A method and system for monitoring and adjusting decision-making of crop planting structure in water-scarce areas

Abstract

The present invention provides a method and system for monitoring and adjusting decisions on crop planting structures in water-scarce areas, belonging to the field of agricultural remote sensing technology, including: obtaining gravity satellite data in underground water-scarce areas, and calculating through spatial interpolation in combination with underground water level data to obtain a distribution map of key underground water-deficient areas; obtaining remote sensing image data of underground water-scarce areas, performing vegetation index selection calculation on the remote sensing image data of the underground water-scarce areas, and classifying according to remote sensing of crop planting in large areas to obtain a crop dominance map; comprehensively combining the distribution map of key underground water-deficient areas and the crop dominance map to obtain a guidance result for crop planting adjustment. The present invention utilizes multi-source remote sensing data, aims at underground water-deficient areas, comprehensively monitors the groundwater isohypses, rapid decline of groundwater, and crop planting dominance by remote sensing, and assists in crop planting decisions, which can improve the efficiency of remote sensing monitoring and decision-making on crop planting dominance, and provide strong technical support for reducing and controlling agricultural water use and slowing down the trend of groundwater level decline.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural remote sensing, and in particular, to a method and system for monitoring and adjusting the crop planting structure and making decisions in water-scarce areas. Background Art

[0002] For a long time, people have generally used artificial irrigation to develop land resources. However, the existing cultivated land area is limited. Especially in arid or semi-arid regions, large-scale artificial reclamation combined with extensive flood irrigation of farmland consumes a large amount of groundwater resources and causes large-scale water-salt migration, recombination, and accumulation within the region, resulting in a sharp reduction in the area of arable and high-yielding agricultural and pastoral land in these areas. Therefore, it is necessary to effectively monitor the cultivated land in water-scarce areas to guide the rational adjustment of the crop planting structure.

[0003] At present, the adjustment of the crop planting structure mainly relies on manually reported statistical data. On the one hand, there is no spatial location information of specific fields, and on the other hand, there is a lack of spatial coupling and superposition analysis with the change of groundwater shortage. Manually reported statistical data has obvious problems of low efficiency and low accuracy.

[0004] In view of the above defects, a new method for guiding crop planting in water-scarce areas needs to be proposed. Summary of the Invention

[0005] The present invention provides a method and system for monitoring and adjusting the crop planting structure and making decisions in water-scarce areas, so as to solve the defects in the prior art that the guidance for adjusting the crop planting structure in water-scarce areas mainly relies on manual work, resulting in low efficiency and accuracy.

[0006] In a first aspect, the present invention provides a method for monitoring and adjusting the crop planting structure and making decisions in water-scarce areas, including:

[0007] Obtaining gravity satellite data of the groundwater-deficient area, and combining spatial interpolation calculation of groundwater level data to obtain a distribution map of key groundwater-deficient areas;

[0008] Obtaining remote sensing image data of the groundwater-deficient area, performing vegetation index selection calculation on the remote sensing image data of the groundwater-deficient area, and classifying according to remote sensing of large-area crop planting to obtain a crop dominance map;

[0009] Combining the distribution map of key groundwater-deficient areas and the crop dominance map to obtain a guidance result for crop planting adjustment.

[0010] According to the method for monitoring and adjusting the crop planting structure and making decisions in water-scarce areas provided by the present invention, the obtaining of the gravity satellite data of the groundwater-deficient area includes:

[0011] Calling the gravity satellite data set through the Google Maps Engine (GEE);

[0012] Extract the total groundwater storage data from the gravity satellite dataset, and screen the relative change data of the average groundwater level within a preset time period in the total groundwater storage data;

[0013] Convert the relative change data to a preset scale range through interpolation to obtain the gravity satellite data of the groundwater-deficient area;

[0014] Obtain groundwater observation record data;

[0015] Perform spatial interpolation on the groundwater observation record data using the Kriging interpolation method to obtain the time series of groundwater level distribution;

[0016] Overlay the gravity satellite data of the groundwater-deficient area and the time series of groundwater level distribution through three-dimensional visualization and threshold method, and analyze to obtain the distribution map of key groundwater-deficient areas;

