| 摘要: |
| 【目的】地表组分温度是定量遥感反演的一个关键参数，在能量平衡过程模型和地表
自然灾害监测中具有重要意义。【方法】过去的几十年中，国内外大量研究人员针对地表
组分温度的反演提出了不同的方法和模型。文章系统回顾了现有的地表组分温度热红外遥
感反演算法，包括多角度算法、多波段算法和时空信息算法，分析了各种反演算法的优缺
点，评述了地表组分温度的验证方法。【结果/ 结论】地表组分温度反演方法发展至今已经
取得了阶段性进展，有些研究成果已得到广泛运用。由于地表结构复杂性、卫星传感器硬
件技术及卫星发射成本等客观因素的影响，地表组分温度反演仍存在一些难点和亟待解决
的问题，如有效比辐射率会随观测角度的变化而改变的问题、多角度和多波段数据的相邻
角度和波段数据之间均存在相关性较高的问题、多角度传感器不同角度观测到的目标面积
和观测时间不一致的问题等。未来地表组分温度遥感反演仍然是一个需要不断深入研究的
内容。 |
| 关键词: 热红外遥感 地表温度 组分温度 |
| DOI：10.12105/j.issn.1672-0423.20190102 |
| 分类号: |
| 基金项目:国家自然科学基金项目“热红外与被动微波遥感地表温度融合方法研究”（41871275） |
|
| Progress in land surface component temperature retrievalalgorithms from remote sensing data |
|
Zhang Shuting, Duan Sibo※, Xing Zefeng, Han Xiaojing, Leng Pei
|
|
Institute of Agricultural Resources and Regional Planning，Chinese Academy of Agricultural Sciences，Beijing 100081，China
|
| Abstract: |
| ［Purpose］Land surface component temperature（LSCT）is a key parameter
in quantitative remote sensing. It’s of great significance in energy balance process model and
natural hazard monitoring.［Method］In the past decades，a variety of methods and models
were put forward to estimate LSCT using thermal infrared（TIR）data including multi-angle
method，multi-channel method and multi-temporal method. In this paper，those methods were
systematically reviewed and the advantages and disadvantages of those methods were analyzed.
The validation methods of LSCT were also described in this paper.［Result/Conclusion］The
development of LSCT retrieval methods has reached several progresses and the results were widely
used in other researches. However，due to the complexity of land surface structure，the limit of
satellite sensor features and launching cost，there are still several unsolved problems in LSCT
retrieval，e. g.，the problem of effective emissivity shifting with view angle，the high correlation
between multi-channel and multi-angle data，and temporal and spatial discrepancies of multiangle
measurements. Further research on LSCT retrieval using remotely sensed data is still
required in the coming future. |
| Key words: thermal infrared remote sensing land surface temperature land surface component
temperature |
