Dr. Xiaoping Chen Profile

Dr. Xiaoping Chen

at Harbin Institute of Tech Shenzhen Graduate Shool

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Proceedings Article | 18 November 2014 Paper

An evaluation of prediction accuracy and stability of a new vegetation index for estimating vegetation leaf area index

Hailing Jiang, Lifu Zhang, Hang Yang, Xiaoping Chen, Shudong Wang, Xueke Li, Kai Liu

Proceedings Volume 9263, 92631B (2014) https://doi.org/10.1117/12.2068403

KEYWORDS: Sensors, Silicon, Vegetation, Reflectivity, Remote sensing, Biological research, Hyperspectral simulation, Near infrared, Earth observing sensors, Landsat

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LAI is a crucial parameter and a basic quantity indicating crop growth situation. Empirical models comprising spectral indices (SIs) and LAI have widely been applied to the retrieval of LAI. SI method already has exhibited feasibility in the estimation of vegetation LAI. However, it is largely subject to the inconsistency from different remote sensors which have varied specifications, such as spectral response features and central wavelength. To address this issue, a new vegetation index (VIUPD) based on the universal pattern decomposition method was proposed. It is expressed as a linear sum of the pattern decomposition coefficients and features in sensor-independency. The aim of this study was to evaluate the prediction accuracy and stability of VIUPD for estimating LAI, compared with three other common-used SIs. In this study, the measured spectra were resampled to simulated TM multispectral data and Hyperion hyperspectral data respectively, using the Gaussian spectral response function. The three typical SIs chosen were including NDVI, TVI and MCARI, which were constructed with the sensitive bands to the LAI. Finally, the regression equations between four selected SIs and LAI were established. The best index evaluated using the simulated TM data was VIUPD which exhibits the best correlation with LAI (R²=0.92) followed by NDVI (R²=0.80). For the simulated Hyperion data, VIUPD again ranks first with R²=0.89, followed by TVI (R2=0.63). Meanwhile, the consistence of VIUPD also was studied based on simulated TM and Hyperion sensor data and the R² reached to 0.95. It is demonstrated that VIUPD has the best accuracy and stability to estimate LAI of winter wheat whether using simulated TM data or Hyperion data, which reaffirms that VIUPD is comparatively sensor independent.

Proceedings Article | 8 November 2014 Paper

Comparison of the sensor dependence of vegetation indices and vegetation water indices based on radiative transfer model

Xiaoping Chen, Shudong Wang, Hailing Jiang, Xia Zhang

Proceedings Volume 9260, 92603G (2014) https://doi.org/10.1117/12.2069033

KEYWORDS: Vegetation, Reflectivity, Atmospheric sensing, Sensors, Radiative transfer, Atmospheric modeling, Absorption, Satellites, Multispectral imaging, Remote sensing

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The vegetation index (VI) and vegetation water index (VIw) have long been used for plant water stress detection indiscriminately, without considering the effects of differences in their band selection. To address this, this study quantitatively compared the difference of sensor dependence for the two indices based on canopy/atmospheric radiative transfer model. Five different bandwidths at canopy and top-of-atmosphere scale were simulated separately for 23 classic indices. The results show that VIws exhibited better correlation with vegetation water content (VWC) at both scale ( R2 : 0.835; 0.812) in comparison with VIs ( R2 : 0.474; 0.475). To quantitatively describe the uncertainty caused by bandwidth, a new index variability was established. VIws and VIs performed entirely differently: at canopy scale, the uncertainty caused by bandwidths for VIws and VIs is 13.703% and 43.451%, respectively. However, at top-of-atmosphere scale, the uncertainty for VIws and VIs is 32.021% and 41.265%. VIws exhibited less dependence on bandwidth and were more affected by atmospheric effect than VIs. We attribute these differences to differences in band selection: VIws based on water absorption features are more sensitive to not only variation of VWC but also atmospheric conditions. Conversely, as chlorophyll absorption features which VIs are calculated on effectively avoid atmospheric absorption features and are located in red edge region, VIs are found less affected by the atmosphere condition and extremely sensitive to bandwidth. Results figure out the differences we should focus on when we choose VI or VIw from different sensors for VWC retrieval.

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