[0017] According to a method for monitoring and adjusting the crop planting structure in water-deficient areas provided by the present invention, the obtaining of remote sensing image data of groundwater-deficient areas and the selection calculation of vegetation indices and remote sensing classification of large-area crop planting for the remote sensing image data of groundwater-deficient areas include:

[0018] Obtain Sentinel-2 image data of different crop planting areas in the first time period and the second time period within a year through GEE;

[0019] Perform image mosaicing, image cropping, and preset band fusion calculation on the Sentinel-2 image data of different crop planting areas, and perform image cloud removal using the preset bands of Sentinel-2 to obtain the initial remote sensing image data of the groundwater-deficient area;

[0020] Perform fusion correction on the initial remote sensing image data of the groundwater-deficient area, and perform masking processing on the initial remote sensing image data of the groundwater-deficient area based on the preset cultivated land range to obtain the remote sensing image data of the groundwater-deficient area;

[0021] Screen the third band data, fourth band data, eighth band data, and eleventh band data of Sentinel-2 in the remote sensing image data of the groundwater-deficient area, as well as the normalized difference vegetation index, enhanced vegetation index, normalized difference water index, chlorophyll index, soil tillage index, and normalized tillage index to obtain the vegetation index set of the remote sensing image data of the groundwater-deficient area;

[0022] Based on the monthly time series combination in the first time period and the second time period within a year, divide the vegetation index set of the remote sensing image data of the groundwater-deficient area into a large-area crop planting remote sensing data set;

[0023] Classify the large-area crop planting remote sensing data set using the random forest classification method to obtain the classification result of large-area crop planting.

[0024] A method for guiding crop planting in water - scarce areas provided by the present invention, the obtaining remote - sensing image data of underground water - scarce areas, performing vegetation index selection calculation on the remote - sensing image data of the underground water - scarce areas and classifying according to remote - sensing classification of large - area crop planting to obtain a crop dominance map, includes:

[0025] Cropping and segmenting the large - area crop - planting classification result, and outputting the planting areas of different crops;

[0026] Calculating the ratios of the planting areas of different crops to the total cultivated land area respectively to obtain the proportions of the planting areas of different crops;

[0027] Drawing and outputting the crop dominance map according to the proportions of the planting areas of different crops.

[0028] In a second aspect, the present invention also provides a system for monitoring and adjusting the crop - planting structure in water - scarce areas, including:

[0029] A first processing module, configured to obtain gravity satellite data of underground water - scarce areas, and combine spatial interpolation calculation of underground water - level data to obtain a distribution map of key underground water - scarce areas;

[0030] A second processing module, configured to obtain remote - sensing image data of underground water - scarce areas, perform vegetation index selection calculation on the remote - sensing image data of the underground water - scarce areas and classify according to remote - sensing classification of large - area crop planting to obtain a crop dominance map;

[0031] An adjustment module, configured to comprehensively obtain a crop - planting adjustment guidance result based on the distribution map of key underground water - scarce areas and the crop dominance map.

[0032] In a third aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the program, it implements the method for monitoring and adjusting the crop - planting structure in water - scarce areas as described in any one of the above.

[0033] In a fourth aspect, the present invention also provides a non - transitory computer - readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for monitoring and adjusting the crop - planting structure in water - scarce areas as described in any one of the above.

[0034] In a fifth aspect, the present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method for monitoring and adjusting the crop - planting structure in water - scarce areas as described in any one of the above.

