Remote sensing technology can be used to quickly extract macro information of the study area, and its advantages in monitoring water resources have become increasingly evident. In this study, Fenhe 2nd reservoir and Jinyang Lake in Taiyuan，Shanxi Province were examined using the remote sensing data obtained from HJ-1B on May 6, 2010. Water area of Fenhe 2nd reservoir was extracted using NDWI and improved segmentation threshold. The distribution of eutrophication and phytoplankton in Jinyang Lake were analyzed using NDPI and a profile map of phytoplankton was produced. Results show that spatial and spectral resolution of HJ-1B can meet the requirements of water resources monitoring well, which are conducive for further promotion and application of HJ-1B remote sensing data.

On 2011 March 7, the Solar Neutron Telescope located at Mt. Sierra Negra, Mexico (4600 m) observed enhancements of the counting rate from 19:57 to 20:04 UT with statistical significance 6.8σ and from 20:36 to 21:03 UT with 5.8σ. One plausible physical explanation for the observation enhancements is that they were produced by solar gamma-rays. The intensities were estimated to be (0.16 ± 0.03) photons cm−2 s−1 for the first flare and (0.22 ± 0.04) photons cm−2 s−1 for the second one at the top of the atmosphere. As far as we know, this is the first report on the detection of solar gamma-rays with a ground-based detector. In association with these events, the solar neutron detector Space Environment Data Acquisition Equipment on board the International Space Station registered two solar neutrons with statistical significances of 7.3σ and 6.6σ. The Large Area Telescope on board the Fermi observatory also observed high-energy gamma-rays from this flare with a statistical significance of 6.7σ. In this paper we propose a unified model to explain the production mechanism of high-energy gamma-rays and neutrons in association with this flare.

Vegetation covering situation is very important for the quality of air quality, soil and water conservation ability and soil forming in an area. By using the remote sensing image of Taiyuan Valley Plain, the application of Normalized Difference Vegetation Index (NDVI) and unsupervised classification, the vegetation coverage map which includes non-cultivated land disposition and cultivated land disposition was obtained using ERDAS Imagine software. To evaluate the accuracy of the results, 200 points were sampled randomly, the high spatial resolution remote sensing image from Google Earth was used as the reference. The overall classification accuracy is 82%, with the Kappa statistic of 0.81. By counting the totally pixel acreage, it was gotten that the vegetation coverage was 46% and the cultivated land coverage ratio was 31% in the study area.