CN112579981B - Remote sensing simulation method for yield of winter wheat in coupling irrigation area scale - Google Patents
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Abstract
The invention belongs to the field of agricultural remote sensing and ecological remote sensing modeling, and particularly relates to a remote sensing simulation method for winter wheat yield in a coupling irrigation area scale, which has the following implementation scheme: firstly, constructing and initializing a coupling irrigation regional scale winter wheat yield simulation remote sensing model to study the regional scale winter wheat yield simulation remote sensing model which is coupled based on the existing multilayer soil water balance simulation, photosynthetic simulation and yield simulation methods to construct and initialize the coupling irrigation regional scale winter wheat yield simulation remote sensing model, and the key point is that the irrigation strategy is constructed and coupled to the existing multilayer soil water balance simulation; and finally, simulating the N-year winter wheat yield of the research area by using the simulation method after N-year data driving localization, wherein the method comprises the steps of locally coupling irrigation mode parameters IPPs in the regional scale winter wheat yield remote sensing simulation method of irrigation, and the yield simulation method coupled with the irrigation estimation strategy can be used for simulating the perennial winter wheat yield of the research area, and has strong universality and excellent market prospect.
Description
The technical field is as follows:
the invention belongs to the field of agricultural remote sensing and ecological remote sensing modeling, and particularly relates to a process model for estimating vegetation productivity based on remote sensing and a crop yield simulation method, which are used for regional scale winter wheat yield simulation.
Background art:
winter wheat is one of three major grain crops in the world and is also the main grain crop in China. Huang-Huai-Hai plain is one of the main winter wheat producing areas in China, and over 50% of the total winter wheat production in China is produced in the area. The method accurately simulates the yield of Huang-Huai-Hai plain winter wheat in regional scale, and is very important for accurately estimating the total yield of Chinese grains and evaluating the national grain safety.
In the regional scale winter wheat yield estimation method, because the field survey and farmland sampling statistical method is high in time and economic cost and cannot acquire field scale winter wheat yield distribution, a model is simulated to be a main means for regional scale winter wheat yield estimation. In the existing crop yield estimation model, an empirical model uses a statistical method to establish an empirical relation between yield and meteorological and remote sensing factors; although the semi-empirical model has a certain ecological mechanism, the characterization of parameters characterizing the photosynthetic capacity of crops in the model is still empirical, which makes the two models have poor universality in time and space. In addition, the model for estimating the regional scale winter wheat yield also comprises mechanism models, such as a crop growth model and a process model (a remote sensing process model for short) for estimating the vegetation productivity based on remote sensing, the model simulates the crop productivity and the crop yield by describing the processes of evapotranspiration, photosynthesis, respiration and the like in the growth and development of crops, has a stronger physiological and ecological mechanism, and has stronger universality in time and space. However, since the crop growth model depends on more accurate meteorological data, field management information and the like and can only be used in a specific area after calibration, the application of the crop growth model in the simulation of large-area-scale crop yield is limited. Compared with a crop growth model, the remote sensing process model taking the remote sensing data as the main driving factor has lower sensitivity to meteorological data errors and dependency on field management information, and has stronger applicability in an area scale. However, the yield of crops simulated by the remote sensing process model still has larger uncertainty, the main reason is that the remote sensing process model lacks the simulation of irrigation information of a farmland ecosystem, the irrigation is a main way for ensuring the stable yield and the high yield of winter wheat in North China plain, and the simulation of the irrigation information is considered in the remote sensing process model and is crucial to accurately estimating the yield of winter wheat in North China plain. Therefore, the remote sensing process model is insufficient in irrigation information simulation, and the simulation precision of the winter wheat yield of the estimated regional scale is limited. Therefore, the invention seeks to provide a remote sensing simulation method for the yield of winter wheat in a coupling irrigation region scale, which can effectively solve the problems.
The invention content is as follows:
the invention aims to provide a remote sensing simulation method for the yield of winter wheat in a coupling irrigation area scale aiming at the problem that the existing remote sensing process model is lack of simulation for irrigation information of a farmland ecosystem. The method provides an irrigation estimation strategy expressed by using Irrigation Pattern Parameters (IPPs), and the irrigation estimation strategy is coupled into a multilayer soil water balance model to simulate a soil water balance process, and meanwhile, the simulation method can simulate the yield of winter wheat in continuous years in a regional scale and has stronger universality.
