Study On Growth Monitoring And Predicting Technique Based On Integration Of Remote Sensed Information And Model In Wheat

Real-time and non-destructive monitoring of crop growth and accurate grain yield and qulities predicting could improve traditional crop cultivation and real-time forecasting techniques, and it's also very important for developments of food security and sustainable agriculture. Integration of remote sensing (RS) and crop growth model is an important approach to improving the ability of monitoring and predicting crop growth. Winter wheat (Triticum aestivum L.) growth prediction techniques were developed by integrating the ground-based and space-borne remote sensed data and winter wheat growth model (WheatGrow) based on the initialization trategy and updating strategy in this paper. Leaf area index (LAI) and leaf nitrogen accumulation (LNA) estimated using ASD, HJ-1 A/B CCD, Landsat-5 TM data were combined to WheatGrow model in three different growth stages by the calibration trategy. While management parameters including the sowing date, sowing rate and nitrogen rate, acquired difficultly at regional scale, were reversed by the initialization scheme. This integrated technique was tested based on independent datasets. The results showed that LNA was better than LAI as an integrated parameter predicting wheat growth and grain yield, and the heading stage was the best integtated period. In addition, predicted results well described the temporal and spatial distribution of winter wheat growth status and productivity in the study area.Based on the updating trategy, a new deterministic algorithm named Ensemble Square Root Filter (EnSRF), which was an improved grounded on Ensemble Kalman Filter (EnKF), was used for integrating remote sensing (ASD spectral data, HJ-1 A/B CCD and Landsat-5 TM data) and WheatGrow model. The analysis value of model variables such as leaf area index (LAI) and leaf nitrogen accumulation (LNA) were calculated based on EnSRF without perturbed observations. This technique was tested on independent datasets. The result showed that values of LAI and LNA based on EnSRF agreed better with actual values than values simulated by WheatGrow model or remote sensed by different sensors. In addition, predicted results based on this newly developed integrated technique also well described the temporal and spatial distribution of winter wheat growth status and productivity in the study area.Definition of management zone based on the crop potential productivity, nutrient utilization efficiency and environmental effects could combine crop, soil, topography and other factors comprehensively, and increase predicting precision and effectivity of crop growth model based on these divided zones. Wheat canopy NDVI (normalized difference vegetation index) data, calculated from HJ-1 A/B CCD images of three different winter wheat growth stages, and soil nutrient indices, including total nitrogen content, organic matter content, available phosphorus content, and available potassium content, were selected as data sources to study the zone division technique of wheat managemen field in county level. With spatial variability analysis and principal component extraction, fuzzy algorithms isodata was used to define management zones. The valuses of variation coefficients of each zone divided by combined of NDVI at heading and soil nutrient indices were between4.49-6.06% for NDVI and 3.25～87.89% for soil nutrient indices, which were lower than the values of zone divided by individal NDVI or soil nutrient index with corresponding value range of 5.76～7.52%,3.44～170.00% and 2.65～12.06%, 8.90-148.00% respectively. This result would contribute to efficient growth management and process simulating in region level.Wheat growth monitoring and predicting system (WGMPS) based on integrating remote sensing and WheatGrow model was developed using object oriented programming technology. Which developed by taking Microsoft.NET Framework 2.0 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 Engine 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. WGMPS performed well in growth monitoring and yield predicting tested using datasets from the weak-gluten wheat production area of Jiangsu province.