Soil moisture agricultural drought index calculation method adaptive to crop root development

Abstract

The invention discloses a soil moisture agricultural drought index calculation method adaptive to crop root development, and relates to the field of agricultural drought. According to the method, soil moisture demands of roots of crops in different growth and development stages in different root zone depths are introduced, so that a soil moisture agricultural drought index MSMADI capable of dynamically adapting to the growth stage of the roots of the crops is constructed, and soil moisture drought conditions of different crops in different growth stages and different root zone depths are reflected more accurately; drought time-space changes and influence factors thereof in different regions and different seasons are disclosed, and accurate evaluation of agricultural drought is realized. The method has the advantages that more accurate evaluation indexes are provided for agricultural drought states of different crop areas, and scientific basis and technical support are provided for precise agricultural irrigation.

Description

Technical Field

[0001] The invention relates to the field of agricultural drought, and in particular to the calculation of soil moisture agricultural drought index dynamically adapted to the crop root development process. Background Art

[0002] Drought is a natural, recurring, global phenomenon. It is a complex and destructive disaster that has caused huge economic, social and environmental impacts. At present, most definitions of drought focus on its scope of impact. Agricultural drought involves the impact of meteorological drought characteristics on agriculture. It generally refers to the phenomenon of reduced crop yield caused by reduced soil moisture over a period of time. It is characterized by soil moisture content and plant growth conditions to reflect the degree of water stress suffered by plants during growth. Agricultural drought usually causes serious agricultural production losses, which in turn affects the food security of society. It is one of the most threatening disasters to agricultural production. Therefore, it is of great significance to establish effective agricultural drought monitoring indicators to monitor the occurrence, scope and severity of agricultural drought for agricultural planning and management. Drought index is used to identify, classify and monitor drought conditions. It is usually calculated in combination with data from climate, hydrology and other fields to obtain numerical values ​​to characterize drought conditions. Drought index can quantitatively evaluate the intensity, duration and spatial range of abnormal climate conditions and support decision-making systems.

[0003] On a shorter time scale, such as the daily scale, crop status is more suitable for assessing agricultural drought. Drought indices based on soil moisture, vegetation status, and surface temperature (taking into account their short-term estimates) will include the main factors describing the impact and causes of drought and are suitable for short- and medium-term agricultural drought monitoring, such as the agricultural drought index SMADI proposed by Sánchez. However, the current soil moisture agricultural drought monitoring only considers the deficit state of soil moisture itself, while ignoring the water demand of the crops planted as an agricultural drought index. Due to the different distribution of plant roots, the water demand at different root zone depths is also different. Summary of the invention

[0004] The purpose of this method is to introduce the soil moisture requirements of roots of different crops at different growth and development stages at different root zone depths into the existing agricultural drought index, to form a soil moisture agricultural drought index calculation method, to more accurately evaluate the agricultural drought status of different crop areas in a small range and within a short period of time, and to provide scientific basis and technical support for precision agricultural irrigation.

[0005] The present invention provides a method for calculating a soil moisture agricultural drought index adapted to the root development of crops. On the basis of the SMADI index, the soil moisture requirements of the roots of crops at different growth and development stages at different root zone depths are introduced to calculate the soil moisture agricultural drought index MSMADI. Using MSMADI, a more accurate agricultural drought status can be provided according to different crop requirements, making agricultural monitoring and management more convenient. The method comprises the following steps:

[0006] Step S1: Data collection and processing: soil moisture related variables NDVI, LST and PRE, soil moisture remote sensing data SSM, RZSM and site soil moisture RZSM are collected and preprocessed;

[0007] Step S2: Construction of drought participation index: Based on the NDVI, LST and SSM data obtained in step S1, the participation indexes VCI, MTCI and SMCI of the soil moisture agricultural drought index are obtained;

[0008] Step S3: Calculation of the root zone soil moisture status index RZSMCI: Based on the RZSM data obtained in step S1, the RZSMCI of the crop is calculated to reflect the soil moisture status of different crops at different depths;

[0009] Step S4: Calculation of crop root length density RLD: Calculate the root length density of crops at different root zone depths at different growth and development stages according to the root distribution and growth law of the crops;

[0010] Step S5: Construction of soil moisture agricultural drought index MSMADI: Based on the drought participation index VCI and MTCI obtained in step S2, the RZSMCI obtained in step S3 and the RLD obtained in step S4, MSMADI is obtained to reflect the soil moisture drought conditions of different crops at different growth stages and different root zone depths;

[0011] Step S6: Calculation of soil water deficit index SWDI: Calculate SWDI based on the site soil water content RZSM data obtained in step S1 to reflect the actual soil water content at different root zone depths;

[0012] Step S7: Comparison and evaluation of agricultural drought indices; compare MSMADI with the existing agricultural drought index SMADI and soil water deficit index SWDI to verify the accuracy and reliability of MSMADI in different crop areas, and then compare MSMADI, SMADI and SWDI with vegetation status NDVI, LST and precipitation PRE to evaluate the response of drought index to environmental factors.

[0013] A storage device stores instructions and data for realizing a soil moisture agricultural drought index calculation method suitable for crop root development.

[0014] A soil moisture agricultural drought index calculation device adapted to crop root development comprises: a processor and a storage device; the processor loads and executes instructions and data in the storage device to implement a soil moisture agricultural drought index calculation method adapted to crop root development.

