Comparison of NDVIs from GOCI and MODIS Data towards Improved Assessment of Crop Temporal Dynamics in the Case of Paddy Rice

The monitoring of crop development can benefit from the increased frequency of observation provided by modern geostationary satellites. This paper describes a four-year testing period from 2010 to 2014, during which satellite images from the world's first Geostationary Ocean Color Imager (GOCI) were used for spectral analyses of paddy rice in South Korea. A vegetation index was calculated from GOCI data based on the bidirectional reflectance distribution function (BRDF)-adjusted reflectance, which was then used to visually analyze the seasonal crop dynamics. These vegetation indices were then compared with those calculated using the Moderate-resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) based on Nadir BRDF-adjusted reflectance. The results show clear advantages of GOCI, which provided four times better temporal resolution than the combined MODIS sensors, interpreting subtle characteristics of the vegetation development. Particularly in the rainy season, when data acquisition under clear weather conditions was very limited, it was possible to find cloudless pixels within the study sites by compiling GOCI images obtained from eight acquisition periods per day, from which the vegetation index could be calculated. In this study, ground spectral measurements from CROPSCAN were also compared with satellite-based vegetation products, despite their different index magnitude, according to systematic discrepancy, showing a similar crop development pattern to the GOCI products. Consequently, we conclude that the very high temporal resolution of GOCI is very beneficial for monitoring crop development, and has potential for providing improved information on phenology.

[1]  H. S. Lim,et al.  Retrieving aerosol optical depth using visible and mid‐IR channels from geostationary satellite MTSAT‐1R , 2008 .

[2]  R. Saunders,et al.  An improved method for detecting clear sky and cloudy radiances from AVHRR data , 1988 .

[3]  P. Atkinson,et al.  Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology , 2012 .

[4]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[5]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[6]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[7]  A. Belward,et al.  The Best Index Slope Extraction ( BISE): A method for reducing noise in NDVI time-series , 1992 .

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

[9]  Danny Lo Seen,et al.  A Comparative Study on Satellite- and Model-Based Crop Phenology in West Africa , 2014, Remote. Sens..

[10]  M. Boschetti,et al.  Multi-year monitoring of rice crop phenology through time series analysis of MODIS images , 2009 .

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

[12]  W. Verhoef,et al.  Reconstructing cloudfree NDVI composites using Fourier analysis of time series , 2000 .

[13]  John F. Mustard,et al.  Extracting Phenological Signals From Multiyear AVHRR NDVI Time Series: Framework for Applying High-Order Annual Splines With Roughness Damping , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Hiroyuki Ohno,et al.  Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers , 2006 .

[15]  David P. Roy,et al.  Generation of Temporally Complete Daily Nadir MODIS Reflectance Time Series , 2010 .

[16]  M. Boschetti,et al.  Comparative Analysis of Normalised Difference Spectral Indices Derived from MODIS for Detecting Surface Water in Flooded Rice Cropping Systems , 2014, PloS one.

[17]  M. Friedl,et al.  Land Surface Phenology from MODIS: Characterization of the Collection 5 Global Land Cover Dynamics Product , 2010 .

[18]  A. B. Harto,et al.  Detecting Rice Phenology in Paddy Fields with Complex Cropping Pattern Using Time Series MODIS Data , 2010 .

[19]  Jiaxin Jin,et al.  Characterizing Spatial-Temporal Variations in Vegetation Phenology over the North-South Transect of Northeast Asia Based upon the MERIS Terrestrial Chlorophyll Index , 2012 .

[20]  Donghui Xie,et al.  Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics , 2013 .

[21]  Jing M. Chen,et al.  Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Clement Atzberger,et al.  A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America , 2011, Int. J. Digit. Earth.

[23]  Michele Meroni,et al.  Evaluation of Agreement Between Space Remote Sensing SPOT-VEGETATION fAPAR Time Series , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[24]  D. Legates,et al.  Crop identification using harmonic analysis of time-series AVHRR NDVI data , 2002 .

[25]  H. Mooney,et al.  Shifting plant phenology in response to global change. , 2007, Trends in ecology & evolution.

[26]  Menghua Wang,et al.  An efficient method for multiple radiative transfer computations and the lookup table generation , 2003 .

[27]  Alan H. Strahler,et al.  Quality assessment of BRDF/albedo retrievals in MODIS operational system , 2008 .

[28]  R. Ahas,et al.  Onset of spring starting earlier across the Northern Hemisphere , 2006 .

[29]  N. Delbart,et al.  Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004) , 2006 .

[30]  Xiangqian Wu,et al.  Overview of Intercalibration of Satellite Instruments , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Mutlu Ozdogan,et al.  The spatial distribution of crop types from MODIS data: Temporal unmixing using Independent Component Analysis , 2010 .

[32]  Yujie Wang,et al.  Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and look-up tables , 2011 .

[33]  Alan H. Strahler,et al.  An algorithm for the retrieval of albedo from space using semiempirical BRDF models , 2000, IEEE Trans. Geosci. Remote. Sens..

[34]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[35]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[36]  A. Strahler,et al.  On the derivation of kernels for kernel‐driven models of bidirectional reflectance , 1995 .

[37]  Clement Atzberger,et al.  Correction: Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. RemoteSens 2013, 5, 1704-1733 , 2013, Remote. Sens..

[38]  Kyung-Soo Han,et al.  Sensitivity analysis of 6S-based look-up table for surface reflectance retrieval , 2015, Asia-Pacific Journal of Atmospheric Sciences.

[39]  Jong-Min Yeom,et al.  Feasibility of using Geostationary Ocean Colour Imager (GOCI) data for land applications after atmospheric correction and bidirectional reflectance distribution function modelling , 2013 .

[40]  J. Roujean,et al.  A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data , 1992 .

[41]  Shobha Kondragunta,et al.  Comparison of GOES and MODIS Aerosol Optical Depth (AOD) to Aerosol Robotic Network (AERONET) AOD and IMPROVE PM2.5 Mass at Bondville, Illinois , 2009, Journal of the Air & Waste Management Association.

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

[43]  Clement Atzberger,et al.  Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection , 2013, Remote. Sens..