Evaluation method, system and device for water ecological suitability of crops in semi-arid windy desert area

Abstract

The invention discloses a semi-arid windy desert area crop water ecological suitability evaluation method, system and device, and relates to the crossing field of agricultural ecology and information technology, and the method comprises the steps: constructing an evaluation index system of crop water ecological suitability; obtaining evaluation target area index data; carrying out growth period inversion on the high-temporal-spatial-resolution NDVI time sequence data to obtain a total precipitation amount in different growth periods, a total water consumption amount of crops and a mean value of soil water contents in different growth periods, and further calculating to obtain precipitation satisfaction rates and water utilization efficiencies in different growth periods; performing standardization processing on the index data; calculating by adopting an entropy weight method to obtain the data weight of each index; based on the standardized data and the data weight of each index, calculating to obtain a crop water ecological suitability index; and evaluating the crop moisture ecological suitability, and determining and evaluating the crop moisture ecological suitability of the target area. The method can provide decision support for crop water-suitable planting structure optimization and a water-saving irrigation technology in the semi-arid windy desert area.

Description

Technical Field

[0001] The present application relates to the interdisciplinary field of agricultural ecology and information technology, and in particular to a method, system and device for evaluating the ecological suitability of crop water in semi-arid sandy areas. Background Art

[0002] Semiarid, windy and sandy regions are water-scarce, with large interannual and seasonal variability in precipitation, resulting in low and unstable crop yields. Furthermore, the region is plagued by severe wind erosion and salinization, posing a serious threat to food security. With global climate change, water resources are becoming increasingly scarce. Accurately assessing regional crop water ecological suitability is crucial for optimizing agricultural layout, ensuring high and stable crop yields, and improving water resource utilization efficiency. Traditional crop water ecological suitability assessments rely on single indicators, such as the water scarcity index and water use efficiency. Data are primarily derived from field experiments or observations at a single site, resulting in significant spatial scale limitations and insufficient representation of spatial heterogeneity. Using a single indicator to assess crop water suitability fails to fully account for the nonlinear interactions of ecological factors. For example, the synergistic effects of soil water retention, such as soil water holding capacity, water content, and salinization, are not quantified, leading to biased assessments of regional water stress. Existing methods fail to incorporate the dynamic changes in crop water requirements at different growth stages into the model framework, leading to errors in identifying water-sensitive periods and reducing assessment accuracy. Although the agricultural water management standard (ISO14046:2014) issued by the International Organization for Standardization (ISO) has established a water balance evaluation framework, it does not cover the dynamic weight adjustment mechanism of ecological factors in the context of climate change. The domestic "Farmland Irrigation Water Quality Standard" (GB 5084-2021) focuses on water quality control and lacks quantitative representation of the water dynamics of the soil-crop-atmosphere continuum (SPAC). Most of the existing agricultural information systems (such as CropWat and AquaCrop) do not have the function of dynamic correction of the growth period, and none of them can achieve real-time calibration of multi-indicator weights. This static evaluation system has been unable to adapt to the development needs of modern agriculture such as frequent extreme weather and diversified planting structures.

[0003] Therefore, how to solve the problems of single data, static models, and insufficient evaluation dimensions in existing technologies has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0004] The purpose of this application is to provide a method, system and device for evaluating the ecological suitability of crop water in semi-arid sandy areas. The method can construct a crop water ecological suitability index that takes into account water supply, water storage and water demand based on the natural ecological characteristics of semi-arid sandy areas, integrate multi-scale spatiotemporal data, and provide high-precision and dynamic regional crop water ecological suitability evaluation, thereby providing decision support for the optimization of crop water-adaptive planting structure and water-saving irrigation technology in the region, and providing guarantees for improving regional crop yields and production stability.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for evaluating the ecological suitability of crop water in a semi-arid sandy region. The method comprises:

[0007] An evaluation index system for crop water ecological suitability is constructed; the evaluation index system for crop water ecological suitability includes: climate suitability index, soil suitability index and crop suitability index; the climate suitability index includes: precipitation and precipitation satisfaction ratio (PSR); the soil suitability index includes: field holding capacity, soil moisture content during the growth period, electrical conductivity (ECE) and alkalinity (ESP); the crop suitability index includes: yield, water consumption during the growth period and water use efficiency (WUE).

[0008] Obtain indicator data for the target area for evaluation; the indicator data for the target area for evaluation include: high temporal and spatial resolution NDVI time series data, daily precipitation, average daily crop water consumption, average daily soil moisture content, field water holding capacity, electrical conductivity, alkalinity and yield.

[0009] Based on the evaluation index system of crop water ecological suitability, the daily precipitation, the average daily crop water consumption and the average daily soil moisture content, the high temporal and spatial resolution NDVI time series data are inverted for the growing period to obtain the average values ​​of the total precipitation in different growing periods, the total water consumption of crops and the soil moisture content in different growing periods.

[0010] Based on the total amount of precipitation in different growth periods, the total water consumption of crops and the average of soil moisture content in different growth periods, the precipitation satisfaction rate and water use efficiency in different growth periods are calculated.

