Method, device and equipment for monitoring and diagnosing low-temperature cold damage of crops in irrigation area and medium

Abstract

The present application relates to a kind of irrigation farmland crop low temperature cold injury monitoring and diagnosis method, device, equipment and medium, comprising: collecting irrigation farmland multi-source data;Key indicators are calculated based on farmland multi-source data, wherein, key indicators include: land surface temperature LST, enhanced vegetation index EVI, sunlight-induced chlorophyll fluorescence SIF and solar declination delta;The key indicators calculated are calculated by the air temperature estimation model of pre-constructed irrigation farmland to obtain farmland daily average air temperature, and based on the low temperature cold injury determination standard of farmland crop, the monitoring and diagnosis of farmland crop low temperature cold injury are carried out.The present application can be based on the actual situation of irrigation farmland crop growth, utilize multi-source remote sensing data, consider the influence of surface temperature, crop growth, canopy physiology, solar radiation and other multi-factor, accurately estimate the air temperature of irrigation farmland, have strong operability, practicality is strong, it is easy to promote.

Description

Technical Field

[0001] This invention relates to a method, device, equipment, and medium for monitoring and diagnosing low-temperature damage to crops in irrigation areas, and relates to the fields of irrigation management and agricultural meteorological disasters. Background Technology

[0002] In recent years, influenced by global climate change, extreme disasters (such as extreme high temperatures, low temperatures, droughts, and precipitation) have occurred frequently, significantly impacting crop growth, physiological development, and final yield. Among these, low-temperature chilling injury is one of the common natural disasters affecting efficient crop production. Its harm primarily manifests as reduced crop yields by slowing crop development, decreasing crop biomass (e.g., plant height, leaf area, tiller number), and damaging reproductive organs, thus affecting normal fruit setting. Strengthening real-time monitoring and diagnosis of low-temperature chilling injury in crops is a crucial link in mitigating agricultural meteorological disasters and ensuring food security. Large irrigation areas and modern farms are the main battleground for ensuring food security. Regional-scale low-temperature chilling injury monitoring and diagnosis research is typically conducted based on accurate estimation of air temperature and the determination of corresponding low-temperature chilling injury criteria. The key issue is the accurate estimation of the air temperature Ta in irrigated farmland. Previously, meteorological station monitoring and spatial interpolation techniques described on the national meteorological website were frequently used to achieve regional-scale air temperature estimation. However, due to factors such as the density of meteorological stations and the spatiotemporal differences of relevant parameters, especially since the national meteorological website is often not located within farmland, the air temperature estimated by the above methods cannot accurately represent the actual conditions of farmland in irrigated areas. In regions with significant spatial heterogeneity, its accuracy is even more uncertain. Furthermore, given the vast area and complex planting structure of irrigated areas, while increasing the number and density of farmland meteorological stations could improve data accuracy, this is difficult to achieve due to the substantial economic investment required.

[0003] Remote sensing technology provides a rapid and effective means for estimating air temperature in farmland on a large scale. Land surface temperature (LST), a remote sensing monitoring data product, links the energy exchange process between land and atmosphere. It is a good indicator in the field of thermal infrared remote sensing, representing the energy balance process driven by solar radiation, and can be used to estimate air temperature. Air temperature estimation methods based on LST mainly include: the temperature-vegetation index method, the energy balance method, and statistical methods. Among them, the temperature-vegetation index method is based on the assumption that the vegetation radiation temperature under a certain vegetation cover is equivalent to the surrounding air temperature, using the negative correlation between the vegetation index and land surface temperature to obtain the air temperature. However, this method is greatly affected by local seasons, ecosystem types, and soil moisture, resulting in relatively poor universality. The energy balance method is based on thermodynamic principles, considering net surface radiation as the sum of soil heat flux, sensible heat flux, and latent heat flux. However, this method requires a large number of parameters, which are inconvenient to obtain. Statistical methods consider many influencing factors and have relatively high accuracy, but they still lack consideration of the impact on crop physiological processes. The estimation results have relatively poor orientation towards the farmland crop environment and the real-time nature of crop responses to meteorological disasters.

