Research On Biomass Estimation Of Crops With Remote Sensing

Crop aboveground biomass(AGB)is one of the most important indicators of agriculture ecosystem monitoring.It is not only closely related to crop growth monitoring,yield and production estimation,but also the important content of the research for global climate change,carbon cycle,material cycle,energy exchange and other aspects.Crop AGB estimation has alwalys been the hot topic in the field of agriculutural research,however,it also faces a tough challegen.In recent years,due to the rapid growth of population,the deterioration of the environment and many other factors,food production,food security,and farmland ecosystem sustainability have also been influenced in varing degrees.Therefore,the accurate estimation and dynamic monitoring of biomass can provide an important information for efficient use of farmland resources.The traditional method for crop biomass and yield mainly involves laborious field observation and can't meet the demand at regional scale.Meanwhile,the remote sensing technology which is characterized with the near real-time observing ability,the crop biomass estimation based on remote sensing has become a hotspot research,which is also of great significance for the research on global change.The main goal of this thesis is taking advantage of multi-source remote sensing data to improve the accuracy of crop biomass estimation.In this research,the biomass of wheat,maize and rice was estimated through using red edge band indices,spatiotemporal data fusion and combinatorial index methods,and then a number of biomass estimation models were proposedand analyzed.Finally,these models were verified by ground measured data.This thesis provides new approachesfor the accurate prediction of crop biomass.(1)In this study,canopy reflectance spectral and aboveground biomass(AGB)were obtained from field experiments,the corresponding reflectance of MERIS bands were simulated using the spectral response function(SRF)and canopy reflectance,then best fit relationships were established between sixteen vegetation indices(VIs)which derived from the simulated bands and AGB of spring maize and winter wheat,finally correlation and sensitivity analyses were worked out.Results show that all the VIs tested in the paper were significantly correlated with AGB.The red-edge chlorophyll index(CIre)and red-edge NDVI(NDVIre)performed best for AGB estimation for maize(R2=0.94,RMSE=0.112 kg/m2)and wheat(R2=0.94,RMSE=0.158 kg/m2),respectively.Several Vis,such as normalized difference vegetation index(NDVI),will saturate at moderate-to-high biomass stages.The indices incorporate red-edge bands were more closely related to biomass and can delay the saturation phenomenon,but insensitive to crop types.The normalized difference vegetation indices and ratio indices were sensitive to biomass variations in the low and moderate-to-high biomass stages,respectively.The red-edge simple ratio(SRre)and MERIS terrestrial chlorophyll index(MTCI)can be used as a stable index for the AGB estimation over the whole growing season.Aim to improve the accuracy of crop AGB,the combined indices are suggested for diffent phenology stage of crops.(2)We mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation(PROBA-V)products in the North China Plain(NCP).First,the daily 100-m land surface reflectance was generated by fusing the PROBA-V 100-m and 300-m S1 products.Our results show that the blended data exhibited high correlations with the referenced data(0.71 ? R2 ? 0.94 for the red band,0.50 ? R2 ? 0.95 for the near-infrared band,and 0.88 ? R2 ? 0.97 for the shortwave infrared band).The time-series Normalized Difference Vegetation Index(NDVI)derived from the synthetic reflectance was then clustered for winter wheat identification.The overall classification accuracy was between 78% and 87%,with a kappa coefficient above 0.57,which was 10%-20% higher than the classification accuracy using the 300-m data.Finally,a light use efficiency model with the input of the remote sensing parameters,was used to estimate the biomass and yield.The estimation results were closely related to the field-measured biomass and yield,with high R2 and low root mean square errors(RMSE)(0.864 ? R2 ? 0.871 and 168 ? RMSE ? 191 g/m2 for biomass;0.631 ? R2 ? 0.663 and 41.8 ? RMSE ? 62.8 g/m2 for yield).This paper shows the greatpotential of using PROBA-V 100-m data to enhance the spatial resolution of PROBA-V 300-m data.The proposed framework in this study was provides a novel approach for crop areas and yields estimation with the latest new satellite data.(3)In this paper,the aboveground biomass of rice was estimated by optical data(GF-1)and radar data(Radarsat-2),respectively.Results showed that all of the selected vegetation indices and most of the polarization parameters were significantly correlated with the measured rice biomass,and CIgreen and Anisotropy have the highest estimation accuracies,respectively(R2=0.6123,RMSE=0.4861 kg/m2;R2=0.6543,RMSE=0.5418 kg/m2).Then,considering the different characteristics of these two remote sensing data,the vegetation indices and the polarization parameters are combined to estimate biomass.Compared with the single index or parameter,the combined new index can improve the estimation accuracy of biomass,and the sensitivity analysis also shows that EVI*Anisotropy and EVI*RVI have good stability during the whole growth period of rice.Therefore,these two new indices could be used to develop the estimation model of rice biomass.Although the methods proposed in this thesis have improved the estimation accuracy of crop biomass,quite a few uncertainties,such as the effection of spatial scales and the validation need a further study.