A drought remote sensing monitoring method and system integrating multi-source remote sensing data

Abstract

The present invention relates to the technical field of drought remote sensing monitoring, and specifically discloses a drought remote sensing monitoring method and system integrating multi-source remote sensing data, the method comprising: dividing the area to be measured into a plurality of position units; obtaining the temperature, altitude and air pressure data of each position unit in the area to be measured, and calculating the saturated water vapor pressure difference of each position unit; simultaneously obtaining the soil moisture and sunlight-induced chlorophyll fluorescence data of each position unit in the area to be measured; performing data normalization; selecting the driest position unit as the reference position unit, comprehensively calculating the saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence dimensions, calculating the distance between other position units and the reference position unit, and obtaining the drought index of each position unit; and using the drought index to realize drought monitoring of different positions in the area to be measured. The present invention can more accurately quantify the degree of drought, comprehensively extract the comprehensive information of drought events, and has good reliability and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of drought remote sensing monitoring, and in particular to a drought remote sensing monitoring method and system integrating multi-source remote sensing data. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Drought is an abnormal lack of water at the interface between land and atmosphere. It begins with a decrease in precipitation (called meteorological drought) and spreads to soil moisture (agricultural drought) and stream, lake water levels and groundwater (hydrological drought) as it persists. Drought can have a huge impact on all aspects of human activities, including water resources, agricultural production, energy production and industrial output. Drought index is an important means to carry out drought monitoring and early warning, assess regional drought conditions and risks, and is the basis for studying the occurrence and development of drought. Accurate monitoring of drought disasters is a key link in disaster prevention and mitigation, and is also an inevitable requirement for improving the effectiveness of meteorological services.

[0004] Drought monitoring involves meteorology, agriculture, hydrology, society, ecology, environment and other aspects, and the change process is complex and continuous. With the development of remote sensing technology, a large number of drought monitoring models and methods have been proposed. By constructing various drought indices, drought characteristics are quantified, and the index that quantitatively describes drought characteristics is used as the research object, and then the severity, change characteristics and spatial distribution of drought are further analyzed.

[0005] Precise monitoring of drought and regional applicability have always been one of the difficulties in drought index research. Drought indices established based on different backgrounds have different pertinence, sensitivity and applicability. So far, about 155 drought monitoring indices have been developed to characterize drought and quantitatively analyze drought conditions. However, these drought monitoring methods disclosed in the prior art still face the following technical problems:

[0006] (1) Indices based on meteorological drought monitoring established at meteorological stations (such as the Standardized Precipitation Index (SPI), the Palmer Drought Index (PDSI), the Meteorological Drought Index (SPEI), and the Comprehensive Meteorological Drought Index (CI)) can effectively evaluate drought events dominated by precipitation. They have clear meanings and are simple to calculate, but their spatial continuity is poor. Some indices lack unified standards when comparing different time and space.

[0007] (2) Although remote sensing drought monitoring indices have gradually become a research hotspot, single remote sensing drought monitoring indices (such as the vegetation condition index (VCI), temperature condition index (TCI), apparent thermal inertia index (ATI), etc.) do not fully consider multiple drought-causing factors such as precipitation and evapotranspiration, and lack a certain degree of accuracy in areas with low vegetation coverage, and cannot meet the needs of assessing actual drought conditions.

[0008] (3) Different comprehensive remote sensing drought indices (such as the Vegetation Health Index, the Temperature-Vegetation-Soil Moisture Drought Index (TVMDI), the Microwave Integrated Drought Index (MIDI), etc.) constructed based on different construction methods (feature space method, principal component analysis method, empirical weight method, constrained optimization method, etc.). Such indices emphasize the mechanism of drought formation and can better reflect the comprehensive impact of various factors on drought. However, since many parameters are involved and the calculation of parameter determination is cumbersome, the scope of use is easily limited. In addition, many drought indices do not take into account the saturated vapor pressure deficit (VPD), which is an important indicator of changes in moisture conditions in arid and semi-arid areas. This may lead to inaccurate drought assessment results. Summary of the invention

[0009] In order to solve the above problems, the present invention proposes a drought remote sensing monitoring method and system that integrates multi-source remote sensing data, comprehensively considers important drought-causing factors such as temperature, soil moisture, vegetation, etc., calculates a comprehensive remote sensing drought index, and can better quantify drought and comprehensively extract comprehensive information of drought events.