[0015] The beneficial effects provided by the present invention are: the method provided by the present invention provides a more accurate assessment index for the agricultural drought status in different crop areas, and provides a scientific basis and technical support for precision agricultural irrigation. Specifically:

[0016] (1) The present invention introduces the soil moisture status of the root zone and the root length density of the crop growth and development process into the soil moisture agricultural drought index, and constructs a soil moisture agricultural drought index that dynamically adapts to the crop root development process to calculate the soil moisture agricultural drought index MSMADI, which can more accurately reflect the agricultural drought conditions and realize precise agricultural monitoring and management.

[0017] (2) The present invention calculated the MSMADI of two main crops, winter wheat and summer corn, in the HOAL region at different growth stages, and conducted a spatial and temporal assessment of the drought situation in the HOAL region in 2019, revealing the drought situation and its influencing factors in different regions and seasons. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart for the implementation of the soil moisture agricultural drought index calculation method that dynamically adapts to the crop root development process;

[0019] Figure 2 It is the distribution of land cover and soil moisture stations in the HOAL study area;

[0020] Figure 3 It is the spatial distribution of the four indices VCI, MTCI, SMCI and SMADI on March 22, 2019;

[0021] Figure 4 is the spatial distribution of the average MSMADI index of winter wheat and summer maize at their respective growth and development stages;

[0022] Figure 5 is the temporal variation trend of MSMADI index and SMADI index at three typical farmland sites in 2019;

[0023] Figure 6 is the temporal variation trend of MSMADI index and SWDI index at three typical farmland sites in 2019;

[0024] Figure 7 It is a scatter plot comparison of the MSMADI index corresponding to the SMADI index and the SWDI index;

[0025] Figure 8 is the spatial distribution of MSAMDI at annual scale for the two crops in the HOAL study area;

[0026] Fig. 9 It is the spatial distribution of frequency and the frequency of drought in HOAL area in 2019;

[0027] Fig.10 It is a schematic diagram of the working of the hardware device. DETAILED DESCRIPTION

[0028] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0029] The present invention first explains the relevant basic concepts and the core points of the present application as follows, and then elaborates on the technical solution of the present invention.

[0030] Please refer to Figure 1 , Figure 1 The present invention is a flow chart of the method; a method for calculating the soil moisture agricultural drought index, comprising the following:

[0031] Step S1: Data collection and processing; soil moisture related variables NDVI, LST and PRE, soil moisture remote sensing data SSM, RZSM and site soil moisture RZSM are collected and preprocessed;

[0032] Step S2: Construction of drought participation index: Based on the NDVI, LST and SSM data obtained in step S1, the soil moisture agricultural drought index participation index VCI, MTCI and SMCI are constructed;

[0033] Step S3: Calculation of the root zone soil moisture status index RZSMCI: Based on the RZSM data obtained in step S1, the RZSMCI of the crop is calculated to reflect the soil moisture status of different crops at different depths;

[0034] Step S4: Calculation of crop root length density RLD: Calculation of root length density of crops at different root zone depths at different growth and development stages according to the root distribution and growth law of the crops;

[0035] Step S5: constructing the soil moisture agricultural drought index MSMADI; according to the drought participation index VCI and MTCI obtained in step S2, the RZSMCI obtained in step S3 and the RLD obtained in step S4, MSMADI is obtained to reflect the soil moisture drought conditions of different crops at different growth stages and different root zone depths, forming a soil moisture agricultural drought index calculation method that can dynamically adapt to the crop root development process;

[0036] Step S6: Calculation of soil water deficit index SWDI: Calculation of SWDI based on the site soil water content RZSM data obtained in step S1 to reflect the soil water content at different root zone depths;

[0037] Step S7: Comparison and evaluation of agricultural drought indices; compare MSMADI with the existing agricultural drought index SMADI and soil water deficit index SWDI to verify the accuracy and reliability of MSMADI in different crop areas, and then compare MSMADI, SMADI and SWDI with vegetation status NDVI, LST and precipitation PRE to evaluate the response of drought index to environmental factors.

[0038] In the method of the present invention, in step S1, soil moisture related variables NDVI and LST data come from remote sensing data products MODIS and Landsat 8, soil moisture remote sensing data come from SMAP L4 and ERA5-Land, and site soil moisture data comes from the International Soil Moisture Network ISMN.

[0039] In step S2, the drought participation index includes the vegetation condition index VCI, the temperature condition index MTCI, and the soil moisture condition index SMCI. The calculation formula is as follows:

[0040]

[0041]

[0042] In the formula, NDVI i is the NDVI value of the i-th day, NDVI min and NDVI max They are the minimum and maximum NDVI values ​​in historical records. The VCI value range is between 0 and 1, where 0 indicates extremely unfavorable vegetation conditions and 1 indicates optimal vegetation conditions. i represents the LST of the i-th day, LST min and LST max The values ​​of MTCI range from 0 to 1, with 1 indicating extreme heat and 0 indicating humidity or no temperature stress. SMCI stands for Soil Moisture Status Index, SSM max and SSMmin It is the normalization of the absolute maximum and minimum values ​​of surface soil moisture in a year.

