Cotton field precision irrigation method, system and medium based on remote sensing image data

Abstract

The application discloses a cotton field precision irrigation recognition method and system based on remote sensing image data and a medium, and comprises the following steps: firstly, a research area range is selected, and image data of the research area range is acquired; then, the image data is subjected to recognition processing to obtain a cotton field irrigation area; secondly, an irrigation period interval is acquired, and image data of the cotton field irrigation area is acquired every week in the irrigation period interval; a short-wave infrared vertical water loss index of different grid areas in the cotton field irrigation area is calculated according to the image data; finally, an irrigation scheme of the grid areas is formulated according to the short-wave infrared vertical water loss index. The cotton field precision irrigation recognition method and system can finely irrigate cotton under different drought conditions, provide a reasonable irrigation scheme, realize precision control of irrigation amount, guarantee water required by cotton growth, and improve cotton yield.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing image data processing, and in particular to a cotton field precision identification irrigation method and system based on remote sensing image data and a medium. BACKGROUND

[0002] China is the largest cotton producer in the world, and Xinjiang is the largest cotton planting base in China. The construction of cotton fields in Xinjiang is of great significance. The cotton planting area in China is very vast, and the related technology of mechanized irrigation of cotton fields has also developed to a certain extent. However, the precise identification of cotton growth at different times and the fine irrigation technology of cotton under different drought conditions have not made much progress. At present, there is still a great waste of water resources. SUMMARY

[0003] The present application aims to overcome the above technical deficiencies and provide a cotton field precision identification irrigation method and system based on remote sensing image data and a medium to solve the technical problem of waste of water resources in the prior art.

[0004] To achieve the above technical purpose, in a first aspect, the technical solution of the present application provides a cotton field precision identification irrigation method based on remote sensing image data, comprising the following steps:

[0005] Selecting a research area range and obtaining image data of the research area range;

[0006] Identifying and processing the image data to obtain a cotton field irrigation area;

[0007] Obtaining an irrigation period interval, and obtaining image data of the cotton field irrigation area every week within the irrigation period interval;

[0008] According to the image data, the shortwave infrared vertical water loss index of different grid areas in the cotton field irrigation area is calculated;

[0009] According to the shortwave infrared vertical water loss index, an irrigation scheme for the grid area is formulated.

[0010] Compared with the prior art, the present application has the following advantages:

[0011] The method comprises the following steps: firstly, selecting a research area range, and acquiring image data of the research area range; then, performing identification processing on the image data to obtain a cotton field irrigation area; secondly, acquiring an irrigation period interval, and acquiring image data of the cotton field irrigation area every week in the irrigation period interval; calculating a short-wave infrared vertical water loss index of different grid areas in the cotton field irrigation area according to the image data; and finally, formulating an irrigation scheme of the grid areas according to the short-wave infrared vertical water loss index. The method can finely irrigate cotton under different drought conditions, provide a reasonable irrigation scheme, realize accurate control of irrigation amount, ensure water required for growth of the cotton, and improve cotton yield.

[0012] According to some embodiments of the present application, the step of formulating the irrigation scheme of the grid areas according to the short-wave infrared vertical water loss index comprises the following steps:

[0013] According to the short-wave infrared vertical water loss index, the cotton field irrigation area is divided into a light drought area, a moderate drought area, a heavy drought area and a special drought area; the irrigation coefficient of the light drought area is a light drought coefficient, the irrigation coefficient of the moderate drought area is a moderate drought coefficient, the irrigation coefficient of the heavy drought area is a heavy drought coefficient, and the irrigation coefficient of the special drought area is a special drought coefficient.

[0014] The irrigation amount of the grid area = irrigation coefficient * area.

[0015] According to some embodiments of the present application, the calculation formula of the short-wave infrared vertical water loss index is as follows:

[0016] SPSI = 0.8096 * (SWIR + 0.725 * NIR) * 0.0001

[0017] wherein, SWIR is an S2A data B11 band, and NIR is an S2A data B8A band.

[0018] According to some embodiments of the present application, when the SPSI value is greater than or equal to 0.4 and less than 0.5, the grid area corresponding to the SPSI value is a light drought area.

[0019] When the SPSI value is greater than or equal to 0.5 and less than 0.6, the grid area corresponding to the SPSI value is a moderate drought area.

[0020] When the SPSI value is greater than or equal to 0.6 and less than 0.7, the grid area corresponding to the SPSI value is a heavy drought area.

