Cotton drip irrigation drought monitoring system and method based on satellite remote sensing

Abstract

The present invention discloses a cotton sub-film drip irrigation drought monitoring system based on satellite remote sensing. The monitoring system comprises: a soil analysis and division module, a meteorological monitoring module, a phenological period observation module, a cotton growth monitoring module, and a drought monitoring and early warning module. In order to better realize the functions of the above system, a cotton sub-film drip irrigation drought monitoring method based on satellite remote sensing is also proposed. The method takes into account the soil differences in different areas of the cotton field. When generating a cotton growth model, a comprehensive growth index is used instead of a single growth index, so that the obtained cotton growth model is more stable.

Description

Technical Field

[0001] The present invention relates to the technical field of drought monitoring, and in particular to a cotton sub-mulch drip irrigation drought monitoring system and method based on satellite remote sensing. Background Art

[0002] Most current research on crop growth monitoring estimates single indicators such as plant water content, nitrogen content, chlorophyll content, and biomass to achieve the purpose of crop growth monitoring. Few studies have comprehensively considered these growth indicators to construct new indicators that can reflect the overall growth status. The monitoring results obtained by monitoring a single indicator are often unstable. At the same time, current research on growth monitoring does not divide crop planting areas based on soil conditions, resulting in supervisors being unable to accurately monitor crop drought conditions in specific areas. At the same time, they are also unable to grasp the severity of drought conditions in different regions and provide accurate irrigation compensation for areas lacking irrigation. Summary of the Invention

[0003] The purpose of the present invention is to provide a cotton drip irrigation drought monitoring system and method based on satellite remote sensing to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a cotton drip irrigation drought monitoring system based on satellite remote sensing, the monitoring system comprising: a soil analysis and classification module, a meteorological monitoring module, a phenological period observation module, a cotton growth monitoring module, and a drought monitoring and early warning module;

[0005] The soil analysis module is used to analyze and classify the different irrigation conditions of soil in different areas of cotton fields;

[0006] Meteorological monitoring module, used to monitor and forecast meteorological data in cotton fields;

[0007] The phenological period observation module is used to receive meteorological information from the meteorological monitoring module and record the growth index data of cotton phenological period in combination with the remote sensing monitoring data obtained from the cotton field;

[0008] The cotton growth monitoring module is used to generate a cotton growth model based on historical cotton growth index data, and determine the final growth index for real-time monitoring of cotton growth in the cotton growth model; at the same time, the growth index in the cotton field is monitored in real time;

[0009] The drought monitoring and early warning module is used to receive relevant data from the soil analysis and classification module, the meteorological monitoring module, the phenological period observation module, and the cotton growth monitoring module to generate the drought level of the cotton field, and report the drought level to the supervisors for early warning.

[0010] Furthermore, the soil analysis module includes: a region division unit, an irrigation setting unit, a sampling unit, a soil analysis unit, and an irrigation gradient setting unit;

[0011] A regional division unit is used to divide the cotton field into regions based on the difference in solar radiation intensity in each region of the cotton field during the day;

[0012] An irrigation setting unit is used to set an irrigation gradient in terms of irrigation water volume and irrigation frequency according to the gradient divided by the area to conduct an irrigation test on the cotton field;

[0013] Sampling units are used to sample cotton field soil in each area;

[0014] Soil analysis unit, used to analyze and measure the physical and chemical properties of the sampled soil;

[0015] The irrigation deficiency gradient setting unit is used to set the gradient data of the irrigation deficiency situation of the soil in each area according to the physical and chemical properties of the soil obtained by analysis and measurement.

