An irrigation water requirement calculation method considering crop fine distribution, an electronic device and a storage medium

Abstract

The application discloses a kind of irrigation water requirement calculation method considering crop fine distribution, electronic equipment and storage medium, to accurately calculate the crop irrigation water requirement and its spatial distribution in target area by remote sensing technology, meteorological data and soil moisture balance model.The method comprises the following steps: collecting remote sensing crop classification, meteorological and soil parameter data;Calculate the reference crop evapotranspiration at the grid scale;Configure crop parameters;Calculate the root zone soil moisture change based on the water balance model;Calculate the crop water requirement and irrigation water requirement using the FAO-56 crop coefficient method.The application provides high-resolution irrigation water requirement distribution information by fine crop distribution and dynamic soil moisture simulation, and provides a scientific basis for agricultural water resource management.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural irrigation, and in particular to a method for calculating irrigation water demand taking into account the fine distribution of crops, as well as corresponding electronic equipment and storage media. Background Art

[0002] Calculating irrigation water requirements is a core component of farmland water management, directly impacting agricultural production efficiency and the rational use of water resources. Accurate irrigation water requirement calculation not only ensures that crops receive adequate water during growth, but also prevents water waste and improves the efficiency of irrigation systems. Against the backdrop of increasingly scarce global water resources, accurately calculating farmland irrigation water requirements is crucial for improving irrigation water utilization efficiency and achieving refined agricultural water resource management.

[0003] Existing irrigation water demand calculation methods, such as the crop coefficient method, are simple and widely used, but have the following drawbacks: (1) they ignore the spatial heterogeneity of crop types and cannot depict the fine distribution of water demand within farmland; (2) they do not dynamically simulate the soil moisture balance process, rely on static parameters, and have insufficient calculation accuracy; (3) they lack high-resolution spatial data support and are difficult to meet the needs of refined water resource management. Among the existing technologies, CN106557658A focuses on climate change prediction, but relies on sensor networks and does not fully consider the impact of crop spatial distribution differences on water demand; CN115687850A uses image segmentation to calculate coverage, but only relies on visual features to extract crop information, which is insufficient for fine crop classification and soil moisture dynamic simulation in complex farmland environments; CN106570627B focuses on future climate scenarios, but its calculation framework focuses more on long-term trend prediction and lacks support for fine description of crop distribution at the current farmland scale and real-time water balance simulation.

[0004] Therefore, there is an urgent need for an irrigation water demand calculation method that combines high-resolution remote sensing classification, dynamic water balance model and multi-source data rasterization processing to achieve accurate estimation of water demand under fine crop distribution. Summary of the Invention

[0005] To solve the above problems, the present invention proposes a method for calculating irrigation water requirements that takes into account the fine distribution of crops, an electronic device, and a storage medium. Based on the crop coefficient method, this method takes into account the fine distribution of crops and detailed farmland water revenue and expenditure, solving the problem that the existing technology cannot reflect the spatial heterogeneity of crop water requirements within crops. Refined estimation of regional irrigation water requirements can be achieved by relying only on conventional meteorological, soil parameters, and remote sensing crop classification data.

[0006] The present invention is implemented as follows. In a first aspect, a method for calculating irrigation water requirements taking into account the fine distribution of crops is provided, comprising the following steps:

[0007] Step S1: collecting remote sensing crop classification data, meteorological data and soil parameter data of the target area, and interpolating the remote sensing crop classification data, meteorological data and soil parameter data into raster data of uniform resolution;

[0008] Step S2: Calculate daily reference crop evapotranspiration at the grid scale based on the FAO-56 Penman-Monteith method;

[0009] Step S3: configuring crop parameters at the grid scale, including: crop planting month; number of days in the initial, developmental, mid-term, and late stages; crop coefficients in the initial, developmental, mid-term, and late stages; maximum root depth and crop soil water critical loss coefficient;

[0010] Step S4: Based on the root zone water balance model, calculate the spatiotemporal changes of soil moisture in the crop root zone, including effective precipitation, subroot layer seepage, actual evapotranspiration, and soil water requirement;

[0011] Step S5: Calculate the water requirement of the crop during its growth period based on the FAO-56 crop coefficient method, and combine the effective precipitation and irrigation efficiency coefficient to calculate the irrigation water requirement of the crop during its growth period.