[0021] When the SPSI value is greater than or equal to 0.7 and less than 1, the grid area corresponding to the SPSI value is a special drought area.

[0022] According to some embodiments of the present application, the identification processing of the image data obtains a cotton field irrigation area, including the following steps:

[0023] Obtaining a growth rule curve of cotton, obtaining a current date, comparing the image data of the current date with the growth rule curve of cotton, and extracting a first area in the research area range that meets the growth rule curve of cotton;

[0024] Obtaining digital elevation data of the research area range, intersecting the first area and the digital elevation data to obtain a second area;

[0025] Traversing each row and each column of each grid area of the second area to calculate the maximum and minimum values of each grid area, calculating the difference between the maximum and minimum values, and the set of grid areas with a difference less than a preset height difference threshold is a third area, and the third area is marked as the cotton field irrigation area.

[0026] According to some embodiments of the present application, after the third area is marked as the cotton field irrigation area, the following steps are included:

[0027] Calculating the minimum mean NDVI growth and the minimum mean water content of cotton growth in August in the research area range;

[0028] Filtering out grid areas in the third area with a normalized vegetation index greater than the minimum mean NDVI growth and a normalized difference moisture index greater than the minimum mean water content;

[0029] The set of grid areas is a fourth area, and the fourth area is marked as the cotton field irrigation area.

[0030] According to some embodiments of the present application, the calculation formula of the normalized vegetation index is:

[0031] NDVI=(NIR-RED) / (NIR+RED)

[0032] The calculation formula of the normalized difference moisture index is:

[0033] NDMI=(NIR-SWIR) / (NIR+SWIR)

[0034] Wherein, NIR is S2A data B8A band, RED is S2A data B4 band, and SWIR is S2A data B11 band.

[0035] According to some embodiments of the present application, after the fourth area is marked as the cotton field irrigation area, the following steps are included:

[0036] obtaining infrared picture data of each day in the fourth region during the cotton seeding and seedling stage;

[0037] According to the infrared picture data, obtaining the ground temperature data of each day in the fourth region during the cotton seeding and seedling stage;

[0038] Calculating all grid regions in the infrared picture data of each day where the ground temperature data is greater than the minimum temperature for cotton seeding, obtaining a fifth region by collecting the grid regions, and marking the fifth region as the cotton field irrigation region.

[0039] In a second aspect, the technical solution of the present application provides a cotton field precision identification irrigation system based on remote sensing image data, which comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to realize the cotton field precision identification irrigation method based on remote sensing image data as described in any one of the first aspect.

[0040] In a third aspect, the technical solution of the present application provides a computer readable storage medium, which stores computer executable instructions for making a computer execute the cotton field precision identification irrigation method based on remote sensing image data as described in any one of the first aspect.

[0041] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter in the description of the application. BRIEF DESCRIPTION OF DRAWINGS

[0042] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the description of the embodiments, taken in conjunction with the accompanying drawings, in which:

[0043] Figure 1 A flowchart of the cotton field precision identification irrigation method based on remote sensing image data provided for an embodiment of the present application;

[0044] Figure 2 A flowchart of the cotton field precision identification irrigation method based on remote sensing image data provided for another embodiment of the present application;

[0045] Figure 3 A flowchart of the cotton field precision identification irrigation method based on remote sensing image data provided for another embodiment of the present application;

[0046] Figure 4 A flowchart of the cotton field precision identification irrigation method based on remote sensing image data provided for another embodiment of the present application. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] It should be noted that although functional modules are divided in the system diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0049] This invention provides a precise irrigation method for cotton fields based on remote sensing image data. It can provide refined irrigation for cotton under different drought conditions, offer reasonable irrigation plans, accurately control irrigation volume, ensure the water required for cotton growth, and increase cotton yield.

[0050] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0051] refer to Figure 1 and Figure 2 , Figure 1 A flowchart illustrating a method for precise identification and irrigation of cotton fields based on remote sensing image data, as provided in an embodiment of the present invention; Figure 2 A flowchart of a method for precise identification and irrigation of cotton fields based on remote sensing image data, provided in another embodiment of the present invention; Figure 3 A flowchart of a method for precise identification and irrigation of cotton fields based on remote sensing image data, provided in another embodiment of the present invention; Figure 4 A flowchart of a cotton field precise identification irrigation method based on remote sensing image data, provided as another embodiment of the present invention.