[0035] The method and system for monitoring and adjusting the crop planting structure in water-scarce areas provided by the present invention utilize multi-source remote sensing data, and for groundwater water-scarce areas, comprehensively monitor the groundwater isohypses, rapid decline of groundwater, and remote sensing of crop planting dominance, assist in crop planting decision-making, can improve the efficiency of remote sensing monitoring and decision-making of crop planting dominance in large areas, and provide strong technical support for reducing and controlling agricultural water use and slowing down the trend of groundwater level decline. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0037] Figure 1 It is one of the flow diagrams of the method for monitoring and adjusting the crop planting structure in water-scarce areas provided by the present invention;

[0038] Figure 2 It is another flow diagram of the method for monitoring and adjusting the crop planting structure in water-scarce areas provided by the present invention;

[0039] Figure 3 It is the structural diagram of the system for monitoring and adjusting the crop planting structure in water-scarce areas provided by the present invention;

[0040] Figure 4 It is the structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0042] Aiming at the defects of the prior art, the present invention mines multi-temporal and multi-spatial remote sensing image information and conducts statistical analysis through the Google Earth Engine (GEE) remote sensing big data platform, providing a reference for remote sensing monitoring of crop planting systems and adjustment of planting structures at the field block scale in arid and semi-arid areas.

[0043] Figure 1 It is one of the flow diagrams of the method for monitoring and adjusting the crop planting structure in water-scarce areas provided by the present invention, as Figure 1 shown, including:

[0044] Step 100: Obtain gravity satellite data of the groundwater-deficient areas, and combine it with spatial interpolation calculation of groundwater level data to obtain a distribution map of key groundwater-deficient areas.

[0045] Step 200: Obtain remote sensing image data of the groundwater-deficient areas, perform vegetation index selection calculation on the remote sensing image data of the groundwater-deficient areas, and conduct remote sensing classification of crop planting in large regions to obtain a crop dominance map.

[0046] Step 300: Integrate the distribution map of key groundwater-deficient areas and the crop dominance map to obtain a guidance result for crop planting adjustment.

[0047] In the present invention, the original data of the water-deficient areas is processed through two branches. One is to use GRACE gravity satellite data to invert the change of groundwater storage in recent years, and combine the groundwater level data of groundwater observation stations to perform spatial interpolation of the groundwater level, so as to obtain a distribution map of key groundwater-deficient areas such as groundwater funnel areas and areas with rapid decline of groundwater level that need to be focused on. The other is for water-deficient areas. Using the GEE remote sensing big data platform, Sentinel-2 image data of large regions is obtained, and the image cloud removal operation is completed relying on the QA60 band of Sentinel-2 in the GEE platform. Combining the regional crop planting situation, a certain number of vegetation indices are selected and calculated, as well as some band data in Sentinel-2 data, to form a feature set. Then, combined with the image data of the specified time period as a time-series remote sensing data set, the random forest algorithm is used to perform supervised classification on the target area range to obtain a crop dominance map.

[0048] Integrate the obtained distribution map of key groundwater-deficient areas and crop dominance map, compare and analyze the crop dominance map with the groundwater isohyet and the distribution of areas with rapid decline of groundwater level, assist in making crop planting adjustment decisions for different regions and produce thematic maps.

[0049] It should be noted that the GEE platform has a global-scale remote sensing database and provides a cloud platform for online visualization calculation and analysis processing. This platform can access materials in satellite images and other Earth observation data databases, and provides sufficient computing power to process these data. GEE can quickly and batch process a large number of images. With the development of satellite gravity detection technology, new observation means have been provided for the high-precision and high spatio-temporal resolution Earth gravity field, making it possible to monitor the total global groundwater storage and its changes using the Earth's time-varying gravity field. The Earth gravity field is a basic physical field that reflects the distribution of material density and the motion state of the Earth's surface and interior. Its changes reflect the mass migration and redistribution of the Earth system's fluids, including the atmosphere, ocean, and terrestrial water. After deducting the mass of the atmosphere, ocean, surface water, and glaciers, the remaining mainly reflects the total terrestrial groundwater storage, and the GRACE satellite can be used to monitor the change of groundwater storage.

[0050] The present invention utilizes multi-source remote sensing data, aiming at groundwater-deficient areas. By comprehensively monitoring the groundwater isohyet, the rapid decline of groundwater, and the remote sensing of crop planting dominance, it assists in crop planting decision-making, can improve the efficiency of remote sensing monitoring and decision-making of crop planting dominance in large areas, and provides strong technical support for reducing and controlling agricultural water use and slowing down the trend of groundwater level decline.

