Soil drought monitoring method and device, electronic equipment and storage medium

Abstract

The application discloses a kind of soil drought monitoring method, device, electronic equipment and storage medium, it is related to drought monitoring technical field, for solving current soil drought monitoring technology uses fixed time scale, resulting in drought monitoring result is not accurate enough, the technical problem of poor index adaptability.The method comprises: obtaining the surface soil moisture data of target monitoring area in preset historical period, and based on surface soil moisture data, construct soil moisture sequence;According to soil moisture sequence, carry out quantification calculation based on first-order autocorrelation, obtain soil moisture memory duration;Based on the soil moisture memory duration, calculate the adaptive soil drought index of target monitoring area;The adaptive soil drought index is used to evaluate the soil drought degree of the target monitoring area.

Description

Technical Field

[0001] This invention relates to the field of drought monitoring technology, and in particular to a method, device, electronic equipment, and storage medium for monitoring soil drought. Background Technology

[0002] Soil drought monitoring is a crucial component of agriculture, water resource management, and environmental protection. Drought can lead to reduced crop yields, water shortages, and ecosystem imbalances. Therefore, timely and accurate monitoring of soil drought conditions is essential for developing effective drought mitigation measures.

[0003] Current mainstream drought monitoring relies on standardized monitoring indices, including the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The core of these indices is the calculation of cumulative rainfall and potential evapotranspiration at specific time scales (such as fixed time scales of 1, 3, 6, and 12 months). Furthermore, current methods primarily compare the performance of the same index at different fixed scales (such as SPI-1 and SPI-3) to qualitatively discuss drought characteristics. This can be understood as a "posterior interpretation" of results across multiple fixed scales.

[0004] Using the above methods, the selection of the fixed time scale for traditional drought indices is subjective and empirical, failing to accurately reflect the actual accumulation and dissipation rate of water deficit. This results in unstable characterization of agricultural and hydrological drought by the index, leading to inaccurate early warnings. Furthermore, the same fixed scale cannot be universally applied to different regions, resulting in poor index adaptability. Summary of the Invention

[0005] This invention provides a method, device, electronic equipment, and storage medium for monitoring soil drought, which solves or partially solves the technical problems of current soil drought monitoring technology using a fixed time scale, resulting in inaccurate drought monitoring results and poor index adaptability.

[0006] This invention provides a method for monitoring soil drought, the method comprising: Acquire surface soil moisture data of the target monitoring area within a preset historical time period, and construct a soil moisture sequence based on the surface soil moisture data; Based on the soil moisture sequence, a first-order autocorrelation-based quantitative calculation is performed to obtain the soil moisture memory duration. Based on the soil moisture memory duration, an adaptive soil drought index is calculated for the target monitoring area; the adaptive soil drought index is used to assess the degree of soil drought in the target monitoring area.

[0007] Optionally, constructing a soil moisture sequence based on the surface soil moisture data includes: The surface soil moisture data are preprocessed to construct a soil moisture anomaly sequence; Determine whether the target monitoring area is a region with uniform rainfall throughout the year; If so, then based on the soil moisture anomaly sequence, a first soil moisture sequence containing the soil moisture conditions for all months within the preset historical period is constructed; If not, based on the soil moisture anomaly sequence, a second soil moisture sequence containing soil moisture conditions for all rainy months within the preset historical period and a third soil moisture sequence containing soil moisture conditions for all dry months within the preset historical period are constructed respectively.

[0008] Optionally, the step of performing a quantization calculation based on first-order autocorrelation according to the soil moisture sequence to obtain the soil moisture memory duration includes: Extract the soil moisture of each month contained in the soil moisture sequence, and calculate the average soil moisture based on the soil moisture of each month; Calculate the first-order autocorrelation coefficient of the soil moisture sequence based on the average soil moisture value and the soil moisture value for each month. The soil moisture memory duration is obtained by performing time dimension conversion on the first-order autocorrelation coefficient. Wherein, when the target monitoring area is a region with uniform rainfall throughout the year, the soil moisture memory duration is the general soil moisture memory duration used for monitoring year-round drought; when the target monitoring area is not a region with uniform rainfall throughout the year, the soil moisture memory duration is the seasonal soil moisture memory duration, which includes the rainy season soil moisture memory duration used for monitoring rainy season drought and the dry season soil moisture memory duration used for monitoring dry season drought.

[0009] Optionally, calculating the adaptive soil drought index for the target monitoring area based on the soil moisture memory duration includes: Acquire real-time meteorological data of the target monitoring area during the soil moisture memory period, calculated backward from the current moment. Based on the real-time meteorological data, and combined with the soil moisture memory duration, the adaptive soil drought index of the target monitoring area is calculated. Wherein, when the target monitoring area is a region with uniform rainfall throughout the year, the soil moisture memory duration is the general soil moisture memory duration used for monitoring year-round drought; when the target monitoring area is not a region with uniform rainfall throughout the year, if the current time is in the rainy season, the soil moisture memory duration is the rainy season soil moisture memory duration used for monitoring drought during the rainy season, and if the current time is in the dry season, the soil moisture memory duration is the dry season soil moisture memory duration used for monitoring drought during the dry season.

[0010] Optionally, the data type of the real-time meteorological data includes precipitation data and water evapotranspiration data; the calculation of the adaptive soil drought index for the target monitoring area based on the real-time meteorological data and the soil moisture memory duration includes: If the real-time meteorological data is precipitation data, then based on the precipitation data, the cumulative precipitation during the soil moisture memory period is calculated backward from the current time, and the cumulative precipitation is standardized to obtain the first adaptive soil drought index of the target monitoring area. If the real-time meteorological data is water evapotranspiration data, then based on the water evapotranspiration data, the cumulative water surplus or deficit during the soil moisture memory period is calculated backward from the current time, and the cumulative water surplus or deficit is standardized to obtain the second adaptive soil drought index of the target monitoring area.

[0011] Optionally, the method further includes: The drought status of the target monitoring area is determined based on the adaptive soil drought index. If the drought status indicates that a new soil drought event has occurred in the target monitoring area, the current drought level is determined based on the adaptive soil drought index. If the current drought level reaches the preset drought severity level, then the drought development time from the drought-free state to the current drought level in the target monitoring area is determined. The drought type of the soil drought event is determined based on the drought development duration and the soil moisture memory duration.

[0012] Optionally, determining the drought type of the soil drought event based on the drought development duration and the soil moisture memory duration includes: The duration of drought development is compared with the duration of soil moisture memory. If the duration of drought development is less than the duration of soil moisture memory, then the drought type of the soil drought event is determined to be a sudden drought type. If the drought event develops to a duration greater than or equal to the soil moisture memory duration, then the drought type of the soil drought event is determined to be a mild drought type.

