As an important economic resource, rubber has rapidly grown in Xishuangbanna of Yunnan Province, China, since the 1990s. Tropical rainforests have been replaced by extensive rubber plantations, which has resulted in ecological problems such as the loss of biodiversity and local water shortages. It is vitally important to accurately map the rubber plantations in this region. Although several rubber mapping methods have been proposed, few studies have investigated methods based on optical remote sensing time series data with high spatio-temporal resolution due to the cloudy and foggy weather conditions in this area. This study presented a rubber plantation identification method that used spatio-temporal optical remote sensing data fusion technology to obtain vegetation index data at high spatio-temporal resolution within the optical remote sensing window in Xishuangbanna. The analysis of the proposed method shows that (1) fused optical remote sensing data with high spatio-temporal resolution could map the rubber distribution with high accuracy (overall accuracy of up to 89.51% and kappa of 0.86). (2) Fused indices have high R² (R² greater than 0.8, where R is the correlation coefficient) with the indices that were derived from the Landsat observed data, which indicates that fusion results are dependable. However, the fusion accuracy is affected by terrain factors including elevation, slope, and slope aspects. These factors have obvious negative effects on the fusion accuracy of high spatio-temporal resolution optical remote sensing data: the highest fusion accuracy occurred in areas with elevations between 1201 and 1400 m.a.s.l., and the lowest accuracy occurred in areas with elevations less than 600 m.a.s.l. For the 5 fused time series indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and tasseled cap angle (TCA)), the fusion accuracy decreased with increasing slope, and increasing slope had the least impact on the EVI, but the greatest negative impact on the NDVI; the slope aspect had a limited influence on the fusion accuracies of the 5 time series indices, but fusion accuracy was lowest on the northwest slope. (3) EVI had the highest accuracy of rubber plantation classification among the 5 time series indices, and the overall classification accuracies of the time series EVI for the four different years (2000, 2005, 2010, and 2015) reached 87.20% (kappa 0.82), 86.91% (kappa 0.81), 88.85% (kappa 0.84), and 89.51% (kappa 0.86), respectively. The results indicate that the method is a promising approach for rubber plantation mapping and the detection of changes in rubber plantations in this tropical area.

As an extreme event driven by global climate change, drought poses a severe threat to terrestrial ecosystems. The Yangtze River Basin (YZRB) and Yellow River Basin (YRB) are key ecological barriers and economic zones in China, holding strategic importance for exploring the evolution of drought patterns and their ecological impacts. Using meteorological station data and Climatic Research Unit Gridded Time Series (CRU TS) data, this study analyzed the spatiotemporal characteristics of drought evolution in the YZRB and YRB from 1961 to 2021 using the standardized precipitation evapotranspiration index (SPEI) and run theory. Additionally, this study examined drought effects on ecosystem carbon sequestration (CS) at the city, county, and pixel scales. The results revealed the following: (1) the CRU data effectively captured precipitation (annual r = 0.94) and temperature (annual r = 0.95) trends in both basins, despite significantly underestimating winter temperatures, with the optimal SPEI calculation accuracy found at the monthly scale; (2) both basins experienced frequent autumn–winter droughts, with the YRB facing stronger droughts, including nine events which exceeded 10 months (the longest lasting 25 months), while the mild droughts increased in frequency and extreme intensity; and (3) the drought impacts on CS demonstrated a significant threshold effect, where the intensified drought unexpectedly enhanced CS in western regions, such as the Garzê Autonomous Prefecture in Sichuan Province and Changdu City in the Xizang Autonomous Region, but suppressed CS in the midstream and downstream plains. The CS responded positively under weak drought conditions but declined once the drought intensity surpassed the threshold. This study revealed a nonlinear relationship between drought and CS across climatic zones, thereby providing a scientific foundation for enhancing ecological resilience.

Context: Under global climate change and intensified human activities, the Yangtze River Basin (YZRB) and Yellow River Basin (YRB) face escalating challenges to ecosystem services (ESs), including water scarcity and biodiversity loss. Understanding ES dynamics is essential for sustainable landscape management. Objectives: This study aimed to (1) predict spatiotemporal changes in land cover and ESs from 2030 to 2100 under SSP1–2.6, SSP3–7.0, and SSP5–8.5 scenarios, and (2) quantify pixel-scale ES trade-offs/synergies and their driving factors. Methods: An integrated SSP–LUH2–PLUS–InVEST/CS framework was developed, combining climate scenarios, land use simulations, and ES assessments. The framework was applied to both basins to project future land cover changes and quantify ES responses under varying radiative forcing scenarios. Results: Forests expanded notably in the YZRB under SSP1–2.6 and SSP3–7.0, largely replacing grasslands and croplands, whereas grasslands remained in the YRB. Water yield (WY), soil conservation (SC), and habitat quality (HQ) improved the most under SSP1–2.6, whereas carbon sequestration (CS) peaked under SSP5–8.5. Synergies prevailed among WY–SC, CS–SC, and SC–HQ across all scenarios, whereas CS–HQ shifts to trade-offs under SSP5–8.5. ES interactions were scenario-dependent, with precipitation, temperature, potential evapotranspiration, land cover, and elevation as key drivers, shifting from land use-driven under low emissions to climate-driven under high emissions. Conclusions: Future ES management in the YZRB and YRB should adopt scenario-specific strategies, optimizing land use under low-emission pathways and applying climate-adaptive interventions under high-emission scenarios to sustain ES provision.