Interdependent Dynamics of LAI-Albedo across the Roofing Landscapes: Mongolian and Tibetan Plateaus

Abstract

The Mongolian Plateau (MP) and Tibetan Plateau (TP) have experienced higher-than-global average warming in recent decades, resulting in many significant changes in ecosystem structure and function. Among them are the leaf area index (LAI) and albedo, which play a fundamental role in understanding many causes and consequences of land surface processes and climate. Here, we focused on the spatiotemporal changes of LAI, albedo, and their spatiotemporal relationships on the two roofing landscapes in Eurasia. Based on the MODIS products, we investigated the spatiotemporal changes of albedo(VIS, NIR and SHO) and LAI from 2000 through 2016. We found that there existed a general negative logarithmic relationship between LAI and three measures of albedo on both plateaus. No significant relationship was found for LAI-albedoNIR on the TP, due to more complex land surface canopy characteristics affected by the NIR reflection there. During 2000–2016, overall, annual mean LAI increased significantly by 119.40 × 103 km2 on the MP and by 28.35 × 103 km2 on the TP, while the decreased areas for annual mean albedoVIS were 585.59 × 103 km2 and 235.73 × 103 km2 on the MP and TP, respectively. More importantly, the LAI-albedo relationships varied substantially across the space and over time, with mismatches found in some parts of the landscapes. Substantial additional efforts with observational and/or experimental investigations are needed to explore the underlying mechanisms responsible for these relationships, including the influences of vegetation characteristics and disturbances.

Comparison of surface energy budgets and feedbacks to microclimate among different vegetation landscape on the Mongolian Plateau

“…6a) For the LAI in the study 17years, the resulting of line regression for four vegetation community types showed increasing trend in annual and most month, especially for TPL in annual, July and August, and for MDW in July and August (Fig.. 2a). This result was conformable with other papers on the MP (Chen et al, 2015;John et al, 2018;Qi, 2017;Shao et al, 2017b;Shen et al, 2015;Tian et al, 2018). However from the month change of slope in the study years in May and September, we did not found the growing season extrension over there.…”

Plant Hydraulic Stress Strategy Improves Model Predictions of the Response of Gross Primary Productivity to Drought Across China

“…The uncertainty in the meteorological forcing data clearly affected the simulation of vegetation growth process in LSMs (Li et al, 2019), and eventually induced bias in GPP estimation. For example, the LAI derived from MODIS product was reported to be averagely 0.70 during 2000–2015 in the TI region (Tian et al, 2018), while the simulated LAI in the present study was larger than MODIS in the majority area of TI region (Figure S8), with the mean annual value being 0.94–0.98 and 1.38–1.47 from simulations with CRUF and CHNF data, respectively. Compared to the CRUF data, the CHNF data, with higher air temperature (Figure S9), induced higher simulated LAI and subsequently resulted in higher simulated GPP in the TI region (Figure S2).…”

“…Journal of Geophysical Research: Atmospheres 2000-2015 in the TI region (Tian et al, 2018), while the simulated LAI in the present study was larger than MODIS in the majority area of TI region ( Figure S8), with the mean annual value being 0.94-0.98 and 1.38-1.47 from simulations with CRUF and CHNF data, respectively. Compared to the CRUF data, the CHNF data, with higher air temperature ( Figure S9), induced higher simulated LAI and subsequently resulted in higher simulated GPP in the TI region ( Figure S2).…”

Sensitivity of Coarse‐Scale Snow‐Free Land Surface Shortwave Albedo to Topography

“…Some studies clearly demonstrated that the surface albedos of different plant species show different spatial and temporal patterns due to different canopy architecture and composition optical properties (Betts, ; Gao et al, ; Kuusinen et al, ; Kuusinen et al, ; Tian et al, ; Yanagi & Costa, ). For instance, there exists a general negative logarithmic relationship between LAI and the albedo on both the Mongolian Plateau and Tibetan Plateau (Tian et al, ); the foliage and branches clumping at the shoot and crown level can increase the probability for multiple interactions of photons in the canopy, thus reducing forest albedo (Hovi et al, ). The variability of snow‐free surface albedo is greatly determined by the optical contrast between the vegetation canopy and the background soil, and vegetation cover over bright‐colored soils reduces the surface albedo, whereas plants on dark soils increase the surface albedo (Rechid et al, ).…”