Study On Growth Predicting Technique Based On Integration Of Remotely Sensed Information And Crop Model In Rice

Rice (Oryza sativa L.) is the most important food crop in China, whose total yield takes the first location in the world. Under the complex climate and economic condition, estimating the rice's growth status and production information in time and accurately is important for China's food security and agricultural sustainable development. Crop model and remote sensing are both useful tools in predicting crop growh and productivities. Integration of remote sensing (RS) and crop growth model is an important approach to improving the ability of monitoring and predicting crop growth. Rice growth prediction techniques were developed by integrating the ground-based and space-borne remote sensed data and RiceGrow model based on the assimilation strategy and updating strategy in this paper. Leaf area index (LAI) and leaf nitrogen accumulation (LNA) of rice were estimated using ASD field spectrometer and HJ-1 A/B CCD, based on statistical remote sensing estimation models. This information was integrated with the RiceGrow model for got three management parameters included sowing date, sowing rate, and nitrogen rate. Then the rice yield can be predicted by inputting these parameters. This integrated technique was tested based on independent datasets. The results showed that both LNA and LNA, was better than either as an integrated parameter for crop model parameter initialization. And the Particle Swarm Optimization (PSO) algorithm was more suitable for using in the initialization process than the Shuffled Complex Evolution-University of Arizona (SCE-UA) optimization algorithm.Coupling remotely sensed information and crop growth model can improve the prediction accuracy of crop model in regional scale. A new technique was developed for estimating rice growth parameters and grain yield in both field and regional scales, based on coupling remotely sensed information and rice growth model (RiceGrow) by combing the updating and assimilation strategies. This technique assimilated two growth parameters into RiceGrow model including remotely sensed leaf area index (LAI) and leaf nitrogen accumulation (LNA). The results showed that the predicted values of LAI, LNA and grain yield for RiceGrow model after using updating and assimilation strategy were more close to measured values than ones only using RiceGrow model or assimilation strategy. In addition, the new developed technique also well described the temporal and spatial distributions of rice growth status and grain yields in the study area, with less than 20% of the RE values for both growth parameter and regional total grain yield. Which indicated that the new crop growth and yield prediction technique had a good prediction precision and actual application for rice crop in both field and reginal scales.Rice growth monitoring and predicting system (RGMPS) based on integrating remote sensing and RicetGrow model was developed using object oriented programming technology. Which was developed by taking Microsoft.NET Framework 3.5 as the development environment and C# as the programming language to definite the system structure and interface, and integrating crop growth model components, ESRI ArcGIS Enginec 9.3 and the remote sensing processing modules developed by IDL. Varied functions were realized by this system, such as image processing and spectral information extraction, growth monitoring based on RS, growth and yield simulating and predicting, thematic mapping, and so on.