Accurately monitoring global vegetation dynamics with modern remote sensing is critical for understanding the functions and processes of the biosphere and its interactions with the planetary climate. The MODerate resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) product has been a primary data source for this purpose. To date, the MODIS team had released several versions of VI products that have widely used in global change studies and practical applications. In this study, we reexamined the global vegetation activity by comparing the recent MODIS Collection 6 (C6) VIs with Collection 5 (C5) VIs including Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from Terra (2001-2015) and Aqua Satellites (2003-2015). We found substantial differences in global vegetation trends betweenTerra-C5 and-C6 VIs, especially EVI. From 2001 to 2015, global vegetation showed a remarkable greening trend in annual EVI from the Terra-C6 (0.28% yr-1 ; P < 0.001), in contrast to the decreasing EVI trend from the Terra-C5 (-0.14% yr-1 , p < 0.01). Spatially, large portions of the browning areas in tropical regions identified by Terra-C5 VIs were not evident in Terra-C6 VIs. In contrast, the widespread greening areas in Terra-C6 VIs were consistent with Aqua-C6 VIs and GIMMS3g NDVI. Our finding of a greening Earth supports the recent studies suggesting an enhanced land carbon sink. Our study suggests that most of the vegetation browning trends detected by MODIS Terra-C5 VIs were likely caused by sensor degradation, particularly for the period after 2007. Therefore, previous studies of temporal vegetation trends based solely on Terra-C5 VIs may need to be reevaluated. Our new analysis offers the most updated understanding of the global vegetation dynamics during the past 15 years and contributes to accurately understanding the role of vegetation played in the Earth's biogeochemical and climatic systems.

Land use/land cover change (LULCC) and climate change are among the primary driving forces for terrestrial ecosystem productivity, but their impacts are confounded. The objective of this paper is to decouple the effects of LULCC and climate change on terrestrial net primary productivity (NPP) in China's Yangtze River Basin (YRB) during 2001-2010 using a light use efficiency model through different scenario designs. During the study period, the YRB witnessed tremendous LULCC and climate changes. A prominent LULCC was the conversion of shrub land to forests as a result of a series of forest restoration and protection programs implemented in the basin. At the same time, notable warming and drying trends were observed based on ground and satellite measurements. Prescribed model simulations indicated that LULCC alone had a significantly positive effect on total NPP (up to 6.1 Tg C yr À1 , p < 0.01) mainly due to reforestation and forest protection, while climate change alone showed an overall negative effect in the basin (as much as À2.7 Tg C yr À1 , p = 0.11). The ensemble effect of LULCC and climate change on total NPP is approximately 3.9 Tg C yr À1 (p = 0.26) during 2001-2010. Our study provides an improved understanding of the effects of LULCC and climate change on terrestrial ecosystem productivity in the YRB. We found that reforestation and forest protection could significantly enhance terrestrial ecosystem productivity, a strategy that could mitigate global warming. It also suggests that NPP models with static land use/land cover could lead to increasingly large errors with time.

Terrestrial gross primary production (GPP) is the largest carbon flux entering the biosphere from the atmosphere, which serves as a key driver of global carbon cycle and provides essential matter and energy for life on land. However, terrestrial GPP variability is still poorly understood and difficult to predict, especially at the annual scale. As a major internal climate oscillation, El Niño-Southern Oscillation (ENSO) influences global climate patterns and thus may strongly alter interannual terrestrial GPP variation. Using a remote sensing-driven ecosystem model with long-term satellite and climate data, we comprehensively examined the impacts of ENSO on global GPP dynamics from 1982 to 2016, focusing on lag effects of ENSO and their spatial heterogeneity. We found a clear seasonal lag effect of previous-year ENSO indices on current-year global GPP variability. The composite Oceanic Niño Index in the previous-year's August-October showed the strongest correlation with global annual GPP (R = −0.51, p < 0.01). Spatially, 20.1% and 11.7% of vegetated land area showed significant negative and positive correlations with the ENSO cycle, respectively. ENSO effects on annual GPP exhibited diverse seasonal evolutions, and the timings of peak ENSO influences were heterogeneous across the globe. Annual GPP from TRENDY land surface model ensemble did not capture the major lag effects of ENSO identified in the satellite-derived GPP and top-down-based land sink. Despite the complexity of the climate system, our efforts linking ENSO with global GPP dynamics provide a simple framework to understand and project climatic influences on the terrestrial carbon cycle.