[0010] In some embodiments, the following technical solutions are adopted:

[0011] A drought remote sensing monitoring method integrating multi-source remote sensing data, comprising:

[0012] Divide the area to be measured into a plurality of location units;

[0013] Obtain the temperature, altitude and air pressure data of each location unit in the area to be measured, and calculate the saturated water vapor pressure difference of each location unit; at the same time, obtain the soil moisture and sunlight-induced chlorophyll fluorescence data of each location unit in the area to be measured;

[0014] The obtained saturated vapor pressure deficit, soil moisture and sunlight-induced chlorophyll fluorescence data were normalized;

[0015] The driest location unit was selected as the reference location unit, and the distances between other location units and the reference location unit were calculated based on the saturated water vapor pressure difference, soil moisture, and sunlight-induced chlorophyll fluorescence dimensions to obtain the drought index of each location unit.

[0016] The drought index is used to realize drought monitoring at different locations in the area to be tested.

[0017] The drought index VMFDI Specifically:

[0018] ;

[0019] in, NVPD Max , NSMMin and NSIF Min They represent the saturated vapor pressure deficit, soil moisture and sunlight-induced chlorophyll fluorescence data corresponding to the driest location unit; NVPD , NSM and NSIF They represent the normalized saturated vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence data of a certain location unit respectively.

[0020] As a further solution, the validity of the drought index is tested by Pearson correlation analysis. The specific process is as follows:

[0021] The time series values ​​of gross primary productivity (GPP), actual evapotranspiration (ET), water use efficiency (WUE), drought intensity index (DSI), meteorological drought index (SPEI) and normalized difference vegetation index (NDVI) of each location unit were obtained as test indicators.

[0022] The validity of the drought index is tested by calculating the Pearson correlation coefficient between the drought index time series and each test index time series value.

[0023] As a further solution, the drought index is used to realize drought monitoring at different locations in the area to be tested, specifically:

[0024] Calculate the drought index time series for each location unit;

[0025] The first Position units at time Drought Index The multi-year average for this location For comparison, if , then at time , No. drought occurs in the location unit, otherwise it is not drought.

[0026] In other embodiments, the following technical solutions are adopted:

[0027] A drought remote sensing monitoring system integrating multi-source remote sensing data, including:

[0028] An area division module is used to divide the area to be measured into multiple location units;

[0029] The indicator acquisition module is used to obtain the temperature, altitude and air pressure data of each location unit in the test area, calculate the saturated water vapor pressure difference of each location unit; and simultaneously obtain the soil moisture and sunlight-induced chlorophyll fluorescence data of each location unit in the test area;

[0030] The indicator normalization module is used to normalize the obtained saturated water vapor pressure deficit, soil moisture and sunlight-induced chlorophyll fluorescence data;

[0031] The drought index calculation module is used to select the driest location unit as the reference location unit, comprehensively consider the saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence dimension, calculate the distance between other location units and the reference location unit, and obtain the drought index of each location unit;

[0032] The drought monitoring module is used to realize drought monitoring of different locations in the area to be tested by using the drought index.

[0033] In other embodiments, the following technical solutions are adopted:

[0034] A terminal device comprises a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store a plurality of instructions, wherein the instructions are suitable for being loaded by the processor and executing the drought remote sensing monitoring method integrating multi-source remote sensing data.

[0035] In other embodiments, the following technical solutions are adopted:

[0036] A computer-readable storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the drought remote sensing monitoring method integrating multi-source remote sensing data.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] (1) The present invention calculates the comprehensive remote sensing drought index based on saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence data, using drought principles and spatial distance models. By comprehensively considering temperature, moisture and the conditions of the plants themselves, the present invention can more accurately quantify the degree of drought and comprehensively extract the comprehensive information of drought events, thus providing scientific guidance for responding to climate change and preventing and resisting drought.

[0039] (2) The present invention divides the area to be measured into multiple location units, and calculates the drought index for each location unit separately, which has high spatial continuity; the standard consistency of the indicator data is ensured by indicator normalization.

[0040] (3) The present invention uses saturated vapor pressure difference VPD as one of the indicators for calculating the drought index. VPD can reflect air temperature and humidity at the same time, realizing the reflection of more dimensional data with fewer indicators, which can reduce the calculation complexity.

