An Event-Based Remote-Sensing Framework for Quantifying Operational Post-Bloom Recovery and Its Environmental Controls in Lake Taihu and Lake Dianchi

Most remote-sensing studies of cyanobacterial blooms have focused on bloom onset, peak intensity, and spatial extent, while the post-bloom recovery stage has received far less attention. Yet this stage is directly relevant to water-quality recovery and to the timing of management response. In this study, we developed an event-based remote-sensing framework to quantify post-bloom recovery using satellite-derived chlorophyll-a as the core indicator, and applied it to Lake Taihu and Lake Dianchi over the period 2000–2022. FAI-derived bloom-area time series were used to identify bloom days and event termination, while satellite-derived chlorophyll-a was used to determine the post-bloom recovery date relative to an event-specific background threshold. Recovery duration was defined as the period from bloom termination to the first return of chlorophyll-a below an event-specific background threshold. Based on this framework, we quantified recovery duration, event frequency, representative recovery trajectories, and recovery-rate metrics, and then examined their controls using statistical comparison, nonlinear modeling, grouped contribution analysis, and path-based validation. The two lakes showed distinct recovery regimes. Lake Taihu had 82 recovery events, with a mean recovery duration of 8.82 days and a median of 6 days, whereas Lake Dianchi had 28 events, with a mean duration of 25.32 days and a median of 22 days. Events lasting more than 20 days accounted for 9.76% of cases in Taihu but 57.14% in Dianchi. By the recovery date, normalized chlorophyll-a and bloom area had decreased to 44.5% and 12.1% of their peak values in Taihu, compared with 20.4% and 4.9% in Dianchi, respectively. Driver analysis indicated that recovery duration was influenced mainly by phase-specific meteorological forcing and lake background, rather than by bloom magnitude alone. Trigger-stage meteorology, lake identity, and transition-stage meteorology contributed 31.53%, 27.58%, and 19.40% of the grouped explanatory signal, together accounting for about 78.5%. These results show that post-bloom recovery can be quantified as an event-scale remote-sensing metric and compared across lakes. The framework extends remote sensing from bloom detection to recovery assessment and provides a basis for lake-specific post-bloom monitoring and management.