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Soil total nitrogen (STN) is a crucial component of the ecosystem's nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to predict the STN content and map its spatial distribution in the northeastern coastal region of Hebei Province, China, employing the random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods. The purpose was to compare the ability of remote sensing images (Landsat-8, Sentinel-1, and Sentinel-2) with different machine learning methods for predicting STN content. The research results show the following: (1) The three machine learning methods accurately predicted the STN content and the optimal model provided by the XGBoost method, with an R2 of 0.627, RMSE of 0.127 g·kg−1, and MAE of 0.092 g·kg−1. (2) The combination of optical and synthetic aperture radar (SAR) images improved prediction accuracy, with the R2 improving by 45.5%. (3) The importance of optical images is higher than that of SAR images in the RF, GBM, and XGBoost methods, with optical images accounting for 87%, 76%, and 77% importance, respectively. (4) The spatial distribution of STN content predicted by the three methods is similar. Higher STN contents are distributed in the northern part of the study area, while lower STN contents are distributed in coastal areas. The results of this study can be very useful for inventories of soil nitrogen and provide data support and method references for revealing nitrogen cycling. [ABSTRACT FROM AUTHOR]

The age of a shelterbelt is not only an important parameter for determining the function of a shelterbelt, it is also strongly related to the biomass and carbon flux of shelterbelt ecosystems. Therefore, timely and accurate identifications of shelterbelt ages are key for shelterbelt monitoring and management. This study developed a method for estimating shelterbelt age (i.e., years after planting) from a time series of remote sensing images. Firstly, the shelterbelts were divided into three states based on a single remote sensing image of each. Then, a three-stage growth process was established by analysis. Finally, the shelterbelt ages were determined based on time series remote sensing images over a two-year monitoring period in the study area. The actual shelterbelt ages based on field measurements were used to analyze the accuracy of the results. The total number of samples was 243. The results showed that the age identification accuracy was 68.7%. The main factors affecting the identification accuracy were missing images, cloud cover, and the length of the monitoring period. Despite some uncertainties, the proposed method may be used to obtain critical data for shelterbelt management and conducting quick surveys of current shelterbelt conditions over a large area. [ABSTRACT FROM AUTHOR]

The interaction between water and green spaces holds significant importance as an urban carbon sink, but there has been insufficient attention to how the specific morphology of waterfront landscapes affects their capacity for carbon sink. This study focuses on typical riverfront spaces in Shanghai, employing an improved Carnegie-Ames-Stanford-Approach (CASA) model fused with remote sensing spatiotemporal images to simulate vegetation fixed carbon within urban riverfront green spaces. Furthermore, an interpretable machine learning method was utilized to unveil the mechanism driving spatial heterogeneity in carbon sink efficiency. The results reveal the carbon sink efficiency of urban riverfront green spaces exhibits noticeable spatial heterogeneity, varying according to the location, type, scale, and river elements; The internal green component factors, including vegetation coverage and tree green ratio, along with surrounding environmental factor water surface ratio, are key factors influencing the carbon sinks efficiency; Hydrological elements within specific thresholds, namely, water surface ratio ranges between 0.245 and 0.281, can effectively enhance the carbon sink capacity of green spaces. And the maximum influencing value of distance from the water body is about 1800 m. The study contributes to developing a more scientific layout for climate-adaptive urban riverfront green spaces on the mesoscale. • Green carbon sink efficiency is affected by spatial morphology and the surrounding environment. • Hydrological elements can enhance carbon sink efficiency in riverfront green patches. • Landscape spatial morphology elements interact with each other during the carbon sequestration process. [ABSTRACT FROM AUTHOR]