[0043] In step S3, the calculation formula of the root zone soil moisture status index RZSMCI is as follows:

[0044]

[0045] Where RZSM max and RZSM min It is the normalization of the absolute maximum and minimum values ​​of the soil moisture in the root zone in a year. There are RZSMs corresponding to the soil depths of 5 cm, 10 cm, 20 cm and 50 cm respectively.

[0046] In step S4, the root length density of the crop is calculated based on its root distribution law and growth law. According to the root distribution law, the root zone depth is divided into 5 cm, 10 cm, 20 cm and 50 cm. According to the growth law, the growth and development stage of summer corn is divided into jointing stage (May-June), silking stage (July-August) and maturity stage (September-October), and the growth and development stage of winter wheat is divided into growth stage (November-March), flowering stage (April) and maturity stage (May-June). According to the requirements of the "Agricultural Meteorological Observation Specification" of the meteorological department, the root distribution length is observed and measured. The measurement time of the root distribution is 1 measurement per development period. The measurement method is to collect soil column samples with a soil drill, wash them, and then use the cross method to calculate the root length of different root zone depths, and calculate the root length density RLD of the corresponding root zone depth.

[0047] In step S5, considering that crops mainly obtain water from the soil through roots, and different species have different root distributions at different growth stages, which affects soil water absorption, it is not sufficient to only consider surface soil moisture when defining the soil moisture agricultural drought index. Therefore, the root zone soil moisture and root length density at the growth and development stage are included in the soil moisture agricultural drought index to construct the soil moisture agricultural drought index MSMADI. The calculation formula is as follows:

[0048]

[0049] Where RZSMCI represents the root zone soil moisture index, RZSMCI 5cm 、RZSMCI 10cm 、RZSMCI 20cm and RZSMCI 50c m represents the soil moisture index of the root zone in the corresponding interval, RLD all Indicates the root length density of crops in the soil at a depth of 0 to 50 cm, RLD 5cm, RLD 10cm , RLD 20cm and RLD 50cm They respectively represent the root length density in the corresponding interval.

[0050] In step S6, the soil moisture deficit index SWDI is calculated using the following formula:

[0051]

[0052] Where RZSM FC is the field capacity, which indicates the maximum water content of the soil; RZSM AWC It is the effective water content of the soil, indicating the amount of water that the soil can provide to plants for use. The RZSM root zone soil moisture uses actual measured data from the site.

[0053] In step S7, in the agricultural drought index comparison and evaluation, the MSMADI of the present invention is compared with the soil drought index SMADI and SWDI used to evaluate the short-term drought conditions in the crop production area. At the same time, the MSMADI, SMADI and SWDI are also compared with the vegetation status (NDVI) and weather conditions (LST and PRE).

[0054] The embodiments take summer corn and winter wheat of the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria as examples to further describe the technical solution of the method for calculating the soil moisture agricultural drought index dynamically adapted to the crop root development process of the present invention. The embodiments are used to illustrate the present invention, but are not used to limit the scope of application of the present invention, and are also applicable to different regions or time periods.

[0055] The implementation flow chart of the method for calculating the soil moisture agricultural drought index suitable for crop root development of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0056] (1) Data collection and processing;

[0057] In this embodiment, three types of soil moisture related data are collected: ① The ground soil moisture station data comes from the International Soil Moisture Network (ISMN). HOAL has the most dense soil moisture stations when most of the land is farmland. The sensors in the HOAL network provide hourly soil moisture data at depths of 5 cm, 10 cm, 20 cm, and 50 cm. In the present invention, the soil moisture measurement values ​​of a total of 31 soil moisture monitoring stations throughout 2019 are used. According to the quality mark of soil moisture measurement provided by ISMN, only those soil moisture values ​​marked with "G" (Good) are retained. The specific distribution of ground soil moisture stations can be found in Figure 2 , Figure 2 (a) is the land cover of the HOAL study area. Figure 2 (b) is the soil moisture station distribution, and Table 1 provides detailed information:

[0058] Table 1 Soil moisture site information

[0059]

[0060]

[0061] ②Model-based data products include SMAP L4 and ERA5-Land. The SMAP L4 product provides surface soil moisture (SSM) of the top 5 cm and root zone soil moisture (RZSM) of 0-100 cm at a spatial resolution of 9 km and a temporal resolution of 3 hours. The SMAP L4 hourly data is synthesized daily. The ERA5-Land product provides PRE precipitation data and soil moisture data at multiple depths of 0-7 cm, 7-28 cm, and 28-100 cm at a spatial resolution of 0.1° and an hourly temporal resolution. The weighted average formula is used to convert multi-layer soil moisture into SSM and RZSM data; ③Remote sensing data products include Landsat8 and MODIS. Landsat 8 products provide surface reflectance data at a spatial resolution of 30 m and a temporal resolution of 15 days, and thermal infrared band data at a spatial resolution of 100 m. Clear sky images (i.e., cloud cover <5%) are selected. The 2019 Landsat The 8 bands of 8 data, the thermal infrared band is resampled to 30m through cubic convolution, the MOD11A1 product provides the surface temperature LST with 1km spatial resolution and daily time resolution, the MOD09GA product provides the surface reflectance with 500m spatial resolution and daily time resolution, the QC information is used to exclude the pixels affected by cloud cover or cloud shadow, only the pixels with good quality or retrieval error less than 1K are selected, and reprojected to the UTM projection of WGS84 benchmark, finally, the soil moisture data of SMAP L4 and ERA5-Land are used, and the downscaling method based on spatiotemporal fusion and point-surface fusion are adopted to generate daily 30m high-precision, high-resolution multi-layer soil moisture data.