[0052] In one embodiment, the method for precise identification of irrigation in cotton fields based on remote sensing image data includes, but is not limited to, steps S110 to S150.

[0053] Step S110: Select the study area and acquire image data of the study area.

[0054] Step S120: The image data is processed to obtain the cotton field irrigation area;

[0055] Step S130: Obtain irrigation period intervals, and acquire image data of the cotton field irrigation area weekly within the irrigation period intervals;

[0056] Step S140: Calculate the shortwave infrared vertical water loss index of different grid areas within the cotton field irrigation area based on the image data;

[0057] Step S150: Develop an irrigation plan for the grid area based on the shortwave infrared vertical water loss index.

[0058] In one embodiment, the method for precise identification of irrigation in cotton fields based on remote sensing image data includes, but is not limited to, steps S210 to S230.

[0059] Step S210: Obtain the growth curve of cotton, obtain the current date, compare the image data of the current date with the growth curve of cotton, and extract the first region within the study area that conforms to the growth curve of cotton.

[0060] Step S220: Obtain digital elevation data for the study area, and intersect the first area and the digital elevation data to obtain the second area;

[0061] Step S230: Traverse the pixel values ​​of each row and each column of each grid area in the second region, calculate the difference between the maximum and minimum pixel values, and the set of grid areas whose difference is less than the preset height difference threshold is the third region. The third region is marked as the cotton field irrigation area.

[0062] In one embodiment, the method for precise identification of irrigation in cotton fields based on remote sensing image data includes, but is not limited to, steps S310 to S330.

[0063] Step S310: Calculate the minimum mean NDVI growth rate and minimum mean moisture content of cotton in August within the study area.

[0064] Step S320: Select grid areas in the third region where the normalized vegetation index is greater than the minimum mean of NDVI growth and the normalized differential humidity index is greater than the minimum mean of water content.

[0065] Step S330: The set of grid regions is the fourth region, and the fourth region is marked as the cotton field irrigation area.

[0066] In one embodiment, the method for precise identification of irrigation in cotton fields based on remote sensing image data includes, but is not limited to, steps S410 to S430.

[0067] Step S410: Obtain infrared image data for each day during the cotton sowing and seedling stage in the fourth region;

[0068] Step S420: Based on the infrared image data, obtain the soil temperature data of the fourth region for each day during the cotton sowing and seedling stage;

[0069] Step S430: Calculate all grid areas in the infrared image data of each day whose ground temperature data is greater than the minimum temperature for cotton sowing, merge the grid areas to obtain the fifth area, and mark the fifth area as the cotton field irrigation area.

[0070] In one embodiment, a method for precise identification and irrigation of cotton fields based on remote sensing image data includes the following steps: First, a study area is selected, and image data of the study area is acquired; then, the image data is processed to identify the irrigated area of ​​the cotton field; second, the irrigation period interval is obtained, and image data of the irrigated area of ​​the cotton field is acquired weekly within the irrigation period interval; the shortwave infrared vertical water loss index of different grid areas within the irrigated area of ​​the cotton field is calculated based on the image data; finally, an irrigation plan for the grid area is formulated based on the shortwave infrared vertical water loss index. This method can provide precise irrigation for cotton under different drought conditions, offering reasonable irrigation plans, accurately controlling irrigation volume, ensuring the water required for cotton growth, and increasing cotton yield.

[0071] Raster data is a data format that divides space into a regular grid, with each grid cell called a unit, and assigns corresponding attribute values ​​to each unit to represent entities. The position of each unit (pixel) is defined by its row and column numbers, and the location of the entity it represents is implicit in the raster row and column positions. Each data point in the data organization represents a non-geometric attribute of a feature or phenomenon or a pointer to its attribute. An excellent compressed data encoding scheme aims to achieve maximum compression while minimizing computer processing time.

[0072] Bands: Bands are also called spectral bands or spectral zones. In remote sensing technology, the electromagnetic spectrum is usually divided into segments of varying sizes. Larger segments are called band regions, such as the visible region and the infrared region; medium-sized segments are such as the near-infrared region and the far-infrared region; and smaller segments are called bands.

