CN108416154B - Regional crop water consumption and yield fine simulation method based on remote sensing information - Google Patents
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Abstract
The invention discloses a regional crop water consumption and yield fine simulation method based on remote sensing information, which is characterized in that the remote sensing is used for carrying out model crop parameter CCx *And Brel *And performing spatial inversion, performing spatial superposition on natural factors, management factors and crop parameters influencing the operation of the model by using a spatial analysis function of ArcGIS software, dividing a research area into simulation units with spatial discreteness, realizing refined division of the parameters of the simulation units, calling the parameters of the simulation units by using the AquaCrop-GIS respectively, realizing extension of the spatial scale of the AquaCrop model in a mechanism, and completing construction of the AquaCrop-RS model based on the parameter spatial heterogeneity. The invention has the advantages that: not only realizes the promotion of the model scale from the mechanism, but also improves the simulation precision. The yield, evapotranspiration amount and water production efficiency of the corn seeds produced in the irrigation area are simulated by utilizing the AquaCrop-RS model, and optimal coupling of water and fertilizer is realized by reasonably adjusting the fertilizing amount, the irrigation amount and the irrigation date, so that the method is an important way for improving the production benefit of crops.
Description
Technical Field
The invention relates to the technical field of accurate water consumption, yield and water use efficiency of crops, in particular to a regional crop water consumption and yield fine simulation method based on remote sensing information.
Background
The distributed crop model can carry out fine simulation on the water consumption of crops and is an effective tool for reasonably allocating water resources. However, how to popularize the crop model to the regional scale is a key problem for model agricultural application, because the crop parameters of the model often have spatial heterogeneity according to the feedback of crops to the environment, the spatial variability of the model parameters is often ignored in the traditional crop model regional simulation, and an immeasurable simulation error is caused. Therefore, it is very important to find and obtain the spatial distribution of crop parameters for the purpose of realizing the fine simulation of the region model.
The technical terms used in the present invention are:
AquaCrop model: the crop model is developed based on the relation between yield and moisture, is simple and stable, has a good user interface, and can be applied to a wide range of objects and fields. The AquaCrop model uses canopy coverage (percentage of canopy shadow area floor area in direct sunlight) instead of leaf area index to express the growth process of crop canopy. The crop canopy development process directly affects the evapotranspiration ability of crops and, together with soil fertility, affects the accumulation of crop dry matter mass. The feedback mechanism of the crop on the soil fertility in the model is through the crop parameter CCx *And Brel *The reaction is carried out. Wherein CCx *Is the maximum value that the canopy can reach under the condition of certain density and in the presence of fertilizer stress, Brel *Refers to the relative dry mass that can be achieved during the flowering phase in the presence of fertilizer stress.
The input data required by the model operation comprise meteorological data (comprising air temperature, rainfall, radiation, humidity and the like), crop parameters (comprising constant parameters which do not need to be calibrated, variety related parameters which need to be calibrated, environment related parameters and the like), soil parameters, irrigation quantity, management parameters (comprising soil fertility, surface coverage and the like), initial soil water contentAnd the like. Wherein, when the field scale is applied, the model parameters can be obtained by field test, and when the field scale is applied, the meteorological data and crop parameters (the invention is mainly CC) are applied in the regionx *And Brel *) The soil parameters, the initial soil moisture content and the management parameters (mainly soil fertility parameter stress) have spatial heterogeneity and need to be obtained by means of spatial inversion of remote sensing data. In the past, the AquaCrop model directly ignores the crop parameter CC during regional applicationx *、Brel *And managing the space change of the stress, and directly taking the numerical value rated by the field point. Analysis of crop parameters CCx *、Brel *Spatial variability and the impact on the model for regional simulation.
Maximum canopy coverage CCx: the maximum value of the canopy can be reached when the crop growth environment condition is optimal under a certain density condition. The index directly influences the development stage and the aging stage of the crop canopy, and further directly influences the formation of crop evapotranspiration and yield. In the optimum condition, CCxIs determined by the crop variety and planting density. CC in the Presence of Fertilizer stressx *Will ratio CCxAnd is reduced.
