A rice maturity suitability partitioning method based on time-series meteorological data analysis

Abstract

The present application belongs to the technical field of meteorological data processing and remote sensing mapping, and particularly relates to a rice maturity suitability zoning method based on time series meteorological data analysis. The present application obtains an annual representative meteorological mode through annual inter-period normalization and time series reconstruction, obtains annual precipitation accumulation and temperature supply through precipitation and temperature index calculation, and combines rice growth mechanism and rice maturity law to perform threshold segmentation on large-area precipitation and temperature indexes, so as to realize rice maturity suitability zoning. The method provided by the present application effectively reconstructs the annual meteorological mode under the background of global climate change and regional climate anomaly, analyzes and extracts meteorological indexes most significantly affecting rice maturity suitability, performs threshold segmentation on key meteorological indexes in combination with rice growth mechanism and rice maturity law, can provide clear and accurate guidance and suggestions for rice maturity planning and agricultural policy making, and solves the problem of blindness in current rice maturity planning.

Description

Technical Field

[0001] This invention belongs to the field of meteorological data processing and remote sensing mapping technology, specifically relating to a method for zoning rice ripening suitability based on time-series meteorological data analysis. Background Technology

[0002] Global climate change and regional climate anomalies are having an increasingly significant impact on rice cultivation, while population growth and accelerated urbanization are placing higher demands on increasing rice yields. Rice cropping system refers to the number of rice planting cycles repeated in a year in a large field (fields used for crop cultivation). One rice planting cycle is also called a single-season rice crop. Considering that rice takes 120 days from transplanting to maturity, the minimum rice cropping system in paddy fields is 1 (single-season rice) and the maximum is 3 (triple-season rice). Under suitable natural and social conditions, increasing the rice cropping system is the most direct way to increase rice yield.

[0003] Southern China has a warm and humid climate, and its low-latitude areas are suitable for double-cropping and even triple-cropping rice. If the actual rice cropping system adopted in a certain area is lower than the suitable system for that region, it will lead to insufficient utilization of arable land resources. Conversely, if the actual rice cropping system adopted is higher than the suitable system, it will result in one or more rice cropping cycles being under unfavorable growing conditions, leading to reduced rice yields or even crop failure. Therefore, there is an urgent need for an efficient and timely method for assessing and geographically zoning rice cropping suitability for large areas. However, currently, agricultural management departments and farmers in various regions rely mainly on long-standing farming habits and lunar calendar experience for rice cropping system decisions and management. They lack analysis of the correlation between rice growth mechanisms and dominant climatic factors, and do not consider the impact of interannual climate change and annual climate anomalies on rice cropping suitability assessments. Therefore, they cannot provide scientific and timely advice and guidance for rice cropping system planning in the context of continuously changing regional climate patterns. Summary of the Invention

[0004] To address the aforementioned problems and shortcomings, and to resolve the current reliance on agricultural experience in assessing rice maturity suitability, which lacks analysis of the correlation between rice growth mechanisms and dominant climate factors, and fails to consider the impact of interannual climate change and annual climate anomalies, this invention provides a rice maturity suitability zoning method based on time-series meteorological data analysis. This method obtains annually representative meteorological models through interannual regularization and time-series reconstruction. It quantifies annual precipitation accumulation and temperature supply under different meteorological models by calculating precipitation and temperature indicators. Combining rice growth mechanisms and rice maturity patterns, it performs threshold segmentation of precipitation and temperature indicators over large areas to achieve rice maturity suitability zoning.

[0005] A method for zoning rice ripening suitability based on time-series meteorological data analysis, the specific steps of which are as follows:

[0006] Step 1. Scale transformation and resampling;

[0007] We acquired time-series meteorological data for the five years preceding the study year, extracting monthly average precipitation and temperature data. We subtracted 273.15 from the monthly average temperature data to convert it from Kelvin to Celsius scale. We then spatially resampled both the monthly average precipitation and temperature data to ensure a spatial resolution of at least 10 meters for each.

[0008] Step 2. Interannual regularization and temporal reconstruction;

[0009] For the target area, the harmonic decomposition algorithm was used to perform time-series fitting on the monthly average precipitation data and monthly average temperature data, respectively.

