| The use of satellite remote sensing technology in monitoring the growth and predicting grain yield and quality of wheat following rice has been extensively studied and gained some achievements in the production practice. The research is conducted in Jiangsu Taixing, jiangyan, Xinghua and Gaoyou with rice stubble wheat as the research object, obtaining key diagnosis parameters, such as LAI biomass, SPAD, leaf nitrogen content, leaf nitrogen accumulation and leaf nitrogen density in jointing stage and booting stage, blooming stage,15 days after blooming stage. Based on the remote sensing variables of the corresponding environmental mitigation satellite HJ-1A/1B imaging, the research builds 4 different combinations of the remote sensing variables,analyzing the combinations and remote sensing variables, establishes and evaluates the better model used in key diagnosis parameters monitoring, discussing the feasibility of monitoring wheat’s key diagnosis parameters through the different combinations of the remote sensing variables, the main research results are as follows:(1) By analyzing the relationship between wheat’s main growth diagnosis parameters and different value combinations of the remote sensing variables,it is feasible to monitor LAI,SPAD and LNC through remote sensing variables’different value combinations,and even better than single variable at jointing stages. From monitoring by single-variable model to the models combined of LAI and B-C,SPAD and A-D,LNC and C-D,there’s an accuracy’s increase of 16.5%,13.8% and 16.5%. It is feasible to monitor LAI,SPAD and LNC through remote sensing variables’different value combinations,and even better than single variable at booting stages.From monitoring by single-variable model to the models combined of LAI and C-D,SPAD and B-D,LNC and B-D,there’s an accuracy’s increase of 31.6%,17.7% and 6.5%. It is feasible to monitor LAI,SPAD,LNC and biomass through remote sensing variables’ different value combinations,and even better than single variable at blooming stages.From monitoring by single-variable model to the models combined of LAI and A-C,SPAD and A-B,biomass and B-C,LNC and A-B,there’s an accuracy’s increase of 6.8%,17%,41% and 31.8%. It is feasible to monitor LAI,SPAD and biomass through remote sensing variables’ different value combinations,and even better than single variable at 15 days after blooming stages.From monitoring by single-variable model to the models combined of LAI and B-F,SPAD and A-B,biomass and E-F,there’s an accuracy’s increase of 14.6%,15.9% and 43.6%.(2)By analyzing the relationship between wheat’s main growth diagnosis parameters and ratio specific value combinations of the remote sensing variables,it is feasible to monitor LAI,SPAD and LNC through remote sensing variables’ratio specific value combinations,and even better than single variable at jointing stages. From monitoring by single-variable model to the models combined of LAI and C/F,SPAD and D/F,LNC and E/F,there’s an accuracy’s increase of 25.3%,7.98% and 43.5%. It is feasible to monitor LAI,SPAD and LNC through remote sensing variables’ratio specific value combinations,and even better than single variable at booting stages for LAI and SPAD.From monitoring by single-variable model to the models combined of LAI and D/F,SPAD and D/F,there’s an accuracy’s increase of 26.8% and 15%,but there’s an accuracy’s decrease of 4.1% from monitoring by single-variable model to the model combined of LNC and C/D. It is feasible to monitor LAI,SPAD,biomass and LNC through remote sensing variables’ratio specific value combinations,and even better than single variable at blooming stages for LAI,LNC and biomass.From monitoring by single-variable models to the model combined of LAI and B/C,biomass and D/F,LNC and D/F, there’s an accuracy’s increase of 24.4%,41.8% and 1.9%. but there’s an accuracy’s decrease of 16.5% from monitoring by single-variable model to the model combined of SPAD and D/E. It is feasible to monitor LAI,SPAD and biomass through remote sensing variables’ ratio specific value combinations,and even better than single variable at 15 days after blooming stages.From monitoring by single-variable model to the models combined of LAI and A/F,SPAD and A/F,biomass and A/E,there’s an accuracy’s increase of 20.2%,32.4% and 37.5%.