CN110347964B - Remote sensing water demand constrained arid region agricultural planting structure optimization method - Google Patents
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Abstract
The invention relates to a remote sensing water demand constrained arid region agricultural planting structure optimization method which is used for carrying out estimation research on crop water demand based on time sequence remote sensing data and optimizing and adjusting a crop planting structure based on the crop water demand. Estimating the instantaneous evapotranspiration of the crops by using an energy balance equation based on remote sensing data, and performing time scale expansion on the instantaneous evapotranspiration on the basis to obtain daily evapotranspiration; further expanding the time scale of the daily evapotranspiration to obtain the evapotranspiration of the whole growing season of the crops; and then, the water demand of the whole growing season of the crops is estimated by combining meteorological data such as rainfall and the like, and a foundation is provided for the optimization and adjustment of the crop planting structure. The method overcomes the current problem that a large amount of measured data is needed based on meteorological and measured data and the defect that the daily water demand of crops can only be estimated by utilizing remote sensing data, and has important significance on the optimal configuration of crops under the constraint of water resources in arid regions.
Description
Technical Field
The invention relates to the technical field of water resource management and agricultural planting structure optimization in arid regions, in particular to an agricultural planting structure optimization method for arid regions with remote sensing water demand constraint.
Background
The shortage of water resources is a main bottleneck for limiting the agricultural development of arid regions, and the enhancement of the reasonable utilization of the water resources is a necessary way for ensuring the sustainable development of agriculture and economic society. The agricultural planting structure describes the types and the spatial distribution of crops in a region or an agricultural production unit, is the basis of regional water resource optimization configuration, and has positive effects on water resource utilization and agricultural economic benefits by adjusting and optimizing the agricultural planting structure.
The estimation of the water demand of crops is the basis for adjusting and optimizing the crop planting structure under the restriction of water resources. Many scholars at home and abroad carry out estimation on the water demand of crops based on meteorological data and measured data, but the method is greatly influenced by climate factor change, and the collection of a large amount of field measured data is difficult. Many scholars also carry out quantitative research on crop evapotranspiration by using a Penman formula, an SEBAL energy balance equation and the like based on remote sensing image data, but most of the research is only limited to the estimation of the crop instantaneous and daily evapotranspiration, and the evapotranspiration and water demand of the crops in the whole growing season are not estimated.
Disclosure of Invention
In view of the above, the invention aims to provide a method for optimizing an agricultural planting structure in an arid region with remote sensing water demand restriction, which fully utilizes the advantages of time series remote sensing images, overcomes the current problems that a large amount of actual measurement data is needed based on meteorological and actual measurement data, and only can estimate the daily water demand of crops by utilizing the remote sensing data, and has important significance on the optimal configuration of crops under the water resource restriction in arid regions.
The invention is realized by adopting the following scheme: a remote sensing water demand constrained arid region agricultural planting structure optimization method specifically comprises the following steps:
step S1: acquiring remote sensing image data, meteorological data (air temperature and air speed) and DEM data of a research area, preprocessing the remote sensing data, and calculating vegetation index, ground surface emissivity, ground surface albedo and ground surface temperature;
step S2: computing groundApparent net radiant flux Rn；
Step S3: calculating soil heat flux G;
step S4: calculating the sensible heat flux H;
step S5: calculating to obtain the instantaneous evapotranspiration of the crops at the moment of acquiring the remote sensing data based on an energy balance equation;
step S6: carrying out time scale expansion on the crop instantaneous evapotranspiration to obtain daily evapotranspiration;
step S7: carrying out time scale expansion on the crop daily evapotranspiration to obtain evapotranspiration of the whole growing season;
step S8: estimating the theoretical water demand of the growing season of the crops based on evapotranspiration and rainfall data of the growing season of the crops;
step S9: establishing an agricultural planting structure optimization model by integrating the theoretical water demand of the crop growing season, the crop planting structure and the agricultural water supply data;
step S10: and solving the agricultural planting structure optimization model by using a particle swarm algorithm to obtain a crop planting structure optimization scheme.
Preferably, the step S1 includes the following steps:
step S11: collecting time sequence remote sensing data of a growing season of a research area, and carrying out preprocessing such as geometric correction and radiation correction;
step S12: calculating a vegetation index NDVI of the preprocessed remote sensing image;
step S13: calculating the ground surface emissivity of the preprocessed remote sensing data;
step S14: calculating the earth surface albedo of the processed remote sensing data;
step S15: calculating the surface temperature of the processed remote sensing data;
step S16: collecting meteorological observation data such as air temperature and wind speed and the like in a research area;
step S17: and collecting DEM elevation data of the research area.
