The Yellow River Delta, with the most typical new wetland system in warm temperate zone of China, is suffering from increasingly serious salinization. The purpose of this study is to utilize five typical surface parameters, including Albedo (the surface Albedo), NDVI (vegetation index), SI (salinity index),WI (humidity index), and I Fe2O3 (Iron oxide index), to construct 10 different feature spaces and, then, propose two different kinds of monitoring models (point-to-point model and point to line model) of soil salinization. The results showed that the inversion accuracy of the I Fe2O3 feature space detection index based on the pointto-point model was the highest with R 2 ¼0.86. However, the inversion accuracy of Albedo-NDVI feature space detection index based on the point-to-point model is the lowest with R 2 ¼0.72. This is due to the fact that NDVI is not sensitive enough to indicate the status of vegetation grown in the region with low (disturbance of soil background) and high (influenced by the saturation effect) vegetation coverage. The chemical weathering is also a primary cause of soil salinization, during which Fe 2 O 3 is formed by the reaction of oxygen present in the atmosphere with primary Fe 2þ minerals in the soil .Therefore, the AlbedoÀI Fe2O3 feature space detection index based on the point-to-point model has a stronger applicability to monitor the information of soil salinization in the Yellow River Delta. This above point-to-point detection model can be utilized as a better approach to provide data and decision support for the development of agriculture, construction of reservoirs, and protection of natural ecological system in the Yellow River Delta.

Current feature space models of desertification were almost linear, which ignored the complicated and non-linear relationships among variables for monitoring desertification. Fully considering the influencing factors of the desertification process in Naiman Banner, four sensitive indices including MSAVI, NDVI, TGSI, and Albedo have been selected to construct five feature spaces. Then, the precisions of different feature space models for monitoring desertification information (including non-linear and linear models) have been compared and analyzed. The non-linear Albedo-MSAVI feature space model for Naiman Banner has higher efficiency with the overall precision of 90.1%, while that of Albedo-TGSI had the worst precision with 0.69. Overall, the feature space model (non-linear) of Albedo-MSAVI has the highest applicability for monitoring the desertification information in Naiman Banner.INDEX TERMS Albedo-MSAVI, monitoring model, feature space, Landsat8 OLI, Naiman Banner.

A new saponin, named ginsenoside-I (a), was isolated from leaves of Panax Ginseng, together with nine known saponins, and its structure was elucidated as 3beta,6alpha,12beta,20(S)-tetrahydroxyldammar-24(25)-ene (20- O-beta-D-glucopyranosyl)-3-O-beta-D-glucopyranoside on the basis of chemical and 2D-NMR methods.

(2020) Desertification detection model in Naiman Banner based on the albedo-modified soil adjusted vegetation index feature space using the Landsat8 OLI images, Geomatics, Natural

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