Efficiently monitoring Soil Organic Carbon(SOC)in farmlands is crucial for environmental and agricul-tural sustainability.Currently,crop spectral variables are primarily employed to estimate SOC in low-relief farmland...Efficiently monitoring Soil Organic Carbon(SOC)in farmlands is crucial for environmental and agricul-tural sustainability.Currently,crop spectral variables are primarily employed to estimate SOC in low-relief farmlands.To enhance SOC estimation,further crop information needs to be excavated.Addi-tionally,few studies have considered the sample size in modeling SOC estimation,which may lead to precision loss and cost waste.Therefore,this study proposed a novel method to improve SOC estimation in low-relief farmlands.This method considers more information on crop growth and minimum sample size.The results showed that:(1)time-series NDVI was established as the characteristic crop spectral variables,based on crop spectral variables extracted from eight-day time-series reflectance products.(2)Seventeen harmonic component variables were derived from time-series NDVI via Fourier trans-formation.(3)Six crop spectral variables and seven harmonic component variables were determined as the optimal SOC estimators.(4)The convolutional neural network model provided higher SOC estimation accuracy(R^(2)=0.81,NRMSE=7.09%)than the random forest model and the back propagation neural network model.And the minimum sample size based on the optimal model was determined to be 250.(5)The proposed method improved SOC estimation at the regional scale,achieving a 2.54%reduction in NRMSE compared to the NDVI-based model.These findings suggest that the proposed method holds the potential for efficient SOC estimation in low-relief farmlands.展开更多
In the applications of COX regression models, we always encounter data sets t<span>hat contain too many variables that only a few of them contribute to the</span> model. Therefore, it will waste much more ...In the applications of COX regression models, we always encounter data sets t<span>hat contain too many variables that only a few of them contribute to the</span> model. Therefore, it will waste much more samples to estimate the “noneffective” variables in the inference. In this paper, we use a sequential procedure for constructing<span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">the fixed size confidence set for the “effective” parameters to the model based on an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size. Fixed design is considered for numerical simulation. The strong consistency, asymptotic distributions and convergence rates of estimates under the fixed design are obtained. In addition, the sequential procedure is shown to be asymptotically optimal in the sense of Chow and Robbins (1965).</span></span></span>展开更多
<span style="font-family:Verdana;">In the applications of Tobit regression models we always encounter the data sets which contain too many variables that only a few of them contribute to the model. The...<span style="font-family:Verdana;">In the applications of Tobit regression models we always encounter the data sets which contain too many variables that only a few of them contribute to the model. Therefore, it will waste much more samples to estimate the “non-effective” variables in the inference. In this paper, we use a sequential procedure for constructing the fixed size confidence set for the “effective” parameters to the model by using an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size based on Tobit regression model. Fixed design is considered for numerical simulation.</span>展开更多
基金supported by the National Key Research and Development Program of China(2021YFD20002).
文摘Efficiently monitoring Soil Organic Carbon(SOC)in farmlands is crucial for environmental and agricul-tural sustainability.Currently,crop spectral variables are primarily employed to estimate SOC in low-relief farmlands.To enhance SOC estimation,further crop information needs to be excavated.Addi-tionally,few studies have considered the sample size in modeling SOC estimation,which may lead to precision loss and cost waste.Therefore,this study proposed a novel method to improve SOC estimation in low-relief farmlands.This method considers more information on crop growth and minimum sample size.The results showed that:(1)time-series NDVI was established as the characteristic crop spectral variables,based on crop spectral variables extracted from eight-day time-series reflectance products.(2)Seventeen harmonic component variables were derived from time-series NDVI via Fourier trans-formation.(3)Six crop spectral variables and seven harmonic component variables were determined as the optimal SOC estimators.(4)The convolutional neural network model provided higher SOC estimation accuracy(R^(2)=0.81,NRMSE=7.09%)than the random forest model and the back propagation neural network model.And the minimum sample size based on the optimal model was determined to be 250.(5)The proposed method improved SOC estimation at the regional scale,achieving a 2.54%reduction in NRMSE compared to the NDVI-based model.These findings suggest that the proposed method holds the potential for efficient SOC estimation in low-relief farmlands.
文摘In the applications of COX regression models, we always encounter data sets t<span>hat contain too many variables that only a few of them contribute to the</span> model. Therefore, it will waste much more samples to estimate the “noneffective” variables in the inference. In this paper, we use a sequential procedure for constructing<span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">the fixed size confidence set for the “effective” parameters to the model based on an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size. Fixed design is considered for numerical simulation. The strong consistency, asymptotic distributions and convergence rates of estimates under the fixed design are obtained. In addition, the sequential procedure is shown to be asymptotically optimal in the sense of Chow and Robbins (1965).</span></span></span>
文摘<span style="font-family:Verdana;">In the applications of Tobit regression models we always encounter the data sets which contain too many variables that only a few of them contribute to the model. Therefore, it will waste much more samples to estimate the “non-effective” variables in the inference. In this paper, we use a sequential procedure for constructing the fixed size confidence set for the “effective” parameters to the model by using an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size based on Tobit regression model. Fixed design is considered for numerical simulation.</span>