Further, in step S2, the net surface radiant flux RnThe following formula is used for the calculation of (c):
Rn＝(1-α)Rs↓+RL↓-RL↑-(1-ε0)RL↓；
wherein α is the ground surface albedo, RnNet radiant flux (J.m) for the earth's surface-2·s-1)，Rs↓ is solar short wave radiation (J. m-2. s-1) incident to the earth surface, RL↓ is incident long wave radiation (J. m-2. s-1), RL×) is reflected long wave radiation; epsilon0Is the surface emissivity. Wherein R iss↓,RL↓,RLThe calculation formula of ≈ e is as follows:
Rs↓＝GSC×sin(θ)×τsw/dr 2
dr＝1+0.033cos[DAY×(2π/365)]
RL↓＝1.08(-lnτsw)0.265×δTa 4
RL↑＝ε0δTs 4
wherein: gSCIs the solar constant (1367 W.m)2) (ii) a θ is the solar altitude (°); tau isswIs the atmospheric one-way transmission; drIs a distance from the sun to the earth (astronomical unit), DAY is the DAY of the year when the remote sensing image is obtained, and delta is a Boltzmann constant (5.67 multiplied by 10)-8W·m-2·K-4),ε0Is the surface emissivity; t isaWith reference to the high atmospheric temperature (K), which can be expressed by the complete vegetation coverage or the surface temperature of the water body area, the invention replaces it with a 'cold spot' pixel that calculates the sensible heat flux; t issIs the surface temperature (K), τswIs the atmospheric one-way transmission.
Further, in step S3, the soil heat flux G is calculated by the following formula:
in the formula, TsIs the surface temperature (K), alpha is the surface albedo, NDVI is the normalized vegetation index, c11Is a satellite correction coefficient, and the transit time is 0.9 before 12 points of local timeAnd 1.0 is taken between 12 and 14 points.
Further, in step S4, the sensible heat flux H is calculated by the following equation:
in the formula, ρairIs the air density (kg. m)-3)，CpIs the specific heat at constant pressure of air (1004 J.kg)-1·K-1) dT is the height Z from the ground1And Z2Temperature difference (usually taking Z)1＝0.1m；Z22m, meteorological station observation height), rabIs the aerodynamic impedance (s.m)-1)。
Wherein, CpAnd (3) calculating:
in the formula, TaAs above, Z is the terrain height (m).
rabAnd (3) calculating:
wherein k is Karman constant (0.41), μ*Is the friction wind speed. Z1＝0.1m；Z22m (for meteorological station observation height). Frictional wind velocity (mu)*) Is a parameter for representing the atmospheric motion intensity, and the calculation of the parameter needs a stable wind speed which is not influenced by the surface roughness. Empirically, at 200m altitude, the effect of the undulations on the atmospheric motion is negligible, and the wind speed over the whole can be considered the same. The wind speed estimation process at 200m is as follows:
Zom＝0.123hav
the formula: mu.sxIs a height of hxWind speed at (m), ZomMomentum surface roughness (m). h isavThe mean height (m) of vegetation is assumed to be 0.01 if no vegetation is present. Firstly, the friction wind speed of the meteorological station is obtained according to the formula 10 and the formula 11, and the wind speed mu at the position of 200m can be obtained by substituting the friction wind speed of the meteorological station into the formula 10 and the formula 11200Is measured by200And a pixel element ZomSubstituting the formula 10 to obtain the friction wind speed mu of each pixel element in the area*The spatial distribution of (a).
Calculating dT: SEABL model hypothesis dT and surface temperature TsThe linear correlation relationship is expressed as follows:
dT＝aTs+b
a. b is a correction coefficient, and is determined by selecting a cold image element and a thermal image element. The "hot spot" is usually selected to be the area of dry bare soil that is not covered by vegetation, with an evapotranspiration of approximately 0, satisfying H ≈ Rn-G. The "cold spots" are usually selected from water areas or areas with high vegetation coverage, satisfying λ ET ═ Rn-G. Due to the instability of the atmosphere, certain errors exist in the stable sensible heat flux obtained by selecting cold and hot points at one time. Therefore, the stable sensible heat flux H (when r isahH is stabilized when the relative error of the two times is less than 2%).
