East Asia is one of the most important sources of aerosols in the world. The distribution of aerosols varies across time and space. Accurate aerosol data is crucial to identify its spatiotemporal dynamics; thus, it is of great significance to obtain and verify new aerosol data for this region. Based on the Aerosol Optical Depth (AOD) data of the Aerosol Robotic Network (AERONET) program for 17 stations from 2011 to 2020, this study comprehensively verified the accuracy and applicability of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD 1 km products among different seasons, elevations, and climate zones over entire East Asia. The results showed that: (1) The overall accuracy of MAIAC AOD was high in East Asia, and the accuracy of Terra was slightly better than that of Aqua. MAIAC AOD showed significant heterogeneity among sites. MAIAC AOD performed well in areas with high vegetation cover and flat terrain, while the inversion accuracy was relatively low in areas with low vegetation cover and high terrain. (2) In general, MAIAC AOD and AERONET AOD showed good agreement in different seasons, presenting as winter > spring > autumn > summer. Yet the accuracy and consistency of Terra AOD product were better than Aqua product. (3) MAIAC AOD showed different accuracy at different elevations and climate zones. It had a high correlation and best inversion accuracy with AERONET AOD at low and medium elevations. MAIAC AOD had better inversion accuracy in the arid and warm temperate zones than that in the equatorial and cold temperate zones. (4) AOD distribution and its trend showed significant spatial differences in East Asia. The high AOD values were dominant in the Sichuan basin and the eastern plains of China, as well as in India and Bangladesh, while the relatively low AOD values were distributed in southwestern China and the areas north of 40°N. AOD in most parts of East Asia showed a negative trend, indicating a great improvement in air quality in these regions.

In recent years, atmospheric aerosol pollution has seriously affected the ecological environment and human health. Understanding the spatial and temporal variation of AOD is essential to revealing the impact of aerosols on the environment. Based on the MAIAC AOD 1 km product from 2011 to 2020, we analyzed AOD’s distribution patterns and trends in different time series across East Asia. The results showed that: (1) The annual average AOD in East Asia varied between 0.203 and 0.246, with a decrease of 14.029%. The areas with high AOD values were mainly located in the North China Plain area, the Sichuan Basin area, and the Ganges Delta area, with 0.497, 0.514, and 0.527, respectively. Low AOD values were mainly found in the Tibetan Plateau and in mountainous areas north of 40° N, with 0.061 in the Tibetan Plateau area. (2) The distribution of AOD showed a logarithmic decreasing trend with increasing altitude. Meanwhile, the lower the altitude, the faster the rate of AOD changes with altitude. (3) The AOD of East Asia showed different variations in characteristics in different seasons. The maximum, minimum, and mean values of AOD in spring and summer were much higher than those in autumn and winter. The monthly average AOD reached a maximum of 0.326 in March and a minimum of 0.190 in November. The AOD showed a continuous downward trend from March to September. The highest quarterly AOD values in the North China Plain occurred in summer, while the highest quarterly AOD values in the Sichuan Basin, the Ganges Delta, and the Tibetan Plateau all occurred in spring, similar to the overall seasonal variation in East Asia.

Forest phenology is sensitive to climate change, and its responses affect many land surface processes, resulting in a feedback effect on climate change. Human activities have been the main driver of climate change’s long-term shifts in temperature and weather patterns. Forest phenology, understood as the timing of the annual cycles of plants, is extremely sensitive to changes in climate. Quantifying the responses of temperate forest phenology under an elevational range of topographic conditions that mimic climate change is essential for making effective adaptive forest ecosystem management decisions. Our study utilized the Google Earth Engine (GEE), gap filling, and the Savitzky–Golay (GF-SG) algorithm to develop a long-time series spatio-temporal remote sensing data fusion. The forest phenology characteristics on the north slope of Changbai Mountain were extracted and analyzed annually from 2013 to 2022. Our study found that the average start of the growing season (SOS) on the north slope of Changbai Mountain occurred between the 120th–150th day during the study period. The end of the growing season (EOS) occurred between the 270th–300th day, and the length of the growing season (LOS) ranged from the 110th–190th day. A transect from the northeast to southwest of the study area for a 10-year study period found that SOS was delayed by 39 d, the EOS advanced by 32 d, and the LOS was gradually shortened by 63 d. The forest phenology on the north slope of Changbai Mountain showed significant topographic differentiations. With an increase of 100 m in altitude, the mean SOS was delayed by 1.71 d (R² = 0.93, p < 0.01). There were no obvious trends in EOS variation within the study area altitude gradient. LOS decreased by 1.23 d for each 100 m increase in elevation (R² = 0.90, p < 0.01). Forests on steep slopes had an earlier SOS, a later EOS, and a longer LOS than forests on gentle slopes. For each degree increase in slope, SOS advanced by 0.12 d (R² = 0.53, p = 0.04), EOS was delayed by 0.18 d (R² = 0.82, p = 0.002), and the LOS increased by 0.28 d (R² = 0.78, p = 0.004). The slope aspect had effects on the EOS and the LOS but had no effect on the SOS. The forest EOS of the south aspect was 3.15 d later than that of the north aspect, and the LOS was 6.47 d longer. Over the 10-year study period, the phenology differences between the north and south aspects showed that the LOS difference decreased by 0.85 d, the SOS difference decreased by 0.34 d, and the EOS difference decreased by 0.53 d per year. Our study illustrates the significance of the coupling mechanism between mountain topography and forest phenology, which will assist our future understanding of the response of mountain forest phenology to climate change, and provide a scientific basis for further research on temperate forest phenology.

