CN109711102A - A kind of crop casualty loss fast evaluation method - Google Patents
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Abstract
The present invention provides a kind of crop casualty loss fast evaluation methods, crop modeling and GLUE parameter estimation tool of the method based on DSSAT system, utilize the historical data of the crop by disaster area, the leaf area index and yield of crop under each scene are obtained based on crop modeling simulation, and then construct regression equation.It is then based on the satellite image of Google Earth Engine (GEE), the leaf area index of each pixel on satellite image is brought into equation of linear regression, obtains the yield of each pixel.Finally fractional yield loss will be obtained by the yield in disaster time in disaster area and the yield comparison of upper one year.Method of the invention does not need a large amount of ground observation data, only a small amount of experimental data is needed to carry out calibration calibration, improves assessment efficiency, reduce the time cost of Disaster Loss Evaluation, provide guarantee to prevent and reduce natural disasters.The method of the invention realizes the qualitative assessments of casualty loss, can damage underproduction rate to different spaces scale and reliably be estimated, improve the quality of the disaster assessment of loss.
Description
Technical Field
The invention relates to the technical field of agricultural remote sensing, in particular to a cross-scale and crop disaster loss quantitative evaluation method based on a remote sensing data processing platform Google Earth (GEE) and a crop model, and particularly relates to a crop disaster loss rapid evaluation method.
Background
Agriculture is closely related to the weather. The whole growth process of crops is in a natural environment, the yield of the crops is very easily stressed and interfered by adverse factors such as meteorological disasters, and the crop yield can be greatly reduced or even dead-grown when the disasters are serious. Currently, the assessment for agricultural meteorological disasters is mainly divided into two categories: risk assessment and impact assessment. The risk means the possibility of disaster occurrence and damage, the technical basis of the evaluation is a risk analysis technology, and the obtained risk intensity division cannot accurately quantify the disaster loss and only can roughly describe the disaster. The following three methods are mainly used for the previous impact evaluation: 1) on the basis of the annual output data of the ground station, the trend output is obtained by utilizing a linear moving average method and an orthogonal polynomial approximation method, the meteorological output is obtained by subtracting the trend output from the annual actual output, and the disaster loss is estimated by calculating the relative meteorological output. The method is based on the assumption that crop yield reduction is caused by cold damage, has poor evaluation effect on single disaster loss, and cannot carry out dynamic evaluation. 2) Based on historical observation data, a regression equation of disaster indexes and yield is constructed, and disaster loss is obtained through extrapolation, but the disaster indexes have strong regionality, are difficult to expand and apply to other regions, and cannot meet damage assessment below the county level. 3) The crop model is applied to the aspects of yield forecast, farmland management decision support, disaster index construction, loss quantification and the like by the advantages of the crop model facing to the crop growth process and strong mechanicalness, can artificially reproduce the continuous process from seeding to maturity of crops on a day-to-hour scale, and reflects the response mode of crop growth to different environments and management factors. However, most crop models are specific field tests, only single-point simulation can be carried out, and the regional scale application of the models can be realized through variety parameter regionalization and meteorological element spatial interpolation technology, but new errors are inevitably introduced. Although the regional crop model can represent the spatial difference, a large amount of driving data is required for construction and operation, and the parameters are very difficult to define due to the large spatial heterogeneity of surface parameters, varieties and management modes, and the large regional research is still not easy to realize. Furthermore, the spatial resolution of the area model depends on meteorological or soil data, making fine loss mapping difficult. Therefore, a reasonable and practical method is actively explored to perform more accurate and faster regional loss assessment, so that ideas can be provided for business operation of agricultural disaster early warning, agricultural insurance and other work.
