MARS is an international group of ecologists from multiple universities and institutes. Our primary goals are Modeling and Remote Sensing of water, energy and carbon fluxes between land surface ecosystem and atmosphere, which modulates from local micrometeorology to global climate.
- Water, energy and carbon fluxes of forest ecosystem;
- Climate change and forest biophysical feedbacks;
- Forest phenology, photosynthesis and drought;
- Land surface model and Remote sensing.
Recent Researches and News
21/8/2021: The Innovation
- Three climate-phenology regimes are identified across tropical and subtropical forest biomes.
- In regions where light and moisture limit plants in dry season, litterfall and productivity both peak in the sunny wet season.
- In regions where light and moisture limit plants in different seasons, litterfall peaks in the sunny dry season but productivity peaks in the cloudy wet season.
- In regions where moisture does not limit plants, litterfall and productivity both peak in the sunny dry season.
27/8/2019: MARS group are opening new positions
Start:2019/09/01 Deadline: 2019/12/31. …more
26/8/2019: Congratulations! MARS group got 3 new projects granted from the National Nature Science Foundation of China.
2020/01/01-2023/12/31, Driving mechanism of canopy phenology and evapotranspiration in tropical and subtropical evergreen broad-leaved forests. PI: Xiuzhi Chen;
2020/01/01-2023/12/31, Representation of the understory energy balance to decompose the individual forest biophysical effect on air and soil temperatures. PI: Yongxian Su;
2020/01/01-2022/12/31, Response mechanism of soil respiration and its components to warming in subtropical forest in southern China. PI: Jianping Wu.
22/8/2019: Environment International
The well-documented energy balance dynamics within forest ecosystems are poorly implemented in studies of the biophysical effects of forests. This results in limitations to the accurate quantification of forest cooling/warming on local air temperature. Taking into consideration the forest air space, this study proposes a three-layered (canopy, forest air space and soil [CAS]) land surface energy balance model to simulate air temperature within forest spaces The novel CAS model provides a feasible way to represent the energy balance within forest ecosystems and to assess its impacts on local air temperatures globally. …more
31/5/2019: Congratulations!!! Liyang wins the 2019 CSC award of Chinese Goverment Scholarship!!!
Liu Liyang is a Ph.D focusing on microwave remote sensing and forest biophysical effect studies. He has published high-quality papers on Remote Sensing of Environment and will visit Philippe Ciais’ group in the Laboratoire des Sciences du Climat et de I’ Environnement (LSCE) for two years.
26/4/2019: MARS group are opening new positions. Expired.
25/3/2019: National Science Review
Any changes in plant community composition, plant species richness and environmental factors that can reduce litter C/N ratio, or climatic changes that increase wetness index, may promote SOC accumulation.…more
15/1/2018: Nature Communications
Prove more robustness of Zhou et al. (2015)’s model. …more
15/9/2017: Remote Sensing of Environment
For the first time we constructed a microwave derived Temperature Vegetation Drought Index (TVDI) using the AMSR-E brightness temperatures. The novel MTVDI could better separate the drought levels in different degrees than MODIS-derived TVDI. …more
12/6/2016: Nature Communications
Prove more robustness of Zhou et al. (2015)’s findings. …more
09/1/2015: Nature Communications
Here, we developed a global model that relates annual water yield (R/P) to a wetness index (P/PET) and watershed characteristics (m). We found that When P/PET<1, the R/P is more responsive to changes in m than it is when P/PET>1, suggesting that any land cover changes in non-humid regions (P/PET<1) or in watersheds of low water retention capacity (m<2) can lead to greater hydrological responses. …more
25/4/2014: Renewable and Sustainable Energy Reviews
Here, we developed a normalized approach for assessing China׳s city-level CO2 emissions of energy consumptions using DMSP/OLS nighttime light imageries and explored major driving forces for proposing feasible mitigation policies. China׳s CO2 emission process was always consistent with its economic development.