[0011] The data of each indicator is standardized to obtain the standardized data.

[0012] The entropy weight method is used to calculate the weight of each indicator data.

[0013] Based on the standardized data and the weight of each indicator data, the crop water ecological suitability index is calculated.

[0014] The crop water ecological suitability index is evaluated to obtain an evaluation result.

[0015] Based on the assessment results, the crop water ecological suitability of the assessment target area is determined.

[0016] Optionally, based on the crop water ecological suitability evaluation index system, the daily precipitation, the daily average crop water consumption and the daily average soil moisture content, the high spatiotemporal resolution NDVI time series data is inverted for the growing period to obtain the average values ​​of the total precipitation in different growing periods, the total crop water consumption and the soil moisture content in different growing periods, specifically including:

[0017] The high spatiotemporal resolution NDVI time series data are subjected to a curve morphology analysis, and the growth stage is determined using the first-order derivative and the second-order derivative; the growth stage includes growth stages divided according to different types of crops, for example, the growth stages of winter wheat include: sowing stage, emergence stage, greening stage, jointing stage, heading stage and maturity stage; the growth stages of corn include: sowing stage, emergence stage, jointing stage, large trumpet stage, heading stage and flowering stage, silking stage and maturity stage.

[0018] Based on the growth stage, according to the daily precipitation, the average daily crop water consumption and the average daily soil moisture content, the total precipitation in different growth periods, the total water consumption of crops and the average soil moisture content in different growth periods are calculated.

[0019] Optionally, the calculation formula for the precipitation satisfaction rate is:

[0020]

[0021] The calculation formula of the water use efficiency is:

[0022]

[0023] Among them, PSR is the precipitation satisfaction rate; P is the precipitation; ET c It is the water consumption of crops during the growth period, expressed by actual evapotranspiration; WUE is water use efficiency; Yield is the crop yield per unit area.

[0024] Optionally, the expression for the normalization process is:

[0025]

[0026] Among them, X′ ij is the standard value of indicator j in year i; X ij is the original value of indicator j in year i; αij is the maximum value of index j in year i; β ij is the minimum value of indicator j in year i.

[0027] Optionally, the calculation formula for the weight of each indicator data is:

[0028]

[0029] d j =1-f j ;

[0030]

[0031] Among them, w j is the weight of indicator j; d j is the entropy redundancy of indicator j; n is the number of level 2 indicators; f j is the information entropy of indicator j; m is the year number; P ij is the proportion of indicator j in year i.

[0032] Optionally, the calculation formula of the crop water ecological suitability index is:

[0033]

[0034] Among them, WESI is the crop water ecological suitability index; w j is the weight of indicator j; p is the number of indicators; X′ ij is the standard value of indicator j in year i.

[0035] In a second aspect, the present application provides a system for evaluating the ecological suitability of crops in semi-arid and sandy areas. The system comprises:

[0036] An evaluation index system construction module is used to construct an evaluation index system for crop water ecological suitability; the evaluation index system for crop water ecological suitability includes: climate suitability index, soil suitability index and crop suitability index; the climate suitability index includes: precipitation and precipitation satisfaction rate; the soil suitability index includes: field water holding capacity, soil moisture content during the growth period, electrical conductivity and alkalinity; the crop suitability index includes: yield, water consumption during the growth period and water use efficiency.

[0037] The evaluation target area indicator data acquisition module is used to obtain the evaluation target area indicator data; the evaluation target area indicator data includes: high temporal and spatial resolution NDVI time series data, daily precipitation, daily average crop water consumption, daily average soil moisture content, field water holding capacity, electrical conductivity, alkalinity and yield.

[0038] The growth period inversion module is used to perform growth period inversion on the high temporal and spatial resolution NDVI time series data based on the evaluation index system of crop water ecological suitability, the daily precipitation, the average daily crop water consumption and the average daily soil moisture content, so as to obtain the average values ​​of the total precipitation in different growth periods, the total water consumption of crops and the soil moisture content in different growth periods.

[0039] The precipitation satisfaction rate and water use efficiency calculation module is used to calculate the precipitation satisfaction rate and water use efficiency in different growth periods based on the total precipitation in different growth periods, the total water consumption of crops and the average of soil moisture content in different growth periods.

[0040] The standardization processing module is used to perform standardization processing on each indicator data to obtain standardized data.

[0041] The module for calculating the weight of each indicator data is used to calculate the weight of each indicator data using the entropy weight method.

[0042] The crop water ecological suitability index calculation module is used to calculate the crop water ecological suitability index based on the standardized data and the weights of each indicator data.

[0043] The evaluation module is used to evaluate the crop water ecological suitability index and obtain an evaluation result.

[0044] The crop water ecological suitability determination module is used to determine the crop water ecological suitability of the assessment target area based on the assessment results.

[0045] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for evaluating crop water ecological suitability in semi-arid sandy areas.

[0046] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for evaluating the ecological suitability of crops in semi-arid sandy areas with respect to water.

[0047] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for evaluating the ecological suitability of crops in semi-arid sandy areas.

