Evaluation of Machine Learning Algorithms in Spatial Downscaling of MODIS Land Surface Temperature
Open accessAbstract
Land surface temperature (LST) is described as one of the most important environmental parameters of the land surface biophysical process. Commonly, the remote-sensed LST products yield a tradeoff between high temporal and high spatial resolution. Thus, many downscaling algorithms have been proposed to address this issue. Recently, downscaling with machine learning algorithms, including artificial neural networks (ANNs), support vector machine (SVM), and random forest (RF), etc., have gained more recognition with fast operation and high computing precision. This paper intends to make a comparison between machine learning algorithms to downscale the LST product of the moderate-resolution imaging spectroradiometer from 990 to 90 m, and downscaling results would be validated by the resampled LST product of the advanced spaceborne thermal emission and reflection radiometer. The results are further compared with the classical algorithm—thermal sharpening algorithm (TsHARP), using images derived from two representatives kind of areas of Beijing city. The result shows that: 1) all machine learning algorithms produce higher accuracy than TsHARP; 2) the performance of TsHARP on urban area is unsatisfactory than rural because of the weak indication of impervious surface by normalized difference vegetation index, however, machine learning algorithms get the desired results on both two areas, in which ANN and RF models perform well with high accuracy and fast arithmetic, SVM also gets a good result but there is a smoothing effect on land surface; and 3) additionally, machine learning algorithms are promising to achieve a universal framework which can downscale LST for any area within the training data from long spatiotemporal sequences.
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Outputs with high resolution may be aggregated via clustering methods to provide insights, while at coarser resolution, statistical downscaling can help to disaggregate data to an appropriate spatial resolution, as seen in applications such as crop yield, wind speed or surface temperature.
“Outputs with high resolution may be aggregated via clustering methods to provide insights [183], while at coarser resolution, statistical downscaling can help to disaggregate data to an appropriate spatial resolution, as seen in applications such as crop yield [249], wind speed [479] or surface temperature [481].”
From Tackling climate change with machine learning (2023) · cites this paper as [481]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
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Self-citation
- Authors
- Li, W., Ni, L., Li, Z.-L., Duan, S.-B., & Wu, H.
- Affiliations
- University of Chinese Academy of Sciences (CN)
- Institute of Remote Sensing and Digital Earth (CN)
- Centre National de la Recherche Scientifique (FR)
- Institute of Agricultural Resources and Regional Planning (CN)
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2019-07
- Volume
- 12
- Issue
- 7
- Pages
- 2299-2307
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2918246750
- Cited by
- 145
- References
- 31
- Citations by year
- 2026: 20 · 2025: 31 · 2024: 24 · 2023: 17 · 2022: 23 · 2021: 17 · 2020: 8 · 2019: 5
- FWCI
- 5.62
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion)
- HAL (submittedVersion, other-oa)
- HAL (submittedVersion)
- Funders
- National Key R&D Program of China (2018YFB0504800)
- Chinese Academy of Sciences (XDA20030302)
- National Natural Science Foundation of China (41771398, 41871267)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- land surface temperature, spatial downscaling, MODIS, machine learning, artificial neural networks, support vector machine, random forest, ASTER, urban land surface, impervious surfaces, NDVI
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