Remote sensing and live data fusion coupling rice high-temperature heat damage influence assessment method

Abstract

The invention relates to the technical field of high-temperature heat damage influence assessment, in particular to a remote sensing and live data fusion coupling rice high-temperature heat damage influence assessment method, which comprises the following steps: acquiring remote sensing data of a rice planting area, and calculating a remote sensing factor for rice high-temperature heat damage influence assessment based on the remote sensing data; collecting fused live CLDAS air temperature data of a rice planting area, and calculating a temperature factor of rice high-temperature heat damage influence evaluation based on the air temperature data; and calculating the high-temperature heat damage influence grade of the rice planting area based on the remote sensing factor and the temperature factor, and completing rice high-temperature heat damage influence evaluation. According to the method, the high-temperature heat damage influence grade of the rice can be accurately evaluated by combining remote sensing data and fusing actual CLDAS air temperature data.

Description

Technical Field

[0001] The invention relates to the technical field of high temperature heat damage impact assessment, and in particular to a method for assessing the impact of high temperature heat damage on rice by coupling remote sensing and fusion real-time data. Background Art

[0002] Rice is a cereal crop of the genus Oryza. According to botanical classification, rice is divided into indica rice and japonica rice; according to the length of the growing period, it is divided into early rice, mid-season rice, late rice, etc.; according to the starch content, it is divided into glutinous rice and non-glutinous rice; according to the seed retention method, it is divided into conventional rice and hybrid rice; and according to the cultivation method, it is divided into rice and upland rice.

[0003] Rice is easily affected by high temperature and heat damage during planting. In the existing technology, there are technical evaluation methods that take meteorological factors into consideration. For example, in the study of high temperature and heat damage to early rice in Jiangxi Province, high temperature weather processes (including duration and occurrence time) were considered to dynamically identify the level of high temperature and heat damage to rice, and a high temperature and heat damage impact assessment model for rice was constructed by combining the duration of high temperature stress and experimental data of different temperature gradients. Sichuan Province selected the main meteorological influencing factors of high temperature and heat damage to early rice, and combined with the principal component analysis method to construct a high temperature and heat damage impact assessment model for rice heading and flowering and filling and fruiting periods in Sichuan Province, and verified it using historical climate data. Jiangsu Province also used historical meteorological data and rice growth period data to use the Logistic curve equation to refine and quantify the impact of high temperature and heat damage on rice yield loss from the perspective of disaster factors and disaster-bearing bodies. In the study of high temperature and heat damage to early rice in Jiangnan and South China, the intensity of high temperature and heat damage reaching the standard, effective days and possible impact weights of different levels were considered to construct an early rice high temperature and heat damage intensity index. There are also methods using remote sensing and other technologies, such as using the Normalized Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI) to evaluate rice damage caused by high temperature at the leaf and canopy scales; and using a rice growth remote sensing monitoring system using drones and portable multispectral sensors in the Wuhu area of ​​the middle and lower reaches of the Yangtze River to study the spectral characteristics of rice plants in the sensitive growth period and establish an inversion and recognition model for rice high temperature stress; using optical satellite remote sensing information to study the spatial distribution of temperature in rice-growing areas, extracting information from the mixed underlying surface temperature of rice fields based on mixed pixel decomposition information, and then evaluating the impact of high temperature heat damage on rice. There are also technical methods that combine satellite remote sensing and meteorological station data, such as using satellite remote sensing data to invert the daily maximum and average temperatures, and interpolating the temperature data of the corresponding stations in the cloud-covered areas to generate "satellite-interpolation" temperature time series data, and then conducting rice high temperature heat damage monitoring and evaluation based on rice high temperature heat damage indicators. There are also those who use the fusion of multi-source data to carry out monitoring and assessment of high temperature heat damage to rice, such as using vegetation index and land surface temperature (LST) data in combination with machine learning models to improve the accuracy of air temperature (Ta) estimation, and then assess the high temperature heat damage situation in rice-growing areas.