[0013] The present invention also provides a soil drought monitoring device, comprising: The soil moisture sequence construction unit is used to acquire surface soil moisture data of the target monitoring area within a preset historical time period, and construct a soil moisture sequence based on the surface soil moisture data. The soil moisture memory duration calculation unit is used to perform a quantization calculation based on first-order autocorrelation according to the soil moisture sequence to obtain the soil moisture memory duration. An adaptive soil drought index calculation unit is used to calculate the adaptive soil drought index of the target monitoring area based on the soil moisture memory duration; the adaptive soil drought index is used to assess the degree of soil drought in the target monitoring area.

[0014] The present invention also provides an electronic device, the device comprising a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the soil drought monitoring method as described above, according to the instructions in the program code.

[0015] The present invention also provides a computer-readable storage medium for storing program code for performing the soil drought monitoring method as described in any of the preceding claims.

[0016] As can be seen from the above technical solutions, the present invention has the following advantages: A method for monitoring soil drought is provided. First, surface soil moisture data of the target monitoring area within a preset historical time period is acquired, and a soil moisture sequence is constructed based on this data. Then, a first-order autocorrelation-based quantification calculation is performed on the soil moisture sequence to obtain the soil moisture memory duration. Based on the soil moisture memory duration, an adaptive soil drought index for the target monitoring area is calculated. This adaptive soil drought index is used to assess the degree of soil drought in the target monitoring area. Thus, the "memory duration," an inherent attribute of regional soil moisture, serves as the unique and adaptive time scale determination basis, replacing subjective, experience-based fixed scale selection. On the one hand, this fundamentally solves the problem that fixed scales are not applicable to all areas, enhancing the adaptability of the soil drought index across different monitoring areas. On the other hand, the soil drought index of the target monitoring area is adaptively corrected and optimized based on the soil moisture memory duration, avoiding the influence of subjective experience on the accuracy of actual detection and improving the overall accuracy of monitoring and assessment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the steps of a soil drought monitoring method. Figure 2 A schematic diagram of the overall process of a soil drought monitoring method; Figure 3 A visualization example of the calculation results of soil moisture retention time at a depth of 20cm in Yunnan Province; Figure 4 A schematic diagram showing the annual variations in soil moisture and precipitation at a depth of 10cm in Yunnan Province; Figure 5 A schematic diagram showing the changes in key parameters of drought monitoring in Yunnan Province from September 2009 to May 2010; Figure 6 This is a schematic diagram showing the changes in key parameters of drought monitoring in Yunnan Province from May to October 2011. Figure 7 This is a structural block diagram of a soil drought monitoring device. Detailed Implementation

[0019] This invention provides a method, device, electronic equipment, and storage medium for monitoring soil drought, which solves or partially solves the technical problems of current soil drought monitoring technologies using a fixed time scale, resulting in inaccurate drought monitoring results and poor index adaptability.

[0020] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0021] As an example, in soil drought monitoring scenarios, current mainstream drought monitoring relies on standardized monitoring indices, including SPI and SPEI. The core of these indices is the calculation of cumulative rainfall and potential evapotranspiration at specific time scales (such as fixed time scales of 1, 3, 6, and 12 months). Furthermore, current methods primarily compare the performance of the same index at different fixed scales (such as SPI-1 and SPI-3) to qualitatively discuss drought characteristics. This can be understood as a "posterior interpretation" of results across multiple fixed scales.

[0022] Using the above methods, the selection of the fixed time scale for traditional drought indices is subjective and empirical, failing to accurately reflect the actual accumulation and dissipation rate of water deficit. This results in unstable characterization of agricultural and hydrological drought by the index, leading to inaccurate early warnings. Furthermore, the same fixed scale cannot be universally applied to different regions, resulting in poor index adaptability.

[0023] Further analysis reveals that current drought type identification lacks objective standards, relying heavily on subjective descriptions or post-event impact assessments to differentiate between different drought types. This lack of in-process, objective, and quantitative physical standards leads to vague early warning information and hinders targeted early warning and response to droughts with varying hazard characteristics. Furthermore, current monitoring models fail to embed "memory duration," reflecting the system's water retention capacity, as a core parameter, resulting in physical biases in the model's depiction of the drought lifecycle. While traditional indices quickly return to normal when meteorological conditions improve, the "memory effect" in soil and groundwater systems means that actual water stress continues, leading to an underestimation of drought duration, severity, and overall impact. This not only results in delayed assessments but also distorted impact quantification.

[0024] Therefore, one of the core inventive points of this invention is to provide a unified solution for soil drought monitoring based on soil physical properties by quantifying the "memory duration" of soil moisture to solve the above-mentioned problems. First, the "memory duration" of regional soil moisture is used as the sole, adaptive time scale determination criterion, replacing subjective, empirical, fixed scale selection. Based on this, a physical drought type discrimination mechanism is established, using "memory duration" as the discrimination threshold to provide clear and calculable physical definitions for drought types "sudden drought" and "slow drought." Furthermore, the aforementioned "memory duration" is parameterized to construct an integrated monitoring model that can simultaneously optimize traditional indices and identify drought types.

[0025] Reference Figure 1 The diagram illustrates a flowchart of a soil drought monitoring method provided by an embodiment of the present invention, which may specifically include the following steps: Step 101: Obtain surface soil moisture data of the target monitoring area within a preset historical time period, and construct a soil moisture sequence based on the surface soil moisture data; In this step, the surface soil moisture data of the target monitoring area within a preset historical period is first obtained, and a soil moisture sequence is constructed based on the surface soil moisture data.

[0026] Specifically, this involves acquiring long-term (e.g., spanning multiple years) and high-precision surface (or shallow) soil moisture data for the target monitoring area. In practical applications, soil layers that best represent the root water supply of the main local vegetation are prioritized. For example, in areas where agriculture is the dominant industry, soil moisture data from 0-20cm is preferred. Similarly, in areas with shrubs and fruit trees, soil moisture data from 20-50cm is preferred. It is understood that this invention does not impose limitations on these aspects. For instance, assuming the target monitoring area is Yunnan Province, calculations and comparisons determine that the 0-20cm layer is the primary moisture data layer.

[0027] Next, the raw data can be preprocessed to eliminate seasonal cycles. Specifically, the multi-year climatic average for each month is calculated, and this monthly average is subtracted from the raw data series to obtain the soil moisture anomaly series. The anomaly series can be used to describe the difference between a time series and its mean or benchmark value, thus reflecting the deviation of the data from the average state.

[0028] In this embodiment of the invention, the soil moisture memory duration is used as the sole input parameter and criterion for subsequent processing steps. Calculating the soil moisture memory duration requires first constructing a soil moisture sequence based on the soil moisture anomaly sequence.

