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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Page ix
... band 384 J.C. Fortea and J. Moreno Downscaling of the seasonal evolution of various crops in the Versailles plain ... bands for the purpose of data pansharpening Katarzyna Siok, Ireneusz Ewiak, Agnieszka Jenerowicz 430 Comparison ...
... band 384 J.C. Fortea and J. Moreno Downscaling of the seasonal evolution of various crops in the Versailles plain ... bands for the purpose of data pansharpening Katarzyna Siok, Ireneusz Ewiak, Agnieszka Jenerowicz 430 Comparison ...
Page 2
... bands in the visible, near infra-red and short wave infra-red spectrum at a 10, 20 and 60 m spatial resolution depending on the band. The remote sensing data used in this study was completed by Sentinel-1A data which were used during ...
... bands in the visible, near infra-red and short wave infra-red spectrum at a 10, 20 and 60 m spatial resolution depending on the band. The remote sensing data used in this study was completed by Sentinel-1A data which were used during ...
Page 10
... bands of the WorldView-2 satellite. The non-linear model introduced by (Stumpf et al. 2003) will be used to ... band from aerial photography (Lyzenga 1978). This model was then expanded to multi-spectral satellite imagery using ...
... bands of the WorldView-2 satellite. The non-linear model introduced by (Stumpf et al. 2003) will be used to ... band from aerial photography (Lyzenga 1978). This model was then expanded to multi-spectral satellite imagery using ...
Page 11
... bands , L ( di ) is the remote sensing radiance after atmospheric and sun - glint corrections for spectral band di , and L∞ ( di ) is the deepwater radiance for spectral band di . The model explained in Eq . 4 referred to as the log ...
... bands , L ( di ) is the remote sensing radiance after atmospheric and sun - glint corrections for spectral band di , and L∞ ( di ) is the deepwater radiance for spectral band di . The model explained in Eq . 4 referred to as the log ...
Page 12
... band from which to extract depth information (Su et al. 2008). Worldview-2 provides a new band; costal blue (400-450 nm), with higher capability of penetrating water (Globe 2009). Also, the yellow band (585-625 nm) will be examined as ...
... band from which to extract depth information (Su et al. 2008). Worldview-2 provides a new band; costal blue (400-450 nm), with higher capability of penetrating water (Globe 2009). Also, the yellow band (585-625 nm) will be examined as ...
Table des matières
RARQS2017p339 | 243 |
RARQS2017p341 | 248 |
RARQS2017p342 | 254 |
RARQS2017s1004 | 260 |
RARQS2017s105 | 265 |
RARQS2017s121 | 270 |
RARQS2017p404 | 276 |
RARQS2017p405 | 282 |
| 47 | |
| 51 | |
RARQS2017s203 | 56 |
RARQS2017s304 | 61 |
RARQS2017s401 | 67 |
RARQS2017s503 | 73 |
RARQS2017p201 | 77 |
RARQS2017p204 | 82 |
RARQS2017p209 | 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