[0048] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0049] The present application provides a method, system and device for evaluating the water ecological suitability of crops in semi-arid sandy areas, the method comprising: constructing an evaluation index system for the water ecological suitability of crops; the evaluation index system for the water ecological suitability of crops comprises: climate suitability index, soil suitability index and crop suitability index; the climate suitability index comprises: precipitation and precipitation satisfaction rate; the soil suitability index comprises: field water holding capacity, soil moisture content during the growing period, electrical conductivity and alkalinity; the crop suitability index comprises: yield, water consumption during the growing period and water use efficiency; by constructing an evaluation index system for the water ecological suitability of crops, indicators are selected from three key dimensions of climate, soil and crops, covering multiple important factors affecting crop growth, making the evaluation results more comprehensive and accurate, and being able to comprehensively reflect the water ecological suitability status of crops in different regions, and being highly targeted, being able to accurately measure the degree of water adaptation of crops in each link, providing a solid foundation for subsequent evaluation. Obtaining indicator data for the target area of ​​assessment; the indicator data for the target area of ​​assessment includes: high-temporal-resolution NDVI time series data, daily precipitation, daily average crop water consumption, daily average soil moisture content, field water holding capacity, electrical conductivity, alkalinity, and yield; facilitating a more comprehensive understanding of crop growth and water conditions in the assessment area. High-temporal-resolution NDVI time series data can more accurately reflect the growth dynamics and spatial distribution differences of crops in different growth stages. Compared with low-resolution data, it can more accurately grasp the spatiotemporal relationship between crop growth and water factors, providing a more reliable basis for subsequent analysis. Based on the evaluation index system for crop water ecological suitability, the daily precipitation, the daily average crop water consumption, and the daily average soil moisture content, the high-temporal-resolution NDVI time series data is inverted during the growth period to obtain the total precipitation in different growth periods, the total crop water consumption, and the average soil moisture content in different growth periods; thus, dynamic monitoring of the crop growth period is achieved, and changes in the crop growth process and the dynamic impact of water conditions can be captured in a timely manner. Compared with traditional static assessment methods, it is more adaptable to the complex and changing farmland ecological environment. Based on the total precipitation during different growth periods, the total water consumption of crops, and the average soil moisture content during different growth periods, the precipitation satisfaction rate and water use efficiency during different growth periods are calculated. The precipitation satisfaction rate can intuitively reflect the degree of match between precipitation and crop water requirements, while the water use efficiency can reflect the yield benefit generated by crops after consuming a certain amount of water, reflecting the crop's ability to utilize water. This has important guiding significance for the rational allocation of water resources and improving agricultural water use efficiency. The data of each indicator is standardized to obtain standardized data. This standardization process can eliminate differences in data types and dimensions of different indicators, making the data of each indicator comparable and ensuring the scientific nature and accuracy of subsequent analysis.The entropy weight method is used to calculate the weight of each indicator data; the entropy weight method can determine the weight of each indicator data according to the degree of variation and the amount of information, avoiding the subjective influence of human factors on the weight distribution, making the weight result more objective and reasonable, and improving the credibility of the evaluation result. Based on the standardized data and the weight of each indicator data, the crop water ecological suitability index is calculated; it can reveal the degree of coordination between the water ecological environment and crop growth requirements of the evaluation area from a holistic perspective, providing a comprehensive reference basis for agricultural production and regional planning. The crop water ecological suitability index is evaluated to obtain an evaluation result; based on the evaluation result, the crop water ecological suitability of the evaluation target area is determined; through the evaluation of the suitability index, the evaluation area can be divided into different water ecological suitability levels, thereby promoting the coordinated development of agriculture, water resources and ecological environment, and achieving the rational layout and sustainable development of regional agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 This is an application environment diagram of a method for evaluating crop water ecological suitability in semi-arid sandy areas in one embodiment of the present application.

[0052] Figure 2 A flow chart of a method for evaluating crop water ecological suitability in semi-arid sandy areas provided in one embodiment of the present application.

[0053] Figure 3 A schematic diagram of the overall structural framework of a method for evaluating crop water ecological suitability in semi-arid sandy areas provided in one embodiment of the present application.

[0054] Figure 4 This is a schematic diagram of the functional modules of a crop water ecological suitability evaluation system for semi-arid sandy areas provided in one embodiment of the present application.

[0055] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0057] The massive amount of data accumulated by satellite remote sensing and Internet of Things technologies provides multi-source and multi-scale spatiotemporal data for high-resolution assessment of crop water ecological suitability at the regional scale. By using multi-source heterogeneous data fusion technology to integrate meteorological, soil, remote sensing, crop and other multi-source spatiotemporal data, it can solve the problems of low spatial resolution and insufficient dynamics of single data sources in traditional methods, and break through technical difficulties such as data standardization, spatiotemporal alignment and enhanced complementarity. This application constructs a multi-factor coupling evaluation model covering precipitation water supply, soil water storage, root water absorption and crop water demand, quantifies the nonlinear relationship between climate, soil and crops, improves the ecological explanatory power of the evaluation results, and combines growth stage correction to achieve precise management. Design a cloud-edge collaborative computing architecture that integrates data acquisition, real-time analysis, and visual decision-making modules to support the digital transformation of agricultural water-saving management.

