scholarly journals Improving Land Surface Temperature Simulation in CoLM Over the Tibetan Plateau Through Fractional Vegetation Cover Derived From a Remotely Sensed Clumping Index and Model‐Simulated Leaf Area Index

2019 ◽  
Vol 124 (5) ◽  
pp. 2620-2642 ◽  
Author(s):  
Chengwei Li ◽  
Hui Lu ◽  
L. Ruby Leung ◽  
Kun Yang ◽  
Hongyi Li ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Surface Temperature ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
The Tibetan Plateau ◽  
Area Index ◽  
Clumping Index

scholarly journals A Trend Analysis of Leaf Area Index and Land Surface Temperature and Their Relationship from Global to Local Scale

Land ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 388
Author(s):  
Azad Rasul ◽  
Sa’ad Ibrahim ◽  
Ajoke R. Onojeghuo ◽  
Heiko Balzter
Keyword(s):  
Surface Temperature ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Regional Scale ◽  
Climate Mitigation ◽  
Area Index ◽  
Detection Analysis ◽  
Moderate Resolution Imaging Spectroradiometer

Although the way in which vegetation phenology mediates the feedback of vegetation to climate systems is now well understood, the magnitude of these changes is still unknown. A thorough understanding of how the recent shift in phenology may impact on, for example, land surface temperature (LST) is important. To address this knowledge gap, it is important to quantify these impacts and identify patterns from the global to the regional scale. This study examines the trend and linear regression modeling of the leaf area index (LAI) and LST derived from the moderate resolution imaging spectroradiometer (MODIS) data, specifically to assess their spatial distribution and changing trends at the continental and regional scales. The change detection analysis of interannual variability in the global LAI and LST between two periods (2003–2010 and 2011–2018) demonstrates more positive LAI trends than negative, while for LST most changes were not significant. The relationships between LAI and LST were assessed across the continents to ascertain the response of vegetation to changes in LST. The regression between LAI and LST was negative in Australia (R2 = 0.487 ***), positive but minimal in Africa (R2 = 0.001), positive in North America (R2 = 0.641 ***), negative in Central America (R2 = 0.119), positive in South America (R2 = 0.253 *) and positive in Europe (R2 = 0.740 ***). Medium temperatures enhance photosynthesis and lengthen the growing season in Europe. We also found a significant greening trend in China (trendp = 0.16 ***) and India (trendp = 0.13 ***). The relationships between LAI and LST in these most prominent greening countries of the world are R2 = 0.06 and R2 = 0.25 for China and India, respectively. Our deductions here are twofold—(1) In China, an insignificant association appeared between greening trend and temperature. (2) In India, the significant greening trend may be a factor in lowering temperatures. Therefore, temperature may stabilize if the greening trend continues. We attribute the trends in both countries to the different land use management and climate mitigation policies adopted by these countries.

scholarly journals An inversion model for estimating the negative air ion concentration using MODIS images of the Daxing’anling region

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242554
Author(s):  
Cui Yue ◽  
Zhao Yuxin ◽  
Zhang Nan ◽  
Zhang Dongyou ◽  
Yang Jiangning
Keyword(s):  
Air Quality ◽  
Surface Temperature ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Area Index ◽  
Inversion Model ◽  
Forest Coverage ◽  
The Relationship

The negative air ion (NAI) concentration is an essential indicator of air quality and atmospheric pollution. The NAI concentration can be used to monitor air quality on a regional scale and is commonly determined using field measurements. However, obtaining these measurements is time-consuming. In this paper, the relationship between remotely sensed surface parameters (such as land surface temperature, normalized difference vegetation index (NDVI), and leaf area index) obtained from MODIS data products and the measured NAI concentration using a stepwise regression method was analyzed to estimate the spatial distribution of the NAI concentration and verify the precision. The results indicated that the NAI concentration had a negative correlation with temperature, leaf area index (LAI), and gross primary production while it exhibited a positive correlation with the NDVI. The relationship between land surface temperature and the NAI concentration in the Daxing’anling region is expressed by the regression equation of y = -35.51x1 + 11206.813 (R2 = 0.6123). Additionally, the NAI concentration in northwest regions with high forest coverage was higher than that in southeast regions with low forest coverage, suggesting that forests influence the air quality and reduce the impact of environmental pollution. The proposed inversion model is suitable for evaluating the air quality in Daxing’anling and provides a reference for air quality evaluation in other areas. In the future, we will expand the quantity and distribution range of sampling points, conduct continuous observations of NAI concentrations and environmental parameters in the research areas with different land-use types, and further improve the accuracy of inversion results to analyze the spatiotemporal dynamic changes in NAI concentration and explore the possibility of expanding the application areas of NAI monitoring.

scholarly journals Evaluation of CMIP5 earth system models in reproducing leaf area index and vegetation cover over the Tibetan Plateau

2014 ◽  
Vol 28 (6) ◽  
pp. 1041-1060 ◽  
Author(s):  
Yan Bao ◽  
Yanhong Gao ◽  
Shihua Lü ◽  
Qingxia Wang ◽  
Shaobo Zhang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Cover ◽  
The Tibetan Plateau ◽  
Earth System ◽  
Area Index ◽  
System Models ◽  
Earth System Models
Sign in / Sign up

Export Citation Format

Share Document