Further, step S5 specifically includes the following steps:
step S51: net radiant flux R from the earth's surfacenThe soil heat flux G and the sensible heat flux H are introduced into an energy balance equation to calculate the latent heat flux lambda ET at the satellite transit time, and the specific calculation formula is as follows:
Rn＝G+H+λET；
in the formula: rnIs the net radiant quantity (J.m)-2·s-1) (ii) a G is the soil heat flux (J.m)-2·s-1) (ii) a H is sensible heat flux (J.m)-2·s-1) (ii) a λ ET is latent heat flux (J · m)-2·s-1)；
Step S52: determining the instant of the satellite transit time by using the latent heat flux λ ET calculated in step S51Evapotranspiration ETinstThe specific calculation formula is as follows:
in the formula, ETinstIs instant evapotranspiration powder (J.m)-2·s-1) (ii) a Lambda is latent heat of vaporization (J.m)-2·s-1)。
Further, step S6 is specifically: the instantaneous evapotranspiration is expanded on a time scale, and daily evapotranspiration is estimated through a sine function, wherein the specific calculation formula is as follows:
in the formula, NEThe time interval from the beginning of evaporation to the end of evaporation to close to 0 is generally 2h less than the sunshine hours, and t is the time interval from sunrise to satellite transit time and has the unit of h, ETdailyFor daily evapotranspiration, ETinstIs an instant evapotranspiration.
Further, step S7 is specifically: according to the trapezoidal method of the mathematical integration method, the scale of the daily evapotranspiration is expanded to obtain the evapotranspiration ET of the whole crop growing seasontotalThe specific calculation formula is as follows:
wherein i is the image sequence value, n is 13, ETiThe evapotranspiration amount of the i-th day is shown, and delta t is the interval days of evapotranspiration of two adjacent days.
Further, step S8 is specifically: and (4) subtracting the effective rainfall by using the evapotranspiration value of the crop growth season calculated by the S7 to estimate the theoretical water demand of the crop growth season.
Further, step S9 specifically includes the following steps:
step S91: taking the planting area of each crop in different areas as a decision variable;
step S92: the following function is used as the objective function:
in the formula, XiI crop area, QiFor i crop yield, PiIs the i crop price.
Further, the method also includes step S93: setting constraint conditions, specifically comprising water resource safety constraint, whole-region cultivated land safety constraint, special economic crop constraint and grain safety constraint; the safety constraint of the whole-area cultivated land specifically comprises the following steps: the optimized planting area of the crops in the whole area should not exceed the current planting area; the special economic crop constraints are specifically as follows: the optimized planting area of the special economic crops is not less than the existing planting area.
The method is used for carrying out estimation research on the water demand of crops based on time sequence remote sensing data and optimizing and adjusting the crop planting structure based on the water demand of the crops. Estimating the instantaneous evapotranspiration of the crops by using an energy balance equation based on time sequence remote sensing data, and performing time scale expansion on the instantaneous evapotranspiration on the basis to obtain daily evapotranspiration; further expanding the time scale of the daily evapotranspiration to obtain the evapotranspiration of the whole growing season of the crops; and then, the water demand of the whole growing season of the crops is estimated by combining meteorological data such as rainfall and the like, and a foundation is provided for the optimization and adjustment of the crop planting structure.
Compared with the prior art, the invention has the following beneficial effects: compared with the research of estimating the water demand of crops by using meteorological data, the method for estimating the water demand of crops in the growing season by combining the time sequence remote sensing data with the meteorological data is not easily influenced by factors such as weather conditions, the problem that a large amount of actual measurement data is difficult to obtain is solved, and the calculation process is simpler. The method fully utilizes the advantage that the drought region can easily obtain time sequence remote sensing data, and expands the crop instantaneous evapotranspiration on a time scale to obtain the water demand of the crop in the whole growing season, thereby providing a basis for optimizing the crop planting structure and avoiding the difficulty that most of the current researches only utilize the remote sensing data to estimate the instantaneous evapotranspiration and the daily evapotranspiration and can not be applied to the crop planting optimization. The method has important significance for the optimal allocation of water resources in arid regions.
Drawings
FIG. 1 is a flow chart of a method according to an embodiment of the present invention.
FIG. 2 shows the result of optimizing the crop planting area under the constraint of remote sensing estimation of the water demand of crops and under the condition of total-area cultivation safety in the embodiment of the present invention.