[0012] Furthermore, the sources of the remote sensing crop classification data in step S1 include publicly available remote sensing inversion datasets and datasets generated by combining high-resolution satellite images with remote sensing crop classification algorithms, wherein: the satellite images include Sentinel-2 and Landsat, and the remote sensing crop classification algorithm includes a random forest algorithm;

[0013] The meteorological data include daily precipitation, temperature, relative humidity, wind speed and sunshine hours;

[0014] The soil parameter data include soil type, field capacity and wilting water content;

[0015] The uniform resolution raster data has a resolution of no less than 30 meters x 30 meters.

[0016] Furthermore, the formula for calculating the daily reference crop evapotranspiration at the grid scale based on the FAO-56 Penman-Monteith method described in step S2 is:

[0017]

[0018] Where ET0 is the daily reference crop evapotranspiration, in mm d -1 ; R n is the net radiation, in MJ m -2 d -1 ; G is the soil heat flux, unit is MJ m-2 d -1 ; T is the daily average temperature, unit is ℃; u2 is the average wind speed at 2 meters, unit is ms -1 ;e s is the saturated water vapor pressure, unit is kPa; e a is the actual water vapor pressure, in kPa; Δ is the slope of the saturated water vapor pressure difference versus temperature curve, in kPa℃ -1 ;γ is the psychrometer constant, unit kPa℃ -1 .

[0019] Furthermore, the calculation of the spatiotemporal variation of soil moisture in the crop root zone based on the root zone water balance model in step S4 specifically includes the following sub-steps:

[0020] Step S41: Calculate the effective precipitation based on the daily precipitation. The formula is:

[0021]

[0022] Where, P is the daily precipitation, unit is mm; P e is the effective precipitation, unit is mm;

[0023] Step S42: Based on soil parameters and soil water storage capacity, calculate the leakage rate of the lower root layer. The formula is:

[0024]

[0025] Where D p is the leakage under the root system, unit: mm d -1 ; F max is the maximum soil infiltration rate, unit: mm d -1 ; S is the soil water storage in the root zone, unit: mm; t is the time step, unit: d; TAW is the total available soil water in the root zone, unit: mm; RAW is the part of TAW that is the soil water in TAW that is easily available to crops, unit: mm;

[0026] The relationship between RAW and TAW is:

[0027] RAW=p×TAW=p×(θ fc -θ wp )×Z r

[0028] Where p is the critical loss coefficient of crop soil water, 0 <p<1,无量纲;θ fc is the field water holding capacity of the soil, in cm 3 cm -3 θ wp is the soil wilting water content, unit: cm 3 cm-3 ; Z r is the thickness of the root layer, in mm;

[0029] Step S43: Calculate the actual evapotranspiration based on the crop coefficient, soil water stress coefficient and daily reference crop evapotranspiration, using the formula:

[0030] ET a =Kc×Ks×ET0

[0031] Where, ET a is the actual evapotranspiration, unit: mm d -1 ; Kc is the crop coefficient, dimensionless; Ks is the soil water stress coefficient, dimensionless; ET0 is the daily reference crop evapotranspiration, unit: mm d -1 ;

[0032] The calculation formula of the soil water stress coefficient Ks is:

[0033]

[0034] Step S44: Based on the root zone water balance model, the soil water requirement is updated and calculated using the formula:

[0035] S(t)=S(t-1)+P e (t)-ET a (t)-D p (t).

[0036] Furthermore, in step S5, the water requirement of the crop during its growth period is calculated based on the FAO-56 crop coefficient method. The specific formula is:

[0037]

[0038] Where, CWD is the water demand during the crop growth period, unit is mm; n is the total number of days in the crop growth period, unit is d;

[0039] The calculation results in the irrigation water requirement during the crop growth period, and the specific formula is:

[0040]

[0041] Where, IWD is the irrigation water demand during the crop growth period, unit is mm; n is the total number of days in the crop growth period, unit is d; I e is the irrigation efficiency coefficient, dimensionless.

[0042] Furthermore, the method of the present invention generates a spatial distribution map of the irrigation water demand of the target area during the crop growth period in step S1 by using GIS technology, and calculates the total amount of irrigation water demand of the target area based on the crop area.

[0043] The second aspect of the present invention is to provide an electronic device comprising a processor, a memory, an input interface, an output interface, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for calculating irrigation water requirements taking into account the fine distribution of crops when executing the computer program.

[0044] A third aspect of the present invention is to provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for calculating irrigation water requirements considering the fine distribution of crops.

[0045] The beneficial effects of the present invention are:

[0046] This invention integrates remote sensing technology, crop coefficient method and water balance model to overcome the limitations of a single data source, comprehensively consider crop distribution heterogeneity and soil moisture dynamics, provide high-resolution irrigation water demand distribution information, improve calculation accuracy, and provide technical support for the refined management of agricultural water resources.

