An Improvement of the Radiative Transfer Model Component of a Land Data Assimilation System and Its Validation on Different Land Characteristics
-
- Hui Lu
- Ministry of Education Key Laboratory for Earth System Modeling, and Center for Earth System Science, Tsinghua University, Beijing 100084, China
-
- Kun Yang
- Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
-
- Toshio Koike
- The Department of Civil Engineering, the University of Tokyo, Tokyo 113-8656, Japan
-
- Long Zhao
- Department of Geological Sciences, The John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, TX 78712, USA
-
- Jun Qin
- Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
書誌事項
- 公開日
- 2015-05-21
- 権利情報
-
- https://creativecommons.org/licenses/by/4.0/
- DOI
-
- 10.3390/rs70506358
- 公開者
- MDPI AG
説明
<jats:p>The paper reports the recent progress in the radiative transfer model (RTM) development, which serves as the observation operator of a Land Data Assimilation System (LDAS), and its validation at two Planetary Boundary Layer (PBL) stations with different weather and land cover conditions: Wenjiang station of humid and cropped field and Gaize station of arid and bare soil field. In situ observed micrometeorological data were used as the driven data of LDAS, in which AMSR-E brightness temperatures (TB) were assimilated into a land surface model (LSM). Near surface soil moisture content output from LDAS, together with the one simulated by a LSM with default parameters, were compared to the in-situ soil moisture observation. The comparison results successfully validated the capability of LDAS with new RTM to simulate near surface soil moisture at various environments, supporting that LDAS can generally simulate soil moisture with a reasonable accuracy for both humid vegetated fields and arid bare soil fields while the LSM overestimates near surface soil moisture for humid vegetated fields and underestimates soil moisture for arid bare soil fields.</jats:p>
収録刊行物
-
- Remote Sensing
-
Remote Sensing 7 (5), 6358-6379, 2015-05-21
MDPI AG