The Qinghai-Tibetan Plateau (QTP) is the highest plateau in the world. Under the background of global change, it is of unique significance to study the net primary productivity (NPP) of vegetation on the QTP. Based on the Google Earth Engine (GEE) cloud computing platform, the spatio-temporal variation characteristics of the NPP on the QTP from 2001 to 2017 were studied, and the impacts of climate change, elevation and human activity on the NPP in the QTP were discussed. The mean and trend of NPP over the QTP were “high in the southeast and low in the northwest” during 2001–2017. The trend of NPP was mostly between 0 gC·m−2·yr−1 and 20 gC·m−2·yr−1 (regional proportion: 80.3%), and the coefficient of variation (CV) of NPP was mainly below 0.16 (regional proportion: 89.7%). Therefore, NPP was relatively stable in most regions of the QTP. Among the correlation coefficients between NPP and temperature, precipitation and human activities, the positive correlation accounted for 81.1%, 48.6% and 56.5% of the QTP area, respectively. Among the two climatic factors, the influence of temperature on NPP was greater than that of precipitation. The change of human activities and the high temperature at low altitude had positive effects on the increase of NPP.

Radiometric correction is one of the most important preprocessing parts of Unmanned Aerial Vehicle (UAV) multispectral remote sensing data analysis and application. In this study, a back propagation (BP) neural network-based radiometric correction method (BPNNRCM) considering optimal parameters was proposed. Firstly, we used different UAV multispectral sensors (K6 equipped on the DJI M600, D-MSPC2000 equipped on the FEIMA D2000) to collect training, validation, testing and cross-validation data. Secondly, the radiometric correction results of BP neural network with different input variables and hidden layer node number were compared to select the best combination of input parameters and hidden layer node number. Finally, the radiometric correction accuracy and robustness of BP neural network considering the optimal parameters were verified. When the number of nodes in the input layer was 5 (digital number, UAV sensor height, wavelength, solar altitude angle and temperature) and the number of nodes in the hidden layer was 8, the BP neural network had the best comprehensive performance in training time of train set and accuracy of validation/test set. In the aspect of accuracy and robustness, the absolute errors of test and crossvalidation images' surface reflectance obtained by the BPNNRCM were all less than 0.054. The BPNNRCM had smaller average absolute error (0.0141), mean squared error (0.0003), mean absolute error (0.0141) and mean relative error (7.1%) comparing with empirical line method (ELM) and radiative transfer model. In general, the research results of this paper prove the feasibility and prospect of BPNNRCM for radiometric correction of UAV multispectral images.

Accurate understanding of the impacts of climate change on hulless barley and river in the Lhasa River Basin is of great significance to food security and water resources management in the plateau region. It is important to explore the relationship between hulless barley and river under the background of climate change for the comprehensive and coordinated development of agriculture and water conservancy. Based on the Google Earth Engine (GEE) cloud computing platform, the Random Forest algorithm was used to obtain the spatio‐temporal variation of hulless barley and river in the Lhasa River Basin from 2010 to 2020. The overall accuracy and Kappa coefficient of classification results were 89.54% and 85.96%, respectively. The average area and Normalized Difference Vegetation Index (NDVI) of hulless barley from 2010 to 2020 were 178.46 km2 and 0.69, respectively. The increase of accumulated precipitation, number of precipitation days, average dew point temperature (ADPT) and average wind speed (AWS) promoted the growth of hulless barley, with NDVI significant increasing rate of 0.0173 (R2 = 0.764, p < 0.001). The combined effects of human activities (construction of water conservancy facilities and mining activities), ADPT and AWS resulted in a significant decrease (decreasing rate: 10.8682 km2/year, p < 0.01) in river area during 2010–2020. There was a significant negative correlation (R2 = 0.722, p < 0.01) between hulless barley NDVI and river area driven by climate factors. The changes in hulless barley gravity center and river gravity center were consistent, and both shifted in the northeast direction. These results provide a scientific understanding of the impacts of plateau climate change on agriculture and water resource. The land cover maps of the Lhasa River Basin with long time series and high spatial resolution were drawn. Our study verified the relevance between hulless barley gravity center and river gravity center as well as the internal relationship between hulless barley growth and river area. Furthermore, we explored the interrelation relationships among hulless barley, the Lhasa River area and climate factors.