[0041] (4) The present invention uses sunlight-induced chlorophyll fluorescence (SIF) as one of the indicators for calculating the drought index. SIF is a signal generated along with vegetation photosynthesis and contains information on the physiological, biochemical and metabolic characteristics of vegetation. It responds more strongly, faster and more accurately to changes in photosynthetic capacity than traditional vegetation indices. Using it for the calculation of the drought index can more accurately reflect the impact of local vegetation on drought conditions.

[0042] (5) The present invention evaluates the effectiveness of the remote sensing drought index VMFDI from the aspects of drought monitoring effectiveness and drought monitoring accuracy. Pearson correlation analysis was performed on meteorological drought index SPEI, gross primary productivity GPP, actual evapotranspiration ET, water use efficiency WUE, GRACE drought severity index DSI, soil moisture SM and normalized difference vegetation index NDVI. The results showed that the VMFDI index had a good temporal correlation with each index and all passed the significance test of 0.01; therefore, the drought index proposed in the present invention has good reliability and accuracy.

[0043] Other features and advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of a drought remote sensing monitoring method that integrates multi-source remote sensing data in an embodiment of the present invention;

[0045] FIG2 (a) is a schematic diagram of the drought index VMFDI in an embodiment of the present invention;

[0046] FIG2( b ) is a schematic diagram showing the relationship between the drought index VMFDI and a single variable in an embodiment of the present invention;

[0047] Figure 3 (a) shows the spatial distribution of VPD in the TerraClimate dataset;

[0048] Figure 3 (b) shows the spatial distribution of VPD in the ERA5-Land dataset;

[0049] Figure 4 A schematic diagram of the correlation between two data set products, TerraClimate VPD and ERA5-Land VPD, in an embodiment of the present invention;

[0050] Figure 5 Schematic diagram of Pearson correlation analysis between drought index VMFDI and related index in an embodiment of the present invention;

[0051] Figure 6 (a) is a schematic diagram of the spatial correlation between VMFDI and meteorological drought index SPEI01;

[0052] Figure 6 (b) is a schematic diagram of the spatial correlation between VMFDI and meteorological drought index SPEI03;

[0053] Figure 6 (c) is a schematic diagram of the spatial correlation between VMFDI and meteorological drought index SPEI12. DETAILED DESCRIPTION

[0054] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0055] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0056] Embodiment 1

[0057] In one or more embodiments, a drought remote sensing monitoring method integrating multi-source remote sensing data is disclosed, combining Figure 1 , specifically including the following process:

[0058] S101: Divide the area to be measured into a plurality of location units, and the size of the location unit can be set according to the spatial resolution of the meteorological data and the requirements of the regional detection accuracy.

[0059] As a specific example, this embodiment selects the Yellow River Basin as the area to be tested; most of the Yellow River Basin (32°10'N to 41°50'N, 95°53'E to 119°05'E) is an arid and semi-arid area, with dramatic climate changes, low total precipitation, large spatial distribution differences, large evaporation, low humidity, high terrain in the west and low terrain in the east, and the western source area is composed of a series of high mountains, with perennial snow and glacial landforms; the central part is loess landforms with serious soil erosion; the eastern part is mainly composed of the Yellow River alluvial plain. Recently, various ecological protection projects have been implemented to improve the ecological environment of the Yellow River Basin.

[0060] The entire Yellow River basin is divided into multiple location units, each of which can be 0.1°×0.1° in size. Of course, the specific size can be set according to actual needs.

[0061] S102: Acquire the temperature, altitude and air pressure data of each location unit in the area to be measured, and calculate the saturated water vapor pressure difference of each location unit; and simultaneously acquire the soil moisture and sunlight-induced chlorophyll fluorescence data of each location unit in the area to be measured.

[0062] This embodiment selects three indicators, namely, saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence, to comprehensively calculate the drought index, taking into account the temperature, moisture and the conditions of the plants themselves, and can more accurately quantify the degree of drought.

