and the surface brightness temperature image of the crop planting area; A hyperspectral vegetation drought index calculation module for calculating the hyperspectral vegetation drought index at each crop planting position in the crop planting area according to the spectral reflectance image of the crop planting area to form a hyperspectral vegetation drought index image of the crop planting area; A surface temperature calculation module for calculating the surface temperature at each crop planting position in the crop planting area according to the surface brightness temperature image of the crop planting area to form a surface temperature image of the crop planting area; A feature space construction module for constructing a feature space with the hyperspectral vegetation drought index as the horizontal axis and the surface temperature as the vertical axis according to the hyperspectral vegetation drought index image and the surface temperature image; A linear fitting module for extracting the dry edge equation and the wet edge equation from the feature space through linear fitting; A temperature hyperspectral vegetation drought index value determination module for determining the temperature hyperspectral vegetation drought index value at each crop planting position in the crop planting area according to the dry edge equation and the wet edge equation to obtain a temperature hyperspectral vegetation drought index image of the crop planting area; The calculation formula for the hyperspectral vegetation drought index at the crop planting position is HVDI=(R NIR -R Red ) / R SWIR Where, HVDI is the hyperspectral vegetation drought index at the crop planting position, and R NIR is the near-infrared band reflectance at the crop planting position in the second band of the spectral reflectance image, and R Red is the red band reflectance at the crop planting position in the first band of the spectral reflectance image, and R SWIR is the shortwave infrared band reflectance at the crop planting position in the fifth band of the spectral reflectance image; The calculation formula for the surface temperature at the crop planting position is LST = DN × 0.02 - 273 In the formula, LST is the surface temperature at the crop planting position, and DN is the pixel gray value corresponding to the crop planting position; Constructing a feature space with the hyperspectral vegetation drought index as the horizontal axis and the surface temperature as the vertical axis according to the hyperspectral vegetation drought index image and the surface temperature image, specifically including: Selecting the pixels in the hyperspectral vegetation drought index image with the hyperspectral vegetation drought index greater than or equal to 0.2, and dividing the hyperspectral vegetation drought index in the hyperspectral vegetation drought index image into multiple intervals with a step size of 0.01; Determine the maximum and minimum values of the hyperspectral vegetation drought index in each interval, as well as the corresponding orbit row and column numbers in the hyperspectral vegetation drought index image for the maximum and minimum values; According to the corresponding orbit row and column numbers of the maximum and minimum values, obtain the temperature values corresponding to the orbit row and column numbers from the land surface temperature image; Using the maximum and minimum values of the hyperspectral vegetation drought index in all intervals as the horizontal axis data points and the corresponding temperature values as the vertical axis data points, construct a feature space with the hyperspectral vegetation drought index as the horizontal axis and the land surface temperature as the vertical axis.

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