Forest canopy height is an important indicator of the forest ecosystem, and an accurate assessment of forest canopy height on a large scale is of great significance for forest resource quantification and carbon sequestration. The retrieval of canopy height based on remote sensing provides a possibility for studying forest ecosystems. This study proposes a new method for estimating forest canopy height based on remote sensing. In this method, the GEDI satellite and ICESat-2 satellite, which are different types of space-borne lidar products, are used to cooperate with the Landsat 9 image and SRTM terrain data, respectively. Two forest canopy height-retrieval models based on multi-source remote sensing integration are obtained using a random forest regression (RFR) algorithm. The study, conducted at a forest site in the northeastern United States, synthesized various remote sensing data sets to produce a robust canopy height model. First, we extracted relative canopy height products, multispectral features, and topographic data from GEDI, ICESat-2, Landsat 9, and SRTM images, respectively. The importance of each variable was assessed, and the random forest algorithm was used to analyze each variable statistically. Then, the random forest regression algorithm was used to combine these variables and construct the forest canopy height model. Validation with airborne laser scanning (ALS) data shows that the GEDI and ICESat-2 models using a single data source achieve better accuracy than the Landsat 9 model. Notably, the combination of GEDI, Landsat 9, and SRTM data (R = 0.92, MAE = 1.91 m, RMSE = 2.78 m, and rRMSE = 12.64%) and a combination of ICESat-2, Landsat 9, and SRTM data (R = 0.89, MAE = 1.84 m, RMSE = 2.54 m, and rRMSE = 10.75%). Compared with the least accurate Landsat 9 model, R increased by 29.58%, 93.48%, MAE by 44.64%, 46.20%, RMSE by 42.80%, 49.40%, and the rRMSE was increased by 42.86% and 49.32%, respectively. These results fully evaluate and discuss the practical performance and benefits of multi-source data retrieval of forest canopy height by combining space-borne lidar data with Landsat 9 data, which is of great significance for understanding forest structure and dynamics. The study provides a reliable methodology for estimating forest canopy height and valuable insights into forest resource management and its contribution to global climate change.

The Chlorophyll-a (Chl-a) concentration is an important indicator of water environmental conditions; thus, the simultaneous monitoring of large-area water bodies can be realized through the remote sensing-based retrieval of the Chl-a concentrations. The back propagation (BP) neural network learning method has been widely used for the remote sensing retrieval of water quality in first and second-class water bodies. However, many Chl-a concentration measurements must be used as learning samples with this method, which is constrained by the number of samples, due to the limited time and resources available for simultaneous measurements. In this paper, we conduct correlation analysis between the Chl-a concentration data measured at Dianshan Lake in 2020 and 2021 and synchronized Landat-8 data. Through analysis and study of the radiative transfer model and the retrieval method, a BP neural network retrieval model based on multi-phase Chl-a concentration data is proposed, which allows for the realization of remote sensing-based Chl-a monitoring in third-class water bodies. An analysis of spatiotemporal distribution characteristics was performed, and the method was compared with other constructed models. The research results indicate that the retrieval performance of the proposed BP neural network model is better than that of models constructed using multiple regression analysis and curve estimation analysis approaches, with a coefficient of determination of 0.86 and an average relative error of 19.48%. The spatial and temporal Chl-a distribution over Dianshan Lake was uneven, with high concentrations close to human production and low concentrations in the open areas of the lake. During the period from 2020 to 2021, the Chl-a concentration showed a significant upward trend. These research findings provide reference for monitoring the water environment in Dianshan Lake.

Mastering the fluctuation of water levels and the water storage capacity of plateau lakes is greatly important for monitoring the water balance of the Tibetan Plateau and predicting regional and global climate change. The water level of plateau lakes is difficult to measure, and the ground measured data of long-time series are difficult to obtain. Ngoring Lake is considered in this study, using spaceborne single-photon lidar ICESat-2/ATL13 inland lake standard data products, the water level values provided by Hydroweb laboratory, and the image data of an optical remote sensing satellite. A new method is proposed in the absence of measured data. The method uses multisource remote sensing data to estimate the long-term changes in the water levels, surface area, and water storage capacity of Ngoring Lake in the past three decades. The results show that the water level values of ICESat-2 and Hydroweb on overlapping observation days are highly correlated, with R² = 0.9776, MAE = 0.420 m, RMSE = 0.077 m, and the average absolute height difference is 0.049 m. The fusion of multiple altimetry data can obtain more continuous long-time series water-level observation results. From 1992 to 2021, the water body information of Ngoring Lake basin fluctuated greatly and showed different variation characteristics in different time periods. The lowest water level in January 1997 was approximately 4268.49 m, and it rose to its highest in October 2009, approximately 4272.44 m. The change in the water level in the basin was mainly affected by natural factors, such as precipitation, air temperature, and human activities. The analysis shows that ICESat-2 can be combined with other remote sensing data to realize the long-time series dynamic monitoring of plateau lakes, showing great advantages in the comprehensive observation of plateau lakes in no man’s land.