Using the recent 10-year (March 2000 to February 2010) MODIS data of aerosol optical depth (AOD), the distributions of annual and seasonal mean AOD over China are presented, and the trends and seasonal variations in AOD over 10 regions in China are analysed. The spatial pattern of annual mean AOD is characterized generally with two low centres and two high centres over China. Two low AOD centres are located in the areas with a high vegetation cover and a sparse population in (1) the high-latitude region in Northeast China with AOD of about 0.2 and (2) the high-altitude region in Southwest China with AOD from 0.1 to 0.2. These two low AOD centres are connected by a low AOD zone (0.2-0.3) in a northeast-southwest direction across China. Demarcated by this low AOD zone, two high centres with AOD of about 0.8 are situated in (1) the most densely populated and industrialized regions in China with high anthropogenic aerosols from North China Plain, Yangtze River areas covering Sichuan Basin, Central China and Yangtze River Delta to South China with Pearl River Delta region and (2) Tarim Basin in Northwest China with high natural aerosols dominated with desert dust. The spatial AOD patterns over China keep seasonally unchanged, but the strengths of the AOD-centres vary from season to season. The wintertime AOD is lowest in China. The monthly AOD variations from March to September in Southern China correspond with high AOD before, after the rain periods and low AOD during the rain periods of Asian summer monsoon. Asian summer monsoons also make a notable impact on the seasonal cycle of aerosols in China. The AOD in Northern China changes monthly with a single peak between April and June and a low in the winter months. The positive trends in AOD occur mostly in the aerosol source regions with higher annual mean AOD (>0.25), while the negative trends are found in the regions with lower annual mean AOD (<0.25) over China.

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Lake surface water temperature (LSWT) is an important factor of water ecological environment. In the context of global warming, the LSWT of global lakes generally reveals an upward trend. With a continuous intensification of human activities and a rapid expansion of the impervious surface, urbanization has exerted an increasing impact on the environment, so the impact of human activities on LSWT cannot be ignored. Because of the special geographical location, the change of LSWT in plateau lakes has important impacts on climate diversity, biodiversity, and cultural diversity. As a result, it is critical to monitor and model the variation characteristics of LSWT in the plateau area. Based on the data set of natural factors representing climate change and human factors representing human activities, this study proposes a classification of lake types by K-Means clustering method. At watershed scale, 11 lakes in the study area are divided into three types: Natural Lake, Semi-urban Lake, and Urban Lake (UL). Based on this classification, the variation characteristics of LSWT for the eleven lakes from 2001 to 2017 are analyzed. The causal relationship and contribution of climate change and human activities to the rise of LSWT are discussed. Results show that (1) from 2001 to 2017, the annual mean of LSWT-day/night and nearsurface air temperature in the 11 lakes show a warming trend, a significant correlation (R = 0.82, α = 0.0164 < 0.5) and a same periodicity, which indicates that near-surface air temperature is one of the main influencing factors of LSWT warming in Yunnan-Guizhou Plateau. (2) LSWT warming trend of UL is more obvious than those of Semi-urban Lake and Natural Lake, indicating that human activities have more significant impact on LSWT of UL. The main driving factors are the impervious surface expansion and population increase. (3) The influence of human activities on the LSWT in Yunnan-Guizhou Plateau is becoming more and more significant, and it is also the main factor in causing the deterioration of lake water environment in Yunnan-Guizhou Plateau.

SUMMARY During the angiosperm (flowering-plant) life cycle, double fertilization represents the hallmark between diploid and haploid generations [1]. The success of double fertilization largely depends on compatible communication between the male gametophyte (pollen tube) and the maternal tissues of the flower, culminating in precise pollen tube guidance to the female gametophyte (embryo sac) and its rupture to release sperm cells. Several important factors involved in the pollen tube reception have been identified recently [2–6], but the underlying signaling pathways are far from being understood. Here, we report that a group of female-specific small proteins, early nodulin-like proteins (ENODLs, or ENs), are required for pollen tube reception. ENs are featured with a plastocyanin-like (PCNL) domain, an arabinogalactan (AG) glycomodule, and a predicted glycosylphosphatidylinositol (GPI) anchor motif. We show that ENs are asymmetrically distributed at the plasma membrane of the synergid cells and accumulate at the filiform apparatus, where arriving pollen tubes communicate with the embryo sac. EN14 strongly and specifically interacts with the extracellular domain of the receptor-like kinase FERONIA, localized at the synergid cell surface and known to critically control pollen tube reception [6]. Wild-type pollen tubes failed to arrest growth and to rupture after entering the ovules of quintuple loss-of-function EN mutants, indicating a central role of ENs in male-female communication and pollen tube reception. Moreover, overexpression of EN15 by the endogenous promoter caused disturbed pollen tube guidance and reduced fertility. These data suggest that female-derived GPI-anchored ENODLs play an essential role in male-female communication and fertilization.

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Terraces are the major land-use type of agriculture and support the main agricultural production in southeast and southwest China. However, due to smallholder farming, complex terrains, natural disasters and illegal land occupations, a light-weight and low cost dynamic monitoring of agricultural terraces has become a serious concern for smallholder production systems in the above area. In this work, we propose a small unmanned aerial vehicle (UAV) based multi-temporal image registration method that plays an important role in transforming multi-temporal images into one coordinate system and determines the effectiveness of the subsequent change detection for dynamic agricultural terrace monitoring. The proposed method consists of four steps: (i) guided image filtering based agricultural terrace image preprocessing, (ii) texture and geometric structure features extraction and combination, (iii) multi-feature guided point set registration, and (iv) feature points based image registration. We evaluated the performance of the proposed method by 20 pairs of aerial images captured from Longji and Yunhe terraces, China using a small UAV (the DJI Phantom 4 Pro), and also compared against four state-of-the-art methods where our method shows the best alignments in most cases.

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