[0004] In summary, existing air temperature estimation methods based on LST consider influencing factors such as vegetation index (greenness), solar zenith angle, longitude, latitude, and altitude. While these parameters can characterize the response of different factors to low-temperature chilling injury to some extent, they are not specifically designed for crop environments. Furthermore, the influencing factors lack consideration of the physiological response of crop canopy to temperature stress, resulting in poor real-time performance and an inability to accurately and promptly reflect the timing, extent, and severity of low-temperature chilling injury suffered by irrigated farmland crops. Summary of the Invention

[0005] The present invention aims to at least solve one of the technical problems existing in the prior art. Therefore, in response to the above-mentioned problems, the object of the present invention is to provide a method, apparatus, equipment, and medium for monitoring and diagnosing low-temperature chilling injury of crops in irrigated areas, capable of accurately estimating the air temperature in farmland and enabling real-time monitoring and diagnosis of the degree of low-temperature chilling injury to crops in irrigated areas.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0007] In a first aspect, the present invention provides a method for monitoring and diagnosing low-temperature chilling injury to crops in irrigation areas, comprising:

[0008] Collect multi-source data on farmland in irrigation areas;

[0009] Key indicators were calculated based on multi-source farmland data. These key indicators include: land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), and solar declination (δ).

[0010] The calculated key indicators are used to calculate the daily average air temperature of farmland through a pre-constructed farmland air temperature estimation model, and the low-temperature chilling injury of farmland crops is monitored and diagnosed based on the low-temperature chilling injury judgment criteria of farmland crops.

[0011] Furthermore, the multi-source data for farmland in the irrigation area includes remote sensing data and time-series data;

[0012] Remote sensing data includes Landsat 8, MOD11A2 / MOD11A1, MOD13A2, and GOSIF data;

[0013] The time series data includes Julian J.

[0014] Furthermore, key performance indicators (KPIs) are calculated, including:

[0015] The enhanced adaptive reflectivity spatiotemporal fusion model ESTARFM was used to fuse LST / EVI and MOD11A2-LST / MOD13A2-EVI data based on Landsat 8 to obtain high spatiotemporal resolution land surface temperature (LST) and enhanced vegetation index (EVI) data.

[0016] Sunlight-induced chlorophyll fluorescence SIF data were obtained using GOSIF data;

[0017] The solar declination δ data was calculated based on the Julian Day J.

[0018] Furthermore, the established model for estimating air temperature in farmland within the irrigation area is as follows:

[0019] Ta = a*LST + b*EVI + c*SIF + d*δ + e

[0020] In the formula, Ta is the average daily air temperature of the farmland, and a, b, c, d, and e are coefficients to be determined.

[0021] Furthermore, low-temperature damage to farmland crops can be categorized into barrier-type, delayed-type, or mixed-type.

[0022] Furthermore, the criteria for determining the obstacle-type low-temperature chilling injury are: the average temperature for 3 consecutive days during the male emergence period is ≤18℃, and the average temperature for 3 consecutive days during the grouting and milk ripening period is ≤16℃.

[0023] The criteria for determining delayed-type low-temperature chilling injury are as follows: when the accumulated temperature anomaly of ≥10℃ is between -70℃·d and -120℃·d, it is a year of general low-temperature chilling injury; when it is below -120℃·d, it is a year of severe chilling injury.

[0024] The criteria for determining mixed-type low-temperature chilling injury are: barrier-type low-temperature chilling injury and delayed-type low-temperature chilling injury occur simultaneously or consecutively in the same growth period;

[0025] The calculation method for accumulated temperature anomalies ≥10℃ is as follows:

[0026]

[0027] In the formula, CDD is the accumulated temperature anomaly ≥10℃; Ta i The average daily temperature on day i that is ≥10℃; The average annual accumulated temperature is ≥10℃, and n is the total number of days.

[0028] Furthermore, the method also includes a step of obtaining the sensitivity of parameters, including: inputting land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), solar declination (δ), and farmland daily average air temperature (Ta) data into IBM SPSS Amos 26 Graphics software, performing path analysis to output the direct and indirect effects of each parameter on farmland daily average air temperature (Ta), and obtaining the sensitivity of the parameters.

[0029] Secondly, the present invention also provides a monitoring and diagnostic device for low-temperature chilling injury of crops in irrigation areas, the device comprising:

[0030] The multi-source data acquisition module is configured to collect multi-source data from farmland in the irrigation area;

[0031] The indicator calculation module is configured to calculate key indicators based on multi-source farmland data. These key indicators include: land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), and solar declination (δ).

[0032] The disaster diagnosis module is configured to calculate the daily average air temperature of farmland by using a pre-built farmland air temperature estimation model based on the calculated key indicators, and to monitor and diagnose low-temperature damage to farmland crops based on the low-temperature damage judgment criteria for farmland crops.

[0033] Thirdly, the present invention also provides an electronic device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods.