In order to achieve the purpose, the remote sensing simulation method for the yield of the winter wheat in the coupling irrigation area scale is realized by the following technical scheme:
s1, constructing and initializing regional scale winter wheat yield simulation remote sensing model for coupling irrigation
The remote sensing model for simulating the yield of the winter wheat in the area scale of coupled irrigation is constructed and initialized by coupling based on the existing multilayer soil water balance simulation, photosynthetic simulation and yield simulation methods, and the key point is that the irrigation strategy is constructed and coupled to the existing multilayer soil water balance simulation.
(1) Irrigation strategy
Judging the ordinal number DOY, namely Day of Yeast, every Day from the green turning period to the mature period of winter wheat, if the winter wheat is in a certain growing and developing stage from the green turning period to the mature period, setting the Soil Water Content SWC, namely Soil Water Content, to the field Water capacity FC (field Capacity) level, wherein the formula is as follows:
Si,RS＝SOSRS+ki·LOSRS (2)
LOSobs＝EOSobs–SOSobs (4)
LOSRS＝EOSRS–SOSRS (5)
wherein SWC represents the sum of water contents of multi-layer soil, FC represents field water holding capacity, Si,RSIndicates the ith growth period of the winter wheat estimated by remote sensing, i is 1,2,3, … and 10, and respectively represents a sowing period, an emergence period, an overwintering period, a green returning period, an elongation period, a booting period, a heading period, a flowering period, a filling period and a mature period. Because the green turning period to the filling period is the main stage of the formation of photosynthetic dry matter, and in order to improve the operational efficiency of the simulation method, only the green turning period to the filling period are concerned in the simulation method, length represents the length of a growth period, and SOSRS、EOSRSAnd LOSRSRespectively representing the SOS (start of growing session), EOS (end of growing session) and LOS (length of growing session) of growing season estimated by remote sensing, and respectively representing the green turning period, the mature period and the length of the green turning-mature period of winter wheat; SOSobs、EOSobsAnd LOSobsRespectively obtaining a green turning period, a mature period and a green turning period-mature period length observed by an agricultural weather station;
indicating slave SOSRSTo EOSRSEach day between DoY values, kiIs defined as Si,obsAnd SOSobsDifference of (D) and LOSobsThe climate observed by the agricultural weather stationDetermining data;
(2) then, the irrigation strategy is coupled into a multilayer soil water balance simulation to realize soil water balance simulation, and the simulation is coupled with photosynthesis simulation and yield simulation to realize the construction and initialization of the remote sensing simulation method for the yield of the regional-scale winter wheat, which is related to the implementation, as shown in fig. 2;
s2 irrigation mode parameters IPPs in remote sensing simulation method for yield of regional scale winter wheat by localized coupling irrigation
(1) Firstly, according to observed phenological data, a pending parameter k is determinediThe method comprises the following steps:
1) collecting original observation data of an agricultural weather station, and extracting weather information in the original observation data;
2) converting the date record format (year-month-day) of the phenology into a sequence (DoY);
3) use of observed Return period DoY as SOSobsUsing observed maturity DoY as EOSobsCalculating LOS according to equation (4)obs；
4) Calculating k according to equation (3)i(i＝4,5,6,…,9)。
(2) Designing a simulation experiment to localize IPPs, wherein the design principle of the simulation experiment is as follows:
1) design S4,RSTo S9,RSNo irrigation at all stages is taken as a control experiment;
2) irrigation occurs only at specific growth stages, such as the green turning stage, or the jointing stage, or other growth stages prior to the maturation stage;
3) irrigation does not occur simultaneously at two adjacent growth and development stages;
(3) selecting reference year, and using the data of the reference year to drive the constructed winter wheat yield simulation method according to the decision coefficient R between the simulation yield and the statistical yield2And root mean square error RMSE to determine IPPs, R of the region of interest2And RMSE is calculated as follows:
wherein: n represents the number of reference years; x is a radical of a fluorine atomiThe simulated yield at the i-th year is shown,
is the average value of the n-year simulated yield; y isiStatistical yield for year i;
is the average value of the statistical yield of n years;
s3, simulating the yield of winter wheat in N years in the research area by using the N-year data-driven localized simulation method, wherein N is an integer greater than 1.
Compared with the prior art, the invention has the following beneficial effects:
1. aiming at the problem that the existing remote sensing process model is lack of simulation of irrigation information of a farmland ecosystem, an irrigation estimation strategy expressed by using irrigation mode parameters is constructed, coupled to a multilayer soil water balance model and coupled with a photosynthesis estimation and yield simulation method to form a region scale winter wheat yield simulation method of coupled irrigation, so that high-precision simulation of the region scale winter wheat yield under the irrigation condition is realized.