FIG. 3 is a diagram of economic benefit changes in counties after crop remote sensing water demand constraints and crop optimization adjustment under the condition of total-area cultivated land safety in accordance with an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the embodiment provides a method for optimizing an agricultural planting structure in an arid region with remote sensing water demand constraint, which specifically comprises the following steps:
step S1: acquiring remote sensing image data, meteorological data (air temperature and air speed) and DEM data of a research area, preprocessing the remote sensing data, and calculating vegetation index, ground surface emissivity, ground surface albedo and ground surface temperature;
step S2: calculating the net surface radiant flux Rn；
Step S3: calculating soil heat flux G;
step S4: calculating the sensible heat flux H;
step S5: calculating to obtain the instantaneous evapotranspiration of the crops at the moment of acquiring the remote sensing data based on an energy balance equation;
step S6: carrying out time scale expansion on the crop instantaneous evapotranspiration to obtain daily evapotranspiration;
step S7: carrying out time scale expansion on the crop daily evapotranspiration to obtain evapotranspiration of the whole growing season;
step S8: estimating the theoretical water demand of the growing season of the crops based on evapotranspiration and rainfall data of the growing season of the crops;
step S9: establishing an agricultural planting structure optimization model by integrating the theoretical water demand of the crop growing season, the crop planting structure and the agricultural water supply data;
step S10: and solving the agricultural planting structure optimization model by using a particle swarm algorithm to obtain a crop planting structure optimization scheme.
Preferably, the step S1 includes the following steps:
step S11: collecting time sequence remote sensing data of a growing season of a research area, and carrying out preprocessing such as geometric correction and radiation correction;
step S12: calculating a vegetation index NDVI of the preprocessed remote sensing image;
step S13: calculating the ground surface emissivity of the preprocessed remote sensing data;
step S14: calculating the earth surface albedo of the processed remote sensing data;
step S15: calculating the surface temperature of the processed remote sensing data;
step S16: collecting meteorological observation data such as air temperature and wind speed and the like in a research area;
step S17: and collecting DEM elevation data of the research area.
In this embodiment, in step S2, the net surface radiant flux RnThe following formula is used for the calculation of (c):
Rn＝(1-α)Rs↓+RL↓-RL↑-(1-ε0)RL↓；
wherein α is the ground surface albedo, RnNet radiant flux (J.m) for the earth's surface-2·s-1)，Rs↓ is solar short wave radiation (J. m-2. s-1) incident to the earth surface, RL↓ is incident long wave radiation (J. m-2. s-1), RL×) is reflected long wave radiation; epsilon0Is the surface emissivity. Wherein R iss↓,RL↓,RLThe calculation formula of ≈ e is as follows:
Rs↓＝GSC×sin(θ)×τsw/dr 2
dr＝1+0.033cos[DAY×(2π/365)]
RL↓＝1.08(-lnτsw)0.265×δTa 4
RL↑＝ε0δTs 4
wherein: gSCIs the solar constant (1367 W.m)2) (ii) a θ is the solar altitude (°); tau isswIs the atmospheric one-way transmission; drIs a distance from the sun to the earth (astronomical unit), DAY is the DAY of the year when the remote sensing image is obtained, and delta is a Boltzmann constant (5.67 multiplied by 10)-8W·m-2·K-4),ε0Is the surface emissivity; t isaWith reference to the high atmospheric temperature (K), which can be expressed by the complete vegetation coverage or the surface temperature of the water body area, the invention replaces it with a 'cold spot' pixel that calculates the sensible heat flux; t issIs the surface temperature (K), τswIs the atmospheric one-way transmission.
In the present embodiment, in step S3, the soil heat flux G is calculated by the following formula:
in the formula, TsIs the surface temperature (K), alpha is the surface albedo, NDVI is the normalized vegetation index, c11Is the satellite correction coefficient, transitTime was 0.9 before 12 o ' clock at local time and 1.0 between 12 o ' clock and 14 o ' clock.
In the present embodiment, in step S4, the sensible heat flux H is calculated by the following equation:
in the formula, ρairIs the air density (kg. m)-3)，CpIs the specific heat at constant pressure of air (1004 J.kg)-1·K-1) dT is the height Z from the ground1And Z2Temperature difference (usually taking Z)1＝0.1m；Z22m, meteorological station observation height), rabIs the aerodynamic impedance (s.m)-1)。
Wherein, CpAnd (3) calculating:
in the formula, TaAs above, Z is the terrain height (m).
rabAnd (3) calculating:
wherein k is Karman constant (0.41), μ*Is the friction wind speed. Z1＝0.1m；Z22m (for meteorological station observation height). Frictional wind velocity (mu)*) Is a parameter for representing the atmospheric motion intensity, and the calculation of the parameter needs a stable wind speed which is not influenced by the surface roughness. Empirically, at 200m altitude, the effect of the undulations on the atmospheric motion is negligible, and the wind speed over the whole can be considered the same. The wind speed estimation process at 200m is as follows:
Zom＝0.123hav
the formula: mu.sxIs a height of hxWind speed at (m), ZomMomentum surface roughness (m). h isavThe mean height (m) of vegetation is assumed to be 0.01 if no vegetation is present. Firstly, the friction wind speed of the meteorological station is obtained according to the formula 10 and the formula 11, and the wind speed mu at the position of 200m can be obtained by substituting the friction wind speed of the meteorological station into the formula 10 and the formula 11200Is measured by200And a pixel element ZomSubstituting the formula 10 to obtain the friction wind speed mu of each pixel element in the area*The spatial distribution of (a).