Relative dry matter amount Brel *: the ratio of dry matter mass to potential dry matter mass in the flowering period of a crop under fertilizer stress is shown in the following formula:
in the formula: b is the accumulated dry matter mass in the flowering phase under the condition of soil fertilizer stress, kg/ha; b is0Kg/ha for potential accumulation of dry matter during flowering.
AquaCrop-GIS: the AquaCrop-GIS software is a software platform developed by the world Food and Agriculture Organization (FAO) in order to promote the application and popularization of an AquaCrop model to a region, and the platform can realize the simultaneous operation of a large number of AquaCrop simulations.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a regional crop water consumption and yield fine simulation method based on remote sensing information, which can effectively solve the problems in the prior art.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a regional crop water consumption and yield fine simulation method based on remote sensing information comprises the following steps:
step 1, obtaining crop parameter-maximum canopy coverage CC by remote sensing methodx *And relative dry matter Brel *Spatial distribution;
step 3, dividing the target area into independent simulation units with spatial discreteness by utilizing ArcGIS software to realize the refined division of simulation unit parameters;
and step 5, comparing the relative error of the area simulation result of the AquaCrop-GIS model with the relative error of the simulation result of the AquaCrop-RS model by using the traditional area application method of the AquaCrop model, and taking the absolute value difference value DARE of the relative error as the precision evaluation index of the model.
Further, in the step 1, the remote sensing data is used for inverting the maximum canopy coverage CCx *The method specifically comprises the following steps:
converting remote sensing NDVI data into a broad dynamic vegetation index and a leaf area index, and further indirectly calculating the canopy coverage of the seed corn; wherein the conversion process of the canopy coverage is shown as the following formula:
CC＝1.005×[1-exp(-0.6LAI)]1.2
in the formula: WDRVI is a wide dynamic range vegetation index; LAImaxThe maximum leaf area index of the corn for seed production; alpha is an empirical value.
The maximum canopy coverage of the seed corn in the growth period is extracted by using a maximum synthesis method, and the formula is shown as follows:
in the formula: CC (challenge collapsar)x *Actual maximum canopy coverage; n is the number of image frames in the selected time period; CC (challenge collapsar)iCanopy coverage for the ith image during the growth period.
Further, in the step 1, remote sensing data is used for inverting the relative dry matter quality Brel *The method specifically comprises the following steps:
the method utilizes the accumulated net photosynthesis in the vegetative growth period of the corn for seed production to replace the accumulated dry matter mass to obtain the relative dry matter mass, and the obtaining process is as follows:
PSNnet＝SUM(PSNnet,1…PSNnet,i-1,PSNnet,i,PSNnet,i+1…PSNnet,n)
in the formula: PSN (Point-to-Point)netKg C day for seed production in target year-1(ii) a n is the image amplitude, PSN, of the selected time periodnet,iKg C day for net photosynthesis in image i of the target year-1；
Extracting the net photosynthesis accumulated in each year of flowering phase of the extracted seed production corn by adopting a maximum synthesis method according to the following formula; obtaining the maximum value spatial distribution of the accumulated net photosynthesis of the seed production corns in the flowering phase in the target year;
PSNnet-max＝MAX(PSNnet,1…PSNnet,j-1,PSNnet,j,PSNnet,j+1…PSNnet,N)
in the formula: PSN (Point-to-Point)net-maxKg C day for the maximum value of net photosynthesis accumulated in the flowering phase of corn for seed production-1(ii) a N is the year; PSN (Point-to-Point)net,jKg C day for the cumulative net photosynthesis of the flowering phase of the corn for seed production in the j year-1；
Finally, according to the inversion principle, the flowering phase B of the maize for seed productionrel *Calculating;
in the formula: b isrel *Is the actual relative dry matter weight kg/ha in the flowering phase, B*Kg/ha, B, the actual dry matter of the flowering phase0Kg/ha for potential accumulation of dry matter in flowering phase; PSN (Point-to-Point)netKg C day for the accumulation of net photosynthesis in the flowering phase of corn for seed production in the target year-1，PSNnet_maxThe maximum value of the cumulative net photosynthesis of the flowering phase of the corn for seed production in the target year, kg C day-1。
Further, in step 3, the target area is divided into independent simulation units with spatial discreteness by using an ArcGIS software tool, namely, ArcToolbox-Analysis-Overlay-artifact.