[0016] Furthermore, the cotton growth model module includes a multi-temporal remote sensing vegetation index calculation unit, a comprehensive growth index acquisition unit, a cotton growth model generation unit, a remote sensing vegetation index sensitivity analysis unit, and a growth index locking unit;

[0017] The multi-temporal remote sensing vegetation index calculation unit is used to quantify the pre-stored historical multi-temporal remote sensing data of cotton from the seedling stage to the flowering and boll stage and obtain the historical multi-temporal remote sensing vegetation index of the cotton field;

[0018] The comprehensive growth index sampling unit is used to collect indicators that will affect the growth of cotton during the growth period, and to comprehensively construct new indicators that can reflect the overall growth status by collecting the collected indicators;

[0019] A cotton growth model generating unit generates a growth model of the cotton growth period using the new index obtained by the receiving comprehensive growth index taking unit;

[0020] The sensitivity analysis unit of remote sensing vegetation index is used to analyze the sensitivity of different remote sensing vegetation indices in the growth model of cotton growth period by combining comprehensive growth assessment index with growth index data appearing in cotton phenological period;

[0021] The growth index locking unit is used to select the remote sensing vegetation index that is most sensitive to the cotton growth level in the cotton growth model as the growth index to perform real-time monitoring of cotton growth.

[0022] Furthermore, the drought monitoring and early warning module includes a soil moisture spatial analysis unit, a data matching unit, a drought level judgment unit, and an early warning unit;

[0023] A soil moisture spatial analysis unit is configured to generate an abnormal signal when the received growth index deviates from the historical index data by more than a deviation threshold, and perform spatial sampling and analysis of the soil moisture in the area based on the abnormal signal;

[0024] a data matching unit for matching the sampling analysis data obtained in the soil moisture spatial analysis unit with the irrigation gradient data obtained in the soil analysis module;

[0025] The drought level judgment unit is used to receive the matching data from the data matching unit and judge the drought level of the cotton field in combination with the meteorological information obtained from the meteorological monitoring module.

[0026] In order to better complete the functions of the above system, a method for monitoring drought conditions of cotton drip irrigation under mulch film based on satellite remote sensing is proposed. The monitoring method includes:

[0027] S100: Divide the cotton field into regions based on the differences in solar radiation intensity across the day; test the soil in each of the divided cotton field regions at different irrigation water volume gradients and irrigation frequencies; sample soil in each of the tested cotton field regions, and analyze and measure the physical and chemical properties of the soil samples; obtain soil moisture gradient data for each region experiencing irrigation deficiency; and classify the soil moisture gradient data into three gradients: mild, moderate, and severe.

[0028] S200: Install a small weather station in the cotton field under drip irrigation to monitor and forecast field weather data; weather data includes rainfall, relative humidity, solar radiation intensity, maximum temperature, and minimum temperature;

[0029] S300: Combine meteorological data with remote sensing technology to record growth index data during cotton phenological periods.

[0030] S400: Based on remote sensing technology, historical cotton growth index data is obtained and a cotton growth model is generated. The remote sensing vegetation index that is most sensitive to cotton growth levels is selected from the cotton growth model as the growth index to perform real-time cotton growth monitoring.

[0031] S500: Retrieving historical index data of the growth index, generating an abnormal signal when the growth index obtained by real-time monitoring in the cotton field in the region deviates from the historical index data by more than a deviation threshold, and sampling and analyzing the soil moisture space in the region;

[0032] S600: Generate the drought level of the cotton field based on the sampling and analysis results in step S500, combined with the soil moisture gradient data in step S100 and the real-time meteorological data received, and report the drought level to supervisors for early warning.

[0033] Furthermore, step S100 includes:

[0034] S101: Based on the standard suitable range of solar radiation intensity for cotton leaves, the duration of time during which the solar radiation intensity of each area of ​​the cotton field is within the standard suitable range is accumulated; the accumulated duration data is combined with remote sensing vegetation index data to set a gradient for regional division;

[0035] S102: performing an irrigation test on the cotton field according to the irrigation gradients set in the irrigation water volume and irrigation frequency;

[0036] S103: Collect 30 cm of soil from the surface layer of each cotton field area, and simultaneously excavate three sections 1 m deep at different locations in each cotton field area. Use a circular cutter to vertically sample the soil at a depth of 10 cm to analyze and measure the physical and chemical properties of the sampled soil. The physical and chemical properties of the soil include soil water content, water storage capacity, water evaporation rate, and water dissipation rate.