[0051] Based on the above embodiments, the method for obtaining gravity satellite data of groundwater-deficient areas and calculating the spatial interpolation of groundwater level data to obtain a distribution map of key groundwater-deficient areas includes:

[0052] Calling the gravity satellite data set through the Google Earth Engine (GEE);

[0053] Extracting the total groundwater storage data in the gravity satellite data set, and screening the relative change data of the average groundwater level within a preset time period in the total groundwater storage data;

[0054] Converting the relative change data to a preset scale range through interpolation to obtain the gravity satellite data of the groundwater-deficient area;

[0055] Obtaining groundwater observation record data;

[0056] Performing spatial interpolation on the groundwater observation record data by using the Kriging interpolation method to obtain a time series of groundwater level distribution;

[0057] Overlaying the gravity satellite data of the groundwater-deficient area and the time series of groundwater level distribution through three-dimensional visualization and threshold method, and analyzing to obtain the distribution map of key groundwater-deficient areas.

[0058] Specifically, the present invention calls the GRACE Tellus satellite data set through the GEE platform. Based on the total groundwater storage obtained from the GRACE satellite and the relative change data relative to the average value of the previous 10 years as the basic data set for analyzing the change of groundwater level, the dimension is divided here with a time resolution of one month and a spatial resolution of 0.5°.

[0059] Then, through the groundwater level observation stations in each place, obtain the observation record data of groundwater observation points in recent years, count the average value, variance, etc. of the discrete groundwater observation point data, and analyze the time change curve.

[0060] Then, the Kriging interpolation method is used to perform spatial interpolation on the groundwater level data, and spatial expression is carried out in GIS to obtain the time-series groundwater level distribution map, so as to calculate the annual average buried depth data of groundwater in the study area. The Kriging interpolation method here is also called the spatial local interpolation method, which is based on the variogram theory and structural analysis, and is a method for unbiased optimal estimation of regionalized variables within a limited area. The applicable range of the Kriging interpolation method is that there is spatial correlation in regionalized variables, that is, if the results of variogram and structural analysis show that there is spatial correlation in regionalized variables, then the Kriging interpolation method can be used for interpolation or extrapolation.

[0061] Furthermore, compare the groundwater volume change monitoring data of gravity satellite data and the spatial interpolation of groundwater level data, and analyze the monitoring of groundwater change trends and changes. Perform overlay analysis on the two, and through three-dimensional visualization and threshold method, set the areas where groundwater has dropped rapidly in recent years and the areas with low groundwater levels as key groundwater water shortage areas, and make a distribution map of key water shortage areas.

[0062] The present invention can obtain a relatively accurate distribution map of key water shortage areas by acquiring big data of underground water shortage areas through gravity satellites and performing preprocessing and analysis.

[0063] Based on any one of the above embodiments, the obtaining of remote sensing image data of underground water shortage areas, the selection and calculation of vegetation indices for the remote sensing image data of underground water shortage areas, and the remote sensing classification of crop planting in large areas include:

[0064] Obtain Sentinel-2 image data of different crop planting areas in the first time period and the second time period within a year through GEE;

[0065] Perform image mosaicking, image cropping and preset band fusion calculation on the Sentinel-2 image data of different crop planting areas, and use the preset bands of Sentinel-2 to remove clouds from the image to obtain the initial remote sensing image data of underground water shortage areas;

[0066] Perform fusion correction on the initial remote sensing image data of underground water shortage areas, and perform masking processing on the initial remote sensing image data of underground water shortage areas based on the preset cultivated land range to obtain the remote sensing image data of underground water shortage areas;

[0067] Screen the third band data, fourth band data, eighth band data and eleventh band data of Sentinel-2 in the remote sensing image data of underground water shortage areas, as well as the normalized difference vegetation index, enhanced vegetation index, normalized difference water index, chlorophyll index, soil tillage index and normalized tillage index to obtain the vegetation index set of the remote sensing image data of underground water shortage areas;

[0068] Based on the monthly time series combination in the first time period and the second time period within a year, divide the vegetation index set of the remote sensing image data of the groundwater-deficient area into a large-area crop planting remote sensing data set;

[0069] Use the random forest classification method to classify the large-area crop planting remote sensing data set to obtain the large-area crop planting classification result;

[0070] Crop and segment the large-area crop planting classification result, and output the planting areas of different crops;

[0071] Calculate the ratios of the planting areas of different crops to the total cultivated land area respectively to obtain the proportions of the planting areas of different crops;

[0072] Draw and output the crop dominance map according to the proportions of the planting areas of different crops.