[0047] The present invention will be explained in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of the method of the present invention;

[0049] Figure 2 A remote sensing crop classification map according to an embodiment of the present invention;

[0050] Figure 3 A spatial distribution map generated by the irrigation water requirement results for crops during their growth period according to an embodiment of the present invention;

[0051] Figure 4 It is a schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0052] Example 1:

[0053] The first part of this embodiment provides a method for calculating irrigation water demand taking into account the fine distribution of crops, such as Figure 1 As shown, the following steps are included:

[0054] Step S1: Collect remote sensing crop classification data, meteorological data, and soil parameter data of the target area, and interpolate the remote sensing crop classification data, meteorological data, and soil parameter data into raster data of uniform resolution.

[0055] This embodiment takes the "Hetao Irrigation District of Inner Mongolia" as the target area. The sources of remote sensing crop classification data include publicly available remote sensing inversion datasets and datasets generated by high-resolution satellite images (such as Sentinel-2 or Landsat satellite images) combined with remote sensing crop classification algorithms (such as random forest algorithms). Meteorological data include daily precipitation, temperature, relative humidity, wind speed, and sunshine hours. Soil parameter data include soil type, field water holding capacity, and wilting water content, and the publicly available national raster soil parameter dataset can be directly used. These data are interpolated into raster data of the same resolution, with a resolution of not less than 30 meters × 30 meters. The details are as follows:

[0056] Crop classification data: The crop classification data in this embodiment is generated based on Landsat satellite images and random forest crop classifiers, such as Figure 2 As shown, it mainly includes four categories: sunflower, corn, wheat and melons and vegetables, with a resolution of 30 meters × 30 meters; crop classification data is stored as raster numbers to facilitate subsequent raster scale calculations.

[0057] Meteorological data: Meteorological data were obtained from national meteorological stations in and around the Hetao Irrigation District. Kriging was used to interpolate the station meteorological data into raster data with a resolution of 30 m × 30 m, ensuring that the spatial resolution matched the crop classification data.

[0058] Soil parameter data: Soil type and parameter data for the whole country were obtained from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences. The Hetao Plain section was intercepted using ArcGIS software. The soil parameter data were also linearly interpolated into raster data with a resolution of 30 m × 30 m.

[0059] Step S2: Based on the FAO-56 Penman-Monteith method and combined with grid meteorological data, calculate the daily reference crop evapotranspiration at the grid scale. The specific formula is:

[0060]

[0061] Where ET0 is the daily reference crop evapotranspiration, in mm d -1 ; R n is the net radiation, in MJ m -2 d -1 ; G is the soil heat flux, unit is MJ m -2 d -1 ; T is the daily average temperature, unit is ℃; u2 is the average wind speed at 2 meters, unit is ms -1 ;e s is the saturated water vapor pressure, unit is kPa; e a is the actual water vapor pressure, in kPa; Δ is the slope of the saturated water vapor pressure difference versus temperature curve, in kPa℃ -1;γ is the psychrometer constant, unit kPa℃ -1 .

[0062] In particular, in this embodiment, the soil heat flux G is negligible on a daily scale.

[0063] Step S3: Configure crop parameters at the grid scale.

[0064] Crop parameters are configured based on the FAO Irrigation and Drainage Technical Report and the actual crop planting conditions in the Hetao Irrigation Area. The crop parameter data that needs to be configured include: crop planting month; number of days in different growth stages (initial stage, development stage, mid-term, late stage); crop coefficient Kc in different growth stages (initial stage, development stage, mid-term, late stage); maximum root depth D mx and the critical loss coefficient of crop soil water, p. The planting periods for sunflower, corn, wheat, and melons and vegetables are: late May to early June, early May, late September to early October, and late April to early May, respectively. The specific configurations of the remaining parameters are shown in the table below:

[0065] Table 1 Parameter configuration table of main crops in Hetao irrigation area

[0066]

[0067] Step S4: Based on the root zone water balance model, calculate the spatiotemporal variation of soil moisture in the crop root zone, including effective precipitation, subroot zone seepage, actual evapotranspiration, and soil water requirement. This specifically includes the following sub-steps:

[0068] Step S41: Calculate the effective precipitation based on the daily precipitation. The formula is:

[0069]

[0070] Where, P is the daily precipitation, unit is mm; P e is the effective precipitation, unit is mm;