Aerosol optical depth (AOD) serves as a significant parameter in aerosol research. With the increasing utilization of satellite data in AOD research, it is crucial to evaluate the satellite AOD data. Using Aerosol Robotic Network (AERONET) in situ measurements, this study investigates the accuracy and applicability of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) AOD data in Asia from June 2006 to June 2023. By matching the CALIPSO AOD data in a 1° × 1° area around the selected AERONET sites, various statistical metrics were used to create a comprehensive evaluation system. The results show that: (1) There is a high correlation between the AODs of CALIPSO and AERONET (R = 0.636), and the AOD values of CALIPSO are only 1.7% higher than those of AERONET on average. The MAE (0.215) and RMSE (0.358) suggest that the error level of CALIPSO AOD is relatively low; (2) In most of the 25 sites throughout Asia CALIPSO AOD have high matching accuracies with the AERONET AOD, and only in three sites has a validation accuracy of ‘Poor’; (3) The accuracy varies across the four seasons, ranked as follows: winter demonstrates the highest accuracy, followed by autumn, spring, and summer; (4) The accuracy varies with surface elevation, with better matching in lowest altitude (500 m) areas, but slightly worse matching in medium altitude (200–500 m) areas and low altitude (50–200 m). The uncertainty in the CALIPSO AOD retrievals varies in seasons, altitudes, and aerosol characteristics.

Under the background of global change, the lake water environment is facing a huge threat from eutrophication. The rapid increase in curly-leaf pondweed (Potamogeton crispus L.) in recent years has seriously threatened the ecological balance and the water diversion safety of the eastern route of China’s South-to-North Water Diversion Project. The monitoring and control of curly-leaf pondweed is imperative in shallow lakes of northern China. Unmanned Aerial Vehicles (UAVs) have great potential for monitoring aquatic vegetation. However, merely using satellite remote sensing to detect submerged vegetation is not sufficient, and the monitoring of UAVs on aquatic vegetation is rarely systematically evaluated. In this study, taking Nansi Lake as a case, we employed Red–Green–Blue (RGB) UAV and satellite datasets to evaluate the monitoring of RGB Vegetation Indices (VIs) in pondweed and mapped the dynamic patterns of the pondweed Fractional Vegetation Coverage (FVC) in Nansi Lake. The pondweed FVC values were extracted using the RGB VIs and the machine learning method. The extraction of the UAV RGB images was evaluated by correlations, accuracy assessments and separability. The correlation between VIs and FVC was used to invert the pondweed FVC in Nansi Lake. The RGB VIs were also calculated using Gaofen-2 (GF-2) and were compared with UAV and Sentinel-2 data. Our results showed the following: (1) The RGB UAV could effectively monitor the FVC of pondweed, especially when using Support Vector Machine that (SVM) has a high ability to recognize pondweed in UAV RGB images. Two RGB VIs, RCC and RGRI, appeared best suited for monitoring aquatic plants. The correlations between four RGB VIs based on GF-2, i.e., GCC, BRI, VDVI, and RGBVI and FVC_SVM calculated by the UAV (p < 0.01) were better than those obtained with other RGB VIs. Thus, the RGB VIs of GF-2 were not as effective as those of the UAV in pondweed monitoring. (2) The binomial estimation model constructed by the Normalized Difference Water Index (NDWI) of Sentinel-2 showed a high accuracy (R² = 0.7505, RMSE = 0.169) for pondweed FVC and can be used for mapping the FVC of pondweed in Nansi Lake. (3) Combined with the Sentinel-2 time-series data, we mapped the dynamic patterns of pondweed FVC in Nansi Lake. It was determined that the flooding of pondweed in Nansi Lake has been alleviated in recent years, but the rapid increase in pondweed in part of Nansi Lake remains a challenging management issue. This study provides practical tools and methodology for the innovative remote sensing monitoring of submerged vegetation.

Identifying the types and vertical distribution of aerosols plays a significant role in evaluating the influence of aerosols on the climate system. Based on the aerosol optical properties obtained from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), this study analyzed the long-term aerosol characteristics of seven cities in East Asia (Ulaanbaatar, Beijing, Lanzhou, Shanghai, Lhasa, Hong Kong, and Bangkok) from 2007 to 2021, including the spatiotemporal variations of aerosol optical depth (AOD), the vertical stratification characteristics of aerosols, and the main aerosol subtype. The results showed that, except for Lhasa, the AOD values of all cities exhibited a trend of initially increasing and then decreasing over the years. Except for Shanghai, the high values of AOD in the other cities occurred in the spring and summer seasons, while the low values occurred in the autumn and winter seasons. In all four seasons, the AOD contribution within the 1–3 km range accounted for more than 50% of the total. In the autumn and winter seasons, this proportion reached over 80%. The main types of aerosols and their contributions varied at different altitudes. Overall, dust, polluted continental/smoke, polluted dust, and elevated smoke dominated in all aerosol layers across each city. On the other hand, clean marine, clean continental, and dusty marine had very small proportions, accounting for less than 5% of all the cities’ aerosol layers.