Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 10⁴ km²), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km²) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes.

Low- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resolution, insufficient water sampling, and higher uncertainty in retrieval accuracy exist. In this paper, a hybrid Chl-a estimation method based on spectral characteristics (i.e., remote sensing reflectance (Rrs)) classification was developed for oligotrophic and mesotrophic waters using high-resolution satellite Sentinel-2 (A and B) data. First, 99 samples and quasi-synchronous Sentinel-2 satellite data were collected from four small- and medium-sized reservoirs in central China, and the usability of the Sentinel-2 Rrs data in inland oligotrophic and mesotrophic waters was verified by accurate atmospheric correction. Second, a new optical classification method was constructed based on different water characteristics to classify waters into clear water, phytoplankton-dominated water, and water dominated by phytoplankton and suspended matter together using the thresholds of Rrs490/Rrs560 and Rrs665/Rrs560. The proposed method has a higher classification accuracy compared to other classification methods, and the band-ratio algorithm is simpler and more effective for satellite sensors without NIR bands. Third, given the sensitivity of the empirical method to water variability and the ease of development and implementation, a nonlinear least squares fitted one-dimensional nonlinear function was established based on the selection of the best-fitting spectral indices for different optical water types (OWTs) and compared with other Chl-a estimation algorithms. The validation results showed that the hybrid two-band method had the highest accuracy with squared correlation coefficient, root mean squared difference, mean absolute percentage error, and bias of 0.85, 2.93, 32.42%, and −0.75 mg/m³, respectively, and the results of the residual values further validated the applicability and reliability of the model. Finally, the performance of the classification and estimation algorithms on the four reservoirs was evaluated to obtain images mapping the Chl-a in the reservoirs. In conclusion, this study improves the accuracy of Chl-a estimation for oligotrophic and mesotrophic waters by combining a new classification algorithm with a two-band hybrid model, which is an important contribution to solving the problem of low resolution and high uncertainty in the retrieval of Chl-a in oligotrophic and mesotrophic waters in small- and medium-sized reservoirs and has the potential to be applied to other optically similar oligotrophic and mesotrophic lakes and reservoirs using similar spectrally satellite sensors.

As economic development and urbanization continue to accelerate, the Yellow River Basin experiences increasing challenges in balancing land use with ecological environmental protection. Understanding their interactions is crucial for sustainable regional development. This study adopts an integrated evaluation system and a coupling model to examine the dynamic interactions between land use functions and ecological quality in the Yellow River Basin section of Henan Province, China, from 2000 to 2020. The primary findings are as follows: (1) Land use functions improved from 0.276 to 0.303, with high-land-use-function areas expanding eastward. (2) Ecological quality initially declined but subsequently improved, with areas having good and excellent ecological quality increasing from 44.47% to 72.61%. (3) Coupling coherence stabilized, with moderate coordination covering 69.80% of the area by 2020. (4) The fractional vegetation cover and leaf area index were identified as critical influencing factors. Overall, these results highlight the importance of balanced land use planning and targeted ecological conservation strategies. This study provides valuable insights for policymakers aiming to enhance sustainable regional development, emphasizing the importance of integrating ecological security with economic growth in rapidly urbanizing areas.

Lake Qinghai, the largest closed-basin lake on the Qinghai–Tibet Plateau, plays a crucial role in maintaining regional ecological stability through its hydrological functions. In recent decades, the lake has exhibited a continuous rise in water level and lake area expansion, sparking growing interest in the mechanisms driving these changes and their future evolution. This study integrates the Soil and Water Assessment Tool (SWAT), simulations under future Shared Socioeconomic Pathways (SSPs) and statistical analysis methods, to assess runoff dynamics and lake level responses in the Lake Qinghai Basin over the next 30 years. The model was developed using a combination of meteorological, hydrological, topographic, land use, soil, and socio-economic datasets, and was calibrated with the sequential uncertainty fitting Ver-2 (SUFI-2) algorithm within the SWAT calibration and uncertainty procedure (SWAT–CUP) platform. Sensitivity and uncertainty analyses confirmed robust model performance, with monthly R² values of 0.78 and 0.79. Correlation analysis revealed that runoff variability is more closely associated with precipitation than temperature in the basin. Under SSP 1-2.6, SSP 3-7.0, and SSP 5-8.5 scenarios, projected annual precipitation increases by 14.4%, 18.9%, and 11.1%, respectively, accompanied by temperature rises varying with emissions scenario. Model simulations indicate a significant increase in runoff in the Buha River Basin, peaking around 2047. These findings provide scientific insight into the hydrological response of plateau lakes to future climate change and offer a valuable reference for regional water resource management and ecological conservation strategies.