[0071] Step S42: Based on soil parameters and soil water storage capacity, calculate the leakage rate of the lower root layer. The formula is:

[0072]

[0073] Where D p is the leakage under the root system, unit: mm d -1 ; F max is the maximum soil infiltration rate, unit: mm d -1 ; S is the soil water storage in the root zone, unit: mm; t is the time step, unit: d; TAW is the total available soil water in the root zone, unit: mm; RAW is the part of TAW that is the soil water in TAW that is easily available to crops, unit: mm;

[0074] The relationship between RAW and TAW is:

[0075] RAW=p×TAW=p×(θ fc -θ wp )×Z r

[0076] Where p is the critical loss coefficient of crop soil water, 0 <p<1,无量纲;θ fc is the field water holding capacity of the soil, in cm 3 cm -3 θ wp is the soil wilting water content, unit: cm 3 cm -3 ; Z r is the thickness of the root layer, in mm;

[0077] Step S43: Calculate the actual evapotranspiration based on the crop coefficient, soil water stress coefficient and daily reference crop evapotranspiration, using the formula:

[0078] ET a =Kc×Ks×ET0

[0079] Where, ET a is the actual evapotranspiration, unit: mm d -1 ; Kc is the crop coefficient, dimensionless; Ks is the soil water stress coefficient, dimensionless; ET0 is the daily reference crop evapotranspiration, unit: mm d -1 ;

[0080] The calculation formula of the soil water stress coefficient Ks is:

[0081]

[0082] Step S44: Based on the root zone water balance model, the soil water requirement is updated and calculated using the formula:

[0083] S(t)=S(t-1)+P e (t)-ET a (t)-D p (t).

[0084] Step S5: Calculate the water requirement of the crop during its growth period based on the FAO-56 crop coefficient method, and combine the effective precipitation and irrigation efficiency coefficient to calculate the irrigation water requirement of the crop during its growth period.

[0085] Actual evapotranspiration ET during the crop growth period a The sum is the water requirement during the crop growth period. The specific formula is:

[0086]

[0087] Where, CWD is the water demand during the crop growth period, unit is mm; n is the total number of days in the crop growth period, unit is d;

[0088] The irrigation water requirement during the crop growth period is calculated using the following formula:

[0089]

[0090] Where, IWD is the irrigation water demand during the crop growth period, unit is mm; n is the total number of days in the crop growth period, unit is d; I e is the irrigation efficiency coefficient, dimensionless.

[0091] Finally, Figure 3 As shown in the figure, GIS technology is used to generate the spatial distribution map of irrigation water demand during the crop growth period in the Hetao Irrigation District of Inner Mongolia, and the total amount of irrigation water demand at the regional scale is calculated based on the crop area.

[0092] Through the above steps, this embodiment achieves a refined calculation of the irrigation water demand of the Hetao Irrigation District in Inner Mongolia, and generates a daily crop irrigation water demand distribution map with a spatial resolution of 30 meters × 30 meters.

[0093] The second part of this embodiment provides an electronic device, such as Figure 4 As shown, the device includes a processor, memory, an input interface, an output interface, and a computer program stored in the memory and executable by the processor. The processor is responsible for reading and processing data and executing the computer program. When executing the program, it implements the aforementioned method for calculating irrigation water requirements that considers the precise distribution of crops and stores the calculation results in the memory. The memory is used to store the computer program, drive data, and intermediate calculation results. The input and output interfaces are used to read drive data and output calculation results.

[0094] The third part of this embodiment provides a non-transitory computer-readable storage medium on which computer programs and data (crop classification data, meteorological data, soil parameter data, and intermediate calculation results) are stored. When the computer program is executed by the processor, it implements the above-mentioned irrigation water demand calculation method considering the fine distribution of crops, and supports the processor to quickly read and write data.

[0095] The above electronic devices and storage media are combined with each other to realize efficient calculation of irrigation water demand.

[0096] Finally, it should be noted that the above merely serves to illustrate the technical solutions of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application.