[0034] Fourthly, the present invention also provides a computer-readable storage medium for storing one or more programs, characterized in that the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods.

[0035] Because the present invention adopts the above technical solution, it has the following characteristics:

[0036] 1. This invention can accurately estimate the air temperature of farmland in irrigated areas based on the actual growth conditions of crops and by utilizing multi-source remote sensing data, comprehensively considering the influence of multiple factors such as surface temperature, crop growth, canopy physiology, and solar radiation. Simultaneously, by combining this with the corresponding low-temperature chilling injury assessment standards for farmland crops, it enables the monitoring and diagnosis of the degree of low-temperature chilling injury in irrigated areas, providing a scientific basis and technical support for the prevention and control of agricultural meteorological disasters in irrigated areas. Furthermore, all parameters used in this invention—Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), Sun-Induced Chlorophyll Fluorescence (SIF), and Solar Declination (δ)—are relatively easy to obtain. The prediction method is simple, data acquisition is convenient, and no professional technical team or business training is required. It has strong operability, high practicality, and is easy to promote.

[0037] 2. Compared with the existing technology, the present invention is based on the actual growth conditions of crops in irrigated farmland, takes into account the real farmland growth environment and crop canopy physiological response in irrigated farmland, and can carry out large-scale, real-time and rapid monitoring and diagnosis of low temperature damage to crops in irrigated farmland, and has good practicality.

[0038] 3. Compared with the existing technology, the air temperature estimation model for irrigation farmland proposed in this invention has been calibrated and verified with long-term measured data from irrigation areas. The predicted results are accurate and the precision has been greatly improved, which can better serve disaster prevention and agricultural production.

[0039] In summary, this invention can be widely applied to the monitoring and diagnosis of low-temperature damage to crops in irrigated farmland. Attached Figure Description

[0040] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0041] Figure 1 This is a flowchart of a method for monitoring and diagnosing low-temperature chilling injury to corn in irrigated farmland according to an embodiment of the present invention.

[0042] Figure 2 This is a flowchart of a method for calibrating and verifying an air temperature estimation model for irrigated farmland according to an embodiment of the present invention.

[0043] Figure 3 This is a schematic diagram of the corn low-temperature chilling injury determination standard according to an embodiment of the present invention.

[0044] Figure 4 This is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0045] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0046] Previous regional air temperature estimations were not specifically designed for farmland crop environments and lacked consideration of crop canopy physiology in response to temperature stress, resulting in poor real-time performance and inaccurate, untimely reflection of the timing, extent, and severity of low-temperature chilling injury to irrigated farmland crops. This invention provides a method, device, equipment, and medium for monitoring and diagnosing low-temperature chilling injury to irrigated farmland crops. The method includes: collecting multi-source data from irrigated farmland; calculating key indicators based on the multi-source data, including land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), and solar declination (δ); calculating the daily average air temperature of the farmland using a pre-constructed air temperature estimation model; and monitoring and diagnosing low-temperature chilling injury to crops based on low-temperature chilling injury criteria. This invention can accurately estimate the air temperature of irrigated farmland crops based on actual crop growth conditions, utilizing multi-source remote sensing data and comprehensively considering the influence of multiple factors such as surface temperature, crop growth, canopy physiology, and solar radiation.

[0047] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0048] Example 1: The method for monitoring and diagnosing low-temperature chilling injury in irrigated crops provided in this example includes:

[0049] S1. Collect multi-source data of farmland in irrigation area

[0050] Specifically, the collected multi-source data of farmland in the irrigation area includes ground data, remote sensing data, and time series data.

[0051] Ground data include the daily average air temperature Ta in farmland, the crop canopy temperature Tc, and the soil temperature Ts.

[0052] The remote sensing data includes Landsat 8, MOD11A2 / MOD11A1, MOD13A2, and GOSIF. Among them, Landsat 8 requires preprocessing such as radiometric calibration, atmospheric correction, image mosaicking, and image cropping. MOD11A2 / MOD11A1 and MOD13A2 require preprocessing such as format conversion, projection conversion, resampling, and image cropping. GOSIF requires preprocessing such as projection conversion, resampling, and image cropping to ensure that their coordinate system, spatial resolution, and spatial extent are consistent with each other.

[0053] The time series data includes Julian J.

[0054] S2, Calculate key indicators

[0055] Specifically, the calculation of key indicators includes:

[0056] S21. Based on the vertical temperature gradient, the average air temperature Ta of the farmland at the monitoring point is obtained using the soil temperature Ts.