The remote sensing technology can monitor the growth and development conditions of crops in a large-scale, dynamic and real-time manner, and is widely applied to various fields such as planting area, pest and disease damage monitoring, disaster monitoring, fine agriculture and the like. The Google Earth Engine (GEE) is a platform which is provided by Google and used for carrying out online visual calculation analysis processing on a large amount of global scale geoscience data (particularly satellite data), provides multisource and multiscale remote sensing data such as global Sentinel, MODIS, Landsat TM/OLI and the like, is a massive remote sensing data processing, archiving and analyzing platform supporting parallel cloud computing, and solves the problems that traditional remote sensing images are difficult to collect, large in storage amount, low in processing efficiency and the like. The combination of remote sensing and crop models can realize continuous yield simulation on different spatial resolutions, solve the problem of regional scale application of site crop models, and have been widely applied, but the following two key problems exist: 1) although the statistical model established by using the remote sensing index is simple and easy to use, the statistical model established based on the single-time phase vegetation index and the actually measured yield is limited to specific time, place and year. 2) Although a certain result is obtained based on crop model assimilation remote sensing data loss estimation, the model is not easy to popularize due to a large amount of input data and a complex operation process, data collection is very complicated, and operation efficiency is low.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method for rapidly evaluating the disaster damage of crops, so as to reduce or avoid the aforementioned problems.
In order to solve the technical problems, the invention provides a method for rapidly evaluating the disaster damage of crops, which comprises the following steps:
step S1: inputting soil data, meteorological data and agricultural production data of crops of a region to be evaluated into a DSSAT system, and respectively generating a soil file S, a meteorological file W and a crop file A which can be called; calling and calculating the previously generated files S, W and A through a GLUE parameter estimation tool, and calculating to obtain a calibration data file C containing calibration values of the regional crop variety parameters; calling the soil file S, the meteorological file W, the crop file A and the obtained calibration data file C through a crop model of a DSSAT system to perform simulation calculation to obtain an LAI file corresponding to the day-by-day LAI and a yield file Y corresponding to the yield;
step S2: setting a multi-year accumulated temperature range flat value of the area as a disaster index H;
step S3: selecting two time windows in each 30 days before and after the peak growth period of the crops as a center, and acquiring the LAI of each day corresponding to the two windows from an LAI file; then obtaining the Yield of each scene type as a dependent variable from a Yield file Y, and obtaining the LAI of one day in the previous windowd 1LAI of one day in the rear windowd 2And disaster indexH is an independent variable, the following linear regression equation is established, and then the obtained coefficients of the linear regression equation and the corresponding dates of the front window and the rear window LAI are stored:
Yield＝β0,d+β1,d*LAId 1+β2,d*LAId 2+β3*H
in the formula: yield under each scene category, LAId 1And LAId 2Respectively representing the LAI of the day d in the front window and the back window under each scene category, wherein H is a disaster index;
step S4: respectively extracting the maximum dynamic vegetation index WDRVI and the corresponding date of each pixel in two time windows based on the satellite image of the area acquired by the GEE platform; and converting WDRVI into LAI;
step S5: according to the dates corresponding to the LAI of the front window and the rear window, the coefficient of the linear regression equation obtained in the step S3, the output is calculated pixel by pixel based on the LAI of the two windows obtained by the GEE satellite image and the disaster index H obtained by actual meteorological data, and finally the output change relative to the previous year is calculated to obtain the relative output loss of the disaster.
Preferably, the formula for calculating the relative yield loss in step S5 is as follows:
in the formula: l isRFor relative yield loss, YlThe yield of the last year (disaster-free year), YnYield for the year in which the disaster occurred; so far, the crop disaster damage rapid assessment process is completed.
Preferably, the step S1 further includes the following steps:
setting a plurality of disaster situations and situation types of management situations, calling and calculating a meteorological file W, a crop file A, a soil file S and a calibration data file C corresponding to each situation type through a crop model in DSSAT, and obtaining a plurality of LAI files and yield files Y with the same number as the situation types.
Preferably, the specific calculation formula for converting WDRVI into LAI in step S4 is as follows:
WDRVI＝-0.681+1.437(1-e-0.351LAI)
in the formula: WDRVI is the maximum dynamic vegetation index, LAI is the leaf area index, pNIRNear infrared band reflectivity, p, measured for sensor loadREDThe reflectivity is in the red light wave band.
