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 62
Page 11
... variable bottom type ( Stumpf et al . 2003 ) , but changes in depth affect the high absorption band more . As a result , the effect of change in ratio because of depth is much greater than that caused by change in bottom reflectance ...
... variable bottom type ( Stumpf et al . 2003 ) , but changes in depth affect the high absorption band more . As a result , the effect of change in ratio because of depth is much greater than that caused by change in bottom reflectance ...
Page 21
... variable of solar and atmospheric conditions. For VNIR region, where spectroradiometer performs better, an average of 5% of uncertainty were gathered. For SWIR region both sources, environmental and instrumental rise to 12% of ...
... variable of solar and atmospheric conditions. For VNIR region, where spectroradiometer performs better, an average of 5% of uncertainty were gathered. For SWIR region both sources, environmental and instrumental rise to 12% of ...
Page 32
... variables, especially for minimum Tair and over Forests and Rugged lands. In general, the improvement was more important (in terms of reducing uncertainty) for the estimation of monthly minimum Tair , than for the estimation of monthly ...
... variables, especially for minimum Tair and over Forests and Rugged lands. In general, the improvement was more important (in terms of reducing uncertainty) for the estimation of monthly minimum Tair , than for the estimation of monthly ...
Page 33
... variable ( Tair ) from the variables that influence the climate of the zone ( the geographic variables and the LST ) . The result is a potential map obtained from the equation of adjustment of the regression that reflects the general ...
... variable ( Tair ) from the variables that influence the climate of the zone ( the geographic variables and the LST ) . The result is a potential map obtained from the equation of adjustment of the regression that reflects the general ...
Page 34
... variables analysed in this study are the root mean square error ( RMSE ) and the coefficient of determination ( R2 ) ... variables ( hereafter called " complex model " ) or all variables except the topographic wetness index and the ...
... variables analysed in this study are the root mean square error ( RMSE ) and the coefficient of determination ( R2 ) ... variables ( hereafter called " complex model " ) or all variables except the topographic wetness index and 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