(3) By analyzing the relationship between wheat’s main growth diagnosis parameters and normalized combinations of the remote sensing variables,it is feasible to monitor LAI,SPAD and LNC through remote sensing variables’normalized combinations,and even better than single variable at jointing stages. From monitoring by single-variable model to the models combined of LAI and (C-F)/(C+F),SPAD and (D-F)/(D+F),LNC and (A-F)/(A+F),there’s an accuracy’s increase of 35.2%,40.2% and 34.8%. It is feasible to monitor LAI,SPAD biomass and LNC through remote sensing variables’normalized combinations,and even better than single variable at booting stages. From monitoring by single-variable model to the models combined of LAI and (D-F)/(D+F),SPAD and (B-D)/(B+D),LNC and (A-D)/(A+D),biomass and (A-F)/(A+F), there’s an accuracy’s increase of 61.9%,11.2%,26.9% and 35.6%. It is feasible to monitor LAI,SPAD,biomass and LNC through remote sensing variables’normalizes combinations,and even better than single variable at blooming stages for LAI,LNC and biomass.From monitoring by single-variable models to the model combined of LAI and (B-C)/(B+C),biomass and (C-D)/(C+D),LNC and (C-D)/(C+D), there’s an accuracy’s increase of 35.1%,38.4% and 9.4%. but there’s an accuracy’s decrease of 12.4% from monitoring by single-variable model to the model combined of SPAD and (D-E)/(D+E). It is feasible to monitor LAI,SPAD and biomass through remote sensing variables’ ratio specific value combinations,and even better than single variable at 15 days after blooming stages for SPAD and LAI.From monitoring by single-variable model to the models combined of LAI and (C-F)/(C+F),SPAD and (A-F)/(A+F),there’s an accuracy’s increase of 22.5% and 21.3%, but there’s an accuracy’s decrease of 15.6% from monitoring by single-variable model to the model combined of LNC and (D-E)/(D+E).(4) By analyzing the relationship between the wheat’s main growth diagnosis parameters amplitude from jointing stage to blooming stage and the combinations of the same remote sensing variable’s different value in 2 adjacent stages,it is feasible to monitor wheat’s main growth diagnosis parameters amplitude through the combination of the same remote sensing variable’s different value in 2 adjacent stages from jointing stage to booting stage. The correlation between LAI amplitude and GNDVI(booting stage-jointing stage) is good(r=0.787),the R2 and RMSE are 0.76 and 0.58, up to scratch; the correlation between SPAD amplitude and NDVI(booting stage-jointing stage) is good too(r=0.860),the R2 and RMSE are 0.86 and 4.45, up to scratch; the correlation between LNC amplitude and RVI(booting stage-jointing stage) is good (r=-0.849),the R2 and RMSE are 0.79 and 0.77, up to scratch; the correlation between biomass amplitude and SIPI(booting stage-jointing stage) is good too(r=0.854),the R2 and RMSE are 0.82 and 4300.92, up to scratch. It is feasible to monitor SPAD and LNC amplitude through the combination of the same remote sensing variable’s different value from booting stage to blooming stage. The correlation between SPAD amplitude and NDVI(blooming stage-booting stage) is good (r=-0.831),the R2 and RMSE are 0.80 and 3.66, up to scratch; the correlation between LNC amplitude and NDVI(blooming stage-booting stage) is good (r=-0.860),the R2 and RMSE are 0.85 and 0.54, up to scratch;(5) Based on the quantitative relationship between growth diagnosis parameters and remote sensing variables, the monitoring and forecasting model can be set up to obtain the spatial distribution of wheat growth diagnosis parameters on remote sensing image. According to the different level’s diagnosis parameters and production division, images of remote sensing monitoring projects in different diagnosis parameters and output forecasting projects can be formulated. The wheat’s diagnosis is timely available which can be effectively controlled.thus we can reasonably guide production, finally realizing the good quality and high yield.
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