RESUMEN
The vertical distribution of crop nitrogen is increased with plant height, timely and non-damaging measurement of crop nitrogen vertical distribution is critical for the crop production and quality, improving fertilizer utilization and reducing environmental impact. The objective of this study was to discuss the method of estimating winter wheat nitrogen vertical distribution by exploring bidirectional reflectance distribution function (BRDF) data using partial least square (PLS) algorithm. The canopy reflectance at nadir, +/-50 degrees and +/- 60 degrees; at nadir, +/- 30 degrees and +/- 40 degrees; and at nadir, +/- 20 degrees and +/- 30 degrees were selected to estimate foliage nitrogen density (FND) at upper layer, middle layer and bottom layer, respectively. Three PLS analysis models with FND as the dependent variable and vegetation indices at corresponding angles as the explicative variables were. established. The impact of soil reflectance and the canopy non-photosynthetic materials, was minimized by seven kinds of modifying vegetation indices with the ratio R700/R670. The estimated accuracy is significant raised at upper layer, middle layer and bottom layer in modeling experiment. Independent model verification selected the best three vegetation indices for further research. The research result showed that the modified Green normalized difference vegetation index (GNDVI) shows better performance than other vegetation indices at each layer, which means modified GNDVI could be used in estimating winter wheat nitrogen vertical distribution
Asunto(s)
Nitrógeno/análisis , Hojas de la Planta/química , Triticum/química , Algoritmos , Análisis de los Mínimos Cuadrados , Análisis EspectralRESUMEN
The present study aims to explore capability of different methods for winter wheat leaf area index inversion by integrating remote sensing image and synchronization field experiment. There were four kinds of LAI inversion methods discussed, specifically, support vector machines (SVM), discrete wavelet transform (DWT), continuous wavelet transform (CWT) and principal component analysis (PCA). Winter wheat LAI inversion models were established with the above four methods respectively, then estimation precision for each model was analyzed. Both discrete wavelet transform method and principal component analysis method are based on feature extraction and data dimension reduction, and multivariate regression models of the two methods showed comparable accuracy (R2 of DWT and PCA model was 0. 697 1 and 0. 692 4 respectively; RMSE was 0. 605 8 and 0. 554 1 respectively). While the model based on continuous wavelet transform suffered the lowest accuracy and didn't seem to be qualified to inverse LAL It was indicated that the nonlinear regression model with support vector machines method is the most eligible model for estimating winter wheat LAI in the study area.