Dr. Tomoaki Miura Profile

Dr. Tomoaki Miura

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Publications (11)

SPIE Journal Paper | 10 October 2018 Open Access

Assessment of cross-sensor vegetation index compatibility between VIIRS and MODIS using near-coincident observations

Tomoaki Miura, Jordan Muratsuchi, Marco Vargas

JARS, Vol. 12, Issue 04, 045004, (October 2018) https://doi.org/10.1117/12.10.1117/1.JRS.12.045004

KEYWORDS: MODIS, Near infrared, Reflectivity, Vegetation, Scattering, Sensors, Mendelevium, Satellites, Climatology, Radiometry

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SPIE Journal Paper | 16 December 2013 Open Access

Derivation of a MODIS-compatible enhanced vegetation index from visible infrared imaging radiometer suite spectral reflectances using vegetation isoline equations

Kenta Obata, Tomoaki Miura, Hiroki Yoshioka, Alfredo Huete

JARS, Vol. 7, Issue 01, 073467, (December 2013) https://doi.org/10.1117/12.10.1117/1.JRS.7.073467

KEYWORDS: Vegetation, MODIS, Sensors, Reflectivity, Atmospheric modeling, Aerosols, Atmospheric particles, Data modeling, Radiometry, Near infrared

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SPIE Journal Paper | 30 October 2012

Scaling effects in area-averaged values of two-band spectral vegetation indices represented in a general form

Kenta Obata, Tomoaki Miura, Hiroki Yoshioka

JARS, Vol. 6, Issue 01, 063585, (October 2012) https://doi.org/10.1117/12.10.1117/1.JRS.6.063585

KEYWORDS: Spatial resolution, Vegetation, Image resolution, Reflectivity, Sensors, Spectral resolution, Satellites, Data modeling, Near infrared, Clouds

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SPIE Journal Paper | 1 January 2011

Moderate-resolution imaging spectroradiometer-based vegetation indices and their fidelity in the tropics

Sunyurp Park, Tomoaki Miura

JARS, Vol. 5, Issue 1, 053555, (January 2011) https://doi.org/10.1117/12.10.1117/1.3643696

KEYWORDS: Vegetation, MODIS, Environmental sensing, Satellites, Near infrared, Ecosystems, Climatology, Short wave infrared radiation, Soil science, Data centers

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SPIE Journal Paper | 1 March 2010

Spectral compatibility of vegetation indices across sensors: band decomposition analysis with Hyperion data

Youngwook Kim, Alfredo Huete, Tomoaki Miura, Zhangyan Jiang

JARS, Vol. 4, Issue 01, 043520, (March 2010) https://doi.org/10.1117/12.10.1117/1.3400635

KEYWORDS: MODIS, Sensors, Near infrared, Vegetation, Reflectivity, Atmospheric modeling, Satellites, Satellite imaging, Optical resolution, Atmospheric sensing

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Proceedings Article | 9 October 2007 Paper

Multisensor reflectance and vegetation index comparisons of Amazon tropical forest phenology with hyperspectral Hyperion data

Youngwook Kim, Alfredo Huete, Zhangyan Jiang, Tomoaki Miura

Proceedings Volume 6679, 667906 (2007) https://doi.org/10.1117/12.734974

KEYWORDS: MODIS, Sensors, Vegetation, Reflectivity, Near infrared, Agriculture, Atmospheric corrections, Remote sensing, Climate change, Aerosols

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Proceedings Article | 27 September 2006 Paper

Assessments of multisensor vegetation index dependencies with hyperspectral and tower flux data

Alfredo Huete, Tomoaki Miura, Youngwook Kim, Kamel Didan, Jeffrey Privette

Proceedings Volume 6298, 629814 (2006) https://doi.org/10.1117/12.681382

KEYWORDS: Sensors, MODIS, Satellites, Vegetation, Near infrared, Atmospheric corrections, Photosynthesis, Ecosystems, Data fusion, Data modeling

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Proceedings Article | 27 September 2006 Paper

