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Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples. Assessment in broad areas is now primarily based on grid units, which do not have a clear physical meaning like slope units, and their accuracy is not ideal. Nevertheless, the large amount of manual editing, due to the incorrectly generated horizontal and vertical lines during slope unit partitioning, limits using slope units for rapid assessment over large areas. Hence, this paper proposes a reliable susceptibility assessment approach to solve this problem based on optimal slope units and negative samples involving prior knowledge. Precisely, an algorithm to automatically extract slope units is designed to eliminate fragmented and erroneous units. Second, a samples labeling index (SLI) is defined based on the certainty factors model to select negative samples reasonably. Sichuan Province, China is selected for experimental analysis, with the results demonstrate that the optimized slope unit and the negative samples selection strategy consider prior knowledge achieve better results in the random forest model, support vector machine model, and artificial neural network model. In particular, the composite performance index AUC of artificial neural network model improved from 0.81 to 0.90. [ABSTRACT FROM AUTHOR]

In this study, Aqua-Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) and C6.1 Dark Target aerosol optical depth (AOD) retrievals at 3 km (DT3K) and 10 km (DT10K), Deep Blue AOD retrievals at 10 km (DB10K), and combined DT and DB (DTB) AOD retrievals at 10 km resolutions were validated from 2002 to 2014 against ground-based sunphotometer AOD measurements obtained from the Chinese aerosol remote sensing network (CARSNET). The CARSNET AOD data were obtained for sites at Mt. Waliguan (MW), Lanzhou (LZ), Ulate (UL), and Zhengzhou (ZZ) located in the Yellow River basin (YERB) region, China. Errors and agreement between satellite and ground data were reported using Pearson's correlation (R) and relative mean bias (RMB). Results showed that the DT3K C6.1 highest quality flag (QF = 3) AOD retrievals were well correlated with the sunphotometer AOD data, with an R of 0.82 and an RMB of 1.01. Overestimation and underestimation in DT AOD retrievals were observed for AOD > 1.1 and AOD < 1.1, respectively. A significant underestimation of 37% in DB10K AOD retrievals was observed across all the sites except ZZ, which was indicated by a low-value RMB (0.63). Spatial distribution maps showed high AOD values (>0.8) over the lower part of the YERB and low AOD values (<0.4) across the upstream part of the YERB. This might be due to a large number of aerosol emissions over the lower developed areas and a scarcity of aerosols over the upstream mountain areas. Overall, this study supports the use of DT10K C6.1 AOD retrievals over the western semi-arid and arid regions of the YERB and DTB10K AOD retrievals over the north-central water system and eastern plain regions of the YERB. [ABSTRACT FROM AUTHOR]