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The main inherent uncertainty sources in trend estimation based on satellite remote sensing data

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Abstract

Satellite data have been extensively used to characterize the trends in environmental and ecological parameters in response to climate change and anthropogenic activities due to the long-term and continuous data record from a global perspective. Nevertheless, trends estimated from different satellite data can be different and even contradict to each other, resulting in inconsistent scientific conclusions about Earth system dynamics. Previous studies were mainly focused on detecting trends or their spatial distribution characteristics, with much less attention to the factors leading up to uncertainties in the results. To address this problem, this review provides a comprehensive analysis of the main factors leading up to the uncertainty of results. The spatial-scale, temporal-scale, platform/sensor-related characteristics, and trend detection algorithms were considered. Their effects as well as the causes of such effects were discussed. And the way to minimize these effects was also suggested. Based upon above, the future development direction was pointed out to obtain more reliable trend estimates from satellite observations. This paper aims to deepen the consciousness and understanding of the detected trends which are not related to actual changes of the Earth surface but caused by perspectives taken for data selection and analysis. Although our discussion on these factors is very preliminary, efforts made here shall provide a first-step guideline that future work can be built up on, with the overarching aim of capturing the real trend of environmental changes using satellite remote sensing.

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Acknowledgements

The data that support the main conclusion of this review are derived from Google Cloud Platform: Google Earth Engine (https://earthengine.google.com/).

Funding

This work was supported by the China High-Resolution Earth Observation System #1 under grant number 21-Y20B01-9001–19/22, the National Natural Science Foundation of China #2 under grant no. 42071296, and the Fundamental Research Funds for the Central Universities #3 under grant lzujbky-2022–09.

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Jianguang Wen and Xiaodan Wu were responsible for the main research ideas and writing the manuscript. Qing Xiao, Dongqin You, and Xuanlong Ma contributed to the manuscript organization. Dujuan Ma, Jingping Wang, and Tingjun Zhang contributed to the literature collection. All the authors thoroughly reviewed and edited this paper.

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Correspondence to Xiaodan Wu.

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Wen, J., Wu, X., You, D. et al. The main inherent uncertainty sources in trend estimation based on satellite remote sensing data. Theor Appl Climatol 151, 915–934 (2023). https://doi.org/10.1007/s00704-022-04312-0

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  • DOI: https://doi.org/10.1007/s00704-022-04312-0

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