[0058] Specifically, the present application relates to a method, system, and device for evaluating crop water ecological suitability in semi-arid sandy areas, which are suitable for agricultural planning, crop planting layout optimization, and water-saving irrigation decision support. The method includes: constructing an evaluation index system for crop water ecological suitability based on the natural ecological characteristics of the semi-arid sandy area, specifically including: climate suitability indicators, including precipitation and precipitation satisfaction rate; soil suitability indicators, including field water holding capacity, soil moisture content during the growth period, electrical conductivity, and alkalinity; crop suitability indicators, including yield, water consumption during the growth period, and water use efficiency; obtaining climate, soil, and crop indicator data for the target area to be evaluated and performing spatial resolution matching; using the range normalization method to standardize the indicator data so that the processed indicator data are all in the range of [0, 1]. The present application uses the entropy weight method to calculate the weight value of the evaluation index; calculating the WESI through linear weighting, and classifying different areas into highly suitable (>0.8), moderately suitable (0.6-0.8), and low suitable (<0.6) according to the WESI value. The evaluation system includes data layer, algorithm layer and application layer. The algorithm layer integrates dynamic weight calculation module and GIS visualization module.

[0059] This application is used to solve the problems of single data, static models and insufficient evaluation dimensions in the existing technology. According to the natural ecological characteristics of semi-arid sandy areas, a crop water ecological suitability index is constructed that takes into account water supply, water storage and water demand. It integrates multi-scale spatiotemporal data to provide high-precision and dynamic regional crop water ecological suitability evaluation, thereby providing decision support for the optimization of crop water-suitable planting structure and water-saving irrigation technology in the region, and providing guarantees for improving regional crop yields and production stability.

[0060] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0061] The method for evaluating crop water ecological suitability in semi-arid sandy areas provided in the present application can be applied to Figure 1In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the evaluation target area index data to the server 104, and the evaluation target area index data includes: high temporal and spatial resolution NDVI time series data, daily precipitation, daily average crop water consumption, daily average soil moisture content, field water holding capacity, electrical conductivity, alkalinity and yield; after the server 104 receives the evaluation target area index data, the server 104 performs growth period inversion on the high temporal and spatial resolution NDVI time series data based on the evaluation index system of crop water ecological suitability, the daily precipitation, the daily average crop water consumption and the daily average soil moisture content, and obtains the average of the total precipitation in different growth periods, the total water consumption of crops and the soil moisture content in different growth periods; the evaluation index system of crop water ecological suitability includes: climate suitability index, soil suitability index and crop suitability index. The climate suitability indicators include: precipitation and precipitation satisfaction rate; the soil suitability indicators include: field water holding capacity, soil moisture content during the growth period, electrical conductivity, and alkalinity; the crop suitability indicators include: yield, water consumption during the growth period, and water use efficiency; based on the total precipitation in different growth periods, the total water consumption of the crop, and the average of the soil moisture content in different growth periods, the precipitation satisfaction rate and water use efficiency in different growth periods are calculated; the data of each indicator is standardized to obtain standardized data; the entropy weight method is used to calculate the weight of each indicator data; based on the standardized data and the weight of each indicator data, a crop water ecological suitability index is calculated; the crop water ecological suitability index is evaluated to obtain an evaluation result; and based on the evaluation result, the crop water ecological suitability of the evaluation target area is determined. The server 104 can provide feedback on the obtained crop water ecological suitability of the evaluation target area to the terminal 102. In addition, in some embodiments, the method for evaluating the ecological suitability of crop water in semi-arid sandy areas can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform the ecological suitability evaluation of crop water in semi-arid sandy areas based on the evaluation target area index data, or the server 104 can obtain the evaluation target area index data from the data storage system and perform the ecological suitability evaluation of crop water in semi-arid sandy areas based on the evaluation target area index data.

[0062] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, and tablet computers. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or a cloud server.

[0063] In an exemplary embodiment, Figure 2 and Figure 3 As shown, a method for evaluating the ecological suitability of crop water in semi-arid sandy areas is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, and can also be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps S1 to S9.

[0064] S1: Construct an evaluation index system for crop water ecological suitability; the evaluation index system for crop water ecological suitability includes: climate suitability index, soil suitability index and crop suitability index; the climate suitability index includes: precipitation and precipitation satisfaction rate; the soil suitability index includes: field water holding capacity, soil moisture content during the growth period, electrical conductivity and alkalinity; the crop suitability index includes: yield, water consumption during the growth period and water use efficiency.

[0065] S2: Obtaining indicator data for the target area for evaluation; the indicator data for the target area for evaluation include: high temporal and spatial resolution NDVI time series data, daily precipitation, daily average crop water consumption, daily average soil moisture content, field water holding capacity, electrical conductivity, alkalinity and yield.