[0073] In one embodiment, a method for precise identification of irrigation in cotton fields based on remote sensing image data includes the following steps: First, a study area is selected, and image data of the study area is acquired; then, the image data is processed to identify the irrigated area of ​​the cotton field; second, an irrigation period interval is obtained, and image data of the irrigated area of ​​the cotton field is acquired weekly within the irrigation period interval; the shortwave infrared vertical water loss index of different grid areas within the irrigated area of ​​the cotton field is calculated based on the image data; finally, an irrigation plan for the grid area is formulated based on the shortwave infrared vertical water loss index. Formulating an irrigation plan for the grid area based on the shortwave infrared vertical water loss index includes the following steps: dividing the irrigated area of ​​the cotton field into mild drought, moderate drought, severe drought, and extreme drought areas based on the shortwave infrared vertical water loss index; the irrigation coefficient for the mild drought area is the mild drought coefficient, the irrigation coefficient for the moderate drought area is the moderate drought coefficient, the irrigation coefficient for the severe drought area is the severe drought coefficient, and the irrigation coefficient for the extreme drought area is the extreme drought coefficient; the irrigation amount for the grid area = irrigation coefficient * area.

[0074] In one embodiment, a method for precise identification of irrigation in cotton fields based on remote sensing image data includes the following steps: First, a study area is selected, and image data of the study area is acquired; then, the image data is processed to identify the irrigated area of ​​the cotton field; second, an irrigation period interval is obtained, and image data of the irrigated area of ​​the cotton field is acquired weekly within the irrigation period interval; the shortwave infrared vertical water loss index of different grid areas within the irrigated area of ​​the cotton field is calculated based on the image data; finally, an irrigation plan for the grid area is formulated based on the shortwave infrared vertical water loss index. Formulating an irrigation plan for the grid area based on the shortwave infrared vertical water loss index includes the following steps: dividing the irrigated area of ​​the cotton field into mild drought, moderate drought, severe drought, and extreme drought areas based on the shortwave infrared vertical water loss index; the irrigation coefficient for the mild drought area is the mild drought coefficient, the irrigation coefficient for the moderate drought area is the moderate drought coefficient, the irrigation coefficient for the severe drought area is the severe drought coefficient, and the irrigation coefficient for the extreme drought area is the extreme drought coefficient; the irrigation amount for the grid area = irrigation coefficient * area. The formula for calculating the shortwave infrared vertical water loss index is as follows: SPSI = 0.8096 * (SWIR + 0.725 * NIR) * 0.0001, where SWIR is the B11 band of S2A data and NIR is the B8A band of S2A data.

[0075] In one embodiment, a method for precise identification of irrigation in cotton fields based on remote sensing image data includes the following steps: First, a study area is selected, and image data of the study area is acquired; then, the image data is processed to identify the irrigated area of ​​the cotton field; second, an irrigation period interval is obtained, and image data of the irrigated area of ​​the cotton field is acquired weekly within the irrigation period interval; the shortwave infrared vertical water loss index of different grid areas within the irrigated area of ​​the cotton field is calculated based on the image data; finally, an irrigation plan for the grid area is formulated based on the shortwave infrared vertical water loss index. Formulating an irrigation plan for the grid area based on the shortwave infrared vertical water loss index includes the following steps: dividing the irrigated area of ​​the cotton field into mild drought, moderate drought, severe drought, and extreme drought areas based on the shortwave infrared vertical water loss index; the irrigation coefficient for the mild drought area is the mild drought coefficient, the irrigation coefficient for the moderate drought area is the moderate drought coefficient, the irrigation coefficient for the severe drought area is the severe drought coefficient, and the irrigation coefficient for the extreme drought area is the extreme drought coefficient; the irrigation amount for the grid area = irrigation coefficient * area.

[0076] The formula for calculating the shortwave infrared vertical water loss index (SPSI) is as follows: SPSI = 0.8096 * (SWIR + 0.725 * NIR) * 0.0001, where SWIR is the B11 band of S2A data and NIR is the B8A band of S2A data. When the SPSI value is above 0.4 and below 0.5, the corresponding grid area is a mildly arid region; when the SPSI value is above 0.5 and below 0.6, the corresponding grid area is a moderately arid region; when the SPSI value is above 0.6 and below 0.7, the corresponding grid area is a severely arid region; and when the SPSI value is above 0.7 and below 1, the corresponding grid area is a severely arid region. This allows for precise irrigation based on the varying degrees of drought in different regions, representing a significant improvement over conventional extensive irrigation methods. It can substantially save water resources, improve water resource utilization in arid areas, and has excellent practical value.

