Name
Abstract | ||
|---|---|---|
Many physical models have been proposed to simulate the directional anisotropy in the thermal infrared (TIR) region over vegetation canopies to produce angular corrected directional brightness temperature or land surface temperature. However, too many input parameters obstruct their operational use. Semiempirical kernel-driven models are designed to be a tradeoff between physical accuracy and operationality. Recently, four kernel-driven models have been proposed: the first two are direct extensions of kernel models in the visible- and near-infrared region and the last two were directly designed for the TIR region. In this paper, 153 continuous and 153 discrete canopies with varying structures and temperature distributions were considered in order to evaluate their accuracies against two physical models (4SAIL and DART). Their error distribution, scatterplots, and directional anisotropy patterns are compared. LSF-Li model, followed by Ross-Li, Vinnikov, and
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model, gave the best fitting results for all the scenes. The
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of all four kernel models can reach up to 0.82 for discrete scenes; however, the kernel-driven models underestimate the hotspot effect from continuous scenes; therefore, further improvements are necessary for operational use with future TIR satellite missions. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tgrs.2019.2899600 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Kernel,Atmospheric modeling,Land surface temperature,Land surface,Remote sensing,MODIS,Surface treatment | Kernel (linear algebra),Satellite,Physical model,Brightness temperature,Anisotropy,Dart,Remote sensing,Atmospheric model,Hotspot (Wi-Fi),Mathematics | Journal |
Volume | Issue | ISSN |
57 | 8 | 0196-2892 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Biao Cao | 1 | 22 | 10.43 |
| Jean-Philippe Gastellu-Etchegorry | 2 | 46 | 14.47 |
| Yongming Du | 3 | 72 | 19.60 |
| hua li | 4 | 26 | 9.11 |
| Zunjian Bian | 5 | 6 | 5.13 |
| Tian Hu | 6 | 2 | 2.07 |
| Wenjie Fan | 7 | 0 | 0.68 |
| Qing Xiao | 8 | 52 | 11.09 |
| Qinhuo Liu | 9 | 280 | 85.97 |