[0037] S104: obtaining gradient data of soil irrigation shortage in each area based on the soil physical and chemical properties obtained in step S103;

[0038] In the above steps, the cotton fields are divided into regions taking into account that different areas in the cotton fields receive different solar radiation intensities during the day, so the actual breeding environment and water shortage degree enjoyed by cotton in different areas in the soil under the same irrigation conditions are also different; based on this difference, different irrigation levels are set for detection to obtain the gradient data of irrigation shortage in each area, which is conducive to the efficient and accurate monitoring of the drought conditions in the cotton fields in each area in the subsequent steps.

[0039] Furthermore, step S400 includes:

[0040] S401: pre-storing historical multi-temporal remote sensing data of cotton from the seedling stage to the flowering and boll stage, and calculating two or more historical multi-temporal remote sensing vegetation indices after quantifying the historical multi-temporal remote sensing data;

[0041] S402: Combine historical multi-temporal remote sensing vegetation indices with leaf area index, aboveground biomass, and plant water content to construct a comprehensive growth index to generate a growth model for the cotton growing season;

[0042] S403: Based on the quantitative comprehensive growth assessment indicators and combined with the growth indicator data appearing in the cotton phenological period, sensitivity analysis of different remote sensing vegetation indices is performed to select the remote sensing vegetation index that is most sensitive to the cotton growth level for cotton growth monitoring;

[0043] The above-mentioned cotton growth model generated by constructing a comprehensive growth index takes into account multiple multi-dimensional indicators in the cotton growth process, so that the results of real-time cotton growth monitoring based on the cotton growth model in the subsequent steps are more accurate; and adding sensitivity analysis of different remote sensing vegetation indices and screening the remote sensing vegetation index that is most sensitive to the cotton growth level to monitor cotton growth can greatly reduce the data processing burden of the system and improve the monitoring efficiency and real-time performance of the system.

[0044] Furthermore, the historical multi-temporal remote sensing data refers to multi-temporal remote sensing data including cotton plant height, cotton leaf area, cotton stem diameter, number of cotton fruiting branches, number of cotton buds, number of cotton flowers, and number of cotton bolls.

[0045] Furthermore, the sampling and analysis of the soil moisture space in the area in step S500 includes:

[0046] S501: setting a random point every interval threshold in the area, determining the number of sampling points based on the set interval threshold, and applying GPS for navigation and positioning;

[0047] S502: For each point, two soil layers are collected and a stratified gradient is set, with the first gradient being the a layer and the second gradient being the b layer; the spatial characteristics of soil moisture and nutrients are analyzed using classical statistics and geostatistics respectively;

[0048] The above-mentioned sampling and analysis of the soil moisture space in the area is actually a process of confirming the abnormal signal of degree, and random sampling of soil can ensure that the soil samples taken are random and representative of the area.

[0049] Furthermore, step S600 includes:

[0050] S601: matching the soil sampling analysis results obtained in step S500 with the three soil moisture gradient data of the irrigation shortage situation in step S100;

[0051] S602: When the matching result shows that the overlap rate between the soil sampling analysis result and the data of the severe gradient is higher than the overlap rate between the data of other gradients, the drought condition of the cotton fields in the area is determined to be the first level;

[0052] S603: When the matching result shows that the overlap rate between the soil sampling analysis results and the data of the moderate gradient is higher than the overlap rate between the data of the other gradients, the real-time meteorological data information is retrieved, and the estimated duration of the irrigation shortage relief is obtained based on the meteorological data information. If the estimated duration is less than the system-set duration threshold, the cotton field drought condition in the area is classified as the second level; if the estimated duration is greater than the system-set duration threshold, the cotton field drought condition in the area is classified as the first level.