Calculating dT: SEABL model hypothesis dT and surface temperature TsThe linear correlation relationship is expressed as follows:
dT＝aTs+b
a. b is a correction coefficient, and is determined by selecting a cold image element and a thermal image element. The "hot spot" is usually selected to be the area of dry bare soil that is not covered by vegetation, with an evapotranspiration of approximately 0, satisfying H ≈ Rn-G. The "cold spots" are usually selected from water areas or areas with high vegetation coverage, satisfying λ ET ═ Rn-G. Due to the instability of the atmosphere, certain errors exist in the stable sensible heat flux obtained by selecting cold and hot points at one time. Therefore, the stable sensible heat flux H (when r isahH is stabilized when the relative error of the two times is less than 2%).
In this embodiment, step S5 specifically includes the following steps:
step S51: net radiant flux R from the earth's surfacenThe soil heat flux G and the sensible heat flux H are introduced into an energy balance equation to calculate the latent heat flux lambda ET at the satellite transit time, and the specific calculation formula is as follows:
Rn＝G+H+λET；
in the formula: rnIs the net radiant quantity (J.m)-2·s-1) (ii) a G is the soil heat flux (J.m)-2·s-1) (ii) a H is sensible heat flux (J.m)-2·s-1) (ii) a λ ET is latent heat flux (J · m)-2·s-1)；
Step S52: using the latency calculated in step S51Heat flux lambda ET, determining instantaneous evaporation and dispersion ET of satellite transit timeinstThe specific calculation formula is as follows:
in the formula, ETinstIs instant evapotranspiration powder (J.m)-2·s-1) (ii) a Lambda is latent heat of vaporization (J.m)-2·s-1)。
In this embodiment, step S6 specifically includes: the instantaneous evapotranspiration is expanded on a time scale, and daily evapotranspiration is estimated through a sine function, wherein the specific calculation formula is as follows:
in the formula, NEThe time interval from the beginning of evaporation to the end of evaporation to close to 0 is generally 2h less than the sunshine hours, and t is the time interval from sunrise to satellite transit time and has the unit of h, ETdailyFor daily evapotranspiration, ETinstIs an instant evapotranspiration.
In this embodiment, step S7 specifically includes: according to the trapezoidal method of the mathematical integration method, the scale of the daily evapotranspiration is expanded to obtain the evapotranspiration ET of the whole crop growing seasontotalThe specific calculation formula is as follows:
wherein i is the image sequence value, n is 13, ETiThe evapotranspiration amount of the i-th day is shown, and delta t is the interval days of evapotranspiration of two adjacent days.
In this embodiment, step S8 specifically includes: and (4) subtracting the effective rainfall by using the evapotranspiration value of the crop growth season calculated by the S7 to estimate the theoretical water demand of the crop growth season.
In this embodiment, step S9 specifically includes the following steps:
step S91: taking the planting area of each crop in the Hexi county, the HeShuo county, the Yanqi county, the Bohu lake county and the Kurler district as a decision variable, as the Kurler district is mainly planted with cotton, hot pepper hardly exists, and the cotton in the four counties of the Yanqi basin is little, the current situation is respected and the cotton is rejected during planning. The specific decision variables are as follows:
step S92: under the condition of ensuring social, economic and ecological safety, the maximum economic benefit is the objective function. The objective function is formulated as follows:
in the formula, XiIs i crop area, unit: hm2，QiIs the yield of i crops in kg/hm2，PiIs i crop price, unit/kg.
Step S93: considering the advantages of the existing planting structure, the benefit maximization under the state of not influencing the ecological environment is met as far as possible by taking cultivated land resources, crop area, grain safety, ecological pressure and the like as constraint conditions.
