Chongya Jiang

Inconsistencies of interannual variability and trends in long‐term satellite leaf area index products

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products.

Urban heat mitigation by roof surface materials during the East Asian summer monsoon

Roof surface materials, such as green and white roofs, have attracted attention in their role in urban heat mitigation, and various studies have assessed the cooling performance of roof surface materials during hot and sunny summer seasons. However, summers in the East Asian monsoon climate region are characterized by significant fluctuations in weather events, such as dry periods, heatwaves, and rainy and cloudy days. This study investigated the efficacy of different roof surface materials for heat mitigation, considering the temperatures both at and beneath the surface of the roof covering materials during a summer monsoon in Seoul, Korea. We performed continuous observations of temperature at and beneath the surface of the roof covering materials, and manual observation of albedo and the normalized difference vegetation index for a white roof, two green roofs (grass (Poa pratensis) and sedum (Sedum sarmentosum)), and a reference surface. Overall, the surface temperature of the white roof was significantly lower than that of the grass and sedum roofs (1.1 °C and 1.3 °C), whereas the temperature beneath the surface of the white roof did not differ significantly from that of the grass and sedum roofs during the summer. The degree of cloudiness significantly modified the surface temperature of the white roof compared with that of the grass and sedum roofs, which depended on plant metabolisms. It was difficult for the grass to maintain its cooling ability without adequate watering management. After considering the cooling performance and maintenance efforts for different environmental conditions, we concluded that white roof performed better in urban heat mitigation than grass and sedum during the East Asian summer monsoon. Our findings will be useful in urban heat mitigation in the region.

Intercomparison and uncertainty analysis of global MODIS, cyclopes, and GLOBCARBON LAI products

Three major global moderate resolution leaf area index (LAI) products: MODIS/TERRA+AQUA (MCD15 C5), SPOT/VEGETATION CYCLOPES V3.1, and the GLOBCARBON V2.0, were compared in this study. Results show that the three products agree very well for grasses/cereal crops and shrubs. The products differ considerably for EBF, where GLOBCARBON shows systematically lower LAIs than MODIS (~1.02) and CYCLOEPS (~0.50). The discrepancies for EBF are attributed to the different LAI definitions and clumping corrections. MODIS and CYCLOPES generally agree with each other for DBF, ENF and DNF during the peak growth period. The product theoretical uncertainties, indicated by the quantitative quality indicators (QQIs), show that MODIS has the lowest uncertainty (0.19) followed by CYCLOPES (0.54) and GLOBCARBON (0.65).

Long-term global radiation, carbon and water fluxes derived from multi-satellite data and a process-based model

The world is experiencing remarkable climate change, which alters vegetation structural and functional status, and the biosphere feedback to the climate might amplify or dampen regional and global climate change. The exchange of energy and mass across the land-atmosphere interface is an essential indicator of the interaction between ecosystem and environment, and it has been widely measured at landscape scale since the establishment of FLUXNET, a global network of micrometeorological flux measurement sites (Baldocchi, 2008; Baldocchi et al., 2001). Furthermore, these site observations have been upscaled to global scale in conjunction with satellite remote sensing data, providing opportunities to investigate the spatial and temporal variations of carbon and water cycles from a macro perspective (Beer et al., 2010; Martin Jung et al., 2010). However, such upscaling approaches are based on data-driven models, which need to be well calibrated using site observations (M. Jung, Reichstein, & Bondeau, 2009; Papale & Valentini, 2003; Xiao et al., 2010; Yang et al., 2007). Consequently, those models are limited by the representativeness, quantity and quality of the training datasets (Sundareshwar et al., 2006), as well as the lack of independent reference datasets for validation. Furthermore, by using the upscaling approach predictors are directly linked with target fluxes through machine learning, while the intermediate variables along with intrinsic mechanisms are hidden.