Preferably, the step S5 further includes the following steps: after the dates corresponding to the LAI of the front window and the rear window are obtained through the GEE satellite images, the regression coefficient corresponding to the combined date is calculated according to the dates corresponding to the LAI of the front window and the rear window, the yield is calculated pixel by pixel, and finally the yield chart of the disaster year is obtained.
Preferably, in the step S5, in the process of calculating the yield in the previous year, the cold damage scenario is removed from the simulation scenarios, and only different management scenarios are simulated to obtain the regression coefficient matrix, so as to obtain the yield map of the disaster-free year.
The crop disaster loss rapid assessment method does not need a large amount of ground observation data, and only needs a small amount of experimental data to carry out calibration. In addition, a large amount of remote sensing data are processed based on the GEE, so that the data processing cost is greatly saved, the evaluation efficiency is improved, the time cost of disaster loss evaluation is reduced, and the guarantee is provided for disaster prevention and reduction. In addition, the method realizes the quantitative evaluation of the disaster loss, can reliably evaluate the yield reduction rate of different spatial scales, improves the quality of the disaster loss evaluation, and can provide a quantitative basis for agricultural insurance.
Drawings
The drawings are only for purposes of illustrating and explaining the present invention and are not to be construed as limiting the scope of the present invention. Wherein,
FIG. 1 is a schematic flow chart of a method for rapidly evaluating damage caused by a crop disaster according to an embodiment of the present invention;
FIG. 2 is a schematic diagram showing the historical loss of cold damage at low temperature according to another embodiment of the present invention;
FIG. 3 is a schematic illustration of regional chilling loss according to yet another embodiment of the present invention.
Detailed Description
In order to more clearly understand the technical features, objects, and effects of the present invention, embodiments of the present invention will now be described with reference to the accompanying drawings. Wherein like parts are given like reference numerals.
Based on the problems of the prior art, the method for rapidly evaluating the disaster damage of the area from the point is established by combining the remote sensing observation data, the meteorological data and the crop model. The method is not limited by ground measured data, can be dynamically evaluated, is easy to operate and strong in generalization capability, can be used for large-scale research, can quantify county-level or even field loss, aims to realize fine mapping of crop yield loss, provides reference for business operation of disaster evaluation and provides guarantee for disaster prevention and reduction.
Specifically, the invention provides a method for rapidly evaluating crop disaster loss, which is based on a crop model of a DSSAT system and a GLUE parameter estimation tool, utilizes soil, weather and crop production data of a disaster area to simulate and obtain a leaf area index and yield of a crop, and then constructs a regression equation. Then, based on the satellite image of the Google Earth Engine (GEE), the leaf area index of each pixel on the satellite image is substituted into a linear regression equation to obtain the yield of each pixel. Finally, comparing the yield of the area needing to be evaluated with the yield of the last year obtains the relative yield loss.
Among them, the dssat (precision Support System for Agro technology transfer) agricultural technology transfer decision Support System is one of the most widely used model systems at present. DSSAT is a comprehensive computer system developed by the american international development agency authorized for the university of weiyi under the sponsorship and guidance of the ibsnat (international Benchmark networks for Agro technology transfer) international reference Network for agricultural technology transfer. DSSAT is not a generic model, it develops different models for different crops. DSSAT currently consists of 26 different main crop simulation models, including various crop models, such as the ceres (crop Environment recovery synthesis) series model, the CROPGRO legume crop model, the substorptopato potato model, the cropsocascava cassava model, and the OILCROP sunflower model. The GLUE parameter estimation tool belongs to a functional module of DSSAT. Google Earth (GEE) is a tool that is subordinate to Google, USA and can process satellite image data in batches, and belongs to a series of tools of Google Earth. Compared with traditional image processing tools such as ENVI and the like, the GEE can rapidly process massive images in batches, and can rapidly calculate vegetation indexes such as NDVI and the like, predict crop related yield, monitor drought growth change and the like.
The above are all the prior known technologies, which are used as the calculation tools in the development of the invention and are not the content of the claims of the invention. It should be understood by those skilled in the art that the above-mentioned prior art computer software systems may have different software versions and modifications, and based on the technical idea disclosed in the present invention, those skilled in the art can select any one of the prior art computer software systems to reproduce, as long as they conform to the idea of the method of the present invention.