(1) Water resource safety constraints
The theoretical water use constraints are as follows:
(2) safety restraint for whole-area cultivated land
In recent years, the agricultural area of research areas is expanding, and the agricultural water consumption is increasing rapidly. Therefore, the optimized whole-area crop planting area is required not to exceed the current planting area. In order to ensure the economic benefit of each county, the economic benefit of other counties and cities is prevented from being reduced due to the fact that a certain crop in a certain county and city is infinitely increased. The planting area of crops in each county is required to be not more than 120% of the current planting situation and not less than 80% of the current planting area, and specific constraint conditions are as follows:
0.8*62.72≤(X1+X2+X3)≤1.2*62.72
0.8*75.58≤(X4+X5+X6)≤1.2*75.58
0.8*55.60≤(X7+X7+X9)≤1.2*55.6
0.8*55.65≤(X10+X11+X12)≤1.2*55.65
0.8*256.45≤(X13+X14+X15)≤1.2*256.45
(3) characteristic commercial crop constraint
Under the current situation simulation planning condition and the situation simulation planning condition of the future 2030, the characteristic economic crop constraint is the same. The bergamot pears are used as unique economic crops in Kuerle city, and the planting area of the bergamot pears after optimization is not lower than the existing planting area:
X16＞22.84
(4) grain safety restraint
Wheat is the main food crop in open river basin, and corn is an important source of livestock feed although only a small part of the crop is used as grain, and the importance of the corn is also self-evident. Therefore, the planting area of the wheat and the corn is limited to be not less than 80 percent of the current planting area.
0.8*102.8≤(X1+X4+X7+X10+X13)
0.8*121.6≤(X2+X5+X8+X11+X14)
Specifically, in this embodiment, step S10 specifically includes the following steps:
step S101: initializing a particle swarm;
step S102: recursion formula and parameter setting;
step S103: setting a fitness function;
step S104: and solving the model by a particle swarm optimization algorithm.
In this embodiment, step S101 specifically includes: optimizing the crops, wherein the method not only has total amount constraint but also has independent variable constraint conditions, and for the problem that the total amount constraint and the independent variable upper and lower limit constraint exist, the ith particle on the n-dimensional space is set as (X)i＝Xi1，Xi2,…Xin). The initial population of particles is generated as follows:
in the formula: q is a total constraint, i.e.
Is the upper limit of the variable;
is the lower limit of the variable; u is [0-1 ]]The random number of (2).
In this embodiment, step S102 specifically includes: in the optimization process, the iterative formula of particle speed and position update is as follows:
in the formula: i is 1,2, …, m; i is 1,2, …, n where m is the number of particles, even if the number of decision variables, n is the variable dimension; c. C1And c2Weight coefficients and for tracking the historical optimum of the particleWeight coefficients that track population optima, generally such that c1＝c2；ξ1And xi2Is [0,1 ]]A random number in between; and w is a weight coefficient used for controlling the influence of the previous speed on the current speed and carrying out balance adjustment on the global searching capability and the local searching capability of the algorithm.
In this embodiment, step S103 specifically includes: the fitness function generally varies from the objective function, and the formula is as follows:
wherein Xi is the irrigation area of i crops in unit: hm2Qi is the yield of i crops in kg/hm2Pi is the price of i crops, unit/kg, and Wi is the irrigation water demand of i crops.
In this embodiment, step S104 specifically includes the following steps:
step S1041: initializing the population scale, position and speed of a particle swarm, and setting the maximum iteration number T;
step S1042: calculating the adaptive value of each particle i, and then determining the individual optimal value pbest of the particle i according to the adaptive valueiAnd global optimum gbesti；
Step S1043: for each particle i, its current adaptation value is compared with the previous individual optima pbestiComparing, if the former is better than the latter, replacing gbestiOtherwise, keeping the original individual optimal value;
step S1044: for each particle i, comparing the current individual optimal value with the population global optimal value, if the current individual optimal value is better, taking the current individual optimal value as the current global optimal value, otherwise, keeping the original global optimal value;
step S1045: updating the speed and the position of the particles according to the constraint conditions;
step S1046: and exiting if the ending condition is met, otherwise, turning to the step S1042 to renew the particles.
The embodiment carries out estimation research on the crop water demand based on the time sequence remote sensing data and carries out optimization and adjustment on the crop planting structure based on the crop water demand. Estimating the instantaneous evapotranspiration of the crops by using an energy balance equation based on time sequence remote sensing data, and performing time scale expansion on the instantaneous evapotranspiration on the basis to obtain daily evapotranspiration; further expanding the time scale of the daily evapotranspiration to obtain the evapotranspiration of the whole growing season of the crops; and then, the water demand of the whole growing season of the crops is estimated by combining meteorological data such as rainfall and the like, and a foundation is provided for the optimization and adjustment of the crop planting structure.