[0062] (2) Construction of drought participation index;

[0063] In this embodiment, it is considered that crop status is more suitable for assessing agricultural drought on a shorter time scale, such as a daily scale. The drought index based on soil moisture, vegetation status and surface temperature will include the main factors describing the impact and causes of drought, and is suitable for medium- and short-term agricultural drought monitoring. Therefore, a soil moisture agricultural drought participation index including the vegetation condition index VCI, the temperature condition index MTCI and the soil moisture condition index SMCI is constructed. Among them, the vegetation condition index VCI can be used to assess the health of surface vegetation. The main function of VCI is to distinguish between vegetation fluctuations caused by short-term weather and long-term ecosystem changes, thereby helping to identify and monitor the impact of drought. Through VCI, the stress on vegetation can be discovered in a timely manner, and important information about the health of vegetation can be provided, especially in agricultural and ecosystem management. The formula for calculating VCI in combination with the NDVI obtained in step (1) is as follows:

[0064]

[0065] In the formula, NDVI i is the NDVI value of the i-th day, NDVI min and NDVI max are the minimum and maximum NDVI values ​​in history, respectively. The VCI value range is between 0 and 1, where 0 indicates an extremely unfavorable vegetation state and 1 indicates the best vegetation state (see Figure 3 (a) in Figure 1). The higher the VCI index, the better the vegetation condition. The lower red areas in the figure mainly appear in the building and farmland areas, and the blue areas are mainly distributed in the forest area, indicating that the VCI index can accurately reflect the vegetation condition in the HOAL area.

[0066] The temperature condition index MTCI reflects the high and low temperature conditions. Since there is a significant inverse relationship between land surface temperature (LST) and vegetation conditions (NDVI), calculating MTCI can highlight the inverse relationship between LST and NDVI and thus reflect the health of surface vegetation. The formula for calculating MTCI combined with the LST obtained in step (1) is as follows:

[0067]

[0068] Where, LST i represents the LST of the i-th day, LST min and LST max The MTCI value ranges from 0 to 1, with 1 indicating extreme heat and 0 indicating humidity or no temperature stress (see Figure 3 (b) in the figure). The higher the MTCI index, the greater the threat of surface temperature. It can be seen that in the high-lying areas to the west of the HOAL region, the temperature threat is stronger, while in the central region, especially in the forest and farmland areas in the middle, the temperature threat is smaller, indicating that vegetation cover has played a role in preventing ground temperature rise.

[0069] The soil moisture status index SMCI can reflect the drought condition of surface soil moisture. The soil moisture status index SMCI is calculated based on the SSM data obtained in step (1). The calculation formula is as follows:

[0070]

[0071] In the formula, SSM max and SSM min It is the normalization of the absolute maximum and minimum values ​​of surface soil moisture in a year. The SMCI value range is also between 0 and 1, where 0 indicates wetness and 1 indicates very dryness (see Figure 3(c) in the figure). SMCI can well judge the drought conditions of crops in the area. The higher the ratio, the worse the survival environment of crops due to temperature, that is, the drier it is. When the SMCI index is higher, the location is in a state of lack of soil moisture compared to the whole year. The SMCI index in the figure is basically consistent with the distribution of local soil moisture.

[0072] (3) Calculation of root zone soil moisture status index (RZSMCI);

[0073] In this embodiment, considering that most of the water demand of crops comes from the root zone rather than the surface, it is not sufficient to only consider the surface soil moisture when defining the soil moisture agricultural drought index. Therefore, the root zone soil moisture RZSM data obtained in step (1) are used as the basis to calculate the root zone soil moisture status index RZSMCI of four layers (5 cm, 10 cm, 20 cm and 50 cm) of summer corn and winter wheat to reflect the soil moisture status of different crops at different depths. The calculation formula is as follows:

[0074]

[0075] Where RZSM max and RZSM min It is the normalization of the absolute maximum and minimum values ​​of soil moisture in the root zone in a year. The value range of RZSMCI is also between 0 and 1, where 0 means moist and 1 means very dry.

[0076] (4) Calculation of crop root length density (RLD);

[0077] In this embodiment, it is considered that the means of obtaining water for crops is mainly to absorb it from the soil through the roots, and different types of crops have different root distributions in different development processes, which leads to the influence on soil water absorption. The previous soil moisture agricultural drought index usually assumes that the use of water by crops is equal in the entire root zone, and ignores this influence. Therefore, this embodiment calculates the root length density of summer corn and winter wheat according to the root distribution law and growth law of two crops, and divides the root zone depth into 5cm, 10cm, 20cm and 50cm according to the root distribution law. According to the growth law, the growth and development stage of summer corn is divided into jointing stage (May-June), silking stage (July-August) and maturity stage (September-October), and the growth and development stage of winter wheat is divided into growth stage (November-March), flowering stage (April) and maturity stage (May-June). For crops at different growth and development stages, the root distribution length was observed and measured in accordance with the requirements of the Meteorological Department's "Agricultural Meteorological Observation Specifications". The root distribution was measured once for each development period. The measurement method was as follows: soil column samples were collected using a soil drill, and after the samples were flushed, the root lengths at different root zone depths were calculated using the cross method. The root length density RLD at the corresponding root zone depth was then calculated based on the obtained root lengths. The estimated root length densities of the two crops at three important development stages are shown in Tables 2 and 3.

