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 92
Page vii
... Climate Data Record of EUMETSAT LSA SAF SEVIRI/MSG LAI, FAPAR and FVC products 191 B. Fuster, J. Sánchez-Zapero, F. Camacho, F. J. García-Haro, M.Campos-Taberner The geostationary and polar orbit LSA SAF vegetation products Francisco ...
... Climate Data Record of EUMETSAT LSA SAF SEVIRI/MSG LAI, FAPAR and FVC products 191 B. Fuster, J. Sánchez-Zapero, F. Camacho, F. J. García-Haro, M.Campos-Taberner The geostationary and polar orbit LSA SAF vegetation products Francisco ...
Page 6
... climate change (Chen et al., 2003; Jangid et al., 2017). Researchers have long investigated the use of remote sensing data to retrieve SST. The split-window method is at present the most popular method for SST estimation. One ...
... climate change (Chen et al., 2003; Jangid et al., 2017). Researchers have long investigated the use of remote sensing data to retrieve SST. The split-window method is at present the most popular method for SST estimation. One ...
Page 21
... Climate (MetEOC), funded by the European Metrology Research Programme. MetEOC is developing new infrastructure and methods to allow higher, traceable, accuracy to be delivered to the European calibration and validation community. Field ...
... Climate (MetEOC), funded by the European Metrology Research Programme. MetEOC is developing new infrastructure and methods to allow higher, traceable, accuracy to be delivered to the European calibration and validation community. Field ...
Page 32
... climate and meteorology of a specific geographic region are essential for the knowledge of the spatial and temporal patterns of the surface air temperature (Tair ), defined as the temperature measured by a thermometer exposed to the air ...
... climate and meteorology of a specific geographic region are essential for the knowledge of the spatial and temporal patterns of the surface air temperature (Tair ), defined as the temperature measured by a thermometer exposed to the air ...
Page 33
... climatic variable ( Tair ) from the variables that influence the climate of the zone ( the geographic variables ... climate . Once this potential mapping is available , it is possible to interpolate the residuals of the regression ...
... climatic variable ( Tair ) from the variables that influence the climate of the zone ( the geographic variables ... climate . Once this potential mapping is available , it is possible to interpolate the residuals of the regression ...
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