[0063] Among them, the saturated water vapor pressure deficit (VPD) represents the difference between the saturated water vapor pressure of the air at a given temperature and the actual water vapor pressure at that temperature. When it increases, it can promote photosynthesis in the stomata of plant leaves. However, when the VPD is too high, vegetation usually reduces stomatal conductance and photosynthesis to reduce water loss, thereby inhibiting plant growth and causing plant tissue to dry up and die. In addition to the physiological effects on plants, excessively high VPD can also cause rapid evaporation of soil moisture, causing plant water and drought. Usually, the saturated water vapor pressure deficit is determined by air temperature and humidity. VPD is an important indicator of changes in water conditions in arid and semi-arid areas, and is also a key factor in determining plant photosynthesis.

[0064] Soil moisture (SM) is a key state variable of the earth's surface, reflecting the complex interactions among water, energy, and carbon cycles. Changes in soil moisture directly affect the availability of plant water, which in turn affects plant productivity and crop yields. Therefore, insufficient soil moisture has a significant impact on agriculture and water supply. Numerous studies have shown that compound droughts caused by high VPD and low SM have posed a strong limitation and negative impact on terrestrial carbon sinks and agricultural production.

[0065] Solar-induced Chlorophyll Fluorescence (SIF) is the energy released by chlorophyll in plants after absorbing short-wave radiation under natural light conditions, which is not used for photosynthesis. It is in the form of long-wavelength 650-800nm. As a signal produced by vegetation photosynthesis, SIF contains information on the physiological, biochemical and metabolic characteristics of vegetation, and responds more strongly, faster and more accurately to changes in photosynthetic capacity than traditional vegetation indices.

[0066] This embodiment mainly obtains meteorological data such as soil moisture content (root zone soil moisture content obtained by adding three layers of 0-7, 7-28, and 28-100 cm), 2m near-surface air temperature, 2m dew point temperature, altitude, air pressure, and average sea level pressure.

[0067] Meteorological data can be obtained from the meteorological reanalysis dataset ERA5-Land, which has a spatial resolution of 0.1°×0.1° and a monthly time scale. Compared with other meteorological datasets, this dataset can provide data with higher spatial resolution at high temporal resolution, especially improving the accuracy of hydrological variables such as soil moisture and lakes.

[0068] In this embodiment, the saturated water vapor pressure difference VPD of each location unit is calculated as follows:

[0069] (1)

[0070] (2)

[0071] (3)

[0072] Among them, SVP is the saturated water vapor pressure (hPa), AVP is the actual water vapor pressure (hPa), Td is the dew point temperature (℃), and Ta is the land air temperature (℃). To enhance the factor, when dealing with humid air instead of pure water vapor, a slight correction must be applied using the relevant factor, is a weak function of temperature and pressure, and is calculated as follows:

[0073] (4)

[0074] (5)

[0075] Where Z is the altitude (m), P mst is the air pressure (hPa), P msl The average sea level pressure is 1013.25hPa.

[0076] As an optional implementation, the TerraClimate dataset of global land surface monthly climate and climate water balance released by the Applied Climate Science Laboratory at the University of Idaho is used to verify the saturated vapor pressure difference VPD calculated above. The spatial resolution of the TerraClimate dataset is 0.1°×0.1°, and the dataset has a low overall mean absolute error and high spatial fidelity.

[0077] Figure 3 (a) and Figure 3 (b) show the spatial distribution of VPD (in hPa) of the TerraClimate dataset and the ERA5-Land dataset, respectively. Different colors in the figure represent different sizes of VPD. It can be seen that the VPD data calculated using ERA-5 Land is consistent with the spatial distribution of VPD in the TerraClimate dataset.

[0078] Combination Figure 4 The accuracy of the saturated vapor pressure difference (VPD) calculated based on the ERA5-Land dataset is evaluated by three continuity indicators: the correlation coefficient Corr, the root mean square error RMSE, and the relative bias Bias. Specifically, by randomly selecting the VPD of TerraCliamte (corresponding to Figure 4 The horizontal axis VPD-Te in the figure) and the VPD calculated based on the ERA5-Land dataset (corresponding to Figure 4 The vertical axis VPD-ERA in the figure is used for correlation analysis on 1% of the grid data in the two corresponding periods of the two datasets (about 1701 data points after removing missing values, and the fitting line in the figure is obtained through these data points). The units of the two datasets are hPa, and the final correlation coefficient Corr is 0.9012, the root mean square error RMSE is 2.7264 hPa, and the relative deviation Bias is 1.2321, indicating that the data accuracy is good and can be used in subsequent analysis.

