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 66
Page 7
... calculated with 5 surface temperatures : To – 5 K , To – 2 Κ , Το Κ , Τo + 2 K and To + 5 K. Then the brightness temperature can be obtained by inverting the Planck function . The atmospheric equivalent temperature can be obtained from ...
... calculated with 5 surface temperatures : To – 5 K , To – 2 Κ , Το Κ , Τo + 2 K and To + 5 K. Then the brightness temperature can be obtained by inverting the Planck function . The atmospheric equivalent temperature can be obtained from ...
Page 8
... calculated result, M ranged from 0.9620~0.9941, close to 1. The first term of Eq. (5) (i.e., (M - 1)(T 10.8 – T11.95 )) is thus small even if multiplied by the maximum of (T10.8 ) (approximately 4 K). While the second term (i.e., (M – 1 ...
... calculated result, M ranged from 0.9620~0.9941, close to 1. The first term of Eq. (5) (i.e., (M - 1)(T 10.8 – T11.95 )) is thus small even if multiplied by the maximum of (T10.8 ) (approximately 4 K). While the second term (i.e., (M – 1 ...
Page 13
... calculations (Updike and Comp 2010). The average solar Zenith angle has to be calculated for the whole scene at the time of acquisition according to the following equation: Eq. 11 Where, sunEl value can be found in the same file *.IDM ...
... calculations (Updike and Comp 2010). The average solar Zenith angle has to be calculated for the whole scene at the time of acquisition according to the following equation: Eq. 11 Where, sunEl value can be found in the same file *.IDM ...
Page 23
... calculated in single and dual mode using Python in - house programme . In case of single mode , HCRF was calculated with the panel interpolated radiance between the before and after acquisitions . In dual mode , the data of Rover and ...
... calculated in single and dual mode using Python in - house programme . In case of single mode , HCRF was calculated with the panel interpolated radiance between the before and after acquisitions . In dual mode , the data of Rover and ...
Page 24
... calculate an overall standard deviation, which takes into account the dispersion of the data of all sampling ... calculated using all the panel measurements using this equation (2) (2) where: k is the number of points; xk is the ...
... calculate an overall standard deviation, which takes into account the dispersion of the data of all sampling ... calculated using all the panel measurements using this equation (2) (2) where: k is the number of points; xk is 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