[0066] S3: Based on the evaluation index system of crop water ecological suitability, the daily precipitation, the average daily crop water consumption and the average daily soil moisture content, the high temporal and spatial resolution NDVI time series data is inverted for the growing period to obtain the average values ​​of the total precipitation in different growing periods, the total water consumption of crops and the soil moisture content in different growing periods.

[0067] S4: Based on the total amount of precipitation in different growth periods, the total water consumption of crops and the average of soil moisture content in different growth periods, the precipitation satisfaction rate and water use efficiency in different growth periods are calculated.

[0068] S5: Perform standardization processing on the data of each indicator to obtain standardized data.

[0069] S6: The entropy weight method is used to calculate the weight of each indicator data.

[0070] S7: Based on the standardized data and the weight of each indicator data, the crop water ecological suitability index is calculated.

[0071] S8: Evaluate the crop water ecological suitability index to obtain an evaluation result.

[0072] S9: Determine the crop water ecological suitability of the target area based on the assessment results.

[0073] Specifically, in step S1, an evaluation index system for crop water ecological suitability is constructed (as shown in Table 1). The system includes three primary indicators: climate suitability index, which characterizes the water supply of the region; soil suitability index, which characterizes the water storage of the region; and crop suitability index, which characterizes the water demand of the crops. The climate suitability index includes two secondary indicators: precipitation and precipitation satisfaction rate. The precipitation satisfaction rate is calculated using formula (1):

[0074]

[0075] Among them, PSR is the precipitation satisfaction rate; P is the precipitation; ET c It is the water consumption of crops during the growth period, expressed by actual evapotranspiration.

[0076] Soil suitability index includes four secondary indicators: field water holding capacity, soil moisture content during the growth period, electrical conductivity, and alkalinity; crop suitability index includes three secondary indicators: yield, water consumption during the growth period, and water use efficiency. The water use efficiency is calculated using formula (2):

[0077]

[0078] Among them, WUE is water use efficiency; Yield is crop yield per unit area.

[0079] Table 1 Evaluation index system for crop water ecological suitability

[0080]

[0081]

[0082] Specifically, in step S2, climate, soil, and crop indicator data for the target area are obtained, preprocessed, and spatially matched. Precipitation data is derived from daily precipitation at regional meteorological observation stations. Outliers are removed from the original data, and missing values ​​are interpolated. The interpolated data are then spatially interpolated using the specialized software Anuspline, typically at a spatial resolution of 1 km. Daily crop water consumption data are derived from satellite remote sensing interpretation data products, with an 8-day time step and a 1 km spatial resolution. Soil field water holding capacity, electrical conductivity, and alkalinity data are derived from the FAO World Soil Database (HWSD) surface layer (0-20 cm) data, with a spatial resolution of 1 km. Daily soil moisture content data are derived from GF-1 satellite remote sensing data products, with a daily time step, a 1 km spatial resolution, and a depth of 0-20 cm. Yield data comes from the first high-resolution spatiotemporal distribution dataset of global crop yields from 2010 to 2020, published in the journal Scientific Data by the CropWatch research team, a group of researchers from the Chinese Academy of Sciences. This dataset, with a resolution of 10 km, was interpolated to a 1 km spatial resolution in ArcGIS. High-resolution NDVI time series data from satellite remote sensing (such as MODIS and Landsat) were obtained. Moving averages, Savitzky-Golay filters, and wavelet transforms were used to remove noise from clouds and atmospheric interference, preserving trend characteristics.

[0083] As an optional implementation, in step S3, based on the crop water ecological suitability evaluation index system, the daily precipitation, the daily average crop water consumption, and the daily average soil moisture content, the high spatiotemporal resolution NDVI time series data is inverted for the growth period to obtain the average values ​​of the total precipitation in different growth periods, the total crop water consumption, and the soil moisture content in different growth periods, specifically including:

[0084] S31: Performing curve morphology analysis on the high spatiotemporal resolution NDVI time series data, and determining the growth stage using the first-order derivative and the second-order derivative; the growth stage includes growth stages divided according to different types of crops, for example, the growth stages of winter wheat include: sowing period, emergence period, greening period, jointing period, heading period and maturity period; the growth stages of corn include: sowing period, emergence period, jointing period, large trumpet period, heading period and flowering period, silking period and maturity period.

[0085] S32: Based on the growth stage, according to the daily precipitation, the average daily crop water consumption and the average daily soil moisture content, calculate the average total precipitation in different growth periods, the total crop water consumption and the soil moisture content in different growth periods.

[0086] Specifically, crop growth stages are inverted based on high-temporal and spatial resolution NDVI time series data. The method includes analyzing the NDVI curve morphology, using the first-order derivative (rate of change) and second-order derivative (acceleration of change) to detect turning points in the growth period, extracting key nodes such as the sowing period (low value), greening period (rapid rise), jointing period (stable high), heading period (peak value), and maturity period (decline), and automatically learning NDVI time series features with networks such as LSTM and CNN to improve classification accuracy. Results are extracted based on the growth period stage.