[0010] set up The monthly average precipitation data is used as the fitting formula for the precipitation time series:

[0011]

[0012] t represents normalized cumulative days, a represents the annual average precipitation, b represents the interannual trend of precipitation, and A represents the magnitude of the cosine term of precipitation. Represents the phase of the cosine term of precipitation, where a, b, A, The coefficients are undetermined and obtained through least squares fitting. To achieve interannual normalization of multi-year precipitation time series data, a, A, and Substituting the least squares fitted value into the precipitation time series fitting formula and removing the bt term, we obtain the following formula:

[0013]

[0014] This represents the reconstructed precipitation time series data after removing the interannual trend.

[0015] Similarly, let The temperature time series fitting formula is as follows, based on the monthly average temperature data:

[0016]

[0017] t represents the normalized cumulative day of the year, g represents the annual average temperature, h represents the interannual trend of temperature, and B represents the amplitude of the temperature cosine term. Represents the phase of the temperature cosine term, where g, h, B, The coefficients are undetermined and obtained through least squares fitting. To achieve interannual normalization of multi-year temperature time series data, g, B, and Substituting the least squares fitted value into the temperature time series fitting formula, and removing the ht term from the formula, we obtain the following formula:

[0018]

[0019] This represents the reconstructed temperature time series data after removing interannual trends.

[0020] Step 3. Calculation of precipitation and temperature indices;

[0021] The formula for calculating the annual cumulative precipitation Acc is as follows:

[0022] dt

[0023] dt represents t as an integral quantity, and the formula for calculating the temperature T of the target region corresponding to the time t1 and t2 is as follows:

[0024]

[0025]

[0026] Substitute the value 10 for T in the above formula, calculate the values ​​of t1 and t2, and obtain the first day of the year with a temperature above 10°C, B. 10 and the last day of the year E 10 Substitute the value 18 for T in the above formula, calculate the values ​​of t1 and t2, and obtain the first day of the year with a temperature higher than 18°C, B. 18 and the last day of the year E 18 .

[0027] Calculate the duration of the warm period D W Duration of the hot season D H D W and D H The unit is days, and the formula is as follows:

[0028] D W = E 10 -B 10

[0029] D H = E 18 -B 18

[0030] Step 4. Zoning based on the suitability of rice ripening;

[0031] For the target area, based on the following rules, the suitability of rice maturity for each region is determined pixel-by-pixel, thus achieving regional mapping of rice maturity suitability. The specific rules are as follows:

[0032] Unsuitable for rice cultivation: Acc < 800, or D W <150, or D H <60;

[0033] Suitable for single-season rice: Acc ≥ 800, 150 ≤ D W <270, 60≤DH <180;

[0034] Suitable for double-cropping rice: Acc ≥ 800, 270 ≤ D W <365, 180≤D H <300;

[0035] Suitable for three-season rice: Acc ≥ 800, D W ≥ 365, D H ≥ 300.

[0036] Furthermore, in step 1, a bilinear interpolation algorithm is used for spatial resampling.

[0037] The principles involved in steps 1 to 4:

[0038] Scale transformation and resampling principles: Meteorological data products use Kelvin (K) for temperature measurement, while agricultural research more commonly uses Celsius (°C). Kelvin is numerically 273.15 degrees larger than Celsius. Large-area meteorological data products typically have a coarser spatial resolution (≥250×250m), while paddy field sizes are usually between 10×10m and 100×100m. To obtain meteorological analysis results at the field scale, the resolution of the meteorological data products needs to be resampled to the smallest paddy field size, i.e., at least 10m.

[0039] The principle of interannual normalization and temporal reconstruction: Temperature and precipitation are key meteorological factors determining the suitability of rice ripening. Their annual fluctuations conform to a first-order cosine function, peaking in the warm, humid month (July) and troughing in the dry, cold month (January). Formulating rice ripening plans requires knowledge of annual meteorological models for each region. Due to increasingly pronounced global climate change and regional climate anomalies, interannual differences in meteorological models are becoming more significant. Therefore, it is necessary to integrate meteorological data from the five years preceding the rice planting year for harmonic analysis and interannual normalization to suppress temporal random noise, remove interannual trends (slope term), and retain common annual characteristics (constant term and cosine term), thereby obtaining a representative regional annual meteorological model and reconstructing temporal meteorological data.