[0053] S604: When the matching result shows that the overlap rate between the soil sampling analysis result and the data of the slight gradient is higher than the overlap rate between the data of other gradients, the drought condition of the cotton fields in the area is determined to be the third level;

[0054] In the above process of determining the drought condition in the cotton field, the matching conditions of the three types of soil moisture gradient data and the meteorological data information corresponding to the irrigation shortage in step S100 are taken into consideration. The estimated duration for alleviating the irrigation shortage based on the meteorological data information can provide a reference data for supervisors, that is, a benchmark for determining whether the drought condition in the cotton field is serious and whether it needs to be dealt with as soon as possible. The larger the drought level number, the less urgent the drought condition, and the supervisors are required to provide irrigation compensation for the cotton field.

[0055] Compared with the existing technology, the beneficial effects achieved by the present invention are: the present invention divides the cotton field into regions based on the differences in light intensity of the cotton field and obtains the irrigation-deficient soil moisture gradient data for the cotton fields in different regions, thereby taking into account the soil differences in different regions of the cotton field; the present invention uses a comprehensive growth index rather than a single growth index when generating a cotton growth model, so that the cotton growth model is more stable; the present invention analyzes and screens the sensitivity of the remote sensing vegetation index in the cotton growth model, and uses the most sensitive vegetation index to monitor the cotton growth, which improves the effectiveness of monitoring the drought in the cotton field. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0057] Figure 1 This is a schematic structural diagram of a cotton drip irrigation drought monitoring system based on satellite remote sensing according to the present invention;

[0058] Figure 2 The present invention is a schematic flow chart of a method for monitoring drought conditions in cotton drip irrigation under film based on satellite remote sensing. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] See also Figure 1-2The present invention provides a technical solution: a cotton drip irrigation drought monitoring system based on satellite remote sensing, the monitoring system includes: a soil analysis and classification module, a meteorological monitoring module, a phenological period observation module, a cotton growth monitoring module, and a drought monitoring and early warning module;

[0061] The soil analysis module is used to analyze and classify the different irrigation conditions of soil in different areas of cotton fields;

[0062] Among them, the soil analysis module includes: regional division unit, irrigation setting unit, sampling unit, soil analysis unit, and irrigation gradient setting unit;

[0063] The regional division unit is used to realize regional division of the cotton field based on the difference in solar radiation intensity of each area of ​​the cotton field during the day; the irrigation setting unit is used to set the irrigation gradient of irrigation water volume and irrigation frequency according to the gradient of regional division to conduct irrigation test on the cotton field; the sampling unit is used to sample the cotton field soil in each area; the soil analysis unit is used to analyze and measure the physical and chemical properties of the sampled soil; the irrigation deficiency gradient setting unit is used to set the gradient data of the soil deficiency situation in each part of the area according to the physical and chemical properties of the soil obtained by analysis and measurement;

[0064] Meteorological monitoring module, used to monitor and forecast meteorological data in cotton fields;

[0065] The phenological period observation module is used to receive meteorological information from the meteorological monitoring module and record the growth index data of cotton phenological period in combination with the remote sensing monitoring data obtained from the cotton field;

[0066] The cotton growth monitoring module is used to generate a cotton growth model based on historical cotton growth index data, and determine the final growth index for real-time monitoring of cotton growth in the cotton growth model; at the same time, the growth index in the cotton field is monitored in real time;

[0067] Among them, the cotton growth model module includes a multi-temporal remote sensing vegetation index calculation unit, a comprehensive growth index acquisition unit, a cotton growth model generation unit, a remote sensing vegetation index sensitivity analysis unit, and a growth index locking unit;

[0068] The multi-temporal remote sensing vegetation index calculation unit is used to quantify the pre-stored historical multi-temporal remote sensing data of cotton from the seedling stage to the flowering and boll stage and obtain the historical multi-temporal remote sensing vegetation index of the cotton field; the comprehensive growth index sampling unit is used to collect indicators that will affect the growth of cotton during the growth period, and comprehensively construct new indicators that can reflect the overall growth conditions based on the collected indicators; the cotton growth model generation unit uses the new indicators obtained by the comprehensive growth index sampling unit to generate a growth model for the cotton growth period; the remote sensing vegetation index sensitivity analysis unit is used to combine the comprehensive growth evaluation index with the growth index data appearing in the cotton phenological period to analyze the sensitivity of different remote sensing vegetation indices in the growth model of the cotton growth period; the growth index locking unit is used to screen the remote sensing vegetation index that is most sensitive to the cotton growth level in the cotton growth model as the growth index to perform real-time monitoring of cotton growth.

