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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Résultats 1-5 sur 85
Page 2
... maximum green rice LAI development (mid-August). A range of 18–24 measurements over every ESU was taken following the guidelines and recommendations of the Validation of Land European Remote sensing Instruments (VALERI) protocol. LAI ...
... maximum green rice LAI development (mid-August). A range of 18–24 measurements over every ESU was taken following the guidelines and recommendations of the Validation of Land European Remote sensing Instruments (VALERI) protocol. LAI ...
Page 8
... maximum of (T10.8 ) (approximately 4 K). While the second term (i.e., (M – 1 + N)T 11.95 – T11.95 ) makes the main contribution to Eq. (5), because of the large value of T11.95 . Taking the structure of the split-window algorithm into ...
... maximum of (T10.8 ) (approximately 4 K). While the second term (i.e., (M – 1 + N)T 11.95 – T11.95 ) makes the main contribution to Eq. (5), because of the large value of T11.95 . Taking the structure of the split-window algorithm into ...
Page 10
... maximum penetration of LiDAR systems is greatly dependent upon water transparency. Average penetration depth for most of currently operated systems are in the range of 30 meters, LADS (Laser Airborne Depth Sounder) developed by Tenix ...
... maximum penetration of LiDAR systems is greatly dependent upon water transparency. Average penetration depth for most of currently operated systems are in the range of 30 meters, LADS (Laser Airborne Depth Sounder) developed by Tenix ...
Page 22
... maximum (FWHM) of nearly 3 nm in the VNIR spectral region, and a FWHM of nearly 10 nm in the SWIR. 3.1 Sources of Uncertainty Single beam, where the same instrument is used to measure both the target and the reference panel spectral ...
... maximum (FWHM) of nearly 3 nm in the VNIR spectral region, and a FWHM of nearly 10 nm in the SWIR. 3.1 Sources of Uncertainty Single beam, where the same instrument is used to measure both the target and the reference panel spectral ...
Page 27
... maximum of carboxylation Vemo , 25 and 125 μmol · m - 2.s - 1 ( for dry and wet vegetation ) , and 2 values of soil resistance to vapor transfer rss , 200 , 2000 s.m ̄1 ( for wet and dry , respectively ) . Gathering all these input data ...
... maximum of carboxylation Vemo , 25 and 125 μmol · m - 2.s - 1 ( for dry and wet vegetation ) , and 2 values of soil resistance to vapor transfer rss , 200 , 2000 s.m ̄1 ( for wet and dry , respectively ) . Gathering all these input data ...
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