Study Of The Optimization Algorithms For Remote Sensing Monitoring Key Growth Diagnosis Parameters Of Winter Wheat At Main Growth Stages

Wheat is one of the main crops in China and Jiangsu province. Compared with the traditional method of monitoring the growth and production of wheat by relying on technical person to implement fixed-point investigation, the remote sensing technique is used for monitoring wheat growth at large scale because of its real-time and non-destructive monitoring in the process of wheat cultivation and management. However, its estimation accuracy should be further improved for guiding on-farm crop management. Therefore, predecessors have carried out relevant research, among which, constructing statistical models based on remote sensing data to monitor growth diagnosis parameters which can reflect the growth status of wheat at main growth stages of wheat has been widely carried out. The research about this direction has showed that algorithms used to construct statistical models are very important for improve monitoring accuracy. However, to our knowledge, only a few studies have related to the algorithms of modeling both at home and abroad recently, and among which, most of them limited on remotely monitoring one growth diagnosis parameter using related algorithms, or only aimed at one growth stage. But few studies have done to monitor multiple growth diagnosis parameters of different main growth stages of wheat based on different algorithms, and furthermore, to systemly analysis, evaluate and compare corresponding model practicability and prediction to determine the best model.In view of the above mentioned, to improve remote sensing monitoring accuracy of wheat growth to guide the production of large area wheat field for eventually realizing high yield, high efficiency, safety, low cost and high quality production of wheat, we carried out experiments in four counties (YiZheng, JiangYan, Xing Hua, and TaiXing) of Jiangsu province, China during the winter wheat growing seasons of 2010 through 2012 including the jointing stage, the booting stage and the anthesis stage respectively. Around wheat growth diagnosis parameters such as leaf area index, biomass, leaf nitrogen content and leaf SPAD at main growth stages, and supporting by synchronous China’s domestic HJ-CCD multi-spectral data, we studied the feasibility and prediction accuracy of different multiple regression models for remotely monitoring each parameter of wheat. The objective of this research is to provide a useful exploratory and predictive tool for improving prediction accuracy of monitoring growth diagnosis parameters at main growth stages of wheat by remote sensing technology. The main research contents and results of this study are as follows:(1) The study on analyzing the correlation between growth diagnosis parameters of wheat at different growth stages and remote sensing variables. The results showed that, based on the significant correlation at 0.01 level, when monitoring wheat leaf area index (LAI), the vegetation indices including NRI、RVI、NDVI, GNDVI, SIPI, SAVI, OSAVI and PSRI could be selected as sensitive remote-sensing variables in the jointing and anthesis stage respectively, and the vegetation indices including NDVI, GNDVI, SIPI, SAVI, OSAVI and PSRI were sensitive to LAI in the booting stage. The vegetation indices such as NDVI、SAVI、OSAVI、NRI、GNDVI、 SIPI、PSRI、RVI、CRI、EVI、MSR、 NLI、RDVI、TVI and MTVI2 could be used to monitoring above ground dry biomass of wheat of the jointing, booting and anthesis respectively. When monitoring nitrogen content in wheat leaves (LNC), the vegetation indices such as NDVI, GNDVI, SIPI, RVI,SAVI, OSAVI, MSAVI and EVI were sensitive remote-sensing variables in the jointing stage, and the vegetation indices including NRI、PSRI、NDVI,GNDVI, SIPI, RVI,SAVI,OSAVI, MSAVI and EVI were sensitive remote-sensing variables in the booting stage, and the vegetation indices such as NDVI, NRI and PSRI could be used. When monitoring wheat leaf SPAD, the sensitive remote-sensing variables respectively were NRI、RVI、NDVI, GNDVI, SIPI, SAVI, OSAVI and PSRI in the jointing stage, and NDVI、NRI、RVI、SAVI and OSAVI in the booting stage, and RVI、NDVI、GNDVI, SIPI，SAVI, OSAVI and PSRI in the anthesis.(2) The study on constructing remote sensing models based on using multiple regression algorithms for monitoring growth diagnosis parameters at main growth stages of wheat. For LAI, LNC, SPAD and biomass of wheat in the jointing, booting and anthesis respectively, taking the pooled data of 2010,2011 and 2012 as the training set, meanwhile, taking the sensitive remote-sensing variables as input variables and each growth parameter as output variables, the monitoring models of each growth parameter were established based on the traditional multiple linear (ML), Partial Least Squares (PLS), Artificial Neural Networks (ANN), Single-Kernel Support Vector Regression (SK-SVR), Double-Kernel Support Vector Regression (DK-SVR) and Random Forest (RF) regression algorithms respectively. Finally, taking the determination coefficients (R~2) and the root mean square error (RMSE) as metrics, meanwhile, combining with the 1:1 relationship between the observed values and predicted values of model, and taking data of each stage from 2013 as the testing set, the practicability and performance of all the models at each stage were not only evaluated on the testing set of corresponding stage but also compared to identify the best model for each growth stage.