[0029] In practical terms, taking China as an example, the rainy season is defined by the period of concentrated, continuous rainfall during the summer monsoon season. For instance, in South China, the rainy season (wet season) is from April to September, with the rest being the dry season; in the Yangtze River basin, the rainy season is from June to July (plum rains), with the rest being the dry season; in North China / Northeast China, the rainy season is from July to August, with the rest being the dry season; and in Southwest China (such as Yunnan), May to October is the typical rainy season, with the rest being the dry season. However, Northwest China and the heart of the Qinghai-Tibet Plateau do not have a distinct rainy season and can be considered regions with evenly distributed rainfall throughout the year.

[0030] In some embodiments, it is assumed that the target monitoring area is a region with uniform rainfall throughout the year, such as the Northwest and the hinterland of the Qinghai-Tibet Plateau, where rainfall is uniform throughout the year. In this case, the distinction between dry and rainy seasons is not necessary. Based on the soil moisture anomaly sequence obtained in the previous steps, a soil moisture sequence containing soil moisture conditions for all months within a preset historical period (long time series) can be constructed (defined as the first soil moisture sequence to distinguish it from other soil moisture sequences). In this case, when calculating the soil moisture memory duration subsequently, for regions with uniform rainfall throughout the year, it is equivalent to calculating only one soil moisture memory duration for monitoring year-round drought (i.e., a general soil moisture memory duration). For example, by collecting and preprocessing five years of historical surface soil moisture data from the hinterland of the Qinghai-Tibet Plateau, a soil moisture anomaly sequence containing soil moisture conditions for these five years can be obtained. Then, based on the soil moisture anomaly sequence, sorting and statistical processing is performed on a monthly basis to construct a soil moisture sequence containing soil moisture conditions for all months within these five years.

[0031] In some other embodiments, it is assumed that the target monitoring area is not a region with uniform rainfall throughout the year. For example, in Yunnan Province, the dry season is from November to April, and the rainy season is from May to October. In this case, based on the soil moisture anomaly sequence, it is necessary to construct a soil moisture sequence containing soil moisture conditions for all rainy months within a preset historical period (defined as the second soil moisture sequence to distinguish it from other soil moisture sequences), and a soil moisture sequence containing soil moisture conditions for all dry months within a preset historical period (defined as the third soil moisture sequence to distinguish it from other soil moisture sequences). In this case, when calculating the soil moisture memory duration, it is equivalent to calculating two seasonally related soil moisture memory durations: the soil moisture memory duration for rainy season drought monitoring (which can be understood as the rainy season soil moisture memory duration) and the soil moisture memory duration for dry season drought monitoring (which can be understood as the dry season soil moisture memory duration). For example, by collecting and preprocessing five years of historical surface soil moisture data from Yunnan Province, a soil moisture anomaly sequence encompassing these five years' worth of soil moisture data can be obtained. Then, considering the seasonal dependence of the climate system, based on the soil moisture anomaly sequence, a combined seasonal and monthly sorting and statistical processing is performed to construct separate soil moisture sequences: one containing soil moisture data for all rainy season months (May to October) within the five years, and another containing soil moisture data for all dry season months (November to April) within the five years. Specifically, the soil moisture data for the six rainy months of each year within the five years is concatenated to obtain a second soil moisture sequence corresponding to the rainy season. Similarly, the soil moisture data for the six dry season months of each year within the five years is concatenated to obtain a third soil moisture sequence corresponding to the dry season.

[0032] Based on the preceding discussion, in specific implementation, the process of constructing a soil moisture sequence based on surface soil moisture data can include: first, preprocessing the surface soil moisture data to construct a soil moisture anomaly sequence; then determining whether the target monitoring area is a region with uniform rainfall throughout the year; in one case, if the target monitoring area is a region with uniform rainfall throughout the year, then based on the soil moisture anomaly sequence, constructing a first soil moisture sequence containing soil moisture conditions for all months within a preset historical period; in another case, if the target monitoring area is not a region with uniform rainfall throughout the year, then based on the soil moisture anomaly sequence, constructing a second soil moisture sequence containing soil moisture conditions for all rainy months within a preset historical period, and a third soil moisture sequence containing soil moisture conditions for all dry months within a preset historical period.

[0033] Step 102: Perform quantization calculation based on first-order autocorrelation on the soil moisture sequence to obtain the soil moisture memory duration; In this step, the soil moisture memory duration is obtained by performing a quantitative calculation based on first-order autocorrelation according to the soil moisture sequence.

[0034] Specifically, for soil moisture sequences, the first-order lag autocorrelation coefficient is calculated to characterize the strength of the soil moisture's ability to "remember" the state of the previous month. For regions with uniform rainfall throughout the year, only the first soil moisture sequence is constructed, and only the first-order lag autocorrelation coefficient of this one sequence needs to be calculated. For regions with uneven rainfall throughout the year, since a second soil moisture sequence (corresponding to the rainy season) and a third soil moisture sequence (corresponding to the dry season) are constructed, the first-order lag autocorrelation coefficients of these two sequences need to be calculated separately for subsequent quantitative calculations of soil moisture memory duration.

[0035] For the first soil moisture series, the second soil moisture series, and the third soil moisture series, the first-order autocorrelation coefficient can be calculated using the following formula: ; In the formula, This represents the first-order autocorrelation coefficient; The soil moisture is the monthly soil moisture contained in the soil moisture series (taking the Southwest (Yunnan region) as an example, the rainy season soil moisture series corresponds to May to October of the current year, and the dry season soil moisture series corresponds to November of the current year to April of the following year); For sample length ( Indicates the number of years; This indicates the sample length considering seasonality; when the soil moisture sequence is the first soil moisture sequence, the sample length should be [length missing]. ); This represents the average value of the sample sequence.

[0036] Furthermore, the calculated first-order autocorrelation coefficient is converted into a physical quantity with time dimensions according to the following formula, thus obtaining the soil moisture memory duration (unit: month): ; in, This indicates the characteristic time during which soil moisture "forgets" its own historical state. The higher the value, the stronger the soil moisture's ability to "remember" abnormal soil conditions, and the slower the system response.

[0037] Based on the preceding discussion, in a specific implementation, the process of obtaining the soil moisture memory duration by performing quantitative calculations based on first-order autocorrelation of the soil moisture sequence can include: extracting the soil moisture of each month contained in the soil moisture sequence, and calculating the average soil moisture based on the soil moisture of each month; calculating the first-order autocorrelation coefficient of the soil moisture sequence based on the average soil moisture and the soil moisture of each month; and performing time dimension conversion on the first-order autocorrelation coefficient to obtain the soil moisture memory duration.

[0038] Specifically, when the target monitoring area is a region with uniform rainfall throughout the year, the soil moisture memory duration is the general soil moisture memory duration used for monitoring year-round drought. When the target monitoring area is not a region with uniform rainfall throughout the year, the soil moisture memory duration is the seasonal soil moisture memory duration, which includes the rainy season soil moisture memory duration used for rainy season drought monitoring and the dry season soil moisture memory duration used for dry season drought monitoring.

