Remote-sensing yield estimation method applicable to crop whole growth period

A remote-sensing yield estimation method applicable to a crop whole growth period, comprising:

• S1:
• constructing a biomass estimation model of the whole growth period;
• S2:
• constructing a biomass contribution rate curve;
• S3:
• predicting crop yield;
• and
• S4:
• performing parameter optimization of optimization algorithm;
• wherein in the step S1, the biomass estimation model of the whole growth period is a layered structure, wherein in a first layer, a relationship model of an enhanced vegetation index EVI2 and biomass AGB is constructed;
• in a second layer, an evolution function is constructed with a coefficient of the first-layer model and a relative accumulated temperature of the growth period;
• a slope k of the first-layer model and the relative accumulated temperature RGS are in exponential law, and an intercept b and the RGS are in linear law, with the specific formula shown below:
• AGB
• i
• =
• k
• *
• EVI
• ⁢
• 2
• +
• b
• ;
• k
• =
• k
• 1
• ·
• e
• b
• 1
• ·
• RGS
• ;
• ⁢
• b
• =
• k
• 2
• *
• RGS
• +
• b
• 2
• ;
• EVI
• ⁢
• 2
• =
• 2.5
• *
• (
• NIR
• -
• R
• )
• 1
• +
• NIR
• +
• 2.4
• *
• R
• ;
• _
• wherein AGBi refers to a crop biomass, k and b refer to a coefficient and an intercept of a first-layer regression equation of the biomass estimation model of the whole growth period, wherein k and b are used as dependent variables of two regression equations respectively in the second layer of the model;
• EVI2 is the enhanced vegetation index which is a dependent variable of the first-layer model and calculated by a red band and a near-infrared band of remote-sensing images;
• NIR and R are a near-infrared band and a red band of remote-sensing images respectively;
• k1, b1, k2 and b2 refer to coefficients of two regression equations of the second layer respectively;
• in the step S2, the biomass contribution rate curve is an allometric growth curve, with a horizontal axis being the relative accumulated temperature value and a vertical axis being a biomass contribution rate;
• by using the biomass of an image obtaining date and the biomass contribution rate curve, a crop harvest biomass is obtained as crop maximal biomass AGBmax, wherein the crop maximal biomass AGBmax and the biomass contribution rate are shown below:
• β
• =
• b
• 3
• 1
• +
• b
• 4
• ×
• e
• b
• 5
• ·
• RGS
• ;
• AGB
• max
• =
• AGB
• β
• ;
• _
• wherein β is a biomass contribution rate, b3, b4, b5 are model coefficients;
• AGBmax is a crop final biomass;
• AGB is a biomass of crop in a growth period;
• in the step S3, the crop yield is a biomass of the last day in the growth period multiplied by a harvest index;
• the harvest index is a harvest index of a region determined, with one county administrative region as unit, based on the characteristics of different crop varieties obtained from seed administration department or agriculture promotion department, wherein the yield prediction formula is as below:
• Yield
• =
• AGB
• max
• ×
• HI
• ;
• in the formula, Yield refers to a crop yield and HI refers to a harvest index corresponding to a crop;
• in the step S4, seven parameters k1, b1, k2, b2, b3, b4, b5 and two independent variables EVI2 and RGS of the biomass estimation model of whole growth period are obtained, wherein EVI2 refers to the enhanced vegetation index, and the seven parameters of the yield estimation model are optimized based on genetic optimization algorithm;
• the step S4 comprises the following steps:
• S4.1, performing parameter initialization and determining a parameter optimization range;
• using parameters of a CBA model built based on ground data as initial values of the parameters of the yield estimation model, wherein the CBA model is the biomass estimation model of the whole growth period;
• S4.2 fitness calculation:
• when performing optimization on the parameters of the biomass estimation model of the whole growth period based on genetic algorithm, selecting a RMSE of a measured yield and a predicted yield as fitness function;
• during the optimization process of the model parameters, using RMSE minimization after performing yield estimation with the yield estimation model by using training data as optimization target:
• calculating an individual fitness as below:
• J
• =
• ∑
• i
• =
• 1
• n
• ⁢
• (
• Y
• gai
• -
• Y
• i
• )
• n
• ;
• wherein J is an individual fitness, n is a number of training samples, Ygai is a yield estimation value of a sample point, and Y is a measured yield value of a sample point;
• S4.3 iterative calculation:
• using the fitness function to perform fitness evaluation on an individual;
• based on a probability that the genetic algorithm is in direct proportion to the individual fitness, determining a chance that each individual in current population is passed down to a next-generation population, and based on a size of the individual fitness, performing reproduction, crossover and inheritance operations;
• performing repetitive iterative operation on the model parameters;
• when two adjacent iterations are <0.01 or reach a preset 500 iterations, ending iterative calculation;
• performing decoding on optimized individuals to obtain optimized model parameter values;
• and
• S4.4, crop yield estimation:
• bringing the obtained optimized parameter values into the yield estimation model, and with the relative accumulated temperature and the enhanced vegetation index EVI2 of a crop in any growth period as inputs, obtaining a yield estimation result by using the yield estimation model at different times.