This dissertation is a compilation of eight chapters. Apart from the Introduction and Conclusion, the six remaining chapters are based on papers or manuscripts that have been published in, submitted to, or are under review for, ISI-indexed journals.
The eight chapters are described as follows:
Chapter 1 introduces the dissertation, background context, motivations, and expression of the research objectives and approaches.
Chapter 1
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Chapter 2 presents a comprehensive review of Ta estimation using MODIS LST data since it became available in the early 2000s.
Chapter 3 presents the spatiotemporal variability of MODIS LST in northern Vietnam.
Chapter 4 evaluates MODIS LST data for Ta estimation in northwestern Vietnam.
Chapter 5 estimates Ta-max and Ta-min in northern Vietnam using MODIS LST.
Chapter 6 compares different methods for Ta estimation using MODIS LST data.
Chapter 7 summarizes the results obtained from Chapters 2 – 7 and answers the research questions in Chapter 1. Recommendations and future research directions are also provided.
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Chapter 2
This chapter is based on two manuscirpts:
Application of MODIS Land Surface Temperature Data: A Systematic Literature Review and Analysis (accepted)
Thanh Noi Phan,a,b, * and Martin Kappas,a
Application of MODIS Land Surface Temperature Data for Air Surface Temperature Estimation: A Review (submitted)
Thanh Noi Phan a,b,, Martin Kappas a, and Stefan Erasmi a
a Department of Cartography, GIS and Remote Sensing, Institute of Geography, University of Göttingen, Göttingen 37077, Germany
b Department of Cartography and Geodesy, Faculty of Land Management, Vietnam National University of Agriculture, Hanoi, Vietnam.
Correspondence: thanh-noi.phan@geo.uni-goettingen.de; Tel.: +49 551 399805, Department of Cartography, GIS and Remote Sensing, Institute of Geography, University of Göttingen, Goldschmidt Street 5, Göttingen 37077, Germany.
8 Abstract
There is an increasing demand of air surface temperature (Ta) data which can capture information for a large area or for a region, because this kind of data is an important parameter for a wide range of applications, such as environment and climate science, hydrology, agriculture, and weather forecasting. However, due to the sparse distribution of meteorological stations, particularly in developing countries and remote regions (e.g.
mountainous areas, the Arctic, or tropical rainforests), the spatial coverage of Ta is often limited. Remotely sensed MODIS land surface temperature (LST) data, which is freely available with global coverage and has very high temporal resolutions (twice daily observations from two satellites, Aqua and Terra), is considered one of the most suitable and important data sources for Ta estimation. Since MODIS data became available in the early 2000s, there has been a rapid increase in applications of MODIS LST data for Ta estimation.
To date, several methods have been proposed, applied, and evaluated to retrieve Ta from MODIS LST data. However, to the best of our knowledge, there are no studies that provide an overall review of the broad field of algorithms and applications for the estimation of Ta from MODIS LST data. In this context, this paper provides a review of methods that have been developed and applied over the last 15 years. Furthermore, we also discuss the advantages, limitations, potential, and future direction of Ta estimation using MODIS LST.
Keywords: MODIS, LST, air surface temperature, land surface temperature, estimation, statistical methods
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