[0039] Specifically, when the target monitoring area is a region with uniform rainfall throughout the year, the soil moisture sequence is the first soil moisture sequence for the whole year. At this time, the soil moisture of each month contained in the sequence is extracted from the first soil moisture sequence, and the corresponding average soil moisture is calculated based on the soil moisture of each month. The first-order autocorrelation coefficient of the first soil moisture sequence is calculated based on the average soil moisture and the soil moisture of each month. The time dimension is transformed into the first-order autocorrelation coefficient to obtain the general soil moisture memory duration.

[0040] When the target monitoring area is not a region with uniform rainfall throughout the year, two sets of soil moisture sequences actually need to be calculated: a second soil moisture sequence and a third soil moisture sequence. For rainy season months, the soil moisture for each rainy season month included in the second soil moisture sequence is extracted, and the corresponding average soil moisture is calculated based on the soil moisture for each rainy season month. The first-order autocorrelation coefficient of the second soil moisture sequence is calculated based on the average soil moisture and the soil moisture for each rainy season month. The first-order autocorrelation coefficient is then converted to time dimensions to obtain the rainy season soil moisture memory duration. For dry season months, the soil moisture for each dry season month included in the third soil moisture sequence is extracted, and the corresponding average soil moisture is calculated based on the soil moisture for each dry season month. The first-order autocorrelation coefficient of the third soil moisture sequence is then calculated based on the average soil moisture and the soil moisture for each dry season month. The first-order autocorrelation coefficient is then converted to time dimensions to obtain the dry season soil moisture memory duration.

[0041] Step 103: Calculate the adaptive soil drought index for the target monitoring area based on the soil moisture memory duration; the adaptive soil drought index is used to assess the degree of soil drought in the target monitoring area.

[0042] In this step, an adaptive soil drought index is calculated for the target monitoring area based on the soil moisture memory duration, in order to assess the degree of soil drought in the target monitoring area.

[0043] In soil moisture monitoring, drought indices are used to assess the severity of drought. The Standardized Precipitation Index (SPI) and Standardized Evapotranspiration Index (SPEI) are two commonly used drought indices. A positive SPI value indicates above-average precipitation (humid), while a negative SPI value indicates below-average precipitation (drought). A larger absolute SPI value indicates a stronger drought or wetness. Similarly, a positive SPEI value indicates a water surplus (humid), while a negative SPEI value indicates a water deficit (drought). A larger absolute SPEI value also indicates a stronger drought or wetness.

[0044] However, as mentioned earlier, traditional indices such as SPI / SPEI or the Meteorological Comprehensive Drought Index (MCI) require a pre-defined time scale k (e.g., k = 1 month, 3 months, 6 months) for calculation, while MCI is a mathematical synthesis of multi-scale SPI indices. The key improvement of this invention lies in replacing the traditionally used subjective fixed scale k with the regionally and seasonally specific soil moisture memory duration obtained through actual calculation.

[0045] Specifically, when calculating the standardized index, the present invention no longer uses fixed periods of 1, 3, and 6 months, but calculates the cumulative precipitation (or water surplus and deficit) within the past soil moisture memory duration, and then performs a standardization process thereon. Among them, standardization, in the fields of statistics and machine learning, refers to scaling raw data proportionally to eliminate the dimensional (unit) and magnitude differences between different indicators, so that they can be placed on the same comparable "scale". Thereby, a new index with adaptive characteristics for evaluating the degree of soil drought can be obtained. For ease of understanding, the present invention defines it as an adaptive soil drought index (i.e., a memory duration adaptive standardized index). Meanwhile, it can be expressed as A-SPI (Adaptive Standardized Precipitation Index) or A-SPEI (Adaptive Standardized Precipitation Evapotranspiration Index). Exemplarily, if the soil moisture memory duration of the important soil layer in a certain monitoring area is 20 days, the cumulative precipitation (or water surplus and deficit) on the 20-day scale is calculated to carry out objective drought monitoring and drought degree assessment.

[0046] It should be pointed out that the time scale of the adaptive soil drought index is dynamically determined by the local soil physical properties. In clay areas with strong memory capacity, the soil moisture memory duration is relatively long, and the index reflects the cumulative effect over a longer period. In sandy soil areas with weak memory capacity, the soil moisture memory duration is relatively short, and the index can respond to recent changes more quickly. This fundamentally solves the problem that a fixed scale is not applicable to all regions.

[0047] Wherein, when verifying the applicability of the soil moisture memory duration, the present invention mainly selects the SPEI index to construct the adaptive soil drought index A-SPEI. The reason is that the SPEI index not only takes precipitation into account, but also considers the temperature-sensitive characteristic of evapotranspiration. The discrimination levels (corresponding to drought degree grades) of the A-SPEI index are consistent with those of the SPEI index.

[0048] Referring to the relevant specification (Meteorological Drought Grades, 2017), the SPEI values are divided into four drought grades, specifically: extreme drought (SPEI≤-2.0), severe drought (-2.0<SPEI≤-1.5), moderate drought (-1.5<SPEI≤-1.0) and light drought (-1.0<SPEI≤-0.5). A-SPEI can also refer to this specification to divide different drought grades.

[0049] Based on the preceding discussion, in specific implementation, the process of calculating the adaptive soil drought index of the target monitoring area based on the soil moisture memory duration can include: acquiring real-time meteorological data of the target monitoring area within the soil moisture memory duration period, calculated backward from the current time; and calculating the adaptive soil drought index of the target monitoring area based on the real-time meteorological data and the soil moisture memory duration.

[0050] Specifically, when the target monitoring area is a region with uniform rainfall throughout the year, the soil moisture memory duration is the same as the general soil moisture memory duration used for monitoring year-round drought; when the target monitoring area is not a region with uniform rainfall throughout the year, if the current time is during the rainy season, the soil moisture memory duration is the same as the rainy season soil moisture memory duration used for monitoring drought during the rainy season, and if the current time is during the dry season, the soil moisture memory duration is the same as the dry season soil moisture memory duration used for monitoring drought during the dry season.

[0051] Specifically, when the target monitoring area is a region with uniform rainfall throughout the year, real-time meteorological data of the target monitoring area is obtained within the general soil moisture memory period, calculated backward from the current time. Based on the real-time meteorological data, the adaptive soil drought index of the target monitoring area is calculated in combination with the general soil moisture memory period.

[0052] When the target monitoring area is not a region with uniform rainfall throughout the year, if the current time is during the rainy season (currently within the rainy season drought monitoring period), real-time meteorological data for the target monitoring area within the rainy season soil moisture memory period, calculated backwards from the current time, is obtained. Based on the real-time meteorological data and the rainy season soil moisture memory period, an adaptive soil drought index for the target monitoring area is calculated. If the current time is during the dry season (currently within the dry season drought monitoring period), real-time meteorological data for the target monitoring area within the dry season soil moisture memory period, calculated backwards from the current time, is obtained. Based on the real-time meteorological data and the dry season soil moisture memory period, an adaptive soil drought index for the target monitoring area is calculated.