[0004] In summary, the existing technologies for evaluating rice heat damage mainly include single-point simulation of meteorological stations, regional remote sensing inversion, and GIS spatial extrapolation. Single-point simulation has a large impact on spatial extrapolation errors due to the discreteness of the station and the complexity of the underlying surface of the environment. The surface temperature inverted based on remote sensing is easily affected by cloud and rain weather, which can cause the loss of remote sensing data, and the integrity of the time series is difficult to ensure. The land surface data assimilation system developed by the National Meteorological Center is currently the only real-time business system in the field of land surface data assimilation systems in my country. It can provide temporally and spatially continuous and high-resolution temperature fusion real-time products. Temperature change monitoring and verification in multiple regions have shown that it has a high accuracy rate. However, there are few studies on monitoring and evaluating rice heat damage based on remote sensing technology and fusion real-time product coupling methods. The combination of remote sensing data and fusion real-time data to evaluate the impact of rice heat damage can not only improve the accuracy of temperature estimation and provide monitoring of rice growth status, but also can be quantitatively evaluated according to rice heat damage indicators, providing a scientific basis for early warning and management of rice heat damage. Summary of the invention

[0005] The purpose of the present invention is to provide a method for evaluating the impact of high temperature heat damage to rice by coupling remote sensing and fusion real-time data, and to accurately evaluate the impact level of high temperature heat damage to rice by combining remote sensing data and temperature data of CLDAS software.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] The rice heat damage impact assessment method coupled with remote sensing and fusion real-time data includes:

[0008] Collecting remote sensing data of rice-growing areas, and calculating remote sensing factors for assessing the impact of high temperature heat damage to rice based on the remote sensing data;

[0009] Collecting fused real-time CLDAS temperature data in rice-growing areas, and calculating temperature factors for evaluating the impact of high temperature heat damage to rice based on the temperature data;

[0010] The high temperature heat damage impact level of the rice growing area is calculated based on the remote sensing factor and the temperature factor, and the high temperature heat damage impact assessment of rice is completed.

[0011] Optionally, the remote sensing factors for calculating the impact assessment of high temperature heat damage to rice based on the remote sensing data include:

[0012] Calculating the Normalized Difference Vegetation Index (NDVI) of the rice-growing area to be evaluated based on the remote sensing data, and constructing an NDVI data set for the high temperature heat damage process;

[0013] The remote sensing factor for assessing the impact of high temperature heat damage to rice is calculated based on the NDVI dataset.

[0014] Optionally, the method for calculating the remote sensing factor for assessing the impact of high temperature heat damage to rice based on the NDVI dataset is:

[0015]

[0016] Among them, VCI is the growth condition of rice; NDVI gj It is the average NDVI value of the high temperature occurrence process in a certain period of a certain year; NDVI max，j The maximum NDVI value of rice during a certain period of high temperature in a certain year; min，j It is the minimum NDVI value of rice during the high temperature occurrence process in a certain year and period.

[0017] Optionally, the temperature factor for evaluating the impact of high temperature heat damage to rice calculated based on the temperature data includes:

[0018] Read temperature component data based on CLDAS;

[0019] Calculate the daily average temperature based on the temperature component data;

[0020] The daily average temperature is corrected using weather station data to obtain the corrected daily average temperature.

[0021] Optionally, the daily average temperature is corrected by using weather station data, and the method for obtaining the corrected daily average temperature is:

[0022] t r =mt c +n

[0023] Among them, t r is the corrected daily average temperature; t c is the corrected average temperature of the previous day; m and n are the coefficients of the regression equation.

[0024] Optionally, calculating the high temperature heat damage impact level of the rice planting area based on the remote sensing factor and the temperature factor includes:

[0025] Based on the remote sensing factor and the temperature factor, respectively obtaining the rice growth grade and the high temperature heat damage grade;

[0026] respectively obtaining scores of the rice growth grade and the high temperature heat damage grade;

[0027] Calculating the rice high temperature heat damage impact index in the rice planting area based on the score;

[0028] The high temperature heat damage impact level of the rice planting area is obtained based on the rice high temperature heat damage impact index.

[0029] Optionally, the method for calculating the rice high temperature heat damage impact index of the rice planting area based on the score is:

[0030]

[0031] Where I is the rice high temperature impact index; I ndvi is the NDVI factor scoring result; I cldas is the CLDAS temperature factor scoring result, and a and b are weight coefficients.