Further, the step 4 is specifically to sort the batch file of the simulation unit according to the construction principle of the AquaCrop-GIS software, and run the software. And displaying the simulation result by using a visualization module of the AquaCrop-GIS software, or performing space analysis on a vector file of the simulation result by using ArcGIS to complete the construction of the AquaCrop-RS model.
Compared with the prior art, the invention has the advantages that: and the spatial scale of the AquaCrop model and the simulation precision are improved in mechanism. The yield, evapotranspiration amount and water production efficiency of the corn seeds produced in the irrigation area are simulated by utilizing the AquaCrop-RS model, and optimal coupling of water and fertilizer is realized by reasonably adjusting the fertilizing amount, the irrigation amount and the irrigation date, so that the method is an important way for improving the production benefit of crops.
Drawings
FIG. 1 is a schematic diagram of an operation of an AquaCrop model on an AquaCrop-GIS platform according to an embodiment of the present invention;
FIG. 2 is a schematic diagram a and a result diagram b of the division of a target area simulation unit according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the AquaCrop-RS model construction according to the embodiment of the invention;
FIG. 4 is a diagram illustrating spatial distribution of relative error absolute Difference (DARE) and simulation accuracy versus the results of an area simulation of an AquaCrop-GIS model and an area simulation of an AquaCrop-RS model according to the conventional area application method of the AquaCrop model of the embodiment of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and examples.
A regional crop water consumption and yield fine simulation method based on remote sensing information comprises the following steps:
step 1, obtaining crop parameter-maximum canopy coverage CC by remote sensing methodx *And relative dry matter Brel *Spatial distribution;
and 3, dividing the target area into independent simulation units with spatial discreteness by utilizing ArcGIS software, and realizing the fine division of the parameters of the simulation units. As shown in fig. 2(a-b), the spatial attributes and management files of the target area, including maximum canopy coverage, relative dry matter, soil type, planting structure, irrigation distribution, weather, are superimposed using the arcgps software tool ArcToolbox-Analysis-Overlay-artifact, and the target area is divided into independent simulation units.
And 4, completing construction of the AquaCrop-RS model based on the AquaCrop-GIS platform. The operational principle of the AquaCrop-GIS platform is shown in figure 1, meteorological files, crop files, soil files, irrigation and management files and the like in an area range are formed into a standard database according to the input data requirement of a single-point AquaCrop model, and an operation module and a visualization module of the AquaCrop-GIS platform respectively call the files of the database and visually analyze a simulation result. As shown in FIG. 3, the AquaCrop-RS model is constructed by inverting the remote sensing crop parameter CCx *And Brel *And substituting the model into a crop module and a management module of the model, endowing the model crop module and the management module with the characteristic of spatial distribution, and enabling a single-point AquaCrop model to respectively run in each independent simulation unit by means of the AquaCrop-GIS platform to complete the regional distributed simulation so as to complete the construction of the AquaCrop-RS model.
And step 5, comparing the relative error of the area simulation result of the AquaCrop-GIS model with the relative error of the simulation result of the AquaCrop-RS model by using the traditional area application method of the AquaCrop model, and taking the absolute value difference value DARE of the relative error as the precision evaluation index of the model. As shown in fig. 4, the 50% area of the target area of the DARE is reduced by more than the area percentage of the DARE increase, so we consider that the AquaCrop-RS model improves the simulation accuracy for the target area as a whole.
Maximum canopy coverage CCx *Spatial inversion:
and respectively re-projecting the remote sensing image into UTM projection by utilizing Modis Tool software.
Masking the remote sensing image by ArcGIS software by utilizing the seed production corn distribution map of the target area, extracting the NDVI space distribution map of the seed production corn planting range of the target area,
and (3) calculating each phase element of the NDVI image by using an ArcGIS grid calculator according to the following formula to obtain a canopy coverage space distribution map of each growth period of the seed corn.
CC＝1.005×[1-exp(-0.6LAI)]1.2
In the formula: WDRVI is a wide dynamic range vegetation index; LAImaxThe maximum leaf area index of the corn for seed production; alpha is an empirical value.
And extracting the maximum canopy coverage of the seed corn in the target area according to the following formula.