[0087] In step S4, the daily average for each eight-day growing period is calculated based on the total water consumption of the crops. Based on the obtained start and end times of each growing period, daily precipitation, daily average crop water consumption, and daily average soil moisture content, the total precipitation and total crop water consumption in different growing periods, as well as the average soil moisture content in different growing periods, are calculated, and then the precipitation satisfaction rate and water use efficiency in different growing periods are calculated.

[0088] Next, we calculate the crop water ecological suitability index. First, we use the range normalization method to normalize the index data so that the processed index data are in the range of [0, 1]. We use formula (3) to calculate the normalized value of the index:

[0089]

[0090] Among them, X′ ij is the standard value of indicator j in year i; X ij is the original value of indicator j in year i; α ij is the maximum value of index j in year i; β ij is the minimum value of indicator j in year i.

[0091] Then the entropy weight method is used to calculate the weight of each indicator data:

[0092]

[0093] d j =1-f j (5);

[0094]

[0095] Among them, w j is the weight of indicator j; d j is the entropy redundancy of indicator j; n is the number of level 2 indicators; f j is the information entropy of indicator j; m is the year number; P ij is the proportion of indicator j in year i.

[0096] Finally, the crop water ecological suitability index is calculated:

[0097]

[0098] Among them, WESI is the crop water ecological suitability index; w j is the weight of index j; p is the number of indices; X′ ij is the standard value of index j in year i.

[0099] Furthermore, crop water ecological suitability assessment is carried out in different regions. According to the calculation results of WESI, the crop water ecological suitability of each spatial grid point at a spatial resolution of 1 km in the assessment area is determined. WESI > 0.8 is highly suitable, 0.6 < WESI < 0.8 is moderately suitable, and WESI < 0.6 is lowly suitable; ArcGIS is used to create a spatial map of crop water ecological suitability with a spatial resolution of 1 km, providing a reference for optimizing crop planting structure and making water-saving irrigation decisions.

[0100] By implementing the above steps S1 to S9, this application integrates meteorological grid data, remotely sensed soil parameters, crop parameters, and high spatio-temporal resolution NDVI time series data. In view of the characteristics of salinization in the semi-arid sandy area, the influence of soil salt content and alkalinity on crop root water uptake is considered, and an evaluation system covering four aspects of precipitation water supply, soil water storage, root water uptake, and crop water demand is constructed. Moreover, growth period data is extracted based on high spatio-temporal resolution NDVI time series data. Compared with the traditional single-source data method, the spatio-temporal heterogeneity characterization ability of the evaluation results is significantly improved, and the accuracy of suitability classification is increased.

[0101] This application improves the decision-making efficiency through spatio-temporal coupling analysis. Through cloud-edge computing devices, real-time monitoring data is obtained to achieve rapid regional evaluation, thus supporting real-time irrigation decisions; the built-in GIS visualization module of the system can automatically generate a graded suitability map, and combined with the yield prediction model, it can provide suggestions for adjusting the planting structure. By establishing a three-level suitability classification standard based on the WESI index, a unified quantitative basis is provided for formulating cross-regional water management policies.

[0102] Through the closed-loop design of "data - model - application", this application has achieved a leapfrog improvement from single-point observation to全域 dynamic evaluation. The accuracy, timeliness, and adaptability of crop water ecological suitability evaluation can also be significantly improved, and it can be widely applied in fields such as agricultural water-saving planning, optimizing regional crop planting structure, and coping with climate change, with significant ecological and economic benefits.

[0103] This application also provides an application scenario, which applies the above-mentioned method for evaluating the ecological suitability of crops in semi-arid and sandy areas. Specifically, the method for evaluating the ecological suitability of crops in semi-arid and sandy areas provided in this embodiment can be applied in the ecological suitability assessment scenario of the target area. The target area ecological suitability assessment scenario includes: the construction of an evaluation index system, the acquisition of indicator data for the target area, the inversion of the growing period, the calculation of the crop water ecological suitability index, and the evaluation. First, an evaluation index system for crop water ecological suitability is constructed. The evaluation index system for crop water ecological suitability includes climate suitability indicators, soil suitability indicators, and crop suitability indicators. The climate suitability indicators include precipitation and precipitation satisfaction rate. The soil suitability indicators include field water holding capacity, soil moisture content during the growing period, electrical conductivity, and alkalinity. The crop suitability indicators include yield, water consumption during the growing period, and water use efficiency. Second, indicator data for the target area are obtained. The indicator data for the target area include high temporal and spatial resolution NDVI time series data, daily precipitation, average daily crop water consumption, average daily soil moisture content, field water holding capacity, electrical conductivity, alkalinity, and yield. Based on the evaluation index system of crop water ecological suitability, the daily precipitation, the average daily crop water consumption and the average daily soil moisture content, the high spatiotemporal resolution NDVI time series data is inverted during the growth period to obtain the total precipitation in different growth periods, the total water consumption of crops and the average soil moisture content in different growth periods; then, based on the total precipitation in different growth periods, the total water consumption of crops and the average soil moisture content in different growth periods, the precipitation satisfaction rate and water use efficiency in different growth periods are calculated; each indicator data is standardized to obtain standardized data; the entropy weight method is used to calculate the weight of each indicator data; based on the standardized data and the weight of each indicator data, the crop water ecological suitability index is calculated; finally, the crop water ecological suitability index is evaluated to obtain an evaluation result; based on the evaluation result, the crop water ecological suitability of the evaluation target area is determined.