[0078] Table 2 Root length density at different root zone depths during the growth and development stages of summer corn

[0079]

[0080] Table 3 Root length density at different root zone depths during the growth and development stages of winter wheat

[0081]

[0082] (5) Construction of soil moisture agricultural drought index (MSMADI)

[0083] In this embodiment, the root zone soil moisture and the root length density of the crop growth and development stage are incorporated into the soil moisture agricultural drought index, and the soil moisture agricultural drought index MSMADI is constructed based on the drought participation index VCI and MTCI obtained in step (2), the RZSMCI obtained in step (3) and the RLD obtained in step (4) to reflect the soil moisture drought conditions of different crops at different growth stages and different root zone depths, thereby improving its performance in agricultural drought monitoring. The calculation formula is as follows:

[0084]

[0085] Where RZSMCI 5cm 、RZSMCI 10cm 、RZSMCI 20cm and RZSMCI50cm Respectively represent the soil moisture index of the root zone in the corresponding interval, RLD all Indicates the root length density of crops in the soil at a depth of 0 to 50 cm, RLD 5cm , RLD 10cm , RLD 20cm and RLD 50cm They respectively represent the root length density in the corresponding interval.

[0086] Based on the soil moisture agricultural drought index of the present invention, the daily MSMADI index (expressed as WSMADI and CSMADI, respectively) for winter wheat and summer corn in the HOAL region was calculated, and the average drought index of the two crops at three important growth and development stages was calculated. The spatial distribution is shown in Figure 4 As shown, Figure 4 (a) is the CSMADI of summer corn at the jointing stage. Figure 4 (b) is the CSMADI of summer corn during the silking period. Figure 4 (c) is the CSMADI of summer corn at maturity. Figure 4 (d) is the WSMADI of winter wheat during its growth period. Figure 4 (e) is the WSMADI of winter wheat during its growth period. Figure 4 (f) in the figure is the WSMADI of winter wheat during maturity. Overall, neither crop encountered a serious drought threat in 2019 during the growth and development stage, and the MSMADI index was between 0 and 0.4, that is, it was wet or slightly dry. From the perspective of water demand during the growth stage, winter wheat has a higher water demand. The winter wheat maturity stage and summer corn jointing stage are in the same time period (both in May and June). From the drought index distribution map, it can be seen that the drought degree of winter wheat during maturity is higher than that of summer corn jointing. There are more yellow and red distribution areas in the figure. This may be due to the more developed root system of winter wheat during maturity, and the lack of soil moisture in the deeper root zone, resulting in a more arid state. The drought degree of summer corn silking stage is significantly stronger than the other stages. This is in July and August 2019. From the temporal change trend of soil moisture in the root zone in previous studies, it can be seen that July and August are the lowest time periods of RZSM in 2019, which leads to the occurrence of agricultural drought during the silking stage of summer corn. The winter wheat growth and flowering period was in the winter and spring of 2019, and the RZSM during this period was the highest in the year, so drought conditions rarely occurred. These results prove that MSMADI has a higher potential in determining weather drought conditions compared with environmental conditions alone.

[0087] (6) Calculation of soil water deficit index (SWDI);

[0088] In this embodiment, the soil moisture deficit index SWDI is calculated to reflect the soil moisture content at different root zone depths. The SWDI index is calculated using the root zone soil moisture site data RZSM provided by the HOAL soil moisture network ISMN obtained in step (1). Therefore, the soil moisture content takes into account the decrease in water volume or the change in soil moisture content, and the calculation formula is:

[0089]

[0090] Where RZSM FC is the field capacity, which indicates the maximum water content of the soil; RZSM AWC It is the effective water content of the soil, indicating the amount of water that the soil can provide to plants for use. The root zone soil moisture uses the actual measured data of the site. AWC It is RZSM FC and withering point (RZSM wp ). RZSM wp It is calculated by the hydraulic characteristic curve of the soil column sampled at each depth for measuring soil moisture content. Since the HOAL study area is relatively small, the soil type and crop type are basically the same. Based on the soil composition analysis of each site, RZSM wp Defined as 0.29m 3 m -3 .

[0091] (7) Comparison and evaluation of agricultural drought index: In this embodiment, a variety of agricultural drought indices are compared and evaluated. First, considering that SMADI is a common agricultural drought index, the MSMADI proposed in the present invention is compared with the soil drought index SMADI and SWDI used to evaluate short-term drought conditions in crop production areas, wherein the SMADI calculation formula is as follows:

[0092]

[0093] SMADI reflects the surface soil moisture drought condition considering only the soil moisture deficit state itself (see Figure 3 (d) in the figure). The SMADI index is calculated by combining the first three indices VCI, MTCI and SMCI. The higher the SMADI value, the greater the impact of soil moisture on the survival conditions of crops in the region. Soil water shortage leads to agricultural drought. As can be seen from the figure, most areas in the HOAL region were humid on March 22. Only a few built-up areas had a high agricultural drought index due to the lack of vegetation, which was manifested as a state of crop water shortage.

