Spatiotemporal variability of near-surface CO and its affecting factors over Mongolia.

We investigate the spatiotemporal variability of near-surface CO concentrations in Mongolia from 2010 to 2019 and the factors affecting it over four climate zones of Mongolia based on the Köppen-Geiger climate classification system, including arid desert climate (BWh), arid steppe climate (BSk), dry climate (Dw), and polar frost climate (ET). Initially, we validate the near-surface CO datasets obtained from the Greenhouse Gases Observing Satellite (GOSAT) using ground-based CO observations obtained from the World Data Center for Greenhouse Gases (WDCGG) and found good agreement. The results showed that CO concentrations over Mongolia steadily increased from 389.

Analysis of spatiotemporal land cover changes in Inner Mongolia using self-organizing map neural network and grid cells method.

Land use has changed dramatically in the Inner Mongolia Autonomous Region because of rapid economic growth and human disturbances. However, little information is available about the medium- and long-term land use changes in this region. The effects of ecological recovery policies have also been evaluated rarely.

Extended averaged learning subspace method for hyperspectral data classification.

Averaged learning subspace methods (ALSM) have the advantage of being easily implemented and appear to outperform in classification problems of hyperspectral images. However, there remain some open and challenging problems, which if addressed, could further improve their performance in terms of classification accuracy. We carried out experiments mainly by using two kinds of improved subspace methods (namely, dynamic and fixed subspace methods), in conjunction with the [0,1] and [-1,+1] normalization methods.