Zhao-Liang Li - Biography#

Zhao-Liang Li received a PhD degree in terrestrial environmental physics-remote sensing from the University of Strasbourg, France, in 1990. Since 1992, he has been a research scientist at CNRS, Illkirch, France. He jointed the Institute of Geographic Sciences and Natural Resources Research, CAS, China,in 2003.

Long-term engagement in quantitative thermal infrared remote sensing and accomplished the international peer-recognized achievements in the key land surface geophysical parameter retrievals from remotely sensed data. Zhao-Liang Li has published more than 330 scientific papers including 212 papers in international peer-reviewed journals, 4 monographs and 15 chapters in books and authorized 18 Chinese national invention patents. According to the Google Scholar searching, his papers have been cited more than 11000 times, the highest cited number of the single paper is more than 800 times and his H index is 46. In addition, Zhao-Liang Li has coordinated as principal investigator and participated in many national and international projects.

His academic activities include being a guest editor for the Remote Sensing Special Issue on "Recent Advances in Thermal Infrared Remote Sensing" (2015), a guest editor for ISPRS International Journal of Geo-Information Special Issue on "Recent Advances in Geodesy and its Applications" (2016), a guest editor for the Remote Sensing Special Issue on "Remote Sensing for Land Surface Temperature (LST) Estimation, Generation, and Analysis" (2018). He served since 2016 as associate editor for IEEE Transactions on Geosciences and Remote Sensing, associate editor for Remote Sensing.

Main Scientific Achievements#

Below are his representative scientific and technological innovations and academic contributions:

(1) Proposed physically and rigorously the temperature-independent spectral indices (TISI) to eliminate the effect of land surface temperature (LST) on the spectral analysis. Thanks to this TISI, accurate spectral analysis in thermal infrared domain and the physical retrieval of LSE from satellite measurements became possible. The TISI allows for the first time performing accurately thermal infrared spectral analysis and fills the gap of the spectral analysis in the whole optical remote sensing domain. In addition, TISI can be used to retrieve physically LSE and establish a bridge between the mid-infrared emissivity and the thermal infrared emissivity. The TISI has significantly promoted the development of quantitative thermal infrared remote sensing.

(2) Proposed and developed originally the “local split-window” method and the physics-based day/night operational method with the combination of mid and thermal infrared data for simultaneously retrieving LST and LSE from satellite data. These two methods are two of four types of methods currently used in the world for retrieving LST from thermal infrared remote sensing data. These two methods are used by NASA to produce operationally daily global MODIS 1 km and 5 km LST retrieval products with LST retrieval error of less than 1 K, representing the current world highest level.

(3) Proposed a radiance-based method for validating the satellite-derived LST and cleverly avoided the difficulties of ground-based LST measurement for a large-scale heterogeneous and non-isothermal pixel. The proposed method can be applied to the surface on which ground LST measurements are unfeasible. In addition, the method can be performed during the daytime and nighttime over homogeneous and non-isothermal surfaces. The method provides a new alternative approach for satellite-derived LST validation over a wide range of biomes and LST and atmospheric regimes. It has been recommended by the International EOS Committee as one of the four existing remote sensing LST retrieval product validation methods.

(4) Proposed and developed two-layer energy-separation method to separate soil and vegetation temperatures from one satellite-derived LST, consequently to effectively retrieve for a first time soil evaporation and vegetation transpiration at the pixel scale using only remote sensing data

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