[0057] S22. Using the enhanced adaptive reflectivity spatiotemporal fusion model ESTARFM, high spatiotemporal resolution land surface temperature LST / enhanced vegetation index EVI data are obtained by fusing LST / EVI data retrieved from Landsat 8 and MOD11A2-LST / MOD13A2-EVI, including:

[0058] Using LST / EVI data and MOD11A2-LST / MOD13A2-EVI data retrieved from Landsat 8 remote sensing images before and after the period to be predicted, as well as MOD11A2-LST / MOD13A2-EVI data for the period to be predicted, LST data with an 8-day timescale and 30m spatial resolution and EVI data with a 16-day timescale and 30m spatial resolution for the period to be predicted are simulated. The fusion operation is performed in the ENVI / IDL environment. This is just one example, and is not limited to this.

[0059] S23. Obtain sunlight-induced chlorophyll fluorescence SIF data using GOSIF data.

[0060] Specifically, SIF (Spectral Intensity Frequency) is the spectral signal (650-800nm) emitted by vegetation at its photosynthetic centers. It has two peaks: red (690nm) and near-infrared (740nm), and can directly reflect the dynamic changes in actual photosynthesis in vegetation, resulting in a more rapid and agile response to environmental stresses. In this embodiment, this parameter is introduced into the air temperature estimation model for irrigated farmland, which can reflect in real time the changes in photosynthesis of crops in irrigated farmland before and after encountering low-temperature chilling injury.

[0061] S24. Calculate the solar declination δ data based on the Julian Day J.

[0062]

[0063] In the formula, δ represents the solar declination data.

[0064] S3. Establish an estimation model for air temperature in farmland within the irrigation area.

[0065] Specifically, based on the daily average air temperature Ta, land surface temperature LST, enhanced vegetation index EVI, sunlight-induced chlorophyll fluorescence SIF, and solar declination δ data, a model for estimating farmland air temperature in irrigated areas was established using the multiple linear regression (MLR) method. The model parameters were then calibrated and the model accuracy was verified.

[0066] Furthermore, the established model for estimating air temperature in farmland within the irrigation area is as follows:

[0067] Ta = a*LST + b*EVI + c*SIF + d*δ + e

[0068] In the formula, a, b, c, d, and e are undetermined coefficients.

[0069] Furthermore, the undetermined coefficients a, b, c, d, and e are obtained in the following way:

[0070] Using ArcGIS software, raster data of remote sensing images—land surface temperature (LST), enhanced vegetation index (EVI), and sunlight-induced chlorophyll fluorescence (SIF)—we sampled to obtain pixel values ​​at corresponding monitoring points. These values ​​were then combined with the calculated solar declination δ and the measured daily average air temperature Ta in farmland, and substituted into the farmland air temperature estimation model in the irrigation area to obtain the coefficient values ​​a, b, c, d, and e in the model.

[0071] Furthermore, additional observational data is needed to validate the air temperature estimation model for farmland in the irrigation area. If the model meets the preset accuracy requirements, the validation is successful; otherwise, data needs to be collected again and the corresponding model needs to be re-established.

[0072] S4. Based on the criteria for determining low-temperature chilling injury of corresponding farmland crops, monitor and diagnose the occurrence time, scope, and degree of low-temperature chilling injury of farmland crops.

[0073] Specifically, the air temperature estimation model for farmland in irrigation areas is applied to the monitoring and diagnosis of low-temperature chilling injury in farmland crops. By inputting the daily land surface temperature (LST), daily enhanced vegetation index (EVI), daily sunlight-induced chlorophyll fluorescence (SIF), and daily solar declination (δ) data of the study area, the daily average air temperature (Ta) of farmland is calculated daily and pixel by pixel. At the same time, combined with the corresponding low-temperature chilling injury judgment criteria for farmland crops, the occurrence time, range, and degree of low-temperature chilling injury in farmland crops in irrigation areas can be monitored and diagnosed.

[0074] Among them, the daily land surface temperature (LST) was obtained from MOD11A1-LST data; the daily enhanced vegetation index (EVI) was obtained from MOD13A2-EVI data after time series smoothing using Savitzky-Golay (SG) filtering; the daily sunlight-induced chlorophyll fluorescence (SIF) was obtained from GOSIF-SIF data after time series smoothing using SG filtering; and the daily solar declination (δ) was calculated using Julian day J.

[0075] Furthermore, the criteria for determining low-temperature chilling injury in farmland crops need to be based on the different accumulated temperature / temperature thresholds required for the growth and production of different crops.