[0053] In some embodiments, the data type of real-time meteorological data may include precipitation data and evapotranspiration data. Further, calculating the adaptive soil drought index for the target monitoring area based on real-time meteorological data and in conjunction with soil moisture memory duration may include: If the real-time meteorological data is precipitation data, then based on the precipitation data, calculate the cumulative precipitation over the soil moisture memory period from the current time backward, and standardize the cumulative precipitation to obtain the first adaptive soil drought index A-SPI for the target monitoring area. If the real-time meteorological data is water evapotranspiration data, then based on the water evapotranspiration data, the cumulative water surplus or deficit during the soil moisture memory period is calculated backward from the current time, and the cumulative water surplus or deficit is standardized to obtain the second adaptive soil drought index A-SPEI for the target monitoring area.

[0054] In some embodiments, the present invention proposes for the first time to use soil moisture memory duration as an objective and quantitative threshold to define the type of drought development rate.

[0055] The term "sudden drought" is defined as the time it takes for a drought event (starting from a drought-free state) to develop to the "moderate drought" level. Shorter than the soil moisture memory duration for the current season in the region. (Right now If the drought develops faster than the soil system "forgets" its normal moisture state, then the event is classified as a "sudden drought." This indicates that the drought is developing faster than the soil system can "forget" its normal moisture state, thus posing a "surprise attack" on the ecosystem.

[0056] Define mild drought (ordinary drought) as: if If the drought rate is within a certain range, it is classified as a "slow drought." This indicates that the drought's development rate has not exceeded the system's own memory decay rate, and it is a relatively slow cumulative process.

[0057] Therefore, in the specific implementation, after calculating the soil moisture memory duration and the adaptive soil drought index, the drought status of the target monitoring area can be determined based on the adaptive soil drought index. If the drought status indicates that a new soil drought event has occurred in the target monitoring area, the current drought level is determined based on the adaptive soil drought index. If the current drought level reaches the preset drought level (such as reaching moderate drought), the drought development duration from the drought-free state to the current drought level in the target monitoring area is determined. Based on the drought development duration and the soil moisture memory duration, the drought type of the soil drought event is determined.

[0058] Furthermore, the implementation process for determining the drought type of a soil drought event based on the drought development duration and soil moisture memory duration may include: comparing the drought development duration with the soil moisture memory duration; in one case, if the drought development duration is less than the soil moisture memory duration, the drought type of the soil drought event is determined to be a sudden drought; in another case, if the drought development duration is greater than or equal to the soil moisture memory duration, the drought type of the soil drought event is determined to be a mild drought.

[0059] Based on the preceding embodiments, this invention essentially constructs an integrated monitoring model and its corresponding identification process for soil drought monitoring scenarios. In practical application, it can specifically include the following key steps: Input: Real-time meteorological data (precipitation, potential evapotranspiration) and pre-calculated soil moisture memory duration.

[0060] Calculate the adaptive soil drought index: Using soil moisture memory duration as a scale, calculate the current adaptive soil drought index (e.g., A-SPEI).

[0061] Determine drought status: Determine whether drought is currently occurring and its level based on the A-SPEI value.

[0062] Drought type identification: When a new drought is detected, the duration of drought development is recorded. When the drought reaches the moderate drought level, the drought development duration is compared with the soil moisture memory duration, and the drought type is automatically output as "sudden drought" or "slow drought" according to the aforementioned rules, triggering the corresponding early warning strategy.

[0063] In this embodiment of the invention, a unified solution for soil drought monitoring based on soil physical properties is provided by quantifying the "memory duration" of soil moisture. First, the "memory duration" of regional soil moisture is used as the sole, adaptive time scale determination criterion, replacing subjective, empirically-based fixed scale selection. Based on this, a physical drought type discrimination mechanism is established, using "memory duration" as the discrimination threshold to provide clear and calculable physical definitions for drought types "sudden drought" and "slow drought." Furthermore, the aforementioned "memory duration" is parameterized to construct an integrated monitoring model that can simultaneously optimize traditional indices and identify drought types.

[0064] The technical solution provided by this invention can bring at least the following beneficial effects: On the one hand, "soil moisture memory duration" is transformed from a background physical quantity into a core functional parameter that can drive models and determine types, realizing a leap from "descriptive features" to "driving applications". This provides an objective basis for scale revision of drought monitoring models and also provides physical criteria for distinguishing drought types.

[0065] On the other hand, all the improvements in this invention (scale revision, type definition) are based on the "memory" physics recognized in soil hydrology, and the conclusions have strong explanatory power, strong scientific rigor, and clear physical significance, overcoming the empirical and subjective nature of traditional methods.

[0066] Meanwhile, this invention uses a unified and objective physical parameter, "soil moisture memory duration," which simultaneously solves three problems: exponential scale optimization, drought type identification, and improved assessment accuracy. The scheme has a unified internal logic and a rigorous structure, which improves the overall accuracy of monitoring and assessment.

[0067] In addition, the "soil moisture memory duration" varies with region, season, and soil depth, enabling this solution to automatically adapt to different underlying surface conditions, thus exhibiting better adaptability and universality.

[0068] To better illustrate this, let's assume the target monitoring area is not a region with uniform rainfall throughout the year, and refer to... Figure 2 This diagram illustrates the overall flow of a soil drought monitoring method according to an embodiment of the present invention. It should be noted that this embodiment only provides a brief description of the general flow of soil drought monitoring. The specific implementation process of each step can be understood by referring to the relevant content in the foregoing embodiments, and will not be elaborated upon here. It is understood that the present invention does not impose any limitations on this.

[0069] Step 201: Obtain surface soil moisture data of the target monitoring area within a preset historical time period, and construct soil moisture sequences for the rainy season and the dry season based on the surface soil moisture data. Step 202: Perform quantitative calculation based on first-order autocorrelation based on the soil moisture sequence of the rainy season to obtain the soil moisture memory duration of the rainy season. At the same time, perform quantitative calculation based on first-order autocorrelation based on the soil moisture sequence of the dry season to obtain the soil moisture memory duration of the dry season. Step 203: If it is determined that the current time is in the dry season, then obtain the precipitation data of the target monitoring area during the dry season soil moisture memory period calculated backward from the current time; Step 204: Calculate the adaptive soil drought index based on precipitation data and the dry season soil moisture memory duration; determine the drought status of the target monitoring area based on the adaptive soil drought index; Step 205: If the drought status indicates that a new soil drought event has occurred in the target monitoring area, the current drought level is determined based on the adaptive soil drought index; if the current drought level reaches the preset drought level, the drought development time from the drought-free state to the current drought level in the target monitoring area is determined. Step 206: Compare the drought development duration with the soil moisture memory duration; if the drought development duration is less than the soil moisture memory duration, the drought type of the soil drought event is determined to be sudden drought; if the drought development duration is greater than or equal to the soil moisture memory duration, the drought type of the soil drought event is determined to be slow drought.

