Improved Vegetation Profiles with GOCI Imagery Using Optimized BRDF Composite

The purpose of this study was to optimize a composite method for the Geostationary Ocean Color Imager (GOCI), which is the first geostationary ocean color sensor in the world. Before interpreting the sensitivity of each composite with ground measurements, we evaluated the accuracy of bidirectional reflectance distribution function (BRDF) performance by comparing modeled surface reflectance from BRDF simulation with GOCI-measured surface reflectance according to composite period. The root mean square error values for modeled and measured surface reflectance showed reasonable accuracy for all of composite days since each BRDF composite period includes at least seven cloud-free angular sampling for all BRDF performances. Also, GOCI-BRDF-adjusted NDVIs with four different composite periods were compared with field-observation NDVI and we interpreted the sensitivity of temporal crop dynamics of GOCI-BRDF-adjusted NDVIs. The results showed that vegetation index seasonal profiles appeared similar to vegetation growth curves in both field observations from crop scans and GOCI normalized difference vegetation index (NDVI) data. Finally, we showed that a 12-day composite period was optimal in terms of BRDF simulation accuracy, surface coverage, and real-time sensitivity.

[1]  B. Pinty,et al.  SeaWiFS for global biosphere applications , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[2]  Molly E. Brown,et al.  Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Jong-Min Yeom,et al.  Application of GOCI-derived vegetation index profiles to estimation of paddy rice yield using the GRAMI rice model , 2015, Comput. Electron. Agric..

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

[5]  J. D. Tarpley,et al.  Global vegetation indices from the NOAA-7 meteorological satellite , 1984 .

[6]  F. Gao,et al.  Detecting vegetation structure using a kernel-based BRDF model , 2003 .

[7]  J. Muller,et al.  Sampling the surface bidirectional reflectance distribution function (BRDF): 1. evaluation of current and future satellite sensors , 1994 .

[8]  B. Duchemin,et al.  VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products , 2004 .

[9]  Alan H. Strahler,et al.  Using a multikernel least-variance approach to retrieve and evaluate albedo from limited bidirectional measurements , 2001 .

[10]  F. E. Nicodemus.  Directional Reflectance and Emissivity of an Opaque Surface , 1965 .

[11]  Jong-Min Yeom,et al.  Comparison of NDVIs from GOCI and MODIS Data towards Improved Assessment of Crop Temporal Dynamics in the Case of Paddy Rice , 2015, Remote. Sens..

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

[13]  Yunyue Yu,et al.  Analysis of vegetation index NDVI anisotropy to improve the accuracy of the GOES-R green vegetation fraction product , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[14]  C. Tucker,et al.  Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor , 2006 .

[15]  André R. S. Marçal,et al.  Fast over‐land atmospheric correction of visible and near‐infrared satellite images , 2008 .

[16]  Frédéric Baret,et al.  Developments in the 'validation' of satellite sensor products for the study of the land surface , 2000 .

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

[18]  Wolfgang Lucht,et al.  Theoretical noise sensitivity of BRDF and albedo retrieval from the EOS-MODIS and MISR sensors with respect to angular sampling , 2000 .

[19]  Michael J. Barnsley,et al.  Global retrieval of bidirectional reflectance and albedo over land , 1997 .

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

[21]  C. Justice,et al.  Analysis of the phenology of global vegetation using meteorological satellite data , 1985 .

[22]  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 .

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

[24]  Rasmus Fensholt,et al.  Analysing the advantages of high temporal resolution geostationary MSG SEVIRI data compared to Polar Operational Environmental Satellite data for land surface monitoring in Africa , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[25]  R. Myneni,et al.  The interpretation of spectral vegetation indexes , 1995 .

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

[27]  A. Strahler,et al.  Multiangle remote sensing: Past, present and future , 2000 .

[28]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

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

[30]  Rasmus Fensholt,et al.  The Normalization of Surface Anisotropy Effects Present in SEVIRI Reflectances by Using the MODIS BRDF Method , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Joo-Hyung Ryu,et al.  Diurnal variation of NDVI from an unprecedented high-resolution geostationary ocean colour satellite , 2013 .

[32]  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 .

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