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Jin Chen  - - - 
Top co-authors
Alfredo Huete

208 shared publications

Ecosystem Dynamics, Health and Resilience, University of Technology Sydney, Ultimo 2007, NSW, Australia

Bunkei Matsushita

37 shared publications

Graduate School of Life and Environmental Studies, University of Tsukuba, Tsukuba, Japan

Guo Yu Qiu

18 shared publications

Key Laboratory for Urban Habitat Environment Science and Technology, School of Environment and Energy, Shenzhen Graduate School of Peking University, Shenzhen 518055, China

Jing Li

7 shared publications

Beijing Normal University

Wei Yang

3 shared publications

Key Laboratory of Environment Change and Natural Disaster, Ministry of Education of China, College of Resource Science and Technology, Beijing Normal University, Beijing 100875, China. s

Publication Record
Distribution of Articles published per year 
(2004 - 2019)
Total number of journals
published in
Publications See all
Article 0 Reads 0 Citations Replacing the Red Band with the Red-SWIR Band (0.74ρred+0.26ρswir) Can Reduce the Sensitivity of Vegetation Indices to S... Xuehong Chen, Zhengfei Guo, Jin Chen, Wei Yang, Yanming Yao,... Published: 09 April 2019
Remote Sensing, doi: 10.3390/rs11070851
DOI See at publisher website ABS Show/hide abstract
Most vegetation indices (VIs) of remote sensing were designed based on the concept of soil-line, which represents a linear correlation between bare soil reflectance at the red and near-infrared (NIR) bands. Unfortunately, the soil-line can only suppress brightness variation, not color differences of bare soil. Consequently, soil variation has a considerable impact on vegetation indices, although significant efforts have been devoted to this issue. In this study, a new soil-line is established in a new feature space of the NIR band and a virtual band that combines the red and shortwave-infrared (SWIR) bands (0.74ρred+0.26ρswir). Then, plus versions of vegetation indices (VI+), i.e., normalized difference vegetation index plus (NDVI+), enhanced vegetation index plus (EVI+), soil-adjusted vegetation index plus (SAVI+), and modified soil-adjusted vegetation index plus (MSAVI+), are proposed based on the new soil-line, which replaces the red band with the red-SWIR band in the vegetation indices. Soil spectral data from several spectral libraries confirm that bare soil has much less variation for VI+ than the original VI. Simulation experiments show that VI+ correlates better with fractional vegetation coverage (FVC) and leaf area index (LAI) than original VI. Ground measured LAI data collected from BigFoot, VALERI, and other previous references also confirm that VI+ derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data correlates better with ground measured LAI than original VI. These data analyses suggest that replacing the red band with the red-SWIR band can reduce the sensitivity of VIs to soil background. We recommend employing the proposed NDVI+, EVI+, SAVI+, and MSAVI+ in applications of large area, sparse vegetation, or when soil color variation cannot be neglected, although sensitivity to soil moisture and clay content might cause slight side effects for the proposed VI+s.
Article 0 Reads 0 Citations A comparative analysis of accessibility measures by the two-step floating catchment area (2SFCA) method Xiang Chen, Pengfei Jia Published: 25 March 2019
International Journal of Geographical Information Science, doi: 10.1080/13658816.2019.1591415
DOI See at publisher website
Article 0 Reads 4 Citations A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savit... Ruyin Cao, Yang Chen, Miaogen Shen, Jin Chen, Ji Zhou, Cong ... Published: 01 November 2018
Remote Sensing of Environment, doi: 10.1016/j.rse.2018.08.022
DOI See at publisher website
Article 0 Reads 0 Citations A Novel Cloud Removal Method Based on IHOT and the Cloud Trajectories for Landsat Imagery Shuli Chen, Xuehong Chen, Xiang Chen, Jin Chen, Xin Cao, Mia... Published: 02 July 2018
Remote Sensing, doi: 10.3390/rs10071040
DOI See at publisher website ABS Show/hide abstract
Cloud removal is a prerequisite for the application of Landsat datasets, as such satellite images are invariably contaminated by clouds. Clouds affect the transmission of radiation signal to different degrees because of their different thicknesses, shapes, heights and distributions. Existing methods utilize pixel replacement to remove thick clouds and pixel correction techniques to rectify thin clouds in order to retain the land surface information in contaminated pixels. However, a major limitation of these methods refers to their deficiency in retrieving land surface reflectance when both thick clouds and thin clouds exist in the images, as the two types of clouds differ in the transmission of radiation signal. As most remotely sensed images show rather complex cloud contamination patterns, an efficient method to alleviate both thin and thick cloud effects is in need of development. To this end, the paper proposes a new method to rectify cloud contamination based on the cloud detection of iterative haze-optimized transformation (IHOT) and the cloud removal of cloud trajectory (IHOT-Trajectory). The cloud trajectory is able to take consideration of signal transmission for different levels of cloud contamination, which characterizes the spectral response of a certain type of land cover under increasing cloud thickness. Specifically, this method consists in four steps. First, the cloud thicknesses of contaminated pixels are estimated by the IHOT. Second, areas affected by cloud shadows are marked. Third, cloud trajectories are fitted with the aid of neighboring similar pixels under different cloud thickness. Last, contaminated areas are rectified according to the relationship between the land surface reflectance and the IHOT. The experimental results indicate that the proposed approach is able to effectively remove both the thin and thick clouds and erase the cloud shadows of Landsat images under different scenarios. In addition, the proposed method was compared with the dark object subtraction (DOS), the modified neighborhood similar pixel interpolator (MNSPI) and the multitemporal dictionary learning (MDL) methods. Quantitative assessments show that the IHOT-Trajectory method is superior to the other cloud removal methods overall. For specific spectral bands, the proposed method performs better than other methods in visible bands, whereas it does not necessarily perform better in infrared bands.
Article 0 Reads 4 Citations The mixed pixel effect in land surface phenology: A simulation study Xiang Chen, Dawei Wang, Jin Chen, Cong Wang, Miaogen Shen Published: 01 June 2018
Remote Sensing of Environment, doi: 10.1016/j.rse.2018.04.030
DOI See at publisher website
Article 0 Reads 0 Citations Inequalities of Nuclear Risk Communication Within and Beyond the Evacuation Planning Zone Xiang Chen, Clayton Frazier, Rejina Manandhar, Zhigang Han, ... Published: 10 May 2018
Applied Spatial Analysis and Policy, doi: 10.1007/s12061-018-9257-7
DOI See at publisher website