About this Research Topic

Manuscript Submission Deadline 18 October 2022

Satellite-based earth observation techniques achieved long-term (more than 50 years) terrestrial remote sensing products that can provide rapid and large-scale land surface monitoring. Less-than-ideal atmospheric conditions (e.g. cloud & shadow cover and aerosol) introduce biases or even gaps in terrestrial remote sensing products retrieved from optical or thermal sensors, which impeded the further usage of these products for land surface dynamic monitoring. Considering that most land surface phenomena (e.g. vegetation growth) were dominated rather gradual dynamics instead of frequent variation like atmospheric components (e.g. cloud cover), numerous Time Series Reconstruction (TSR) algorithms had been proposed for gap-filling and smoothing processing for remote sensing products since 1980s. TSR algorithms mainly predict contaminated or missing observations based on temporally neighbor observations with high quality.

In recent years, many new TSR methods (e.g. by adopting the theory of machine learning, and spatial-temporal fusion) had been developed either to improve reconstruction performance or to adapt existing algorithms to new reconstruction requirements. As with booming of cloud-based earth observation platforms that can powerful storage and computation capacity, huge volume earth observation data cube is easily accessible for broader users, which bring TSR research new challenges and chances. By the constellation of Landsat and Sentinel-2 satellites lunched in past 10 years, we can retrieve very dense optical observations with rather high spatial resolution (<100m). Most traditional TSR algorithms were proposed and evaluated based on product with much coarser spatial resolution. How can the TSR algorithms be applied to these high-resolution products is still under discussion. In addition, how to objectively quantify the accuracy of a TSR algorithm had attracted wide attention recently. The signal smoothing effect of TSR processing may eliminate detail variation of rime series, which may impact rapid disturbance detection. In this case, the impact of TSR processing on downstream applications still needed to be addressed urgently.

This research topic welcome original research or review articles focusing on time series reconstruction processing of remote sensing products. Relevant topics could be (but not limit to):
• Novel time series reconstruction algorithms for remote sensing products;
• Methods, metrics, strategies for performance evaluation of time series reconstructions;
• The adopting of time series reconstruction techniques for product for different parameters (e.g. chlorophyll, radar or fluorescence) or high spatial resolution sensors (e.g. Landsat, Sentinel-2, and Planet CubeSat);
• The impacts of TSR processing on downstream applications such as drought monitoring, phenology extraction, crop yield prediction, and disturbance detection;
• Near real-time processing protocol & software development for time series reconstruction;
• Spatial-temporal fusion for time series reconstruction.

Keywords: time series reconstruction, gap-filling, remote sensing, optical sensors, terrestrial monitoring, time series analysis


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Satellite-based earth observation techniques achieved long-term (more than 50 years) terrestrial remote sensing products that can provide rapid and large-scale land surface monitoring. Less-than-ideal atmospheric conditions (e.g. cloud & shadow cover and aerosol) introduce biases or even gaps in terrestrial remote sensing products retrieved from optical or thermal sensors, which impeded the further usage of these products for land surface dynamic monitoring. Considering that most land surface phenomena (e.g. vegetation growth) were dominated rather gradual dynamics instead of frequent variation like atmospheric components (e.g. cloud cover), numerous Time Series Reconstruction (TSR) algorithms had been proposed for gap-filling and smoothing processing for remote sensing products since 1980s. TSR algorithms mainly predict contaminated or missing observations based on temporally neighbor observations with high quality.

In recent years, many new TSR methods (e.g. by adopting the theory of machine learning, and spatial-temporal fusion) had been developed either to improve reconstruction performance or to adapt existing algorithms to new reconstruction requirements. As with booming of cloud-based earth observation platforms that can powerful storage and computation capacity, huge volume earth observation data cube is easily accessible for broader users, which bring TSR research new challenges and chances. By the constellation of Landsat and Sentinel-2 satellites lunched in past 10 years, we can retrieve very dense optical observations with rather high spatial resolution (<100m). Most traditional TSR algorithms were proposed and evaluated based on product with much coarser spatial resolution. How can the TSR algorithms be applied to these high-resolution products is still under discussion. In addition, how to objectively quantify the accuracy of a TSR algorithm had attracted wide attention recently. The signal smoothing effect of TSR processing may eliminate detail variation of rime series, which may impact rapid disturbance detection. In this case, the impact of TSR processing on downstream applications still needed to be addressed urgently.

This research topic welcome original research or review articles focusing on time series reconstruction processing of remote sensing products. Relevant topics could be (but not limit to):
• Novel time series reconstruction algorithms for remote sensing products;
• Methods, metrics, strategies for performance evaluation of time series reconstructions;
• The adopting of time series reconstruction techniques for product for different parameters (e.g. chlorophyll, radar or fluorescence) or high spatial resolution sensors (e.g. Landsat, Sentinel-2, and Planet CubeSat);
• The impacts of TSR processing on downstream applications such as drought monitoring, phenology extraction, crop yield prediction, and disturbance detection;
• Near real-time processing protocol & software development for time series reconstruction;
• Spatial-temporal fusion for time series reconstruction.

Keywords: time series reconstruction, gap-filling, remote sensing, optical sensors, terrestrial monitoring, time series analysis


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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