[0094] Although SWDI uses root zone soil moisture as a key parameter, it uses site measured data, while MSMADI uses high-precision, high-resolution field-scale multi-layer soil moisture data generated in the aforementioned study. Therefore, the data sources of the two are independent and can be compared. The agricultural soil drought status represented by different value ranges of the three main drought indices is shown in Table 4.

[0095] Table 4 The value range of agricultural drought index under different agricultural drought conditions

[0096]

[0097] In order to verify the MSMADI index, three representative farmland sites located in the HOAL study area, HOAL-08, HOAL-17 and HOAL-21, were selected to calculate their daily MSAMDI index, SMADI index and SWCI index in 2019. The time variation curves of the three indices at these three sites in 2019 were plotted. Figure 5 The temporal changes of the MSMADI index (WSMADI and CSMADI, respectively) and SMADI for winter wheat and summer maize are shown. Figure 5 (a) is the HOAL-08 area. Figure 5 (b) is the HOAL-17 region. Figure 5 (c) in the figure is the HOAL-21 region. Overall, the temporal variation trends of the three indices remain strongly similar, fluctuating between 0 and 0.6 throughout the year, with obvious seasonality. The drought degree is significantly higher in the hot summer, and most of the time it is above 0.2, indicating that it has experienced abnormal dryness and mild drought, which is consistent with the seasonal distribution of soil moisture. The drought index is lowest in April and May, when the HOAL temperature is low and the rainfall is sufficient, which is in line with the local climatic conditions. The MSMADI values ​​of the three stations are slightly lower than SMADI most of the time, because SMADI only considers the surface soil moisture, while the MSMADI study covers the entire root zone soil. This is because the surface soil moisture will evaporate faster from the ground due to the daily soil temperature difference and the influence of ground evaporation, while the deeper the soil moisture, the smaller the daily soil temperature difference and the less affected by soil evaporation, so it can retain more soil moisture. MSMADI reacts more strongly than SMADI when there is a certain intensity of abnormal dryness and agricultural drought. As can be seen from the figure, when the agricultural drought index is greater than 0.3, the value of the MSMADI index is higher than that of the SMADI index. These results show that MSMADI is more able to consider the impact of agricultural meteorology on crops than SMADI.

[0098] Figure 6The time variation trend of WSMADI, CSMADI and SWDI is shown. Figure 6 (a) is the HOAL-08 area. Figure 6 (b) is the HOAL-17 region. Figure 6 (c) in the figure is the HOAL-21 region. Overall, the temporal changes of the two different agricultural drought indices show a relative trend. As the MSMADI index increases, the SWDI index decreases, which shows that both indices can correctly reflect the agricultural drought status in the HOAL region. Especially in the summer when agricultural drought is most severe, both indices can accurately reflect whether the farmland site is in an abnormally dry mild drought state or a moderate drought. From Tables 2 and 3, it can be seen that in July and August in summer, crops mainly absorb water from 20cm to 50cm in the root zone, and the soil in this range is less likely to be lost. Therefore, the water content is high, and the crops will not be subjected to strong soil drought stress. The research results show that both MSMADI and SWDI indices can well reflect the drought stress of crops due to low soil moisture content in the root zone, and the MSMADI index can provide a more accurate agricultural soil drought status through analysis of the crop root system.

[0099] In order to accurately compare the three agricultural drought indices, the three agricultural drought indices at the daily scale in 2019 were calculated for all stations, and the scatter plots of the MSMADI index, SMADI and SWDI indexes were drawn, as shown in Figure 2. Figure 7 As shown. In the figure, the x-axis represents the SMDAI index and the SWDI index, the positive triangle represents the WSMADI index, and the inverted triangle represents the CSMADI index. Figure 7 As can be seen in (a), the two MSMADI indices are highly positively correlated with the SMADI index, with correlation coefficients of 0.996 and 0.997. The scatter points are basically distributed near the fitting line. When the index value is less than 0.4, most of the scatter points are located below the fitting line, indicating that when the drought degree is no drought or mild drought, the MSMADI index will be underestimated than the SMADI index; when the index value is greater than 0.4, most of the scatter points are located above the fitting line, indicating that the MSMADI index is overestimated than the SMADI index. This is consistent with the previous research results on time changes. Figure 7 As can be seen in (b), the MSMADI index is negatively correlated with the SWDI index, with correlation coefficients of -0.62 and -0.63, respectively. When SWDI is positive, it means there is no drought. At this time, the corresponding MSMADI index is mostly between 0 and 0.2, and the drought situation described is consistent with the SWDI index. The correlation between the SMADI index and the SWDI index is -0.587, which is lower than the MSMADI index, indicating that the MSMADI index can better reflect the overall drought situation of the soil in the study area.

[0100] Secondly, in order to evaluate the response of the three drought indices to the environmental factors in the study area, MSMADI, SMADI and SWDI were compared with vegetation status (NDVI) and weather conditions (LST and PRE), and the correlations between the three agricultural drought indices and LST, NDVI, RZSM and PRE were calculated. The results are shown in Table 5.

[0101] Table 5 Correlation between four agricultural drought indices and environmental factors

[0102]

[0103]

[0104] Overall, the correlations are consistent with the logic of change. When LST increases, it will become more arid, and when NDVI, RZSM and PRE are smaller, it will become more arid. The correlation of the MSMADI index of the two crops is higher than that of the SMADI index and the SWDI index, which proves that it can better reflect the environmental and climate changes in the study area. Among the four environmental factors, rainfall is not a key parameter in the change of agricultural drought index. It can be seen that the strongest correlation between CSMADI and WSMADI is -0.436 and -0.443. Rainfall is one of the most commonly used drought monitoring variables. The best correlation with rainfall also proves the feasibility of using the MSMADI index to evaluate short-term small-area precision agriculture drought.