Particularly, in the embodiment, an agricultural area of an open river basin in Xinjiang is used as a research area, the water demand of the growing period of crops is estimated by using remote sensing image data of Landsat7 and Landsat8 in the 2016 research area, on the basis, the current situation data of the crop planting structure and the statistical data of agricultural irrigation water, which are obtained by using a remote sensing or statistical method, are combined, a crop planting structure optimization model is constructed under the constraint condition of current situation theoretical water, and the model is solved by using a particle swarm algorithm, so that the optimization scheme of the crop planting structure is obtained. And obtaining an optimization result of the crop planting area based on the condition of the whole-area cultivated land safety with unchanged water amount according to the optimization result, as shown in figure 2. The water resource consumption is 21.34 multiplied by 109m3Under the safety constraint of the whole cultivated land, the total planting area of the optimized crops is unchanged, and the economic benefit is increased by 7.91 multiplied by 109And (5) Yuan. The total planting area of the wheat is reduced by 18.72 multiplied by 10 according to various crop changes3hm2The economic benefit is reduced by 2.40 multiplied by 109Element; the total planting area of the corn is increased by 10.71 multiplied by 103hm2The economic benefit is increased by 3.74 multiplied by 109Element; the total increase of the planting area of the pepper is 12.31 multiplied by 103hm2Increase economic benefit by 5.06X 109Element; the cotton planting area is reduced by 8.88 multiplied by 103hm2The economic benefit is reduced by 2.89 multiplied by 109Element; the increased area of the bergamot pear is 4.57 multiplied by 103hm2Increase economic benefit by 4.41X 109And (5) Yuan. The current situation is that the economic benefit changes in each county after the optimization and adjustment of crops under the theoretical water use and the safety condition of the whole cultivated land, as shown in fig. 3. The economic benefit of each county is increased after optimization and adjustment, and the economyThe benefit increases from big to small are that lake county > and quiet county > and Shuo county > Yanqi county > Kuerle district in turn. Wherein the cultivated land area of Bohu county is increased by 8.4 multiplied by 103hm2The economic benefit is increased by 2.73 multiplied by 109Element: the economic benefits of wheat, corn and pepper are respectively increased by 0.05 multiplied by 109Yuan, 0.87 × 109Meta sum 1.81X 109And (5) Yuan. Increase of 10.40X 10 of the cultivated land area of He county3hm2The economic benefit is increased by 2.32 multiplied by 109Element: the economic benefits of wheat, corn and pepper are respectively increased by 0.33 multiplied by 109Yuan, 1.17X 109The sum of elements is 0.82 × 109And (5) Yuan. Increase of 1.97X 10 of cultivated land area in Heshuo county3hm2The economic benefit is increased by 1.89 multiplied by 109Element: the economic benefit of wheat is reduced by 1.01 multiplied by 109The economic benefits of corn, pepper and hot pepper are respectively increased by 1.58 multiplied by 109Meta sum 1.32 × 109And (5) Yuan. The area of cultivated land in Yanqi county is reduced by 1.92 × 103hm2The economic benefit is increased by 0.96 multiplied by 109Element: the economic benefit of wheat is reduced by 1.26 multiplied by 109The economic benefits of corn, pepper and hot pepper are increased by 1.11X 109And (5) Yuan. The cultivated land area in the region of Kurler is reduced by 18.88 multiplied by 103hm2The economic benefit is increased by 0.01 multiplied by 109Element: the economic benefits of wheat, corn and cotton are respectively reduced by 0.51 multiplied by 109Yuan, 0.99X 109Yuan He 2.89X 109The economic benefit of Yuan-Xiang pear is increased by 4.41X 109And (5) Yuan.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (6)
1. A remote sensing water demand constraint arid region agricultural planting structure optimization method is characterized by comprising the following steps:
step S1: acquiring remote sensing image data, meteorological data and DEM data of a research area, and calculating a vegetation index, an earth surface specific radiance, an earth surface albedo and an earth surface temperature;
step S2: calculating the net surface radiant flux Rn；
Step S3: calculating soil heat flux G;
step S4: calculating the sensible heat flux H;
step S5: calculating to obtain the instantaneous evapotranspiration of the crops at the moment of acquiring the remote sensing data based on an energy balance equation;
step S6: carrying out time scale expansion on the crop instantaneous evapotranspiration to obtain daily evapotranspiration;
step S7: carrying out time scale expansion on the crop daily evapotranspiration to obtain evapotranspiration of the whole growing season;
step S8: estimating the theoretical water demand of the growing season of the crops based on evapotranspiration and rainfall data of the growing season of the crops;
step S9: establishing an agricultural planting structure optimization model by integrating the theoretical water demand of the crop growing season, the crop planting structure and the agricultural water supply data;
step S10: solving the agricultural planting structure optimization model by using a particle swarm algorithm to obtain a crop planting structure optimization scheme;
in step S3, the soil heat flux G is calculated according to the following formula:
in the formula, TsIs the surface temperature, alpha is the surface albedo, NDVI is the normalized vegetation index, c11The satellite correction coefficient is obtained, the transit time is 0.9 before 12 points at the local time, and 1.0 is obtained between 12 points and 14 points;
wherein, step S8 specifically includes: subtracting the effective rainfall from the evapotranspiration value of the crop growth season calculated by S7 to estimate the theoretical water demand of the crop growth season;
wherein, step S9 specifically includes the following steps:
step S91: taking the planting area of each crop in different areas as a decision variable;
step S92: the following function is used as the objective function:
in the formula, XiI crop area, QiFor i crop yield, PiIs i crop price;
wherein, also include step S93: setting constraint conditions, specifically comprising water resource safety constraint, whole-region cultivated land safety constraint, special economic crop constraint and grain safety constraint; the safety constraint of the whole-area cultivated land specifically comprises the following steps: the optimized planting area of the crops in the whole area should not be higher than the existing planting area; the special economic crop constraints are specifically as follows: the optimized planting area of the special economic crops is not less than the existing planting area.
