Is Time Series Smoothing Function Necessary for Crop Mapping? - Evidence from Spectral Angle Mapper After Empirical Analysis

Time series smoothing functions have been frequently applied to fit multi-temporal vegetation index for better extraction of plant seasonal/growing parameters. Questions are raised that whether the smoothing is necessary for crop mapping. Four time series smoothing functions, namely, HANTS, Savitzky-Golay (S-G), double logistics and asymmetric Gaussian, were used to smooth 23 MODIS 16-days composite NDVI images in one year. The effectiveness were compared through visual check, correlation coefficient R, root mean square error (RMSE), and local signal noise ratio (SNR). The best smoothing time series NDVI images, along with the original time series images, were then used to map corn and soybeans by spectral angle mapper (SAM) method and their mapping accuracies were compared. Comparison of smoothing results showed that S-G fitted data got the strongest correlation coefficient R, the lowest RMSE and lower local SNR. Comparison of mapping results further showed that time smoothing function does not improve the classification accuracy obviously with the same training sample and same temporal bands. The whole analysis indicates that it is the mapping method that matters more than time series smoothing function for classification precision.

[1]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[2]  P. Beck,et al.  Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI , 2006 .

[3]  Andrew E. Suyker,et al.  A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data , 2010 .

[4]  Per Jönsson,et al.  Seasonality extraction by function fitting to time-series of satellite sensor data , 2002, IEEE Trans. Geosci. Remote. Sens..

[5]  Jennifer N. Hird,et al.  Noise reduction of NDVI time series: An empirical comparison of selected techniques , 2009 .

[6]  Carsten Jentsch,et al.  A test for second order stationarity of a multivariate time series , 2015 .

[7]  Matthew C. Hansen,et al.  Corn and Soybean Mapping in the United States Using MODIS Time‐Series Data Sets , 2007 .

[8]  Massimo Menenti,et al.  Reconstruction of global MODIS NDVI time series: performance of harmonic analysis of time series (HANTS). , 2015 .

[9]  P. Eilers,et al.  Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements , 2011 .

[10]  Jude H. Kastens,et al.  Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data , 2013 .

[11]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[12]  Damien Arvor,et al.  Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil , 2014, TheScientificWorldJournal.

[13]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[14]  Li Chuanrong,et al.  Review on Methods for SNR Estimation of Optical Remote Sensing Imagery , 2010 .

[15]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[16]  M. Duggin.  Factors limiting the discrimination and quantification of terrestrial features using remotely sensed radiance , 1985 .

[17]  Fengmei Yao,et al.  Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information , 2014 .

[18]  B. Wardlow,et al.  Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains , 2008 .

[19]  R. Lamparelli,et al.  Mapping and discrimination of soya bean and corn crops using spectro-temporal profiles of vegetation indices , 2015 .