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Shrubs are typical vegetation on the Qinghai – Tibet Plateau, and their spatial distribution pattern is crucial for understanding the regional ecological environment. Existing global land cover products cannot represent shrubs well in this area, and the relationship between shrubs and the corresponding environmental factors is still unclear. In this study, using the Dogxung Zangbo River Basin as an example, Sentinel-2 images were applied to estimate the regional shrub fraction image, using the random forest regression model trained with samples generated from fine-resolution Jilin-1 images. The estimated shrub fraction image was further analysed using an elevation dataset to explore the impact of topography on the spatial distribution of shrubs. The results showed that using Sentinel-2 images acquired in both plant growing and non-growing seasons can better distinguish shrubs from grass than using only one season of data, and the coefficient of determination and root-mean-square error derived from this model were 0.821 and 0.087, respectively. The shrub distribution was strongly correlated with topography. In the study area, areas with a southeast or south orientation and large slope are more suitable for shrub growth. [ABSTRACT FROM AUTHOR]

Detailed soil maps contribute to the understanding of agricultural and food value chains. PlanetScope satellite constellations have high spatial and temporal resolution and could be used to develop Synthetic Soil Image (SYSI) at specific dates of field crops calendar. This work aimed to derive a SYSI from PlanetScope time series and estimate soil clay content, an important attribute related to several soil functions. The normalization process was applied to make the radiometries of the different Doves' images compatible and, subsequently, land cover classification was performed to select the bare soil class, obtaining the SYSI-Planet. The clay content was predicted for 2337 topsoil samples using the SYSI-Planet and Random Forest algorithm. The normalization of PlanetScope images were based on reference images, which mitigated the radiometric differences from acquisition dates and different radiometric calibrations. SYSI-Planet resulted in an excellent clay content prediction, resulting in a coefficient of determination (R2) equal to 0.93 and Root-Mean-Square Error (RMSE) equal to 70.90. The predicted clay mapped revealed spatial patterns with a high spatial resolution (~3 m), which can enhance precise agricultural management. [ABSTRACT FROM AUTHOR]