[0073] Specifically, the present invention determines 8 months each year as the distinguishable key times for different planted crops according to the crop growth phenological calendar, which are March - August and November - December each year. The Sentinel-2 images of these 8 months are obtained through the GEE remote sensing data cloud platform to form time series features for differentiating different crop plantings.

[0074] Precisely because of the Sentinel-2 image data obtained based on the GEE platform, the overall quality of this image data is good. It is the bottom-of-atmosphere reflectance data after atmospheric correction, and it has been orthorectified and sub-pixel geometrically rectified.

[0075] Screen the annual high-quality image data within the target area through the GEE platform. After the screening is completed, image mosaicking, image cropping, band calculation, and fusion calculation are completed within the GEE platform, and the QA60 band in Sentinel-2 is used for image cloud removal operation to obtain the initial remote sensing image data of the groundwater-deficient area.

[0076] Then download and export the initial remote sensing image data of the groundwater-deficient area to the ENVI software for fusion and correction, and then use the publicly available cultivated land range to perform masking processing on the study area image to remove other areas outside the cultivated land, completing the image preprocessing to obtain the remote sensing image data of the groundwater-deficient area.

[0077] Then, select typical and representative vegetation indices for calculation in the remote sensing image data of the groundwater-deficient area underground, including: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), Green Chlorophyll Vegetation Index (GCVI), Soil Tillage Index (STI), and Normalized Difference Tillage Index (NDTI).

[0078] Among them, NDVI can quantitatively characterize photosynthetic capacity, water stress, and vegetation productivity. EVI can reduce soil reflectance and enhance the contrast between bare land and vegetation. NDWI is sensitive to the surface water content and is also an important vegetation index for water body extraction. GCVI has a strong response to the fertilizer application situation in the cultivated land area of the target area. STI is an effective vegetation index for identifying cultivated land. Vegetation indices are often used for reasons such as the extraction of vegetation or vegetation information.

[0079] Based on the above six vegetation indices, four band data of Sentinel-2, namely B3, B4, B8, and B11, are additionally selected as the vegetation index set of the remote sensing image data of the groundwater-deficient area underground for remote sensing classification of different planting methods.

[0080] Furthermore, the time series combination of 8 months from November to December and March to August and the feature combination of 10 vegetation indices (NDVI, EVI, NDWI, GCVI, STI, NDTI6, B3, B4, B8, B11) per month, a total of 80 features, are put into a random forest classifier for large-area crop planting classification.

[0081] Taking the groundwater funnel area in the southern plain of a certain region as an example, the random forest algorithm is used to classify one-season crops and two-season crops in a year to investigate the planting distribution of different crops. The F1-score of the classification result of one-season crops reaches 95.97%, and the F1-score of the classification result of two-season crops in a year reaches 98.70%, which can meet the requirements of remote sensing monitoring of crop planting distribution at the field block scale.

[0082] The present invention defines the crop dominance as the ratio of the crop planting area to the total cultivated land area, which reflects the proportion of different crop planting systems within a certain range. Based on the delimited area as the basic unit, the remote sensing monitoring results of crop planting distribution at the field scale are cropped and segmented, and the area tabulation tool of Arcgis software is used to count the classification results of different crops within the delimited area, and calculate the crop planting dominance. It can be understood that based on the delimited area, the distribution of crop planting systems in different regions can be intuitively seen, and a crop planting dominance map can be drawn.

[0083] Finally, by comparing the crop dominance map and the distribution map of key groundwater-deficient areas, and combining information such as groundwater isobaths, in order to achieve the purpose of water-saving planting, auxiliary crop planting adjustment decisions and suggestions can be put forward.