2. The irrigation estimation strategy expressed by the irrigation mode parameters avoids the condition that a large amount of remote sensing data is used for inverting the annual irrigation information of winter wheat, and the irrigation mode parameters can represent the irrigation mode of a specific research area, so that the yield simulation method coupled with the irrigation estimation strategy can be used for the yield simulation of the winter wheat in the research area, has strong universality and has excellent market prospect.
Description of the drawings:
FIG. 1 is a schematic flow diagram of the irrigation estimation strategy for winter wheat according to the present invention.
FIG. 2 is a schematic flow diagram of a winter wheat yield simulation method according to the present invention.
FIG. 3 is a schematic diagram of the spatial distribution of winter wheat yield in North China simulated by the present invention (2016-.
The specific implementation mode is as follows:
the invention is further illustrated by way of example and with reference to the accompanying drawings. The remote sensing simulation method for the yield of the winter wheat in the coupling irrigation area scale is realized by the following technical scheme:
s1, constructing and initializing regional scale winter wheat yield simulation remote sensing model for coupling irrigation
(1) First, an irrigation estimation strategy suitable for winter wheat was established.
Judging the daily number DOY (day of Yeast) every day from the green returning period to the mature period of winter wheat, and if the winter wheat is in a certain growth development stage from the green returning period to the mature period, setting the soil Water content SWC (soil Water content) to the field Water holding capacity FC (field Capacity) level, wherein the following formula is shown:
Si,RS＝SOSRS+ki·LOSRS (9)
LOSobs＝EOSobs–SOSobs (11)
LOSRS＝EOSRS–SOSRS (12)
wherein SWC represents the sum of water contents of multi-layer soil, FC represents field water holding capacity, Si,RS(i-4, 5,6, …,9) represents the growth and development stage of winter wheat estimated using remote sensing and observation phenological data, i-4, 5,6, …,9 represents the green return stage, the heading stage, the booting stage, the heading stage, the flowering stage and the filling stage, respectively; SOSRS、EOSRSAnd LOSRSRespectively representing the beginning of growing season and growing season extracted by using remote sensing dataEnd and growth season length; si,obs(i-4, 5,6, …,9) representing observed phenological data, i-4, 5,6, …,9 representing the green return period, the jointing period, the booting period, the heading period, the flowering period and the filling period of winter wheat, respectively; DoY, the order of the orders is shown,
indicating slave SOSRSTo EOSRSDay by day DoY values in between; k is a radical ofiIs Si,obsAnd SOSobsDifference of (D) and LOSobsThe ratio of the agricultural weather station to the agricultural weather station is determined by using the weather data observed by the agricultural weather station; length represents the length of a growth period, which is set to a constant of 10 in this embodiment, representing the length of one ten day.
(2) The irrigation strategy is coupled into a multilayer soil water balance simulation to realize soil water balance simulation, and the construction and initialization of the simulation method related to the implementation are realized by coupling with photosynthesis simulation and yield simulation. The multilayer soil water balance simulation, the photosynthesis simulation and the yield simulation adopted in the embodiment are as follows:
1) multilayer soil water balance simulation
This section estimates crop evapotranspiration et (evapotranspiraration) using the following formula:
wherein λ E is equivalent to the crop evapotranspiration ET,
and
wet canopy evaporation, dry canopy transpiration, saturated soil surface layer evaporation, and wet soil evaporation are indicated, respectively.
2) And (4) estimating the photosynthesis. The concrete formula is as follows:
Acanopy＝AsunLAIsun+AshadeLAIshade (14)
GPP＝Acanopy×daylength×FactorGPP (15)
NPP＝GPP-Ra (16)
in the formula, AcanopyIndicating the canopy photosynthetic assimilation Rate (. mu.mol m)-2s-1)；AsunAnd AshadeRespectively representing the photosynthetic assimilation rates of the male leaves and the female leaves; GPP denotes the Total Primary Productivity (gC m) at the canopy Scale-2d-1) (ii) a daylength represents the day length(s); factorGPPIs to convert GPP units into gCm-2d-1A unit conversion factor of (d); LAIsunAnd LAIshdRespectively representing the leaf area indexes of male leaves and female leaves; NPP is net primary productivity; raIndicating autotrophic respiration. LAIsunAnd LAIshdIs calculated as follows:
LAIsun＝2cosθ(1-exp(-0.5ΩLAI/cosθ)) (17)
LAIshade＝LAI-LAIsun (18)
wherein LAI is the leaf area index, Ω is the clustering index, θ is the average solar altitudenoonThe solar altitude at noon, lat latitude, julianday.