2. The method for optimizing an agricultural planting structure in an arid area with remote sensing water demand constraint according to claim 1, wherein in step S2, the net radiant flux R of the earth' S surfacenThe following formula is used for the calculation of (c):
Rn＝(1-α)Rs↓+RL↓-RL↑-(1-ε0)RL↓；
wherein α is the ground surface albedo, Rs↓ is solar short wave radiation incident to the earth surface, RL↓ is incident long wave radiation, RL×) is reflected long wave radiation; epsilon0Is the surface emissivity.
3. The remote sensing water demand constrained arid region agricultural planting structure optimization method according to claim 1, wherein in the step S4, the calculation of the sensible heat flux H adopts the following formula:
in the formula, ρairIs the density of air, CpIs the specific heat of air at constant pressure, dT is the height Z from the ground1And Z2Temperature difference of (d) ofabIs the aerodynamic impedance.
4. The remote sensing water demand constrained arid region agricultural planting structure optimization method according to claim 1, wherein the step S5 specifically comprises the following steps:
step S51: net radiant flux R from the earth's surfacenThe soil heat flux G and the sensible heat flux H are introduced into an energy balance equation to calculate the latent heat flux lambda ET at the satellite transit time, and the specific calculation formula is as follows:
Rn＝G+H+λET；
in the formula: rnIs the net dose; g is soil heat flux; h is sensible heat flux; λ ET is latent heat flux;
step S52: calculating the instantaneous evaporation and dispersion ET of the satellite transit time by using the latent heat flux lambda ET calculated in the step S51instThe specific calculation formula is as follows:
in the formula, ETinstIs an instant evapotranspiration; λ is latent heat of vaporization.
5. The remote sensing water demand constrained arid region agricultural planting structure optimization method according to claim 1, wherein the step S6 is specifically as follows: the instantaneous evapotranspiration is expanded on a time scale, and the daily evapotranspiration is estimated through a sine function, wherein the specific calculation formula is as follows:
in the formula, NEThe number of evapotranspiration per day, t is the time interval from sunrise to satellite transit time, ETdailyFor daily evapotranspiration, ETinstIs an instant evapotranspiration.
6. The remote sensing water demand constrained arid region agricultural planting structure optimization method according to claim 1, wherein the step S7 is specifically as follows: according to the trapezoidal method of the mathematical integration method, the scale of the daily evapotranspiration is expanded to obtain the evapotranspiration ET of the whole crop growing seasontotalThe specific calculation formula is as follows:
wherein i is the image sequence value, n is 13, ETiThe evapotranspiration quantity of the day of the i-th period is shown, and delta t is the interval days of evapotranspiration of two adjacent days.
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Non-Patent Citations (5)
| Title |
|---|
| Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin;Li, Nana等;《Journal of Applied Remote Sensing》;20170131;全文 * |
| São Paulo.Estimation of soil heat flux in a neotropical Wetland region using remote sensing techniques.《Revista Brasileira de Meteorologia》.2014, * |
| 基于SEBAL 模型的北京市日蒸散发区域分布规律;程明瀚等;《中国农学通报》;20190510;全文 * |
| 基于遥感和蚁群算法的多目标种植结构优化;张智韬等;《排灌机械工程学报》;20110331;第29卷(第2期);全文 * |
| 近50 年基于农作物种植结构的新疆绿洲农田;吕娜娜等;《地理研究》;20170831;第36卷(第8期);全文 * |
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