[0076] In a preferred embodiment, the method further includes a sensitivity analysis step, which involves selecting measured data within the observation period based on the farmland air temperature estimation model of the irrigation area, using path analysis to evaluate the sensitivity of land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), and solar declination (δ), analyzing the direct and indirect effects of the model parameters LST, EVI, SIF, and δ on the daily average air temperature (Ta) of farmland, and obtaining sensitive parameters to facilitate future disaster monitoring and early warning.

[0077] like Figure 1 As shown, the following describes in detail the method for monitoring and diagnosing low-temperature chilling injury of maize in irrigated farmland provided by the present invention, using maize as a specific example, including:

[0078] S1. Collect multi-source data of farmland in irrigation area

[0079] Specifically, in this embodiment, the Thermal Infrared Multi-Parameter Real-Time Acquisition System (CTMS) and the Low-Power Regional Soil Moisture Monitor (LESW) are used for observation and data acquisition. The acquired multi-source data of farmland in the irrigated area includes ground data, remote sensing data, and time-series data. Among them, the ground data acquired in this embodiment mainly includes the daily average air temperature Ta, daily canopy temperature Tc, and daily soil temperature at different depths (e.g., 10, 20, 40 cm) Ts. The daily average air temperature Ta and daily canopy temperature Tc are obtained using a CTMS temperature sensor or an infrared sensor, while the daily soil temperature Ts at different depths is obtained by the CTMS and LESW using temperature sensors.

[0080] The remote sensing data collected in this embodiment mainly includes: Landsat 8, MOD11A2 / MOD11A1, MOD13A2, and GOSIF data. The Landsat 8, MOD11A2 / MOD11A1, and MOD13A2 data were downloaded from the NASA website (https: / / www.nasa.gov / ), and the GOSIF data were downloaded from the Global Ecology Group website (https: / / globalecology.unh.edu / ). After acquisition, the remote sensing data underwent preprocessing including radiometric calibration, atmospheric correction, mosaicking, cropping, and resampling to ensure consistency in coordinate system, spatial resolution, and spatial extent.

[0081] The time series data mainly includes: Julian J.

[0082] S2. Calculate key indicators, including:

[0083] S21. Based on the vertical temperature gradient, calculate the daily average air temperature Ta at the monitoring point using the daily soil temperature Ts at different depths.

[0084] To obtain more sample data for model training and validation, it is necessary to use the daily soil temperature Ts at different depths monitored by LESW to obtain the corresponding daily average air temperature Ta at the monitoring points. The specific method is as follows: First, perform multiple regression analysis on the daily soil temperature Ts at different depths of CTMS monitoring points and the daily average air temperature Ta at the same location to obtain the vertical temperature gradient relationship Ta=f(Ts). Then, use this vertical temperature gradient relationship to calculate the daily average air temperature Ta of the farmland at the current location from the daily soil temperature Ts measured by LESW as the measured sample data.

[0085] S22. The enhanced adaptive reflectance spatiotemporal fusion model ESTARFM was used to fuse LST / EVI retrieved from Landsat 8 and MOD11A2-LST / MOD13A2-EVI to obtain high spatiotemporal resolution land surface temperature (LST) and enhanced vegetation index (EVI). Specifically, the Landsat 8 image data was preprocessed using ENVI / IDL software, including radiometric calibration, atmospheric correction, mosaicking, and cropping. The LST was calculated using atmospheric correction, and the EVI was calculated using band calculation tools. The MODIS Reprojection Tool (MRT) was used to preprocess MOD11A2 and MOD13A2 product data, including format conversion and projection conversion, and resampled to a 30m grid size to obtain LST and EVI within the study area. The Landsat 8 data from the study area was then analyzed using the Landsat 8 image data retrieved before and after the predicted period. LST / EVI data and MOD11A2-LST / MOD13A2-EVI data retrieved from 8 remote sensing images, along with MOD11A2-LST / MOD13A2-EVI data for the period to be predicted, were used to simulate LST land surface temperature at an 8-day timescale and 30m spatial resolution, and EVI enhanced vegetation index data at a 16-day timescale and 30m spatial resolution for the period to be predicted. The above fusion operation was performed in the ENVI / IDL environment.