[0105] In order to evaluate the drought situation in the HOAL region in 2019, the annual scale CSMADI index and WSMADI index were calculated for the region. The spatial distribution is shown in Figure 8 As shown, Figure 8 (a) shows the annual average spatial distribution of CSMADI. Figure 8(b) in the figure shows the annual average spatial distribution of WSMADI. Overall, the HOAL region suffers less drought, and the annual scale MSMADI index of the entire region is between 0 and 0.4, that is, it is in a humid state without drought or an abnormally dry and slightly drought state. In the central and northern parts of the study area, the climate is humid and there is sufficient water, so crops will not be affected by drought. There is a large-scale slight drought phenomenon in the west and southwest of the study area. This is due to the high terrain and fast wind speed, which accelerates ground evaporation, resulting in excessive loss of soil moisture and causing agricultural drought. The eastern and southeastern parts of the study area belong to the construction area and the farmland affected by the construction area. The temperature in the construction area is relatively high, which leads to the occurrence of agricultural drought. The wettest place in the study area is located in the farmland and woodland area in the middle. This is because the area is surrounded by forests, which can effectively control the surface temperature, and the forest acts as a windbreak, effectively controlling ground evaporation. The spatial distribution of the CSMADI index and the WSMADI index are generally similar, but there are differences in some places. In the northwest of the study area, the value of the CSMADI index is significantly higher than the WSMADI index. This phenomenon can also be found in other areas. This is caused by the different root length densities of winter wheat and summer corn. Therefore, when planting summer corn, more water should be irrigated than when planting winter wheat to meet the growth needs of the plant.

[0106] In addition, the drought frequency at each station in the study area was calculated and their spatial distribution was mapped (see Fig. 9 In (a)), the drought frequency is defined as the ratio of the number of days with drought in a year to the whole year at the station. It can be seen that the drought frequency is lower in forests and stations around forests, proving that forests can effectively prevent agricultural droughts. The drought frequency is higher in grasslands and farmlands because grasslands and farmlands have large changes in vegetation cover due to changes in planting seasons, and their relative moisture retention capacity is poor, making them more susceptible to agricultural droughts. The total drought frequency of all stations in each month is as follows: Fig. 9 As shown in (b), it can be seen that the frequency of drought is lowest in spring and autumn because of the high rainfall and low temperature. Although there is more precipitation in summer, the frequency of drought is high due to the high temperature and high water demand of plants. This proves that the MSMADI index proposed by the present invention can accurately grasp the agricultural drought status of different crop areas in space and time, and provide scientific help for precision agricultural irrigation in a small range and short time.

[0107] See also Fig.10 , Fig.10 It is a schematic diagram of the working of the hardware device of an embodiment of the present invention, and the hardware device specifically includes: a soil moisture agricultural drought index calculation device 401, a processor 402 and a storage device 403.

[0108] A soil moisture agricultural drought index calculation device 401 adapted for crop root system development: The soil moisture agricultural drought index calculation device 401 implements the soil moisture agricultural drought index calculation method adapted for crop root system development.

[0109] Processor 402: The processor 402 loads and executes the instructions and data in the storage device 403 to implement the soil moisture agricultural drought index calculation method suitable for crop root development.

[0110] Storage device 403: The storage device 403 stores instructions and data; the storage device 403 is used to implement the soil moisture agricultural drought index calculation method suitable for crop root development.

[0111] The beneficial effects of the present invention are:

[0112] (1) The present invention introduces the soil moisture status of the root zone into the original SMADI index, and constructs a soil moisture agricultural drought index calculation method that dynamically adapts to the crop root development process to calculate the index MSMADI, which can more accurately reflect the agricultural drought status and realize precise agricultural monitoring and management.

[0113] (2) The present invention calculated the MSMADI of two main crops, winter wheat and summer corn, in the HOAL region at different growth stages, and conducted a spatial and temporal assessment of the drought situation in the HOAL region in 2019, revealing the drought situation and its influencing factors in different regions and seasons.

[0114] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calculating the soil moisture agricultural drought index adapted to crop root development, characterized in that: The following steps are involved: Step S1: Data collection and processing: soil moisture related variables NDVI, LST and PRE, soil moisture remote sensing data SSM, RZSM and site soil moisture RZSM are collected and preprocessed; Step S2: Construction of drought participation index: Based on the NDVI, LST and SSM data obtained in step S1, the participation indexes VCI, MTCI and SMCI of the soil moisture agricultural drought index are obtained; Step S3: Calculation of the root zone soil moisture status index RZSMCI: Based on the RZSM data obtained in step S1, the RZSMCI of the crop is calculated to reflect the soil moisture status of different crops at different depths; Step S4: Calculation of crop root length density RLD: Calculate the root length density of crops at different root zone depths at different growth and development stages according to the root distribution and growth law of the crops; Step S5: Construction of soil moisture agricultural drought index MSMADI: Based on the drought participation index VCI and MTCI obtained in step S2, the RZSMCI obtained in step S3 and the RLD obtained in step S4, MSMADI is obtained to reflect the soil moisture drought conditions of different crops at different growth stages and different root zone depths; Step S6: Calculation of soil water deficit index SWDI: Calculate SWDI based on the site soil water content RZSM data obtained in step S1 to reflect the actual soil water content at different root zone depths; Step S7: Comparison and evaluation of agricultural drought indices; compare MSMADI with the existing agricultural drought index SMADI and soil water deficit index SWDI to verify the accuracy and reliability of MSMADI in different crop areas, and then compare MSMADI, SMADI and SWDI with vegetation status NDVI, LST and precipitation PRE to evaluate the response of drought index to environmental factors.