[0077] In one embodiment, the method for precise identification of irrigation in cotton fields based on remote sensing image data includes the following steps: First, a study area is selected, and image data of the study area is acquired; then, the image data is processed to identify the irrigated area of ​​the cotton field; second, the irrigation period interval is obtained, and image data of the irrigated area of ​​the cotton field is acquired weekly within the irrigation period interval; the shortwave infrared vertical water loss index of different grid areas within the irrigated area of ​​the cotton field is calculated based on the image data; finally, an irrigation plan for the grid area is formulated based on the shortwave infrared vertical water loss index.

[0078] The process of identifying and processing image data to determine the cotton field irrigation area includes the following steps: obtaining the cotton growth curve, obtaining the current date, comparing the image data of the current date with the cotton growth curve, and extracting the first region within the study area that conforms to the cotton growth curve; obtaining digital elevation data of the study area, and intersecting the first region and the digital elevation data to obtain the second region; traversing the pixel values ​​of each row and each column of each grid area in the second region, calculating the maximum and minimum pixel values ​​of each grid area, calculating the difference between the maximum and minimum pixel values, and defining the set of grid areas whose difference is less than a preset height difference threshold as the third region, which is then labeled as the cotton field irrigation area.

[0079] The cotton field irrigation identification method based on remote sensing image data provided in this embodiment can effectively improve the accuracy of identifying cotton field irrigation areas, improve water resource utilization, and facilitate refined management of cotton planting.

[0080] In one embodiment, the method for precise identification of irrigation in cotton fields based on remote sensing image data includes the following steps: First, a study area is selected, and image data of the study area is acquired; then, the image data is processed to identify the irrigated area of ​​the cotton field; second, the irrigation period interval is obtained, and image data of the irrigated area of ​​the cotton field is acquired weekly within the irrigation period interval; the shortwave infrared vertical water loss index of different grid areas within the irrigated area of ​​the cotton field is calculated based on the image data; finally, an irrigation plan for the grid area is formulated based on the shortwave infrared vertical water loss index.

[0081] The process of identifying and processing image data to determine the cotton field irrigation area includes the following steps: obtaining the cotton growth curve, obtaining the current date, comparing the image data of the current date with the cotton growth curve, and extracting the first region within the study area that conforms to the cotton growth curve; obtaining digital elevation data of the study area, and intersecting the first region and the digital elevation data to obtain the second region; traversing the pixel values ​​of each row and each column of each grid area in the second region, calculating the maximum and minimum pixel values ​​of each grid area, calculating the difference between the maximum and minimum pixel values, and defining the set of grid areas whose difference is less than a preset height difference threshold as the third region, which is then labeled as the cotton field irrigation area.

[0082] After designating the third region as the cotton field irrigation area, the following steps were taken: the minimum mean NDVI growth rate and the minimum mean moisture content of cotton growth in August were calculated within the study area; grid areas within the third region with a normalized vegetation index greater than the minimum mean NDVI growth rate and a normalized differential humidity index greater than the minimum mean moisture content were selected; the set of grid areas was designated as the fourth region, and the fourth region was designated as the cotton field irrigation area.

[0083] This embodiment of the cotton field precise irrigation identification method based on remote sensing image data introduces the minimum mean NDVI growth and minimum mean moisture content of cotton growth in August as references. By comparing the normalized vegetation index and the minimum mean NDVI growth, normalized differential humidity index and minimum mean moisture content in the third region, the accuracy of cotton field irrigation area identification is further improved, water resource utilization is enhanced, and refined management of cotton planting is facilitated.

[0084] In one embodiment, the method for precise identification of irrigation in cotton fields based on remote sensing image data includes the following steps: First, a study area is selected, and image data of the study area is acquired; then, the image data is processed to identify the irrigated area of ​​the cotton field; second, the irrigation period interval is obtained, and image data of the irrigated area of ​​the cotton field is acquired weekly within the irrigation period interval; the shortwave infrared vertical water loss index of different grid areas within the irrigated area of ​​the cotton field is calculated based on the image data; finally, an irrigation plan for the grid area is formulated based on the shortwave infrared vertical water loss index.

[0085] The process of identifying and processing image data to determine the cotton field irrigation area includes the following steps: obtaining the cotton growth curve, obtaining the current date, comparing the image data of the current date with the cotton growth curve, and extracting the first region within the study area that conforms to the cotton growth curve; obtaining digital elevation data of the study area, and intersecting the first region and the digital elevation data to obtain the second region; traversing the pixel values ​​of each row and each column of each grid area in the second region, calculating the maximum and minimum pixel values ​​of each grid area, calculating the difference between the maximum and minimum pixel values, and defining the set of grid areas whose difference is less than a preset height difference threshold as the third region, which is then labeled as the cotton field irrigation area.