(3) Each model performance for monitoring wheat LAI of primary growth stages was made clear. The results showed that, the DK-SVR model had the best prediction accuracy in the jointing, booting and antehsis stage respectively. The coefficients of determination (R~2) of estimated-versus-measured LAI values respectively were 0.76,0.80 and 0.67, meanwhile the corresponding root mean square errors (RMSE) were 0.29,0.47 and 0.55, and the^predicted values of the model were in good agreement with the measured values. Models on the basis of ML and ANN were unable to monitor LAI of each stage. From high to low, the performance of the other 3 models in jointing stage were SK-SVR model (R~2=0.71, RMSE=0.43), PLS model (R~2=0.65, RMSE=0.40) and RF model (R~2=0.49, RMSE=1.41); in booting stage the order was SK-SVR model (R~2=0.78, RMSE=0.58), PLS model (R~2=0.75, RMSE=0.7) and RF model (R~2=0.32, RMSE=1.13); in anthesis the order was RF model (R~2=0.52, RMSE=0.57)、PLS model (R~2=0.45, RMSE=0.64) and SK-SVR model (R~2=0.33, RMSE=0.84).(4) Each model performance for monitoring wheat above ground dry biomass of primary growth stages was identified. The results pointed out, the model based on RF algorithm respectively showed the best predictive ability among the six models of each stage including jointing, booting and anthesis. The determination coefficients (R~2) and the root mean square error (RMSE) of estimated-versus-measured biomass values successively were 0.53 and 477 kg.hm（-2）,0.72 and 1126 kg.hm（-2）,0.79 and 1808 kg.hm（-2）. In the jointing, the DK-SVR model (R~2=0.50, RMSE=505.5 kg.hm（-2）) and SK-SVR model (R~2=0.47, RMSE=509.5 kg.hm（-2）) were the suboptimal models, and meanwhile, the results also showed that models based on ML, PLS and ANN were unable to monitor biomass of this stage. In the booting and anthesis, the ANN models were unable to monitoring biomass, and from high to low, the performance of the other 4 models of these two stages were DK-SVR model (R~2=0.67 and RMSE=1389.2 kg.hm"2 in the booting; R~2=0.65 and RMSE=2058.1 kg.hm（-2） in the anthesis), SK-SVR model (R~2=0.51 and RMSE=1422.3 kg.hm"2 in the booting; R~2=0.62 and RMSE=2174.2 kg.hm（-2） in the anthesis), ML model (R~2=0.53 and RMSE=1461.5 kg.hm"2 in the booting; R~2=0.49 and RMSE=2454.4 kg.hm"2 in the anthesis) and PLS model (R~2=0.48 and RMSE=1521.7 kg.hm（-2） in the booting; R~2=0.49 and RMSE=2803.6 kg.hm（-2） in the anthesis).(5) Each model performance for monitoring wheat leaf SPAD of primary growth stages was identified. Considering the determination coefficients (R~2) and the root mean square error (RMSE) between measured values and predicted values of the model, meanwhile considering the consistency between the estimated-versus-measured values, models based on ML was unable to monitor leaf SPAD of jointing, booting and anthesis stage respectively. In the jointing, the DK-SVR model was the best model, and its R~2 and RMSE were 0.65 and 1.58 respectively, and meanwhile, from high to low, the performance of the other 4 models were RF model (R~2=0.55 and RMSE=2.11), SK-SVR model (R~2=0.57 and RMSE=2.31), ANN model (R~2=0.43 and RMSE=3.06) and PLS model (R~2=0.40 and RMSE=3.42). In the booting, the best model was the RF model (R~2=0.72 and RMSE=2.2), the other 4 models successively were DK-SVR model (R~2=0.57 and RMSE=2.10), SK-SVR model (R~2=0.52 and RMSE=2.30), PLS moel (R~2=0.47 and RMSE=5.76) and ANN model (R~2=0.43 and RMSE=2.80). In the anthesis, RF model (R~2=0.60 and RMSE=3.16) showed the best performance, while PLS model was unable to monitoring leaf SPAD values, and the other 3 models successively were DK-SVR model (R~2=0.52 and RMSE=3.03), SK-SVR model (R~2=0.48 and RMSE=3.07) and ANN model (R~2=0.46 and RMSE=3.20).(6) Each model performance for monitoring wheat LNC of primary growth stages was made clear. The results indicated that, the DK-SVR model showed the best prediction accuracy in the jointing, booting and antehsis stage respectively. Good agreement was observed between the estimated values and the measured values in each stage, and the determination coefficients (R~2) and the root mean square error (RMSE) of estimated-versus-measured LNC values were 0.73 and 0.13 in the jointing,0.82 and 0.21 in the booting, and 0.75 and 0.20 in the anthesis. Both SK-SVR model and PLS model were the suboptimal model of each stage, and the R~2 and RMSE of SK-SVR model successively were 0.61 and 0.16,0.77 and 0.29,0.72 and 0.21, meanwhile, the R~2 and RMSE of PLS model successively were 0.59 and 0.23,0.77 and 0.31,0.52 and 0.26. The results also showed that models on the basis of ML, ANN and RF were unable to monitor LNC of each stage.