[0104] Based on the same inventive concept, the present application also provides a system for evaluating the ecological suitability of crops in semi-arid sandy areas, which is used to implement the aforementioned method for evaluating the ecological suitability of crops in semi-arid sandy areas. The solution provided by this system is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the system for evaluating the ecological suitability of crops in semi-arid sandy areas provided below can be found in the limitations of the method for evaluating the ecological suitability of crops in semi-arid sandy areas described above, and will not be repeated here.

[0105] In an exemplary embodiment, Figure 4As shown, a system for evaluating the ecological suitability of crops in semi-arid and sandy areas is provided. The system for evaluating the ecological suitability of crops in semi-arid and sandy areas comprises:

[0106] The evaluation index system construction module M1 is used to construct an evaluation index system for crop water ecological suitability; the evaluation index system for crop water ecological suitability includes: climate suitability index, soil suitability index and crop suitability index; the climate suitability index includes: precipitation and precipitation satisfaction rate; the soil suitability index includes: field water holding capacity, soil moisture content during the growth period, electrical conductivity and alkalinity; the crop suitability index includes: yield, water consumption during the growth period and water use efficiency.

[0107] The evaluation target area indicator data acquisition module M2 is used to obtain the evaluation target area indicator data; the evaluation target area indicator data includes: high temporal and spatial resolution NDVI time series data, daily precipitation, daily average crop water consumption, daily average soil moisture content, field water holding capacity, electrical conductivity, alkalinity and yield.

[0108] The growing period inversion module M3 is used to perform growing period inversion on the high temporal and spatial resolution NDVI time series data based on the evaluation index system of crop water ecological suitability, the daily precipitation, the average daily crop water consumption and the average daily soil moisture content, and obtain the average values ​​of the total precipitation in different growing periods, the total water consumption of crops and the soil moisture content in different growing periods.

[0109] The precipitation satisfaction rate and water use efficiency calculation module M4 is used to calculate the precipitation satisfaction rate and water use efficiency in different growth periods based on the total precipitation in different growth periods, the total water consumption of crops and the average of soil moisture content in different growth periods.

[0110] The standardization processing module M5 is used to perform standardization processing on each indicator data to obtain standardized data.

[0111] The indicator data weight calculation module M6 is used to calculate the weight of each indicator data using the entropy weight method.

[0112] The crop water ecological suitability index calculation module M7 is used to calculate the crop water ecological suitability index based on the standardized data and the weights of each indicator data.

[0113] The evaluation module M8 is used to evaluate the crop water ecological suitability index and obtain an evaluation result.

[0114] The crop water ecological suitability determination module M9 is used to determine the crop water ecological suitability of the assessment target area according to the assessment result.

[0115] It should be noted that in the evaluation target area indicator data acquisition module M2, outlier elimination and missing value interpolation are also performed to obtain a complete data set of spatiotemporal series; the spatial resolution of the evaluation data is determined, and the data that does not meet the spatial resolution is interpolated and resampled. For example, the precipitation data observed at the station is spatially interpolated using Anuspline software, and the crop yield data obtained with a spatial resolution of 10km is interpolated and resampled to match the spatial resolution of the evaluation data.

[0116] As an optional implementation, the system further includes: a spatiotemporal analysis and GIS visualization module for spatiotemporal pattern analysis of the crop water ecological suitability index and visualization based on ArcGIS.

[0117] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store evaluation target area indicator data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for evaluating the ecological suitability of crop water in semi-arid sandy areas is implemented.

[0118] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0119] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the above method embodiments when executing the computer program.

[0120] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above-mentioned method embodiments when executed by a processor.

[0121] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the above method embodiments are implemented.

[0122] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0123] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0124] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0125] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for evaluating crop water ecological suitability in semi-arid sandy areas, characterized in that: The method for evaluating crop water ecological suitability in semi-arid sandy areas includes: Constructing an evaluation index system for crop water ecological suitability; the evaluation index system for crop water ecological suitability includes: climate suitability index, soil suitability index, and crop suitability index; the climate suitability index includes: precipitation and precipitation satisfaction rate; the soil suitability index includes: field water holding capacity, soil moisture content during the growth period, electrical conductivity, and alkalinity; the crop suitability index includes: yield, water consumption during the growth period, and water use efficiency; Obtaining indicator data for the target area for assessment; the indicator data for the target area for assessment include: high temporal and spatial resolution NDVI time series data, daily precipitation, daily average crop water consumption, daily average soil moisture content, field water holding capacity, electrical conductivity, alkalinity, and yield; Based on the crop water ecological suitability evaluation index system, the daily precipitation, the daily average crop water consumption, and the daily average soil moisture content, the high temporal and spatial resolution NDVI time series data is inverted during the growth period to obtain the average values ​​of the total precipitation during different growth periods, the total water consumption of the crops, and the soil moisture content during different growth periods; Based on the total amount of precipitation in different growth periods, the total water consumption of crops and the average of soil moisture content in different growth periods, the precipitation satisfaction rate and water use efficiency in different growth periods are calculated; Performing standardization on the data of each indicator to obtain standardized data; The entropy weight method is used to calculate the weight of each indicator data; Based on the standardized data and the weights of each indicator data, the crop water ecological suitability index is calculated; Evaluating the crop water ecological suitability index to obtain an evaluation result; Based on the assessment results, the crop water ecological suitability of the assessment target area is determined.