2. The method for calculating the soil moisture agricultural drought index suitable for crop root development according to claim 1, characterized in that: In step S1, the soil moisture related variables NDVI and LST data are from remote sensing data products MODIS and Landsat 8, soil moisture remote sensing data are from SMAP L4 and ERA5-Land, and site soil moisture data are from the International Soil Moisture Network ISMN.

3. The method for calculating the soil moisture agricultural drought index suitable for crop root development according to claim 1, characterized in that: In step S2, the drought participation index includes the vegetation condition index VCI, the temperature condition index MTCI, and the soil moisture condition index SMCI, and the calculation formula is as follows: In the formula, NDVI i is the NDVI value of the i-th day, NDVI min and NDVI max are the minimum and maximum NDVI values ​​in historical records, respectively. The VCI value range is between 0 and 1, where 0 indicates extremely unfavorable vegetation conditions and 1 indicates the best vegetation conditions; LST i represents the LST of the i-th day, LST min and LST max are the maximum and minimum LST in history respectively; MTCI ranges from 0 to 1, 1 indicates extreme heat, while 0 indicates humidity or no temperature stress; SMCI stands for Soil Moisture Status Index, SSM i represents the SSM of the i-th day, SSM max and SSM min It is the normalization of the absolute maximum and minimum values ​​of surface soil moisture in a year.

4. The method for calculating the soil moisture agricultural drought index suitable for crop root development according to claim 1, characterized in that: In step S3, the calculation formula of the root zone soil moisture status index RZSMCI is as follows: Where RZSM i is the RZSM of the i-th day, RZSM max and RZSM min It is the normalization of the absolute maximum and minimum values ​​of soil moisture in the root zone during the year.

5. The method for calculating the soil moisture agricultural drought index suitable for crop root development according to claim 1, characterized in that: In step S4, for crops at different growth and development stages, the root distribution length is observed and measured in accordance with the requirements of the "Agricultural Meteorological Observation Specifications" of the meteorological department. The root distribution is measured once for each development period. The measurement method is: use a soil drill to collect soil column samples, flush the samples, and use the cross method to calculate the root length of different root zone depths, and then calculate the root length density RLD of the corresponding root zone depth based on the obtained root length.

6. A method for calculating the soil moisture agricultural drought index suitable for crop root development as claimed in claim 5, characterized in that: In step S4, the crops are summer corn and winter wheat. The root zone depths are divided into 5 cm, 10 cm, 20 cm and 50 cm according to the root distribution law. The growth and development stages of summer corn are divided into jointing stage, silking stage and maturity stage according to the growth law, and the growth and development stages of winter wheat are divided into growth stage, flowering stage and maturity stage.

7. A method for calculating the soil moisture agricultural drought index suitable for crop root development as claimed in claim 6, characterized in that: In step S5, considering that crops mainly obtain water from the soil through roots, and different species have different root distributions at different growth stages, which affects soil water absorption, it is not sufficient to only consider surface soil moisture when defining the soil moisture agricultural drought index. Therefore, the root zone soil moisture and root length density at the growth and development stage are included in the soil moisture agricultural drought index to construct the soil moisture agricultural drought index MSMADI. The calculation formula is as follows: Where RZSMCI represents the root zone soil moisture index, RZSMCI 5cm 、RZSMCI 10cm 、RZSMCI 20cm and RZSMCI 50cm It represents the soil moisture status index of the root zone of crops at a depth of 5cm, 10cm, 20cm and 50cm respectively. VCI represents the drought participation index including the vegetation condition index. RLD all Indicates the root length density of crops in the soil at a depth of 0 to 50 cm, RLD 5cm , RLD 10cm , RLD 20cm and RLD 50cm Respectively represent the root length density of crops at depths of 5cm, 10cm, 20cm and 50cm.

8. The method for calculating the soil moisture agricultural drought index suitable for crop root development according to claim 1, characterized in that: In step S6, the soil water deficit index SWDI is calculated using the following formula: Where RZSM FC is the field capacity, which indicates the maximum water content of the soil; RZSM AWC It is the effective water content of the soil, indicating the amount of water that the soil can provide for plants to use; RZSM represents the soil moisture at the site, using the actual measured data of the site.

9. A storage device, characterized in that: The storage device stores instructions and data for implementing a method for calculating a soil moisture agricultural drought index suitable for crop root development as described in any one of claims 1 to 8.

10. A soil moisture agricultural drought index device adapted to crop root development, characterized in that: include: A processor and a storage device; the processor loads and executes instructions and data in the storage device to implement a method for calculating a soil moisture agricultural drought index suitable for crop root development as described in any one of claims 1 to 8.

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