[0069] The drought monitoring and early warning module is used to receive relevant data from the soil analysis and classification module, the meteorological monitoring module, the phenological period observation module, and the cotton growth monitoring module to generate the drought level of the cotton field and report the drought level to the supervisors for early warning;

[0070] Among them, the drought monitoring and early warning module includes soil moisture spatial analysis unit, data matching unit, drought level judgment unit, and early warning unit;

[0071] The soil moisture spatial analysis unit is used to generate an abnormal signal when the received growth index deviates from the historical index data by more than a deviation threshold, and to sample and analyze the soil moisture space in the area based on the abnormal signal; the data matching unit is used to match the sampling analysis data obtained in the soil moisture spatial analysis unit with the irrigation gradient data obtained in the soil analysis module; the drought level judgment unit is used to receive the matching data from the data matching unit and judge the drought level of the cotton field in combination with the meteorological information obtained in the meteorological monitoring module.

[0072] In order to better complete the functions of the above system, a method for monitoring drought conditions of cotton drip irrigation under mulch film based on satellite remote sensing is proposed. The monitoring method includes:

[0073] S100: Divide the cotton field into regions based on the differences in solar radiation intensity across the day; test the soil in each of the divided cotton field regions at different irrigation water volume gradients and irrigation frequencies; sample soil in each of the tested cotton field regions, and analyze and measure the physical and chemical properties of the soil samples; obtain soil moisture gradient data for each region experiencing irrigation deficiency; and classify the soil moisture gradient data into three gradients: mild, moderate, and severe.

[0074] Wherein, step S100 includes:

[0075] S101: Based on the standard suitable range of solar radiation intensity for cotton leaves, the duration of time during which the solar radiation intensity of each area of ​​the cotton field is within the standard suitable range is accumulated; the accumulated duration data is combined with remote sensing vegetation index data to set a gradient for regional division;

[0076] S102: performing an irrigation test on the cotton field according to the irrigation gradients set in the irrigation water volume and irrigation frequency;

[0077] S103: Collect 30 cm of soil from the surface layer of each cotton field area, and simultaneously excavate three sections 1 m deep at different locations in each cotton field area. Use a circular cutter to vertically sample the soil at a depth of 10 cm to analyze and measure the physical and chemical properties of the sampled soil. The physical and chemical properties of the soil include soil water content, water storage capacity, water evaporation rate, and water dissipation rate.

[0078] S104: obtaining gradient data of soil irrigation shortage in each area based on the soil physical and chemical properties obtained in step S103;

[0079] S200: Install a small weather station in the cotton field under drip irrigation to monitor and forecast field weather data; weather data includes rainfall, relative humidity, solar radiation intensity, maximum temperature, and minimum temperature;

[0080] S300: Combine meteorological data with remote sensing technology to record growth index data during cotton phenological periods.

[0081] S400: Based on remote sensing technology, historical cotton growth index data is obtained and a cotton growth model is generated. The remote sensing vegetation index that is most sensitive to cotton growth levels is selected from the cotton growth model as the growth index to perform real-time cotton growth monitoring.

