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Detecting soil salinity with MODIS time series VI data

文献类型： 外文期刊

作者： Zhang, Ting-Ting 1 ; Qi, Jia-Guo 2 ; Gao, Yu 1 ; Ouyang, Zu-Tao 1 ; Zeng, Sheng-Lan 3 ; Zhao, Bin 4 ;

作者机构： 1.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Key Lab East China Sea & Ocean Fishery Resources, Sci Observing & Expt Stn Fisheries Resources & En, Shanghai 200090, Peoples R China

2.Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48823 USA

3.Chengdu Univ Informat Technol, Coll Atmospher Sci, Plateau Atmosphere & Environm Key Lab Sichuan Pro, Chengdu 610225, Sichuan Provinc, Peoples R China

4.Fudan Univ, Inst Biodivers Sci, Key Lab Biodivers Sci & Ecol Engn,Minist Educ, Coastal Ecosyst Res Stn Yangtze River Estuary, Shanghai 200438, Peoples R China

关键词： MODIS time series data;Phenological metrics;Seasonal integral vegetation index;The Yellow River Delta;Vegetation heterogeneity

期刊名称：ECOLOGICAL INDICATORS （ 影响因子：4.958； 五年影响因子：5.846 ）

ISSN： 1470-160X

年卷期： 2015 年 52 卷

页码：

收录情况： SCI

摘要： Mapping of salinization using the satellite derived vegetation indices (VIs) remains difficult at broad regional scales due to the low classification accuracy. Satellite derived VIs from the Moderate Resolution Imaging Spectroradiometer (MODIS) have more potential because the MODIS balances the requirements of spatial detail, spectral and temporal density and tends to reflect vegetation responses through time. However, the relationship between MODIS data and salinity may be underestimated in previous studies because the MODIS time series data were not investigated thoroughly, especially regarding vegetation phenology. This study assessed the applicability of MODIS time series VI data for monitoring soil salinization with a series of MODIS pixels selected in the Yellow River Delta, China. The hidden information in vegetation phenology was investigated by improving the quality of VIs time series data with the Savitzky-Golay filter, extracting the phenological markers and differentiating VIs time series data based on vegetation types. The results showed that the quality of the enhanced vegetation index (EVI) time series data were improved by the Savitzky-Golay filter, which could provide more accurate thresholds of phenological stages than the empirical definition. The seasonal integral of EVI (EVI-SI) extracted from the smoothed EVI time series profile was verified as the best indicator of the degree of soil salinity. Additionally, the correlation of EVI-SI and soil salinity was highly dependent on land cover heterogeneity, and the ranges of correlation coefficients were as high as 0.59-0.92. EVI-SI was linearly correlated with ECe in cropland with a high model fit (R-2 = 0.85). The relationship of EVI-SI and ECe fit best with a binomial line and EVI-SI was able to explain 70% of the variance of ECe. Despite the poor fit of the linear regression model in mixed sites limited by spatial resolution (R-2 = 0.32), MODIS time series VI data, as well as the extracted seasonal parameters, still show great potential to assess large-scale soil salinization. (C) 2015 Elsevier Ltd. All rights reserved.

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