AsunAnd AshadeAll the following formulas are adopted for calculation:
A＝min(Wc,Wj)-Rd (21)
Rd＝0.015Vm (24)
wherein A represents the leaf-scale net photosynthetic rate (. mu.mol m)-2s-1)；WcAnd WjRespectively representing photosynthesis rate limited by Rubisco activity and photosynthesis rate (mum) dependent on electron transfer-2s-1)；RdIndicating dark respiration (. mu.mol m)-2s-1)；CiIs intercellular CO2A concentration (Pa); gamma is CO2A concentration compensation point (Pa); k is an enzyme kinetic parameter (Pa); j is the electron conduction rate (. mu.mol m)-2s-1)；VmRepresents the maximum carboxylation rate (. mu. mol m)-2s-1)。
3) And (5) simulating the yield. The concrete formula is as follows:
Yield＝NPPsum×Tc×HI×unit_factor (25)
wherein Yield of the simulated winter wheat is shown by Yield; NPPsumRepresents the sum of the net primary productivity during the growing season; t is a unit ofcFor a particular crop, T is the conversion factor between carbon and dry mattercIs a constant. For winter wheat, the carbon content is about 45%, and its TcThe value was 2.22. HI denotes the harvest index, which is taken to be 0.48 in this example. unit _ factor is a unit conversion factor, and converts the simulation result into t/hm2。NPPiRepresents the NPP at day i.
S2 irrigation mode parameters IPPs in regional scale winter wheat yield remote sensing simulation method for localized coupled irrigation
(1) Firstly, according to observation weather data of an agricultural weather station, undetermined parameters are determined, and the method comprises the following steps:
1) collecting original observation data (including longitude and latitude, crop species, phenological date, yield and the like of all sites) of 140 agricultural meteorological stations in the research area in 2010-2012, and extracting phenological information in the original observation data;
2) converting the date record format (year-month-day) of the phenology into a sequence (DoY), and calculating the DoY mean value of each phenology period;
3) observed mean of the green return period DoY was used as SOSobsUsing observed maturity DoY mean as EOSobsCalculating LOS according to equation (11)obs；
4) Calculating k according to equation (10)i(i＝4,5,6,…,9)。
K obtained by calculationiThe values are shown in table 1.
TABLE 1 calculated kiParameter(s)
(2) Then, an irrigation simulation experiment is designed according to the experiment design principle. The designed irrigation simulation experiment is shown in table 2:
table 2 irrigation simulation experiments designed for localized IPPs in this example. The greenish color in the table indicates that irrigation occurred.
(3) The yield simulation method for the winter wheat is characterized in that 2010-2015 years are selected as reference years, the data (including daily meteorological data, soil attribute data, remote sensing phenological data, remote sensing leaf area index data, remote sensing vegetation index data, land utilization data and harvest index) of the reference years are used for driving and constructing, and the winter wheat is simulated in sequence according to the settings in the table 2. Then sequentially counting R between the simulated yield and the statistical yield under each simulated scene2And RMSE, selecting R2IPPs under the simulated scenario of maximum and RMSE minimum are irrigation mode parameters for winter wheat in the study area. The IPPs selected in this embodiment are irrigation mode parameters under a T18 simulation scenario, that is, 3 times of irrigation from the green turning period to the milk stage, and irrigation is performed in the green turning period, the booting period, and the milk stage, respectively.
3. And (3) driving a regional scale winter wheat yield simulation method comprising localized irrigation mode parameters by using 2016-2018 data to simulate spatial distribution of the yield of the multi-year winter wheat.
