联合PROSAIL模型和植被水分指数的低矮植被含水量估算

植被冠层含水量CWC(Canopy Water Content)和植被地上部分含水量VWC(Vegetation Water Content)对于植被健康状况和土壤干旱监测具有重要意义.本文联合PROSAIL辐射传输模型和植被水分指数NDWI(Normalized Difference Water Index),发展了...     

Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy

Fine scale maps of vegetation biophysical variables are useful status indicators for monitoring and managing national parks and endangered habitats. Here, we assess in a comparative way four different retrieval methods for estimating leaf area index (LAI) in grassland: two radiative transfer model (RTM) inversion methods (one based on look-up-tables (LUT) and one based on predictive equations) and two statistical modelling methods (one partly, the other entirely based on in situ data). For prediction, spectral data were used that had been acquired over Majella National Park in Italy by the airborne hyperspectral HyMap instrument. To assess the performance of the four investigated models, the normalized root mean squared error (nRMSE) and coefficient of determination (R²) between estimates and in situ LAI measurements are reported (n = 41). Using a jackknife approach, we also quantified the accuracy and robustness of empirical models as a function of the size of the available calibration data set. The results of the study demonstrate that the LUT-based RTM inversion yields higher accuracies for LAI estimation (R² = 0.91, nRMSE = 0.18) as compared to RTM inversions based on predictive equations (R² = 0.79, nRMSE = 0.38). The two statistical methods yield accuracies similar to the LUT method. However, as expected, the accuracy and robustness of the statistical models decrease when the size of the calibration database is reduced to fewer samples. The results of this study are of interest for the remote sensing community developing improved inversion schemes for spaceborne hyperspectral sensors applicable to different vegetation types. The examples provided in this paper may also serve as illustrations for the drawbacks and advantages of physical and empirical models.     

Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3

Sentinel-2 is planned for launch in 2014 by the European Space Agency and it is equipped with the Multi Spectral Instrument (MSI), which will provide images with high spatial, spectral and temporal resolution. It covers the VNIR/SWIR spectral region in 13 bands and incorporates two new spectral bands in the red-edge region, which can be used to derive vegetation indices using red-edge bands in their formulation. These are particularly suitable for estimating canopy chlorophyll and nitrogen (N) content. This band setting is important for vegetation studies and is very similar to the ones of the Ocean and Land Colour Instrument (OLCI) on the planned Sentinel-3 satellite and the Medium Resolution Imaging Spectrometer (MERIS) on Envisat, which operated from 2002 to early 2012. This paper focuses on the potential of Sentinel-2 and Sentinel-3 in estimating total crop and grass chlorophyll and N content by studying in situ crop variables and spectroradiometer measurements obtained for four different test sites. In particular, the red-edge chlorophyll index (CI_red-edge), the green chlorophyll index (CI_green) and the MERIS terrestrial chlorophyll index (MTCI) were found to be accurate and linear estimators of canopy chlorophyll and N content and the Sentinel-2 and -3 bands are well positioned for deriving these indices. Results confirm the importance of the red-edge bands on particularly Sentinel-2 for agricultural applications, because of the combination with its high spatial resolution of 20 m.     

A shadow- eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains

ABSTRACT

The effect of terrain shadow, including the self and cast shadows, is one of the main obstacles for accurate retrieval of vegetation parameters by remote sensing in rugged terrains. A shadow- eliminated vegetation index (SEVI) was developed, which was computed from only red and near-infrared top-of-atmosphere reflectance without other heterogeneous data and topographic correction. After introduction of the conceptual model and feature analysis of conventional wavebands, the SEVI was constructed by ratio vegetation index (RVI), shadow vegetation index (SVI) and adjustment factor (f (Δ)). Then three methods were used to validate the SEVI accuracy in elimination of terrain shadow effects, including relative error analysis, correlation analysis between the cosine of solar incidence angle (cosi) and vegetation indices, and comparison analysis between SEVI and conventional vegetation indices with topographic correction. The validation results based on 532 samples showed that the SEVI relative errors for self and cast shadows were 4.32% and 1.51% respectively. The coefficient of determination between cosi and SEVI was only 0.032 and the coefficient of variation (std/mean) for SEVI was 12.59%. The results indicate that the proposed SEVI effectively eliminated the effect of terrain shadows and achieved similar or better results than conventional vegetation indices with topographic correction.     

一种协同反演气溶胶与水汽含量的高光谱图像大气校正算法

大气校正是高光谱图像定量反演地表参数的前提。为充分利用高光谱数据本身的光谱特点,提出了一种协同反演大气气溶胶光学厚度(aerosol optical thickness,AOT)与水汽含量(water vapor content,WV)的大气校正方法,在同时考虑了气溶胶模式、AOT和WV这3个因素的综合影响基础上,采用循环迭代的思想,基于6S辐射传输模型,反演大气参数及地表反射率,弥补了现有反演算法中没有同时考虑AOT与WV的不足;并以武汉市Hyperion高光谱图像为例,验证了该算法的有效性。从与FLAASH算法及MOIDS提供的AOT和WV产品对比来看,该算法能较好地校正气溶胶与水汽对高光谱图像的影响,且反演过程中所有的输入均来自图像数据本身或6S辐射传输模型,无需输入额外的参数。