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The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) is an active spaceborne remote sensing system that utilizes photon-counting LiDAR to capture highly detailed information about under-vegetation terrain and forest structure over vast spatial regions. It facilitates the accurate retrieval of terrain elevation and canopy height information, critical for assessing the global carbon budget and understanding the role of forests in climate change mitigation. However, challenges arise from the characteristics of the ICESat-2 photon-counting LiDAR data, such as their linear distribution, extensive spatial coverage, and substantial residual noise. These challenges hinder the performances of the state-of-the-art methods when applied on ICESat-2 data for extracting ground or top of canopy, while they perform well on airborne LiDAR that is featured with planar distribution, small coverage, and high signal-to-noise ratio. Consequently, this study proposes a novel algorithm termed Adaptive Linear Cloth Simulation Filtering (ALCSF), for the automated extraction of ground and top-of-canopy photons from ICESat-2 signal photons. The ALCSF algorithm innovatively introduces a cloth strip model as a reference to accommodate the distribution characteristics of ICESat-2 photons. Additionally, it employs a terrain-adaptive strategy to adjust the rigidity of the cloth strip by utilizing terrain slope information, thus making ALCSF applicable to large-scale areas with significant topographical changes. Furthermore, the proposed ALCSF addresses noise interference by simultaneously considering the movability of particles of the cloth strip model and the photon distribution during iterative adjustments of the cloth strip. The performance of the ALCSF is evaluated by comparing it with the ICESat-2 Land–Vegetation Along-Track Products (ATL08) across twelve datasets that encompass various times of day and scenes. In the results, the ALCSF exhibits notable improvements over ATL08 products, effectively reducing the root mean square error (RMSE) of ground elevation by 21.8% and canopy height by 25.8%, with superior performance in preserving terrain details. This highlights the significance of ALCSF as a valuable tool for enhancing the accuracy of ICESat-2 land and vegetation products, ultimately contributing to the estimation of the global carbon budget in future studies. [ABSTRACT FROM AUTHOR]

Wetlands provide numerous ecosystem services essential for biodiversity conservation, climate change mitigation, and human well-being. Nevertheless, there are still no complete and uniform wetland maps providing information on the location, distribution, size, and changing status of wetlands in Africa. In this study, we used 130,000 Landsat satellite images and 270,000 sampling points evenly distributed across Africa to classify wetlands with a random forest classifier based on the Google Earth Engine (GEE) cloud platform. This resulted in the first systematic and comprehensive map of existing wetlands in Africa that quantified the area and distribution of these wetlands at a spatial resolution of 30 m, with an overall wetland producer accuracy of 90.22 % and user accuracy of 95.74 %. To address the seasonal variation in African wetlands, we used a dynamic threshold segmentation method based on the Otsu method, which can extract the extent of wetlands under different periods more accurately based on Landsat time series and African phenology data. Our results show that the total African existing wetlands extent from 2019 to 2021 (here in 2020), was approximately 1,341,500 km2, excluding shallow marine waters, comprises 3.98 % of Africa's total land area. Of these wetlands, inland wetlands account for 81 %, and coastal wetlands account for 19 %. The wetlands in Africa were symmetrically distributed around the equator and decreased with increasing latitude, accounting for 97 % of the total wetland area being within the 20°N and 20°S latitudes. African marshes were highly dynamic, expanding by 92.22 % from the dry to wet season. Our study demonstrates the substantial potential of large data samples, multiple wetland classification features (spectral-temporal, topographic, and hydrological features), and random forest classification in GEE for large-scale wetland mapping. The resulting wetland maps provide new baseline data for large-scale monitoring of seasonal dynamics and long-term trends in African wetlands. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is a critical measure for ecosystem health and offers opportunities to understand carbon fluxes and associated implications. However, SOC can be significantly influenced by anthropogenic land use change, with intensive and extensive disturbances resulting in considerable SOC loss. Consequently, understanding the spatial distribution of SOC across different land uses, particularly at national level characterised by different biomes, is vital for integrated land-use planning and climate change mitigation. Remote sensing and deep learning (DL) offer a reliable largescale mapping of SOC by leveraging on their big data provision and powerful analytical prowess, respectively. This study modelled SOC stocks across South Africa's major land uses using Deep Neural Networks (DNN) and Sentinel-3 satellite data. Based on 1936 soil samples and 31 spectral predictors, results show a relatively high accuracy with an R2 and RMSE value of 0.685 and 10.15 t/h (26% of the mean), respectively. From the seven land uses evaluated, grasslands (31.36%) contributed the most to the overall SOC stocks while urban vegetation (0.04%) contributed the least. Moreover, although SOC stock was found to be relatively proportional to land coverage, commercial (46.06 t/h) and natural (44.34 t/h) forests showed a higher carbon sequestration capacity. These findings provide an important guideline to managing SOC stocks in South Africa, useful in climate change mitigation through sustainable land-use practices. Whereas landscape restoration, and other relevant interventions are encouraged to improve SOC storage, care must be taken within land-use decision making to maintain an appropriate balance between carbon sequestration, biodiversity, and general ecosystem functions. [ABSTRACT FROM AUTHOR]