[0040] Calculation principles for precipitation and temperature indices: Rice is a crop that thrives in warm and humid conditions, requiring ample water and high temperatures during its growth period. The water supply in the field comes from the accumulated rainfall throughout the year, therefore, it is necessary to calculate the total annual precipitation. Rice requires a sufficiently long period of field temperature supply to complete one or more planting cycles. The two most important temperature points for rice growth are 10°C and 18°C; therefore, it is necessary to calculate the duration of temperatures above 10°C (warm period) and above 18°C ​​(hot period) in each region.

[0041] The principle of rice ripening suitability zoning: To ensure sufficient water supply during the rice growing season, the annual cumulative rainfall should not be less than 800 mm, which is the traditional threshold for annual rainfall required for rice cultivation. A rice planting cycle can be divided into seedling stage, transplanting stage, vegetative growth stage, reproductive growth stage, and harvesting stage, each lasting approximately 30 days. The minimum tolerable temperature for rice during the vegetative and reproductive growth stages is 18°C, and the minimum tolerable temperature during the remaining stages is 10°C. Therefore, areas suitable for single-season rice cultivation should have at least 60 days of hot weather and 150 days of warm weather. For multi-season rice, the harvesting period of the previous season overlaps with the seedling stage of the subsequent season, and from the first season to the last season, the temperature shows a trend of first rising and then falling. Therefore, regions suitable for double-cropping rice should have at least 180 days of hot weather to ensure a temperature of 18°C ​​from the first vegetative growth stage to the second reproductive growth stage, and at least 270 days of warm weather to ensure a temperature of 10°C from the first seedling stage to the second harvest stage. Regions suitable for triple-cropping rice should have at least 300 days of hot weather to ensure a temperature of 18°C ​​from the first vegetative growth stage to the third reproductive growth stage, and should also have a warm period throughout the year to ensure a temperature of 10°C from the first seedling stage to the third harvest stage.

[0042] In summary, this invention obtains annually representative meteorological models through interannual regularization and temporal reconstruction. By calculating annual cumulative precipitation and temperature supply under different meteorological models using precipitation and temperature indicators, and combining rice growth mechanisms and rice ripening patterns, threshold segmentation of precipitation and temperature indicators is performed over a large area to achieve rice ripening suitability zoning. This can provide scientific and efficient guidance for regional and national-level rice ripening planning and agricultural policy formulation, solving the problem of blindness in current rice ripening planning. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the process of the present invention;

[0044] Figure 2 The meteorological data time series reconstruction diagram is shown in the example.

[0045] Figure 3 The following is a graph showing precipitation and temperature indices for an example. Detailed Implementation

[0046] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0047] A method for zoning rice ripening suitability based on time-series meteorological data analysis (e.g.) Figure 1 As shown in the figure, the development environment in this embodiment is GEE (Google Earth Engine), and the programming language is JavaScript.

[0048] Step 1: Divide China into North and South using the Qinling-Huaihe line as the boundary. Retrieve ERA5-Land monthly average precipitation and monthly average temperature data for Southern China from 2018 to 2022. Use the image.subtract function to scale the monthly average temperature data. Use the image.resample('bilinear') function to resample the monthly average precipitation and monthly average temperature data to 10m resolution.

[0049] Step 2: Use the imageCollection function to construct time-series precipitation and time-series temperature respectively, and use the image.linearRegression function to implement harmonic fitting of time-series precipitation and time-series temperature respectively, obtain undetermined coefficients, and reconstruct time-series precipitation and time-series temperature.

[0050] Step 3: Use the mathematical operators add, subtract, multiply, divide, and acos to calculate the annual cumulative precipitation Acc and the start date of the warm period (B). 10 and E 10 ) and duration D W The start date of the hot season (B 18 and E 18 ) and duration D H Perform the calculation.

[0051] Step 4: Use the logical operators and, or, not, gt, and lt to apply the parameters Acc and D. W and D H Data is segmented and assigned values, and areas unsuitable for rice cultivation and areas suitable for single, double, and triple-cropping rice cultivation are identified pixel by pixel.

[0052] This embodiment processes multi-year time-series temperature and precipitation data from ERA5-Land. Figure 2 This is a time-series reconstruction diagram of meteorological data for an example. Figure 3 The following is a graph showing precipitation and temperature indices for an example.

