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. |
À l'intérieur du livre
Résultats 1-5 sur 58
Page v
... uncertainty model for Field Spectroscopy M. Jiménez, O. Gutiérrez de la Cámara, A. Moncholí, F. Muñoz 21 Interest of VNIR directional measurements for parameterizing the TIR directional anisotropy Zunjian Bian, Jean-Louis Roujean, Mark ...
... uncertainty model for Field Spectroscopy M. Jiménez, O. Gutiérrez de la Cámara, A. Moncholí, F. Muñoz 21 Interest of VNIR directional measurements for parameterizing the TIR directional anisotropy Zunjian Bian, Jean-Louis Roujean, Mark ...
Page vii
... Uncertainty Analysis of the Automated Radiometric Calibration over Baotou Cal&Val Site in China 211 Lingling Ma, Yongguang Zhao, Emma R. Woolliams, Yaokai Liu, Ning Wang, Xinhong Wang, Caihong Dai, Caixia Gao, Chuanrong Li, Lingli Tang ...
... Uncertainty Analysis of the Automated Radiometric Calibration over Baotou Cal&Val Site in China 211 Lingling Ma, Yongguang Zhao, Emma R. Woolliams, Yaokai Liu, Ning Wang, Xinhong Wang, Caihong Dai, Caixia Gao, Chuanrong Li, Lingli Tang ...
Page 17
... / 2 images . Generally , cloud is easy to be separated from 35 N 30 N 25 N Towards a complete spectral reflectance uncertainty model for Field Spectroscopy. 17 Recent Advances in Quantitative Remote Sensing - RAQRS 2017 RARQS2017-p1.19.
... / 2 images . Generally , cloud is easy to be separated from 35 N 30 N 25 N Towards a complete spectral reflectance uncertainty model for Field Spectroscopy. 17 Recent Advances in Quantitative Remote Sensing - RAQRS 2017 RARQS2017-p1.19.
Page 21
... uncertainty of the spectral reflectance measured must be estimated and reported, taken into account as many important sources of uncertainty as possible. This work presents the initial approach to establish a complete model of uncertainty ...
... uncertainty of the spectral reflectance measured must be estimated and reported, taken into account as many important sources of uncertainty as possible. This work presents the initial approach to establish a complete model of uncertainty ...
Page 22
... uncertainties c8: Expanding uncertainties Follow these eight steps, ensures the development of the complete uncertainty model for a measurand. Furthermore, determining a simplified version of the uncertainty model and then adding ...
... uncertainties c8: Expanding uncertainties Follow these eight steps, ensures the development of the complete uncertainty model for a measurand. Furthermore, determining a simplified version of the uncertainty model and then adding ...
Table des matières
RARQS2017p339 | 243 |
RARQS2017p341 | 248 |
RARQS2017p342 | 254 |
RARQS2017s1004 | 260 |
RARQS2017s105 | 265 |
RARQS2017s121 | 270 |
RARQS2017p404 | 276 |
RARQS2017p405 | 282 |
| 47 | |
| 51 | |
| 56 | |
| 61 | |
| 67 | |
| 73 | |
| 77 | |
| 82 | |
| 90 | |
RARQS2017p212 | 95 |
RARQS2017p217 | 104 |
RARQS2017p225 | 107 |
RARQS2017p226 | 112 |
RARQS2017p231 | 118 |
RARQS2017p234 | 123 |
RARQS2017p235 | 128 |
RARQS2017p241 | 138 |
RARQS2017s604 | 144 |
RARQS2017p304 | 151 |
RARQS2017p307 | 157 |
RARQS2017p308 | 162 |
RARQS2017p309 | 168 |
RARQS2017p311 | 174 |
RARQS2017p315 | 180 |
RARQS2017p318 | 186 |
RARQS2017p319 | 191 |
RARQS2017p322 | 197 |
RARQS2017p323 | 202 |
RARQS2017p324 | 206 |
RARQS2017p327 | 211 |
RARQS2017p330 | 217 |
RARQS2017p331 | 223 |
RARQS2017p332 | 227 |
RARQS2017p335 | 232 |
RARQS2017p337 | 238 |
RARQS2017p406 | 287 |
RARQS2017p407 | 292 |
RARQS2017p408 | 296 |
RARQS2017p412 | 303 |
RARQS2017p417 | 312 |
RARQS2017p420 | 318 |
RARQS2017p424 | 323 |
RARQS2017p427 | 327 |
RARQS2017p428 | 332 |
RARQS2017p434 | 337 |
RARQS2017p437 | 342 |
RARQS2017p440 | 346 |
RARQS2017p441 | 351 |
RARQS2017p501 | 356 |
RARQS2017p503 | 362 |
RARQS2017p504 | 368 |
RARQS2017p506 | 374 |
RARQS2017p508 | 379 |
RARQS2017p509 | 384 |
RARQS2017p510 | 387 |
RARQS2017p511 | 391 |
RARQS2017p512 | 396 |
RARQS2017p514 | 402 |
RARQS2017p519 | 407 |
RARQS2017p524 | 412 |
RARQS2017p525 | 418 |
RARQS2017p527 | 425 |
RARQS2017p528 | 430 |
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