[0086] After designating the third region as the cotton field irrigation area, the following steps were taken: the minimum mean NDVI growth rate and the minimum mean moisture content of cotton growth in August were calculated within the study area; grid areas within the third region with a normalized vegetation index greater than the minimum mean NDVI growth rate and a normalized differential humidity index greater than the minimum mean moisture content were selected; the set of grid areas was designated as the fourth region, and the fourth region was designated as the cotton field irrigation area.

[0087] The formula for calculating the normalized vegetation index is:

[0088] NDVI = (NIR - RED) / (NIR + RED)

[0089] The formula for calculating the normalized differential humidity index is:

[0090] NDMI = (NIR - SWIR) / (NIR + SWIR)

[0091] Among them, NIR is the B8A band of S2A data, RED is the B4 band of S2A data, and SWIR is the B11 band of S2A data.

[0092] In one embodiment, the method for precise identification of irrigation in cotton fields based on remote sensing image data includes the following steps: First, a study area is selected, and image data of the study area is acquired; then, the image data is processed to identify the irrigated area of ​​the cotton field; second, the irrigation period interval is obtained, and image data of the irrigated area of ​​the cotton field is acquired weekly within the irrigation period interval; the shortwave infrared vertical water loss index of different grid areas within the irrigated area of ​​the cotton field is calculated based on the image data; finally, an irrigation plan for the grid area is formulated based on the shortwave infrared vertical water loss index.

[0093] The process of identifying and processing image data to determine the cotton field irrigation area includes the following steps: obtaining the cotton growth curve, obtaining the current date, comparing the image data of the current date with the cotton growth curve, and extracting the first region within the study area that conforms to the cotton growth curve; obtaining digital elevation data of the study area, and intersecting the first region and the digital elevation data to obtain the second region; traversing the pixel values ​​of each row and each column of each grid area in the second region, calculating the maximum and minimum pixel values ​​of each grid area, calculating the difference between the maximum and minimum pixel values, and defining the set of grid areas whose difference is less than a preset height difference threshold as the third region, which is then labeled as the cotton field irrigation area.

[0094] After designating the third region as the cotton field irrigation area, the following steps were taken: the minimum mean NDVI growth rate and the minimum mean moisture content of cotton growth in August were calculated within the study area; grid areas within the third region with a normalized vegetation index greater than the minimum mean NDVI growth rate and a normalized differential humidity index greater than the minimum mean moisture content were selected; the set of grid areas was designated as the fourth region, and the fourth region was designated as the cotton field irrigation area.

[0095] After designating the fourth area as the cotton field irrigation area, the following steps are included:

[0096] Acquire infrared image data for each day during the cotton sowing and seedling stage in the fourth region;

[0097] Based on infrared image data, obtain the soil temperature data of the fourth region every day during the cotton sowing and seedling stage;

[0098] Calculate all grid areas in the infrared image data for each day whose ground temperature is higher than the minimum temperature for cotton sowing, merge the grid areas to obtain the fifth area, and mark the fifth area as the cotton field irrigation area.

[0099] In one embodiment, the method for precise identification of irrigation in cotton fields based on remote sensing image data includes the following steps:

[0100] First, the study area R1 was selected, and cotton field areas were identified within this area.

[0101] The growth period of cotton can be divided into the sowing and seedling stage, seedling stage, bud stage, flowering and boll-forming stage, and boll-opening stage, spanning from April to October. Among them, the irrigation ratio during the flowering and boll-forming stage is 50%-60%, and the irrigation plan formulated at this time is of the greatest significance. Therefore, one raster image data per month from April to August of the current season with relatively complete Sentinel-2 Level-2A (S2A) area and less cloud cover is selected.

[0102] Based on the cotton growth curve (the NDVI (Normalized Difference Vegetation Index) growth rate increases from April to August), region D1 that conforms to the cotton growth curve is extracted.