[0032] Optionally, obtaining the high temperature heat damage impact level of the rice planting area based on the rice high temperature heat damage impact index includes:

[0033] Based on the preset rice high temperature heat damage impact assessment threshold range, the range of the rice high temperature heat damage impact index is determined to obtain the high temperature heat damage impact level.

[0034] The beneficial effects of the present invention are as follows: the present invention can realize accurate identification of rice-growing areas, integrate the advantages of real-time product time series data acquisition, high-precision estimation of maximum temperature and average temperature, and quantitative assessment of the degree of high-temperature heat damage, providing a scientific basis for early warning and management of high-temperature heat damage to rice. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 The present invention is a flowchart of a method for assessing the impact of high temperature heat damage to rice by coupling remote sensing and fusion of real-time data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] This embodiment provides a method for assessing the impact of high temperature heat damage to rice by coupling remote sensing and fusion real-time data. Figure 1 As shown, including:

[0040] Collect remote sensing data of rice-growing areas and calculate remote sensing factors for assessing the impact of high temperature heat damage to rice based on remote sensing data;

[0041] Collect temperature data in rice-growing areas and calculate temperature factors for assessing the impact of high temperature heat damage to rice based on the temperature data;

[0042] The impact level of high temperature heat damage in rice-growing areas is calculated based on remote sensing factors and temperature factors, and the impact assessment of high temperature heat damage to rice is completed.

[0043] The specific steps include:

[0044] Step 1, download MODIS / FY3 satellite remote sensing data and preprocess them, including: ① strip noise removal, ② image bowtie effect processing, ③ cloud interference processing, ④ geometric correction, ⑤ radiation calibration and atmospheric correction, ⑥ multi-source data fusion, ⑦ quality control and data verification;

[0045] Step 2, calculate the normalized difference vegetation index NDVI and generate the NDVI data set of the high temperature heat damage process;

[0046] Download daily MODIS / FY3 satellite remote sensing data during the period of high temperature heat damage to rice, including data from the disaster year and the same period in history, calculate NDVI, and generate a data set:

[0047]

[0048] NDVI max，j =max(NDVI aj )

[0049] NDVI min，j =min(NDVI aj )

[0050] Where:

[0051] R nir ——Reflectivity in near infrared band;

[0052] R red ——Reflectivity in the red light band;

[0053] NDVI i ——Daily NDVI value during high temperature occurrence;

[0054] NDVI gj ——The average NDVI value of the high temperature occurrence process in a certain period of a certain year;

[0055] i——number of days of high temperature process;

[0056] d——the cumulative number of days of high temperature process;

[0057] j——the year when the high temperature process occurred;

[0058] NDVI max，j ——The maximum value of rice NDVI during the high temperature occurrence process in a certain period of a certain year;

[0059] NDVI min，j ——The minimum NDVI value of rice during a high temperature period in a certain year.

[0060] Step 3, calculate the remote sensing factors for evaluating the impact of high temperature heat damage to rice;

[0061] The extreme value model is used to evaluate the growth status of rice using NDVI. The extreme value model is as follows:

[0062]

[0063] The rice growth levels corresponding to the preset VCI thresholds are shown in Table 1.

[0064] Table 1

[0065] Rice growth grade VCI Threshold good [0.7,1.0] middle [0.5～0.7) Difference [0,0.5)

[0066] Step 4: Download the fusion live product CLDAS data and pre-process it;

[0067] Land surface data assimilation technology is an effective means to obtain high-quality soil moisture data. CLDAS soil moisture products use data fusion assimilation technology to fuse data from multiple sources such as ground observations, satellite monitoring, and numerical model products to obtain high-quality atmospheric driving field (temperature, air pressure, humidity, wind speed, precipitation, radiation) grid data, which in turn drives the road process model and ultimately obtains soil moisture data. The data range covers the entire East Asia region, with a grid resolution of 0.0625° (6.5km) and 0.01° (1km), and a time resolution of 1h. The data starts on January 1, 2009.