Light use efficiency (LUE) is a key biophysical parameter characterizing the ability of plants to convert absorbed light to carbohydrate. However, the environmental regulations on LUE, especially moisture stress, are poorly understood, leading to large uncertainties in primary productivity estimated by LUE models. The objective of this study is to investigate the effects of moisture stress on LUE for a wide range of ecosystems on daily, 8 day, and monthly scales. Using the FLUXNET and Moderate Resolution Imagine Spectroradiometer data, we evaluated moisture stress along the soil-plant-atmosphere continuum, including soil water content (SWC) and soil water saturation (SWS), land surface wetness index (LSWI) and plant evaporative fraction (EF), and precipitation and daytime atmospheric vapor pressure deficit (VPD). We found that LUE was most responsive to plant moisture indicators (EF and LSWI), least responsive to soil moisture (SWC and SWS) variations with the atmospheric indicator (VPD) falling in between. LUE showed higher sensitivity to SWC than VPD only for grassland ecosystems. For evergreen forest, LUE had better association with VPD than LSWI. All moisture indicators (except soil indicators) were generally less effective in affecting LUE on the daily and 8 day scales than on the monthly scale. Our study highlights the complexity of moisture stress on LUE and suggests that a single moisture indicator or function in LUE models is not sufficient to capture the diverse responses of vegetation to moisture stress. LUE models should consider the variability identified in this study to more realistically reflect the environmental controls on ecosystem functions.

Intel Software Guard eXtension (SGX), a hardware supported trusted execution environment (TEE), is designed to protect security critical applications. However, it does not terminate traditional memory corruption vulnerabilities for the software running inside enclave, since enclave software is still developed with type unsafe languages such as C/C++. This paper presents Rust-SGX, an efficient and layered approach to exterminating memory corruption for software running inside SGX enclaves. The key idea is to enable the development of enclave programs with an efficient memory safe system language Rust with a Rust-SGX SDK by solving the key challenges of how to (1) make the SGX software memory safe and (2) meanwhile run as efficiently as with the SDK provided by Intel. We therefore propose to build Rust-SGX atop Intel SGX SDK, and tame unsafe components with formally proven memory safety. We have implemented Rust-SGX and tested with a series of benchmark programs. Our evaluation results show that Rust-SGX imposes little extra overhead (less than 5% with respect to the SGX specific features and services compared to software developed by Intel SGX SDK), and meanwhile have stronger memory safety. CCS CONCEPTS• Security and privacy → Formal methods and theory of security; Systems security;

No abstract

No abstract

Knowledge of nutrient storage and partitioning in forests is imperative for ecosystem models and ecological theory. Whether the nutrients (N, P, K, Ca, and Mg) stored in forest biomass and their partitioning patterns vary systematically across climatic gradients remains unknown. Here, we explored the global-scale patterns of nutrient density and partitioning using a newly compiled dataset including 372 forest stands. We found that temperature and precipitation were key factors driving the nutrients stored in living biomass of forests at global scale. The N, K, and Mg stored in living biomass tended to be greater in increasingly warm climates. The mean biomass N density was 577.0, 530.4, 513.2, and 336.7 kg/ha for tropical, subtropical, temperate, and boreal forests, respectively. Around 76% of the variation in biomass N density could be accounted by the empirical model combining biomass density, phylogeny (i.e., angiosperm, gymnosperm), and the interaction of mean annual temperature and precipitation. Climate, stand age, and biomass density significantly affected nutrients partitioning at forest community level. The fractional distribution of nutrients to roots decreased significantly with temperature, suggesting that forests in cold climates allocate greater nutrients to roots. Gymnosperm forests tended to allocate more nutrients to leaves as compared with angiosperm forests, whereas the angiosperm forests distributed more nutrients in stems. The nutrient-based Root:Shoot ratios (R:S), averaged 0.30 for R:S N , 0.36 for R:S P , 0.32 for R:S K , 0.27 for R:S Ca , and 0.35 for R:S Mg , respectively. The scaling exponents of the relationships describing root nutrients as a function of shoot nutrients were more than 1.0, suggesting that as nutrient allocated to shoot increases, nutrient allocated to roots increases faster than linearly with nutrient in shoot. Soil type significantly affected the total N, P, K, Ca, and Mg stored in living biomass of forests, and the Acrisols group displayed the lowest P, K, Ca, and Mg.