[0084] Taking the groundwater funnel area in the southern part of a certain plain area as an example, by comparing the classification results of single-season and double-season crops at the field scale, the crop dominance map of two crops per year in a certain plain funnel area and the distribution map of the buried depth of deep groundwater in a certain plain, it can be seen that the main crop types in the main groundwater funnel areas are single-season crops per year, and the dominance of two crops per year is relatively low. However, there are also some funnel areas where the crop types are still mainly two crops per year. In the core area of the groundwater funnel area, the proportion of two crops per year is relatively large, which requires a large amount of agricultural water and is not conducive to maintaining the groundwater level. It is recommended to reduce the planting of two crops per year in the key control areas; while in the marginal areas of the groundwater funnel area, and the planting proportion of two crops per year is slightly smaller, it is recommended to reduce the planting of two crops per year in the secondary control areas to maintain the groundwater level.

[0085] Take Figure 2 Figure 2 showing the flow chart of the method for monitoring and adjusting the crop planting structure in water-deficient areas, including the complete steps of the solution of the present invention:

[0086] (1) Obtaining gravity satellite data in groundwater-deficient areas and calculating groundwater changes;

[0087] (2) Spatial interpolation of groundwater level data;

[0088] (3) Mapping key groundwater-deficient areas;

[0089] (4) Obtaining multi-temporal satellite remote sensing images based on the GEE platform;

[0090] (5) Calculating satellite remote sensing vegetation indices;

[0091] (6) Remote sensing monitoring of large-area crop planting;

[0092] (7) Mapping crop planting dominance at the field scale;

[0093] (8) Making decisions on adjusting the dominance of large-area crop planting.

[0094] The present invention provides a method for assisting crop planting decision-making by comprehensively monitoring the groundwater contour line, the rapid decline of groundwater, and the remote sensing of crop planting dominance in groundwater-deficient areas, which can improve the efficiency of remote sensing monitoring and decision-making of crop planting dominance in large areas and provide technical support for reducing and controlling agricultural water use to slow down the decline trend of groundwater levels.

[0095] The system for monitoring and adjusting the crop planting structure in water-deficient areas provided by the present invention will be described below. The system for monitoring and adjusting the crop planting structure in water-deficient areas described below can be correspondingly referred to the method for monitoring and adjusting the crop planting structure in water-deficient areas described above.

[0096] Figure 3 is a schematic structural diagram of the system for monitoring and adjusting the crop planting structure in water-deficient areas provided by the present invention, as Figure 3 shown, including: a first processing module 31, a second processing module 32, and an adjustment module 33, where:

[0097] The first processing module 31 is used to obtain gravity satellite data of the groundwater-deficient area, and combine spatial interpolation calculation of groundwater level data to obtain a distribution map of key groundwater-deficient areas; the second processing module 32 is used to obtain remote sensing image data of the groundwater-deficient area, perform vegetation index selection calculation on the remote sensing image data of the groundwater-deficient area, and classify according to remote sensing of large-area crop planting to obtain a crop dominance map; the adjustment module 33 is used to comprehensively combine the distribution map of key groundwater-deficient areas and the crop dominance map to obtain a guidance result for crop planting adjustment.

[0098] The present invention uses multi-source remote sensing data, and for groundwater-deficient areas, comprehensively monitors the groundwater contour line, the rapid decline of groundwater, and the remote sensing of crop planting dominance to assist crop planting decision-making, which can improve the efficiency of remote sensing monitoring and decision-making of crop planting dominance in large areas and provide strong technical support for reducing and controlling agricultural water use to slow down the decline trend of groundwater levels.

[0099] Figure 4 illustrates a schematic structural diagram of an electronic device, as Figure 4As shown in the figure, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440. Among them, the processor 410, the communications interface 420, and the memory 430 complete mutual communication through the communication bus 440. The processor 410 may call the logical instructions in the memory 430 to execute the method for monitoring and adjusting the decision-making of the crop planting structure in the water-deficient area. The method includes: obtaining gravity satellite data in the underground water-deficient area, and combining spatial interpolation calculation of the groundwater level data to obtain a distribution map of key groundwater-deficient areas; obtaining remote sensing image data in the underground water-deficient area, performing vegetation index selection calculation on the remote sensing image data in the underground water-deficient area, and classifying the remote sensing of crop planting in the large area to obtain a crop dominance map; comprehensively combining the distribution map of key groundwater-deficient areas and the crop dominance map to obtain a guidance result for crop planting adjustment.