[0103] Download the 30-meter digital elevation data (DEM) of the study area R1. Of course, it is not limited to 30-meter DEM; 20-meter or 40-meter DEMs are also acceptable, as long as they can achieve the technical effect of this embodiment. This embodiment does not impose any limitations on it. Overlay the DEMs in the D1 area and find the intersection to obtain DEM2. Iterate through each region IMG1, IMG2, IMG3...IMGn in DEM2 in sequence. For example, for IMG1, iterate through the pixel values ​​of each row and each column of IMG1, find the maximum value max and the minimum value min, calculate the difference, and obtain the regions D2 whose difference is less than V1.

[0104] Note: V1 is the set height difference threshold (e.g., 10 meters means that the difference between each grid cell in the DEM elevation image does not exceed 10 meters).

[0105] Calculate the NDVI (Normalized Difference Vegetation Index) and crop moisture NDMI during the peak cotton growing season in August.

[0106] (Normalized Differential Humidity Index), calculated using the following formula:

[0107] NDVI=(NIR-RED) / (NIR+RED), NDMI=(NIR-SWIR) / (NIR+SWIR)

[0108] Note: NIR is the B8A band of S2A data, RED is the B4 band of S2A data, and SWIR is the B11 band.

[0109] August is the peak growing season for cotton, with good growth and high crop moisture content. Region D3 was selected where NDVI>V3 and NDMI>V4. The intersection of D2 and D3, D4, was calculated.

[0110] Note: V3 sets the minimum average NDVI growth value reflecting cotton growth in August, and V4 sets the minimum average moisture content reflecting cotton growth in August.

[0111] The sowing and seedling raising period for cotton in Xinjiang is generally in April. This was determined by consulting the sowing period in the first edition of a book by cotton planting technology experts and the dates recorded in survey forms filled out by local cotton farmers. The data was then combined into a union of DATE1-DATE2, and a dataset M1 consisting of one raster image per day between DATE1-DATE2 from the MOD11A1 satellite data within the study area R1 was downloaded. Figures 1-2) The physiological zero-degree temperature of cotton is 20°C. Only when the ground temperature is greater than 20°C can cotton seeds be sown to prevent the emerged seedlings from being frozen to death. The MOD11A1 Terra Land Surface Temperature image data can be used to obtain the ground temperature data. Calculate the areas in each image data in M1 where the value > 20°C, and take the union of the superimposed areas of each region to obtain D5.

[0112] Calculate the intersection area D6 of D4 and D5.

[0113] According to the water requirement law of cotton, the flowering and boll-setting period is the period with the largest water requirement. Obtain the irrigation interval W1 - W2 during this period (refer to the book knowledge of Xinjiang cotton planting techniques and the planting experience of cotton farmers. Generally, it is from mid-July to mid-August). Calculate 1 image data per week of S2A during the period of W1 - W2, and calculate the SPSI (Shortwave Infrared Vertical Water Loss Index). The formula is as follows:

[0114] SPSI = 0.8096 * (SWIR + 0.725 * NIR) * 0.0001

[0115] In the formula, SWIR is the B11 band of S2A data, and NIR is the B8A band of S2A data.

[0116] Within the range of the D6 area, screen out the grid areas RS1, RS2, RS3, and RS4 with SPSI values of 0.4 - 0.5, 0.5 - 0.6, 0.6 - 0.7, and 0.7 - 1, which respectively correspond to the light drought area, medium drought area, severe drought area, and extreme drought area, and calculate the corresponding areas AREA1, AREA2, AREA3, and AREA4. Develop corresponding irrigation plans, with the light drought area coefficient C1, medium drought area coefficient C2, severe drought area C3, and extreme drought area C4, where C1 < C2 < C3 < C4. The irrigation amount = irrigation coefficient * area, achieving precise control, ensuring the water required for the growth of cotton, and increasing the cotton yield.

[0117] The present invention also provides a cotton field precise identification and irrigation system based on remote sensing image data, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the cotton field precise identification and irrigation method based on remote sensing image data as described above.

[0118] The processor and the memory can be connected through a bus or other means.

[0119] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0120] It should be noted that the cotton field precision identification irrigation system based on remote sensing image data in this embodiment may include a business processing module, an edge database, a server version information register, and a data synchronization module. When the processor executes the computer program, it implements the cotton field precision identification irrigation method based on remote sensing image data as described above in the cotton field precision identification irrigation system based on remote sensing image data.

[0121] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] Furthermore, one embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor or controller, for example, by a processor in the above-described terminal embodiment, enabling the processor to execute the cotton field precision identification irrigation method based on remote sensing image data in the above-described embodiment.