[0079] In this embodiment, the soil moisture data of each location unit in the measured area can be obtained by adding three layers of ERA5-Land soil moisture data 0~7, 7~28, and 28~100cm; the sunlight-induced chlorophyll fluorescence data of each location unit in the measured area can be obtained through the global gridded data set of sunlight-induced chlorophyll fluorescence SIF through pre-processing methods such as data masking and cropping.

[0080] S103: Normalize the obtained saturated vapor pressure difference VPD, soil moisture SM and sunlight-induced chlorophyll fluorescence SIF data.

[0081] In this embodiment, since the value ranges of the saturated vapor pressure difference VPD, soil moisture SM and sunlight-induced chlorophyll fluorescence SIF data are different, the three indicators need to be normalized, so each axis in the three-dimensional data space is normalized to the range of [0,1].

[0082] In addition, in order to avoid errors caused by outlier points, this embodiment screens and processes outliers of saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence data before data normalization. The specific process is as follows:

[0083] Calculate the cumulative probability of the saturated water vapor pressure difference data value of each location unit, remove the saturated water vapor pressure difference data value with a cumulative probability less than a set first threshold (set to 0.1% in this embodiment) as an abnormal point, and modify the saturated water vapor pressure difference data value of the corresponding location unit to the saturated water vapor pressure difference data value corresponding to the current minimum cumulative probability (i.e., the minimum cumulative probability after removing the abnormal point);

[0084] The saturated water vapor pressure difference data value whose cumulative probability is greater than the set second threshold value (set to 99.9% in this embodiment) is removed as an abnormal point, and the saturated water vapor pressure difference data value of the corresponding position unit is modified to the saturated water vapor pressure difference data value corresponding to the current maximum cumulative probability (i.e., the maximum cumulative probability after removing the abnormal point);

[0085] The same method is used to screen and process abnormal points in soil moisture and sunlight-induced chlorophyll fluorescence data.

[0086] S104: The driest location unit is selected as the reference location unit, and the distances between other location units and the reference location unit are calculated based on the saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence dimension to obtain the drought index of each location unit.

[0087] Combined with Figure 2 (a), point D (1,0,0) is the maximum drought point, which is used as the reference location unit and corresponds to the minimum VMFDI value of 0; point W (0,1,1) is the wettest point and corresponds to the maximum VMFDI value. ; Line DW represents the dry and wet edge, with a length from 0 to Combined with Figure 2 (b), taking the multi-year average data as an example, the relationship between VMFDI and a single variable, the red, blue, and green are the corresponding points in each dimension, indicating the correlation between VPD-SM, VPD-SIF, and SM-SIF. The size of the black dot represents the VMFDI value. The larger the black dot, the smaller the VMFDI value. Among them, the unit of VPD is (hPa), and the unit of SM is (m 3 / m 3 ), the unit of SIF is (m*W / m 2 / um / sr).

[0088] The calculation method of the drought index of each location unit in this embodiment is:

[0089] (6)

[0090] in, NVPD Max , NSM Min and NSIF Min They represent the saturated water vapor pressure deficit, soil moisture and sunlight-induced chlorophyll fluorescence data values ​​corresponding to the driest location unit respectively; NVPD , NSM and NSIF They respectively represent the normalized saturated vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence data values ​​of a certain location unit.

[0091] It should be noted that the values ​​involved in the above formula (6) are all normalized dimensionless values.

[0092] This example tests the validity of the VMFDI index through Pearson correlation analysis.

[0093] The Pearson correlation coefficient is calculated as follows:

[0094] (7)

[0095] Among them, X i and Y i Represents the time series of correlated variables, R is the correlation coefficient (-1≤R≤1), and the absolute value of R indicates the strength of linear correlation. R of 0 indicates no correlation, while R of -1 and 1 indicate strong negative correlation and positive correlation, respectively. The correlation coefficient R is used to study temporal autocorrelation.

[0096] This example calculates the correlation between the drought index VMFDI and other indices (meteorological drought indices SPEI01, SPEI03 and SPEI12 at different time scales, gross primary productivity GPP, actual evapotranspiration ET, water use efficiency WUE, GRACE drought severity index DSI, soil moisture SM, normalized difference vegetation index NDVI, land surface temperature LST, saturated vapor pressure deficit VPD and sunlight-induced chlorophyll fluorescence SIF, etc.). The calculation results are shown in the figure. Figure 5 shown.