[0100] In addition, when the logical instructions in the above-mentioned memory 430 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0101] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for monitoring and adjusting the decision-making of the crop planting structure in the water-deficient area provided by the above-mentioned various methods. The method includes: obtaining gravity satellite data in the underground water-deficient area, and combining spatial interpolation calculation of the groundwater level data to obtain a distribution map of key groundwater-deficient areas; obtaining remote sensing image data in the underground water-deficient area, performing vegetation index selection calculation on the remote sensing image data in the underground water-deficient area, and classifying the remote sensing of crop planting in the large area to obtain a crop dominance map; comprehensively combining the distribution map of key groundwater-deficient areas and the crop dominance map to obtain a guidance result for crop planting adjustment.

[0102] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a method for monitoring and adjusting the decision-making of the crop planting structure in the water-deficient area provided by the above-mentioned various methods. The method includes: obtaining gravity satellite data in the underground water-deficient area, and combining spatial interpolation calculation of underground water level data to obtain a distribution map of key underground water-deficient areas; obtaining remote sensing image data in the underground water-deficient area, performing vegetation index selection calculation on the remote sensing image data in the underground water-deficient area, and classifying remote sensing of crop planting in large areas to obtain a crop dominance map; comprehensively combining the distribution map of key underground water-deficient areas and the crop dominance map to obtain a guidance result for crop planting adjustment.

[0103] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0104] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring and adjusting the decision-making of crop planting structure in water-deficient areas, characterized in that, it includes: Obtain gravity satellite data in underground water-deficient areas, and combine spatial interpolation calculation of underground water level data to obtain a distribution map of key underground water-deficient areas; Obtain remote sensing image data of underground water-deficient areas, perform vegetation index selection calculation on the remote sensing image data of the underground water-deficient areas, and conduct remote sensing classification of crop planting in large areas to obtain a crop dominance map; Integrate the distribution map of key underground water-deficient areas and the crop dominance map to obtain a guidance result for crop planting adjustment; The obtaining of remote sensing image data of underground water-deficient areas, performing vegetation index selection calculation on the remote sensing image data of the underground water-deficient areas, and conducting remote sensing classification of crop planting in large areas includes: Obtain Sentinel-2 image data of different crop planting areas in the first time period and the second time period within a year through GEE; Perform image stitching, image cropping and preset band fusion calculation on the Sentinel-2 image data of different crop planting areas, and use the preset band of Sentinel-2 to remove clouds from the image to obtain initial remote sensing image data of underground water-deficient areas; Perform fusion correction on the initial remote sensing image data of underground water-deficient areas, and perform masking processing on the initial remote sensing image data of underground water-deficient areas based on a preset cultivated land range to obtain the remote sensing image data of the underground water-deficient areas; Screen the third band data, fourth band data, eighth band data and eleventh band data of Sentinel-2 in the remote sensing image data of the underground water-deficient areas, as well as the normalized difference vegetation index, enhanced vegetation index, normalized difference water index, chlorophyll index, soil tillage index and normalized tillage index, to obtain a set of vegetation indices of the remote sensing image data of the underground water-deficient areas; Based on the monthly time series combination in the first time period and the second time period within a year, divide the set of vegetation indices of the remote sensing image data of the underground water-deficient areas into a set of remote sensing data for crop planting in large areas; Use the random forest classification method to classify the set of remote sensing data for crop planting in large areas to obtain a classification result of crop planting in large areas; The obtaining of remote sensing image data of underground water-deficient areas, performing vegetation index selection calculation on the remote sensing image data of the underground water-deficient areas, and conducting remote sensing classification of crop planting in large areas to obtain a crop dominance map includes: Crop and segment the classification result of crop planting in large areas, and output the planting areas of different crops; Calculate the ratios of the planting areas of different crops to the total cultivated land area respectively to obtain the proportions of the planting areas of different crops; Draw and output the crop dominance map according to the proportions of the planting areas of different crops.