[0123] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0124] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

[0125] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for precise identification of irrigation in cotton fields based on remote sensing image data, characterized in that, Includes the following steps: Select a study area and acquire image data of the study area; The image data is processed to identify the cotton field irrigation area; The irrigation period interval is obtained, and image data of the cotton field irrigation area is acquired weekly within the irrigation period interval. The shortwave infrared vertical water loss index of different grid areas within the cotton field irrigation area was calculated based on the image data. An irrigation plan for the grid area is formulated based on the shortwave infrared vertical water loss index. The process of identifying and processing the image data to obtain the cotton field irrigation area includes the following steps: Obtain the growth curve of cotton, obtain the current date, compare the image data of the current date with the growth curve of cotton, and extract the first region within the study area that conforms to the growth curve of cotton; Obtain digital elevation data for the study area, and intersect the first area and the digital elevation data to obtain the second area; Traverse the pixel values ​​of each row and each column of each grid area in the second region, calculate the maximum and minimum pixel values ​​of each grid area, calculate the difference between the maximum and minimum pixel values, and the set of grid areas whose difference is less than a preset height difference threshold is the third region, and the third region is marked as the cotton field irrigation area. After the third area is marked as the cotton field irrigation area, the following steps are included: The minimum mean NDVI growth and minimum mean moisture content of cotton in August within the study area were calculated. Filter out grid areas in the third region where the normalized vegetation index is greater than the minimum mean of NDVI growth and the normalized differential humidity index is greater than the minimum mean of water content. The collection of the grid regions is the fourth region, and the fourth region is marked as the cotton field irrigation area; After the fourth area is marked as the cotton field irrigation area, the following steps are included: Acquire infrared image data for each day during the cotton sowing and seedling stage within the fourth region; Based on the infrared image data, obtain the soil temperature data of the fourth region for each day during the cotton sowing and seedling stage; Calculate all grid areas in the infrared image data for each day whose ground temperature is greater than the minimum temperature for cotton sowing, combine these grid areas to obtain a fifth area, and label the fifth area as the cotton field irrigation area.

2. The method for precise identification and irrigation of cotton fields based on remote sensing image data according to claim 1, characterized in that, The step of formulating an irrigation scheme for the grid area based on the shortwave infrared vertical water loss index includes the following steps: The cotton field irrigation area is divided into mild drought area, moderate drought area, severe drought area and extreme drought area according to the shortwave infrared vertical water loss index. The irrigation coefficient of the mild drought area is the mild drought coefficient, the irrigation coefficient of the moderate drought area is the moderate drought coefficient, the irrigation coefficient of the severe drought area is the severe drought coefficient, and the irrigation coefficient of the extreme drought area is the extreme drought coefficient. The irrigation amount for the grid area = irrigation coefficient × area.

3. The method for precise identification and irrigation of cotton fields based on remote sensing image data according to claim 2, characterized in that, The formula for calculating the shortwave infrared vertical water loss index is as follows: SPSI = 0.8096 × (SWIR+ 0.725 × NIR) × 0.0001 Among them, SWIR is the B11 band of S2A data, and NIR is the B8A band of S2A data.

4. The method for precise identification and irrigation of cotton fields based on remote sensing image data according to claim 3, characterized in that, When the SPSI value is above 0.4 and less than 0.5, the grid area corresponding to the SPSI value is a slightly arid area; When the SPSI value is above 0.5 and less than 0.6, the grid area corresponding to the SPSI value is a moderately arid area; When the SPSI value is above 0.6 and less than 0.7, the grid area corresponding to the SPSI value is a severely drought-stricken area; When the SPSI value is above 0.7 and less than 1, the grid area corresponding to the SPSI value is a drought-stricken area.

5. The method for precise identification and irrigation of cotton fields based on remote sensing image data according to claim 1, characterized in that, The formula for calculating the normalized vegetation index is as follows: NDVI=(NIR -RED) / (NIR + RED) The formula for calculating the normalized differential humidity index is as follows: NDMI = (NIR - SWIR) / (NIR + SWIR) Among them, NIR is the B8A band of S2A data, RED is the B4 band of S2A data, and SWIR is the B11 band of S2A data.

6. A precise identification irrigation system for cotton fields based on remote sensing image data, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the cotton field precision identification irrigation method based on remote sensing image data as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the cotton field precision identification and irrigation method based on remote sensing image data as described in any one of claims 1 to 5.

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