Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.
LIU Liang-yunHUANG Wen-jiangPU Rui-liangWANG Ji-hua
Satellite-based remote sensed phenology has been widely used to assess global climate change.However,it is constrained by uncertain linkages with photo-synthesis activity.Two dynamic threshold methods were employed to retrieve spring phenology metrics from four Moderate Resolution Imaging Spectro-radiometer(MODIS)products,including fraction of Absorbed Photosyntheti-cally Active Radiation(fAPAR),Leaf Area Index(LAI),Normalized Difference Vegetation Index(NDVI),and Enhanced Vegetation Index(EVI)for three temperate deciduous broadleaf forests in North America between 2001 and 2009.These MODIS-based spring phenology metrics were subsequently linked to the photosynthetic curves(daily gross primary productivity,GPP)measured by an eddy covariance flux tower.The 20% dynamic threshold spring onset metrics from MODIS products were closer to the photosynthesis onset metrics at the date of 2% GPP increase for NDVI and fAPAR,and closer to the date of 5%and 10% increase of GPP for EVI and LAI,respectively.The 50% dynamic threshold onset metrics were closer to the photosynthesis onset metrics at the date of 10%GPP increase for NDVI,and closer to the date of 20% GPP increase for fAPAR,LAI and EVI,respectively.These results can improve our knowledge on the photosynthesis activity status of remotely sensed spring phenology metrics.
Time-series remote sensing images were previously employed to detect land use and land-cover changes and to analyze related trends. However,land-cover change mapping using time-series remote sensing data,especially medium-resolution imagery,was often constrained by a lack of high-quality training and validation data,especially for historical satellite images. In this study,we tested and evaluated a generalized classifier for time series Landsat Thematic Mapper( TM) imagery based on spectral signature extension. First,a new atmospheric correction procedure and a robust relative normalization method were performed on time-series images to eliminate the radiometric differences between them and to retrieve the surface reflectance. Second,we selected one surface reflectance image from the time series as a source image based on the availability of reliable ground truth data. The spectral signature was then extracted from the training data and the source image. Third,the spectral signature was extended to all the corrected time-series images to build a generalized classifier. This method was tested on a time series consisting of five Landsat TM images of the Tibetan Plateau,and the results showed that the corrected time-series images could be classified effectively from the reference image using the generalized classifier. The overall accuracy achieved was between 88. 35% and 94. 25%,which is comparable with the results obtained using traditional scene-by-scene supervised classification. Results also showed that the performance of the extension method was affected by the difference in acquisition times of the source image and target image.
