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Xiangming Xiao

Researcher at University of Oklahoma

Publications -  437
Citations -  26780

Xiangming Xiao is an academic researcher from University of Oklahoma. The author has contributed to research in topics: Normalized Difference Vegetation Index & Environmental science. The author has an hindex of 74, co-authored 369 publications receiving 19835 citations. Previous affiliations of Xiangming Xiao include Chinese Ministry of Education & Florida State University College of Arts and Sciences.

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Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

TL;DR: In this paper, the authors developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy Rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period flooding and rice transplanting.

Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

TL;DR: In this paper, the authors developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy Rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period flooding and rice transplanting.
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Satellite-based modeling of gross primary production in an evergreen needleleaf forest

TL;DR: In this article, a satellite-based Vegetation Photosynthesis Model (VPM) was developed to estimate the seasonal dynamics and interannual variation of gross primary production (GPP) of evergreen needleleaf forests.
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Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images

TL;DR: In this article, the authors developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia using 8-day composite images (500m spatial resolution) in 2002 from the MODIS sensor onboard the NASA EOS Terra satellite.
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Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

TL;DR: In this paper, a satellite-based Vegetation Photosynthesis Model (VPM) was developed and validated using site-specific CO2 flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA.