Crop growth monitoring method and device based on satellite remote sensing

Abstract

The invention relates to the technical field of satellite image analysis, and discloses a crop growth monitoring method and device based on satellite remote sensing, and the method comprises the steps: obtaining a satellite remote sensing image of a target monitoring region, extracting the geographic position and collection time information of the satellite remote sensing image, converting the remote sensing image according to the geographic position information and the collection time, and obtaining a crop growth monitoring result; the method comprises the steps of forming basic analysis units, analyzing crop growth characteristics and environmental influence characteristics for each basic analysis unit, correcting the crop growth characteristics according to the environmental influence characteristics, generating definite feedback information, and performing time sequence analysis on the feedback information of each basic analysis unit to obtain a comprehensive crop growth monitoring result of a target monitoring area. Interference of environmental factors can be accurately recognized and corrected, the growth vigor monitoring precision is improved, a more comprehensive and dynamic growth vigor monitoring result is obtained, and the problem that crop growth vigor information cannot be accurately extracted through satellite remote sensing images in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite image analysis, and in particular to a method and device for monitoring crop growth based on satellite remote sensing. Background Art

[0002] In agricultural production, the growth of crops directly affects the yield and quality, so timely and accurate monitoring of crop growth is crucial for agricultural management and decision-making. Traditional crop growth monitoring methods rely on ground observations, manual surveys and other means, which are not only labor-intensive, but also have limited coverage, making it difficult to achieve large-scale, real-time monitoring.

[0003] With the continuous development of remote sensing technology, crop growth monitoring methods based on satellite remote sensing have gradually become mainstream. This method obtains image data of crop growing areas through remote sensing satellites, and can obtain real-time and comprehensive crop growth information over a large area, thereby providing a scientific basis for agricultural management.

[0004] However, satellite remote sensing image data is affected by environmental and geographical factors. Under different temporal and spatial conditions, there are temporary and local deviations in the growth status of crops, which makes it difficult to extract real information on crop growth under the influence of complex environmental factors. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for monitoring crop growth based on satellite remote sensing, aiming to solve the problem in the prior art that crop growth information cannot be accurately extracted through satellite remote sensing images.

[0006] The present invention is implemented in this way. In a first aspect, the present invention provides a method for monitoring crop growth based on satellite remote sensing, comprising: Acquire satellite remote sensing images of a plurality of target monitoring areas, and convert the analysis units of the satellite remote sensing images according to the geographical location information and acquisition time information corresponding to the satellite remote sensing images, so as to obtain basic analysis units corresponding to the satellite remote sensing images; Analyze the crop growth factors and environmental impact factors of each basic analysis unit to obtain the crop growth characteristics and environmental impact characteristics of each basic analysis unit; wherein the environmental impact characteristics are used to describe the temporary morphological impact of environmental factors on crops when acquiring satellite remote sensing images; Performing growth feedback correction processing on the crop growth characteristics of each basic analysis unit according to the environmental impact characteristics of each basic analysis unit to obtain confirmed feedback information of the crop growth characteristics of each basic analysis unit; A comprehensive analysis of the time-series relationship of the confirmed feedback information of each of the basic analysis units is performed to obtain the crop growth monitoring result of the target monitoring area.

[0007] In a second aspect, the present invention provides a crop growth monitoring device based on satellite remote sensing, which is used to implement a crop growth monitoring method based on satellite remote sensing as described in any one of the first aspects, comprising: A preliminary analysis unit is used to obtain satellite remote sensing images of several target monitoring areas, and convert the satellite remote sensing images into analysis units according to the geographical location information and acquisition time information corresponding to the satellite remote sensing images, so as to obtain basic analysis units corresponding to the satellite remote sensing images; An element analysis unit is used to analyze the crop growth elements and environmental impact elements of each of the basic analysis units to obtain the crop growth characteristics and environmental impact characteristics of each of the basic analysis units; wherein the environmental impact characteristics are used to describe the temporary morphological impact of environmental factors on crops when acquiring satellite remote sensing images; A feedback correction unit, used for performing growth feedback correction processing on the crop growth characteristics of each basic analysis unit according to the environmental impact characteristics of each basic analysis unit, so as to obtain confirmed feedback information of the crop growth characteristics of each basic analysis unit; The comprehensive analysis unit is used to conduct a comprehensive analysis of the time series relationship of the confirmed feedback information of each basic analysis unit to obtain the crop growth monitoring result of the target monitoring area.

[0008] The present invention provides a method for monitoring crop growth based on satellite remote sensing, which has the following beneficial effects: The present invention obtains a satellite remote sensing image of a target monitoring area, and extracts its geographical location and acquisition time information, converts the remote sensing image according to the geographical location information and the acquisition time, forms a basic analysis unit, analyzes the growth characteristics of crops and the environmental impact characteristics of each basic analysis unit respectively, corrects the growth characteristics of crops according to the environmental impact characteristics, generates certain feedback information, performs time series analysis on the feedback information of each basic analysis unit, obtains a comprehensive crop growth monitoring result of the target monitoring area, can accurately identify and correct the interference of environmental factors, improves the growth monitoring accuracy, obtains a more comprehensive and dynamic growth monitoring result, and solves the problem in the prior art that crop growth information cannot be accurately extracted through satellite remote sensing images. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 1 is a schematic diagram of the steps of a method for monitoring crop growth based on satellite remote sensing provided by an embodiment of the present invention; Figure 2It is a structural schematic diagram of a crop growth monitoring device based on satellite remote sensing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0011] The implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0012] Reference Figure 1 , Figure 2 As shown, a preferred embodiment of the present invention is provided.

[0013] In a first aspect, the present invention provides a method for monitoring crop growth based on satellite remote sensing, comprising: S1: Acquire satellite remote sensing images of a plurality of target monitoring areas, and convert the analysis units of the satellite remote sensing images according to the geographical location information and acquisition time information corresponding to the satellite remote sensing images to obtain basic analysis units corresponding to the satellite remote sensing images; S2: analyzing the crop growth factors and environmental impact factors for each of the basic analysis units to obtain the crop growth characteristics and environmental impact characteristics of each of the basic analysis units; wherein the environmental impact characteristics are used to describe the temporary morphological impact of environmental factors on crops when acquiring satellite remote sensing images; S3: performing growth feedback correction processing on the crop growth characteristics of each basic analysis unit according to the environmental impact characteristics of each basic analysis unit, so as to obtain confirmed feedback information of the crop growth characteristics of each basic analysis unit; S4: Performing a comprehensive analysis of the time-series relationship of the confirmed feedback information of each of the basic analysis units to obtain the crop growth monitoring result of the target monitoring area.

[0014] Specifically, in step S1 of the embodiment provided by the present invention, satellite remote sensing images of each designated target monitoring area are acquired through a satellite terminal. These images come from different remote sensing satellites. The satellite remote sensing images include image data under normal visual conditions and may also include image data of specific resolution and frequency.

[0015] More specifically, according to the monitoring needs, the target monitoring area is selected. The area can be a specific agricultural area, farmland, regional planting area, etc. The satellite image data of the selected area is obtained through remote sensing data providers or corresponding platforms (such as Google Earth Engine, USGSEarth Explorer, etc.), and each image will be accompanied by its collected geographic information and time information.

[0016] More specifically, it is necessary to ensure that the acquired data has sufficient spatial resolution (such as 10 meters, 30 meters, etc.) and temporal resolution (such as daily or weekly data) to enable accurate dynamic monitoring. Appropriate image data needs to be selected based on the periodicity and timeliness requirements of monitoring.

[0017] More specifically, each remote sensing image has corresponding geographic location information (such as coordinates, projection, coordinate system, etc.), which helps to accurately correspond the image to the location on the ground. Using the image's geographic reference information, the satellite image is aligned with the actual geographic coordinates through the Geographic Information System (GIS) to ensure that each pixel (or pixel) in the image corresponds to a real geographic location.

[0018] More specifically, the acquisition time information of satellite images is also very important, especially when monitoring agricultural growth. The image acquisition time can help understand the different stages of crop growth and the impact of environmental factors on crops. The acquisition time is marked with the image data to ensure the time span and frequency of the analysis.

[0019] It should be noted that in this step, the satellite terminal monitors multiple target monitoring areas simultaneously. That is to say, along with the relative movement of the satellite, the satellite terminal collects images of different target monitoring areas at predetermined intervals to obtain satellite remote sensing images of each target monitoring area, so as to realize the simultaneous monitoring of multiple target monitoring areas.

[0020] More specifically, a satellite remote sensing image of a target monitoring area at a time acquisition node is converted into a basic analysis unit, which feeds back the crop status of the target monitoring area at the time acquisition node, that is, each target monitoring area has multiple basic analysis units arranged in chronological order.

[0021] More specifically, each target monitoring area has a fixed relative geographical relationship, so each basic analysis unit can also provide information feedback to the target monitoring area in the adjacent area. In other words, each basic analysis unit can perform correlation analysis in time and space to enhance the effectiveness and accuracy of the analysis.

[0022] Specifically, in steps S2 and S3 of the embodiment provided by the present invention, crop growth factors and environmental impact factors are analyzed for each basic analysis unit to obtain crop growth characteristics and environmental impact characteristics of each basic analysis unit.

[0023] It can be understood that the crop growth characteristics reflect the growth form of crops in the target monitoring area, and the environmental impact characteristics are used to describe the temporary morphological impact of environmental factors on crops when acquiring satellite remote sensing images.

[0024] Specifically, the growth condition of crops can be judged and identified by the external morphology of crops displayed in the image. Under the influence of specific climatic environments, such as strong winds and after rainy days, the influence of these external environmental factors will temporarily change the external morphology of crops to a certain extent, thus leading to misjudgment of the growth condition of crops.

[0025] For example, in windy climates, the external shape of crops appears to be tilted, which leads to errors in the judgment of the growth characteristics of crops, and they are mistakenly judged as crops growing tilted, or the height and area of ​​crops cannot be accurately identified in the tilted state, thereby misjudging the growth status of crops.

[0026] Therefore, it is necessary to analyze the crop growth characteristics and environmental impact characteristics of the basic analysis units at the same time, so as to obtain the analysis of the crop growth characteristics and environmental impact characteristics fed back by the basic analysis units, and further interactively analyze the crop growth characteristics and environmental impact characteristics of each basic analysis unit according to the time relationship and spatial relationship between each basic analysis unit, so as to correct the growth feedback of the crop growth characteristics through the environmental impact characteristics, so as to obtain the confirmed feedback information of the crop growth characteristics of each basic analysis unit, that is, each basic analysis unit has credible information feedback on crop growth after eliminating the interference of environmental impact characteristics.

[0027] Specifically, in step S4 of the embodiment provided by the present invention, a comprehensive analysis of the time series relationship of the confirmation feedback information of each basic analysis unit is performed to obtain the crop growth monitoring result of the target monitoring area. The same target monitoring area has multiple basic analysis units arranged in chronological order. Each basic analysis unit feeds back the crop growth status of the target monitoring area at the corresponding time node. The crop growth monitoring result of the target monitoring area is obtained by summarizing and analyzing the characteristics of the time series relationship of the confirmation feedback information of each basic analysis unit of the target monitoring area.

[0028] It should be noted that the confirmed feedback information of the basic analysis unit will change as more basic analysis units are collected over time. That is, through the crop growth characteristics and environmental impact characteristics fed back by subsequent basic analysis units, it can be determined whether the feedback correction of crop growth characteristics through environmental impact characteristics in the previous basic analysis units is correct, thereby obtaining more accurate and effective confirmation feedback information, and on this basis, obtaining newer, more accurate and more effective crop growth monitoring results.

[0029] The present invention provides a method for monitoring crop growth based on satellite remote sensing, which has the following beneficial effects: The present invention obtains a satellite remote sensing image of a target monitoring area, and extracts its geographical location and acquisition time information, converts the remote sensing image according to the geographical location information and the acquisition time, forms a basic analysis unit, analyzes the growth characteristics of crops and the environmental impact characteristics of each basic analysis unit respectively, corrects the growth characteristics of crops according to the environmental impact characteristics, generates certain feedback information, performs time series analysis on the feedback information of each basic analysis unit, obtains a comprehensive crop growth monitoring result of the target monitoring area, can accurately identify and correct the interference of environmental factors, improves the growth monitoring accuracy, obtains a more comprehensive and dynamic growth monitoring result, and solves the problem in the prior art that crop growth information cannot be accurately extracted through satellite remote sensing images.

[0030] Preferably, the steps of acquiring satellite remote sensing images of a plurality of target monitoring areas, and converting the satellite remote sensing images into analysis units according to the geographical location information and acquisition time information corresponding to the satellite remote sensing images to obtain basic analysis units corresponding to the satellite remote sensing images include: S11: remote sensing images of a plurality of target monitoring areas are collected at predetermined time intervals by a satellite terminal to obtain satellite remote sensing images of each of the target monitoring areas, and corresponding geographical location information and collection time information are generated for the satellite remote sensing images according to the pointing location and timestamp mark when the satellite terminal collects the remote sensing images; S12: performing regional positioning of a target monitoring area on the satellite remote sensing image according to the geographical location information to obtain a target monitoring area corresponding to the satellite remote sensing image, and generating a regional mark for the satellite remote sensing image according to the target monitoring area corresponding to the satellite remote sensing image to obtain a satellite remote sensing image with a regional mark; S13: Generating a time tag for the satellite remote sensing image according to the acquisition time information to obtain a satellite remote sensing image with a time tag; S14: analyzing the interactive influence of image feedback information of each satellite remote sensing image according to the region mark and time mark of each satellite remote sensing image to obtain a content feedback vector of each satellite remote sensing image; wherein the content feedback vector is used to describe the influence of the image information fed back by the satellite remote sensing image on other satellite remote sensing images; S15: Preprocessing and format compression of the satellite remote sensing image data to obtain the form of the satellite remote sensing image to be analyzed, and combining the form of the satellite remote sensing image to be analyzed with the content feedback vector to obtain the basic analysis unit.

[0031] Specifically, remote sensing images of several target monitoring areas are collected at predetermined time intervals through satellite terminals. The satellite terminals automatically take remote sensing images according to the set time intervals. The satellite terminals are used to automatically acquire remote sensing images to ensure that images are collected on demand within the predetermined time intervals and to effectively monitor changes in the target areas. Each time a remote sensing image is collected, the satellite terminal will record the coordinate information of the location and the corresponding timestamp (collection time), providing a spatial and temporal positioning basis for subsequent analysis. Each remote sensing image corresponds to precise geographic location information and timestamps, ensuring that subsequent analysis can accurately reflect the specific location and time of image collection, and support spatiotemporal analysis.

[0032] More specifically, based on the geographic location information of the satellite remote sensing image, the image is mapped to the target monitoring area. This process accurately locates the image based on the image's geographic coordinates, confirms its corresponding monitoring area, ensures that each remote sensing image corresponds to the target monitoring area, and provides an accurate spatial reference for subsequent analysis. According to the boundary of the target monitoring area, the satellite remote sensing image is marked with regional labels. Through regional labeling technology, different areas in the remote sensing image can be effectively distinguished, so that in-depth analysis of the target area can be carried out.

[0033] More specifically, a time tag is added to each image based on the acquisition time information of the remote sensing image. The tag is used to represent the timestamp information corresponding to the remote sensing image, ensuring that the temporal relationship of the images can be tracked during analysis. Each remote sensing image has a clear time label, which facilitates subsequent comparison and analysis of changes in different periods, and understanding of issues such as crop growth or environmental changes from a time series perspective.

[0034] More specifically, based on the regional label and time label of each satellite remote sensing image, the interactive influence analysis of image feedback information is performed. Specifically, the content feedback vector of a remote sensing image can be calculated. This vector describes how the feedback information of the image affects other images, especially the spatial and temporal relationships. It is used to quantify the influence of a certain image on other images. This may be achieved through image processing technology, feature extraction, similarity calculation, etc., to evaluate the spatial and temporal correlation between images. For example, the impact of changes in a certain area in an image (such as changes in crop growth) on images at other time points can be calculated.

[0035] More specifically, by generating image feedback vectors, we can effectively capture the spatiotemporal correlation between satellite images and reveal the spatiotemporal evolution characteristics of phenomena such as crop growth and environmental changes. We can not only observe the state at a single moment, but also analyze the dynamic changes between images in different periods, thus providing data support for further growth prediction, abnormal event detection, etc.

[0036] More specifically, necessary preprocessing steps are performed on satellite remote sensing images, including denoising, color correction, geometric correction, radiation correction, etc., to ensure that the image data quality meets the analysis requirements. Through the preprocessing steps, the noise in the image is removed and the errors caused by sensors, environment and other factors are reduced, thereby improving the accuracy of subsequent analysis. The processed image is format compressed to make the image data easy to store, transmit and subsequently analyze and process. Through data compression, the storage space occupancy is reduced, the data transmission efficiency is improved, and support is provided for large-scale remote sensing image data processing.

[0037] More specifically, the content feedback vector is combined with the form of the image to be analyzed, and the preprocessed and compressed satellite remote sensing image is combined with the generated content feedback vector to finally form a complete basic analysis unit. This analysis unit contains not only image data, but also the spatial and temporal association information of the image. Combined with the content feedback vector, the spatiotemporal background of each image can be better understood, making the analysis results more comprehensive and accurate. These basic analysis units provide rich data support for further crop growth monitoring, environmental change analysis, etc. Through the fusion of space, time, and feedback information, a high-quality analysis unit is constructed to support more complex analysis and prediction models.

[0038] Preferably, the steps of analyzing the crop growth factors and environmental impact factors for each of the basic analysis units to obtain the crop growth characteristics and environmental impact characteristics of each of the basic analysis units include: S21: identifying the crop type of the basic analysis unit according to a preset crop type identification standard to obtain the crop type characteristics fed back by the basic analysis unit; S22: according to the crop type characteristics, the corresponding crop growth morphology recognition standard is retrieved from the database to analyze the crop growth factors of the basic analysis unit, so as to obtain the growth stage morphology characteristics and the growth quality morphology characteristics fed back by the basic analysis unit, and the growth stage morphology characteristics and the growth quality morphology characteristics are used together as the crop growth characteristics of the basic analysis unit; S23: Analyze the environmental impact factors of the basic analysis unit according to the preset environmental impact identification standard to obtain the environmental impact type and environmental impact degree fed back by the basic analysis unit, and combine the environmental impact type and the environmental impact degree to obtain the environmental impact characteristics of the basic analysis unit.

[0039] Specifically, according to the preset crop type identification standards, the basic analysis unit is used to identify the crop type, and the crops appearing in the remote sensing image are classified through image analysis technology (such as deep learning image classification, feature extraction, etc.), and the specific crop type represented by the image (such as wheat, rice, corn, etc.) is identified. Using advanced image classification technology (such as convolutional neural network CNN, etc.), the crop type in the image can be automatically and quickly identified, thereby improving the efficiency of crop monitoring. Through the preset identification standards, the accuracy of the identification results is ensured, human intervention and misidentification are reduced, and the accuracy and reliability of the analysis are improved.

[0040] More specifically, based on the identified characteristics of the crop type, the crop growth morphology recognition standard corresponding to the crop type is retrieved from the database. The database contains characteristic data and standards of different crops at different growth stages, such as growth height, leaf area, color tone, etc. The basic analysis unit is parsed using the crop growth morphology recognition standard in the database to obtain the morphological characteristics of the growth stage and the morphological characteristics of growth quality.

[0041] More specifically, the growth stage morphological characteristics represent the current growth stage of the crop (such as seedling stage, tillering stage, heading stage, etc.), which can be obtained through quantitative analysis of remote sensing images (such as leaf area index LAI, vegetation index NDVI, etc.).

[0042] More specifically, the morphological characteristics of good or bad growth describe the growth condition of crops, whether it is good or deteriorated. This characteristic is related to the appearance of vegetation, such as upright state, greenness, healthiness, leaf condition, etc., and is judged by the color, texture, contrast and other characteristics in the remote sensing image. The morphological characteristics of the growth stage and the morphological characteristics of good or bad growth will be jointly used as the growth characteristics of crops in this basic analysis unit.

[0043] More specifically, the environmental impact factors of the basic analysis unit are analyzed according to the preset environmental impact identification standards. These standards are usually related to environmental variables (such as temperature, precipitation, soil moisture, climate conditions, light intensity, etc.) and their impact on crop growth. By analyzing the image data and related environmental data of the basic analysis unit, the type and degree of environmental impact are extracted.

[0044] More specifically, the environmental impact type identifies the type of environmental factors that temporarily affect the external form of crops at the current moment, such as strong winds and heavy rains, which change the upright state and color of crops; based on the environmental impact type, the degree of impact of environmental factors on crops is evaluated, that is, the degree of impact of this type of environmental impact factor on the real-time state of crops; finally, the environmental impact characteristics of the basic analysis unit are obtained by combining the environmental impact type and the environmental impact degree.

[0045] More specifically, by combining the growth characteristics of crops (including morphological characteristics of the growth stages and morphological characteristics of good and bad growth) with the environmental impact characteristics (including the type of environmental impact and the degree of environmental impact), and by comprehensively analyzing these two types of characteristics, we can more comprehensively describe the growth conditions and influencing factors of crops.

[0046] Preferably, the step of retrieving the corresponding crop growth morphology recognition standard from the database according to the crop type characteristics and parsing the crop growth elements of the basic analysis unit to obtain the growth stage morphology characteristics and growth quality morphology characteristics fed back by the basic analysis unit includes: S221: Retrieving corresponding crop growth morphology recognition standards from a database according to the crop type characteristics, so as to obtain crop growth morphology recognition standards corresponding to the crop type characteristics; S222: performing multiple standard image grid division on the basic analysis unit to obtain a plurality of image grid frames; wherein the image grid frames are used to perform grid division on the image data fed back by the basic analysis unit to obtain a plurality of grid units divided and arranged according to specific standards, and each of the image grid frames has a different specific standard for grid division of the basic analysis unit; S223: performing element analysis of growth stage morphology and growth quality morphology on each of the image grid frames according to the crop growth morphology recognition standard, so as to obtain growth morphology feature distribution fed back by each of the image grid frames; wherein the growth morphology feature distribution is used to describe the growth stage morphology and growth quality morphology of the crops in each grid unit of the image grid frame; S224: Performing an interactive analysis between grid units on the growth morphological feature distribution fed back by each of the image grid frames to obtain the growth stage morphological features and growth quality morphological features of the basic analysis unit as a whole.

[0047] Specifically, the growth standards are retrieved according to the characteristics of the crop type. Starting from the crop type characteristics identified by the basic analysis unit, the corresponding crop growth morphology recognition standards are extracted from the database according to the characteristics. These standards usually include characteristic data of various crops at different growth stages, such as vegetation index (NDVI), leaf area index (LAI), growth rate, color characteristics, etc., to ensure that the morphological standards of each crop at its specific growth stage are correctly extracted, avoiding confusion between different crop types and growth stages, providing a unified growth standard library, and being able to systematically and quickly retrieve corresponding standards according to crop types, thereby improving the degree of automation of analysis.

[0048] More specifically, the image data in the basic analysis unit is divided into multiple small grid units. Each grid unit represents an image area, which is used to concentrate on analyzing the growth of crops in the area. The grid division standard can be adjusted according to different needs. For example, the grid can be divided based on factors such as pixel size, image resolution, and terrain differences within the area. The purpose of grid division is to enable each grid unit to independently perform growth morphology analysis through detailed regional segmentation, thereby ensuring the accuracy and localization of the analysis results.

[0049] More specifically, through multi-level grid division, analysis can be performed at different scales, thereby capturing local differences within agricultural areas. The division criteria of the grid framework can be adjusted according to the actual needs of different regions, so that the analysis can adapt to different farmland environments and imaging conditions.

[0050] More specifically, based on the crop growth morphology recognition standards obtained from the database, a detailed growth morphology element analysis is performed on each grid frame, specifically: Growth stage morphology feature analysis: Analyze the crop growth stage in each grid unit, for example, which growth stage the crop is in (such as seedling stage, jointing stage, heading stage, etc.), and judge the growth progress of the crop based on its characteristics; Growth quality morphology feature analysis: Identify the health status of the crop, including whether the growth status is good or affected by certain adverse factors (such as drought, pests and diseases, etc.), and extract features related to vegetation health, color difference, density, etc.

[0051] More specifically, the growth stage and morphological characteristics of growth quality of each grid unit are quantified, and a "growth morphological characteristics distribution" map of the entire grid framework is generated to describe the growth status of crops in the area.

[0052] More specifically, an interactive analysis is performed on the distribution of growth morphological characteristics fed back by each image grid framework, that is, by analyzing the relationship between each grid unit, examining how they influence each other in space or time, and further refining the growth stage and health status of each grid unit. Based on the growth characteristic distribution of each grid unit and combined with the results of the interactive analysis, the growth stage morphological characteristics and growth quality morphological characteristics of the entire basic analysis unit are finally extracted and comprehensively summarized. The final result generated by this process is the overall growth characteristics of the basic analysis unit, covering the growth stage, health status and environmental influences of the crop.

[0053] Preferably, the steps of parsing the environmental impact elements of the basic analysis unit according to a preset environmental impact identification standard to obtain the environmental impact type and environmental impact degree fed back by the basic analysis unit, and combining the environmental impact type and the environmental impact degree to obtain the environmental impact characteristics of the basic analysis unit include: S231: performing preliminary division of the content of the image information of the basic analysis unit to obtain a crop representation part and a non-crop representation part; wherein the non-crop representation part includes a ground representation part, an atmosphere representation part, and a vegetation representation part; S232: performing environmental impact factor analysis on the crop embodiment part and the non-crop embodiment part respectively according to a preset environmental impact identification standard, so as to obtain an environmental impact analysis result fed back by the crop embodiment part and an environmental impact analysis result fed back by the non-crop embodiment part; S233: performing a weighted comprehensive analysis on the environmental impact analysis results fed back by the crop embodiment part and the environmental impact analysis results fed back by the non-crop embodiment part, so as to obtain the environmental impact type and environmental impact degree of the basic analysis unit, S234: Combine the environmental impact type and the environmental impact degree to obtain the environmental impact characteristics of the basic analysis unit.

[0054] Specifically, the image information in the basic analysis unit is preliminarily divided, and the content in the image is divided into a crop-representing part and a non-crop-representing part. The crop-representing part refers to all crop-related areas contained in the image, which involve the crops themselves and the growth environment around the crops. The non-crop-representing part includes parts of the image that are not related to crop growth, mainly the embodiment of environmental factors, which can usually be divided into: ground manifestation part: refers to ground areas such as soil surface, agricultural facilities, roads, etc., atmosphere manifestation part: including rain and wind direction in the air, vegetation manifestation part: mainly includes plant communities or weeds other than crops.

[0055] More specifically, through the preliminary division of the image, it is possible to effectively distinguish between the crop growth area and the environmental impact area, laying the foundation for the subsequent environmental impact analysis. By distinguishing between the crop-representing part and the non-crop-representing part, it is possible to ensure the independent analysis of environmental factors and crop growth factors, thereby improving the accuracy of the environmental impact analysis. That is, the environmental impact characteristics can be more accurately identified through the manifestation of the non-crop part, thereby judging the impact of the crop part under the environmental impact characteristics.

[0056] More specifically, according to the environmental impact identification standards, the environmental impact factors of the crop-representing part and the non-crop-representing part are analyzed separately. According to the preset environmental impact identification standards, the environmental impact factors of the crop-representing part are analyzed to identify the environmental characteristics that have a temporary impact on the external form of the crops at the current moment, such as wind that changes the upright state of the crops and rain that changes the color of the crops.

[0057] More specifically, according to the preset environmental impact identification standards, the environmental impact factors of the non-agricultural crop part are analyzed, that is, the environmental impact characteristics of the non-agricultural crop part are identified, that is, the environmental impact characteristics occurring in the area at that moment are judged by the influence of the environmental impact characteristics on the non-agricultural crop part.

[0058] More specifically, the environmental impact factors of the crop-related part and the non-crop-related part are analyzed separately to ensure that the analysis can cover all environmental factors affecting crop growth, and a weighted comprehensive analysis is performed on the environmental impact analysis results of the crop-related part and the non-crop-related part. The environmental impact analysis results of the crop-related part and the non-crop-related part are combined for weighted analysis, and different weights are assigned to different environmental impact factors according to the actual impact of each part on crop growth. The specific weights can be determined based on the empirical data of agricultural production or the predicted results of the environmental model.

[0059] More specifically, the influencing factors of the crop part and the non-crop part are weighted and synthesized respectively to obtain the assessment results of the overall environmental impact. The weighted analysis can comprehensively consider the relationship between crop growth and the surrounding environment to ensure a more accurate assessment of the environmental impact. According to the characteristics of different agricultural environments, the weights of various factors can be flexibly adjusted to ensure the accuracy and applicability of the analysis.

[0060] Preferably, the step of performing growth feedback correction processing on the crop growth characteristics of each basic analysis unit according to the environmental impact characteristics of each basic analysis unit to obtain the confirmed feedback information of the crop growth characteristics of each basic analysis unit includes: S31: taking the designated basic analysis unit as a first analysis unit, and performing relevance matching on the remaining basic analysis units according to the content feedback vector of the first analysis unit, so as to obtain a plurality of basic analysis units having content feedback relevance with the first analysis unit; S32: dividing and marking the association types of a plurality of basic analysis units having content feedback association with the first analysis unit, so as to obtain a second analysis unit that is in the same target monitoring area as the first analysis unit but at a different time, and a third analysis unit that is in an adjacent target monitoring area as the first analysis unit and at the same time; S33: performing difference analysis on the environmental impact characteristics of the first analysis unit according to the environmental impact characteristics of the second analysis unit to obtain environmental impact difference characteristics between the second analysis unit and the first analysis unit; S34: performing difference analysis on the crop growth characteristics of the first analysis unit according to the crop growth characteristics of the second analysis unit to obtain the difference characteristics of the crop growth between the second analysis unit and the first analysis unit; S35: performing causal matching processing on the crop growth difference characteristics and the environmental impact difference characteristics according to the time difference between the second analysis unit and the first analysis unit, so as to obtain the causal matching degree of the environmental impact difference characteristics corresponding to the crop growth difference characteristics under the time difference; wherein the causal matching degree is used to describe the causal matching degree of the environmental impact difference characteristics as the difference cause of the crop growth difference characteristics under the time difference; S36: performing an environmental impact confidence assessment process on the environmental impact characteristics of the first analysis unit according to the environmental impact characteristics of the third analysis unit to obtain a characteristic confidence of the environmental impact characteristics of the first analysis unit; S37: Performing a weighted comprehensive analysis on the causal matching degree and the feature confidence degree according to a preset standard to obtain the growth manifestation influence characteristics of the environmental impact characteristics in the first analysis unit on the growth characteristics of the crop; S38: Correcting the crop growth characteristics according to the growth-influencing characteristics to obtain confirmed feedback information of the crop growth characteristics of each basic analysis unit.

[0061] Specifically, the designated basic analysis unit is used as the first analysis unit, and a content feedback vector of the unit is extracted. The content feedback vector is used to describe the influence of the image information fed back by the satellite remote sensing image on the other satellite remote sensing images.

[0062] More specifically, based on the content feedback vector of the first analysis unit, correlation matching is performed on the remaining basic analysis units to find other analysis units that are correlated with the first analysis unit, namely, the basic analysis units of the same target monitoring area in the previous time period and the basic analysis units of the target monitoring area in the adjacent area at the same time.

[0063] More specifically, through correlation matching of content feedback vectors, other units related to the target analysis unit can be accurately identified, thereby performing more effective growth correction analysis.

[0064] More specifically, the analysis units associated with the first analysis unit are divided into two categories: second analysis units, which are in the same target monitoring area as the first analysis unit but different in time, that is, the basic analysis units at the present moment are continuously analyzed by the basic analysis units collected in the previous time period; third analysis units: these units are in the target monitoring area adjacent to the first analysis unit, but the time is the same as the first analysis unit. By dividing the analysis units according to time and space, it is ensured that the influence of each type of unit is analyzed independently, thereby improving the accuracy of the correction processing, being able to distinguish the effects of time and space differences on the growth of crops, and helping to understand the spatiotemporal effects of environmental influences on growth.

[0065] It should be noted that the third analysis unit is the basic analysis unit of the target monitoring area in the adjacent range at the same time, which means that the environmental impact characteristics received by each third analysis unit should be consistent. Based on this feature, the reliability of the environmental impact characteristics of the first analysis unit can be judged.

[0066] It should be noted that, for the second analysis unit, each second analysis unit is the basic analysis unit of the same target monitoring area in the previous time period. Therefore, the crop growth characteristics and environmental impact characteristics of the second analysis unit can be compared and analyzed with the crop growth characteristics and environmental impact characteristics of the first analysis unit: if in the previous second analysis unit, it has been shown that the crops in the target monitoring area show abnormal external manifestations without the interference of environmental impact characteristics, then the external manifestations of the crops in the first analysis unit at the current moment are not caused by the environmental impact characteristics, but there is a problem with the crops themselves in the target monitoring area. If the crop growth characteristics of the second analysis unit in the target monitoring area are normal when it was not affected by the environmental impact characteristics before, the credibility of the environmental impact characteristics of the first analysis unit will be improved. Based on this feature and analysis method, further correlation analysis can be performed.

[0067] More specifically, a difference analysis is performed based on the environmental impact characteristics of the second analysis unit and the environmental impact characteristics of the first analysis unit to find out the environmental impact difference characteristics between the two, and a difference analysis is performed based on the crop growth characteristics of the second analysis unit and the growth characteristics of the first analysis unit to obtain the difference characteristics of crop growth. Through the difference analysis, the changes between environmental impact and crop growth can be clearly identified, providing valuable information to support subsequent causal analysis, providing clear environmental and growth difference characteristics for subsequent causal matching, and ensuring the reliability of the analysis results.

[0068] More specifically, based on the time difference between the second analysis unit and the first analysis unit, the environmental impact difference characteristics are causally matched with the crop growth difference characteristics. The purpose of the matching is to determine whether the impact of environmental factors on crop growth is delayed in time and to quantify this impact relationship. By calculating the matching degree of environmental impact difference characteristics and crop growth difference characteristics under time difference, the causal matching degree between the two is obtained, which describes how the difference in environmental impact leads to differences in crop growth.

[0069] More specifically, through causal matching analysis, we can clearly identify how environmental influences affect crop growth, especially the time lag effects of environmental factors. Through causal matching quantification, we can accurately describe the causal relationship between environmental influences and crop growth, thereby providing data support for crop growth corrections.

[0070] More specifically, based on the environmental impact characteristics of the third analysis unit, the confidence of the environmental impact characteristics of the first analysis unit is evaluated. The confidence reflects the spatial similarity between the third analysis unit and the first analysis unit, as well as the degree of influence of environmental factors on crop growth. By comparing the environmental impact characteristics of the third analysis unit and the first analysis unit, the confidence of the environmental impact characteristics of the first analysis unit is obtained.

[0071] More specifically, environmental impact confidence assessment can identify the reliability of environmental impacts and help determine the strength of association between different analysis units. The level of confidence reflects the spatial consistency of environmental impact characteristics, thereby improving the accuracy of crop growth predictions.

[0072] More specifically, a weighted comprehensive analysis is performed on the causal matching degree and the environmental impact confidence. By setting appropriate weights, combining the causal matching degree and the environmental impact confidence, a comprehensive growth-reflecting impact characteristic is obtained, and the weighted results are standardized according to the preset standards to ensure that the final output impact characteristics meet the predetermined decision-making standards. Through weighted comprehensive analysis, a more accurate basis for correcting the environmental impact on crop growth can be obtained, reducing uncertainty, providing a scientific basis for correcting the predicted results of crop growth, and improving the effectiveness and accuracy of decision support.

[0073] More specifically, the growth characteristics of crops in the first analysis unit are corrected according to the obtained growth impact characteristics. This correction process adjusts the growth prediction through causal analysis of environmental impacts to make it more accurate, and finally generates certain feedback information on the growth characteristics of crops in each basic analysis unit, which is used to guide agricultural production management, risk assessment and prediction.

[0074] Preferably, the step of performing a comprehensive analysis of the time series relationship of the confirmed feedback information of each of the basic analysis units to obtain the crop growth monitoring result of the target monitoring area includes: S41: Classifying and sorting the confirmation feedback information of each basic analysis unit according to the geographical location information and collection time information corresponding to each basic analysis unit to obtain a time series analysis sequence of the target monitoring area; wherein the time series analysis sequence includes confirmation feedback information of a plurality of basic analysis units arranged in chronological order; S42: extracting features of the time series relationship of the time series analysis sequence based on a plurality of preset information dimensions to obtain analysis results of the time series analysis sequence corresponding to each information dimension, and combining the analysis results of the time series analysis sequence corresponding to each information dimension to obtain past growth results; wherein the information dimensions include a growth speed dimension, a growth quality dimension, and a change trend dimension of growth speed and quality; S43: analyzing the current growth status of the target monitoring area according to the time series analysis sequence to obtain the current growth status of the target monitoring area; S44: performing a growth prediction of the target monitoring area at a future time according to the past growth results and the current growth status, so as to obtain a future growth prediction of the target monitoring area; S45: The past growth results, the current growth status and the future growth forecast together constitute the crop growth monitoring result of the target monitoring area.

[0075] Specifically, the certainty feedback information of the basic analysis units is classified and sorted to obtain a time series analysis sequence, and the classification and sorting are performed according to the geographical location information and the collection time information. Each basic analysis unit corresponds to a specific geographical location and collection time. The certainty feedback information of each basic analysis unit is classified and sorted according to its geographical location and time sequence to obtain a time series analysis sequence.

[0076] More specifically, the time series analysis sequence is a collection of basic analysis units arranged in chronological order, containing the certain feedback information of each unit, which reflects the growth conditions of crops in the area at different time periods.

[0077] More specifically, accurate time series data can be obtained by classifying and sorting the confirmed feedback information, which lays the foundation for subsequent time series relationship analysis. The time series analysis sequence can truly reflect the growth changes of crops in the target monitoring area and facilitate historical and future growth predictions.

[0078] More specifically, feature extraction is performed on the time series analysis sequence based on multiple preset information dimensions, which include: Growth rate dimension: describes the change in crop growth rate, such as the rate of increase of biomass, growth quality dimension: describes the growth quality of crops, such as changes in growth indicators such as leaf area and plant height, and the change trend of growth rate and quality. Dimension: analyzes the changing trend of growth rate and quality, and finds out characteristics such as fluctuations, change cycles or continuous growth.

[0079] More specifically, corresponding feature information is extracted from each dimension to reveal different aspects of crop growth. Feature extraction of time series data from multiple dimensions and comprehensive analysis of different aspects of crop growth can reveal the complex dynamic characteristics of crop growth. By extracting dimensions such as growth rate, quality and change trends, it helps to fully grasp the changes in crop growth and provide an accurate basis for subsequent growth expectations.

[0080] More specifically, the analysis results extracted from different information dimensions (growth rate, quality, and change trends) are combined to obtain past growth results. These results reflect the historical growth trends of crops in the target monitoring area, and provide historical data support for analyzing current and future growth conditions. By combining the analysis results of different dimensions, the past growth performance of crops can be fully and accurately described, providing more comprehensive historical background data for current and future growth forecasts. By extracting trends and features from historical data, it can help researchers and agricultural managers identify potential problems or patterns in crop growth.

[0081] More specifically, the current growth condition analysis of the time series analysis sequence is based on the time series analysis sequence and combined with the data analysis at the current moment to evaluate the current growth condition of crops in the target monitoring area. This analysis combines past growth results and currently collected data to obtain the latest growth status of crops in the target monitoring area. Through the time series analysis of the current data, the growth condition of crops in the target area can be grasped in real time, which helps to discover problems in a timely manner and take effective agricultural management measures, provide accurate current growth assessment results, and help agricultural producers adjust management measures according to actual conditions.

[0082] More specifically, based on past growth results and current growth conditions, combined with the periodicity and trend of crop growth, growth forecasts for future moments are made. This speculation integrates the growth change trends in the time series data and predicts the growth conditions of crops in the target monitoring area in the future.

[0083] More specifically, through scientific inference methods, it provides forecasts of future crop growth trends in the target monitoring area, which helps farmers and agricultural managers to make long-term plans. By combining past growth with current growth, it enhances the reliability of future forecasts and reduces the impact of unpredictable factors on the future.

[0084] More specifically, a comprehensive analysis of past growth results, current growth conditions and future growth forecasts is conducted to obtain crop growth monitoring results. A comprehensive analysis of past growth results, current growth conditions and future growth forecasts is conducted to obtain the final crop growth monitoring results. These results will together constitute a comprehensive growth monitoring picture of crops in the target monitoring area, which can help agricultural managers fully grasp the growth status, changing trends and future development of crops. By combining past, current and future growth data, a complete crop growth monitoring picture is provided to help agricultural production managers fully understand the growth of crops, provide multi-dimensional monitoring data for agricultural management and decision-making, help make more accurate management decisions, optimize resource allocation, and improve crop production efficiency.

[0085] Reference Figure 2 As shown, in a second aspect, the present invention provides a crop growth monitoring device based on satellite remote sensing, which is used to implement a crop growth monitoring method based on satellite remote sensing as described in any one of the first aspects, comprising: A preliminary analysis unit is used to obtain satellite remote sensing images of several target monitoring areas, and convert the satellite remote sensing images into analysis units according to the geographical location information and acquisition time information corresponding to the satellite remote sensing images, so as to obtain basic analysis units corresponding to the satellite remote sensing images; An element analysis unit is used to analyze the crop growth elements and environmental impact elements of each of the basic analysis units to obtain the crop growth characteristics and environmental impact characteristics of each of the basic analysis units; wherein the environmental impact characteristics are used to describe the temporary morphological impact of environmental factors on crops when acquiring satellite remote sensing images; A feedback correction unit, used for performing growth feedback correction processing on the crop growth characteristics of each basic analysis unit according to the environmental impact characteristics of each basic analysis unit, so as to obtain confirmed feedback information of the crop growth characteristics of each basic analysis unit; The comprehensive analysis unit is used to conduct a comprehensive analysis of the time series relationship of the confirmed feedback information of each basic analysis unit to obtain the crop growth monitoring result of the target monitoring area.

[0086] In this embodiment, for the specific implementation of each module in the above-mentioned device embodiment, please refer to the above-mentioned method embodiment, which will not be described in detail here.

[0087] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring crop growth based on satellite remote sensing, characterized in that: include: Acquire satellite remote sensing images of a plurality of target monitoring areas, and convert the analysis units of the satellite remote sensing images according to the geographical location information and acquisition time information corresponding to the satellite remote sensing images, so as to obtain basic analysis units corresponding to the satellite remote sensing images; Analyze the crop growth factors and environmental impact factors of each basic analysis unit to obtain the crop growth characteristics and environmental impact characteristics of each basic analysis unit; wherein the environmental impact characteristics are used to describe the temporary morphological impact of environmental factors on crops when acquiring satellite remote sensing images; Performing growth feedback correction processing on the crop growth characteristics of each basic analysis unit according to the environmental impact characteristics of each basic analysis unit to obtain confirmed feedback information of the crop growth characteristics of each basic analysis unit; A comprehensive analysis of the time-series relationship of the confirmed feedback information of each of the basic analysis units is performed to obtain the crop growth monitoring result of the target monitoring area.

2. The method for monitoring crop growth based on satellite remote sensing according to claim 1, characterized in that: The steps of acquiring satellite remote sensing images of a plurality of target monitoring areas and converting the analysis units of the satellite remote sensing images according to the geographical location information and acquisition time information corresponding to the satellite remote sensing images to obtain basic analysis units corresponding to the satellite remote sensing images include: Collecting remote sensing images of several target monitoring areas at predetermined time intervals through a satellite terminal to obtain satellite remote sensing images of each of the target monitoring areas, and generating corresponding geographical location information and collection time information for the satellite remote sensing images according to the pointing location and timestamp mark when the satellite terminal collects the remote sensing images; Performing regional positioning of a target monitoring area on the satellite remote sensing image according to the geographical location information to obtain a target monitoring area corresponding to the satellite remote sensing image, and generating a regional mark on the satellite remote sensing image according to the target monitoring area corresponding to the satellite remote sensing image to obtain a satellite remote sensing image with a regional mark; Generating a time tag for the satellite remote sensing image according to the acquisition time information to obtain a satellite remote sensing image with a time tag; Analyzing the interactive influence of image feedback information of each satellite remote sensing image according to the region mark and time mark of each satellite remote sensing image to obtain a content feedback vector of each satellite remote sensing image; wherein the content feedback vector is used to describe the influence of the image information fed back by the satellite remote sensing image on other satellite remote sensing images; The satellite remote sensing image is subjected to image data preprocessing and format compression to obtain a form of the satellite remote sensing image to be analyzed, and the form of the satellite remote sensing image to be analyzed is combined with the content feedback vector to obtain the basic analysis unit.

3. The method for monitoring crop growth based on satellite remote sensing according to claim 1, characterized in that: The steps of analyzing the crop growth factors and environmental impact factors for each of the basic analysis units to obtain the crop growth characteristics and environmental impact characteristics of each of the basic analysis units include: Identify the crop type of the basic analysis unit according to a preset crop type identification standard to obtain the crop type characteristics fed back by the basic analysis unit; According to the crop type characteristics, the corresponding crop growth morphological recognition standard is retrieved from the database to analyze the crop growth factors of the basic analysis unit to obtain the growth stage morphological characteristics and the growth quality morphological characteristics fed back by the basic analysis unit, and the growth stage morphological characteristics and the growth quality morphological characteristics are used together as the crop growth characteristics of the basic analysis unit; The environmental impact factors of the basic analysis unit are analyzed according to the preset environmental impact identification standard to obtain the environmental impact type and environmental impact degree fed back by the basic analysis unit, and the environmental impact type and the environmental impact degree are combined to obtain the environmental impact characteristics of the basic analysis unit.

4. The method for monitoring crop growth based on satellite remote sensing according to claim 3, characterized in that: The steps of retrieving the corresponding crop growth morphology recognition standard from the database according to the crop type characteristics and analyzing the crop growth factors of the basic analysis unit to obtain the growth stage morphology characteristics and growth quality morphology characteristics fed back by the basic analysis unit include: Retrieving corresponding crop growth morphology recognition standards from a database according to the crop type characteristics to obtain crop growth morphology recognition standards corresponding to the crop type characteristics; Performing multiple standard image grid division on the basic analysis unit to obtain a plurality of image grid frames; wherein the image grid frames are used to perform grid division on the image data fed back by the basic analysis unit to obtain a plurality of grid units divided and arranged according to specific standards, and each of the image grid frames has a different specific standard for grid division of the basic analysis unit; According to the crop growth morphology recognition standard, each of the image grid frames is respectively analyzed for the elements of growth stage morphology and growth quality morphology, so as to obtain the growth morphology characteristic distribution fed back by each of the image grid frames; wherein the growth morphology characteristic distribution is used to describe the growth stage morphology and growth quality morphology of the crops in each grid unit of the image grid frame; The distribution of growth morphological characteristics fed back by each of the image grid frames is interactively analyzed between grid units to obtain the overall growth stage morphological characteristics and growth quality morphological characteristics of the basic analysis unit.

5. The method for monitoring crop growth based on satellite remote sensing as claimed in claim 3, characterized in that: The steps of analyzing the environmental impact elements of the basic analysis unit according to the preset environmental impact identification standard to obtain the environmental impact type and environmental impact degree fed back by the basic analysis unit, and combining the environmental impact type and the environmental impact degree to obtain the environmental impact characteristics of the basic analysis unit include: Preliminarily dividing the content of the image information of the basic analysis unit to obtain a crop representation part and a non-crop representation part; wherein the non-crop representation part includes a ground representation part, an atmosphere representation part, and a vegetation representation part; According to the preset environmental impact identification standard, the environmental impact elements of the crop embodiment part and the non-agricultural crop embodiment part are analyzed respectively to obtain the environmental impact analysis results fed back by the crop embodiment part and the environmental impact analysis results fed back by the non-agricultural crop embodiment part; Performing a weighted comprehensive analysis on the environmental impact analysis results fed back by the crop embodiment part and the environmental impact analysis results fed back by the non-crop embodiment part, so as to obtain the environmental impact type and environmental impact degree of the basic analysis unit; The environmental impact type and the environmental impact degree are combined to obtain the environmental impact characteristics of the basic analysis unit.

6. The method for monitoring crop growth based on satellite remote sensing according to claim 2, characterized in that: The step of performing growth feedback correction processing on the crop growth characteristics of each basic analysis unit according to the environmental impact characteristics of each basic analysis unit to obtain the confirmed feedback information of the crop growth characteristics of each basic analysis unit includes: The designated basic analysis unit is used as a first analysis unit, and the remaining basic analysis units are matched for relevance according to the content feedback vector of the first analysis unit to obtain a plurality of basic analysis units having content feedback relevance with the first analysis unit; Dividing and marking the association types of several basic analysis units that have content feedback association with the first analysis unit to obtain a second analysis unit that is in the same target monitoring area as the first analysis unit but at a different time, and a third analysis unit that is in an adjacent target monitoring area as the first analysis unit and at the same time; Performing difference analysis on the environmental impact characteristics of the first analysis unit according to the environmental impact characteristics of the second analysis unit to obtain environmental impact difference characteristics between the second analysis unit and the first analysis unit; Performing difference analysis on the crop growth characteristics of the first analysis unit according to the crop growth characteristics of the second analysis unit to obtain difference growth characteristics of the crops between the second analysis unit and the first analysis unit; Performing causal matching processing on the crop growth difference characteristics and the environmental impact difference characteristics according to the time difference between the second analysis unit and the first analysis unit, so as to obtain a causal matching degree of the environmental impact difference characteristics corresponding to the crop growth difference characteristics under the time difference; wherein the causal matching degree is used to describe the causal matching degree of the environmental impact difference characteristics as the difference cause of the crop growth difference characteristics under the time difference; Performing an environmental impact confidence assessment process on the environmental impact characteristics of the first analysis unit according to the environmental impact characteristics of the third analysis unit to obtain a characteristic confidence of the environmental impact characteristics of the first analysis unit; Performing a weighted comprehensive analysis on the causal matching degree and the feature confidence degree according to a preset standard to obtain the growth manifestation influence characteristics of the environmental impact characteristics in the first analysis unit on the growth characteristics of the crop; The crop growth characteristics are corrected according to the growth-influencing characteristics to obtain confirmed feedback information of the crop growth characteristics of each basic analysis unit.

7. The method for monitoring crop growth based on satellite remote sensing according to claim 1, characterized in that: The step of performing a comprehensive analysis of the time series relationship of the confirmed feedback information of each of the basic analysis units to obtain the crop growth monitoring result of the target monitoring area includes: According to the geographical location information and collection time information corresponding to each of the basic analysis units, the certain feedback information of each of the basic analysis units is classified and sorted to obtain a time series analysis sequence of the target monitoring area; wherein the time series analysis sequence includes certain feedback information of a plurality of basic analysis units arranged in chronological order; Based on the preset multiple information dimensions, the time series analysis sequence is respectively subjected to feature extraction of the time series relationship to obtain the analysis results of the time series analysis sequence corresponding to each information dimension, and the analysis results of the time series analysis sequence corresponding to each information dimension are combined to obtain the past growth results; wherein the information dimensions include the growth speed dimension, the growth quality dimension, and the change trend dimension of the growth speed and quality; Performing a growth condition analysis of the target monitoring area at the current moment according to the time series analysis sequence to obtain the current growth condition of the target monitoring area; Predicting the growth of the target monitoring area at a future time according to the past growth results and the current growth status, so as to obtain a future growth forecast of the target monitoring area; The past growth results, the current growth status and the future growth forecast together constitute the crop growth monitoring results of the target monitoring area.

8. A crop growth monitoring device based on satellite remote sensing, characterized in that: A method for monitoring crop growth based on satellite remote sensing for implementing any one of claims 1 to 7, comprising: A preliminary analysis unit is used to obtain satellite remote sensing images of several target monitoring areas, and convert the satellite remote sensing images into analysis units according to the geographical location information and acquisition time information corresponding to the satellite remote sensing images, so as to obtain basic analysis units corresponding to the satellite remote sensing images; An element analysis unit is used to analyze the crop growth elements and environmental impact elements of each of the basic analysis units to obtain the crop growth characteristics and environmental impact characteristics of each of the basic analysis units; wherein the environmental impact characteristics are used to describe the temporary morphological impact of environmental factors on crops when acquiring satellite remote sensing images; A feedback correction unit, used for performing growth feedback correction processing on the crop growth characteristics of each basic analysis unit according to the environmental impact characteristics of each basic analysis unit, so as to obtain confirmed feedback information of the crop growth characteristics of each basic analysis unit; The comprehensive analysis unit is used to conduct a comprehensive analysis of the time series relationship of the confirmed feedback information of each basic analysis unit to obtain the crop growth monitoring result of the target monitoring area.

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