Fifth recent advances in quantitative remote sensingJosé Antonio Sobrino Rodríguez Universitat de València, 14 déc. 2018 - 481 pages
The Fifth International Symposium on Recent Advances in Quantitative Remote Sensing was held in Torrent, Spain from 18 to 22 September 2018. It was sponsored and organized by the Global Change Unit (GCU) from the Image Processing Laboratory (IPL), University of Valencia (UVEG), Spain. This Symposium addressed the scientific advances in quantitative remote sensing in connection with real applications. Its main goal was to assess the state of the art of both theory and applications in the analysis of remote sensing data, as well as to provide a forum for researcher in this subject area to exchange views and report their latest results. In this book 89 of the 262 contributions presented in both plenary and poster sessions are arranged according to the scientific topics selected. The papers are ranked in the same order as the final programme. |
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... pixels (healthy and damaged). It can be seen the anomalous temporal LAI evolution over the same field which is ... pixel whereas the red one corresponds to a damaged pixel. 4 CONCLUSIONS This study presented multi-source LAI ...
... pixels (healthy and damaged). It can be seen the anomalous temporal LAI evolution over the same field which is ... pixel whereas the red one corresponds to a damaged pixel. 4 CONCLUSIONS This study presented multi-source LAI ...
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... pixels. The values of these pixels are calculated as a function of the amount of the spectral radiance enters the telescope aperture and the instrument conversion of that radiation into a digital signal. Therefore, image pixel data are ...
... pixels. The values of these pixels are calculated as a function of the amount of the spectral radiance enters the telescope aperture and the instrument conversion of that radiation into a digital signal. Therefore, image pixel data are ...
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... pixels Fig.2. The flow chart of cloud detection 3.1 The Detection of High Clouds Using NO.4 Channel High clouds are ... pixel would be a cloud pixel if the brightness temperature of NO.4 channel was lower than 250K, that is, T4 ...
... pixels Fig.2. The flow chart of cloud detection 3.1 The Detection of High Clouds Using NO.4 Channel High clouds are ... pixel would be a cloud pixel if the brightness temperature of NO.4 channel was lower than 250K, that is, T4 ...
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... pixel are more than 2.2K, that is, T4-T5>2.2K, the pixel is labeled as a cloud pixel. 4 THE ELIMINATION OF CLOUD PIXELS FROM AVHRR/2 IMAGES After cloud detection is complete for a AVHRR/2 image, each cloud pixel of the AVHRR/2 image is ...
... pixel are more than 2.2K, that is, T4-T5>2.2K, the pixel is labeled as a cloud pixel. 4 THE ELIMINATION OF CLOUD PIXELS FROM AVHRR/2 IMAGES After cloud detection is complete for a AVHRR/2 image, each cloud pixel of the AVHRR/2 image is ...
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... pixels of the study area . Product Daytime Nighttime ATCDR 10:26 21:24 MOD11B3 11:11 21:59 MYD11B3 13:08 2:04 The meteorological data were provided by the Spanish National Meteorological Agency ( AEMET ) and the 3 METHODOLOGY Catalan ...
... pixels of the study area . Product Daytime Nighttime ATCDR 10:26 21:24 MOD11B3 11:11 21:59 MYD11B3 13:08 2:04 The meteorological data were provided by the Spanish National Meteorological Agency ( AEMET ) and the 3 METHODOLOGY Catalan ...
Table des matières
RARQS2017p339 | 243 |
RARQS2017p341 | 248 |
RARQS2017p342 | 254 |
RARQS2017s1004 | 260 |
RARQS2017s105 | 265 |
RARQS2017s121 | 270 |
RARQS2017p404 | 276 |
RARQS2017p405 | 282 |
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RARQS2017p212 | 95 |
RARQS2017p217 | 104 |
RARQS2017p225 | 107 |
RARQS2017p226 | 112 |
RARQS2017p231 | 118 |
RARQS2017p234 | 123 |
RARQS2017p235 | 128 |
RARQS2017p241 | 138 |
RARQS2017s604 | 144 |
RARQS2017p304 | 151 |
RARQS2017p307 | 157 |
RARQS2017p308 | 162 |
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RARQS2017p311 | 174 |
RARQS2017p315 | 180 |
RARQS2017p318 | 186 |
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RARQS2017p327 | 211 |
RARQS2017p330 | 217 |
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RARQS2017p335 | 232 |
RARQS2017p337 | 238 |
RARQS2017p406 | 287 |
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RARQS2017p412 | 303 |
RARQS2017p417 | 312 |
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RARQS2017p501 | 356 |
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RARQS2017p506 | 374 |
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RARQS2017p524 | 412 |
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RARQS2017p527 | 425 |
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RARQS2017p532 | 436 |
RARQS2017p535 | 443 |
RARQS2017p538 | 449 |
RARQS2017p539 | 454 |
RARQS2017p540 | 460 |
RARQS2017 | 464 |
Expressions et termes fréquents
accuracy Agricultural albedo algorithm analysis angle applications atmospheric average backscatter band Beijing brightness temperature C-band calculated calibration China classification climate coefficient comparison computed correlation crop datasets derived detection emissivity error estimation evaluate evapotranspiration FAPAR field Figure FPGA Geoscience and Remote global GNSS grass production grassland greenup date Guangxi hyperspectral IEEE imagery images infrared interpolation inversion Journal land surface temperature Landsat landslide leaf area index linear Livingston Island MAIAC mapping maximum measurements meteorological method MODIS monitoring multispectral NDVI obtained optimized parameters pixels polarization precipitation processing radar radiance radiation reflectance region Remote Sensing remote sensing data Research retrieval rice RMSE sampling satellite data scale Sciences Sensing of Environment sensor shows signal simulated soil moisture solar spatial resolution spectral study area temporal thermal infrared Tibetan Plateau trend TWSA uncertainty validation values variables variation vegetation index Zhang