Zhao Guohui, Zhang Yaonan, Tan Junlei, Li Cong, Ren Yanrun
Science Big Data Center of Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China.
Institute of Remote Sensing and Digital Earth, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2020 Aug 4;20(15):4337. doi: 10.3390/s20154337.
Land surface temperature (LST) is a critical state variable of land surface energy equilibrium and a key indicator of environmental change such as climate change, urban heat island, and freezing-thawing hazard. The high spatial and temporal resolution datasets are urgently needed for a variety of environmental change studies, especially in remote areas with few LST observation stations. MODIS and Landsat satellites have complementary characteristics in terms of spatial and temporal resolution for LST retrieval. To make full use of their respective advantages, this paper developed a pixel-based multi-spatial resolution adaptive fusion modeling framework (called pMSRAFM). As an instance of this framework, the data fusion model for joint retrieval of LST from Landsat-8 and MODIS data was implemented to generate the synthetic LST with Landsat-like spatial resolution and MODIS temporal information. The performance of pMSRAFM was tested and validated in the Heihe River Basin located in China. The results of six experiments showed that the fused LST was high similarity to the direct Landsat-derived LST with structural similarity index () of 0.83 and the index of agreement () of 0.84. The range of was 0.65-0.88, the root mean square error yielded a range of 1.6-3.4 °C, and the averaged was 0.6 °C. Furthermore, the temporal information of MODIS LST was retained and optimized in the synthetic LST. The ranged from 0.7 °C to 1.5 °C with an average value of 1.1 °C. When compared with in situ LST observations, the mean absolute error and were reduced after fusion with the mean absolute bias of 1.3 °C. The validation results that fused LST possesses the spatial pattern of Landsat-derived LSTs and inherits most of the temporal properties of MODIS LSTs at the same time, so it can provide more accurate and credible information. Consequently, pMSRAFM can be served as a promising and practical fusion framework to prepare a high-quality LST spatiotemporal dataset for various applications in environment studies.
地表温度（LST）是地表能量平衡的关键状态变量，也是气候变化、城市热岛效应和冻融灾害等环境变化的关键指标。各种环境变化研究迫切需要高时空分辨率数据集，尤其是在LST观测站较少的偏远地区。MODIS和Landsat卫星在LST反演的时空分辨率方面具有互补特性。为充分利用它们各自的优势，本文开发了一种基于像素的多空间分辨率自适应融合建模框架（称为pMSRAFM）。作为该框架的一个实例，实现了从Landsat-8和MODIS数据联合反演LST的数据融合模型，以生成具有类似Landsat空间分辨率和MODIS时间信息的合成LST。在中国的黑河流域对pMSRAFM的性能进行了测试和验证。六个实验的结果表明，融合后的LST与直接从Landsat获得的LST高度相似，结构相似性指数（）为0.83，一致性指数（）为0.84。的范围为0.65 - 0.88，均方根误差的范围为1.6 - 3.4°C，平均为0.6°C。此外，MODIS LST的时间信息在合成LST中得到保留和优化。范围为0.7°C至1.5°C，平均值为1.1°C。与原位LST观测值相比，融合后的平均绝对误差和减小，平均绝对偏差为1.3°C。验证结果表明，融合后的LST具有Landsat衍生LST的空间模式，同时继承了MODIS LST的大部分时间特性，因此它可以提供更准确和可靠的信息。因此，pMSRAFM可以作为一个有前途且实用的融合框架，为环境研究中的各种应用准备高质量的LST时空数据集。