2. The method for monitoring and adjusting the decision-making of crop planting structure in water-deficient areas according to claim 1, characterized in that, The obtaining of gravity satellite data in underground water-deficient areas, and combining spatial interpolation calculation of underground water level data to obtain a distribution map of key underground water-deficient areas includes: Call the gravity satellite data set through the Google Maps Engine GEE; Extract the total underground water storage data in the gravity satellite data set, and screen the relative change data of the average underground water level within a preset time period in the total underground water storage data; Convert the relative change data to a preset scale range through interpolation to obtain the gravity satellite data of the groundwater-deficient area; Obtain groundwater observation record data; Perform spatial interpolation on the groundwater observation record data by using the Kriging interpolation method to obtain the time series of groundwater level distribution; Overlay the gravity satellite data of the groundwater-deficient area and the time series of groundwater level distribution through three-dimensional visualization and threshold method, and analyze to obtain the distribution map of the key groundwater-deficient areas; 3. A system for monitoring and adjusting the crop planting structure in water-deficient areas, characterized in that, it includes: A first processing module, configured to obtain the gravity satellite data of the groundwater-deficient area, and combine the spatial interpolation calculation of the groundwater level data to obtain the distribution map of the key groundwater-deficient areas; A second processing module, configured to obtain the remote sensing image data of the groundwater-deficient area, perform vegetation index selection calculation on the remote sensing image data of the groundwater-deficient area, and classify according to the remote sensing classification of crop planting in large areas to obtain a crop dominance map; An adjustment module, configured to comprehensively obtain the crop planting adjustment guidance result based on the distribution map of the key groundwater-deficient areas and the crop dominance map; The second processing module is specifically configured to: The obtaining the remote sensing image data of the groundwater-deficient area, performing vegetation index selection calculation on the remote sensing image data of the groundwater-deficient area, and classifying according to the remote sensing classification of crop planting in large areas includes: Obtain Sentinel-2 image data of different crop planting areas in the first time period and the second time period within a year through GEE; Perform image mosaicing, image cropping and preset band fusion calculation on the Sentinel-2 image data of different crop planting areas, and perform image cloud removal using the preset bands of Sentinel-2 to obtain the initial remote sensing image data of the groundwater-deficient area; Perform fusion correction on the initial remote sensing image data of the groundwater-deficient area, and perform masking processing on the initial remote sensing image data of the groundwater-deficient area based on the preset cultivated land range to obtain the remote sensing image data of the groundwater-deficient area; Screen the third band data, fourth band data, eighth band data and eleventh band data of Sentinel-2 in the remote sensing image data of the groundwater-deficient area, as well as the normalized difference vegetation index, enhanced vegetation index, normalized difference water index, chlorophyll index, soil tillage index and normalized tillage index, to obtain the vegetation index set of the remote sensing image data of the groundwater-deficient area; Based on the monthly time series combination in the first time period and the second time period within a year, divide the vegetation index set of the remote sensing image data of the groundwater-deficient area into a large-area crop planting remote sensing data set; Use the random forest classification method to classify the large-area crop planting remote sensing data set to obtain the large-area crop planting classification result; The second processing module is also specifically configured to: The obtaining the remote sensing image data of the groundwater-deficient area, performing vegetation index selection calculation on the remote sensing image data of the groundwater-deficient area, and classifying according to the remote sensing classification of crop planting in large areas to obtain a crop dominance map includes: Crop and segment the large-area crop planting classification result, and output the planting areas of different crops; Calculate the ratios of the planting areas of different crops to the total cultivated land area respectively to obtain the proportions of the planting areas of different crops; According to the proportion of the planting areas of different crops, draw and output the crop dominance map.

4. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the program, the method for monitoring and adjusting the decision-making of the crop planting structure in the water-deficient area as described in any one of claims 1 to 2 is implemented.

5. A non-transitory computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the method for monitoring and adjusting the decision-making of the crop planting structure in the water-deficient area as described in any one of claims 1 to 2 is implemented.

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