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Editorial: Rising stars in remote sensing 2025: advancements in time series analysis
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DOI：10.3389/frsen.2026.1931285.png)
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Abstract
En 中文
A central theme across the collection is the movement from mapping change to diagnosing response trajectories. In ["Recording seagrass growth in Mosquito Lagoon; Florida after Hurricanes Ian and Nicole"]; Insalaco et al. use semi-monthly Harmonized Landsat-Sentinel imagery and Random Forest classification to examine rapid seagrass recovery following two major hurricanes in 2022. Their work shows the value of dense temporal monitoring for capturing ecological responses that would be difficult to interpret from isolated pre-and post-event imagery. The study documents a striking post-disturbance trajectory: seagrass remained limited into early 2023 but recovered to pre-collapse levels by summer 2023; raising important questions about disturbance; recovery; water quality; and coastal ecosystem resilience.Disturbance detection is also central to ["Mapping small-scale logging disturbances in tropical forests using Sentinel-1 time series and an extensive ground truth dataset"]; where Mercier et al. address one of the persistent challenges in tropical forest monitoring: identifying small-scale forest degradation that may remain invisible in global forest-cover products. Using dense Sentinel-1 SAR time series; extensive treefelling records; and UAV-borne lidar data; the authors develop and evaluate approaches for detecting very small logging disturbances in the Congo Basin. Their Fused-Lasso Change Detection approach represents the clearest methodological innovation in the collection; showing how radar time series can help overcome cloud-cover limitations and improve detection of subtle; short-lived forest disturbances; especially where selective and artisanal logging create canopy gaps much smaller than conventional monitoring thresholds.The collection also demonstrates the importance of long-term time series for monitoring hydrological change. In ["Monitoring the dual-season hydrological dynamics of the Pong reservoir in Himachal Pradesh; India"]; Sarda and Kumar use three decades of Landsat imagery to evaluate pre-and postmonsoon reservoir dynamics. By comparing water indices; hydrological consistency; and relative water depth; the study identifies seasonal and multi-decadal changes in the Pong Reservoir; an important Ramsar site. MNDWI proved effective for mapping reservoir water cover; and areas of high hydrological consistency declined substantially in the pre-monsoon season. This work illustrates how established spectral-index approaches; when applied consistently through time; can provide useful information for conservation planning; water-resource management; and monitoring of human-modified aquatic systems. The broader conceptual significance of these applied studies is developed in ["Terrestrial ecosystems are in transition"]. Wang et al. synthesize evidence that climate change; land-use change; altered disturbance regimes; and biogeochemical imbalances are eroding ecosystem resilience and increasing the risk of critical transitions. Their review emphasizes the importance of remote sensing time series for detecting early-warning signals; monitoring vegetation dynamics; and identifying ecosystems approaching thresholds. In doing so; the article provides a wider framework for interpreting the other contributions in this Research Topic: time series remote sensing is not only a mapping tool; but also a means of diagnosing resilience; vulnerability; and possible transition pathways.Taken together; the articles in this Research Topic show the diversity of questions now being addressed through remote sensing time series analysis (Figure 1). They span marine and coastal vegetation recovery; tropical forest degradation; reservoir hydrology; dryland vegetation monitoring; and ecosystem transition theory. Most contributions apply well-established tools; including machine learning classification; spectral water and vegetation indices; pixel-based trend analysis; and multi-decadal image archives. Their value lies in applying these approaches to environmental systems where timing; persistence; and spatial heterogeneity matter. At the same time; Mercier et al.'s radar time-series analysis points toward the need for further algorithmic development for difficult monitoring problems; including cloud-prone environments; small disturbances; mixed pixels; and rapidly changing landscape features.Several cross-cutting lessons emerge from the collection. First; disturbance detection depends strongly on sensor choice and temporal density: optical time series are powerful for tracking vegetation and water dynamics; while SAR offers key advantages where clouds or canopy structure limit optical observation. Second; validation remains a central challenge. High-resolution UAV; lidar; field; and imageinterpretation datasets can build confidence at local scales; but applying methods to coarser global products or to regions without detailed ground data remains difficult. Third; many studies can diagnose what changed; when; and where; but the attribution of drivers-such as rainfall variability; water quality; land management; grazing; sedimentation; or human activity-remains a major frontier.The work highlighted here points toward three priorities for the field: integrating AI and time series methods with ecological theory; bridging scale gaps between local validation and regional or global products; and expanding robust monitoring approaches in data-limited regions; including much of the Global South. This Research Topic therefore celebrates a set of strong individual contributions while also identifying broader challenges for the emerging generation of remote sensing scientists. As satellite archives lengthen; revisit times improve; and analytical methods continue to advance; remote sensing time series will play an increasingly central role in diagnosing environmental response trajectories and supporting timely; spatially explicit decisions for conservation; restoration; water-resource management; and climate adaptation.
Keywords:
environmental change
hydrological dynamics
forest degradation
coastal recovery
dryland vegetation dynamics
ecosystem transitions
remote sensing time series
Journal
F
IF:
3.7
Papers:
560
Citations:
993
Organization
Cited Papers