Investigation on functional form in cross-calibration of spectral vegetation index

Hiroki Yoshioka, Tomoaki Miura, Hirokazu Yamamoto

Proceedings Volume 6298, 629813 (2006) https://doi.org/10.1117/12.681564

KEYWORDS: Vegetation, Sensors, Calibration, Atmospheric modeling, Satellites, Earth observing sensors, Spectral calibration, Atmospheric physics, Environmental monitoring, Atmospheric optics

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Proceedings Article | 20 January 2005 Paper

Relationships of spectral vegetation indices for continuity and compatibility of satellite data products

Hiroki Yoshioka, Tomoaki Miura, Hirokazu Yamamoto

Proceedings Volume 5655, (2005) https://doi.org/10.1117/12.578630

KEYWORDS: Sensors, Vegetation, Calibration, Satellites, Environmental monitoring, Near infrared, Earth observing sensors, Optical calibration, Optical sensors, Landsat

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Proceedings Article | 20 January 2005 Paper

An application of hyperspectral remote sensing to multisensor calibration of spectral vegetation indices for environmental monitoring

Tomoaki Miura, Hiroki Yoshioka

Proceedings Volume 5655, (2005) https://doi.org/10.1117/12.579326

KEYWORDS: Reflectivity, Sensors, MODIS, Vegetation, Near infrared, Environmental monitoring, Satellites, Calibration, Remote sensing, Earth observing sensors

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There is increasing interest among the user communities for using satellite data products from multiple sensors for improved environmental monitoring. Spectral vegetation indices (VIs) are one of the more important products in observing spatial and temporal variations of vegetation biophysical properties and photosynthetic activities as well as in biogeochemical cycle modeling. To accomplish this goal, VIs from multiple sensors need to be normalized for differences in sensor characteristics and algorithms. In this study, we evaluated several empirical strategies in cross-calibrating VIs from different sensors for the spectral band pass filter differences. A satellite-borne hyperspectral image was obtained with the Earth Observing-1 (EO-1) Hyperion sensor over a tropical forest-savanna transitional area in South America. The image was first spectrally convolved to simulate Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) band passes and corrected for atmospheric effects. Data were then extracted from six land cover types with a wide range of biogeophysical conditions and used to empirically derive cross-calibration (translation) equations for the Normalized Difference Vegetation Index (NDVI). The empirical strategies examined included: cross-calibration at the VI level using the NDVI as a predictor variable, cross-calibration at the reflectance level using the reflectance as a predictor variable, and cross-calibration at the reflectance level using the NDVI as a predictor variable. We also examined a two-steps approach in which the cross-calibrations were performed first at the reflectance level and then at the NDVI level. Overall, all of the cross-calibration methods performed well, resulting in root mean square errors less than .05 NDVI units. In nearly all the cases, however, the translations resulted in large residual bias errors with their values reaching .16 units for dark, little or non-vegetated land targets. Depending on cross-calibration methods used, both the magnitudes and directions of bias errors varied significantly. Although the NDVI-based cross-calibration of the NDVI produced the best results with small RMSE values (< .01 unit), there still existed small bias errors. These results indicate that data continuity studies require a theoretical basis in developing a mechanistic understanding of discontinuity and that cross-calibration results need to be evaluated from a real application point of view in order to assess the impact of persistent bias errors and to establish acceptable difference, or error levels in multi-sensor data sets.

Proceedings Article | 23 January 2001 Paper

Validation of the MODIS vegetation indices over a global set of test sites: preliminary results

Alfredo Huete, Kamel Didan, Tomoaki Miura, Hiroki Yoshioka, Laerte Ferreira, Xiang Gao, Karim Batchily

Proceedings Volume 4171, (2001) https://doi.org/10.1117/12.413945

KEYWORDS: MODIS, Vegetation, Composites, Clouds, Reflectivity, Aerosols, Bidirectional reflectance transmission function, Sensors, Atmospheric corrections, Contamination

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Showing 5 of 11 publications

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