Claims (1)
1. A remote sensing simulation method for the yield of winter wheat in a coupling irrigation area scale is characterized by being realized by the following technical scheme:
s1, constructing and initializing regional scale winter wheat yield simulation remote sensing model for coupling irrigation
Researching an area scale winter wheat yield simulation remote sensing model for constructing and initializing coupling irrigation by coupling based on the existing multilayer soil water balance simulation, photosynthetic simulation and yield simulation methods, wherein the key point is to construct an irrigation strategy and couple the irrigation strategy into the existing multilayer soil water balance simulation;
(1) irrigation strategy
Judging the number of days DOY every day from the green turning period to the mature period of the winter wheat, and setting the water content of the soil to the field water holding capacity level if the winter wheat is in a certain growth development stage from the green turning period to the mature period, wherein the formula is as follows:
Si,RS＝SOSRS+ki·LOSRS (2)
LOSobs＝EOSobs–SOSobs (4)
LOSRS＝EOSRS–SOSRS (5)
wherein SWC represents the sum of water contents of multi-layer soil, FC represents field water holding capacity, Si,RSIndicates the ith growth period of winter wheat estimated by remote sensing, i is 1,2,3, …,10, respectively representing sowing period, emergence period, and overwintering periodA green turning period, an elongation period, a booting period, a heading period, a flowering period, a filling period and a maturation period; because the green turning period to the filling period is the main stage of the formation of photosynthetic dry matter, and in order to improve the operational efficiency of the simulation method, only the green turning period to the filling period are concerned in the simulation method, length represents the length of a growth period, and SOSRS、EOSRSAnd LOSRSRespectively representing the beginning of a growing season, the ending of the growing season and the length of the growing season estimated by remote sensing, and respectively representing the green turning period, the mature period and the length of the green turning period-mature period of the winter wheat; SOSobs、EOSobsAnd LOSobsRespectively obtaining a green turning period, a mature period and a green turning period-mature period length observed by an agricultural weather station;
indicating slave SOSRSTo EOSRSEach day between DoY values, kiIs defined as Si,obsAnd SOSobsDifference of (D) and LOSobsThe ratio of the agricultural weather station to the agricultural weather station is determined by using the weather data observed by the agricultural weather station;
(2) then, the irrigation strategy is coupled into a multilayer soil water balance simulation to realize soil water balance simulation, and the soil water balance simulation is coupled with photosynthesis simulation and yield simulation to realize the construction and initialization of the regional scale winter wheat yield remote sensing simulation method;
s2 irrigation mode parameters IPPs in regional scale winter wheat yield remote sensing simulation method for localized coupled irrigation
(1) Firstly, according to observed phenological data, a pending parameter k is determinediThe method comprises the following steps:
1) collecting original observation data of an agricultural weather station, and extracting weather information in the original observation data;
2) converting the date record format of phenology from year to month to day into a sequence DoY;
3) use of observed Return period DoY as SOSobsUsing observed maturity DoY as EOSobsCalculating LOS according to equation (4)obs；
4) Calculating k according to equation (3)i,i＝4,5,6,…,9；
(2) Designing a simulation experiment to localize IPPs, wherein the design principle of the simulation experiment is as follows:
1) design S4,RSTo S9,RSNo irrigation at all stages is taken as a control experiment;
2) irrigation only occurs in the green turning stage, or the jointing stage, or other growth and development stages before the mature stage;
3) irrigation does not occur simultaneously at two adjacent growth and development stages;
(3) selecting reference year, and using the data of the reference year to drive the constructed winter wheat yield simulation method according to the decision coefficient R between the simulation yield and the statistical yield2And root mean square error RMSE to determine IPPs, R of the region of interest2And RMSE is calculated as follows:
wherein: n represents the number of reference years; x is the number ofiThe simulated production at the i-th year is shown,
is the average value of the n-year simulated yield; y isiStatistical yield for year i;
is the average value of the statistical yield of n years;
s3, simulating the yield of the winter wheat in N years in the research area by using the N-year data-driven localized simulation method, wherein N is an integer larger than 1.
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| CN109800921A (en) * | 2019-01-30 | 2019-05-24 | 北京师范大学 | A kind of Regional Fall Wheat yield estimation method based on remote sensing phenology assimilation and particle swarm optimization algorithm |
| CN109919395A (en) * | 2019-04-01 | 2019-06-21 | 安徽大学 | A kind of winter wheat yield monitoring method based on short cycle remote sensing area data |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109800921A (en) * | 2019-01-30 | 2019-05-24 | 北京师范大学 | A kind of Regional Fall Wheat yield estimation method based on remote sensing phenology assimilation and particle swarm optimization algorithm |
| CN109919395A (en) * | 2019-04-01 | 2019-06-21 | 安徽大学 | A kind of winter wheat yield monitoring method based on short cycle remote sensing area data |
Non-Patent Citations (3)
| Title |
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| "Developing a Method to Estimate Maize Area in North and Northeast of China Combining Crop Phenology Information and Time-Series MODIS EVI";Zhang sha et.al.;《IEEE》;20191001;全文 * |
| "基于MODIS-EVI及物候差异免阈值提取黄淮海平原冬小麦面积";张莎等;《农业工程学报》;20180630;第34卷(第11期);全文 * |
| "山东省遥感干旱指数的适用性及干旱对冬小麦产量的影响";王兆雪等;《山东农业科学》;20191231;第51卷(第12期);全文 * |
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