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 72
Page v
... radiation reflected from bare soils in Europe Jerzy Cierniewski, Jakub Ceglarek, Cezary Kaźmierowski, Jean-Louis Roujean 56 TRISHNA : a new high resolution thermal infrared Indo-French misión concept Jean-Pierre Lagouarde, Bimal K ...
... radiation reflected from bare soils in Europe Jerzy Cierniewski, Jakub Ceglarek, Cezary Kaźmierowski, Jean-Louis Roujean 56 TRISHNA : a new high resolution thermal infrared Indo-French misión concept Jean-Pierre Lagouarde, Bimal K ...
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... between incoming radiation and vegetation though radiative transfer models (RTMs). Figure 1. Hybrid retrieval methodology including some examples of the 1 Recent Advances in Quantitative Remote Sensing - RAQRS 2017 RARQS2017-p1.06.
... between incoming radiation and vegetation though radiative transfer models (RTMs). Figure 1. Hybrid retrieval methodology including some examples of the 1 Recent Advances in Quantitative Remote Sensing - RAQRS 2017 RARQS2017-p1.06.
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... radiation process and at- sensor brightness temperatures with the surface temperatures ranging from T0 – 5 K, T0– 2 K, T0 K, T0 + 2 K and T0 + 5 K. Based on 115 simulated situations (23 profiles * 5 surface temperatures), the SST was ...
... radiation process and at- sensor brightness temperatures with the surface temperatures ranging from T0 – 5 K, T0– 2 K, T0 K, T0 + 2 K and T0 + 5 K. Based on 115 simulated situations (23 profiles * 5 surface temperatures), the SST was ...
Page 13
... radiation into a digital signal. Therefore, image pixel data are unique to WorldView-2 and should not be directly compared to imagery from other sensors in a radiometric/spectral sense. Instead, image pixels should be converted to a top ...
... radiation into a digital signal. Therefore, image pixel data are unique to WorldView-2 and should not be directly compared to imagery from other sensors in a radiometric/spectral sense. Instead, image pixels should be converted to a top ...
Page 18
... radiation from the earth's surface into a sensor, however, the other part of the spectral radiation can still reach the sensor. Consequently, ground objects under thin clouds can be seen in remote sensing images, but not clearly. The ...
... radiation from the earth's surface into a sensor, however, the other part of the spectral radiation can still reach the sensor. Consequently, ground objects under thin clouds can be seen in remote sensing images, but not clearly. The ...
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