[0070] To enable those skilled in the art to better understand the technical solutions of the present invention, the embodiments of the present invention will be described below through several specific examples.

[0071] Taking Yunnan Province as an example, to understand the effects of temperature and precipitation during the dry and wet seasons on soil moisture, this invention uses a relative contribution rate formula to calculate the contribution rates of precipitation and temperature to soil moisture in the 0-20cm depth in Yunnan. The calculations show that the contribution rate of precipitation to soil moisture is slightly greater than that of temperature in both the dry and wet seasons. The contribution rates of precipitation to soil moisture in the 0-20cm depth are 57.72% and 52.97% in the dry and wet seasons, respectively, while the contribution rates of temperature are 42.28% and 47.03%, respectively. Further analysis of the annual variations in soil moisture and precipitation in the 10-20cm depth in the low-latitude plateau region reveals that the changes in precipitation and soil moisture are consistent in the wet season, while there are slight differences in the dry season. Specifically, in the wet season, soil moisture content gradually increases from May with increasing rainfall, reaching its maximum in July and August. Then, as rainfall decreases, soil moisture decreases accordingly, reaching its minimum in March, with a lag of about one month compared to the minimum precipitation value.

[0072] This invention utilizes daily soil moisture data (20cm shallow soil layer) from 125 meteorological stations in Yunnan Province from 1981 to 2023, processing it into standard monthly averages, and then further processing it into monthly soil moisture sequences for the wet season (May to October) and the dry season (November to April of the following year). Based on this, and combined with the memory duration calculation method provided in the previous embodiments, the soil moisture memory duration in Yunnan Province is calculated. The calculated soil moisture memory duration results are as follows: Figure 3 As shown in the figure. Soil moisture memory is measured by the autocorrelation coefficient of the time series with a one-month lag; the larger the coefficient, the stronger the memory. To briefly illustrate the differences in soil moisture memory duration across different regions of Yunnan during the dry and wet seasons, [the following is a partial translation of the original text]. Figure 3 The soil moisture memory duration was spatially averaged across all areas within the Yunnan region's border area to illustrate the differences in soil memory duration between dry and wet seasons. Specifically, spatial averaging revealed that the average soil moisture memory duration is 1.3 months in the dry season and 0.8 months in the wet season.

[0073] For example, Figure 4 This diagram illustrates the annual and monthly variations of soil moisture at a depth of 10 cm (corresponding to the red line sm10) and precipitation (corresponding to the blue line R) in Yunnan Province. It should be noted that... Figure 4 The purpose of this example is to, based on the aforementioned statistical analysis over many years, analyze the changing characteristics of soil moisture and rainfall, which are closely related, by comparing the dry and wet seasons within the year. This is to further demonstrate from a data analysis perspective that the response of soil moisture to changes in precipitation differs between the dry and wet seasons. Figure 4It can be seen that the changes in precipitation and soil moisture are consistent during the wet season (May to October), while there is a slight difference during the dry season (November to April of the following year). During the wet season, soil moisture gradually increases with increasing rainfall starting in May, reaching its maximum in July and August. Then, as rainfall decreases, soil moisture also decreases, reaching its lowest point in March, with a lag of about one month compared to the lowest precipitation value. This also indirectly proves, from the perspective of average values, that the memory of soil moisture is shorter during the wet season, and its trend is almost synchronous with the change in precipitation. In contrast, during the dry season, especially in February and March, the trend of soil moisture changes lags behind the change in precipitation, demonstrating that the memory of soil moisture is different. Therefore, the occurrence and development of drought depend on the continuous accumulation of precipitation deficit, exhibiting significantly different lags between the dry and rainy seasons.

[0074] Furthermore, two typical drought monitoring cases in Yunnan Province are used to demonstrate the reliability of the above results.

[0075] Drought monitoring case 1: Yunnan experienced a continuous drought in the winter and spring of 2009.

[0076] For example, Figure 5 This diagram illustrates the changes in key parameters of drought monitoring in Yunnan Province from September 2009 to May 2010. Figure 5 It includes the following sub-plots: Daily rainfall anomalies from September 2009 to May 2010 (corresponding to...) Figure 5 'a' in mm, daily temperature anomaly (corresponding to...) Figure 5 b in the text refers to the unit: °C), and the daily SPEI index (corresponding to...). Figure 5 c), the SPEI index at different time scales, including 20 days (corresponding to Figure 5 e), 30 days (corresponding to) Figure 5 (g) 40 days (corresponding to) Figure 5 d) 50 days (corresponding to Figure 5 f), 60 days (corresponding to Figure 5 (h in the middle).

[0077] A comparison of the SPEI index response characteristics at different time scales to the continuous winter-spring drought in the low-latitude plateau from September 2009 to May 2010 reveals that while the 1-day SPEI can accurately capture daily precipitation and temperature changes, its strong susceptibility to daily weather disturbances and frequent interruptions in positive values ​​makes it difficult to characterize the accumulation and persistence of drought. Although the 20-day and 30-day scales suppress short-term fluctuations to some extent, they still showed significant increases multiple times during this period due to localized precipitation, indicating insufficient ability to continuously identify moderate to severe drought levels.

[0078] In contrast, the 40-day SPEI scale demonstrated superior overall monitoring performance. Firstly, its time series has been consistently negative since September 2009, fully reflecting the formation, maintenance, and mitigation process of this drought. Secondly, moderate to severe drought conditions were concentrated in late October to early December 2009 and January to mid-March 2010, highly consistent with the actual drought periods in the region, and achieving a better balance between noise suppression and signal preservation.

[0079] However, when the timescale is further extended to 50 and 60 days, although the SPEI index can still indicate the overall duration of drought, the excessively long integration window leads to an over-filtering effect on the drought evolution signals driven by water surplus and deficit. On the one hand, some drought mitigation or local relief signals caused by precipitation processes are unreasonably smoothed out. On the other hand, the phased intensification and weakening processes of drought intensity are also weakened, resulting in a blurred characterization of the internal structure of drought and making it difficult to clearly distinguish its dynamic process of development-maintenance-mitigation.

[0080] Further comparison with soil moisture memory revealed that the optimal SPEI monitoring scale, which balances drought persistence and evolution details, is approximately 40 days, highly consistent with the average dry season soil moisture memory duration estimated in the previous example (≈1.3 months). Therefore, when the SPEI integral scale approaches the typical memory cycle of regional soil moisture anomalies, it can effectively suppress synoptic-scale noise while reasonably preserving the physical evolution process of drought, thus possessing greater application value.