2. The method for evaluating crop water ecological suitability in semi-arid sandy areas according to claim 1, characterized in that: Based on the crop water ecological suitability evaluation index system, the daily precipitation, the daily average crop water consumption and the daily average soil moisture content, the high spatiotemporal resolution NDVI time series data is inverted during the growth period to obtain the average values ​​of the total precipitation in different growth periods, the total crop water consumption and the soil moisture content in different growth periods, specifically including: Performing curve morphology analysis on the high temporal and spatial resolution NDVI time series data, and determining the growth stage using first-order derivatives and second-order derivatives; the growth stage includes growth stages divided according to different types of crops; Based on the growth stage, according to the daily precipitation, the average daily crop water consumption and the average daily soil moisture content, the total precipitation in different growth periods, the total water consumption of crops and the average soil moisture content in different growth periods are calculated.

3. The method for evaluating crop water ecological suitability in semi-arid sandy areas according to claim 1, characterized in that: The calculation formula of the precipitation satisfaction rate is: The calculation formula of the water use efficiency is: Among them, PSR is the precipitation satisfaction rate; P is the precipitation; ET c It is the water consumption of crops during the growth period, expressed by actual evapotranspiration; WUE is water use efficiency; Yield is the crop yield per unit area.

4. The method for evaluating crop water ecological suitability in semi-arid sandy areas according to claim 1, wherein: The expression of the standardization process is: Among them, X′ ij is the standard value of indicator j in year i; X ij is the original value of indicator j in year i; α ij is the maximum value of index j in year i; β ij is the minimum value of indicator j in year i.

5. The method for evaluating crop water ecological suitability in semi-arid sandy areas according to claim 1, characterized in that: The calculation formula for the weight of each indicator data is: d j =1-f j ; Among them, w j is the weight of indicator j; d j is the entropy redundancy of indicator j; n is the number of level 2 indicators; f j is the information entropy of indicator j; m is the year number; P ij is the proportion of indicator j in year i.

6. The method for evaluating crop water ecological suitability in semi-arid sandy areas according to claim 1, characterized in that: The calculation formula of the crop water ecological suitability index is: Among them, WESI is the crop water ecological suitability index; w j is the weight of indicator j; p is the number of indicators; X′ ij is the standard value of indicator j in year i.

7. A crop water ecological suitability evaluation system for semi-arid sandy areas, characterized by: The crop water ecological suitability evaluation system in semi-arid sandy areas includes: An evaluation index system construction module is used to construct an evaluation index system for crop water ecological suitability; the evaluation index system for crop water ecological suitability includes: climate suitability index, soil suitability index, and crop suitability index; the climate suitability index includes: precipitation and precipitation satisfaction rate; the soil suitability index includes: field water holding capacity, soil moisture content during the growth period, electrical conductivity, and alkalinity; the crop suitability index includes: yield, water consumption during the growth period, and water use efficiency; An evaluation target area indicator data acquisition module is used to acquire evaluation target area indicator data; the evaluation target area indicator data includes: high temporal and spatial resolution NDVI time series data, daily precipitation, daily average crop water consumption, daily average soil moisture content, field water holding capacity, electrical conductivity, alkalinity and yield; A growth period inversion module is used to perform growth period inversion on the high spatiotemporal resolution NDVI time series data based on the crop water ecological suitability evaluation index system, the daily precipitation, the daily average crop water consumption, and the daily average soil moisture content, to obtain the average values ​​of the total precipitation in different growth periods, the total crop water consumption, and the soil moisture content in different growth periods; A precipitation satisfaction rate and water use efficiency calculation module is used to calculate the precipitation satisfaction rate and water use efficiency in different growth periods based on the total precipitation in different growth periods, the total water consumption of crops and the average of soil moisture content in different growth periods; A standardization processing module is used to perform standardization processing on each indicator data to obtain standardized data; The weight calculation module of each indicator data is used to calculate the weight of each indicator data using the entropy weight method; A crop water ecological suitability index calculation module is used to calculate the crop water ecological suitability index based on the standardized data and the weights of each indicator data; An evaluation module is used to evaluate the crop water ecological suitability index and obtain an evaluation result; The crop water ecological suitability determination module is used to determine the crop water ecological suitability of the assessment target area based on the assessment results.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for evaluating crop water ecological suitability in semi-arid sandy areas according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating crop water ecological suitability in semi-arid sandy areas according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for evaluating crop water ecological suitability in semi-arid sandy areas according to any one of claims 1 to 6 is implemented.

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