Rice Monitoring Based On The Connection Of ASAR Data With Crop Growth Simulation Model

Rice crop is one of most important grain crop in the world and our country, and itsinformation of planting area and growth status is valuable for grain safety, international trade,et al., so it is important to set up an efficient rice monitoring system. Remote sensing is themain monitoring tools for crops; however, it is difficult to gather the rice information by opticalremote sensing image due to cloudy and rainy weather condition during the tropic andsub-tropic rice season. SAR is the reliable remote sensing image for rice monitoring withall-weather and all-daily characteristics, so it is important to find the way to monitor rice bySAR. Rice is a special crop with a lay of water under the canopy in the whole growth season,and the backscattering almost come from the rice plant itself which makes it more feasible tomonitor the rice with SAR images.It is not feasible to continuously monitor rice crop in large area by remote sensing becauseof several difficultness, and there are a lot of time gaps of rice monitoring in the whole riceseason. In addition radar remote sensing just gathers the presentational rice information andcan not retrieve the internal growth information. While rice growth simulation model canunderstand the whole information of rice growth owing to its mechanism of process. Socoupling ASAR and rice simulation model is the efficient way to continuously gather the wholeinformation of rice growth in large area.In this study, it has been done in Xinhua, Jiangsu to gather systemically the direct-sowedrice parameters including plant height, above-ground wet matter, dry matter, leaves dry matter,heads biomass, stems biomass, plant morphological parameters and phenological developmentstages. In addition the roughness of soil surface has been measured. Four temporal ENVISATASAR data and daily weather data in rice season were also collected. The main study contentsare as following:1. Analyzed the backscattering characteristic of rice based on 4 temporal ASAR.Compared the precision of decision tree classification with supervised classification. 2. Understood the relationship between canopy water content and LAI, plant height,biomass, et al. Studied the inversion of rice parameters based on water-cloud modeland experiential model.3. Analyzed the obstacle to coupling ASAR and rice simulation model. And studied themethod to upscale rice simulation model.4. Found out the way to connect the inversion data to the upscaled rice simulation model.The main results of this papar are as following:1. The producer precision and the classification precision are both about 80% formultitemporal ASAR data by the decision tree ruler, and are 77% for single temporalASAR. By the supervised classification method, the classification precision is about87%, while the producer classification precision is about 70% for multitemporal ASARdata.2. Before the middle of rice season, there is positive linear relationship between canopywater content and biomass, LAI, plant height.3. Successfully upscaled the point-scale rice simulation model to field-scale rice model byintroducing the new concept of virtual rice variety and comprehensive nutrient factor.4. The main parameters to connect the inversing data of ASAR and upscaled ricesimulation model is comprehensive nutrient factor. The coupled method can get thegrowth information in whole rice season.