Estimating Winter Wheat Nitrogen Vertical Distribution Based on Bidirectional Canopy Reflected Spectrum
YANG Shao-yuan1, 2, 3, HUANG Wen-jiang1*, LIANG Dong2, 3, HUANG Lin-sheng2, 3, YANG Gui-jun4, ZHANG Dong-yan2, 3, CAI Shu-hong5
1. Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China 2. Key Laboratory of Intelligent Computer & Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China 3. School of Electronic and Information Engineering, Anhui University, Hefei 230039, China 4. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China 5. Hebei Agricultural Technique Extension Station, Shijiazhuang 050000, China
摘要： 作物氮素具有随植株高度层垂直分布的特性，快速、无损探测作物氮素垂直分布状况，对于指导合理施肥、提高肥料利用率和减少环境污染具有重要意义。本文提出了利用偏最小二乘(partial least square，PLS)算法，运用多角度光谱数据估计冬小麦氮素含量垂直分布的方法。分别选用前向和后向不同观测角度组合形成的光谱数据组建植被指数，建立不同高度层的叶片氮素含量探测模型，其中选用±50°和±60°的组合，建立了冬小麦上层叶位叶片氮密度反演模型;选用±30°和±40°的组合，建立了中层叶位叶片氮密度反演模型;选用±20°和±30°的组合，建立了下层叶位叶片氮密度反演模型。针对氮素反演容易受到作物背景(土壤、作物残渣)影响的问题，引入R700/R670比值，改进七种常见的植被指数，利用改进了的植被指数建立了冬小麦上层、中层、下层叶片氮密度垂直分布模型。建模实验结果改进了叶片氮密度上层、中层、下层垂直分布估算结果，验证实验选取建模实验中表现最好的三个植被指数进行进一步研究，结果表明改进后的绿光归一化植被指数(green normalized difference vegetation index，GNDVI)在反演上层、中层、下层叶片氮密度时效果最好，达到了极显著的水平，可用于植被氮素含量的垂直分布探测。
关键词：冬小麦;氮密度;冠层光谱;多角度;垂直分布;偏最小二乘
Abstract：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 enviro nmental 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°and ±60°; at nadir, ±30°and ±40°; and at nadir, ±20°and ±30°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
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