遥感地表温度产品时空融合方法研究综述

李冉; 王猛猛; 张正加; 胡添; 刘修国

doi:10.11834/jrs.20210294

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遥感地表温度产品时空融合方法研究综述
A review of spatiotemporal fusion methods for remotely sensed land surface temperature
2022年26卷第12期页码：2433-2450
纸质出版日期： 2022-12-07 ，
DOI： 10.11834/jrs.20210294

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李冉，王猛猛，张正加，胡添，刘修国.2022.遥感地表温度产品时空融合方法研究综述.遥感学报，26（12）： 2433-2450

Li R，Wang M M，Zhang Z J，Hu T and Liu X G. 2022. A review of spatiotemporal fusion methods for remotely sensed land surface temperature. National Remote Sensing Bulletin， 26（12）：2433-2450
李冉，王猛猛，张正加，胡添，刘修国.2022.遥感地表温度产品时空融合方法研究综述.遥感学报，26（12）： 2433-2450 DOI： 10.11834/jrs.20210294.

Li R，Wang M M，Zhang Z J，Hu T and Liu X G. 2022. A review of spatiotemporal fusion methods for remotely sensed land surface temperature. National Remote Sensing Bulletin， 26（12）：2433-2450 DOI： 10.11834/jrs.20210294.

摘要

受传感器性能的制约，利用单一卫星热红外遥感数据反演到的地表温度产品难以兼顾时间分辨率和空间分辨率，而时空融合技术能够发挥多传感器互补的优势，获得时间密集度高、空间细节丰富的地表温度产品，从而在算法层面上解决这一矛盾。随着时空融合研究的深入，地表温度开始显露出与其他产品有着明显区别的融合特性，而内在机理和应用潜力都有待被整理和挖掘。本文就立足于地表温度与时空融合的交叠区，对两者结合而生的研究成果进行收集、分析和总结，系统地概述了该领域的研究背景、原理、方法和应用，并重点突出与泛在时空融合技术的之间的联系与区别；最后，基于地表温度数据的特点和时空融合的局限性，总结了该领域所面临的主要挑战，并对可行的解决方法和发展方向进行了展望。

Abstract

Remotely sensed Land Surface Temperature (LST) from a single source rarely has high temporal and spatial resolutions due to the sensors’ optical characteristics. Spatiotemporal fusion uses data from multiple sources to retrieve LST with high temporal frequency and spatial detail

and the spatiotemporal contradiction is disentangled in the fusion process. According to an in-depth study of spatiotemporal fusion

LST exhibits unique features distinct from other land surface variables. However

the inherent mechanism and potential application of LST spatio-temporal fusion have yet to be compiled and extensively explored. Based on the intersection between LST and spatiotemporal fusion

this work collects

analyzes

and summarizes the state-of-the-art developments in LST spatio-temporal fusion. The research background

principles

methods

and applications of this field are systematically elaborated. In particular

the relations and differences with the ubiquitous spatiotemporal fusion technology are emphasized.

In essence

spatiotemporal fusion methods extract exquisite temporal variation of pixels from the low spatial resolution images and obtain spatial correspondence from images at various scales to predict high spatial resolution images. The spatiotemporal fusion shows great promise over homogeneous and stable land surface

but has an unsatisfactory performance over heterogeneous landscapes with unstable thermal conditions. In comparison with Land Surface Reflectance (LSR)

the spatiotemporal fusion for LST can be less sensitive to the land cover classification uncertainties because of its lower spatial resolution and lower diversity among different land types

but it is difficult to achieve using the general laws for accurate prediction due to the drastic temporal variation of LST.

After spatiotemporal fusion was successfully implemented in LSR

several studies adapted it to LST with some improvements based on the thermal characteristics. In the existing five categories of spatiotemporal fusion models based on weight and learning

Bayesian and hybrid models have been applied to LST. Among these models

the weight models are more mature

robust

and effective

but they cannot easily capture the temporal change of LST. Furthermore

the improvement is relatively limited based on STARFM

ESTARFM

or other classical weight models. Learning models can realize a nonlinear prediction based on the structural similarity of training data when supported by reliable network architecture and abundant training. In particular

the deep learning models have more superior ability to depict and extract the LST with weak spectral characteristics

but suitable neural networks and model parameters must be selected and optimized. Although fusion studies based on the Bayesian framework (including maximum a posterior and Bayesian maximum entropy) are relatively rare

they have shown great potential for achieving unbiased and nonlinear predictions and low-quality requirements for the initial data as LST. The hybrid models can integrate the preponderances of the above-mentioned models and acquire more flexible

efficient

and accurate prediction results compared with a single fusion model

which could be the mainstream of the future spatiotemporal fusion model.

Although the spatiotemporal fusion models are consistently developed

most of them only focus on generating fused products

with a lack of quantitative and qualitative analysis with respect to the practical applications of the fused LST products

such as agriculture and ecology. In this work

the applications in this field are divided into six aspects: land temperature

sea surface temperature

agroforestry

urban heat island

public health

and others

which cover the majority of remote sensing service fields. However

the breadth and depth of the application of the LST fusion products are less than those of LSR fusion products. The mutual development between theoretical research and application demand is urgently needed.

The primary impediment to the application and dissemination of spatiotemporal fusion is the data itself

as evidenced by the diversity of multi-source data

the spatial continuity of image

and the sensitivity of temperature in time series. The angular effect

unstable inversion accuracy

and dramatical diurnal variation significantly constrain their potential applications. Considering these characteristics of LST and existing defects of the spatiotemporal fusion model

this work proposed the future work prospects

such as improving LST inversion accuracy

complementing the strengths of multi-source data

employing a deep learning model

enhancing algorithm flexibility

and constructing a spatiotemporal fusion integrated procedure. The implementation of these strategies will propel the development of theoretical research and operational application of LST with the spatiotemporal fusion technology.

关键词

遥感地表温度时空融合密集时间序列空间降尺度

Keywords

remote sensingland surface temperaturespatio-temporal fusiondense time seriesspatial downscaling

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