[0086] To ensure the accuracy of the data fusion results, the LST fusion results were verified using LST retrieved from Landsat 8 and the canopy temperature Tc observed by CTMS at 11:00 AM on the same day, respectively. Specifically, the LST pixel values ​​retrieved from Landsat 8 and those fused from ESTARFM were compared. If the values ​​met the preset accuracy requirements, the verification was successful; otherwise, the data was reprocessed and the data fusion was repeated. The ESTARFM-fused LST data was sampled using ArcGIS software to extract the LST at the CTMS monitoring point locations. This value was then compared with the canopy temperature Tc observed by CTMS at 11:00 AM on the same day. If the values ​​met the preset accuracy requirements, the verification was successful; otherwise, the data was reprocessed and the data fusion was repeated. The EVI fusion results were verified using EVI retrieved from Landsat 8 at the same time and area, using the same method.

[0087] S23. Use ArcGIS software to perform preprocessing on GOSIF data, such as projection transformation, resampling, and graphic cropping, to obtain crop SIF data for the study area, ensuring that its coordinate system, spatial resolution, and spatial extent are consistent with other remote sensing data.

[0088] S24. Calculate the solar declination δ data based on J:

[0089]

[0090] S3. Based on the daily average air temperature Ta, land surface temperature LST, enhanced vegetation index EVI, sunlight-induced chlorophyll fluorescence SIF, and solar declination δ data, an air temperature estimation model for farmland in the irrigation area was established using the MLR method. The model parameters were calibrated and the model accuracy was verified.

[0091] Specifically, the established model for estimating air temperature in farmland within the irrigation area is as follows:

[0092] Ta = a*LST + b*EVI + c*SIF + d*δ + e

[0093] like Figure 2 As shown, in order to determine the undetermined coefficients a, b, c, d, and e in the farmland air temperature estimation model for the irrigation area, model parameter calibration is required, including:

[0094] Using ArcGIS software, raster data of land surface temperature (LST), enhanced vegetation index (EVI), and sunlight-induced chlorophyll fluorescence (SIF) in the study area were sampled. Pixel values ​​at the corresponding monitoring points were extracted. Combined with the calculated solar declination (δ) data and the measured daily average air temperature (Ta) data of farmland, one year's observation data was substituted into the farmland air temperature estimation model of the irrigation area to obtain the coefficient values ​​a, b, c, d, and e in the model.

[0095] To ensure the accuracy of the air temperature estimation in the irrigated farmland in this embodiment, a verification step is introduced to check the model accuracy. The specific process is as follows:

[0096] The air temperature estimation model for farmland in the irrigation area was validated using at least one year's worth of data. Specifically, land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), and solar declination (δ) data collected from the study area were substituted into the established air temperature estimation model to obtain an estimated daily average air temperature (Ta) for farmland in the study area. Using sampling tools in ArcGIS, pixel values ​​at monitoring points were extracted, and this estimated value was compared with the measured Ta values ​​in the field. The accuracy of the air temperature estimation model was calculated. If the model's accuracy met the preset accuracy requirements, it became the final air temperature estimation model for farmland in the irrigation area. If the accuracy did not meet the preset accuracy requirements, new year's experimental parameters needed to be obtained and substituted back into the air temperature estimation model for calibration and validation until the obtained air temperature estimation model met the preset accuracy requirements.

[0097] S4. Apply the air temperature estimation model to maize crops. Based on the established air temperature estimation model for farmland in the irrigated area, the daily land surface temperature (LST), daily enhanced vegetation index (EVI), daily sunlight-induced chlorophyll fluorescence (SIF), and daily solar declination (δ) data of the study area are input. The average daily air temperature (Ta) of the farmland in the study area is calculated daily and pixel-by-pixel. Combined with the criteria for judging low-temperature chilling injury to maize, the occurrence time, scope, and severity of low-temperature chilling injury encountered by maize in the study area are monitored and assessed. "Calculating the average daily air temperature (Ta) of the farmland in the study area" refers to obtaining the daily average air temperature (Ta) of farmland within the study area (all pixels). The specific process is as follows: For each pixel, the daily land surface temperature (LST), daily enhanced vegetation index (EVI), daily sunlight-induced chlorophyll fluorescence (SIF), and daily solar declination (δ) data of that location are substituted into the pre-constructed air temperature estimation model to obtain the daily average air temperature (Ta) of the farmland at that pixel location. The entire study area contains several pixels; calculating each pixel individually yields the daily average air temperature (Ta) of farmland within the study area for that day.

[0098] Furthermore, the daily land surface temperature (LST) was obtained from MOD11A1-LST data; the daily enhanced vegetation index (EVI) was obtained from MOD13A2-EVI data after SG filtering and time series smoothing; the daily sunlight-induced chlorophyll fluorescence (SIF) was obtained from GOSIF-SIF data after SG filtering and time series smoothing; and the daily solar declination (δ) was calculated using Julian day J.