[0081] Drought monitoring case 2: Yunnan summer and autumn drought in 2011.

[0082] and Figure 5 Similarly, Figure 6 This diagram illustrates the changes in key parameters of drought monitoring in Yunnan Province from May to October 2011.

[0083] Daily SPEI sequence averaged from May to October 2011 in the low-latitude plateau region ( Figure 6 As can be seen in (c), the 1-day SPEI fluctuation is related to the precipitation during the same period ( Figure 6 a) Temperature ( Figure 6 (b) The data shows high synchronization and frequent alternation between positive and negative values. Mild drought and above levels are mainly concentrated in July and August, consistent with periods of low precipitation during the same period. This indicates that the 1-day SPEI is suitable for reflecting real-time wet and dry conditions and sudden drought events, but its ability to characterize sustained processes is limited.

[0084] 20 days ( Figure 6 e) and 30 days ( Figure 6The SPEI variation characteristics at the g) scale are highly consistent. Except for periods of positive values ​​before mid-May and from late September to early October 2011, the values ​​remained negative for the rest of the time. Drought characteristics were not obvious from mid-May to June, but the drought gradually intensified from July onwards, with moderate drought and above mainly concentrated before mid-August. Compared with the 1-day scale, the 20-day scale effectively suppressed spurious fluctuations caused by short-term precipitation, and accurately depicted the occurrence, development, and mitigation of this drought without losing key turning point information.

[0085] When the timescale is further extended to 40 days ( Figure 6 d) 50 days Figure 6 f) and 60 days ( Figure 6 In the case of h), although the SPEI sequence also showed a drought situation from June to mid-October, its response to the high-frequency dry-wet transitions characteristic of the wet season was significantly dulled. On the one hand, the drought incubation signals before July and the signs of relief after September were excessively smoothed out. On the other hand, the phased fluctuations in drought intensity were prolonged, leading to a blurred characterization of the internal structure of drought (development-peak-regression). In summary, a timescale of about 20 days can avoid synoptic-scale noise while retaining key details of drought evolution, demonstrating the best comprehensive monitoring capability in the wet season case in this study. It is worth noting that this optimal timescale is highly consistent with the estimated average memory duration of soil moisture in the wet season (about 0.8 months, or about 24 days) in the previous example. From the perspective of drought monitoring, this can be further verified that the self-memory time of regional soil moisture anomalies can provide a clear physical basis for the rational selection of the SPEI timescale.

[0086] Reference Figure 7 The diagram shows a structural block diagram of a soil drought monitoring device provided in an embodiment of the present invention, which may specifically include: The soil moisture sequence construction unit 701 is used to acquire surface soil moisture data of the target monitoring area within a preset historical period, and construct a soil moisture sequence based on the surface soil moisture data. Soil moisture memory duration calculation unit 702 is used to perform quantization calculation based on first-order autocorrelation according to the soil moisture sequence to obtain the soil moisture memory duration. The adaptive soil drought index calculation unit 703 is used to calculate the adaptive soil drought index of the target monitoring area based on the soil moisture memory duration; the adaptive soil drought index is used to assess the degree of soil drought in the target monitoring area.

[0087] In one optional embodiment, the soil moisture sequence construction unit 701 includes: A soil moisture anomaly sequence construction unit is used to preprocess the surface soil moisture data and construct a soil moisture anomaly sequence. The unit for determining areas with uniform rainfall throughout the year is used to determine whether the target monitoring area is an area with uniform rainfall throughout the year; The first soil moisture sequence construction unit is used to construct a first soil moisture sequence containing the soil moisture conditions of all months within the preset historical period, based on the soil moisture anomaly sequence, when the target monitoring area is a region with uniform rainfall throughout the year. A seasonal soil moisture sequence construction unit is used to construct, based on the soil moisture anomaly sequence, a second soil moisture sequence containing soil moisture conditions for all rainy months within the preset historical period and a third soil moisture sequence containing soil moisture conditions for all dry months within the preset historical period, when the target monitoring area is not a region with uniform rainfall throughout the year.

[0088] In one optional embodiment, the soil moisture memory duration calculation unit 702 includes: The average soil moisture calculation unit is used to extract the soil moisture of each month contained in the soil moisture sequence from the soil moisture sequence, and calculate the average soil moisture based on the soil moisture of each month. The first-order autocorrelation coefficient calculation unit is used to calculate the first-order autocorrelation coefficient of the soil moisture sequence based on the average soil moisture and the soil moisture of each month. The time dimension conversion unit is used to perform time dimension conversion on the first-order autocorrelation coefficient to obtain the soil moisture memory duration. Wherein, when the target monitoring area is a region with uniform rainfall throughout the year, the soil moisture memory duration is the general soil moisture memory duration used for monitoring year-round drought; when the target monitoring area is not a region with uniform rainfall throughout the year, the soil moisture memory duration is the seasonal soil moisture memory duration, which includes the rainy season soil moisture memory duration used for monitoring rainy season drought and the dry season soil moisture memory duration used for monitoring dry season drought.

[0089] In one optional embodiment, the adaptive soil drought index calculation unit 703 includes: The real-time meteorological data acquisition unit is used to acquire real-time meteorological data of the target monitoring area during the soil moisture memory period, calculated backward from the current moment. An adaptive soil drought index calculation and execution unit is used to calculate the adaptive soil drought index of the target monitoring area based on the real-time meteorological data and the soil moisture memory duration. Wherein, when the target monitoring area is a region with uniform rainfall throughout the year, the soil moisture memory duration is the general soil moisture memory duration used for monitoring year-round drought; when the target monitoring area is not a region with uniform rainfall throughout the year, if the current time is in the rainy season, the soil moisture memory duration is the rainy season soil moisture memory duration used for monitoring drought during the rainy season, and if the current time is in the dry season, the soil moisture memory duration is the dry season soil moisture memory duration used for monitoring drought during the dry season.

[0090] In one optional embodiment, the data type of the real-time meteorological data includes precipitation data and water evapotranspiration data; the adaptive soil drought index calculation and execution unit is specifically used for: If the real-time meteorological data is precipitation data, then based on the precipitation data, the cumulative precipitation during the soil moisture memory period is calculated backward from the current time, and the cumulative precipitation is standardized to obtain the first adaptive soil drought index of the target monitoring area. If the real-time meteorological data is water evapotranspiration data, then based on the water evapotranspiration data, the cumulative water surplus or deficit during the soil moisture memory period is calculated backward from the current time, and the cumulative water surplus or deficit is standardized to obtain the second adaptive soil drought index of the target monitoring area.

[0091] In one alternative embodiment, the device further includes: A drought status determination unit is used to determine the drought status of the target monitoring area based on the adaptive soil drought index. The current drought level determination unit is used to determine the current drought level based on the adaptive soil drought index when the drought status indicates that a new soil drought event has occurred in the target monitoring area. The drought development duration determination unit is used to determine the drought development duration from the drought-free state to the current drought level when the current drought level reaches the preset drought severity level; The drought type determination unit is used to determine the drought type of the soil drought event based on the drought development duration and the soil moisture memory duration.