The following describes the method for rapidly evaluating crop disaster damage according to the present invention in further detail by way of specific examples, as shown in fig. 1, which is a schematic flow chart of a method for rapidly evaluating crop disaster damage according to an embodiment of the present invention, and as shown in the figure, the method for rapidly evaluating crop disaster damage according to the present invention includes the following steps:
step S1: inputting soil data, meteorological data and agricultural production data of crops of a region to be evaluated into a DSSAT system, and respectively generating a soil file S, a meteorological file W and a crop file A which can be called.
For example, the maize of the autonomous city flag of encyclophana Orenchun, inner Mongolia can be used as a research object, and the data of soil, weather and agricultural production (planting date, planting density, irrigation and nitrogen application related to the maize) in the area between 2013 and 2017 can be input into the DSSAT system to respectively generate callable files S, W and A. The meteorological data, soil data and agricultural production data of the corn of the inner Mongolia Oruespring autonomous flag can be acquired from the yearbook of the local agricultural department and the observation data of the meteorological department, and the data are public data and only need to be extracted.
Then, through a GLUE parameter estimation tool, calling and calculating the generated files S, W and A, and calculating to obtain a calibration data file C containing calibration values of the regional crop variety parameters.
The GLUE parameter estimation tool is a functional module which is built in the DSSAT system, and the module can read various data files of the DSSAT system, repeatedly iterate through a trial and error method, calculate and obtain calibration values of specific characteristic parameters related to the crops, and automatically generate files containing the calibration values.
For example, also using the maize of the autonomous city flag of encyclopedia of Oruenchun, inner Mongolia as an example, files S, W and A were generated by inputting the historical data of soil, weather and agricultural production in 2013-. And then calling the files through GLUE, and generating a calibration data file C containing calibration values of a plurality of characteristic parameters after operation, wherein the calibration values in the calibration data file C are not changed for the maize of the autonomous flag of Oruenchun city, inner Mongolia, so that the calibration data file C can also be called in the subsequent steps and can also be used for other research works of the maize in the region.
For example, in one embodiment of the present invention, the following specific characteristic parameters are more relevant to the study of maize:
it should be understood that the calibration values of the characteristic parameters calculated by the GLUE, which are calculated after the iteration of the data files S, W and A generated from the historical data of the crops in the area, can be regarded as the local calibration data of the crops in the area, which are not changed for the same crops in the same area, so that the calibration values of the specific characteristic parameters can be used to simulate the LAI (leaf area index) and yield of the crops at all times in the subsequent steps of the present invention. For example, the calibration values of the six characteristic parameters in the above list represent the date of flowering, date of harvest, number of leaves, etc. of the maize from the autonomous flag of the inner Mongolia Oruenchun. The characteristic parameters of corn in different regions are different, and of course, the characteristic parameters required by different crops are different, for example, the characteristic parameters of potato and the like are not selected from G2 or G3, because the characteristic parameters are not related to potato.
In another embodiment of the present invention, for example, the history data of 2013-year old 2017 of the corn of the inner Mongolia Oruenchun autonomous flag may be split, the data of 2013-year 2015 may be used to calculate the calibrated values of the six characteristic parameters through GLUE, and the data of 2016-year 2017 may be used to calculate a standard Root Mean square error (NRMSE) to perform reliability check on the calibrated values of the six calculated characteristic parameters, so as to control the precision and accuracy. Further, for example, after obtaining the calibration values of the six specific characteristic parameters P1, P2, P5, G2, G3 and PHINT related to the foregoing list of corns through the GLUE, the GLUE will automatically record the calibration values of the six characteristic parameters in the generated calibration data file C, that is, the values of the six characteristic parameters of each piece of data in the calibration data file C are the calibration values after iterative computation, so that the obtained calibration data file C is modified to form a localized data file of the corn having an autonomous intra-mongolia-orenchun flag, and in the subsequent steps, the values of the six characteristic parameters of the corn having the autonomous intra-mongolia-orenchun flag can be determined to be equal to the calibration values after iterative computation.