[0097] Specifically, gross primary productivity (GPP) characterizes the total amount of organic carbon fixed by vegetation through its own photosynthesis; actual evapotranspiration (ET) characterizes the sum of evaporation from the earth's surface to the atmosphere and plant transpiration, and is an important ecological process connecting the atmosphere-hydrosphere-biosphere. The degree of photosynthesis and water utilization of vegetation are usually related to its coverage and growth status. The higher the vegetation coverage, the better the regional water, light and heat conditions. Therefore, GPP and ET can indirectly characterize the water deficit of regional vegetation. Specifically, when subjected to drought stress, vegetation will show characteristics such as decreased photosynthesis and respiratory metabolism. When vegetation coverage decreases, GPP and ET will also decrease. Both can be used as effective tools for VMFDI index testing.

[0098] Water use efficiency (WUE) refers to the ratio of carbon (biomass) to water flux at all scales, which is closely related to the carbon and water cycle of the ecosystem. The different sensitivities of GPP and ET to drought control the differences in the relationship between WUE and drought in different vegetation types. Therefore, testing VMFDI through WUE can help us better understand the changes in water use efficiency of different vegetation under drought conditions, and further guide our decisions and actions in responding to climate change and water resources management.

[0099] The larger the VMFDI value, the smaller the VPD value, and the larger the SM and SIF values, the wetter it is. VMFDI is positively correlated with GPP, ET, WUE, NDVI, DSI, and SPEI, indicating that the larger the drought value, the more carbon fixed by green vegetation per unit time, the larger the actual evapotranspiration, the higher the water use efficiency of the ecosystem, the higher the vegetation coverage, and the wetter DSI and SPEI are.

[0100] The DSI is used to reflect hydrological drought. It is established on the basis of GRACE inversion of terrestrial water storage changes. Based on satellite gravity observations, it can provide globally consistent drought monitoring, even in places where ground readings are difficult to obtain, and track changes in groundwater storage, which affects soil moisture recharge and drought recovery.

[0101] SPEI is a typical meteorological drought index with the characteristics of multi-time scale drought conditions. Here, the correlation analysis of SPEI and VMFDI index at 1, 3 and 12 months time scales is carried out. It can be seen that with the increase of time scale, VMFDI shows the characteristics of weakening correlation, indicating that VMFDI short-term monitoring ability is better than medium and long-term. In terms of spatial distribution, the correlation between VMFDI and SPEI also has spatial heterogeneity, showing a strong positive correlation in most areas of the basin, accounting for more than 97.1%. In general, the VMFDI index is reliable for meteorological drought monitoring.

[0102] The results show that the VMFDI index has a good temporal correlation with each index, and all passed the significance test of 0.01. Combined with Figures 6 (a)-6 (c), the widely used multi-time scale SPEI01, SPEI03, and SPEI12 were selected to compare and evaluate the drought monitoring ability of the index in time and space. Among them, Figure 6 (a) is a schematic diagram of the spatial correlation between VMFDI and the meteorological drought index SPEI01, and the proportion of consistent pixels is 99.251%; Figure 6 (b) is a schematic diagram of the spatial correlation between VMFDI and the meteorological drought index SPEI03, and the proportion of consistent pixels is 97.160%; Figure 6 (c) is a schematic diagram of the spatial correlation between VMFDI and the meteorological drought index SPEI12, and the proportion of consistent pixels is 97.150%. It can be seen that with the increase of the time scale, the correlation weakens, indicating that the short-term monitoring ability of VMFDI is better than that of the medium and long term, and the highest proportion of consistent pixels reaches 99.251%.

[0103] S105: Using the drought index to implement drought monitoring at different locations in the area to be tested.

[0104] Specifically, the drought index time series is calculated for each location unit;

[0105] The first Position units at time Drought Index The multi-year average for this location For comparison, if , then at time , No. drought occurs in the location unit, otherwise it is not drought.

[0106] The multi-year average value of this location is It is obtained by calculating the average value based on the historical data of the location over many years. By comparing it with the historical average value of the location, it is possible to accurately determine whether the location is droughty. The judgment result is highly regional and targeted, and is more accurate and reliable.