In the formula: CC (challenge collapsar)x *Actual maximum canopy coverage; n is the number of image frames in the selected time period; CC (challenge collapsar)iCanopy coverage for the ith image during the growth period.
Relative dry matter amount Brel *Spatial inversion of (a):
and respectively re-projecting the remote sensing image into UTM projection by utilizing Modis Tool software.
Masking the remote sensing image by ArcGIS software by utilizing the seed production corn distribution map of the target area to extract PSN of the seed production corn planting rangenetThe spatial distribution map is a map of the spatial distribution,
using ArcGIS grid calculator for each year of PSNnetAnd (4) superposing and summing the spatial distribution maps to obtain a spatial distribution map of the accumulated net photosynthesis of the seed production corns in the flowering phase, wherein the spatial distribution map is represented by the following formula. PSN (Point-to-Point)net＝SUM(PSNnet,1…PSNnet,i-1,PSNnet,i,PSNnet,i+1…PSNnet,n)
In the formula: PSN (Point-to-Point)netKg C day for the accumulation of net photosynthesis in the flowering phase of corn for seed production in the target year-1(ii) a n is the image amplitude, PSN, of the selected time periodnet,iKg C day for net photosynthesis in image i of the target year-1。
Extracting the net photosynthesis accumulated in each annual flowering phase of the extracted corn for seed production in the target region by adopting a maximum synthesis method MVC (model-view controller), wherein the formula is as follows; obtaining the maximum value spatial distribution of the cumulative net photosynthesis of the seed production corn in the flowering phase of the target year
PSNnet-max＝MAX(PSNnet,1…PSNnet,j-1,PSNnet,j,PSNnet,j+1…PSNnet,N)
In the formula: PSN (Point-to-Point)net-maxThe maximum value of the accumulated net photosynthesis of the flowering phase of the corn for the seed production in the target year, kg C day-1(ii) a N is selected research year; PSN (Point-to-Point)net,jAccumulated net light for the flowering phase of the corn for seed production in the j yearCombined action, kg C day-1。
According to the inversion principle, the flowering phase B of the maize for seed productionrel *The calculation is performed as follows.
In the formula: b isrel *Is the actual relative dry matter weight kg/ha in the flowering phase, B*Kg/ha, B, the actual dry matter of the flowering phase0Kg/ha for potential accumulation of dry matter during flowering. PSNnetKg C day for the accumulation of net photosynthesis in the flowering phase of corn for seed production in the target year-1，PSNnet_maxThe maximum value of the accumulated net photosynthesis of the flowering phase of the corn for the seed production in the target year, kg C day-1。
Analog unit division:
the method comprises the steps of utilizing an ArcGIS software tool ArcToolbox-Analysis-Overlay-artifact to stack spatial attributes and management files of a target area, including maximum canopy coverage, relative quality, soil type, planting structure, irrigation distribution and weather, and dividing the target area into independent simulation units according to different attributes of the files.
Constructing an AquaCrop-RS model:
constructing attribute files of a target area, wherein the attribute files comprise a crop file, an initial soil water file, a soil parameter file, a groundwater file, a crop management file, a meteorological file and an irrigation file which are generated by an AuqaCrop single-point model, and encoding parameters of each file according to different attributes;
constructing a batch file, setting an exclusive code WSTA for each simulation unit, encoding an exclusive simulation environment of each WSTA according to a rule, and calling the attribute file by software according to the encoding;
constructing a geographic information file (shp vector file) of a target area, and adding a WSTA attribute to the file, wherein the WSTA of the file corresponds to the WSTA of the batch file;
and storing the files into specified folders according to requirements, and keeping the closed states of all the files. And selecting a simulation scene, and starting the AquaCrop-GIS. The software operation module classifies and sorts the output result of the single-point model;
and a visualization module of the software visualizes the output result classification.
