This review focuses on polymer field-effect transistor (PFET) based gas sensor with polymer as the sensing layer, which interacts with gas analyte and thus induces the change of source-drain current (ΔI_SD). Dependent on the sensing layer which can be semiconducting polymer, dielectric layer or conducting polymer gate, the PFET sensors can be subdivided into three types. For each type of sensor, we present the molecular structure of sensing polymer, the gas analyte and the sensing performance. Most importantly, we summarize various analyte–polymer interactions, which help to understand the sensing mechanism in the PFET sensors and can provide possible approaches for the sensor fabrication in the future. Full article

In this study, a new parameterization scheme of evaporative fraction (EF) was developed from the contextual information of remotely sensed radiative surface temperature ( $T_s$ ) and vegetation index (VI). In the traditional $T_s-VI$ triangle methods, the Priestley-Taylor parameter $\mathrm{\varnothing }$ of each pixel was interpolated for each VI interval; in our proposed new parameterization scheme (NPS), it was performed for each isopiestic line of soil surface moisture. Specifically, $\mathrm{\varnothing }$ of mixed pixels was determined as the weighted-average value of bare soil $\mathrm{\varnothing }$ and full-cover vegetation $\mathrm{\varnothing }$ . The maximum $T_s$ of bare soil ( $T_{smax}$ ) is the sole parameter needed as the constraint of the dry edge. This has not only bypassed the task involved in the determination of the maximum $T_s$ of fully vegetated surface ( $T_{cmax}$ ), but also made it possible to reduce the reliance of the $T_s-VI$ triangle methods on the determination of the dry edge. Ground-based measurements taken during 21 days in 2004 were used to validate the EF retrievals. Results show that the accuracy achieved by the NPS is comparable to that achieved by the traditional $T_s-VI$ triangle methods. Therefore, the simplicity of the proposed new parameterization scheme does not compromise its accuracy in monitoring EF. Full article

Based on a high-quality dataset of 713 daily precipitation series, changes in daily precipitation events during 1960–2013 were observed in China’s ten largest river basins. Specifically, the amount of precipitation in four categories defined by fixed thresholds and their proportion on total precipitation were analyzed on annual and seasonal time scales. Results showed annual precipitation increased by 1.10 mm/10yr in China, but with obvious spatial differences. Regionally, annual precipitation increased significantly in northwestern rivers, upstream areas of the Yangtze River, the Yellow River, southwestern rivers (due to increase in light and moderate precipitation); and in southeastern rivers, downstream areas of the Yangtze River, and the Pearl River (due to increase in heavy and extreme precipitation). Annual precipitation decreased significantly in the mid-Yangtze River and upstream Pearl River (due to decrease in light, moderate, and heavy precipitation). Seasonally, precipitation decreased only in autumn; this was attributable to a decrease in light and moderate precipitation. Results show that the distribution of precipitation intensity over China has shifted to intense categories since the 1960s, there has been an increase in moderate precipitation in Northwestern and Northern China, and an increase in extreme precipitation in Southeastern China. This shift was detected in all seasons, especially in summer. Precipitation extremes were investigated in the categories of extreme precipitation and results show that the risk of flood has been exacerbated over the past half-century in the Huaihe River, the mid- and lower Yangtze River, the Pearl River, and southeastern rivers. Full article

Accurate rainfall estimations based on ground-based rainfall observations and satellite-based rainfall measurements are essential for hydrological and environmental modeling in the Qaidam Basin of China. We evaluated the accuracy of daily and monthly scale Tropical Rainfall Measuring Mission (TRMM) rainfall products in the Qaidam Basin. A Geographically Weighted Regression (GWR) was used to estimate the spatial distribution of the TRMM product error using altitude and geographical latitude and longitude as independent variables. Finally, a rainfall model was developed by combining ground-based and satellite-based rainfall measurements, and the model precision was validated with a cross-validation method based on rainfall gauge measurements. The TRMM precipitation observations may contain errors compared with the ground-measured precipitation, and the error for daily data was higher than that for monthly data. A time series of TRMM rainfall measurements at the same location showed errors at certain time intervals. The ground-based and satellite-based rainfall GWR model improved the error in the TRMM rainfall products. This rainfall estimation model with a 1-km spatial resolution is applicable in the Qaidam Basin in which there is a sparse network of rainfall gauges, and is significant for spatial investigations of hydrology and climate change. Full article

Using National Oceanographic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Climatic Research Unit (CRU) climate datasets, we analyzed interannual trends in the growing-season Normalized Difference Vegetation Index (NDVI) in China from 1982 to 2011, as well as the effects of climatic variables and human activities on vegetation variation. Growing-season (period between the onset and end of plant growth) NDVI significantly increased (p < 0.01) on a national scale and showed positive trends in 52.76% of the study area. A multiple regression model was used to investigate the response of vegetation to climatic factors during recent and previous time intervals. The interactions between growing-season NDVI and climatic variables were more complex than expected, and a lag existed between climatic factors and their effects on NDVI. The regression residuals were used to show that over 6% of the study area experienced significantly human-induced vegetation variations (p < 0.05). These regions were mostly located in densely populated, reclaimed agriculture, afforestation, and conservation areas. Similar conclusions were drawn based on land-use change over the study period. Full article

The knowledge of water storage variations in ungauged lakes is of fundamental importance to understanding the water balance on the Tibetan Plateau. In this paper, a simple framework was presented to monitor the fluctuation of inland water bodies by the combination of satellite altimetry measurements and optical satellite imagery without any in situ measurements. The fluctuation of water level, surface area, and water storage variations in Lake Qinghai were estimated to demonstrate this framework. Water levels retrieved from ICESat (Ice, Cloud, and and Elevation Satellite) elevation data and lake surface area derived from MODIS (Moderate Resolution Imaging Spectroradiometer) product were fitted by linear regression during the period from 2003 to 2009 when the overpass time for both of them was coincident. Based on this relationship, the time series of water levels from 1999 to 2002 were extended by using the water surface area extracted from Landsat TM/ETM+ images as inputs, and finally the variations of water volume in Lake Qinghai were estimated from 1999 to 2009. The overall errors of water levels retrieved by the simple method in our work were comparable with other globally available test results with r = 0.93, MAE = 0.07 m, and RMSE = 0.09 m. The annual average rate of increase was 0.11 m/yr, which was very close to the results obtained from in situ measurements. High accuracy was obtained in the estimation of surface areas. The MAE and RMSE were only 6 km2, and 8 km2, respectively, which were even lower than the MAE and RMAE of surface area extracted from Landsat TM images. The estimated water volume variations effectively captured the trend of annual variation of Lake Qinghai. Good agreement was achieved between the estimated and measured water volume variations with MAE = 0.4 billion m3, and RMSE = 0.5 billion m3, which only account for 0.7% of the total water volume of Lake Qinghai. This study demonstrates that it is feasible to monitor comprehensively the fluctuation of large water bodies based entirely on remote sensing data. Full article