[0082] Wherein, step S400 includes:

[0083] S401: pre-storing historical multi-temporal remote sensing data of cotton from the seedling stage to the flowering and boll stage, and calculating two or more historical multi-temporal remote sensing vegetation indices after quantifying the historical multi-temporal remote sensing data;

[0084] S402: Combine historical multi-temporal remote sensing vegetation indices with leaf area index, aboveground biomass, and plant water content to construct a comprehensive growth index to generate a growth model for the cotton growing season;

[0085] S403: Based on the quantitative comprehensive growth assessment indicators and combined with the growth indicator data appearing during the cotton phenological period, sensitivity analysis of different remote sensing vegetation indices is performed to select the remote sensing vegetation indices most sensitive to the cotton growth level for cotton growth monitoring. The sensitivity analysis here uses the EFAST method, and the professional sensitivity analysis software Simlab is used.

[0086] S500: Retrieving historical index data of the growth index, generating an abnormal signal when the growth index obtained by real-time monitoring in the cotton field in the region deviates from the historical index data by more than a deviation threshold, and sampling and analyzing the soil moisture space in the region;

[0087] Among them, historical multi-temporal remote sensing data refers to multi-temporal remote sensing data including cotton plant height, cotton leaf area, cotton stem diameter, cotton fruiting branch number, cotton bud number, cotton flower number, and cotton boll number;

[0088] The sampling and analysis of the soil moisture space in the area in step S500 includes:

[0089] S501: setting a random point every interval threshold in the area, determining the number of sampling points based on the set interval threshold, and applying GPS for navigation and positioning;

[0090] S502: For each point, two soil layers are collected and a stratified gradient is set, with the first gradient being the a layer and the second gradient being the b layer; the spatial characteristics of soil moisture and nutrients are analyzed using classical statistics and geostatistics respectively;

[0091] S600: Generate the drought level of the cotton field based on the sampling and analysis results in step S500, combined with the soil moisture gradient data in step S100 and the real-time meteorological data received, and report the drought level to supervisors for early warning.

[0092] Step S600 includes:

[0093] S601: matching the soil sampling analysis results obtained in step S500 with the three soil moisture gradient data of the irrigation shortage situation in step S100;

[0094] S602: When the matching result shows that the overlap rate between the soil sampling analysis result and the data of the severe gradient is higher than the overlap rate between the data of other gradients, the drought condition of the cotton fields in the area is determined to be the first level;

[0095] S603: When the matching result shows that the overlap rate between the soil sampling analysis results and the data of the moderate gradient is higher than the overlap rate between the data of the other gradients, the real-time meteorological data information is retrieved, and the estimated duration of the irrigation shortage relief is obtained based on the meteorological data information. If the estimated duration is less than the system-set duration threshold, the cotton field drought condition in the area is classified as the second level; if the estimated duration is greater than the system-set duration threshold, the cotton field drought condition in the area is classified as the first level.

[0096] S604: When the matching result shows that the overlap rate of the soil sampling analysis result with the data of the slight gradient is higher than the overlap rate of the data of other gradients, the drought condition of the cotton fields in the area is determined to be the third level.