[0099] In this embodiment, the low-temperature damage encountered by corn can be mainly divided into three types: barrier type, delayed type, and mixed type.

[0100] like Figure 3 As shown, the criteria for determining the obstacle-type low-temperature chilling injury are: the average temperature for 3 consecutive days during the male emergence period is ≤18℃, and the average temperature for 3 consecutive days during the grouting and milk ripening period is ≤16℃.

[0101] The criteria for determining delayed-type low-temperature chilling injury are as follows: when the accumulated temperature anomaly of ≥10℃ is between -70℃·d and -120℃·d, it is a year of general low-temperature chilling injury; when it is below -120℃·d, it is a year of severe chilling injury.

[0102] The criteria for determining mixed-type low-temperature chilling injury are: both barrier-type and delayed-type low-temperature chilling injury occur simultaneously or consecutively during the same growth period. The calculation method for accumulated temperature anomalies ≥10℃ is as follows:

[0103]

[0104] In the formula, CDD is the accumulated temperature anomaly ≥10℃; Ta i The average daily temperature on day i that is ≥10℃; It is the annual average value of accumulated temperature with a daily average temperature ≥10℃.

[0105] Furthermore, based on path analysis, the direct and indirect effects of model parameters—land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), and solar declination (δ)—on farmland daily average air temperature (Ta) were analyzed to obtain sensitive parameters for future disaster monitoring and early warning, including:

[0106] The land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), solar declination (δ), and farmland daily average air temperature (Ta) data obtained in step S2 are input into IBM SPSS Amos 26 Graphics software for path analysis. The direct and indirect effects of each parameter on farmland daily average air temperature (Ta) are output, and the sensitivity of the parameters is obtained. Changes in sensitive parameters can conveniently and quickly reflect the actual situation of farmland daily average air temperature (Ta), which is beneficial for real-time monitoring and early warning of farmland disasters.

[0107] Example 2: Following the method for monitoring and diagnosing low-temperature chilling injury to crops in irrigated farmland provided in Example 1, this example provides a device for monitoring and diagnosing low-temperature chilling injury to crops in irrigated farmland. The device provided in this example can implement the method for monitoring and diagnosing low-temperature chilling injury to crops in irrigated farmland as described in Example 1. This device can be implemented through software, hardware, or a combination of both. For ease of description, this example is described by dividing the functionality into various units. Of course, in practice, the functions of each unit can be implemented in one or more software and / or hardware components. For example, the device may include integrated or separate functional modules or units to execute the corresponding steps in the methods of Example 1. Since the device in this example is basically similar to the method example, the description process of this example is relatively simple. For relevant details, please refer to the description in Example 1. The example of the device for monitoring and diagnosing low-temperature chilling injury to crops in irrigated farmland provided by this invention is merely illustrative.

[0108] This embodiment provides a device for monitoring and diagnosing low-temperature chilling injury to crops in irrigated farmland, including:

[0109] The multi-source data acquisition module is configured to collect multi-source data from farmland, including ground data, remote sensing data, and time-series data.

[0110] The indicator calculation module is configured to calculate key indicators, including land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), and solar declination (δ).

[0111] The disaster diagnosis module is configured to input the daily land surface temperature (LST), daily enhanced vegetation index (EVI), daily sunlight-induced chlorophyll fluorescence (SIF), and daily solar declination (δ) during the current growing season into a pre-constructed farmland air temperature estimation model to obtain the daily and pixel-by-pixel average daily farmland air temperature (Ta). At the same time, combined with the preset low-temperature chilling injury judgment criteria, it diagnoses the time, range, and degree of low-temperature chilling injury suffered by farmland crops in the irrigation area.

[0112] Example 3: This example provides an electronic device corresponding to the low-temperature cold damage monitoring and diagnosis method for farmland crops in irrigation areas provided in Example 1. The electronic device can be an electronic device for the client, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of Example 1.

[0113] like Figure 4 As shown, the electronic device includes a processor, memory, communication interface, and bus. The processor, memory, and communication interface are connected via the bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the method of Embodiment 1. The implementation principle and technical effects are similar to those of Embodiment 1, and will not be repeated here. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computing device on which the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0114] In a preferred embodiment, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), and optical discs.

[0115] In a preferred embodiment, the processor can be any type of general-purpose processor such as a central processing unit (CPU) or a digital signal processor (DSP), and is not limited thereto.