[0068] Step 5: CLDAS temperature unit conversion and correction;

[0069] CLDAS data format conversion:

[0070] CLDAS data is stored in NC format. The downloaded NC data needs to be read and converted. Usually, the .nc file is converted to a .img file to read the temperature component data.

[0071] CLDAS temperature conversion:

[0072] Since the CLDAS temperature component is stored in Kelvin, it needs to be converted to Celsius. The calculation method is as follows:

[0073] t=T-273.15

[0074] Where:

[0075] T——original Kelvin temperature value;

[0076] t——Celsius temperature value after conversion.

[0077] The daily average temperature is needed in the rice high temperature monitoring and evaluation, and the daily average temperature needs to be calculated using the hourly data of CLDAS. The formula is as follows:

[0078]

[0079] Where:

[0080] t e ——Hourly temperature values;

[0081] t c ——Daily average temperature.

[0082] CLDAS temperature data correction:

[0083] According to the principle of effective monitoring range of meteorological data of 10km, CLDAS temperature data corresponding to the location of meteorological stations are extracted. The temperature data of June, July and August of each meteorological station with a time series of more than 5 years and CLDAS temperature data are selected to establish temperature correction models by month. Usually, a univariate linear regression model is selected, and the CLDAS temperature data is corrected using the correction model.

[0084] t r =mt c +n

[0085] Where:

[0086] t r ——The corrected daily average temperature;

[0087] t c ——Correct the average temperature of the previous day;

[0088] m and n – coefficients of the regression equation.

[0089] Step 6, calculating the temperature factor for evaluating the impact of high temperature heat damage to rice;

[0090] An example analysis of the evaluation was conducted based on the rice heat damage classification index determined based on historical experience. As shown in Table 2, if the daily average temperature (〒) ≥ 12.0℃ or the daily maximum temperature (Tmax) ≥ 35℃ lasts for 3 to 5 days, it is mild heat damage weather; if 〒 ≥ 12.0℃ or Tmax ≥ 35℃ lasts for 6 to 8 days, it is moderate heat damage weather; if 〒 ≥ 12.0℃ or Tmax ≥ 35℃ lasts for ≥ 9 days, it is severe heat damage weather.

[0091] The temperature component data corrected by CLDAS are used to assess the level of high temperature heat damage.

[0092] Table 2

[0093] High temperature heat damage level 〒≥30℃ for continuous days or Tmax≥35℃ for continuous days Mild 3-5 days Moderate 6-8 days serious ≥ 9 days

[0094] Step 7: Use the remote sensing factors and temperature factors in steps 3 and 6 to jointly evaluate the impact of high temperature heat damage on rice growth.

[0095] Based on the calculated remote sensing factors and temperature factors, the rice growth grade and high temperature heat damage grade are obtained according to Table 1 and Table 2 respectively;

[0096] Corresponding to Tables 3 and 4, the evaluation factor (VCI, CLDAS) scores were obtained, and based on the scoring results, the rice high temperature impact index was calculated using the weighted comprehensive method;

[0097] Corresponding to Table 5, obtain the high temperature heat damage impact level in rice-growing areas.

[0098] The calculation method of rice high temperature impact index is as follows:

[0099]

[0100] Where:

[0101] I——rice high temperature impact index;

[0102] I ndvi ——VCI factor scoring results;

[0103] I cldas ——CLDAS temperature factor scoring results;

[0104] a,b——weight coefficients, taking values ​​as 0.7 and 0.3 respectively.

[0105] Table 3

[0106] grade score good 10 middle 7 Difference 3

[0107] Table 4

[0108] grade score Mild 10 Moderate 7 serious 3

[0109] The thresholds for assessing the impact of high temperature on rice are shown in Table 5. The division method is as follows:

[0110]

[0111] Where:

[0112] ——Multi-year average of the rice high temperature impact index;

[0113] f——rice high temperature impact index in the i-th year;

[0114] I stdv ——Standard deviation of rice high temperature impact index;

[0115] I error ——Standard error of rice high temperature impact index;

[0116] I max ——The upper limit of the rice high temperature impact index;

[0117] I min ——The lower limit of the rice high temperature impact index;

[0118] x – year number.