[0097] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0098] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A cotton drip irrigation drought monitoring system based on satellite remote sensing, characterized in that: The monitoring system includes: soil analysis and division module, meteorological monitoring module, phenological period observation module, cotton growth monitoring module, and drought monitoring and early warning module; The soil analysis and division module is used to analyze and divide the different situations of soil irrigation shortage in different areas of the cotton field; The meteorological monitoring module is used to monitor and forecast meteorological data in the cotton field; The phenological period observation module is used to receive the meteorological information from the meteorological monitoring module and record the growth index data of the cotton phenological period in combination with the remote sensing monitoring data obtained from the cotton field; The cotton growth monitoring module is used to generate a cotton growth model based on historical cotton growth index data, and determine the growth index for real-time monitoring of cotton growth in the cotton growth model; and simultaneously monitor the growth index in the cotton field in real time; The drought monitoring and early warning module is used to receive relevant data from the soil analysis and classification module, the meteorological monitoring module, the phenological period observation module, and the cotton growth monitoring module to generate a drought level for the cotton field, and to report the drought level to supervisors for early warning; The soil analysis and division module includes: a region division unit, an irrigation setting unit, a sampling unit, a soil analysis unit, and an irrigation gradient setting unit; The region division unit is used to realize regional division of the cotton field based on the difference in solar radiation intensity of each region of the cotton field during the day; The irrigation setting unit is used to set the irrigation gradient in irrigation water volume and irrigation frequency according to the regional division gradient to perform irrigation test on the cotton field; The sampling unit is used to sample the cotton field soil in each area; The soil analysis unit is used to analyze and measure the physical and chemical properties of the sampled soil; The irrigation deficiency gradient setting unit is used to set the gradient data of the irrigation deficiency situation of the soil in each part of the area according to the physical and chemical properties of the soil obtained by analysis and measurement; The cotton growth monitoring module includes a multi-temporal remote sensing vegetation index calculation unit, a comprehensive growth index acquisition unit, a cotton growth model generation unit, a remote sensing vegetation index sensitivity analysis unit, and a growth index locking unit; The multi-temporal remote sensing vegetation index calculation unit is used to quantify the pre-stored historical multi-temporal remote sensing data of cotton from the seedling stage to the flowering and boll stage and obtain the historical multi-temporal remote sensing vegetation index of the cotton field; The comprehensive growth index sampling unit is used to collect indicators that may affect the growth of cotton during its growth period, and to comprehensively construct a new indicator that can reflect the overall growth status based on the collected indicators; The cotton growth model generating unit generates a growth model of the cotton growth period by using the new index received from the comprehensive growth index taking unit; The remote sensing vegetation index sensitivity analysis unit is used to analyze the sensitivity of different remote sensing vegetation indices in the growth model of the cotton growth period by combining the comprehensive growth assessment index with the growth index data appearing in the cotton phenological period; The growth index locking unit is used to screen the remote sensing vegetation index that is most sensitive to the cotton growth level in the cotton growth model as the growth index to perform real-time monitoring of cotton growth.

2. The cotton drip irrigation drought monitoring system based on satellite remote sensing according to claim 1 is characterized in that: The drought monitoring and early warning module includes a soil moisture spatial analysis unit, a data matching unit, a drought level judgment unit, and an early warning unit; The soil moisture spatial analysis unit is configured to generate an abnormal signal when the received growth index deviates from the historical index data by more than a deviation threshold, and perform spatial sampling analysis of the soil moisture in the area based on the abnormal signal; The data matching unit is used to match the sampling analysis data obtained in the soil moisture spatial analysis unit with the irrigation deficiency gradient data obtained in the soil analysis division module; The drought level judgment unit is used to receive the matching data from the data matching unit and judge the drought level of the cotton field in combination with the meteorological information obtained from the meteorological monitoring module.

