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Nitrogen (N) resorption is an important pathway of N conservation, contributing to an important proportion of plant N requirement. However, whether the ratio of N resorption to N requirement may be affected by environmental factors, mycorrhizal types or atmospheric CO2 concentration remains unclear. Here, we conducted a meta‐analysis on the impacts of environmental factors and mycorrhizal types on this ratio. We found this ratio in ectomycorrhizal (EM) trees decreased with mean annual precipitation, mean annual temperature, soil total N content and atmospheric CO2 concentration and was significantly lower than that in arbuscular mycorrhizal (AM) trees. An in situ15N tracing experiment further confirmed that AM trees have a stronger reliance on N resorption than EM trees. Our study suggests that AM and EM trees potentially have different strategies for alleviation of progressive N limitation, highlighting the necessity of incorporating plant mycorrhizal types into Earth System Models. [ABSTRACT FROM AUTHOR]

Land surface albedo is an important variable for Earth's radiation and energy budget. Over the past decades, many surface albedo products have been derived from a variety of remote sensing data. However, the estimation accuracy, temporal resolution, and temporal continuity of these datasets still need to be improved. We developed a multi-sensor strategy (MSS) based on the direct-estimation algorithm (DEA) and Statistical-Based Temporal Filter (STF) to improve the quality of land surface albedo datasets. The moderate-resolution imaging spectroradiometer (MODIS) data onboard Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi-National Polar-orbiting Partnership (NPP) were used as multi-sensor data. The MCD43A3 product and in situ measurements from the Surface Radiation Budget Network (SURFRAD) and FLUXNET sites were employed for validation and comparison. The results showed that the proposed MSS method significantly improved the temporal continuity and estimation accuracy during the snow-covered period, which was more consistent with the measurements of SURFRAD (R = 0.9498, root mean square error (RMSE) = 0.0387, and bias = −0.0017) and FLUXNET (R = 0.9421, RMSE = 0.0330, and bias = 0.0002) sites. Moreover, this is a promising method to generate long-term, spatiotemporal continuous land surface albedo datasets with high temporal resolution. [ABSTRACT FROM AUTHOR]

A high-spatial resolution aerosol optical depth (AOD) dataset is critically important for regional meteorology and climate studies. Chinese Huanjing-1 (HJ-1) A/B charge-coupled diode (CCD) data are a suitable data source for retrieving AODs. However, AOD cannot be retrieved based on the dark target method due to the absence of a shortwave infrared band. In this study, an AOD estimation method based on the relationships between visible bands of HJ-1 A/B CCDs is proposed. The Polarization and Directionality of the Earth's Reflectances (POLDER) Bidirectional Reflectance Distribution Function (BRDF) dataset was used to construct a lookup table for interband regression coefficients that varied by solar/view angle and land cover type. Finally, high-spatial resolution AODs could be retrieved with the aerosol lookup table and constraints. The results showed that the AODs retrieved from the HJ-1 A/B CCD data had the same range of distribution and trends as a visual interpretation of the images and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products did. The validation results using four sites of the Aerosol Robotic Network (AERONET) in Beijing showed that the value of the correlation coefficient R was 0.866, the root mean square error (RMSE) was 0.167, the mean absolute error (MAE) was 0.131, and the expected error (EE) was 53.9%. If the measurements of an AERONET site were used as prior knowledge, AOD retrieval results could be much more accurately obtained by this method (R is 0.989, RMSE is 0.052, MAE is 0.042, and EE is 96.7%). [ABSTRACT FROM AUTHOR]

Nitrogen (N) resorption is a strategy for plant N conservation through which plants withdraw N from senescing leaves prior to litterfall and its underlying mechanisms are important for better understanding of N cycling. However, most current studies focused on the impacts of soil and leaf nutrients on leaf N resorption efficiency (NRE), and plant physiological regulation that is species dependent is still unclear.Here, we conducted a field experiment to investigate the variations of leaf NRE along an altitudinal gradient in a temperate forest of Northeastern China.Results showed that leaf NRE of Quercus mongolica and Fraxinus mandshurica increased with altitude, while leaf NRE of Tilia amurensis, Acer mono and Acer pseudosieboldianum exhibited an opposite trend, although the relationships were not significant for F. mandshurica and A. mono. The inconsistent responses of leaf NRE of different species to increasing altitude were primarily due to the effect of leaf Glutamate dehydrogenase (GDH), an enzyme responsible for N translocation. Leaf GDH activity in senescing leaves explained the variation of NRE more than soil and climate factors did, suggesting that different plant species had different physiological regulation strategies for their N conservation under similar environment.Synthesis. Our study highlights the role of leaf enzyme as a pivotal regulator of leaf NRE and helps us better understand and predict N cycling under climate change in forest ecosystems. [ABSTRACT FROM AUTHOR]