And then, calling the soil file S, the meteorological file W, the crop file A and the obtained calibration data file C through a crop model of the DSSAT system to perform simulation calculation, so as to obtain an LAI (Leaf area index) file corresponding to the day-by-day LAI and a yield file Y corresponding to the yield.
The LAI file records the daily LAI of the crop in one year, that is, the LAI file records data of the LAI of the crop not exceeding 365 days and the corresponding date, and of course, since the growth date of the crop does not exceed one year, generally, less than 365 pieces of valid LAI data are available. In addition, the yield of the crops corresponding to the date is recorded in the yield file Y corresponding to the yield, only because the growth period of the crops is long, the yield is 0 in many times, the yields on different dates are different, for the crops, the yield value on the final harvest date is the maximum, and the maximum yield value is the yield value which is actually needed in the yield file Y. That is, after the crop model of the DSSAT system is called and calculated, two files are automatically generated, one is an LAI file and the other is a Yield file Y, wherein the LAI file records the value of the LAI of each day corresponding to the date, the Yield file Y records a maximum Yield value, and the maximum Yield value is the Yield needed in the future of the invention.
For example, a LAI file and a yield file Y can be generated after the files S, W, and a of the Maize in the area and the calibration data file C are called by the cores-Maize model related to the Maize in the DSSAT system and the cores-Maize model is run, also using the Maize of the inner mongolia-orchards as a research object.
Among these, the CERES-Maize model is one of the models associated with corn plants in the DSSAT system. Of course, if the study is related to legume crops, the DSSAT-CROPGRO model may be used, and so on, and those skilled in the art may extend to other existing DSSAT system crop models. It should be noted that the CERES-Maize model is a relatively comprehensive model system related to Maize, which requires the input of three types of data items, including meteorological data, soil data, and agricultural production data, each data item including a plurality of small items, such as meteorological data including: day by day maximum temperature, minimum temperature, rainfall and radiation; the soil data includes: soil type, profile characteristics, soil physicochemical properties, namely soil name, soil color, soil water retention performance, soil texture of each layer, organic carbon, total nitrogen, PH and cation exchange capacity; agricultural production data include: management data such as sowing date, flowering date, maturity date, single yield, irrigation and fertilization.
Of course, there is only one yield data per year due to historical data in regional yearbooks. For example, for the inner Mongolia Oruenchun autonomous flag, it may only record the annual total yield of corn over the county area, and not refine to every village. Therefore, in the Y file obtained by the crop model calculation, only one available Yield is obtained, and only one LAI file is obtained corresponding to the Yield, wherein the LAIs of not more than 365 crops are recorded (the amount of data available in the LAI file is less in consideration of the growth cycle of the crops), the number of samples is small, which is not beneficial to the subsequent component regression equation, and therefore, in order to improve the calculation accuracy, the number of samples needs to be increased.
Thus, in yet another embodiment of the present invention, a solution for increasing the number of samples is further provided. That is, a plurality of disaster situations and situation types of management situations can be set according to the actual cultivation and management situation of crops in the local area. In one particular embodiment, for example, seven disaster scenario categories and sixty-four management scenario categories may be set for corn. For example, seven disaster scenario categories may be: the temperature of the corn in the growth period is respectively reduced by 1, 2 and 3 ℃, and four corn barrier cold hazards are set by striking which is lower than the lower limit of the growth temperature of the seeding stage, the jointing stage, the spinning stage, the grouting stage and the maturing stage at random. Sixty-four management scenario categories may be, eight planting dates and eight planting densities, which results in 8 by 8 — 64 management scenario categories. Seven disaster scenario categories and sixty-four management scenarios are combined, and a total of 7 × 8 — 448 scenario categories can be obtained. Of course, more scene categories may be set according to the actual cultivation management situation at the site, and for example, different categories may be set according to the variety, irrigation, and fertilization, respectively.