[0116] Example 4: This example provides a computer program product. The computer program product may include a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by the computer, the computer can execute the method provided in Example 1 above. Its implementation principle and technical effects are similar to those in Example 1, and will not be repeated here.

[0117] In a preferred embodiment, the computer-readable storage medium may be a tangible device for holding and storing instructions used by an instruction execution device, such as, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. The computer-readable storage medium stores computer program instructions that cause a computer to perform the method provided in Embodiment 1 above.

[0118] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In the description of this specification, the terms "a preferred embodiment," "furthermore," "specifically," "in this embodiment," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments in this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0119] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring and diagnosing low-temperature chilling injury in crops in irrigated areas, characterized in that... include: Collect multi-source data on farmland in irrigation areas; Key indicators were calculated based on multi-source farmland data, including land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), and solar declination. δ ; The calculated key indicators are used to calculate the daily average air temperature of farmland using a pre-built farmland air temperature estimation model. Based on the criteria for determining low-temperature chilling injury in farmland crops, monitoring and diagnosis of low-temperature chilling injury are then conducted. The established model for estimating air temperature in farmland within the irrigation area is as follows: ； In the formula, Ta is the average daily air temperature in the farmland. a , b , c , d , e These are coefficients to be determined; Low-temperature chilling injury to farmland crops includes barrier type, delayed type, or mixed type. The criteria for determining barrier type low-temperature chilling injury are: an average temperature of ≤18℃ for three consecutive days during the tasseling stage, and an average temperature of ≤16℃ for three consecutive days during the grain-filling and milk-ripening stages. The criteria for determining delayed type low-temperature chilling injury are: an accumulated temperature anomaly of ≥10℃ between -70℃·d and -120℃·d indicates a year of general low-temperature chilling injury, while an anomaly below -120℃·d indicates a year of severe chilling injury. The criteria for determining mixed type low-temperature chilling injury are: barrier type and delayed type low-temperature chilling injury occurring simultaneously or consecutively during the same growth period. The calculation method for the accumulated temperature anomaly of ≥10℃ is as follows: In the formula, CDD The accumulated temperature anomaly is ≥ 10 ℃; Ta i For the first i The average daily temperature is ≥ 10℃; This refers to the annual average of accumulated temperature ≥ 10℃. n This represents the total number of days.

2. The method for monitoring and diagnosing low-temperature chilling injury to crops in irrigated areas according to claim 1, characterized in that, Multi-source data for farmland in irrigated areas includes remote sensing data and time-series data, among which, Remote sensing data includes Landsat 8, MOD11A2 / MOD11A1, MOD13A2, and GOSIF data; The time series data includes Julian J.

3. The method for monitoring and diagnosing low-temperature chilling injury to crops in irrigated areas according to claim 2, characterized in that, Calculate key metrics, including: The enhanced adaptive reflectivity spatiotemporal fusion model ESTARFM was used to fuse LST / EVI and MOD11A2-LST / MOD13A2-EVI data based on Landsat 8 to obtain high spatiotemporal resolution land surface temperature (LST) and enhanced vegetation index (EVI) data. Sunlight-induced chlorophyll fluorescence SIF data were obtained using GOSIF data; Calculation of solar declination based on Julian J δ data.

4. The method for monitoring and diagnosing low-temperature chilling injury to crops in irrigated areas according to claim 1, characterized in that, It also includes steps for obtaining the sensitivity of parameters, including: land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), and solar declination. δ The daily average air temperature Ta data of farmland was input into IBM SPSS Amos 26 Graphics software to perform path analysis, outputting the direct and indirect effects of each parameter on the daily average air temperature Ta of farmland, and obtaining the sensitivity of the parameters.

5. An apparatus for implementing the method for monitoring and diagnosing low-temperature chilling injury of crops in irrigated areas as described in any one of claims 1-4, characterized in that, The device includes: The multi-source data acquisition module is configured to collect multi-source data from farmland in the irrigation area; The indicator calculation module is configured to calculate key indicators based on multi-source farmland data. These key indicators include: land surface temperature (LST), enhanced vegetation index (EVI), sunlight-induced chlorophyll fluorescence (SIF), and solar declination. δ ; The disaster diagnosis module is configured to calculate the daily average air temperature of farmland by using a pre-built farmland air temperature estimation model based on the calculated key indicators, and to monitor and diagnose low-temperature damage to farmland crops based on the low-temperature damage judgment criteria for farmland crops.

6. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 4.

7. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 4.

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