[0119] Table 5

[0120] grade Interval Mild <![CDATA[[I max ,9999]]]> Moderate <![CDATA[[I min ,I max )]]> serious <![CDATA[[-9999,I min )]]>

[0121] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for assessing the impact of high temperature heat damage to rice by coupling remote sensing and fusion real-time data, characterized in that: include: Collecting remote sensing data of rice-growing areas, and calculating remote sensing factors for assessing the impact of high temperature heat damage to rice based on the remote sensing data; Collecting fused real-time CLDAS temperature data in rice-growing areas, and calculating temperature factors for evaluating the impact of high temperature heat damage to rice based on the temperature data; The high temperature heat damage impact level of the rice growing area is calculated based on the remote sensing factor and the temperature factor, and the high temperature heat damage impact assessment of rice is completed.

2. The method for assessing the impact of high temperature heat damage to rice by coupling remote sensing and fusion real-time data according to claim 1, characterized in that: The remote sensing factors for calculating the impact assessment of high temperature heat damage to rice based on the remote sensing data include: Calculating the Normalized Difference Vegetation Index (NDVI) of the rice-growing area to be evaluated based on the remote sensing data, and constructing an NDVI data set for the high temperature heat damage process; The remote sensing factor for assessing the impact of high temperature heat damage to rice is calculated based on the NDVI dataset.

3. The method for assessing the impact of high temperature heat damage to rice by coupling remote sensing and fusion real-time data according to claim 2, characterized in that: The method for calculating the remote sensing factor for evaluating the impact of high temperature heat damage to rice based on the NDVI dataset is: Among them, VCI is the growth condition of rice; NDVI gj It is the average NDVI value of the high temperature occurrence process in a certain period of a certain year; NDVI max，j The maximum NDVI value of rice during a certain period of high temperature in a certain year; min，j It is the minimum NDVI value of rice during the high temperature occurrence process in a certain year and period.

4. The method for assessing the impact of high temperature heat damage to rice by coupling remote sensing and fusion real-time data according to claim 1, characterized in that: The temperature factors for evaluating the impact of high temperature heat damage to rice based on the temperature data include: Read temperature component data based on CLDAS; Calculate the daily average temperature based on the temperature component data; The daily average temperature is corrected using weather station data to obtain the corrected daily average temperature.

5. The method for assessing the impact of high temperature heat damage to rice by coupling remote sensing and fusion real-time data according to claim 4, characterized in that: The daily average temperature is corrected by using the weather station data, and the method for obtaining the corrected daily average temperature is: t r =mt c +n Among them, t r is the corrected daily average temperature; t c is the corrected average temperature of the previous day; m and n are the coefficients of the regression equation.

6. The method for assessing the impact of high temperature heat damage to rice by coupling remote sensing and fusion real-time data according to claim 1, characterized in that: Calculating the high temperature heat damage impact level of the rice planting area based on the remote sensing factor and the temperature factor includes: Based on the remote sensing factor and the temperature factor, respectively obtaining the rice growth grade and the high temperature heat damage grade; respectively obtaining scores of the rice growth grade and the high temperature heat damage grade; Calculating the rice high temperature heat damage impact index in the rice planting area based on the score; The high temperature heat damage impact level of the rice planting area is obtained based on the rice high temperature heat damage impact index.

7. The method for assessing the impact of high temperature heat damage to rice by coupling remote sensing and fusion real-time data according to claim 6, characterized in that: The method for calculating the rice high temperature heat damage impact index of the rice planting area based on the score is: Where I is the rice high temperature impact index; I ndvi is the NDVI factor scoring result; I cldas is the CLDAS temperature factor scoring result, and a and b are weight coefficients.

8. The method for assessing the impact of high temperature heat damage to rice by coupling remote sensing and fusion real-time data according to claim 7, characterized in that: Obtaining the high temperature heat damage impact level of the rice planting area based on the rice high temperature heat damage impact index includes: Based on the preset rice high temperature heat damage impact assessment threshold range, the range of the rice high temperature heat damage impact index is determined to obtain the high temperature heat damage impact level.

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