Claims

1. A method for calculating irrigation water requirements taking into account the fine distribution of crops, characterized in that: The following steps are involved: Step S1: collecting remote sensing crop classification data, meteorological data and soil parameter data of the target area, and interpolating the remote sensing crop classification data, meteorological data and soil parameter data into raster data of uniform resolution; Step S2: Calculate daily reference crop evapotranspiration at the grid scale based on the FAO-56 Penman-Monteith method; Step S3: configuring crop parameters at the grid scale, including: crop planting month; number of days in the initial, developmental, mid-term, and late stages; crop coefficients in the initial, developmental, mid-term, and late stages; maximum root depth and crop soil water critical loss coefficient; Step S4: Based on the root zone water balance model, calculate the spatiotemporal variation of soil moisture in the crop root zone, including effective precipitation, subroot layer seepage, actual evapotranspiration, and soil water requirement. This specifically includes the following sub-steps: Step S41: Calculate the effective precipitation based on the daily precipitation. The formula is: Where, P is the daily precipitation, unit is mm; P e is the effective precipitation, unit is mm; Step S42: Based on soil parameters and soil water storage capacity, calculate the leakage rate of the lower root layer. The formula is: Where D p is the leakage of the lower layer of the root system, unit: mmd -1 ; F max is the maximum soil infiltration rate, unit: mmd -1 ; S is the soil water storage in the root zone, unit: mm; t is the time step, unit: d; TAW is the total available soil water in the root zone, unit: mm; RAW is the part of TAW that is the soil water in TAW that is easily available to crops, unit: mm; The relationship between RAW and TAW is: RAW=p×TAW=p×(θ fc -θ wp )×Z r where p is the critical loss coefficient of crop soil water, 0 < p < 1, dimensionless; θ fc is the soil field capacity, in cm 3 cm -3 ; θ wp is the soil wilting moisture content, in cm 3 cm -3 ; Z r is the root layer thickness, in mm; Step S43: Calculate the actual evapotranspiration based on the crop coefficient, soil water stress coefficient and daily reference crop evapotranspiration, using the formula: AND a =Kc×Ks×ET0 Where, ET a is the actual evapotranspiration, unit: mm d -1 ; Kc is the crop coefficient, dimensionless; Ks is the soil water stress coefficient, dimensionless; ET0 is the daily reference crop evapotranspiration, unit: mm d -1 ; The calculation formula of the soil water stress coefficient Ks is: Step S44: Based on the root zone water balance model, the soil water requirement is updated and calculated using the formula: S(t)=S(t-1)+P e (t)-ET a (t)-D p (t) Step S5: Based on the FAO-56 crop coefficient method, the water requirement of the crop during the growth period is calculated. In combination with the effective precipitation and irrigation efficiency coefficient, the irrigation water requirement of the crop during the growth period is calculated. The specific formula is: Where, CWD is the water demand during the crop growth period, unit is mm; n is the total number of days in the crop growth period, unit is d; The calculation results in the irrigation water requirement during the crop growth period, and the specific formula is: Where, IWD is the irrigation water demand during the crop growth period, unit is mm; n is the total number of days in the crop growth period, unit is d; I e is the irrigation efficiency coefficient, dimensionless.

2. The method for calculating irrigation water demand considering the fine distribution of crops according to claim 1, characterized in that: The sources of the remote sensing crop classification data in step S1 include publicly available remote sensing inversion datasets and datasets generated by combining high-resolution satellite images with remote sensing crop classification algorithms, wherein: the satellite images include Sentinel-2 and Landsat, and the remote sensing crop classification algorithm includes a random forest algorithm; The meteorological data include daily precipitation, temperature, relative humidity, wind speed and sunshine hours; The soil parameter data include soil type, field capacity and wilting water content; The uniform resolution raster data has a resolution of no less than 30 meters x 30 meters.

3. The method for calculating irrigation water demand considering fine distribution of crops according to claim 1, characterized in that: The formula for calculating daily reference crop evapotranspiration at the grid scale based on the FAO-56 Penman-Monteith method described in step S2 is: Where ET0 is the daily reference crop evapotranspiration, in mm d -1 ; Rn is the net radiation, in MJ m -2 d -1 ; G is the soil heat flux, in MJ m -2 d -1 ; T is the daily average temperature, unit is ℃; u2 is the average wind speed at 2 meters, unit is ms -1 ; es is the saturated water vapor pressure, unit is kPa; ea is the actual water vapor pressure, unit is kPa; Δ is the slope of the curve of the relationship between the saturated water vapor pressure difference and temperature, unit is kPa℃ -1 ;γ is the psychrometer constant, unit kPa℃ -1 .

4. The method for calculating irrigation water demand considering the fine distribution of crops according to claim 1, characterized in that: The method uses GIS technology to generate a spatial distribution map of the irrigation water demand of the target area during the crop growth period in step S1, and calculates the total amount of irrigation water demand of the target area based on the crop area.

5. An electronic device comprising a processor, a memory, an input interface, an output interface, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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