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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Page v
... soil moisture over alpine vegetation region of Tibetan Plateau Long Xia, Xiaoning Song, Pei Leng, Yawei Wang, Yanbin ... soils in Europe Jerzy Cierniewski, Jakub Ceglarek, Cezary Kaźmierowski, Jean-Louis Roujean 56 TRISHNA : a new ...
... soil moisture over alpine vegetation region of Tibetan Plateau Long Xia, Xiaoning Song, Pei Leng, Yawei Wang, Yanbin ... soils in Europe Jerzy Cierniewski, Jakub Ceglarek, Cezary Kaźmierowski, Jean-Louis Roujean 56 TRISHNA : a new ...
Page 35
... soil moisture over alpine vegetation region. RMSEsimple – RMSEcomplex ( K ) Fig. 6. Relationships between GI and NDVI in each pixel. Table 4. Maximum ( mean ) difference in the RMSE of Tair observed during years 2003-2011 on models with ...
... soil moisture over alpine vegetation region. RMSEsimple – RMSEcomplex ( K ) Fig. 6. Relationships between GI and NDVI in each pixel. Table 4. Maximum ( mean ) difference in the RMSE of Tair observed during years 2003-2011 on models with ...
Page 37
... soil moisture (SSM), as an important surface process parameter, plays a very important role in water resources management, crop growth, land degradation, vegetation coverage and global climate change research. In this study, the ...
... soil moisture (SSM), as an important surface process parameter, plays a very important role in water resources management, crop growth, land degradation, vegetation coverage and global climate change research. In this study, the ...
Page 38
... soil moisture on the base of LST-NDVI triangle feature space, and Goward et al. (2002) established the linear relationship between TVDI and soil moisture for the SSM retrieval. In the LST-NDVI feature space, the slope indicates that ...
... soil moisture on the base of LST-NDVI triangle feature space, and Goward et al. (2002) established the linear relationship between TVDI and soil moisture for the SSM retrieval. In the LST-NDVI feature space, the slope indicates that ...
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... soil moisture for different vegetation types in the situation of the highest temperature , and we call this " dry edge " . Meanwhile , the bottom edge of the triangle indicates that the soil moisture is sufficient and will not become a ...
... soil moisture for different vegetation types in the situation of the highest temperature , and we call this " dry edge " . Meanwhile , the bottom edge of the triangle indicates that the soil moisture is sufficient and will not become a ...
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