In another embodiment, the 448 scene categories are obtained by dividing, each scene category corresponds to a different weather file W and crop file a corresponding to the scene category, so that 448 weather files W, crop files a, soil files S (same as original without change) and calibration data files C (common without change) can be obtained, after the 448 combined files are calculated by CERES-Maize calling, one yield file Y related to yield and a LAI file recording day-by-day LAI of the corn growth period under the scene can be simulated for each scene, and finally 448 LAI files and corresponding 448 yield files Y can be obtained by dividing the 448 scene categories, compared with only one yield per year (1 LAI file and 1 yield file Y), this clearly greatly expands the sample size.
Step S2: and setting the average value of the local multi-year accumulated temperature as a disaster index H for depicting the influence of the disaster on the crops.
For example: for low-temperature cold damage, the average value of the accumulated temperature of each simulation site for 5-9 months can be used as a disaster index H, and the calculation formula is as follows:
wherein H is the mean daily temperature not less than 10 ℃ and the accumulated temperature distance is flat (DEG C ● d) within the study period, TiThe day average temperature (DEG C) of more than or equal to 10 ℃ on the ith day, n is the number of days in the calculation period,the daily average temperature is more than or equal to 10 ℃ and the activity accumulated temperature is the perennial average.
Alternatively, in another embodiment, for example, for corn, a 30-year average value for 5-9 months of accumulated temperature for corn may be used as the disaster indicator H to characterize the effect of cold damage on corn growth.
Step S3: selecting two time windows in each 30 days before and after the peak growth period of the crop as a center, and extracting and obtaining the LAI of each day corresponding to the two windows from the LAI file; then, the Yield of each scene type in the Yield file Y is taken as a dependent variable, and the LAI (LAI) of one day in the previous windowd 1) LAI of one day in the back window (LAI)d 2) And establishing the following linear regression equation by taking the disaster index H as an independent variable, and then saving the obtained coefficient of the linear regression equation and the corresponding dates of the front window LAI and the rear window LAI:
Yield＝β0,d+β1,d*LAId 1+β2,d*LAId 2+β3*H
in the formula: yield under each scene category, LAId 1And LAId 2And LAI of the day d in the front window and the rear window under each scene category respectively, wherein H is a disaster index.
By combining the LAIs of the two windows for each LAI file and production file Y, a total of 30 × 30 to 900 regression relationships can be obtained for the first 30 days and the last 30 days, and a total of 448 sets of the same number of regression relationships can be obtained for the 448 LAI files and production file Y. Then, the calculation can be performed by an algorithm such as a least square method, and the obtained coefficients of the regression equation and the corresponding dates of the front and rear windows LAI are stored.
For example, in one embodiment, two time windows may be selected 30 days before and after the laying date of corn, and the LAI of each day of the two windows may be obtained; then the yield of 448 scenario categories output by the crop model is used as a dependent variable. Specifically, for each LAI file and production file Y, the front window is set to 6-7-17 months per year, and the rear window is set to 7-8-17 months per year. And selecting one day of the front window, wherein 30 days of the rear window can be selected arbitrarily, and the like forms 30-900 choices. Since 448 scene categories are set, for 448 LAI files and yield files Y, each LAI file and yield file Y can have 900 choices, and the combination of these two files forms a very large sample for regression calculation, and the coefficients of the equation obtained by least squares regression are more accurate.
Step S4: respectively acquiring the maximum dynamic vegetation index WDRVI Of each pixel element and the image Of the corresponding date DOY (Day Of the Year) in two time windows based on the satellite image Of the area acquired by GEE; the maximum dynamic vegetation index (WDRVI) is then converted to LAI.
For example, in one embodiment, all Landsat-8 and Sentinel-2 images from the GEE platform for 6 months 18 to 7 months 17 (front window) and 7 months 18 to 8 months 17 (back window) of each year may be acquired, processed for cloud removal, then the maximum dynamic vegetation index (WDRVI) and its date are extracted two time windows before and after each pel of each year and converted to LAI, and the specific calculation formula for converting WDRVI to LAI is:
WDRVI＝-0.681+1.437(1-e-0.351LAI)
in the formula: WDRVI is the maximum dynamic vegetation index, LAI is the leaf area index, pNIRNear infrared band reflectivity, p, measured for sensor loadREDThe reflectivity is in the red light wave band.
