Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content （LCC）. This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge...Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content （LCC）. This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed （Brassica napus L.） and wheat （Triticum aestivum L.） crops. The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC. This finding allowed the construction of a new red edge param- eter, defined as red edge symmetry （RES）. Compared to the commonly used red edge parameters （red edge position, red edge amplitude, and red edge area）, RES was a better predictor of LCC. Furthermore, RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm （R675 and R755） and reflectance of red edge center wavelength at 718 nm （R718）, with the equation RES = （R71s - R675）/（R755 - R675）. In addition, RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion. The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data. This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources, as well as ground-based spectral reflectance data.展开更多
Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron ...Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron （MLP） artificial neural network （ANN） based prediction system was presented for predicting the leaf population chlorophyll content from the cotton plant images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cotton plant images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained leaf green pixels, the system training was carried out by applying a back propagation algorithm. The proposed system was tested to predict the chlorophyll content from the cotton plant images. The results using the proposed system were in sound agreement with those obtained by the destructive method. The average prediction relative error for the chlorophyll density （μg cm^-2） in the 17 testing images was 8.41%.展开更多
基金Supported by the Program for New Century Excellent Talents in University of China(No.NCET-08-0797)the National Natural Science Foundation of China(No.30871448)+1 种基金the Natural Science Foundation of Jiangsu Province,China(No.BK2008330)the Program for the Creative Scholars of Jiangsu Province,China(No.BK20081479)
文摘Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content （LCC）. This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed （Brassica napus L.） and wheat （Triticum aestivum L.） crops. The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC. This finding allowed the construction of a new red edge param- eter, defined as red edge symmetry （RES）. Compared to the commonly used red edge parameters （red edge position, red edge amplitude, and red edge area）, RES was a better predictor of LCC. Furthermore, RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm （R675 and R755） and reflectance of red edge center wavelength at 718 nm （R718）, with the equation RES = （R71s - R675）/（R755 - R675）. In addition, RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion. The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data. This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources, as well as ground-based spectral reflectance data.
基金supported by the Chinese Scholarship Council (CSC) and the Minzu University of China(CUN0246)
文摘Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron （MLP） artificial neural network （ANN） based prediction system was presented for predicting the leaf population chlorophyll content from the cotton plant images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cotton plant images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained leaf green pixels, the system training was carried out by applying a back propagation algorithm. The proposed system was tested to predict the chlorophyll content from the cotton plant images. The results using the proposed system were in sound agreement with those obtained by the destructive method. The average prediction relative error for the chlorophyll density （μg cm^-2） in the 17 testing images was 8.41%.