This study focuses on deriving the total absorption coefficients based on field measurements and satellite data. An extended quasi−analytical algorithm (QAA−GRI) was developed based on the two in situ datasets collected from inland waters of Lake Qiandaohu (QDH) and oceanic waters of the East China Sea (ECS). The key model between absorption coefficients at 510 nm ($a(510)$) and green red index ($GRI$) was established using power function in the extended QAA−GRI algorithm. The results reveal that the extended QAA−GRI algorithm performs better than the original quasi−analytical algorithm (QAA−v5) and Garver–Siegel–Maritorena’s algorithm (GSM), and the red–green quasi−analytical algorithm (QAA−RGR), at least for the two in situ datasets from the ECS and QDH. For QAA−GRI, the averaged mean absolute percentage error (MAPE) value of retrieved versus in situ total absorption coefficients is approximately 20%. Subsequently, the extended QAA−GRI algorithm was applied to the OLCI satellite imagery, which is the new successor of MERIS with three specific bands (510, 560, and 620 nm). The implementation of the extended QAA−GRI algorithm on OLCI imagery yielded similar results comparable to that of the QAA−v5 in the ECS region. Furthermore, the application of the algorithm on seasonal and annual MERIS satellite imagery help clarify the combined influences from Yangtze River discharge and coastal currents on the distribution of total absorption in the ECS waters. This study suggests that the extended QAA−GRI algorithm is an alternative for retrieving total absorption coefficient, although it is not recommended for highly turbid waters.

Atmospheric effects substantially influence remote-sensing reflectance retrieval in optically complex inland waters. This study evaluated seven atmospheric correction approaches (QUAC, FLAASH, Sen2Cor, LaSRC, 6S, C2RCC, and ACOLITE) for Sentinel-2 MSI and Landsat-8/9 OLI imagery over the Danjiangkou and Luhun reservoirs. The evaluation used 67 quality-controlled, temporally matched in situ spectral observations and satellite matchups. Performance was quantified using the squared Pearson correlation coefficient (r²), root mean square error (RMSE), and average unsigned relative error (AURE). Laboratory-measured chlorophyll-a (Chl-a) concentrations were used to develop sensor-specific retrieval models and to examine how atmospheric-correction differences propagated into Chl-a estimates and spatial patterns. Because residual aerosol and sun-glint effects may remain after atmospheric correction, an exploratory SWIR-based adjustment was evaluated for the C2RCC visible-band outputs. In the pooled-band analysis, C2RCC yielded the most favorable balance of the evaluated metrics for both sensor datasets. However, performance varied among bands, and the Landsat-8/9 B5 output showed very weak covariation with the in situ measurements. Within the model-development dataset, Sen2Cor achieved the highest Sentinel-2 r² (0.762), whereas C2RCC achieved the lowest Sentinel-2 RMSE (2.30 mg/m³). C2RCC achieved both the highest Landsat-8/9 r² (0.689) and the lowest RMSE (3.18 mg/m³). Independent temporal validation used 14 Luhun observations from 2024. Sen2Cor yielded the lowest Sentinel-2 RMSE and AURE (1.658 mg/m³ and 29.28%). For Landsat-8/9 OLI, C2RCC yielded the highest r² (0.536), lowest RMSE (3.468 mg/m³), and lowest AURE (44.08%). Relative errors increased in weak-signal near-infrared bands, underscoring the need for band-specific interpretation. The SWIR-based adjustment improved both RMSE and AURE for Sentinel-2 MSI but did not provide a consistent improvement for Landsat-8/9 OLI. An exploratory comparison of quality-screened imagery from 2016 to 2025 showed broadly similar reservoir-scale Chl-a patterns in C2RCC-derived products from the two sensors. These results provide reservoir-specific evidence for atmospheric-correction selection and Chl-a retrieval under the sampled conditions.