The Landsat-8 is a remote sensing image obtained by an 8 th satellite of a series of earth observation for detecting earth resources and environment in the United states, the spatial resolution of wave bands 1-7 and 9-11 is 30m, the wave band 8 is a panchromatic wave band with the resolution of 15m, the revisiting period is 16 days, and the spatial resolution is 30m and the time resolution is 30 m. Sentinel-2 is the second satellite for the "global environment and safety monitoring" program, and transmits on 23 days 6 months 2015 with a spatial resolution of 10m and a revisit cycle of 10 days. In addition, it is not complicated to extract the maximum dynamic vegetation index WDRVI and its date through the satellite image of the GEE, and the GEE platform provides a programming interface and can be directly obtained from the GEE platform through input codes.
The following is example code for extracting WDRVI by the GEE platform:
step S5: according to the dates corresponding to the LAIs of the front and rear windows, the coefficients of the linear regression equation obtained in the step S3, the yield is calculated pixel by pixel based on the LAIs of the two windows obtained from the GEE satellite images and the disaster index H obtained from the actual meteorological data, and finally the relative yield loss of the disaster is obtained by calculating the yield change relative to the previous year:
in the formula: l isRFor relative yield loss, YlThe yield of the last year (disaster-free year), YnYield for the year in which the disaster occurred; so far, the crop disaster damage rapid assessment process is completed.
In particular, the coefficients of the linear equation obtained by regression may vary slightly due to the difference in the historical data at different times. Therefore, in a specific embodiment, after the dates corresponding to the previous and next windows LAI are acquired through the GEE satellite images, the regression coefficients corresponding to the combined dates can be calculated according to the dates corresponding to the previous and next windows LAI, the yield is calculated pixel by pixel, and finally the yield chart of the disaster year is obtained. Similarly, in the process of calculating the yield in the previous year (disaster-free year), the cold damage scenes can be removed from the simulation scenes, and only different management scenes are simulated to obtain the regression coefficient matrix, so that the yield map of the disaster-free year is obtained, and further the relative yield loss calculation of the low-temperature cold damage can be quantified.
In another embodiment of the present invention, the yield loss of the year of severe low temperature chilling injury of the autonomous flag of orchids, monster of inner mongolia, since 1980, was evaluated and integrated to a county scale and compared with statistical data, as shown in fig. 2, which shows a schematic diagram of a historical loss graph of low temperature chilling injury according to another embodiment of the present invention, it can be seen that the measured losses are all within one-time variance of the estimated losses, indicating that the method can reliably quantify the yield reduction caused by low temperature chilling injury.
In addition, in another embodiment of the present invention, as shown in the regional cold damage schematic diagram of fig. 3, the present invention, based on the Sentinel-2 image, evaluates the obstacle type low temperature cold damage occurring in 2018, 9, 8 and obtains the yield loss of 43 villages in orenchun, the result of which is very close to the actual statistical result, and can be refined to a smaller regional level.
In conclusion, the method for rapidly evaluating the crop disaster damage can carry out cross-scale crop disaster damage evaluation based on the Google Earth Engine (GEE) and the crop model, and can improve the accuracy and timeliness of regional crop damage evaluation.
Compared with the traditional assessment method, the rapid assessment method does not need a large amount of ground observation data, and only needs a small amount of experimental data to carry out calibration and calibration; moreover, the method can remove the influence of single disaster species, and can reliably and quantitatively estimate the loss of large scale or county or even field; the method has the advantages of less limitation, strong space generalization capability and high universality, and can be transplanted to loss evaluation of other areas, crops and disaster species; according to the invention, a large amount of remote sensing data is processed based on the GEE platform, so that the operation cost is reduced, and the loss evaluation efficiency is greatly improved; in addition, the method of the invention can dynamically evaluate, has strong timeliness and easy operation, is not limited by ground measured data, and is beneficial to realizing business popularization and application.