[0107] This embodiment clarifies the relationship between drought-causing factors based on the drought mechanism, and uses multi-source remote sensing data saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence to construct a simple, efficient and reliable new comprehensive remote sensing drought index. The drought index comprehensively considers important drought-causing factors such as soil moisture, precipitation, temperature, and vegetation, ensuring that the single variable information presentation in the construction of the drought index is maximized, while avoiding the errors caused by a single indicator in the data production process. The effectiveness and reliability of the VMFDI drought index are tested and evaluated from multiple aspects, so as to better quantify drought, comprehensively extract comprehensive information on drought events, and provide scientific guidance for responding to climate change and preventing and resisting drought.

[0108] Embodiment 2

[0109] In one or more embodiments, a drought remote sensing monitoring system integrating multi-source remote sensing data is disclosed, comprising:

[0110] An area division module is used to divide the area to be measured into multiple location units;

[0111] The indicator acquisition module is used to obtain the temperature, altitude and air pressure data of each location unit in the test area, calculate the saturated water vapor pressure difference of each location unit; and simultaneously obtain the soil moisture and sunlight-induced chlorophyll fluorescence data of each location unit in the test area;

[0112] The indicator normalization module is used to normalize the obtained saturated water vapor pressure deficit, soil moisture and sunlight-induced chlorophyll fluorescence data;

[0113] The drought index calculation module is used to select the driest location unit as the reference location unit, comprehensively consider the saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence dimension, calculate the distance between other location units and the reference location unit, and obtain the drought index of each location unit;

[0114] The drought monitoring module is used to realize drought monitoring of different locations in the area to be tested by using the drought index.

[0115] It should be noted that the specific implementation method of the above modules is the same as that in Example 1 and will not be described in detail.

[0116] Embodiment 3

[0117] In one or more embodiments, a terminal device is disclosed, which includes a processor and a memory, wherein the processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the drought remote sensing monitoring method based on integrated multi-source remote sensing data described in Example 1.

[0118] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0119] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0120] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software.

[0121] Embodiment 4

[0122] In one or more embodiments, a computer-readable storage medium is disclosed, in which a plurality of instructions are stored, wherein the instructions are suitable for being loaded by a processor of a terminal device and executing the drought remote sensing monitoring method integrating multi-source remote sensing data described in Example 1.

[0123] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A drought remote sensing monitoring method integrating multi-source remote sensing data, characterized in that: include: Divide the area to be measured into a plurality of location units; Obtain the temperature, altitude and air pressure data of each location unit in the test area, and calculate the saturated water vapor pressure difference of each location unit; at the same time, obtain the soil moisture and sunlight-induced chlorophyll fluorescence data of each location unit in the test area; calculate the saturated water vapor pressure difference of each location unit VPD , specifically: the saturated water vapor pressure difference of each location unit is the difference between the saturated water vapor pressure of the location unit and the actual water vapor pressure; the saturated water vapor pressure is obtained based on the land air temperature and the relevant enhancement factor, and the actual water vapor pressure is obtained based on the dew point temperature and the relevant enhancement factor; specifically as follows: （1） （2） （3） in, SVP is the saturated water vapor pressure (hPa), AVP is the actual water vapor pressure (hPa), T d is the dew point temperature (°C), T a is the land air temperature (℃); To enhance the factor, when dealing with humid air instead of pure water vapor, a slight correction must be applied using the relevant factor, is a weak function of temperature and pressure, and is calculated as follows: （4） （5） in, Z is the altitude (m), P mst is the air pressure (hPa), P msl The average sea level pressure is 1013.25hPa; The obtained saturated vapor pressure deficit, soil moisture and sunlight-induced chlorophyll fluorescence data were normalized; The driest location unit was selected as the reference location unit, and the distances between other location units and the reference location unit were calculated based on the saturated water vapor pressure difference, soil moisture, and sunlight-induced chlorophyll fluorescence dimensions to obtain the drought index of each location unit. The drought index is used to realize drought monitoring at different locations in the area to be tested.

