Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures
Balancing prediction accuracy and generalization ability: A hybrid framework for modelling the annual dynamics of satellite-derived land surface temperatures
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平衡预测精度和泛化能力：用于模拟卫星地表温度年度动态的混合框架
DOI：
10.1016/j.isprsjprs.2019.03.013
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发表时间：
2019-05
影响因子：
12.7
通讯作者：
Zou Zhaoxu
中科院分区：
文献类型：
--
作者：
Liu Zihan;Zhan Wenfeng;Lai Jiameng;Hong Falu;Quan Jinling;Bechtel Benjamin;Huang Fan;Zou Zhaoxu
年温度循环（ATC）模式可以对地表温度（LST）动态进行多时间尺度分析，因此具有广泛的应用价值。然而，目前可用的ATC模型集中在预测精度或泛化能力和灵活的ATC建模框架，不同数量的热观测是缺乏的。在这里，我们提出了一个混合ATC模型（ATCH），同时考虑预测精度和泛化能力，我们的方法结合了多个谐波与线性函数的LST相关的因素，包括地表气温（SAT），植被指数，植被覆盖度，土壤湿度和相对湿度。基于所提出的ATCH，各种参数减少方法（PRA）的设计，以提供模型的衍生物，可以适应不同的情况。使用Terra/MODIS每日LST产品作为评估数据，ATCH与原来的正弦ATC模型（称为ATCO）及其变体，并与两个常用的间隙填充方法（回归克里格插值（RKI）和遥感每日地面温度重建（RSDAST）），在晴朗的天空条件下。此外，在阴天条件下，由ATCH产生的LST直接比较in-situLST测量。比较表明，ATCH提高了预测精度和整体RMSE分别减少了1.8和0.7 K相比，在白天和夜间的ATCO。此外，ATCH表现出更好的泛化能力比RKI和表现优于RSDAST时，LST间隙大小是空间上大和/或时间上长。通过采用LST相关控制（例如，与仅采用晴空信息作为模式输入的方法相比，ATCH能更好地预测云下的LST。进一步的属性分析表明，将正弦函数（ASF），SAT，NDVI和其他LST相关的因素，提供了约16%，40%，15%和30%的贡献，以提高精度。我们的分析是潜在的有用的设计PRA的各种实际需要，通过减少每次最小的贡献因子。我们的结论是，ATCH是有价值的，进一步提高质量的LST产品，可以潜在地提高时间序列分析的土地表面和其他应用。
Annual temperature cycle (ATC) models enable the multi-timescale analysis of land surface temperature (LST) dynamics and are therefore valuable for various applications. However, the currently available ATC models focus either on prediction accuracy or on generalization ability and a flexible ATC modelling framework for different numbers of thermal observations is lacking. Here, we propose a hybrid ATC model (ATCH) that considers both prediction accuracy and generalization ability; our approach combines multiple harmonics with a linear function of LST-related factors, including surface air temperature (SAT), NDVI, albedo, soil moisture, and relative humidity. Based on the proposed ATCH, various parameter-reduction approaches (PRAs) are designed to provide model derivatives which can be adapted to different scenarios. Using Terra/MODIS daily LST products as evaluation data, the ATCH is compared with the original sinusoidal ATC model (termed the ATCO) and its variants, and with two frequently-used gap-filling methods (Regression Kriging Interpolation (RKI) and the Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST)), under clear-sky conditions. In addition, under overcast conditions, the LSTs generated by ATCH are directly compared within-situLST measurements. The comparisons demonstrate that the ATCH increases the prediction accuracy and the overall RMSE is reduced by 1.8 and 0.7 K when compared with the ATCO during daytime and nighttime, respectively. Moreover, the ATCH shows better generalization ability than the RKI and behaves better than the RSDAST when the LST gap size is spatially large and/or temporally long. By employing LST-related controls (e.g., the SAT and relative humidity) under overcast conditions, the ATCH can better predict the LSTs under clouds than approaches that only adopt clear-sky information as model inputs. Further attribution analysis implies that incorporating a sinusoidal function (ASF), the SAT, NDVI, and other LST-related factors, provides respective contributions of around 16%, 40%, 15%, and 30% to the improved accuracy. Our analysis is potentially useful for designing PRAs for various practical needs, by reducing the smallest contribution factor each time. We conclude that the ATCH is valuable for further improving the quality of LST products and can potentially enhance the time series analysis of land surfaces and other applications.
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DOI：
10.3390/rs70100905
发表时间：
2015-01
期刊：
Remote. Sens.
影响因子：
--
作者：
Xiaoyu Zhang;Jing Pang;Lingling Li
通讯作者：
Xiaoyu Zhang;Jing Pang;Lingling Li
卷云下 MODIS 热红外数据提取白天地表温度
DOI：
10.3390/s150509942
发表时间：
2015-04-28
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Fan X;Tang BH;Wu H;Yan G;Li ZL
通讯作者：
Li ZL
DOI：
10.3390/rs10040650
发表时间：
2018-04
期刊：
Remote. Sens.
影响因子：
--
作者：
Z. Zou;W. Zhan;Zihan Liu;B. Bechtel;Lun Gao;Falu Hong;F. Huang;Jiameng Lai
通讯作者：
Z. Zou;W. Zhan;Zihan Liu;B. Bechtel;Lun Gao;Falu Hong;F. Huang;Jiameng Lai
重建受云污染的遥感地表温度的两步框架
DOI：
10.1016/j.isprsjprs.2018.04.005
发表时间：
2018-07
影响因子：
12.7
作者：
Zeng Chao;Long Di;Shen Huanfeng;Wu Penghai;Cui Yaokui;Hong Yang
通讯作者：
Hong Yang
DOI：
--
发表时间：
2010
期刊：
--
影响因子：
--
作者：
Li Bingyuan
通讯作者：
Li Bingyuan