[0092] In one alternative embodiment, the drought type determination unit includes: The duration comparison unit is used to compare the drought development duration with the soil moisture memory duration; The drought type determination unit is used to determine the drought type of the soil drought event as a sudden drought type when the drought development duration is less than the soil moisture memory duration. The drought type determination unit is used to determine the drought type of the soil drought event as a mild drought type when the drought development duration is greater than or equal to the soil moisture memory duration.

[0093] As the device embodiment is basically similar to the method embodiment, it is described in a relatively simple way. For relevant details, please refer to the description of the method embodiment above.

[0094] It should be noted that, in order to enable those skilled in the art to better distinguish data of the same type but with different actual meanings, some technical features in the embodiments of the present invention are distinguished by the terms "first", "second", and "third". The terms "first", "second", and "third" are used only for data differentiation and have no other special meaning. It is understood that the present invention does not impose any limitations on this.

[0095] This invention also provides an electronic device, which includes a processor and a memory: The memory is used to store program code and transfer the program code to the processor; The processor is used to execute the soil drought monitoring method of any embodiment of the present invention according to the instructions in the program code.

[0096] This invention also provides a computer-readable storage medium for storing program code for executing the soil drought monitoring method of any embodiment of the invention.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0098] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0099] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring soil drought, characterized in that, include: Acquire surface soil moisture data of the target monitoring area within a preset historical time period, and construct a soil moisture sequence based on the surface soil moisture data; Based on the soil moisture sequence, a first-order autocorrelation-based quantitative calculation is performed to obtain the soil moisture memory duration. Based on the soil moisture memory duration, an adaptive soil drought index is calculated for the target monitoring area; the adaptive soil drought index is used to assess the degree of soil drought in the target monitoring area.

2. The soil drought monitoring method according to claim 1, characterized in that, The construction of a soil moisture sequence based on the surface soil moisture data includes: The surface soil moisture data are preprocessed to construct a soil moisture anomaly sequence; Determine whether the target monitoring area is a region with uniform rainfall throughout the year; If so, then based on the soil moisture anomaly sequence, a first soil moisture sequence containing the soil moisture conditions for all months within the preset historical period is constructed; If not, based on the soil moisture anomaly sequence, a second soil moisture sequence containing soil moisture conditions for all rainy months within the preset historical period and a third soil moisture sequence containing soil moisture conditions for all dry months within the preset historical period are constructed respectively.

3. The soil drought monitoring method according to claim 1, characterized in that, The step of performing a quantization calculation based on first-order autocorrelation according to the soil moisture sequence to obtain the soil moisture memory duration includes: Extract the soil moisture of each month contained in the soil moisture sequence, and calculate the average soil moisture based on the soil moisture of each month; Calculate the first-order autocorrelation coefficient of the soil moisture sequence based on the average soil moisture value and the soil moisture value for each month. The soil moisture memory duration is obtained by performing time dimension conversion on the first-order autocorrelation coefficient. Wherein, when the target monitoring area is a region with uniform rainfall throughout the year, the soil moisture memory duration is the general soil moisture memory duration used for monitoring year-round drought; when the target monitoring area is not a region with uniform rainfall throughout the year, the soil moisture memory duration is the seasonal soil moisture memory duration, which includes the rainy season soil moisture memory duration used for monitoring rainy season drought and the dry season soil moisture memory duration used for monitoring dry season drought.

4. The soil drought monitoring method according to claim 1, characterized in that, The calculation of the adaptive soil drought index for the target monitoring area based on the soil moisture memory duration includes: Acquire real-time meteorological data of the target monitoring area during the soil moisture memory period, calculated backward from the current moment. Based on the real-time meteorological data, and combined with the soil moisture memory duration, the adaptive soil drought index of the target monitoring area is calculated. Wherein, when the target monitoring area is a region with uniform rainfall throughout the year, the soil moisture memory duration is the general soil moisture memory duration used for monitoring year-round drought; when the target monitoring area is not a region with uniform rainfall throughout the year, if the current time is in the rainy season, the soil moisture memory duration is the rainy season soil moisture memory duration used for monitoring drought during the rainy season, and if the current time is in the dry season, the soil moisture memory duration is the dry season soil moisture memory duration used for monitoring drought during the dry season.

5. The soil drought monitoring method according to claim 4, characterized in that, The data types of the real-time meteorological data include precipitation data and water evapotranspiration data; the calculation of the adaptive soil drought index for the target monitoring area based on the real-time meteorological data and the soil moisture memory duration includes: If the real-time meteorological data is precipitation data, then based on the precipitation data, the cumulative precipitation during the soil moisture memory period is calculated backward from the current time, and the cumulative precipitation is standardized to obtain the first adaptive soil drought index of the target monitoring area. If the real-time meteorological data is water evapotranspiration data, then based on the water evapotranspiration data, the cumulative water surplus or deficit during the soil moisture memory period is calculated backward from the current time, and the cumulative water surplus or deficit is standardized to obtain the second adaptive soil drought index of the target monitoring area.

6. The soil drought monitoring method according to claim 1, characterized in that, Also includes: The drought status of the target monitoring area is determined based on the adaptive soil drought index. If the drought status indicates that a new soil drought event has occurred in the target monitoring area, the current drought level is determined based on the adaptive soil drought index. If the current drought level reaches the preset drought severity level, then the drought development time from the drought-free state to the current drought level in the target monitoring area is determined. The drought type of the soil drought event is determined based on the drought development duration and the soil moisture memory duration.

7. The soil drought monitoring method according to claim 6, characterized in that, The step of determining the drought type of the soil drought event based on the drought development duration and the soil moisture memory duration includes: The duration of drought development is compared with the duration of soil moisture memory. If the duration of drought development is less than the duration of soil moisture memory, then the drought type of the soil drought event is determined to be a sudden drought type. If the drought development duration is greater than or equal to the soil moisture memory duration, then the drought type of the soil drought event is determined to be a mild drought type.

8. A soil drought monitoring device, characterized in that, include: The soil moisture sequence construction unit is used to acquire surface soil moisture data of the target monitoring area within a preset historical time period, and construct a soil moisture sequence based on the surface soil moisture data. The soil moisture memory duration calculation unit is used to perform a quantization calculation based on first-order autocorrelation according to the soil moisture sequence to obtain the soil moisture memory duration. An adaptive soil drought index calculation unit is used to calculate the adaptive soil drought index of the target monitoring area based on the soil moisture memory duration; the adaptive soil drought index is used to assess the degree of soil drought in the target monitoring area.

9. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the soil drought monitoring method according to any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for performing the soil drought monitoring method according to any one of claims 1-7.

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