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.

The vegetation ecosystem of the Qinghai-Tibet Plateau in China, considered to be the ′′natural laboratory′′ of climate change in the world, has undergone profound changes under the stress of global change. Herein, we analyzed and discussed the spatial-temporal change patterns and the driving mechanisms of net primary productivity (NPP) in the Qinghai-Tibet Plateau from 2000 to 2015 based on the gravity center and correlation coefficient models. Subsequently, we quantitatively distinguished the relative effects of climate change (such as precipitation, temperature and evapotranspiration) and human activities (such as grazing and ecological construction) on the NPP changes using scenario analysis and Miami model based on the MOD17A3 and meteorological data. The average annual NPP in the Qinghai-Tibet Plateau showed a decreasing trend from the southeast to the northwest during 2000-2015. With respect to the inter-annual changes, the average annual NPP exhibited a fluctuating upward trend from 2000 to 2015, with a steep increase observed in 2005 and a high fluctuation observed from 2005 to 2015. In the Qinghai-Tibet Plateau, the regions with the increase in NPP (change rate higher than 10%) were mainly concentrated in the Three-River Source Region, the northern Hengduan Mountains, the middle and lower reaches of the Yarlung Zangbo River, and the eastern parts of the North Tibet Plateau, whereas the regions with the decrease in NPP (change rate lower than-10%) were mainly concentrated in the upper reaches of the Yarlung Zangbo River and the Ali Plateau. The gravity center of NPP in the Qinghai-Tibet Plateau has moved southwestward during 2000-2015, indicating that the increment and growth rate of NPP in the southwestern part is greater than those of NPP in the northeastern part. Further, a significant correlation was observed between NPP and climate factors in the Qinghai-Tibet Plateau. The regions exhibiting a significant correlation between NPP and precipitation were mainly located in the central and eastern Qinghai-Tibet Plateau, and the regions exhibiting a significant correlation between NPP and temperature were mainly located in the southern and eastern Qinghai-Tibet Plateau. Furthermore, the relative effects of climate change and human activities on the NPP changes in the Qinghai-Tibet Plateau exhibited significant spatial differences in three types of zones, i.e., the climate change-dominant zone, the human activity-dominant zone, and the climate change and human activity 2

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