It will be appreciated by those of ordinary skill in the art that the examples described herein are intended to assist the reader in understanding the manner in which the invention is practiced, and it is to be understood that the scope of the invention is not limited to such specifically recited statements and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (1)
1. A regional crop water consumption and yield fine simulation method based on remote sensing information is characterized by comprising the following steps:
step 1, obtaining crop parameter-maximum canopy coverage CC by remote sensing methodx *And relative dry matter Brel *Spatial distribution;
step 2, collecting soil attribute, planting structure, irrigation and meteorological space distribution data of a target area;
step 3, dividing the target area into independent simulation units with spatial discreteness by utilizing ArcGIS software to realize the refined division of simulation unit parameters;
step 4, completing construction of an AquaCrop-RS model based on the AquaCrop-GIS platform;
step 5, comparing the relative error of the area simulation result of the AquaCrop-GIS model with the relative error of the simulation result of the AquaCrop-RS model by using the traditional area application method of the AquaCrop model, and taking the absolute value difference value DARE of the relative error as the precision evaluation index of the model;
in the step 1, the remote sensing data is used for inverting the maximum canopy coverage CCx *The method specifically comprises the following steps:
converting remote sensing NDVI data into a broad dynamic vegetation index and a leaf area index, and further indirectly calculating the canopy coverage of the seed corn; wherein the conversion process of the canopy coverage is shown as the following formula:
CC＝1.005*[1-exp(-0.6LAI)]1.2
in the formula: WDRVI is a wide dynamic range vegetation index; LAImaxThe maximum leaf area index of the corn for seed production; alpha is an empirical value;
the maximum canopy coverage of the seed corn in the growth period is extracted by using a maximum synthesis method, and the formula is shown as follows:
in the formula: CC (challenge collapsar)x *Actual maximum canopy coverage; n is the number of image frames in the selected time period; CC (challenge collapsar)iCanopy coverage for the ith image during the growth period;
in the step 1, the relative dry matter mass B is inverted by using remote sensing datarel *The method specifically comprises the following steps:
the method utilizes the accumulated net photosynthesis in the vegetative growth period of the corn for seed production to replace the accumulated dry matter mass to obtain the relative dry matter mass, and the obtaining process is as follows:
PSNnet＝SUM(PSNnet,1...PSNnet,i-1,PSNnet,i,PSNnet,i+1...PSNnet,n)
in the formula: PSN (Point-to-Point)netKg C day for seed production in target year-1(ii) a n is the image amplitude, PSN, of the selected time periodnet,iKg C day for net photosynthesis in image i of the target year-1；
Extracting the net photosynthesis accumulated in each year of flowering phase of the extracted seed production corn by adopting a maximum synthesis method according to the following formula; obtaining the maximum value spatial distribution of the accumulated net photosynthesis of the seed production corns in the flowering phase in the target year;
PSNnet-max＝MAX(PSNnet,1...PSNnet,j-1,PSNnet,j,PSNnet,j+1...PSNnet,N)
in the formula: PSN (Point-to-Point)net-maxKg C day for the maximum value of net photosynthesis accumulated in the flowering phase of corn for seed production-1(ii) a N is the year; PSN (Point-to-Point)net,jKg C day for the cumulative net photosynthesis of the flowering phase of the corn for seed production in the j year-1；
Finally, according to the inversion principle, the flowering phase B of the maize for seed productionrel *Calculating;
in the formula: b isrel *Is the actual relative dry matter weight kg/ha in the flowering phase, B*Kg/ha, B, the actual dry matter of the flowering phase0Kg/ha for potential accumulation of dry matter in flowering phase; PSN (Point-to-Point)netKg C day for the accumulation of net photosynthesis in the flowering phase of corn for seed production in the target year-1，PSNnet_maxThe maximum value of the cumulative net photosynthesis of the flowering phase of the corn for seed production in the target year, kg C day-1；
In the step 3, an ArcGIS software tool ArcToolbox-Analysis-Overlay-artifact is utilized to stack the spatial attributes and management files of the target area, including maximum canopy coverage, relative dry matter quantity, soil type, planting structure, irrigation distribution and weather, and the target area is divided into independent simulation units according to different attributes of the files;
step 4, specifically, the batch file of the simulation unit is arranged according to the construction principle of the AquaCrop-GIS software, and the software is operated; and displaying the simulation result by using a visualization module of the AquaCrop-GIS software, or performing space analysis on a vector file of the simulation result by using ArcGIS to complete the construction of the AquaCrop-RS model.
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| 基于RS数据和GIS方法的冬小麦水分生产函数估算;彭致功 等;《农业机械学报》;20140831;第167-171页 * |
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