| 半湿润雨养农业区冬小麦冠层反射光谱与长势的相关性分析 |
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| 引用本文： | 王静,郭铌,王小平,邓超平,郭海英,张谋草,王银珍.半湿润雨养农业区冬小麦冠层反射光谱与长势的相关性分析[J].资源科学,2008,30(8):1261-1267. |
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| 作者姓名： | 王静 郭铌 王小平 邓超平 郭海英 张谋草 王银珍 |
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| 作者单位： | 中国气象局兰州干旱气象研究所，甘肃省干旱气候变化与减灾重点实验室，中国气象局干旱气候变化与减灾重点开放实验室，兰州 730020;中国气象局兰州干旱气象研究所，甘肃省干旱气候变化与减灾重点实验室，中国气象局干旱气候变化与减灾重点开放实验室，兰州 730020;中国气象局兰州干旱气象研究所，甘肃省干旱气候变化与减灾重点实验室，中国气象局干旱气候变化与减灾重点开放实验室，兰州 730020;中国气象局兰州干旱气象研究所，甘肃省干旱气候变化与减灾重点实验室，中国气象局干旱气候变化与减灾重点开放实验室，兰州 730020;中国气象局兰州干旱气象研究所，甘肃省干旱气候变化与减灾重点实验室，中国气象局干旱气候变化与减灾重点开放实验室，兰州 730020;中国气象局兰州干旱气象研究所，甘肃省干旱气候变化与减灾重点实验室，中国气象局干旱气候变化与减灾重点开放实验室，兰州 730020;中国气象局兰州干旱气象研究所，甘肃省干旱气候变化与减灾重点实验室，中国气象局干旱气候变化与减灾重点开放实验室，兰州 730020 |
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| 基金项目： | 国家自然科学基金项目:"MODIS植被指数在西北地区应用研究"
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中国气象局气象新技术推广项目:"青藏高原东北部退牧还草效益遥感监测与评估" |
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| 摘 要： | 本文研究了黄土高原半湿润代表区甘肃省庆阳市西峰区冬小麦主要生育期（返青-抽穗）冠层反射光谱与反映长势的指标覆盖度及叶面积指数间的关系。结果表明，冬小麦覆盖度及叶面积指数同450nm、550nm、650nm、1 650nm波段反射率呈负相关关系，同850nm波段反射率呈正相关关系。起身前，冬小麦长势指标覆盖度及叶面积指数同各波段反射率相关关系大多不显著。起身以后，冬小麦覆盖度和叶面积指数与各波段反射率呈极显著相关关系。另外，为了更好的监测冬小麦长势，研究了8个常用的植被指数同冬小麦长势间的相关关系，这8个植被指数在起身后同冬小麦覆盖度及叶面积指数间存在着极显著的相关关系。并且，利用这8个植被指数建立了冬小麦长势线性及非线性回归监测模型，其中，线性模型能更好的监测冬小麦覆盖度，指数模型能更好的监测冬小麦叶面积指数，但不同的植被指数，拟合程度不同，其中，尤以NDVI拟合程度最高。
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| 关 键 词： | 冬小麦 反射光谱 长势 植被指数 相关性 |
Correlation Analysis of Canopy Reflectance and Growth of Winter Wheat in a Semi-Wet Rainfed Agriculture Area |
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| WANG Jing,GUO Ni,WANG Xiao-ping,DENG Chao-ping,GUO Hai-ying,ZHANG Mou-cao and WANG Yin-zhen.Correlation Analysis of Canopy Reflectance and Growth of Winter Wheat in a Semi-Wet Rainfed Agriculture Area[J].Resources Science,2008,30(8):1261-1267. |
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| Authors: | WANG Jing GUO Ni WANG Xiao-ping DENG Chao-ping GUO Hai-ying ZHANG Mou-cao and WANG Yin-zhen |
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| Abstract: | Crop growth characteristics such as leaf area index and coverage are critical to yield formation throughout the entire period of growth. Thus, crop growth monitoring is important for prediction of crop yield and evaluation of agricultural production. At present, ground-based remote sensing technology, with many advantages as it is timely, dynamic, economical and readily available, has become a main method for crop growth monitoring. By studying the relationship of spectral features and growth indices and constructing models between them, the technology can better be used to monitor crop growth conditions. However, due to differences in crop type and variety, environmental conditions, and available sensors, existing monitoring models can not be used across all regions, and regionally relevant models are required.
This research studies the relationship between growth indices and canopy reflectance to construct relevant monitoring models and provide information on winter wheat production in semi-wet rainfed agricultural areas. The focus is the relationship between growth condition indices (coverage and leaf area index) and canopy reflectance of winter wheat during the main growth stages (regreening-heading) in Xifeng district of Qingyang City of Gansu, which is a typical semi-wet region. The result showed that winter wheat growth had a negative correlation with the spectral reflectance bands of 450nm, 550nm, 650nm, and 1650nm, and positive correlation with the 850nm band. The correlation was not significant before the erecting stage, and was constrained by crop growing stages and spectral characteristics. After the erecting stage, it was significant because the wheat was growing.
To improve monitoring of crop growth, the correlations of eight normal vegetation indexes including NDVI, EVI, RVI, DVI, SAVI, MSAVI, RDVI, and NIR/G with coverage and leaf area index were analyzed. Just as with the single band reflectance, the correlation was not significant before the erecting stage but highly significant after erecting. The correlations were higher than with single band reflectance, which suggests that vegetation indices are better for monitoring winter wheat growth.
Finally, linear and exponential monitoring models of winter wheat growth were constructed using the eight vegetation indexes. The correlation coefficients of models all exceeded 0.6. For coverage, all linear regression models performed better than the exponential models, but the opposite was true for leaf area index. Among all linear and exponential models, the fitting degree of the model using NDVI was best. Overall, for semi-wet rainfed agricultural regions, a linear regression model of NDVI is better for monitoring winter wheat coverage, while an exponential model of NDVI is better for monitoring winter wheat leaf area index. |
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| Keywords: | Winter wheat Reflectance spectra Growth Vegetation index Correlation |
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