Mapping the distribution of coniferous forests is of great importance to the sustainable management of forests and government decision-making. The development of remote sensing, cloud computing and deep learning has provided the support of data, computing power and algorithms for obtaining large-scale forest parameters. However, few studies have used deep learning algorithms combined with Google Earth Engine (GEE) to extract coniferous forests in large areas and the performance remains unknown. In this study, we thus propose a cloud-enabled deep-learning approach using long-time series Landsat remote sensing images to map the distribution and obtain information on the dynamics of coniferous forests over 35 years (1985–2020) in the northwest of Liaoning, China, through the combination of GEE and U2-Net. Firstly, to assess the reliability of the proposed method, the U2-Net model was compared with three Unet variants (i.e., Resnet50-Unet, Mobile-Unet and U-Net) in coniferous forest extraction. Secondly, we evaluated U2-Net’s temporal transferability of remote sensing images from Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI. Finally, we compared the results obtained by the proposed approach with three publicly available datasets, namely GlobeLand30-2010, GLC_FCS30-2010 and FROM_GLC30-2010. The results show that (1) the cloud-enabled deep-learning approach proposed in this paper that combines GEE and U2-Net achieves a high performance in coniferous forest extraction with an F1 score, overall accuracy (OA), precision, recall and kappa of 95.4%, 94.2%, 96.6%, 95.5% and 94.0%, respectively, outperforming the other three Unet variants; (2) the proposed model trained by the sample blocks collected from a specific time can be applied to predict the coniferous forests in different years with satisfactory precision; (3) Compared with three global land-cover products, the distribution of coniferous forests extracted by U2-Net was most similar to that of actual coniferous forests; (4) The area of coniferous forests in Northwestern Liaoning showed an upward trend in the past 35 years. The area of coniferous forests has grown from 945.64 km2 in 1985 to 6084.55 km2 in 2020 with a growth rate of 543.43%. This study indicates that the proposed approach combining GEE and U2-Net can extract coniferous forests quickly and accurately, which helps obtain dynamic information and assists scientists in developing sustainable strategies for forest management.

(1) Background: The preservation of soil organic carbon (SOC) by soil aggregates (SA) is a key mechanism for the stability of the soil carbon (C) pool. (2) Methods: Soil samples were collected at a 0–20 cm depth from 75 sites across the forest regions of the Greater Khingan Mountains, China, and were fractionated as SA of 0.25–2 mm, 0.053–0.25 mm, and <0.053 mm by a wet-sieving method. The spatial patterns of SA and associated organic C (OC) were investigated, as well as their associations with environmental factors. (3) Results: The predominant SA was the SA fraction (SAF) of 0.25–2 mm. The spatial pattern of SA, with moderate spatial autocorrelation, was found to be associated with aggregate size. SOC was mainly accumulated in the 0.25–2 mm SAF, accounting for 50.39% of the total content of aggregate SOC; the total SOC content in all SAF showed strong spatial autocorrelations without significant differences. No significant correlations were found between temperature and SA variables. Precipitation presented significantly positive and negative correlations with the SAF of <0.053 mm and 0.053–0.25 mm, respectively. SOC was favorably associated with macroaggregate, geometric mean diameter (GMD), and mean weight diameter (MWD); however, the correlation between SOC and aggregate SOC lessened as particle size decreased. The aggregate SOC contents were significantly linked with NH4+-N, AK, and TP, while 0.25–2 mm SA content was negatively related with pH. (4) Conclusions: Precipitation was helpful for the development of clay aggregates (<0.053 mm), i.e., increasing rainfall-induced aggregate disruption. SOC encourages small SA cementing to large SA, which improves SA stability. OC associated with the SAF of 0.25–2 mm and 0.053–0.25 mm was positively correlated with soil nutrients such as nitrogen, phosphorus, and potassium, suggesting that the formation of aggregates was conducive to the preservation of soil nutrients.