It should be appreciated by those of skill in the art that while the present invention has been described in terms of several embodiments, not every embodiment includes only a single embodiment. The description is given for clearness of understanding only, and it is to be understood that all matters in the embodiments are to be interpreted as including technical equivalents which are related to the embodiments and which are combined with each other to illustrate the scope of the present invention.
The above description is only an exemplary embodiment of the present invention, and is not intended to limit the scope of the present invention. Any equivalent alterations, modifications and combinations can be made by those skilled in the art without departing from the spirit and principles of the invention.
Claims (6)
1. A method for rapidly evaluating crop disaster damage comprises the following steps:
step S1: inputting soil data, meteorological data and agricultural production data of crops of a region to be evaluated into a DSSAT system, and respectively generating a soil file S, a meteorological file W and a crop file A which can be called; calling and calculating the previously generated files S, W and A through a GLUE parameter estimation tool, and calculating to obtain a calibration data file C containing calibration values of the regional crop variety parameters; calling the soil file S, the meteorological file W, the crop file A and the obtained calibration data file C through a crop model of a DSSAT system to perform simulation calculation to obtain an LAI file corresponding to the day-by-day LAI and a yield file Y corresponding to the yield;
step S2: setting a multi-year accumulated temperature range flat value of the area as a disaster index H;
step S3: selecting two time windows in each 3 days before and after the peak growth period of the crop as a center, and acquiring daily LAI corresponding to the two windows from an LAI file; then obtaining the Yield of each scene type from the Yield file Y as a dependent variable, and obtaining the LAI of one day in the previous windowd 1LAI of one day in the rear windowd 2And establishing the following linear regression equation by taking the disaster index H as an independent variable, and then saving the obtained coefficient of the linear regression equation and the corresponding dates of the front window LAI and the rear window LAI:
Yield＝β0,d+β1,d*LAId 1+β2,d*LAId 2+β3*H
in the formula: yield under each scene category, LAId 1And LAId 2Respectively representing the LAI of the day d in the front window and the back window under each scene category, wherein H is a disaster index;
step S4: respectively acquiring the maximum dynamic vegetation index WDRVI of each pixel in two time windows and an image of a corresponding date based on the satellite image of the region acquired by the GEE; and converting WDRVI into LAI;
step S5: according to the dates corresponding to the LAI of the front window and the rear window, the coefficient of the linear regression equation obtained in the step S3, the output is calculated pixel by pixel based on the LAI of the two windows obtained by the GEE satellite image and the disaster index H obtained by actual meteorological data, and finally the output change relative to the previous year is calculated to obtain the relative output loss of the disaster.
2. The method of claim 1, wherein the formula for calculating the relative yield loss in step S5 is:
in the formula: l isRFor relative yield loss, YlThe yield of the last year (disaster-free year), YnYield for the year in which the disaster occurred; so far, the crop disaster damage rapid assessment process is completed.
3. The method according to claim 1, wherein the step S1 further comprises the steps of:
setting a plurality of disaster situations and situation types of management situations, calling and calculating a meteorological file W, a crop file A, a soil file S and a calibration data file C corresponding to each situation type through a crop model in DSSAT, and obtaining a plurality of LAI files and yield files Y with the same number as the situation types.
4. The method of claim 1, wherein the specific calculation formula for converting WDRVI into LAI in step S4 is:
WDRVI＝-0.681+1.437(1-e-0.351LAI)
in the formula: WDRVI is the maximum dynamic vegetation index, LAI is the leaf area index, pNIRNear infrared band reflectivity, p, measured for sensor loadREDThe reflectivity is in the red light wave band.
5. The method according to claim 1, wherein the step S5 further comprises the steps of: after the dates corresponding to the LAI of the front window and the rear window are obtained through the GEE satellite images, the regression coefficient corresponding to the combined date is calculated according to the dates corresponding to the LAI of the front window and the rear window, the yield is calculated pixel by pixel, and finally the yield chart of the disaster year is obtained.
6. The method as claimed in claim 5, wherein in step S5, in the production calculation process of the previous year, the cold damage scenario is eliminated from the simulation scenarios, and only different management scenarios are simulated to obtain the regression coefficient matrix, so as to obtain the production chart of the disaster-free year.
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