[0053] As can be seen from the above embodiments, the present invention has achieved the zoning of rice ripening suitability in southern China in 2023. The method provided by the present invention effectively reconstructs the annual meteorological model against the background of global climate change and regional climate anomalies, analyzes and extracts the meteorological indicators that have the most significant impact on rice ripening suitability, and performs threshold segmentation of key meteorological indicators in combination with rice growth mechanism and rice ripening law, so as to scientifically and efficiently evaluate the rice ripening suitability of different regions, and can provide clear and accurate guidance for rice ripening planning and agricultural policy formulation.

Claims

1. A method for zoning rice ripening suitability based on time-series meteorological data analysis, characterized in that, The specific steps are as follows: Step 1. Scale transformation and resampling; Acquire time-series meteorological data for the five years preceding the study year, and extract monthly average precipitation and monthly average temperature data; subtract 273.15 from the monthly average temperature data to achieve conversion from Kelvin to Celsius scale; spatially resample the monthly average precipitation and monthly average temperature data respectively to ensure that the spatial resolution of both is at least 10m. Step 2. Interannual regularization and temporal reconstruction; For the target area, the harmonic decomposition algorithm was used to perform time-series fitting on the monthly average precipitation data and monthly average temperature data, respectively. Let U[t] be the monthly average precipitation data. The precipitation time series fitting formula is as follows: t represents normalized cumulative days, a represents the annual average precipitation, b represents the interannual trend of precipitation, and A represents the magnitude of the cosine term of precipitation. Represents the phase of the cosine term of precipitation, where a, b, A, The coefficients are undetermined and obtained through least squares fitting; to achieve interannual normalization of multi-year precipitation time series data, a, A, Substituting the least squares fitted value into the precipitation time series fitting formula and removing the bt term, we obtain the following formula: Y[t] represents the reconstructed precipitation time series data after removing the interannual trend; Let V[t] be the monthly average temperature data. The temperature time series fitting formula is as follows: V[t]=g+ht+B cos[2πt-γ] t represents the normalized cumulative day of the year, g represents the annual mean temperature, h represents the interannual temperature trend, B represents the amplitude of the temperature cosine term, and γ represents the phase of the temperature cosine term. In the formula, g, h, B, and γ are undetermined coefficients obtained through least squares fitting. To achieve interannual normalization of multi-year temperature time series data, the least squares fitted values ​​of g, B, and γ are substituted into the temperature time series fitting formula, and the ht term is removed, resulting in the following formula: Z[t]=g+B cos[2πt-γ] Z[t] represents the reconstructed temperature time series data after removing the interannual trend; Step 3. Calculation of precipitation and temperature indices; The formula for calculating the annual cumulative precipitation Acc is as follows: dt represents t as an integral quantity, and the formula for calculating the temperature T of the target region corresponding to the times t1 and t2 is as follows: Substituting the value 10 for T in the above formula, calculate the values ​​of t1 and t2 to obtain the first day of the year with a temperature above 10℃, B. 10 and the last day of the year E 10 Substitute the value 18 for T in the above formula, calculate the values ​​of t1 and t2, and obtain the first day of the year with a temperature higher than 18℃, B. 18 and the last day of the year E 18 ; Calculate the duration of the warm period D W Duration of the hot season D H D W and D H The unit is days, and the formula is as follows: D W =E 10 -B 10 D H =E 18 -B 18 Step 4. Zoning based on the suitability of rice ripening; For the target area, based on the following rules, the suitability of rice maturity for each region is determined pixel by pixel, thereby realizing the regional mapping of rice maturity suitability. The rules are as follows: Unsuitable for rice cultivation: Acc < 800, or D W <150, or D H <60; Suitable for single-season rice: Acc ≥ 800, 150 ≤ D W <270 and 60≤D H <180; Suitable for double-cropping rice: Acc ≥ 800, 270 ≤ D W <365 and 180≤D H <300; Suitable for three-season rice: Acc ≥ 800, D W ≥365 and D H ≥300.

2. The rice ripening suitability zoning method based on time-series meteorological data analysis as described in claim 1, characterized in that: In step 1, a bilinear interpolation algorithm is used for spatial resampling.

3. The rice ripening suitability zoning method based on time-series meteorological data analysis as described in claim 1, characterized in that: In step 2, the imageCollection function is used to construct time-series precipitation and time-series temperature respectively, and the image.linearRegression function is used to implement harmonic fitting of time-series precipitation and time-series temperature respectively.

Images