Cosmogenic Be flux from the atmosphere is a proxy for rainfall. Using this proxy, we derived a 550,000-year-long record of East Asian summer monsoon (EASM) rainfall from Chinese loess. This record is forced at orbital precession frequencies, with higher rainfall observed during Northern Hemisphere summer insolation maxima, although this response is damped during cold interstadials. TheBe monsoon rainfall proxy is also highly correlated with global ice-volume variations, which differs from Chinese cave δO, which is only weakly correlated. We argue that both EASM intensity and Chinese cave δO are not governed by high-northern-latitude insolation, as suggested by others, but rather by low-latitude interhemispheric insolation gradients, which may also strongly influence global ice volume via monsoon dynamics.

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The remote sensing technology provides a new means for the determination of chlorophyll content in apple trees that includes a rapid analysis, low cost and large monitoring area. The Back-Propagation Neural Network (BPNN) and the Supported Vector Machine Regression (SVMR) methods were both frequently used method to construct estimation model based on remote sensing imaging. The aim of this study was to find out which estimation model of apple tree canopy chlorophyll content based on the vegetation indices constructed with visible, red edge and near-infrared bands of the sensor of Sentinel-2 was more accurate and stabler. The results were as follows: The calibration set coefficient of determination (R2) value of 0.729 and validation set R2 value of 0.667 of the model using the SVMR method based on the vegetation indices (NDVIgreen + NDVIred + NDVIre) were higher than those of the model using the BPNN method by 8.2% and 11.0%, respectively. The calibration set root mean square error (RMSE) of 0.159 and validation set RMSE of 0.178 of the model using the SVMR method based on the vegetation indices (NDVIgreen + NDVIred + NDVIre) were lower than those of the model using the BPNN method by 5.9% and 3.8%, respectively.

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