Yaomin Zheng

TL;DR: In this article, the first 30 m resolution global land cover maps using Landsat Thematic Mapper TM and enhanced thematic mapper plus ETM+ data were produced. And the authors used four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier.

TL;DR: Wang et al. as mentioned in this paper used the Normalized Difference Vegetation Index (NDVI) trajectory to detect major land cover dynamics in Beijing and classified the land cover types in 2015 with the Google Earth Engine (GEE) cloud calculation.

TL;DR: Wang et al. as mentioned in this paper analyzed the 2008 wetland distribution in China and discussed wetland changes and their drivers over the past 30 years using four wetland maps for all China have been produced, based on Landsat and CBERS-02B remote sensing data.

TL;DR: A global validation data-set based on interpreting Landsat Thematic Mapper and Enhanced TM Plus images for a total of 38,664 sample units pre-determined with an equal-area stratified sampling scheme, which indicates some of the homogeneous sample units in the data- set have already been used in assessing other classification results or as training data for land-cover mapping with coarser-resolution data.

TL;DR: The Soil Moisture Experiment in the Luan River (SMELR) was conducted from 2017 to 2018 in the semi-arid Luan river watershed located in the North of China.