3. A method for monitoring drought conditions in cotton under-mulch drip irrigation based on satellite remote sensing, characterized in that: The monitoring method comprises: S100: Dividing the cotton field into regions based on differences in solar radiation intensity across the cotton field during the day; testing the soil in each of the divided cotton field regions with different gradient irrigation water amounts and irrigation frequencies; sampling soil in each of the tested cotton field regions, and analyzing and measuring the physical and chemical properties of the soil samples; obtaining soil moisture gradient data for each region where irrigation is insufficient; and classifying the soil moisture gradient data into three gradients: mild, moderate, and severe. S200: Installing a small weather station in a cotton field planted with drip irrigation under film to monitor and forecast field weather data; the weather data includes rainfall, relative humidity, solar radiation intensity, maximum temperature, and minimum temperature; S300: Recording growth index data of cotton phenological periods based on remote sensing technology in combination with the meteorological data; S400: Acquire historical cotton growth index data based on remote sensing technology and generate a cotton growth model, and select the remote sensing vegetation index that is most sensitive to the cotton growth level in the cotton growth model as the growth index to perform real-time monitoring of cotton growth; S500: Retrieving historical index data of the growth index, generating an abnormal signal when the growth index obtained by real-time monitoring in the cotton field in the region deviates from the historical index data by more than a deviation threshold, and sampling and analyzing the soil moisture space in the region; S600: generating a drought level for the cotton field based on the sampling and analysis results in step S500 in combination with the soil moisture gradient data in step S100 and the real-time received meteorological data, and reporting the drought level to supervisors for early warning; S100 includes: S101: Based on the standard suitable range of solar radiation intensity for cotton leaves, the duration of time during which the solar radiation intensity of each area of ​​the cotton field is within the standard suitable range is accumulated; the accumulated duration data is combined with remote sensing vegetation index data to set a gradient for regional division; S102: performing an irrigation test on the cotton field according to the irrigation gradients set in the irrigation water volume and irrigation frequency; S103: collecting 30 cm of soil from the surface layer of each cotton field area, and simultaneously excavating three sections 1 m deep at different locations in each cotton field area, sampling vertically at a depth of 10 cm using a circular cutter to analyze and measure the physical and chemical properties of the sampled soil; the physical and chemical properties of the soil include soil water content, water storage capacity, water evaporation rate, and water dissipation rate; S104: obtaining gradient data of soil irrigation shortage in each area based on the soil physical and chemical properties obtained in step S103; S400 includes: S401: pre-storing historical multi-temporal remote sensing data of cotton from the seedling stage to the flowering and boll stage, and calculating two or more historical multi-temporal remote sensing vegetation indices after quantifying the historical multi-temporal remote sensing data; S402: Combine historical multi-temporal remote sensing vegetation indices with leaf area index, aboveground biomass, and plant water content to construct a comprehensive growth index to generate a growth model for the cotton growing season; S403: Based on the quantified comprehensive growth evaluation index and combined with the growth index data appearing in the cotton phenological period, sensitivity analysis of different remote sensing vegetation indices is performed, and the remote sensing vegetation index that is most sensitive to the cotton growth level is selected to perform cotton growth monitoring.

4. The method for monitoring drought conditions in cotton drip irrigation under film based on satellite remote sensing according to claim 3, characterized in that: The historical multi-temporal remote sensing data refers to multi-temporal remote sensing data including cotton plant height, cotton leaf area, cotton stem diameter, number of cotton fruiting branches, number of cotton buds, number of cotton flowers, and number of cotton bolls.

5. The method for monitoring drought conditions in cotton drip irrigation under mulch film based on satellite remote sensing according to claim 3, characterized in that: The sampling and analysis of the soil moisture space in the region in step S500 includes: S501: setting a random point every interval threshold in the area, determining the number of sampling points based on the set interval threshold, and applying GPS for navigation and positioning; S502: For each point, two layers of soil are collected and a stratified gradient is set, with the first gradient being layer a and the second gradient being layer b; classical statistics and geostatistics are used to analyze the spatial characteristics of soil moisture and nutrients.

6. The method for monitoring drought conditions in cotton drip irrigation under film based on satellite remote sensing according to claim 3, characterized in that: The step S600 includes: S601: matching the soil sampling analysis results obtained in step S500 with the three soil moisture gradient data of the irrigation shortage situation in step S100; S602: When the matching result shows that the overlap rate between the soil sampling analysis result and the data of the severe gradient is higher than the overlap rate between the data of the other gradients, the drought condition of the cotton fields in the area is determined to be the first level; S603: When the matching result shows that the overlap rate between the soil sampling analysis result and the data of the medium gradient is higher than the overlap rate between the data of the other gradients, the real-time meteorological data information is retrieved, and an estimated duration for the irrigation shortage to be alleviated is obtained based on the meteorological data information. When the estimated duration is less than a system-set duration threshold, the drought condition of the cotton fields in the area is classified as the second level; when the estimated duration is greater than the system-set duration threshold, the drought condition of the cotton fields in the area is classified as the first level. S604: When the matching result shows that the overlap rate of the soil sampling analysis result with the data of the slight gradient is higher than the overlap rate of the data of other gradients, the drought condition of the cotton fields in the area is determined to be the third level.

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