Estimating rice nitrogen status with satellite remote sensing in Northeast China

Rice farming in Northeast China is crucially important for China's food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production, but also improve N use efficiency and protect the environment. Handheld chlorophyll meter (CM) and active crop canopy sensors have been used to improve rice N management in this region. However, these technologies are still time consuming for large scale applications. Satellite remote sensing provides a promising technology for large scale crop growth monitoring and precision management. The objective of this study was to evaluate the potential of using Formosat-2 satellite remote sensing to estimate rice N status at key growth stages in Northeast China. A village of approximately 2000 ha rice fields in Qixing Farm was selected in 2011, and two Formosate-2 satellite images were collected at the panicle initiation and heading stages. Ground truth data were collected from different farmer's fields, including tiller numbers, biomass, leaf area index (LAI), plant N concentration, plant N uptake, chlorophyll meter (CM) readings, and N nutrition index (NNI). Preliminary analysis results indicated that normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), ratio vegetation index (RVI), green ratio vegetation index (GRVI), enhanced vegetation index (EVI), chlorophyll index (CI), and plant senescence reflectance index (PSRI) were all significantly correlated with biomass, LAI, nitrogen uptake, CM readings and NNI (P<;0.01) at the panicle initiation stage. NDVI and GNDIV were best correlated with biomass (R²=0.41), LAI (R²=0.50) and N uptake (R²=0.44), respectively, while RVI was best correlated with CM readings (R²=0.41) and NNI (R²=0.32). Variety-specific correlations between RVI and CM readings were significantly better than the overall correlation between these two variables, reducing the root mean square error (RMSE) from 2.59 to 2.21 and 2.12 and 2.04, respectively. However, plant N concentration could not be estimated satisfactorily.