2. The drought remote sensing monitoring method of integrating multi-source remote sensing data according to claim 1, characterized in that: Before normalizing the obtained saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence data, the abnormal points of the saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence data are screened and processed respectively. The specific process is as follows: Calculate the cumulative probability of the saturated water vapor pressure difference data value of each location unit, remove the saturated water vapor pressure difference data value with a cumulative probability less than the set first threshold as an abnormal point, and modify the saturated water vapor pressure difference data value of the corresponding location unit to the saturated water vapor pressure difference data value corresponding to the current minimum cumulative probability; The saturated water vapor pressure difference data value whose cumulative probability is greater than the set second threshold is removed as an abnormal point, and the saturated water vapor pressure difference data value of the corresponding position unit is modified to the saturated water vapor pressure difference data value corresponding to the current maximum cumulative probability; The same method is used to screen and process abnormal points in soil moisture and sunlight-induced chlorophyll fluorescence data.

3. The drought remote sensing monitoring method of integrating multi-source remote sensing data according to claim 1, characterized in that: The drought index is specifically: ； in, VMFDI is the drought index, NVPD is Max , NSM Min and NSIF Min They represent the saturated vapor pressure deficit, soil moisture and sunlight-induced chlorophyll fluorescence data corresponding to the driest location unit; NVPD , NSM and NSIF They represent the normalized saturated vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence data of a certain location unit respectively.

4. The drought remote sensing monitoring method integrating multi-source remote sensing data according to claim 1, characterized in that: It also includes testing the validity of the drought index through Pearson correlation analysis; the specific process is: The time series values ​​of gross primary productivity (GPP), actual evapotranspiration (ET), water use efficiency (WUE), drought intensity index (DSI), meteorological drought index (SPEI) and normalized difference vegetation index (NDVI) of each location unit were obtained as test indicators. The validity of the drought index is tested by calculating the Pearson correlation coefficient between the drought index time series and each test index time series value.

5. The drought remote sensing monitoring method integrating multi-source remote sensing data according to claim 1, characterized in that: The drought index is used to realize drought monitoring at different locations in the tested area, specifically: Calculate the drought index time series for each location unit; The first Position units at time Drought Index The multi-year average for this location For comparison, if , then at time , No. drought occurs in the location unit, otherwise it is not drought.

6. A drought remote sensing monitoring system integrating multi-source remote sensing data, characterized in that: include: An area division module is used to divide the area to be measured into multiple location units; The indicator acquisition module is used to obtain the temperature, altitude and air pressure data of each location unit in the test area, and calculate the saturated water vapor pressure difference of each location unit; at the same time, the soil moisture and sunlight-induced chlorophyll fluorescence data of each location unit in the test area are obtained; the saturated water vapor pressure difference of each location unit is calculated, specifically: the saturated water vapor pressure difference of each location unit is the difference between the saturated water vapor pressure of the location unit and the actual water vapor pressure; the saturated water vapor pressure is obtained based on the land air temperature and the relevant enhancement factor, and the actual water vapor pressure is obtained based on the dew point temperature and the relevant enhancement factor; the details are as follows: （1） （2） （3） in, SVP is the saturated water vapor pressure (hPa), AVP is the actual water vapor pressure (hPa), T d is the dew point temperature (°C), T a is the land air temperature (℃); To enhance the factor, when dealing with humid air instead of pure water vapor, a slight correction must be applied using the relevant factor, is a weak function of temperature and pressure, and is calculated as follows: （4） （5） in, Z is the altitude (m), P mst is the air pressure (hPa), P msl The average sea level pressure is 1013.25hPa; The indicator normalization module is used to normalize the obtained saturated water vapor pressure deficit, soil moisture and sunlight-induced chlorophyll fluorescence data; The drought index calculation module is used to select the driest location unit as the reference location unit, comprehensively consider the saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence dimension, calculate the distance between other location units and the reference location unit, and obtain the drought index of each location unit; The drought monitoring module is used to realize drought monitoring of different locations in the area to be tested by using the drought index.

7. A terminal device, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions, characterized in that: The instructions are suitable for being loaded by a processor and executing the drought remote sensing monitoring method integrating multi-source remote sensing data as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a plurality of instructions, characterized in that: The instructions are suitable for being loaded by a processor of a terminal device and executing the drought remote sensing monitoring method based on integrated multi-source remote sensing data as described in any one of claims 1 to 5.

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